Qualitative Data Analysis: Key Approaches [1 ed.] 1529730414, 9781529730418

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QUALITATIVE DATA ANALYSIS KEY APPROACHES

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Sara Miller McCune founded SAGE Publishing in 1965 to support the dissemination of usable knowledge and educate a global community. SAGE publishes more than 1000 journals and over 800 new books each year, spanning a wide range of subject areas. Our growing selection of library products includes archives, data, case studies and video. SAGE remains majority owned by our founder and after her lifetime will become owned by a charitable trust that secures the company’s continued independence. Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne

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QUALITATIVE DATA ANALYSIS KEY APPROACHES

EDITED BY

PETER A. J. STEVENS 00_STEVENS_FM.indd 3

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 Peter A. J. Stevens 2023 Apart from any fair dealing for the purposes of research, private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may not be reproduced, stored or transmitted in any form, or by any means, without the prior permission in writing of the publisher, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publisher.

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In loving memory of my father Daniël. Whoever saves one life saves the world entire.

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TABLE OF CONTENTS

About the Authorsxi 1

Introduction: Walking On and Off the Beaten Track

1

Peter A. J. Stevens Research questions: the foundations for choosing qualitative data analysis approaches4 References15 2

Critical Discourse Analysis: The Articulation of Power and Ideology in Texts

17

Stijn Joye and Pieter Maeseele Chapter objectives

18

Key features, debates and historical development

18

Doing critical discourse analysis step by step

26

Conclusion and discussion

36

Summary checklist

37

Doing critical discourse analysis yourself

37

Recommended reading

40

References41 3

Grounded Theory: Key Principles and Two Different Approaches

43

Peter A. J. Stevens and Lore Van Praag Chapter objectives

43

Key features, debates and historical developments

44

Doing grounded theory step by step

46

Conclusion and discussion

76

Summary checklist

78

Doing grounded theory yourself

78

Recommended reading

80

References80

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Narrative Analysis: Analysing ‘Small Stories’ in Social Sciences

83

Peter A. J. Stevens Chapter objectives

83

Key features, debates and historical developments

84

Doing narrative analysis step by step

89

Conclusion and discussion

102

Summary checklist

103

Doing narrative analysis yourself

103

Recommended reading

105

References106 5

NVivo: An Introduction to Textual Qualitative Data Analysis with Software

109

Charlotte Maene Chapter objectives

109

Introduction110

6

Organizing your NVivo project

115

Coding the data

135

Continued data analysis

157

Summary checklist

176

Using NVivo yourself

176

Recommended reading

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Process Tracing: Making Single Case Studies Transparent and Convincing179 Ferdi De Ville, Niels Gheyle, Yf Reykers and Thijs Van de Graaf Chapter objectives

179

Key features, debates and historical development

180

Doing process tracing step by step

183

Conclusion and discussion

200

Summary checklist

201

Doing process tracing yourself

202

Recommended reading

204

References205 7

Qualitative Comparative Analysis: A Qualitative Method for Uncovering Complex Causal Relations

209

Tim Haesebrouck

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Chapter objectives

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Key features, debates and historical developments

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Doing qualitative comparative analysis step by step

216

Conclusion and discussion Summary checklist

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Doing qualitative comparative analysis yourself 233 Recommended reading 235 References236 8

Qualitative Content Analysis: A Practical Introduction Charlotte Maene

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Chapter objectives 239 Key features, debates and historical development 240 Doing qualitative content analysis step by step 244 Conclusion and discussion 265 Summary checklist 266 Doing qualitative content analysis yourself 266 Recommended reading 268 References269 9

Textual Analysis: A Practical Introduction to Studying Texts in Media and Cultural Studies Frederik Dhaenens and Sofie Van Bauwel

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Chapter objectives 271 Key features, debates and historical development 271 Doing textual analysis step by step 277 Conclusion and discussion 286 Summary checklist 287 Doing textual analysis yourself 287 Recommended reading 289 References289 10 Thematic Analysis: An Analytical Method in its Own Right Davide Dusi and Peter A. J. Stevens

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Chapter objectives 293 Key features, debates and historical development 294 Doing thematic analysis step by step 297 Conclusion and discussion 308 Summary checklist 310 Doing thematic analysis yourself 310 Recommended reading 313 References314 11 Conclusions: Comparing Destinations and Road Maps Peter A. J. Stevens References

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Appendix325 Index379

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ABOUT THE AUTHORS

Charlotte Maene obtained her MA in sociology at Ghent University in 2016. She later started as a teaching assistant at the Department of Sociology at Ghent University and supported the courses Introduction to Qualitative Methods and Applied Qualitative Methods, actively teaching bachelor students on qualitative content analysis and the use of NVivo software. Charlotte obtained her PhD in 2022 at the research group Cultural Diversity: Opportunities and Socialization with a special research interest in ethnic inequality in secondary education, adolescence’s identity development and regionalism in Belgium. Davide Dusi is a postdoctoral researcher at the Centre for Higher Education Governance Ghent. He received his PhD at the Department of Sociology of Ghent University, obtained his BA in education sciences at University of Verona and his MA in sociology at University of Trento. His research experience and interests encompass students’ roles and positions within higher education systems, university community engagement, and more broadly higher education policy. In recent years, Davide has been involved in diverse projects on university community engagement supported by both national and international funding bodies. Ferdi De Ville is associate professor of European politics at Ghent University, where he obtained his PhD in 2011. His main research interests include the political economy of the European Union and international trade policy. Ferdi’s work has been published in journals including the Journal of European Public Policy, the Journal of European Integration, New Political Economy and the British Journal of Politics and International Relations. He is the co-author of TTIP: The Truth about the Transatlantic Trade and Investment Partnership (Polity, 2016) and Rising Powers and Economic Crisis in the Euro Area (Palgrave, 2016). Frederik Dhaenens is an assistant professor at Ghent University, where he teaches courses that deal with media, (popular) culture and diversity. His research is situated within the field of critical media studies and cultural studies, while focusing on queer theory, LGBTQ+ representation, sex and sexuality, and masculinities in relation to popular film, television and music. He acts as vice chair of the Popular Culture Working Group at the International Association of Media and Communication Research (IAMCR). He also co-organizes the LGBTQ+ forum − a Flemish network of researchers, civil society actors and policymakers working on sexual and gender diversity.

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Lore Van Praag (BA, MA, PhD Ghent University) is assistant professor at the Erasmus University of Rotterdam. She has worked in the areas of sociology of education, race and ethnic relations, multilingualism, environmental migration and migration studies, using qualitative research methods and mixed methods. Her work has been published in various journals in the field of education and migration studies, including the Journal of Ethnic and Migration Studies, the International Journal of Intercultural Relations, the British Educational Research Journal and Human Ecology. Niels Gheyle is an FNRS postdoctoral researcher at UCLouvain (Louvain-la-Neuve) and affiliated researcher at Ghent University. He obtained his PhD in 2019 with a dissertation on the origins, dynamics and consequences of the politicization of EU trade policy. His main research interests cover democracy and conflict in European and global governance, with an emphasis on EU trade policy and political strategy. Peter A. J. Stevens (BA, MA Ghent University; MA, PhD Warwick University) is associate professor in qualitative research methods at the Department of Sociology, Ghent University. Peter’s research interests cover the areas of sociology of education and race and ethnic relations. His work has been published in leading journals in the field of education, race and ethnic relations and sociology, including the Review of Educational Research, the Journal of Ethnic and Migration Studies and Sociology of Education. Along with Gary Dworkin, Peter is editor of The Palgrave Handbook of Race and Ethnic Inequalities in Education (2nd edition) (Palgrave, 2019). Pieter Maeseele (BA, MA, PhD Ghent University) is associate research professor at the Department of Communication Sciences, University of Antwerp. His research and teaching are situated at the nexus between media studies and political communication, with a focus on the role and performance of different formats, genres and styles of journalism and popular culture in terms of democratic debate, diversity and pluralism. Pieter is the current vice chair of the Antwerp Media in Society Centre and of IAMCR’s Environment, Science and Risk Communication group, and the former vice-chair of the European Communication Research and Education Associations’ (ECREA) Science and Environment Communication Section. He is a member of the editorial boards of Science Communication, the Journal of Alternative and Community Media, Frontiers in Communication, the Journal of Science Communication, Palgrave Studies in Media and Environmental Communication and World Scientific Publishing. Sofie Van Bauwel is an associate professor at the Department of Communication Sciences at Ghent University, where she teaches cultural media studies, gender and media and television studies. She is part of the CIMS and her main field of interest is gender, sexuality, media and film and television. Sofie is involved in several projects with a focus on the media as signifying articulations in visual popular culture. She was vice chair of ECREA’s Gender and Communication Section (2006–12). She is also a member of

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interdisciplinary research consortium Digital Innovation for Humans and Society. She publishes internationally and nationally on popular media, gender and popular culture. Stijn Joye (BA, MA, PhD Ghent University) is associate professor at the Department of Communication Sciences, Ghent University. His research interests cover the areas of international news studies with a focus on the representation of suffering and crises alongside an interest in issues of domestication, colonial heritage and the practices of seriality and artistic imitation in film. Stijn is associate editor of the International Communication Gazette, book review editor of Communications, former chair of ECREA’s International and Intercultural Communication Section, current vice chair of TWG Ethics of Mediated Suffering, treasurer of the Netherland–Flanders Communication Association (NeFCA) and vice chair of NeFCA’s Intercultural Communication and Diversity Section. Thijs Van de Graaf is associate professor of international politics at Ghent University, where he obtained his PhD in 2012. He is also a non-resident fellow with the Payne Institute, Colorado School of Mines and the Initiative for Sustainable Energy Policy at Johns Hopkins University. Thijs teaches and conducts research in the areas of energy politics, international relations and global governance. His most recent books include Global Energy Politics (Polity, 2020) and The Palgrave Handbook of the International Political Economy of Energy (Palgrave, 2016). Tim Haesebrouck (MA, PhD Ghent University) is a postdoctoral researcher at Ghent University. His research interests include military intervention, defence burden sharing and foreign policy analysis. In addition, he has a strong interest in the development of configurational comparative methods, such as qualitative content analysis. His work has been published in the Journal of Conflict Resolution, the Journal of Peace Research, Foreign Policy Analysis, the Journal of European Public Policy, the European Political Science Review and Sociological Methods and Research. He is editor along with Jeroen Joly of Foreign Policy Change in Europe Since 1991 (Palgrave MacMillan, 2021). Yf Reykers (BA, MA, PhD KU Leuven) is assistant professor (tenured) in international relations at the Department of Political Science, Maastricht University. Yf’s research interests cover the areas of European security and defence policy. His work has been published in journals in the field of international relations, political science and European politics, including Contemporary Security Policy, the Journal of European Integration, Parliamentary Affairs and Third World Quarterly. He is co-editor of Multinational Rapid Response Mechanisms: From Institutional Proliferation to Institutional Exploitation (Routledge Global Institutions Series, 2019).

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1 Introduction: Walking On and Off the Beaten Track Peter A. J. Stevens

How should I analyse my qualitative data (from interviews, observations, media messages)? This is a question that many students and researchers ask themselves when they start thinking about the data that they have collected, or preferably even earlier, before they start collecting data and begin thinking about what they would like to research and how. Qualitative data analysis (QDA) can be done in many ways, but you might not know where to turn when exploring the vast landscape of literature on QDA. This handbook is designed to help you find direction in your journey; to identify which approach to QDA is most useful for what you want to do. In addition, it also shows you how you could apply such an approach by describing the key steps that are involved in the different approaches to QDA. Finally, by providing exercises and annotated bibliographies for each approach discussed, this handbook offers tools to further deepen your knowledge of and skills in conducting QDA. At the same time, this handbook is not intended as a guided tour, in that you can only see where the guide points you, remaining firmly on a pre-defined route. More generally, this handbook falls within a broader continuum of approaches to QDA. On one extreme there is what I call a ‘whatever works’ approach to QDA, which assumes that each individual researcher applies a unique approach to data analysis that can be judged only in terms of the quality of the end product (Corbin, 2009). The notion of ‘quality’ is defined and assessed in different ways in social science research, but often relates to the question of whether the end product of your analysis is both believable and theoretically relevant (Hammersley, 1982; Lincoln and Guba, 1985). Applying a ‘whatever works’ approach does not necessarily mean that researchers need no guidance or inspiration in terms of how they analyse their data, but that they should ignore whatever ‘does not work’ for them and value and apply what ‘works’ in developing

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quality output. Such an approach offers researchers maximum freedom in terms of how they conduct data analysis and necessarily does not prescribe any specific steps that the researcher must take in order to come up with a meaningful end product of QDA. At the other extreme, we can find ‘orthodox approaches’, which expect the researcher to follow a specific road map to produce relevant output. Here, the quality of the data analysis is not only measured by the quality of the end product, but also by if, when and how well the researcher has followed particular steps. In fact, both the end product and the process are related: the more you deviate from the well-beaten track, the more doubt is cast on the quality of the end product. As a result, this approach to QDA offers the researcher the least amount of freedom, as it is essential to follow in the footsteps of others to conduct ‘good’ QDA. In the middle we can find more ‘pragmatic approaches’. These start from a set of general assumptions about what a typical process of conducting QDA looks like; often highlighting steps and characteristics of QDA that are considered typical for almost any QDA approach, including many orthodox approaches. Although each writer often presents their unique approach (e.g., Bazeley, 2013; Maxwell, 2005; Miles, Huberman & Saldaña, 2020; Mortelmans, 2013), they often overlap significantly and usually refer to the following principles that should be considered when conducting QDA: 1. Focus on research questions that: a) emphasize the development of a deeper understanding or explanation; b) explore new hypotheses over testing existing hypotheses; c) interpret meaning; d) provide a rich, contextual description; and/or e) focus on process. 2. Collect and analyse data in a cyclical manner, so that short periods of data collection are followed up by short periods of data analysis, which in turn informs a new wave of data collection, and so on, until a theory has been developed. 3. Reflect constantly on how data collection and analysis can be improved. 4. Focus on interpretation of text rather than statistical analysis of quantitative data. 5. Apply a three-step coding process, in which raw text (e.g., an interview transcript) is first reduced to a smaller set of meaningful codes or labels attached to portions of text. Afterwards, the researcher tends to focus more on this particular set of codes and explores relationships between them, often reducing the number of codes and developing new, more abstract codes in the process. Synthesizing the final network of codes and their connections in relation to the research questions constitutes the last step in the coding process. 6. Use writing as a key tool in analysing (interpreting) data. 7. Sample text, cases or contexts based on their theoretical relevance (i.e., how well they will probably help you to gain valuable information in relation to your research topic or questions) and not because they are representative of some population. 8. Use (fairly) raw data, such as interview extracts or photographs and visual representations (e.g., coding trees or thematic maps) in presenting the outcome of data analysis.

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9. Present your findings and their theoretical relevance together in an integrated way, not separately, which is more common in quantitative research. Pragmatic approaches are widely used, as they offer a useful balance between providing direction but also allowing the researcher freedom in terms of deciding which steps to take in producing quality output. This handbook positions itself between pragmatic and more orthodox approaches to QDA. Or, put differently, while we think it is a good idea to consider the detailed road map prescribed by orthodox approaches, at the same time there might be good reasons to deviate from the prescribed path. More specifically, this handbook presents and compares the following more orthodox approaches to QDA: critical discourse analysis; grounded theory analysis; narrative analysis; process tracing; qualitative comparative analysis; qualitative content analysis; textual analysis and thematic analysis. Each of these approaches sets out particular steps that researchers are encouraged to follow in order to develop a particular type of quality output. However, in describing these approaches, we do not encourage readers to follow religiously the road map prescribed in each approach. First, we will show that within many orthodox approaches, some variability exists in terms of how the approach should be applied and for what purposes (often based on philosophical assumptions that researchers hold on to). Second, we will see that many authors in each orthodox approach encourage researchers to adopt a more pragmatic mindset to QDA, meaning that it can be perfectly motivated, including on pragmatic grounds, not to follow all the steps characteristic of a particular approach. Third, it will become clear that researchers can switch to different approaches during their research and that it often makes sense to do so. In so doing, we also warn against adopting a form of ‘methodolatry’ (Janesick, 2000) by considering orthodox approaches as necessarily better or ideal, irrespective of what you aim to develop in terms of knowledge and the quality of your end product. More generally, researchers can produce equally high-quality output using a whatever-works approach or pragmatic approach and might feel that such an approach fits better with how they can and want to conduct research. At the same time, there are different reasons why paying attention to more orthodox QDA approaches is beneficial. First, for novice researchers, most students and even PhD students, having a road map that tells them how to get to a ‘quality output’ is reassuring and helpful. Not everybody likes to be thrown into a jungle and to find a way out by themselves. While some are okay with basic survival gear and a compass (i.e., a pragmatic approach), others prefer to have a clear path with road signs in front of them. Second, orthodox approaches can effectively help you to develop quality output. Although every orthodox approach is usually developed by particular key authors in the field, they do not constitute individualistic approaches to QDA. Instead, they represent a fairly shared but always developing view among a group of researchers that conducting QDA in a particular way can result in a particular type of quality output. This means that the strengths, limitations, challenges and motivations for applying a particular approach

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are often well debated and evaluated in orthodox approaches. This allows the researcher to make better informed and more specific decisions about which steps to take and why. Third, in applying an orthodox, established road map, your notes can be added to those that already exist and your journey to X can help further improve the road map, effectively helping future travellers who also want to reach X. So, in applying a more orthodox approach, you can build on a developing body of knowledge that critically assesses the usefulness of the prescribed steps characteristic of this approach. Fourth, although the quality of the end product is a key criterion by which to judge the quality of research more generally, adding transparency of the process that resulted in a particular outcome is also essential. Orthodox approaches often offer specific yardsticks or criteria to measure the quality of the process and in so doing help you to make stronger claims about the quality of your work. This does not mean that you cannot and should not be transparent in how you conduct your research when applying a pragmatic or whatever-works approach, but simply that more orthodox approaches are more likely to stimulate you to do so and in relation to specific milestones along your journey. Finally, reflecting appropriately and openly on how your approach is different from or similar to particular, orthodox approaches helps to maintain boundaries between approaches in terms of the road that they describe in order to reach a particular destination. Not doing this (appropriately), can blur boundaries to the extent that people claim to use approaches in a way that poorly resembles the real thing. Hood (2007), for instance, makes this claim specifically in relation to grounded theory (GT). She argues that the label GT has been misused to such an extent that just applying some form of coding constitutes GT for some researchers. This is problematic, as it replaces the whatever-works road map with the road maps of more orthodox approaches but removes the benefits of adopting those orthodox approaches. This would be the equivalent of having ten different road maps, all of them claiming to show you the road to X, but sometimes confusing destination X with destination Y.

1.1 Research questions: the foundations for choosing qualitative data analysis approaches When you want to travel, you should always first identify your destination, then decide how to get there. We rarely step onto a bus not knowing where it will drop us off (although it might be exciting to do so!). The same logic applies to choosing a particular approach to QDA. Your destination represents the kind of knowledge that you aim to produce or the research questions that you want to answer. Your QDA approach represents your means of transport, or how you aim to develop answers to these questions. So, every approach to QDA is suited to finding answers to specific kinds of research question. Conversely, one single QDA approach cannot answer all types of research question. As a result, it is essential that you know which approaches can be used for what type of research questions. The following sections will do this for the approaches to QDA

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discussed in this handbook, so that you can easily identity a suitable form of QDA for particular types of research questions. In so doing, we will also see that the various QDA approaches also differ in terms of how much the research questions can change over the course of the research project. However, we will first describe how to develop good research questions in qualitative research and the issues that inform this process. More specifically, we will look at the formal requirements of research questions, the importance of personal interests, knowledge and experiences, literature and the philosophical paradigms that underpin your research. At the end of this chapter, you should feel comfortable with developing research questions that are appropriate for (qualitative) research, theoretically innovative, of interest to the audience that you write for and, ideally, for yourself. In addition, you will be able to distinguish different philosophical paradigms and know how they stimulate us to ask specific kinds of research question, which in turn leads us to select particular approaches to QDA.

1.1.1 Formal requirements: focus, scope, coherence and feasibility Good research questions are focused, limited and coherent. This means that each question should focus separately on one specific issue (focused). In addition, together they should be connected to each other and to an overarching theme in a logical way, without overlapping too much (coherent). This also introduces an important difference between a research question (RQ) and a research theme (RT). While an RT describes the general topic of interest which you wish to research, an RQ describes a specific question that you want to answer in relation to this theme. As a result, RTs do not usually have question marks at the end, while RQs do. Finally, you should not have too many or too few RQs (limited): too many might result in you not saying enough about each of them, while too few might result in not being able to say much about something at all. Put differently, if you plan to visit fifteen locations in a city in one day, you will probably see little of each, but if you only plan to visit one location, you will not see much of the city altogether. As a rule of thumb, you should aim for two to four RQs in a typical MA or PhD study. Note that the focus and scope of your RQs also have implications for the feasibility of your study (see below). For instance, let us say that we would like to do research on communication between patients and medical doctors (RT). We could develop the following two RQs, based on this RT: 1) How do doctors and patients define ‘good communication’? 2) How can we explain variability in such views? These two RQs are coherent, they do not overlap, but relate to each other, as RQ2 builds on RQ1 by trying to explain an (expected) variability in views; the latter we want to describe through RQ1. In addition, these questions are focused, in that they each try to answer one particular question. Finally, there are only two RQs, which makes the scope limited but not to the extent that we will not be able to tell a substantial story.

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Table 1.1  Checklist: are my research questions focused, limited and coherent? My RQs: •  Are not too broad and not too narrow (too vague or ambitious/too restrictive)? •  Do not overlap too much (repetition/not enough coherence)? •  Are logically related to each other and to an overarching theme? •  Are focused, in that there are no repeated research questions (confusing)? •  Are limited in that I do not have too many or too few RQs (too/not too ambitious)? • Use theoretical concepts consistently (e.g., if you want to focus on attitudes only, use the concept of attitudes only, and do not use other, related concepts such as beliefs, values, etc.)?

A final, formal requirement for evaluating RQs is that they are feasible. This means that we can only go for particular RQs if we are convinced that we can find answers to these questions, based on the knowledge that we have at the time of developing these research questions. This more practical criterion involves an assessment of what it would reasonably take to carry out our research, the challenges involved in doing so and if and how we can overcome these challenges. Based on the travelling analogy, this means that we only decide to travel to a certain destination, if we can reasonably assume that we can get there. I might, for instance, decide not to climb Mount Everest because I do not have the training, skills or knowledge to undertake such a trip, or the financial resources and support to do this successfully. It is here, too, that starting from more orthodox approaches to QDA can help, as such approaches often give very detailed road maps that show what is needed to reach the destination, as well as all the milestones in between. For instance, the typical ‘road map’ offered by GT tells us that we should expect to change our sample and research questions over the research process in order to develop a thorough explanation or understanding of a phenomenon (RQs or our destination). If this creates too much uncertainty and/or you fear that you might not have the time to do this, then you could decide not to use GT as a means to answer these questions as it is simply not feasible to do so. This might stimulate you to opt for other (orthodox) approaches and/or change your research questions altogether. However, questions about the feasibility of researching your research questions not only relate to the issue of having essential knowledge, skills and resources (time and money), but also to any potential ethical issues involved in pursuing these particular RQs. If you plan to travel to mountain X, but in so doing you must go through a nature reserve that is closed to the public, you should probably not travel to that particular mountain (or not use this particular road map). Questions about whether we can pursue particular RQs based on our available resources also relate to the formal requirement of RQs in terms of focus and scope: RQs must be focused and not too broad, as it could prove unrealistic to study them. Because we do not always have the information to make such assessments accurately at the start of our research, and because the context in which we conduct research often changes over time, questions about the feasibility of reaching your ‘destination’ often

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remain throughout your entire project. What appears ‘feasible’ at the start, may no longer appear as such after time, which can stimulate you to change your destination (RQs) or your road map (approach to QDA). Being a good researcher not only means that you assess the feasibility early on, but that you are continually prepared to make changes if necessary. For instance, in conducting our research on communication between patients and medical doctors (see RQ1 and RQ2), we might opt for doctors and their current patients, as this would allow us to compare how doctors and patients evaluate each other in relation to a shared communicative experience. However, as it might be difficult for doctors to discuss known patients, we could, as a plan B, decide to interview doctors about their (anonymous) patients and a group of patients about their (anonymous) doctors and simultaneously sample both groups in such a way that minimizes the chance of them being connected (e.g., by sampling patients from hospital X and doctors from hospital Y). The latter could be considered as (ethically) more feasible.

Table 1.2  Checklist: is it feasible to study/answer these research questions? • Is it likely that I (will) have the necessary knowledge and skills to answers these research questions? • Is it likely that I will be able to follow all the steps typical of a particular orthodox approach (and related to both data collection and analysis) within the time that is available to me? •  Can I deal appropriately with the ethical issues that arise from doing this research?

1.1.2 Literature The literature is a very well established, if not an essential source of inspiration for anyone who wants to develop RQs and do research more generally. However, reading the literature and contemplating how doing so might help you to develop RQs involves four main questions: 1) What do I read? 2) How much do I read? 3) When do I read? 4) What do I consider in my research from what I have read? In relation to the first question, researchers are encouraged to read at least the scientific literature in their field of interest, or the literature that relates to the research theme(s) in which they are interested. As we aim as researchers to build on existing scientific knowledge, and the extent to which we can do so determines the impact of our study, it is important to know what other researchers have studied before, so that you can identify new RQs or destinations that have not yet been visited. At the same time, it is equally recognized that reading more popular (i.e., less scientific) literature can be very inspirational in developing research themes and questions. In relationship to the ‘how much?’ questions, researchers are encouraged to be pragmatic and to prioritize reading what looks more relevant first, as it is virtually impossible to read ‘everything’. While qualitative researchers would generally agree on how to address the first two questions, there is more disagreement over the last two. To understand why qualitative researchers disagree over when we should read and how we use what we have read

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in our research, we must clarify the difference between more inductive and deductive approaches to qualitative research. Deductive reasoning means that we use existing concepts, theories or research findings to set out expectations about what to find in our data and/or how to interpret our data in relation to specific phenomena and to relationships between the data. Inductive reasoning means that we develop conclusions about what is in our data and/or how to interpret it in relation to specific phenomena and the relationships between them by analysing our data. Although most approaches to QDA use both forms of reasoning, they often use one more than the other. Regarding ‘when’ you should read, most researchers argue that you should definitely read before you start developing RQs. In fact, most researchers would argue that a solid literature review will help you to identify RQs that are worth pursuing. More inductive approaches to QDA – such as the original version of GT as developed by Glaser and Strauss (1967) – argue that you should not read a lot at the beginning of your research project, as this might lead you too much into a particular direction or stimulate you to overlook alternative or new interpretations of your data. Instead, they recommend that you read mainly at the later stages of your research, when you have decided on the specific nature of your RQs more inductively, based on the analysis of your data. Here, you develop only initial RQs at the start of your research, based in part on a limited reading of the literature, but then change the RQs (often meaning that they become more specific and focused) over the course of your research and in line with the theory that gradually emerges and develops over the data analysis process. Once you have decided on a specific focus or particular RQs, and you have developed a basic theory to understand or explain these RQs, you can consult specific literature sources that relate to your theory and that help you to further develop that theory. This also shows that the question ‘when do we read?’ relates to the final question ‘how do we use the literature?’. More inductive approaches to QDA will advise you to keep an open mind in considering the literature, meaning that you should put whatever has been found or stated in brackets and show a willingness to question its validity and consider completely different views. Here, existing concepts, theories and findings derived from the literature are used as ‘sensitizing concepts’, which make us aware of or sensitive to their potential relevance for the development of our theories; but these concepts, theories and findings should never determine your focus or interpretation of the data. In contrast, more deductive approaches to QDA, such as qualitative comparative analysis, process tracing, but also more deductive approaches to qualitative content analysis and thematic analysis allow you to use the literature in a more deductive way, in which your RQs and interpretation of your data is strongly informed by concepts, theories and findings derived from the literature. Typically, RQs tend to change more over the course of the research (and data analysis) process when you adopt a more inductive approach, while they tend to be more fixed or stable from the beginning when you employ more deductive QDA approach. More inductive and deductive approaches to QDA not only tell us when we should read and how we should use existing knowledge in developing RQs and interpreting our

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data, they also stimulate us to investigate particular kinds of RQ. Using the travelling metaphor, reading literature tells us which destinations (RQs) have been well travelled, which parts of these popular places are less well known and which locations are pristine or uncharted. This means that we can use the literature in different ways when developing RQs: a) we can confirm the existence of particular destinations (i.e., concepts, theories or findings derived from the literature); b) we can look into lesser-known parts of well-travelled destinations; and c) we can decide to explore uncharted territory. While the first destination corresponds to RQs that aim to validate or test particular existing hypotheses, the second and third destinations involve RQs that emphasize exploration and the development of new hypotheses. In selecting your destination (RQs) you can choose to see what has been seen already (deduction) or explore what is out there (induction). As a result, QDA approaches that emphasize deduction will be particularly suited to RQs that aim to validate or test particular hypotheses or theories (often in new or different contexts), while QDA approaches that emphasize induction will be more appropriate for RQs that aim to explore. Both approaches require a somewhat different way of presenting your RQs: while the former will follow logically from a critical and comprehensive literature review, the latter will be presented not only in terms of how they were formulated initially following a literature review, but also how and why they have changed over the course of your research.

1.1.3 Personal interests, knowledge and experiences There are good reasons to study research questions that interest you personally and/or that focus on themes about which you have knowledge or experience. As research can be demanding and take you to inhospitable lands, over broken bridges and damaged roads, it is important to remain intrinsically motivated in reaching your destination. The more motivated you are, the more likely you will persevere in reaching your destination. Knowing something about your destination or having experienced it in a particular way can also help, in that it might make it easier for you to navigate your way. This relates to the entire research process and not just data analysis and involves issues such as selecting sources of data and negotiating access to them, knowing about particular ethical issues, knowing how much time it might take to get from A to B and what might be innovative RQs (or new destinations) and how to interpret what you see.

1.1.4 Philosophical paradigms Every researcher makes certain philosophical assumptions in their research, implicitly or explicitly. They usually refer to three broad questions: Ontological questions: Is there ‘a’ reality and what is the nature of reality? Epistemological questions: What is the relationship between the researcher and the reality and what, as researchers, can we know about this reality?

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Methodological questions: What kind of procedures does the researcher need to follow to know something about this reality? There are many different ways in which we can answer these fundamental questions and the social science literature boasts a considerable number of classifications of what we call philosophical paradigms, or ways in which particular views on these three questions relate to each other and form a coherent approach in terms of what we study (i.e., what kind of reality?) and how we study our social world (i.e., are we connected or separated from the reality that we study? And what kinds of procedures do we use to know more about reality?). In this handbook, I start from the much-cited classification developed by Guba and Lincoln (1994), which makes a distinction between four philosophical paradigms: a positivist perspective; a post-positivist perspective; a constructivist perspective; and a critical perspective. A positivistic approach assumes that we can see reality as it is and, as a result, develop theories that accurately predict what happens around us. We do this by adopting scientific methods, usually based on systematic observation and experimental designs. It assumes that as researchers we are separated from this reality and that we can therefore study it in an unbiased way (dualistic view). However, given that qualitative research usually does not follow the assumptions of a positivistic paradigm, we will focus only on the following three. A post-positivist approach assumes that we can develop knowledge about and observe reality as it is, albeit in an imperfect way. A mixed-methods research design is often considered, in which we use qualitative and quantitative data to develop theories that approximate reality as much as possible, knowing that we will never develop a ‘perfect theory’ that predicts (parts of) reality fully. This approach starts from the assumption that there is an objective reality that is separate from the researcher but that we lack the capacity to see it in its full complexity. People adopting such an approach usually pursue RQs that aim to develop explanatory models for certain phenomena, test particular hypotheses or try to describe phenomena in an accurate way.

Case study 1.1 Post-positivist research: racism in education For instance, Stevens and Görgöz (2010) employ an ecological approach in studying teacher prejudice and observe that teachers in a British secondary school are less prejudiced towards Turkish minority students compared to their Belgian colleagues. In their qualitative case study they try to explain this observed variability in teacher prejudice. They point to differences in school ethnic composition of the student and staff population and differences in school and education-wide policies between England and Belgium to account for these differences. Stevens and Görgöz assume that they can observe levels of prejudice between teachers and at the same time identify known factors and processes

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(i.e., test hypotheses) and new ones (i.e., develop new hypotheses) about influences on teachers’ levels of prejudice. However, given the small, non-random nature of their sample, they are unable to make claims about the population of teachers more generally (so their findings are not generalizable). In addition, as they are not relying on statistical analysis, they cannot make inferences about the strength of relationships between characteristics or variables. The key value of this study is that it identifies additional hypotheses related to school features and national education policy that seem to inform teachers’ levels of ethnic prejudice. These findings can be taken up by other researchers and, if confirmed, help to develop a more accurate (but never complete) picture of what causes teachers to think in a more prejudiced way of their ethnic minority students.

A constructivist approach assumes that we (researchers and research participants) cannot know or observe reality as it is, but that researchers can build an understanding of how others see and present their subjective reality (Lincoln, Lynham & Guba, 2017). Qualitative research methods are employed as they are better equipped to describe and understand people’s subjective perceptions of reality and how such perceptions develop through interactions between actors in particular contexts. The researcher is part of reality and co-constructs an interpretation of reality, which means that you must be critical of your role in the production of knowledge about reality. Researchers working from this perspective focus on RQs that explore the shared meanings that actors give to particular phenomena (including themselves), and how such meanings are developed through socialization and learning processes. In addition, constructivist researchers explore how such meanings inform our behaviour and how we change the way we look at things according to the context in which we find ourselves, and the kinds of strategies that we use to change (other people’s) views.

Case study 1.2 Constructivist research: racism in education For instance, Stevens (2008) employs a symbolic interactionist approach to describe when and how Turkish minority Belgian-secondary-school students define their teachers as ‘a racist teacher’ and explores how these students, in interaction with each other and their teachers, adapt their view of teachers as ‘racist’. Teachers are considered racist when they express a variety of different attitudes that show that they have a less favourable opinion of students from an ethnic/racial minority background, and when teachers express different forms of behaviour that have less favourable outcomes for pupils from a minority ethnic/racial group (refers to shared views). However, teachers try to ‘manipulate’ students’ views of them as racist (refers to strategies) and students consider the intentionality and universal application of racist behaviour of teachers, teachers’ overall role performance and particular cultural scripts (refers to context) in judging whether teachers are either ‘racist’ or ‘not racist’.

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Finally, a critical approach is similar to a constructivist approach in that it assumes that people develop a subjective understanding of reality (like constructivist researchers claim) but that such an understanding is informed by the real, objective structures in which people are situated. In particular, critical researchers look at how structures of inequality in society (e.g., social class, race, sexuality and gender) structure people’s experiences and interests so that people occupying the same position see and present reality in a somewhat similar way (Lincoln et al., 2017). A key implication is that the presentation of reality is not without purpose or consequence, as it often serves particular interests and a division of scarce resources. Here, too, the researcher is seen as an important actor in the production of knowledge and as a result you should explore how your own positions of relative power inform the process of producing knowledge about reality. For instance, how does your position in terms of colour, education, sexuality and gender inform the process of developing knowledge on a particular topic? In addition, critical researchers often have a social policy agenda, in that they hope that their findings lead to a more fair and just society. The RQs that are studied by critical researchers often focus on how social groups (e.g., men and women) present reality, the particular ways in which reality is presented (i.e., the rhetorical tools that are used to do so), how these are tied to particular group interests and how such presentations reproduce or challenge social inequalities.

Case study 1.3 Critical research: racism in education For instance, Gillborn (2008) employs a critical race theory approach to explore how white people in positions of power in the British educational system (e.g., the Ministry of Education) use various strategies to hide and/or reject the importance of racism in explaining persistent achievement gaps between ethnic minority and majority students in the United Kingdom. A first strategy involves emphasizing the narrowing of the achievement gap between whites and non-whites over time instead of focusing on the persistence of the gap over time. A second strategy involves highlighting how the current government introduced particular policy initiatives but is being silent over how effective these are. A third strategy involves emphasizing the importance of other inequalities such as social class or gender over racial inequalities. A final strategy involves highlighting ‘successful’ (model) minority groups, such as British Chinese students, as proof that racism cannot explain educational inequality. The author focuses mainly on how people in power present a version of reality (i.e., racial inequalities in the British educational system), the way they do this and how this serves their (white people’s) interests and reinforces existing (racial) inequalities.

Most of the time, we are unaware of our assumptions and how they guide us in pursuing particular RQs. However, it can be very stimulating to think carefully about how

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we and other researchers in our areas of interest approach these fundamental questions. For instance, if you realize that most of the research carried out in your field employs a post-positivist approach, then developing RQs from a constructivist or critical perspective can often yield new insights as they pursue very different RQs. As philosophical paradigms also make assumptions about the procedures or steps that you must follow to develop knowledge about our world, it should not surprise us that different, orthodox approaches to QDA are rooted in particular philosophical approaches. In other words, while some QDA approaches start from a post-positivist philosophical approach, others follow the principles of constructivism or critical research. The implication is that when you pick a particular orthodox approach to QDA, you select not only a specific road map, but also particular types of destination or RQ. Table 1.3 gives an overview of how these three philosophical traditions relate to particular RQs and more orthodox QDA approaches. A distinction is made between the following approaches to QDA, each of which is also described in detail in this handbook: CGT: constructivist grounded theory CTA: constructivist thematic analysis (also called ‘reflexive TA’) CDA: critical discourse analysis NASS: narrative analysis of small stories PPGT: post-positivistic grounded theory PPTA: post-positivistic thematic analysis PT: process tracing QComA: qualitative comparative analysis QContA: qualitative content analysis TexA: textual analysis Please note the some of these approaches are discussed together in a particular chapter, such as CGT and PPGT (Chapter 3), CTA and PPTA (Chapter 10). In addition, the classification presented below does not include all existing orthodox approaches to QDA, only a number of key approaches are discussed in this handbook. Classifying these approaches necessarily involves a process of interpretation and abstraction. As a result, not all the variation and nuance within each approach can be presented by such a table. For instance, some approaches (e.g., CTA and PPTA) argue that they can be employed by researchers using very different philosophical assumptions. However, at the same time, we are confident that the classification below offers an accurate and useful overview of key approaches, their key assumptions and the RQs for which they can be used.

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Table 1.3  Philosophical traditions, research questions and approaches to QDA RQs for different philosophical paradigms

Orthodox approach suitable for RQs

Post-positivist RQs How can we explain that something happened?

QComA, PT, PPGT

How do particular social phenomena cause something to happen?

QComA, PT, PPGT

How can we describe a phenomenon in terms of its key constitutive features?

QContA

How do key constitutive features of a social phenomenon relate to more abstract concepts?

PPTA

Constructivist RQs How can we develop a deeper understanding of a social phenomenon?

CGT

How do people experience and define a phenomenon?

QContA, TA and CGT

How do people’s experiences and definitions of a phenomenon relate to their social context?

CTA and CGT

Critical RQs What kind of image of social reality is presented through more overt/direct messages?

NASS, CDA

What kind of image of social reality is presented through more hidden/subtle messages?

TexA, NASS, CDA

Why is social reality presented in a particular way?

TexA, NASS, CDA

How do these two questions relate to the macro, meso and micro context in which such presentations take place?

TexA, NASS, CDA

The handbook contains a chapter on NVivo software. As NVivo can be used to assist with very different approaches to QDA, it falls somewhat out of the discussion in this Introduction. However, given the popularity of NVivo in qualitative research, we decided to include a chapter on NVivo; one that allows a thorough introduction for those of you who have never used NVivo but would like to consider using it in analysing data, either alone or as part of a group of researchers or students working on the same project. While at this stage, you can only take note of how different approaches to QDA are suitable for investigating particular RQs, and how this in turn relates to the philosophical roots of these approaches, it is not yet clear why that is the case and what characterizes these approaches specifically. The chapters in the handbook will take you to this next step. Each chapter is structured in the same way, so that comparing approaches becomes easier for you. More specifically, for each approach each chapter subsequently describes: 1) the key aims; 2) the chapter objectives; 3) the key features, debates and historical developments; 4) a step-by-step description of how you could apply this approach; 5) a concluding section; 6) a summary checklist; 7) a worked out example or exercise of how to apply this approach; and 8) an annotated bibliography.

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The concluding chapter applies all these approaches to a particular research theme: higher education students’ involvement with sex work, based on a published master’s thesis in sociology conducted at Ghent University (Van Schuylenbergh, 2017). More specifically, we will illustrate what it would mean, in general terms, if we applied these different approaches to QDA to the RQs in Table 1.3, but then applied to a specific RT. Those of you who are interested in a particular approach to QDA can go immediately to the chapter on this approach and afterwards read the conclusions, so that you can compare your approach of interest with the others discussed in this handbook as applied on a particular example. However, if you want to get a more concrete idea of what it means in general terms to apply these different approaches to a particular RT, reading the conclusions now will be helpful.

1.2 References Bazeley, P. (2013). Qualitative data-analysis: practical strategies. Thousand Oaks, CA: SAGE. Corbin, J. (2009). Taking an analytic journey. In J. M. Morse (ed.), Developing grounded theory (pp. 35–53). Walnut Creek, CA: Left Coast Press, Inc. Gillborn, D. (2008). Racism and education: coincidence or conspiracy? London: Routledge. Glaser, B. G. and Strauss, A. (1967). The discovery of grounded theory: strategies for qualitative research. New Brunswick: Aldine Transaction. Guba, E. G. and Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin and Y. S. Lincoln (eds), Handbook of qualitative research (pp. 105–17). London: SAGE. Hammersley, M. (1982). What’s wrong with ethnography? New York: Harper & Row. Hood, J. (2007). Orthodoxy vs. Power: the defining traits of grounded theory. In A. Bryant and K. Charmaz (eds), The SAGE handbook of grounded theory (pp. 151–64). Thousand Oaks, CA: SAGE. Janesick, V. J. (2000). The choreography of qualitative research design: minuets, improvisations and crystallization. In N. K. Denzin and Y. S. Lincoln (eds), Handbook of qualitative research (2 ed.) (pp. 379–99). Thousand Oaks, CA: SAGE. Lincoln, Y. S. and Guba, E. G. (1985). Naturalistic inquiry. Newbury Park: SAGE. Lincoln, Y. S., Lynham, S. A. and Guba, E. G. (2017). Paradigmatic controversies, contradictions, and emerging confluences, revisited. In N. K. Denzin and Y. S. Lincoln (eds), The SAGE handbook of qualitative research (pp. 108–50). London: SAGE. Maxwell, J. A. (2005). Qualitative research design: an interactive approach. Thousand Oaks, CA: SAGE. Miles, M. B., Huberman, M. A. and Saldaña, J. (2020). Qualitative data analysis: a methods sourcebook. London: SAGE. Mortelmans, D. (2013). Handboek Kwalitatieve Onderzoeksmethoden. Leuven: Acco. Stevens, P. A. J. (2008). Exploring pupils’ perceptions of teacher racism in their context: a case study of Turkish and Belgian Vocational education pupils in a Belgian school. British Journal of Sociology of Education, 29(2), 175–87.

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Stevens, P. A. J. and Görgöz, R. (2010). Exploring the importance of institutional contexts for the development of ethnic stereotypes: a comparison of schools in Belgium and England. Ethnic and Racial Studies, 33(8), 1350–71. Van Schuylenbergh, J. (2017). Identiteit en image management bij studenten werkzaam in de seksindustrie. Ethiek & Maatschappij, 19(1–2), 1–31.

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2 Critical Discourse Analysis: The Articulation of Power and Ideology in Texts Stijn Joye and Pieter Maeseele

In memory of Jan Blommaert, who introduced us to CDA and taught us the power of language. Critical discourse analysis (CDA) is a systematic, linguistic analysis of discourse in its social context. As a qualitative method, CDA regards a text as the empirical manifestation of an underlying discourse and hence relates the analysis of the text to broader discursive practices as well as to social, economic and political processes. When applying CDA, you start from the idea that language is not neutral in the sense that by representing the world through the use of language, you will always actively construct a specific reality with a specific meaning given to it. CDA clearly manifested itself within the field of social sciences from the mid-2000s onwards and has become a popular methodological option to consider when a study is set up around social issues or questions of power/ exclusion and ideology, driven by a critical stance on behalf of the researcher. Despite its increasing popularity as a qualitative method to examine semiotic content and texts within their broader context, undertaking a research project with discourse analysis often proves to be quite challenging to many students and scholars alike. While there is no single or simple definition of discourse, as such, the same goes for the number and nature of approaches to discourse analysis. In addition, and contrary to, for example, surveys or experiments, there is no straightforward or clear hands-on methodological toolbox. Approaching discourse analysis for the first time can feel confusing and complex. There are a wide range of options and approaches and a lot of seemingly abstract and conceptual ideas. But it is also a flexible and powerful approach. It allows you to select, combine and use the methodology’s inherent flexibility to create an approach that is perfectly suited for one’s own research plans.

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2.1 Chapter objectives In what follows, we will explore one of the most accessible and thus popular approaches to analysing discourse: CDA. This brief and hands-on introduction aims to give you a better insight into what can feel like a heterogeneous and abstract framework to study texts and language: •

• •

First, we will start with a contextualization of CDA by addressing its broader philosophical and theoretical framework, shared by social constructionist approaches to discourse analysis and their basic principles. Second, we explore CDA’s key concepts of power, ideology and articulation, followed by some points of criticism. Third, we will present a hands-on discussion of the methodology itself and an exercise to get you acquainted with the specific logic and dos and don’ts of a research project applying CDA.

2.2 Key features, debates and historical development Let us begin with a basic definition of the central notion of discourse. Textbook definitions generally characterize the heterogeneous field as constituted of a wide range of assumptions, approaches and methodologies. Reflecting the rising popularity of discourse analysis, definitions of discourse itself are abundant but, according to Schiffrin, Tannen and Hamilton (2001, p. 1), three main categories can be distinguished: discourse as 1) anything beyond the sentence; 2) language use; and (3) a broader range of social practice that includes non-linguistic and non-specific instances of language. Combining the three categories is Jørgensen and Phillips’ (2002, p. 1) general definition of discourse as ‘a particular way of talking about and understanding the world’. In other words, discourse in the sense of language use as a social practice. Throughout this chapter, we will refer mainly to language in terms of words, but we should point out that language is to be understood in the widest possible sense of everything that carries meaning, ranging from visuals to objects. Indeed, even the way you are dressed today might be interpreted as a discursive act in a sense that you may have made explicit and motivated choices regarding the combination of pieces of clothing you are wearing, thus expressing or articulating parts of your identity, current mood, values, belonging to a social group, musical taste, etc. – for example, as hipsters or goths do in their typical dress and manner. In other words, they convey meaning to others by the specific discursive choices they have made regarding clothing, hair style, make-up, music or language in general. Through language, they disclose or represent something that is underlying. Here, we see the dual nature of discourse popping up. On the one hand, discourse as a representation of the world that reflects a specific reality. On the other hand, discourse is also a constitutive act. It ‘actively constructs a specific reality by giving meaning to reality, identities, social relations’ (Jørgensen & Phillips, 2002, p. 1, italics added).

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The latter implies that we use language with a reason, intention or objective. This can be done or expressed in an explicit way but generally people will prefer to conceal their true intentions or act in implicit and subtle manners. To that end, a central principle of discourse analysis is the tenet that language is therefore not neutral. Every single minute of the day, we make a considered, thoughtout decision to select particular words out of the enormously rich vocabulary that, for instance, the English language offers to its users. Those words and that specific selection (i.e., discourse) are meant to create meaning and to intentionally construct a specific reality. The classic example here is that of ‘terrorist’ and ‘freedom fighter’. Two entirely different labels to identify or represent the same individual, but the different words construct two very different realities with different outcomes, perceptions and evaluations. Most people will feel more positive and empathic towards the ideas and values associated with or created by the use of ‘freedom fighter’. Consequently, the language users opting to use ‘terrorist’ over ‘freedom fighter’ do so for a specific reason and with a certain interest. Language is not just an instrument to transfer meaning from person A to person B, it is also symbolic and constitutive.

2.2.1 A broader context and brief history This chapter refers to social constructionist approaches to discourse, such as Laclau and Mouffe’s discourse theory (1985), discursive psychology (e.g., Potter & Wetherell, 1987) and CDA (e.g., Fairclough, 1992; 1995). Due to the scope of this chapter, our attention will be focused on CDA, but before doing so, it is important to sketch out the broader philosophical framework that informs CDA as a particular type of discourse analysis. What social constructionist approaches have in common is first of all their critical stance, most commonly illustrated by the already mentioned premise that language is not neutral: it should not be taken for granted but challenged or contested from a critical mindset. Discourse theorists within this school consider language to be both constitutive of the social world as well as constituted by other social practices (Phillips, 2006). Language can be considered as an element or instrument of power, used by actors with particular intentions in particular social interactions. This implies that discourse should not be reduced to language alone and that discourse should be empirically analysed within its social context, thus linked to institutions, power dynamics, ideologies that circulate within a society, socio-cultural hierarchies, specific social actors and their objectives, etc. (Jørgensen & Phillips, 2002). Likewise, the analysis of discourse is to be defined as an ‘[a]nalysis of relationships between concrete language use and the wider social and cultural structures’ (Titscher, Meyer, Wodak & Vetter, 2000). When a presidential candidate delivers a speech on public television to announce their support for lowering taxes that would benefit only a happy few multinationals, this particular speech cannot be fully understood if you do not take into account the broader network of interests, political and economic actors, ideologies, etc., that are tied to this particular person and their speech. These relationships of social, political, economic, cultural

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nature, among others, that are all part of the context of text, help us reconstruct the (underlying) meaning and intention of the speech. A critical social constructionist approach therefore is about unravelling the structures of power that are embedded in texts. Equally, following from the above is the advice or disclaimer to students and scholars alike that an analysis of the content or text alone is not sufficient to earn the label of discourse analysis. A second common trait of social constructionist approaches to discourse analysis is that they see an important relationship between knowledge and social action. Put simply, the way that someone talks about climate change and global warming for instance will entail, produce and reproduce certain knowledge on that matter. In a way, the discourse created here will allow specific forms of action and at the same time will exclude other forms of action. If you use words and statements such as ‘conspiracy theory’, ‘no scientific evidence’ or ‘economic progress’, among others, and thus thrive on the ‘knowledge’ that climate change is a hoax and not really an urgent concern, the reality constructed by said discourse will please movements, organizations and governments that are in denial of climate change and thus support them in, for instance, allowing industries to continue burning fossil fuels and not in signing international agreements on reducing carbon dioxide emissions. This example ties nicely into the third shared principle of the social constructionist school; that is, an understanding of discourse as being historical and culturally specific. The discourse surrounding climate change is different today from what it was fifty years ago. The main discourse is probably different in China from what it would be in Belgium. In other words, the way of interpreting and representing something – such as climate change – is contingent (i.e., dependent on or variable in a specific context in terms of space and time). Discourses on a certain topic can change over time and place, once again stressing the importance of taking into account the context when studying a text and conducting discourse analysis. The different social constructionist strands tend to differ in terms of their analytical focus (see Jørgensen & Phillips, 2002, for an in-depth discussion). As this falls beyond the scope of this chapter, we will restrict ourselves here to CDA and a broader description of its history and position in the academic field. Characteristic for CDA is its focus on the specific while acknowledging the general, methodologically manifesting itself in an analysis of texts by incorporating their context. As a methodology, CDA emerged in the late 1980s as an interdisciplinary European school of discourse studies and ‘[s]ince then, it has become one of the most influential and visible branches of discourse analysis’ (Blommaert & Bulcaen, 2000, p. 447). Illustrated by Figure 2.1, CDA clearly manifested itself within the social sciences from the mid-2000s onwards, growing steadily in popularity ever since. The approach is found particularly in scholarly areas of communication, linguistics and educational research. Recently, a number of eminent scholars have voiced their doubts about the appropriateness of using the label of ‘critical discourse analysis’ as a methodology or indication of the field, instead proposing to speak of ‘critical discourse studies’

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Figure 2.1  Inclusion of term ‘critical discourse analysis’ in ‘topic’ in Web of Science publications over time (snapshot taken in March 2022)

and even updating the titles of their seminal works (Flowerdew & Richardson, 2019; Wodak & Meyer, 2016). According to van Dijk (quoted in Flowerdew & Richardson, 2019, p. 2) ‘the rationale for this change of designation resides in the fact that CDA was increasingly not restricted to applied analysis, but also included philosophical, theoretical, methodological and practical developments’. Elsewhere, van Dijk also referred to CDA as not being an explicit method and methodologically as diverse as discourse analysis in general (Wodak & Meyer, 2016, p. 3). Indeed, residing under the new label of critical discourse studies we find a broad group of ‘varying approaches each with distinctive, but also overlapping methods’ (Flowerdew & Richardson, 2019, p. 2), including but not limited to socio-cognitive approach, discourse historical approach, (multi-modal) critical discourse analysis, cognitive linguistic critical discourse studies, cultural critical discourse analysis and discourse-theoretical analysis. All share a central interest in a systematic investigation of semiotic data such as newspaper articles, conversations in the playground, public speeches, films, interviews, etc., in order to unravel how people use language to create meaning, to persuade others to think about events in a particular way, and to manipulate those people while simultaneously concealing their own intentions (Hansen & Machin, 2013, p. 115). In other words, echoing one of the main characteristics of social constructionist approaches to discourse analysis, the plethora of approaches linked to critical discourse studies are all inherently defined by a critical stance. Therefore, we should regard critical discourse studies more as an interdisciplinary and heterogeneous school, paradigm or research stance that starts from, is fully informed by and ends with a critical state of mind and attitude, manifesting itself in all steps of the research process (cf. 2.3.2). As CDA is one of the most solicited students of this school, it will be our focal point for the remainder of this chapter.

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2.2.2 CDA: what’s in a name? Some key concepts and principles So CDA is a systematic, linguistic analysis of discourse – existing of written and spoken language, non-verbal communication and images – in social interactions and within its broader context. Given the critical stance, CDA aims to deconstruct ideology and power relations that are articulated by means of a (socially shared) group of statements, ideas, images, etc., regarding a specific topic. To achieve this goal, analysts working from a CDA framework will relate their discursive analysis to broader social, economic and political processes. In the above brief description of the method we have highlighted in italics the three building blocks that form the basis for a research project inspired by CDA: power, ideology and articulation. Let us take a closer look at these three key concepts.

Power As with all approaches covering social constructionist discourse analysis, Foucault’s (1972) interpretation of power as productive rather than oppressive and bound up with knowledge is central to CDA. Foucault argues that ‘power operates through discourse by creating our social world and identities in particular ways’ (Foucault cited in Schrøder & Phillips, 2007, p. 894). Power in general and issues of power asymmetries, manipulation and exploitation in particular, are the central focus of many investigations within the field. Research questions set out by critical discourse analysts typically stress ‘patterns of domination whereby one social group is dominated by another’ (Phillips, 2006, p. 288). Underlying this process is the unequal distribution of power and resources within society as power is derived from the privileged access to social resources such as education, wealth, knowledge, etc. According to Machin and Mayr (2012), this privileged access and thus power provides authority, status and influence to those with access while enabling them to dominate, coerce and control subordinate groups who do not have such access or who have only limited resources to their disposal. In a CDA framework, these power structures or relations of inequality, exploitation, exclusion and manipulation are produced through, maintained by and embedded in texts. Therefore, in applying CDA, you will approach apparently neutral and objective news reports from a critical perspective, posing the questions: what kind of a world or reality is constructed in or by the text? Whose benefits are served by that constructed representation and reality? To offer an example, numerous studies have examined how the UK press has covered the EU Referendum leading up to Brexit and have disclosed how national newspapers basically created two alternate realities tied to the ‘leave camp’ or the ‘remain camp’ by their choice of front-page stories, titles, tone, actors cited and issues addressed or neglected (see, e.g., Levy, Aslan & Bironzo, 2016). This observed bias in the news coverage is of course tied to the differences in viewpoints and opinions of political parties, economic actors and other social organizations in the United Kingdom with regard to the decision to be taken at that time. Media outlets such as the Daily Mail, the Daily Telegraph and The Sun were found to be biased in their reports in favour of stepping out of the European

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Union, hence generating and legitimating the power position of the leave camp in their texts while simultaneously challenging or resisting the reality constructed by media outlets affiliated with the remain camp.

Ideology The case of the EU Referendum in the United Kingdom is, furthermore, a good illustration of the notion of ideology and its ties to discourse and CDA. Ideology can be defined as ‘some organised belief system or set of values that is disseminated or reinforced by communication’ (McQuail, 2000, p. 497) and is the basis of a discourse as language is the way that ideologies materialize and manifest themselves, according to van Dijk (1988). In the example of the referendum, UK news media have operated on an ideological level by distributing and supporting the values, norms and ideas (thus ideology) of specific social groups (i.e., the remain camp and the leave camp) with particular interests (i.e., to put it bluntly, to remain in or leave the European Union, respectively). To be clear, these interests are generally concealed in texts and it must be noted that the above is not always an active or conscious effort on behalf of individual journalists, but often achieved or produced through (internalized) routines and practices related to the news organization, profession or broader societal structures in which one operates. In addition, on the level of texts, we need to point towards the idea of intertextuality, defined as a blended environment in which different kinds of text condition each other in order to legitimate certain worldviews (Chouliaraki & Fairclough, 1999). One single opinion piece on the European Union should thus be interpreted as one particular element in a rich intertextual network of other opinion pieces, news reports, interviews by key actors, policy documents, etc., all generating meaning and conditioning each other’s meanings. Or, as the attentive reader will have remarked by now, text and context.

Articulation How do we now connect or integrate the notions of power and ideology in our concrete research design? The answer to this question is the concept of articulation. In short, it refers to how texts express or articulate discourses. It is your empirical entry point into conducting a discourse analysis. As mentioned earlier, CDA researchers are interested in addressing questions of power. On a societal level, we can identify a wide range of actors who all have particular or different ideas, values, norms, etc., and access to resources (cf. the definition of power). These actors are caught in networks and relations of power that are, per definition, unequal. People with similar ideologies or interests might find each other and form a group. For instance, a political party named ‘Education First’ that has a clear and shared vision on how the educational system should be organized. They use language to express their thoughts, make statements about the central role of teachers, construct an ideal model of what a school should look like, and so on. In other words, they produce a discourse on education. Of course, other people may have a different take on this and might also form a political party, ‘Make Children Great Again’, that holds

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opposing ideas regarding educational matters. ‘Make Children Great Again’ could, for instance, degrade the role of teachers and instead discursively construct an educational system that centres on the individual child. The latter group’s discourse clearly conflicts with the former one and only one of them can be the (temporarily) dominant one and form the basis for a reform of the educational system. This struggle between conflicting views on education is also found in the texts that these parties produce or that are being produced over them. According to Kress (1985), ‘[e]very text arises out of a conflict between discourses and the struggle over which discourse is to impose its own meaning as the “natural” meaning of a text’. In other words, the discourses of both our fictitious political parties are articulated through texts such as a campaign leaflet of the party (i.e., a manifest articulation of the discourse as the party will probably be very explicit on their position and ideas about education) or newspaper reports (i.e., a more latent or concealed articulation as journalists will report on the issue in detached and factual ways). Reminding ourselves of the earlier definition of discourse as a group of statements regarding a specific topic – education in this example – not all statements will find their way to concrete texts. For instance, Figure 2.2 portrays a situation in which five statements or elements of a discourse are picked up by journalists or whoever authors a text and are accordingly articulated in a text. The way we – as audiences or as researchers – get in contact with a discourse is thus through texts. These are tangible, these are the empirical manifestations of a discourse and consequently our starting point as a researcher. When analysing these texts, we must look for specific statements on educational matters that can be assigned to one of our fictitious actors and need to identify remarkable patterns in the texts. If a certain newspaper

SOCIAL CONTEXT - SOCIETY

DISCOURSE

Manifest - explicit Latent - implicit

Interview transcripts

Television show

TEXT

Radio broadcast

Newspaper

Speech

Figure 2.2  The concept of articulation

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covers a speech by the president of ‘Education First’ but always refers to her as ‘insecure’, ‘stammering’, ‘grey personality’, this kind of motivated choice of words should attract our attention as researchers since the representation is telling about the reality that is being constructed by that particular journalist who is apparently not in favour of the party, the president or their ideas. To put it more generally, we focus on texts to draw out a discourse that is concealed by or buried within a particular text and which supports the interests of a particular group of people in any given society. Our analysis of the linguistic choices in the text are put in a broader context by relating them to the power relations and struggles of the social world. The idea of articulation allows us to empirically engage in a process of re-assembling a discourse by looking at particular linguistic choices in a text that attract our attention. According to Richardson (2007), CDA is thus mainly used to explore how discourses are realized linguistically in texts to constitute knowledge and social relations. Focus is put on the way that these power relations are enacted, reproduced and challenged by discourse. This approach raises the complex issue of agency attributed to the author of a text. On the one hand, we have stressed multiple times how texts are produced with a certain intention of and motivation by the author, stressing the deliberate and thoughtful choices made by an author and placing the latter in the driver’s seat. On the other hand, texts are said to be moulded by and interwoven with social structures, rather hinting at a determining role of structure and thus not awarding much agency to the author of a text. While at first sight this might seem to be a contradiction, it is actually a prime example of how texts and their authors cannot be analysed without taking into account the context and, secondly, an example of how power is not an unidimensional given but something that operates on different levels of society, combining bottom-up and top-down relations of power. Now, what are some of the other most uttered criticisms on CDA (see, e.g., Carvalho, 2008)? First, critics refer to the lack of longitudinal studies and diachronic analyses in the field, as most studies focus on only one particular event, what can be regarded as a snapshot rather than an overview. Second, there appears to be a bias in terms of the actors who are being examined. Many studies opt to analyse discourse in mainstream news outlets and/or articulated by dominant (political, economic, cultural) actors in society, hence paying little attention to the discursive strategies of less dominant social actors such as alternative news media or non-governmental organizations. Third, we have already hinted several times at the importance of studying the text within its context. It regularly happens that people claim to have conducted CDA while said person has focused too much, if not entirely, on the text, getting lost in a purely linguistic analysis and therefore ignoring the broader context. Often, research papers display too strong a connection to the text and not enough attention is paid to the (journalistic) production process, the consumption or public understanding of texts, the power relations in society – in other words, the context of a text. Finally, critical discourse analysts are frequently criticized for being biased and negative, implying the idea of a self-fulfilling prophecy whereby the socially committed and engaged researcher

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starts off the study with a well-delineated research question dealing with, for example, power inequalities and subsequently finding evidence of said premise. To avoid these criticisms, de Lange, Schuman and Montesano Montessori (2012) assert that a critical attitude as a researcher also includes necessary moments of critical self-reflection, acknowledging your own subjectivity, while always guaranteeing transparency about the research process and the position that you take as a (engaged) researcher. The latter is common to all forms of qualitative methods and is generally referred to as reflexivity. That is, being aware of your personal bias, coming from a certain (temporal, spatial, cultural, political) context and having a specific background, training and knowledge. In other words, everyone is biased and so are our observations. Therefore, the prerequisite to be reflexive on and attentive towards these biases is a key part of your methodology and research. In the context of CDA and other forms of qualitative research, this is achieved through systematic analysis, where you provide your audience with a rich argument and description of all steps taken in the process and explain the reasoning behind them, in addition to these decisions being informed by theory and methodology.

2.3 Doing critical discourse analysis step by step Before going into the more practical organization and set up of a research design under the heading of CDA, it is important to be reminded of the fact that CDA is a qualitative methodology, which implies that this road map is not carved in stone. Some studies will not involve all steps or delve into all levels of the presented model to equal depth and focus. Most projects will rather follow a non-linear path and are characterized by a cyclical process of research. Having said that, this is not a free pass to regard a discourse analysis as merely summarizing a select number of texts and give your own personal account or interpretation of them, as is unfortunately too often the case with student projects. We stress the idea of CDA as a systematic analysis of texts within their context. One way of taking a systematic approach is to build your research design around an established model within the field. For this chapter, we will follow the seminal model of Norman Fairclough (1992, 1995) and its further (empirical) refinements by the likes of Chouliaraki (2006), Richardson (2007) and Machin and Mayr (2012).

2.3.1 Fairclough’s three dimensions of analysis Language carries a unique signifying power, a power to represent events in particular ways (Fairclough, 1995). This idea is central to Fairclough’s (1992) model for the study of the relations between discourse and social and cultural phenomena. The model consists of three dimensions: text; discursive practice; and the wider social practice. The first dimension, text, should be understood ‘as a complex set of discursive strategies that is situated in a special cultural context’ (Barthes cited in Fürsich, 2009, p. 240). According

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to Chouliaraki (2006) and Richardson (2007), analysing a text is basically analysing the choices made by the author of that text. Put differently, given the status of a text as being the empirical manifestation of a discourse or several discourses, researchers will focus on the linguistic characteristics of a text, including but not restricted to the use of vocabulary, sentence construction, verb conjugations, etc., as well as a number of so-called ‘discursive devices’, all resulting from decisions taken by the author. When embarking on a multimodal analysis (see Machin & Mayr, 2012), the analysis of visual images and/or sound is added to the study of the written language. For instance, in the case of television news items, researchers will transcribe the voice-over for the linguistic analysis and will further examine the footage by observing who or what is in frame and how is it represented (Chouliaraki, 2006). In this respect, it is important to ‘recognize that textual or journalistic meaning is communicated as much by absence as by presence; as much by what is “missing” or excluded as by what is remembered and present’ (Richardson, 2007, p. 93). In other words, while it is probably the default setting of any researcher to go ahead with the presented, sampled or retrieved data, it might prove useful to take a moment and deliberately look for ‘missing’ data. When studying international news coverage, for instance, it might take some time before an event attracts the attention of the international media. A prime example of this was the 2003 SARS outbreak and arguably the same applies for the covid-19 pandemic. Although the first known case of SARS occurred in November 2002, it was mid-February 2003 before the disease was reported on by Chinese media and another month before the western news media picked up on the epidemic. This period of global media silence or absence of data is very meaningful to critical discourse analysts. For one, it can help to unravel the dominant selection criteria applied by (Western) media, thriving on proximity and an ethnocentric vision of the world. What is missing or excluded in the news coverage is also a gateway into revealing the underlying global power relations and hierarchies that are reflected in journalistic practices and the international news output (Joye, 2009, 2010), echoing the power of the media to sway public perception by choosing what and what not to publish (Berry et al., 2007). The other two dimensions of the model are concerned with discursive and social practices. Representations of reality are – as we have explored throughout this chapter – always social constructs, embedded in a particular and layered context. But to surpass the level of mere text analysis,1 we must refer to discursive practices in the sense of ways of using language in general and, more particularly, the structural and functional properties of the production, dissemination and consumption processes that may limit or inform the textual choices of the author. Discourse is, after all, context-dependent (Phillips, 2006) and language use – such as news reports, propaganda, radio broadcasts – is the outcome of a range of specific practices, often of professional and institutional nature (Fairclough, 1995). Here, we explicitly use the term of ‘text analysis’ instead of ‘textual analysis’, so as not to raise confusion with the methodology of textual analysis discussed in Chapter 9. Although both methods share some similar ideas and assumptions, textual analysis – to put it bluntly and without nuance – is more appropriate when dealing with (audio-)visual data (e.g, fiction series, music videos) rather than written texts for which CDA is more suited. 1

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If we take the example of international news correspondence, this particular production context is engraved with numerous editorial practices that might not all be evident to the individual journalist operating within this constellation but do have the potential to either facilitate or hinder them in their daily routines, work and thus choices (of language use). To name a few, we can refer to economic conditions of news production, such as the high costs related to foreign correspondence in relation to decreasing budgets of (national) news media, which could limit the opportunities to go abroad and report events live on site, the editorial policy of the news outlet that hires you to cover a story, the fierce competition with other journalists for (privileged) access to sources and information, among other things. You could also look into the genre of the texts being studied. Are we dealing with a peer-reviewed scholarly article, an election pamphlet of a right-wing political party, or a popularizing opinion piece on a satirical blog? In all three cases, we could find an item or a text on migration and even the same statements or ideas. However, the intention and meaning of the three texts will undoubtedly differ substantially according to the particular (productional) context of the three authors, respectively academia, politics and comedy. In short, the genre and the particular context in which these texts are written can help explain why the authors have made the specific choices we have observed on the previous dimension of text. In addition, this dimension also entails the notions of interdiscursivity and intertextuality. The former refers to the fact that different discourses can be articulated in one single communicative event (Fairclough, 1995), struggling to impose their ideas as the dominant or ‘natural’ meaning of said text (cf. 2.2.2). Intertextuality on the other hand starts from the understanding that each text or unit of discourse is produced as being conditioned by previous units and should thus be interpreted and analysed as such. New meanings are created through the relationships between texts (van Dijk, 2009, p. 192). For instance, someone who watches the most successful film of all times, ‘Avengers: Endgame’ (Russo & Russo, 2019) as an isolated cinematic feature will not share the same viewing experience as someone who have seen the twenty-one other preceding movies that are part of the larger Marvel cinematic universe. The former will most likely not understand the plethora of ‘easter eggs’ and nods to the other texts that are included in ‘Avengers: Endgame’, while the latter will fully enjoy the intertextual references and award additional meaning to the same text. This is also an example of how the dimension of discursive practices entails the context in which texts are consumed in addition to the productional context in order to understand how meaning is constructed through language use. While the dimension of discursive practices is thus a contextualization of the textual dimension, the last level of social practices invites the critical discourse analyst to delve into the broader social context of the two other dimensions. Discourse is also permeated by structures, institutions and values from outside the author’s immediate surroundings or context such as economy, politics and ideology (Richardson, 2007). Neither individual authors nor larger organizations can escape the fact that they are tied to a broader social system (Shoemaker, 1991). As indicated above, discourses are both socially constitutive

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and socially conditioned: they (re)produce social structures as well as reflect them. In our research design, this is consequently the place where we must ask the essential question of what kind of reality is being discursively constructed. Will the observed and analysed texts help to continue or reproduce existing inequalities, unwanted identities and other undesirable social practices? Or do the texts and their (hidden) discourses contribute towards social change and thus offer an alternative vision or moment of resistance to and contestation of the established power relations and structures? It is at this point where our discourse analysis earns the prefix ‘critical’. This dimension of Fairclough’s model refers essentially to ideological effects and hegemonic processes (Blommaert & Bulcaen, 2000), whereby ideology is generally interpreted as ‘meaning in the service of power’ (Thompson, 1990, p. 7). Although van Dijk (2009, p. 199) admits that it is theoretically and empirically impossible to provide a complete and detailed ‘account of the ideologies involved and the structures of news that are controlled by them’, he states that a polarization between the ingroup (‘us’, a positive self-image of a social group) and the outgroup (‘them’, assessed and represented in a negative way) is characteristic of many such ideological structures. Next to relating the textual findings and the discursive practices to the broader social field or system where the communicative event takes place, you are also invited to relate your findings and interpretations to a broader theoretical framework and dwell on grand theories in the line of Orientalism, neoliberalism, imperialism and othering, among others, to further provide context and analytical depth. As will become evident in the following section, these three dimensions of analysis play a prominent role in shaping the actual research process and the separate steps of CDA.

2.3.2 Phases of the research design When setting up a research design for CDA, we can identify six steps that are part of a cyclical process, meaning that researchers can go back and forth between the different steps outlined below. (1) Choice of research problem The first step is inherently tied to the broader philosophical and theoretical framework of CDA and one of its main characteristics is propagating and being determined by a critical mindset and stance. Critical discourse analysts will typically look for research problems related to inequalities in social relations and instances of power manipulation, imbalances and biases within society. This may come from a personal observation or it may equally be informed or suggested by an existing theory, a literature review, the empirical findings of a prior research project or an already identified discourse. If that is the case, the empirical focus tends to shift towards an interest in exploring how a certain discourse is then articulated in a particular data set. While this might entail that the researcher thus already has a good prior overview of the problem and a possibly firm idea of the discourses to be confronted with, do not forget the previously

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discussed critique on a potentially biased and negative position of the critical discourse analyst. In other words, it is crucial to look at the data with an ‘open’ mind and not to start off with an ambition to just cherry-pick and select evidence of an already determined outcome. (2) Formulating research questions Following its qualitative nature, a discourse analysis will not start from research questions that deal with frequencies, numbers or percentages. One might of course make general statements in terms of which discourse is dominant, but it is nonsensical to ask how many times a discourse is found within a certain text or how many discourses are present in a data set, as such questions imply a perspective on discourses as perfectly delineated and measurable entities. Assumptions that contradict many of the philosophical and theoretical underpinnings of a social constructionist approach to discourse. Instead, questions will deal with issues of representation and meaning in the vein of how a particular issue is being represented in news reports, which political ideas are articulated through policy reports, how power inequalities are discursively constructed and confirmed in presidential speeches, etc. Terminology and concepts proper to the CDA framework such as articulation, discursive practices, representation, and power will be frequently used in formulating these research questions. Another option is to structure possible sub-questions according to the three dimensions of Fairclough’s model discussed earlier. (3) Choice of data and sampling As texts are the empirical manifestations of a discourse, you will gather semiotic data such as newspaper articles, household conversations, public speeches, films, interviews, etc., in order to unravel how people use language to create meaning. The sampling process depends on the research questions of the project, your prior knowledge, your access to data, etc. The latter is a very pragmatic consideration, but often turns out to be the most determining factor. You might hold the most amazing research objectives, but if you are not granted access to the data, there is simply no research at all. The other side of the spectrum, too much data, can also present a challenge. It is the question that anyone will be confronted with at some point or another in the research process: how many cases do I need? A clear-cut answer is firmly desired but never acquired. The level of detail that is inherently common to a qualitative in-depth analysis of a text and its context implies a huge investment of time and labour. To keep everything feasible, a clear research objective or a wellthought-out selection of discursive devices can help to keep the focus and allow for a larger number of cases to be studied. However, most scholars will opt for a reduced sample based on critical discourse moments (Chilton, 1988; Raeijmaekers, 2018). These are periods of several days in which we see a shift in the discourse following specific demarcated events (a political hearing, a disaster, a world summit, an anniversary, a public statement, etc.) that may challenge ‘established’ views and trigger debate in society (Carvalho, 2008, p 166). In general, media coverage will be affected by these events, both in quantity (e.g., a peak in news attention) and quality

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(e.g., new perspectives, different discourses) (Raeijmaekers, 2018). Once a critical discourse moment is identified, you may make a second selection of the data according to the criterion of relevance. After all, not all documents, news reports, interviews, etc., that are produced or published within the defined time are fit or significant for the research. In the case of a newspaper article, a rule of thumb could be that at least the majority of the article and/or the title and lead should refer directly to the event under study to be considered relevant. (4) Transcription of data When working with written language and text, a transcription is unnecessary and you can immediately work on the data set as it is. In the case of texts in the sense of visuals and spoken language, you need to insert an additional phase of detailed transcription. What is being said or shown (e.g., camera angle, montage) is to be written out with keen attention paid to extra-textual markers such as tone of voice, pauses, framing of images, etc., as these might also be signifiers of meaning (cf. 2.3.3). (5) Analysis of the data While the previous steps can be considered the necessary preparatory phase, the majority of the workload is to be situated in the fifth step of the research project. Here, you delve into the data and conduct the actual systematic analysis, generally organized according to the three dimensions of Fairclough’s model. Given the centrality and importance of this stage, we will go into it in more detail below. For now, we would like to point out that the phase departs from a cyclical reading of the data. In several rounds, you will go back to the data for different, subsequent readings of the same data. In a first round, we encourage you to just get acquainted with the data, go through the selected texts with an open mind, and write down the first thoughts or reflections. Were there any patterns that caught your eye? Remarkable quotes or ways of representing certain issues? Any notable choices made by the author? Of course, these observations at face value could also be retrieved during the previous fourth step when you are transcribing the (visual and spoken) data. A second reading of the corpus allows you to further elaborate on said ‘raw’ analysis. Do you find more evidence for or examples of the identified patterns of the first reading? Or rather elements that oppose or qualify those preliminary first reflections? Additionally, this is also the moment where the researcher adds a more systematic approach to the analysis by introducing the discursive devices as a way to examine the text in more detail, depth and focus. Finally, a third reading is meant to further complete the text analysis while simultaneously moving towards the essential step of contextualizing the textual or linguistic findings by incorporating the dimensions of discursive and social practices. (6) Writing out the research and valorization The final and logical step of your process is to write out the most relevant findings of the literature review and the empirical study into a coherent paper or book chapter and submit it to a publisher or academic journal.

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2.3.3 A closer look at the phase of text analysis Here, we will focus exclusively on the dimension of text as the majority of the analytical work happens on this level. To briefly recap, our attention is attracted towards a linguistic analysis of the author’s choices by means of discursive devices (DD). In a later stage, this text analysis is to be complemented with a discussion of the context by means of the discursive and social practices. The following overview is a selection of the most used discursive devices within CDA and largely dwells on the works of Richardson (2007) and Machin and Mayr (2012). First, we will go into some basic lexical choices (DD1), after which attention is paid to a selection of discursive devices to represent actions and people (DD2–DD9). Finally, we will briefly discuss some visual representational strategies (DD10). To be clear, these discursive devices are to be seen as anchor points or handles that can guide you through your empirical analysis of the text. On the one hand, they can help to dig further and examine in more detail the patterns that were identified during your first reading of the data by showing you what particular kind of language use to look out for in the data when undertaking the second reading. On the other hand, they give you an established framework within which you can interpret and categorize your personal reflections, findings and identified patterns by matching the latter to the several discursive devices offered by scholarly literature.

DD1: Lexical choices Every language offers its users a wide range of words with the complimentary option to form many meaningful combinations with these words. From these practically unlimited possibilities available to an author or user of said language, one only is eventually chosen and printed, spoken out, written down, etc. In other words, the semiotic choices of an author are motivated choices and are therefore very relevant for critical discourse analysts to take a closer look at. By ‘motivated’ we mean that there is a reason or particular intention behind the word choice. Why is someone referring to their new living place as ‘house’ and not as ‘home’, something that might only occur after some months? In the end, the person is talking about the same material place but the different words clearly trigger different associations, feelings or connotations. When you talk about your living place as your ‘home’, you articulate a warm and positive relation with that particular place as compared to the shallow or material bond that is articulated when using ‘house’ instead. Analysing texts means observing patterns in the use of nouns, adjectives, metaphors, superlatives, tone, among others, in addition to paying attention towards the predominance and absence of particular kinds of word. Suppose you always meet up with a friend to have a drink at the end of a work week. If week after week he talks about his colleague Donald in a negative manner, only mentioning the mistakes being made and regularly calling him ‘incompetent’, ‘not fit for the job’ and so on, while in the weekly account of his other colleague Joe, your friend is repeatedly applauding him for again doing a ‘tremendous’ job, being ‘simply the best’ and so on, you will quickly get a firm idea of the discourse that is underlying your weekly updates of your friend’s work life and

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his relationship with his colleagues. Of course, if the texts under study are more factual and objective of nature than the everyday pub talk during a social Friday, it will be more challenging for you to grasp the discourse as its articulation is more implicit than in the case of a biased or subjective text or a personal account. We previously mentioned that it is important to look at what is absent in a text, what is missing or downplayed. Likewise, the opposite is also true, as the overuse of certain words is equally revealing. This practice of overlexicalization refers to the repetition, abundance and overuse of particular words and their synonyms, hinting at a sense of over-persuasion and ideological contention (Machin & Mayr, 2012). The typical example here is that of ‘male nurse’ and ‘female doctor’. The use of respectively ‘male’ and ‘female’ is not necessary here and even not meaningful as the same person or author would probably not talk about a ‘female nurse’ and ‘male doctor’. The gender classifications are thus an example of overlexicalization and cues to a dominant, underlying ideology for the critical researcher, articulating unspoken expectations about gender roles in relation to professions that are tied to patriarchal power structures within a society.

DD2: Transitivity A second cluster of discursive devices (DD2, 3 and 4) analyses how actions or events are being represented in texts. A first option is to study the words, verb tense, sentence structures, etc., of a statement or group of statements from the viewpoint of transitivity. Here we ask ourselves the question of how events and processes are (un)connected to subjects and objects. Consider the following headline of a news report on the restructuring of a fictitious company ADC: ‘100 workers were fired yesterday’. Reminding you of the fact that the headline and its exact wording are motivated choices made by an author, you should immediately start to wonder what could be an alternative headline to cover the same story? Or what could be a different combination of words to describe the event of 100 people being laid off by the company? An option could be the following: ‘ADC fired 100 workers yesterday’. So, if you opt to use the passive verb tense as was the case in our first headline, the actor ADC who is responsible for firing those 100 people remains concealed or hidden from the audience. The social actor with the power to lay off so many people is not held accountable nor even being addressed by the semiotic choices of the journalist. In addition, the fact that 100 people are fired is represented as if it concerns a natural(ized) way of affairs, something that just happened or was meant to happen as it apparently occurred without any (human) interference. The focus is on the effect or outcome of the process, not on the process itself nor on its initiator.

DD3: Nominalization A similar strategy of concealing the actor in power to take decisions is the discursive device of nominalization whereby a noun replaces the process. Instead of ‘ADC reorganizes its staff’, the headline would read ‘A reorganization will be implemented’. The verb ‘to reorganize’ is transformed into the noun ‘reorganization’, discursively hiding not

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only the social actor who is responsible for the reorganization but also those affected by the process. Nominalization thus entails strategies of concealment and depersonalization by obscuring the agency and responsibility of the actors involved. Why would an author choose to do so? Echoing the critical nature of CDA, critical discourse analysts will argue that the use of nominalization and its resulting way of representing the event is in the benefit of the author. The reason could be found in personal interests or motivations of the journalist; for instance, if they are an individual shareholder of ADC. Or the journalist could work for a newspaper that is part of the same conglomerate of ADC and thus experience ‘editorial’ pressure to opt for nominalization in covering the news story. The latter reflection is actually a good example of Fairclough’s second dimension of discursive practices as we tried to explain the author’s textual choices by looking at their context of the newsroom.

DD4: Modality For a critical discourse analyst, direct citations in a text are a fruitful source of data, as these quotes often entail explicit articulations of a discourse or claims on behalf of the speaker. Equally important are paraphrased statements of a speaker as these are presented by the author to the audience of a text in a particular way. In both instances – citations and paraphrased quotes – you should look at how the relation between a statement and the source of said statement is discursively constructed. How confidently is the author being represented, how coherent is the argumentation presented and how persuasive is the message? These questions deal with modality. A high level of modality would mean that the author is very committed to and certain about what is being stated. Compare ‘it is cold’ to ‘I am cold’ and ‘maybe it is a little cold’. While all three statements refer to the temperature as experienced by the speaker, in the first example there is no room for doubt. The feeling is being communicated as a scientific fact, representing a high modality. Words like ‘maybe’ or ‘potentially’ tend to downplay the modality of a statement and create an image of the speaker who is uncertain and less credible (Machin & Mayr, 2012; Richardson, 2007). Again, underlying power relations can inform the author of a text to attribute high or low modality to speakers and their statements. Likewise, the author of a text will make use of so-called ‘quoting verbs’ to connect speakers to their statements. In a neutral manner, the author can use words such as ‘say’ or ‘report’ to describe how someone has spoken. Other verbs might express something about the person’s mood, attitude or character. Consider choices such as ‘she declared’, ‘he grumbled’, ‘he stammered’, ‘she shouted’, and so on. Depending on what is chosen by the author of a text, it transmits additional meaning by which the author can pass judgement, influence the credibility of a statement and/or shape the audience’s perception of someone. This fourth discursive device is a nice lead into the next domain of representational strategies of people, that are the semiotic and visual choices to represent social actors (see DD5–DD9). Similar to the representation of events, language allows the author to highlight, conceal or omit certain features and aspects of one’s identity that can be associated

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with certain kinds of discourse. Machin and Mayr (2012) clarify this by considering the following two possible headlines of a western newspaper and urge us to take a look at the adjectives that indicate specific characteristics of a person (in italics): ‘Muslim man arrested for fraudulently claiming benefits’ and ‘Father of two daughters arrested for fraudulently claiming benefits’. Both headlines report on the same event and same actor, but different articulations, discourses and realities are being constructed by the language use.

DD5: Individualization and collectivization The focus of a text can be on the individual actor or on collective entities or communities. Again, a motivated choice of the author with potentially different outcomes. For example, in the context of the recent refugee crisis, Van Haelter and Joye (2020) showed that some of the news reports on Belgian television focused on individuals in terms of visual choices (close-ups) and the main narrative angle (personal testimonials), hence applying a strategy of individualization that can help to reduce distance and raise empathy for the cause of these forcibly displaced people who are being humanized. This could occasionally lead to personalization, which means that certain individuals become the subject of a report and thus can tell their story in a more comprehensive way (Machin & Mayr, 2012). On the other hand, the study demonstrated that the majority of the news items applied the discursive device of collectivization and represented the refugees in large groups, referring to them as a collective and attributing stories and statements to the entire group, creating a more detached position of author and audience towards a collective other.

DD6: Impersonalization Collectivization is similar to impersonalization whereby the author decides not to mention the name of the individual actor or institution, but prefers to refer to a larger entity. Not so much with an intention to create distance or detachment as was the case with the previous device, but rather to give additional weight to the statement. ‘Belgium says no to Europe’ resonates more deeply than ‘Belgian politician says no to the proposal by a Spanish member of the European parliament’.

DD7: Functionalization Another strategy to grant more gravitas to statements, claims or ideas is to mention the job title of the actor. If a statement on the health risks of covid-19 is made by ‘medical expert professor X’ or by ‘stand-up comedian Y’, we have a discursively constructed difference in perception related to the authority, status and legitimacy of both speakers visà-vis the issue at hand. It is unlikely that you will be comparing professors to comedians, but, think about it; you might be comparing statements made by male and female doctors, by politicians and activists, or by the chairman of the World Health Organization and by an obscure entity hidden behind the figure of QAnon.

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DD8 and DD9: Anonymization and aggregation The last two devices to represent actors are taken together as they are often used together. When anonymization is applied, the author intentionally adds a degree of uncertainty to their text to avoid having to develop a detailed and coherent argument (Machin & Mayr, 2012). For instance, politicians starting an argument by saying that ‘many people told me that’ deliberately leaves it open to the audience’s guessing about the number of people, their identity and so on, but it does manage to get the underlying message across that they are supported by a lot of people. A similar strategy is aggregation where the author treats people as objective statistics and numbers. ’10,000 people are protesting’ is an example of aggregation. While we all know that the number of people was not precisely 10,000, van Dijk (in Machin & Mayr, 2012) stresses that this way of phrasing gives the impression of objective research and scientific credibility, thus strengthening the statement and the point being made by the author.

DD10: Visual semiotic choices Of course, non-linguistic features such as images also substantially contribute to the meaning embedded in a text. Machin and Mayr (2012) suggest analysing elements such as the setting of visuals, use of iconography and the notion of salience. The latter is very relevant from the previously discussed idea of concealment. When taking a picture or editing visuals by means of software, choices are made regarding the composition or framing. Some features are more salient than others as they stand out and consequently demand the audience’s attention while others are pushed towards the background – literally and figuratively speaking. Authors can accomplish this by manipulating size, colour, focus or zoom, and the camera angle, among other things. For instance, a long shot of an immigrant alone on a coast will communicate feelings of loneliness and isolation, while a close-up of the same person creates a sense of intimacy and proximity.

2.4 Conclusion and discussion Coming to the end of this brief and hands-on introduction to the field of critical discourse analysis, we hope you have gained a better insight into what is often regarded as a heterogeneous and abstract framework to study texts and language. While we cannot deny the often conceptually heavy theoretical writings on discourse analysis nor the absence of a straightforward or clear hands-on methodology, we believe CDA is an essential part of the researcher’s toolbox when considering a qualitative analysis of texts within their context. If issues or questions of power and ideology are at the centre of your research project, CDA should be at the top of your list with

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methodological options. It not only allows you to present a fine-grained analysis of the language that is being used in a text, it also further helps to understand how texts are being shaped and moulded by contexts that surpass the mere linguistic choices of an author. Moreover, its inherently critical stance or state of mind challenges you to look further and deeper, beyond the apparently neutral surface and to make explicit what is implicit, to contest and to question what is taken for granted or discursively presented as such. Texts – ranging from written, spoken to visual forms of language – are approached as the empirical manifestations of discourse, allowing us to systematically deconstruct ideologies, social struggles and power relations.

2.5 Summary checklist Acknowledging the challenging nature of getting acquainted with a heterogenous and layered methodology such as CDA, after going through this chapter, you should be able to do the following: •

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Understand the idea of approaching language as a discourse and its implications in terms of how people use language to represent the world and thereby actively construct a particular reality. Situate CDA within a broader philosophical and theoretical framework of social constructionist approaches to discourse analysis and identify its corresponding and diverging or unique features. Consequently, you should also be able to recognise when CDA is an appropriate method to consider. Relate key concepts of ideology, power and articulation to the methodological framework and objectives of CDA. Set up a CDA-informed research design step by step, from identifying a research problem, operationalizing and formulating adequate research questions and selecting a well-motivated sample of data, to the actual cyclical analysis of the data and the final reporting on your findings. Select the most relevant discursive devices in your analysis of the text and subsequently relate or explain your findings on the dimension of text to or by the broader contexts of said text such as discursive and social practices.

2.6 Doing critical discourse analysis yourself Let us go from theory to practice by presenting an exercise that focuses on the textual mode of discourse. Below you will find a fictitious newspaper article that you can analyse according to the framework of CDA. Important contextual information is that we are dealing with a mainstream up-market newspaper that can be identified as left wing in terms of its political orientation and that is not owned by the fictitious company The Media Home.

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Case study 2.1 Analysing newspaper articles according to critical discourse analysis Our media threatened by plain commercial profiteering Is the crucial ideal of diversity definitively given up to the benefit of a neoliberal media landscape with the sole ambition to maximize profits and benefit shareholders? Several shareholders of our country’s largest media concern, The Media Home, reported in a press release that ‘a further rationalization of the workforce will be carried out’. Socialist Minister of Media Sonja Red immediately declared at a press conference: ‘This is undoubtedly a disaster for the local media sector and a danger for its degree of diversity and pluralism!’ Many academic experts provided support for her statement and clearly indicated that the developments will inevitably have a negative impact on the overall quality of our media. Confronted with these facts, CEO of The Media Home and liberal member of parliament Mark Richman reacted in a somewhat negative tone and stammered that ‘it will probably not be that bad, I guess’. Additionally, leading newspaper, The Standard, also owned by The Media Home, defined the operation in its editorial as ‘a completely logical economic decision after 67 journalists had already been dismissed’. From our perspective, we believe good, high-quality and critical journalism costs money and requires a well-equipped, well-staffed editorial team to expose and address social inequalities and problems, but it is clear that not every (political) party is convinced of that. As usual, some groups in our society clearly benefit from the media being a watchdog with small teeth and little to no power.

The short extract is of course modified to meet the purpose of our exercise to get the reader acquainted with the basic framework and design of CDA. When discussing the fragment, the following questions or reminders can help you to conduct the actual analysis: 1. Keep in mind that there are three dimensions to consider. In other words, remember that you perform a critical discourse analysis and not a traditional content analysis. 2. Typical of CDA is the notion that language and words are not neutral but are motivated and deliberate choices of a certain author or language user. Who are the authors here? Why and how does someone say something? 3. Consider the fact that CDA involves a specific jargon or terminology. Use these key concepts in writing up your analysis of the text. 4. Illustrate your findings with concrete examples from the text to provide ample evidence for your cause. Next up is a basic outline of a preliminary analysis, organized according to the three dimensions of Fairclough’s model.

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2.6.1 Dimension of text: a selection of discursive devices •

•

•

•

•

Lexical choices: the use of adjectives such as ‘neoliberal’ or ‘plain commercial’ is clearly motivated in the article and meant to articulate the left-wing discourse of the newspaper. Modality: in a news media context, this is an important tool for assessing the credibility of a source and the persuasive nature of their statements. Consider the sentences of ‘[t]his is undoubtedly a disaster’ and ‘many academic experts … clearly indicated that’. In both instances there is a high modality that is further supported by the use of specific quoting verbs. For example, ‘Sonja Red immediately declared’ strongly contrasts with ‘Mark Richman … stammered that’, making the intention of the journalist quite evident and again articulating a certain ideological position of the authors, being the journalist and the news outlet that they are writing for. Functionalization: mentioning the function or job title of someone tends to give the actors and their statements more authority, status and legitimacy. There are several examples throughout the short article (e.g., ‘Minister of Media’, ‘academic experts’, ‘CEO’ or ‘liberal member of parliament’). Nominalization: the quote by the shareholders that ‘a further rationalization of the workforce will be carried out’ makes use of this discursive device. Accordingly, the downsizing of the staff is presented as a natural process by concealing the actor responsible for it as well as the staff who are let down. Transitivity: similar to the previous finding, transitivity is also applied by some actors in the text to portray the dismissal as the natural state of affairs. The use of a passive verb tense in ‘67 journalists had already been dismissed’ keeps the actor out of the picture. Significantly, this quote was taken from the editorial of a newspaper that is owned by The Media Home, responsible for the dismissal. This brings us to the dimension of discursive practices.

2.6.2 Dimension of discursive practices •

• •

First, the production context of the article should attract your attention as a researcher. The journalist or author of this text writes for a left-orientated newspaper and it is clear that this editorial context helps to explain some of the textual findings and language choices of the author discussed above. In terms of the genre, the text is not an editorial but a regular newspaper article, thus suggesting a neutral or objective style of reporting. Interdiscursivity: the text itself actually displays two conflicting discourses on the issue. In this case the socialist discourse of the newspaper is dominant, but through the use of quotes from the company’s CEO, its shareholders and the editorial from The Standard, the neoliberal counter-discourse is also incorporated in the text, however, in a rather negative way and contrasting the other discourse.

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2.6.3 Dimension of social practices •

•

•

To contextualize the previous findings, we can pay attention to broader social structures, institutions and values. Here, we see an ideological or political component in the sense of an ‘us-them’ polarization, ‘them’ referring to the neoliberal discourse or identity of The Media Home, shareholders, The Standard, CEO, etc., which are all presented from a negative perspective. Additionally, words and phrases such as ‘threatened by plain commercial profiteering’, ‘the sole ambition to maximize profits and benefit the shareholders’, etc., underwrite this representation. On the other hand, the ‘us’ refers to a left-wing, socialist discourse or identity of the newspaper, the quoted minister, academic experts, etc., which are represented positively. The latter articulation of a socialist discourse is of course implicit as news media reports are presented as seemingly neutral and objective. However, as our fictitious article demonstrates, even factual accounts do disseminate social values, in this case values or ideals such ​​ as ‘diversity’, ‘media as watchdog’, and the role of media to expose social inequalities and problems. To conclude, the reason why this article attracts the attention of critical discourse analysts is to be found in the final paragraph, echoing the central idea of power ​​ and a critical stance: ‘to expose and address social inequalities and problems, but it is clear that not every (political) party is convinced of that. As usual, some groups in our society clearly benefit from the media being a watchdog with small teeth and little to no power’.

2.7 Recommended reading Carvalho, A. (2008). Media(ted) discourse and society: rethinking the framework of critical discourse analysis. Journalism Studies, 9(2), 161–77. Maeseele, P. and Raeijmaekers, D. (2020). Nothing on the news but the establishment blues? Towards a framework of (de)politicization and agonistic media pluralism. Journalism, 21(11), 1593–610. Both works provide the reader with a different take on CDA by developing an alternative to Fairclough’s model, based on Carvalho’s approach of mediated discourses. Jørgensen, M. W. and Phillips, L. (2002). Discourse analysis as theory and method. London: SAGE. The ideal starting point for who is new to the field of discourse analysis as it presents a broad overview of the different strands and approaches. Fairclough, N. (1995). Media discourse. London: Edward Arnold. In terms of theorybuilding, this is one of the seminal works in the field of discourse analysis, including a conceptualization of the three-dimensional model discussed in the chapter.

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Machin, D. and Mayr, A. (2012). How to Do critical discourse analysis: a multimodal approach. London: SAGE. A ‘how to’ guide into the practice of doing multimodal CDA, illustrated with many rich examples. Richardson, J. (2007). Analyzing newspapers: an approach from critical discourse analysis. New York: Palgrave Macmillan. This acclaimed book is very accessible and offers the reader a hands-on introduction to CDA.

2.8 References Berry, T. R., Wharf-Higgins, J. and Naylor, P. J. (2007). SARS Wars: an examination of the quantity and construction of health information in the news media. Health Communication, 21(1), 35–44. Blommaert, J. and Bulcaen, C. (2000). Critical discourse analysis. Annual Review of Anthropology, 29, 447–66. Carvalho, A. (2008). Media(ted) discourse and society: rethinking the framework of critical discourse analysis. Journalism Studies, 9(2), 161–77. Chilton, P. (1988). Critical discourse moments and critical discourse analysis. San Diego, CA: University of California. Chouliaraki, L. (2006). The spectatorship of suffering. London: Sage. Chouliaraki, L. and Fairclough, N. (1999). Discourse in late modernity. Edinburgh: Edinburgh University Press. de Lange, R., Schuman, H. and Montesano Montessori, N. (2012). Kritische discoursanalyse: De macht en kracht van taal en tekst. Brussels: Academic & Scientific Publishers. Fairclough, N. (1992). Discourse and social change. Cambridge: Polity Press. Fairclough, N. (1995). Media discourse. London: Edward Arnold. Flowerdew, J. and Richardson, J. E. (eds) (2019). The Routledge handbook of critical discourse studies. London: Routledge. Foucault, M. (1972). The archaeology of knowledge. London: Routledge. Fürsich, E. (2009). In defense of textual analysis. Journalism Studies, 10(2), 238–52. Hansen, A. and Machin, D. (2013). Media and communication research methods. New York: Palgrave Macmillan. Jørgensen, M. W. and Phillips, L. (2002). Discourse analysis as theory and method. London: SAGE. Joye, S. (2009). The hierarchy of global suffering: a critical discourse analysis of television news reporting on foreign natural disasters. Journal of International Communication, 15(2), 45–61. Joye, S. (2010). News discourses on distant suffering: a critical discourse analysis of the 2003 SARS outbreak. Discourse & Society, 21(5), 586–601. Kress, G. (1985). Linguistic processes in sociocultural practice. Geelong: Deakin University Press.

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Laclau, E. and Mouffe, C. (1985). Hegemony and socialist strategy. London: Verso. Levy, D. A., Aslan, B. and Bironzo, D. (2016). UK press coverage of the EU referendum. Oxford: Reuters Institute for the Study of Journalism. Machin, D. and Mayr, A. (2012). How to Do critical discourse analysis: a multimodal approach. London: SAGE. McQuail, D. (2000). McQuail’s mass communication theory. London: SAGE. Phillips, L. J. (2006). Doing discourse analysis: a brief introduction to the field. In N. Carpentier et al. (eds), Media technologies and democracy in an enlarged Europe (pp. 285–94). Tartu: Tartu University Press. Potter, J. and Wetherell, M. (1987). Discourse and social psychology. London: SAGE. Raeijmaekers, D. (2018). Little debate: ideological media pluralism and the transition from a pillarized to a commercialized newspaper landscape (Flanders, 1960–2014). Antwerp: University of Antwerp. Richardson, J. (2007). Analyzing newspapers: an approach from critical discourse analysis. New York: Palgrave Macmillan. Russo, A. and Russo, J. (2019). Avengers: Endgame. Marvel Studios & Walt Disney Pictures. Schiffrin, D., Tannen, D. and Hamilton, H.E. (eds) (2001). The handbook of discourse analysis. Malden: Blackwell. Schrøder, K. C. and Phillips, L. (2007). Complexifying media power: a study of the interplay between media and audience discourses on politics. Media, Culture & Society, 29(6), 890–915. Shoemaker, P. (1991). Gatekeeping. London: SAGE. Thompson, J. (1990). Ideology and Modern Culture. Cambridge: Polity Press. Titscher, S., Meyer, M., Wodak, R. and Vetter, E. (2000). Methods of text and discourse analysis. London: SAGE. van Dijk, T. A. (1988). News analysis: case studies of international and national news in the press. Hillsdale, N.J.: Erlbaum. van Dijk, T. A. (2009). News, discourse, and ideology. In K. Wahl-Jorgensen and T. Hanitzsch (eds), The handbook of journalism studies (pp. 191–204). New York: Routledge. Van Haelter, H. and Joye, S. (2020). Heart for or hard on refugees? A critical discourse analysis of the news discourse about Syrian refugees by the public and commercial broadcaster in Flanders. Tijdschrift voor Communicatiewetenschap, 48(2), 112–27. Wodak, R. and Meyer, M. (2016). Methods of critical discourse studies. California: SAGE.

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3 Grounded Theory: Key Principles and Two Different Approaches Peter A. J. Stevens and Lore Van Praag

Grounded theory (GT) constitutes a well-developed, popular but often misunderstood approach to qualitative data analysis (QDA). It is highly appropriate if you want to move beyond description and instead build a more thorough explanation or understanding of a particular phenomenon. It requires an open mind regarding the focus of your research and the kinds of data you will sample, as these are expected to change as you progress with the analysis of the data. Although GT is characterized by specific features that set it apart from other approaches to qualitative data analysis, different philosophical assumptions adopted by GT researchers result in somewhat different views on how to conduct GT and for which purposes.

3.1 Chapter objectives In this chapter you will: • • • • •

get an introduction to the main features of GT; learn how to analyse data using two different approaches: a post-positivistic and a constructivist approach to GT; discuss the main strengths and weaknesses of these two approaches and of GT in general; understand how you can develop a GT research design (through an in-book exercise) and code data using a GT approach (through an exercise); and know where to find additional reading on this topic.

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3.2 Key features, debates and historical developments GT is currently one of the most popular approaches to QDA in the social sciences. For instance, an analysis of publications included in the Web of Science (WoS) that include the term ‘grounded theory’, shows 11,254 publications at the end of 2020. Although GT was first introduced to the world by Glaser and Strauss’s landmark publication The Discovery of Grounded Theory in 1967, citations to GT only started to pick up after the publication of Strauss and Corbin’s highly influential book in 1990, Basics of Qualitative Research. Grounded Theory Procedures and Techniques. Another sharp increase followed from the publication of Charmaz’s equally influential approach to GT in 2006 (Constructing Grounded Theory). Both approaches to GT are discussed in detail in this chapter.

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Figure 3.1  Inclusion of term ‘grounded theory’ in ‘topic’ in WoS publications in selected psychology and social science disciplines over time Most GT publications can be found in the disciplines of nursing, health sciences and psychology, which can be explained by the fact that Glaser and Strauss (and later Strauss and Corbin and Charmaz) applied and developed GT primarily within these fields. GT publications also stem from the fields of educational sciences and to a lesser extent from the disciplines of sociology, political sciences, communication sciences, anthropology and social work. Initially, GT was developed as a reaction to a sociology that built theory almost exclusively through deductive or ‘top-down’ reasoning and the use of quantitative methods, in which middle-range theories were deducted from grand theories and tested or verified

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by means of statistical techniques on large datasets. Glaser and Strauss (1967) argued that theory could also be developed bottom up (i.e., inductively and in a way that produced valid and reliable explanations or an understanding of social reality). Although they argued that GT could be applied both on quantitative and qualitative data, it was primarily advanced as a way to analyse qualitative data at a time when such analysis was largely considered impressionistic and un-scientific by the sociological research community. Given the dominance of positivism at that time, Glaser and Strauss conceptualized GT as an approach to data analysis that could show us (parts of) reality as it really is, albeit with a focus on developing theory bottom up instead of testing hypotheses based on existing theory. Over the years, there have been various changes, developments in and interpretations of GT. Many of these diverse developments have also affected the practical procedures when carrying out data analyses. However, despite its increased popularity in recent years, it is argued that the term and approach are often misunderstood and not applied appropriately by researchers. Indeed, Hood observes that: ‘For some authors, the use of the term “grounded theory” is simply a justification for engaging in a qualitative data analysis or doing some form of coding’ (Hood, 2007, p. 152). This contrasts with the initial idea of GT to develop clear guidelines and procedures for researchers who wish to apply qualitative research methods and build new theories. Part of the confusion over what constitutes GT, is that, as Dey argues: ‘there is no such a thing as “grounded theory” if we mean by that a single, unified methodology, tightly defined and clearly specified’ (Dey, 2004). Instead, there are different versions of GT that have been developed over time, with key authors sometimes engaging in fierce debates over what constitutes GT. Although the first textbook on GT was written by both Glaser and Strauss (1967), Strauss (with Corbin) and Glaser developed different, more post-positivistic approaches of GT (Glaser, 1978; Strauss & Corbin, 1990). In addition, other authors, often former students of Glaser and/or Strauss developed their own versions of GT, such as Charmaz’s constructivist GT (Charmaz, 2014) and Clarke’s (postmodern) situational analysis (Clarke, 2005). To do proper justice to GT, we should not only give an overview of all the key authors, but also show how their conceptualizations of GT developed over time and are related to the historical context in which they were situated (see, for instance, Charmaz, 2006 for a partial, historical analysis of Glaser and Strauss & Corbin’s approaches). However, such a review goes well beyond the key purpose of this chapter: to offer you a practical ‘how to’ of this approach, assuming that you have no prior experience with GT. At the same time, different GT approaches share similar points of departure that, taken together, allow for the formulation of a particular GT approach. Hood (2007) compares a GT approach with what she calls the ‘generic inductive qualitative model’ (GIQM), based on Maxwell (2005), which represents a common, general approach towards qualitative data analysis that is often wrongly confused with GT.

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According to Hood (2007), a GIQM approach is characterized by: •

• • • •

a focus on processes rather than explaining variance (or how rather than if, to what extent and why something happens); a research design that is continuously adapted as analysis and data collection inform each other through various cycles; samples that are purposely selected based on theoretical grounds; a reliance on literature for defining research questions and interpreting data; researchers who write memo-type synthesis in analysing the data; coding of data that is focused on themes and sometimes on theoretical categories; and

•

data collection that stops when additional cases no longer add new information.

•

Although these features seem relevant to most QDA approaches, GT is characterized by a particular shared understanding regarding how to sample your data, how you must compare cases and when you have reached theoretical saturation. In addition, GT researchers have a particular but not always shared view on what the end product should be of GT analysis, the importance of using existing literature in doing a GT analysis and how you should code your data.

3.3 Doing grounded theory step by step The following sections explain the different steps in doing a GT analysis. We first look at three features that are shared by different approaches to GT: sampling; comparing cases; and reaching theoretical saturation. Afterwards, we investigate three additional features of GT on which there is more discussion between GT researchers: what it means to develop a ‘theory’ in GT; the use of existing literature; and the way you should code your data.

3.3.1 Sampling, comparing cases and reaching theoretical saturation GT employs a form of theoretical sampling that is often not applied in GIQMinspired research. Following GIQM, you often decide at the very start of your study who you will interview, observe and/or what types of documents you will analyse (or other forms of text that you want to include in your sample). Although this form of sampling is often informed by theory, in that categories or groups of actors or forms of text are chosen because you expect to gain theoretically relevant knowledge by studying these, the sample is often not modified throughout the research and particular comparisons between cases and contexts are often built-in a priori in the research design through sampling. In sharp contrast, GT expects your sample to change over the course of your research. You start in GT with a basic or initial, more homogeneous sample which is used to develop a basic theory, a basic understanding or explanation of the phenomena

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in which you are interested. Once you have developed such a basic theory, you select a particular phenomenon as the key focus of your research. Afterwards, you develop a theoretical sample in which cases are selected because they allow you to further develop your theory in relation to this particular phenomenon.

Case study 3.1 Parenting styles: sampling For instance, we want to study parenting styles and develop a sample of fathers and mothers as the literature suggests that men and women have different views on how to be a good parent, but little is known about why this is the case. Following a GIQM, we could select ten fathers and ten mothers and compare their insights and experiences regarding parenting. In contrast, in a GT study you might interview four fathers and four mothers as your initial sample, and subsequently decide on who to interview afterwards based on your emerging findings. These could indicate that you need to interview additional fathers and mothers, but equally, that it is important to interview children, grandparents, adult men and women without children, or other categories of respondents; categories that, after analysing data from these initial interviews, emerge as meaningful in developing a deeper understanding or explanation of a particular phenomenon. In GT you would then continue to adapt your sample as long as necessary to develop a theory about a particular phenomenon.

While applying a constant comparative approach in QDA is a common feature, in that you analyse your data by constantly comparing additional interviews, field notes or other forms of text with those analysed previously, in GT this process is more openended and directed by the emerging theory. More specifically, applying a GT approach does not mean that you compare pre-defined categories of respondents/contexts, but that you compare the data as a whole at the start, and make increasingly more comparisons according to categories that are developed more inductively.

Case study 3.2 Parenting styles: constant-comparative method For instance, in studying parenting styles between fathers and mothers we would, after conducting four interviews, not only compare men with women in relation to parenting styles, but we would analyse the data as a whole, by looking at what kind of information can be obtained from the interviews in general (not only on parenting styles) and we would compare case by case (also women with women and men with men). Out of this (Continued)

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initial analysis, we will develop particular ideas about what is more and less important, and focus our comparisons increasingly more on these issues, themes and codes (see also coding below) that we consider as theoretically relevant (i.e., they relate to issues that we want to explain or understand). The result is that other issues that we initially did not relate directly to ‘parenting styles’ and/or to father/mother differences, might emerge as important and become the focus of our (comparative) analysis. For instance, we might notice that respondents’ views on work-life balance is an important topic to consider in explaining or understanding differences in how men and women define appropriate parenting styles. The topic of ‘work-family-balance’ might end up becoming your most important topic and you might, as a result, focus future comparisons (and sampling of cases) on gaining more information about this theme.

While a GIQM approach to data analysis often stops analysing data when analysis of additional data does not reveal new insights (in other words, you have a feeling that ‘you have heard it all before’), GT only stops analysing (and collecting) data when you feel that you have developed a comprehensive understanding or explanation of a particular phenomenon (called theoretical saturation).

Case study 3.3 Parenting styles: theoretical saturation For instance, after conducting and analysing interviews with ten men and ten women, you might feel that you hear the same stories over and over again and that there is little additional information to be gained from collecting and analysing additional data. However, as a GT researcher you would perhaps, after analysing four interviews, highlight work-life balance as an important theme and decide to keep on collecting and analysing data until you developed a comprehensive understanding of the importance of this theme for parents’ views on parenting styles. If, after conducting interviews with twenty respondents, you still have questions unanswered regarding this theme, you would further investigate the collected data and perhaps collect and analyse additional data (even using different or adapted interview questionnaires) until a comprehensive theory has been developed to understand or explain this phenomenon.

For Hood, misconceptions about these three issues often explain why researchers call their study an example of GT, while in fact what they are doing can be classified more accurately as a GIQM study. In addition to these three features of QDA, we would like to highlight three additional features of GT: its focus on building a theory; the use of existing literature; and the specific procedures that are used to code data. Although these three issues

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set GT apart from other forms of QDA, they are also more debated and contested within the GT field. In what follows and for the remainder of this chapter, we will compare two different approaches to GT: Strauss and Corbin’s more post-positivistic approach and Charmaz’s more constructivist approach. The choice for doing so is motivated by two main reasons. First, after the publication of the seminal Discovery book by Glaser and Strauss, they constitute the currently most cited and hence influential approaches to GT analysis, as illustrated in Table 3.1.

Table 3.1  Citations (in 2020) to key GT textbooks Publication date

Google scholar citations

Glaser and Strauss

1967

118,480

Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory

Strauss and Corbin

1998

53,291

Constructing Grounded Theory

Charmaz

2006

29,933

Theoretical Sensitivity

Glaser

1978

16,323

Title work

Author(s)

The Discovery of Grounded Theory: Strategies for Qualitative Research

As a result, you will be able to form a better judgement of what it means to employ one of these two, more established approaches to GT. Second, they allow for a comparison of two approaches that start from different philosophical paradigms. This allows us to show how GT is influenced by philosophical developments and indeed can speak to audiences with different philosophical preferences or assumptions. However, this choice also leads to a first limitation of this chapter, in that much historical context and variety of approaches is left undiscussed, which necessarily leads to a partial exhibition of the richness that the GT family can boast of. The next sections will focus on three other features that characterize GT in relation to other forms of data analysis, while simultaneously highlighting some important internal discussions and variety in terms of how GT should be employed. First, we will look at what exactly is meant by developing ‘theory’ in GT, then we will look at the role that GT researchers attach to existing research in developing theory and finally we will describe and compare different strategies for coding data in GT.

3.3.2 Theory in GT First, as is evident from the name, but often forgotten in practice, a key purpose of GT is to build a theory. However, differences between GT authors in terms of their basic philosophical assumptions leads to differences in what they consider as ‘theory’ or a valid end product of a GT analysis (Apramian, Cristancho, Watling & Lingard, 2017). In this handbook, we make a distinction between three different philosophical paradigms:

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post-positivism, constructivism and critical theory. Please read the Introduction (Chapter 1) for more information on what characterizes these different philosophical approaches. For this chapter on GT, post-positivism and constructivism appear to have the strongest influence on how GT developed over time. Starting from a more post-positivistic approach, Straus and Corbin (1998) see it as a goal for GT analysis to develop explanatory models that help us to understand how reality is. For them, a theory is ‘a set of well-developed concepts related through statements of relationship, which together constitute an integrated framework that can be used to explain or predict phenomena’ (p. 15). They stress the importance of developing dense theories that show the complexity of social phenomena and to develop theories that account for all the observed variation in the sample. The idea that a GT analysis should aim for explanation is often used to differentiate GT from other QDA approaches; approaches that aim primarily for description (Birks, Hoare & Mills, 2019). However, as a constructivist, Charmaz rejects the idea that you should aim to develop explanations of phenomena that are as accurate as possible. Instead, she starts from the premise that there are necessarily multiple realities, and that you are a co-author in developing representations of these realities. The goal of GT should therefore be to show what people see as their reality and how they construct and act on their views of reality (Charmaz, 2014). In so doing, we can show patterns in and between the various perceptions of reality and how actors respond to them. Although Charmaz does not see it as essential that a theory tries to explain something, developing explanations or an understanding of why certain phenomena occur, can also be the outcome of a constructivist GT. In fact, in developing a thorough understanding of people’s views and actions, it is almost impossible not to consider the personal and social context that might inform actors’ views. However, answering the ‘why’ question is not necessarily the goal of constructivist GT. In answering ‘why’ questions, Charmaz does not restrict herself to developing explanations of phenomena, as such answers can also include ‘abstract understandings that theorize relationships between concepts’ (p. 228).

Case study 3.4 Parenting styles: what kind of theory? For instance, if we apply Strauss and Glaser’s perspective on GT, we could aim for developing a theory that explains how work-family balance relates to parents’ views on good parenting (a phenomenon that we want to explain). The focus of this theory was not determined at the start of the research but was developed inductively by analysing data. However, once we have decided on this focus, we should aim our analysis efforts to develop an explanatory model that explains all cases in our sample, and actively seek for contradicting cases, to develop an explanation that accounts for all perceived variability in relationship to this phenomenon. In contrast, if we were to adopt Charmaz’s perspective

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of GT, we try to develop an understanding of how parents see work-family balance as important in relation to their parenting and explore how they act on such views. In so doing, we might (but do not necessarily have to) develop a typology of different views of these relationships, and in turn, develop a deeper understanding (by considering personal and social context) of why parents seem to have these particular views.

In sum, different philosophical assumptions stimulate GT researchers to look for different kinds of ‘theory’. A more post-positivistic approach emphasizes the development of explanatory models that explain and show ‘how the world works’, while a more constructivist version stresses the need to understand and represent how people see reality. Furthermore, the attention that the researcher gives to their own role in developing theory varies according to such views. These differences in terms of what constitutes ‘good theory’ will resurface when comparing how these two approaches code their data, as they will emphasize different kinds of ‘good’ end products that should emerge from coding.

3.3.3 The use of literature in GT Although engagement with scientific literature prior to primary data collection is a standard practice in social sciences, GT researchers critically question the extent to which you should engage with existing research, what kind of research should be consulted and at what time (Dunne, 2011). In their original publication of The Discovery of Grounded Theory, Glaser and Strauss argued explicitly against reading substantive literature before starting the process of data collection. Instead they argued that you should delay the literature review until after completing the analysis (Glaser & Strauss, 1967). Their position can be explained by their criticism of (at that time) more dominant, positivistic scientists who generated theory based on a priori assumptions. Instead, they wanted to develop a methodology that helps to develop theory about the real world inductively. More specifically, they argued that doing a literature review prior to data collection could stimulate you to force pre-conceived ideas and concepts on the data and give preference to theoretical concepts or models that you admire. On a more practical note, they argued that it was not time efficient to conduct a literature review before collecting data, as a GT researcher cannot possibly know at this stage which literature is relevant, given that the focus of research develops and shifts over time in GT analysis (Dunne, 2011). Although more recently, GT researchers commonly acknowledge the importance of doing a literature review before starting with the collection of data, Straus and Corbin (1990) take a somewhat ambivalent position towards the use of existing literature in doing GT. On the one hand, they argue that reading technical and non-technical literature can develop a theoretical sensitivity to certain categories or relationships, stimulate the asking of particular questions and help develop a theoretical sample. It can

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also be used as additional validity of the emerging findings and as a source of secondary data. However, at the same time they claim that there is no need to review all relevant literature because: a) good GT develops new theoretical insights that no one will have developed beforehand; and b) it constrains and stifles the creativity or the ability to think outside the box. In addition, they suggest that the choice of what you read is determined by what you find in your analysis (or that analysis precedes the consultation of literature and the latter is guided by the outcome of the analyses) and should also be focused on elements of their coding paradigm (see below on coding) and as a result, a more restricted focus on particular kinds of knowledge. On the other hand, Charmaz aligns herself with GT researchers who more strongly advocate the importance of doing a thorough literature review before starting with the collection of the data and in fact, interact with the literature, albeit in a critical, reflexive way, as you move through the process of analysing data. The key motivation for doing so is both pragmatic and theoretical: you are simply expected by examination boards, supervisors and funding bodies to conduct a rigorous review prior to data collection. At the same time, doing such a review allows you to gain confidence about the innovative nature of the knowledge that you want to produce, about any preconceived ideas you might have, about gaps in the literature and it can also develop a theoretical sensitivity and awareness of sensitizing concepts (see below: coding). What is more important is how you deal with the literature. Dunne (2011), Thornberg (2012) and Charmaz (2014) call for a reflective and critical stance towards the literature. While the latter refers to an attitude in which you constantly doubt the validity of existing research, the former is defined as an awareness of how your identity and background impact on the research process. Dunn argues that certain analytical procedures that are typical for GT, such as writing memos (see section 3.3.4 on coding) and employing a constant-comparative approach (see above), help to put this into practice. The result is that you aim for ‘informed grounded theory’ that shows ‘theoretical agnosticism’ instead of ‘theoretical ignorance’ (Henwood & Pidgean, 2006). However, in reflecting on his own PhD research, Dunne (2011) points out that in applying GT, you might find it challenging to develop a traditional literature review as often prescribed in dissertations, which usually take the format of ‘literature review → findings → discussion’. Given that focus, findings and the selection and interpretation of literature change over the course of the research process in GT, it might become difficult to structure a literature review in such a format. Dunn suggests that there are no straightforward answers to this issue, and that you must make time for tackling this challenge and develop sound expectations with your supervisor about how this has to be solved. One way to deal with this issue is that the traditional literature review does not contain all concepts and theories used in the various empirical papers, but offers a general ‘geography of a subject’ (McMenamin, 2006), which helps to identify key discussions, concepts, findings and gaps in the literature. These can be used as a platform to develop more specific research questions in various empirical papers.

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In sum, GT has a developing but critical attitude towards the use of literature in the process of data analysis. While the founding fathers advocated the use of literature only at the final stages of a research project, more contemporary GT scholars emphasize the need to engage with the literature at the start and at various stages of the research project, albeit in a critical and reflective way. The overarching concern is that literature should never determine the focus of a study or the interpretation of the data.

Case study 3.5 Parenting styles: use of literature For instance, in studying parenting styles with fathers and mothers we align ourselves with less ‘purist’ (Dunne, 2011) views on the role of literature in GT analysis and start reading about this topic before collecting data. If we follow Strauss and Corbin, we would limit this reading to essential studies in this field, but we would not try to develop a comprehensive review of research conducted in the field. In the process of analysing our data, and particularly after developing certain themes (e.g., work-life balance), we might get back to the literature to read more about these particular topics. All the time, we take a critical and reflective stance towards the literature that we read. Following Charmaz, we would perhaps conduct a more thorough review, knowing that the eventual data analysis might bring us to different themes and related bodies of literature. Here too, we would continuously try to remain critical and reflective, and use existing concepts carefully, as sensitizing concepts that can stimulate our thinking about the data but that cannot determine its focus or interpretation. In both cases, the finding from the literature that fathers and mothers look differently at parenting did not determine our analysis; instead, we looked more inductively at our data and allowed another, related theme (work-life balance) to surface and take prominence in our analysis.

3.3.4 Coding Coding is the process whereby you cut your data into segments, each of which are given a label or short description that represents your interpretation of each segment of data. The process of coding represents one of the most fundamental aspects of data analysis in qualitative research (not just in GT), as it refers to how you interpret data or attach meaning to text. Coding in GT moves through different phases, which (when you go through them) help you to develop more focus in your analysis while simultaneously developing an explanation or understanding of a particular phenomenon. Although there are different approaches to coding in GT, they contain three subsequent (but overlapping and interacting) phases:

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1. Reducing text into meaningful codes. 2. Reducing and integration of codes. 3. Building of a focused theory. In what follows, we will describe each of these phases in a general sense and how they are put in practice by two different versions of GT: a post-positivistic approach of GT based on Strauss and Corbin (1990) and a constructivist approach of GT based on Charmaz (2014). At the end of each phase, we will describe and explain the key differences between each approach.

Reducing text into meaningful codes When starting with the first phase of coding, you are usually confronted with a substantial amount of data, such as a couple of fully transcribed interviews, written field notes of several days of observation and/or several policy documents or news features. At this early stage of data analysis it is vital to bear in mind that analysis (and therefore coding) should start early in GT. Given the inter-related nature of data collection and analysis in most forms of QDA, and the importance of this feature for GT (see 3.3.1), you should refrain from collecting all or most of your data before beginning coding. Ideally, you would collect a small chunk of data (as described here) and start coding these, before collecting additional data (but see 3.3.1 sampling). Even if you start with a relatively modest amount of data, the goal ‘to make sense of this’ might feel daunting, given that you can easily end up with forty or more pages of written text. Hence, a first goal of coding is to break down this large amount of data into smaller and more meaningful parts, which are called ‘codes’, ‘labels’ or ‘categories’. Each code is given a specific name by you; a name which represents the meaning that you give to a particular chunk or fragment of data. The result is a massive form of data reduction; for instance, from forty or more pages of text to a limited set of codes. Coding is achieved by reading a text (e.g., an interview transcript) and simultaneously adding codes to fragments of this text, which can be assigned to a sentence, a paragraph or several pages (even the whole document). In addition, in GT, the same text fragment might be given different labels (or refer to different codes) if you think that it relates to different meanings. Adding codes is the result of you asking fundamental questions about the data – three key questions can help with any form of qualitative coding: 1. What is this text (fragment) about? 2. How relevant is this for my research theme/focus? 3. How should I name this fragment so that it reflects the interpretations that I want to give? There are tensions built into these questions. The first question emphasises induction, by focusing on what is in the text rather than on what you want to find in the text. By asking this question, you explore in a more open way what kind of information can be retrieved from the text, irrespective of its relevance for your research topic. The second

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question focuses more on deduction, by linking the text to a particular research focus and/or related research questions. Here, you are stimulated to look in a more focused way for information that can be considered theoretically important. The third question relates to some extent to the former two, in that the meaning or labels assigned to text fragments might stay close to the actual wording as presented in the text (also called ‘in vivo codes’), or more abstract and refer more to theoretical constructs rather than the words used in the fragment itself. We will see below how different versions of GT coding sometimes differ in terms of which questions they ask of the data and they do so for different reasons, but with the same goal in mind (developing a theory that can be used to explain or understand a phenomenon). In practice, the process of coding can be done in various ways; for instance, by making notes on a hard copy of an interview transcription or by adding ‘comments’ on a Word document file of a transcribed interview. The latter also shows that software packages can help to make this process more efficient (see also Chapter 5 on NVivo) but will never replace the interpretative work (i.e., asking questions about your data and trying to develop answers on these questions) that lies at the heart of coding.

3.3.5 Phase 1 of coding according to Strauss and Corbin Strauss and Corbin call the first phase of coding open coding, which they describe as: ‘The process of breaking down, examining, comparing, conceptualizing, and categorizing data’ (Strauss & Corbin, 1990, p. 61). A key goal of open coding is that you develop a set of more abstract or theoretical codes that are described in terms of their key characteristics and relationships between them. This is achieved by asking questions and making comparisons. First, you ask inductive questions about the data, such as ‘What is this?’ and ‘What does it represent?’. However, in answering these questions and assigning meaning to fragments of text, you do not attach labels that stay close to the wording as used in the text fragments itself (in vivo coding), but instead use short, abstract labels to represent text.

Case study 3.6 Open coding: developing concepts For instance, we might observe bodybuilders in a gym and notice that they spend time on looking at themselves in the mirror and explaining to other gym-goers how to do certain exercises. In coding these data through open coding, we could label the first activity as ‘admiring themselves’ and the second one as ‘giving advice’. If we would use in vivo codes, we might label the first activity as ‘looking in the mirror’ and the second one as ‘explains to somebody how to do an exercise’. Hence, applying open coding stimulates us to develop more abstract codes (‘concepts’ in Strauss and Corbin’s terminology) and interpretations (‘admiring’ and ‘advice’) of what we observed.

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As a result, you will develop a set of more short, abstract codes that are called concepts. At the same time, you are encouraged to compare these concepts with each other, so that they can be grouped/related to each other, which leads to the development of additional, more abstract codes called categories. The key task then is to describe these categories in terms of their constitutive properties and dimensions. While the former correspond to what we would call ‘variables’ in quantitative research terminology, the latter correspond to the ‘values’ of these variables.

Case study 3.7 Open coding: developing categories For instance: we observe bodybuilders in a gym and notice that they are predominantly involved with ‘training’, ‘teaching’, ‘socializing’ and ‘admiring’ (all abstract categories). Each of these categories might be composed of certain subcategories, which might overlap with certain concepts that we developed earlier. For instance, ‘admiring’ might be composed of ‘admiring oneself’ (for instance, through looking in the mirror or looking at oneself) and ‘admiring others’ (for instance, by giving a compliment). Both main and subcategories can be described in terms of their properties. For instance, ‘admiring’ might have the property ‘object of admiration’, ‘means of admiration’ and ‘frequency of admiration’. Each of these properties can then be assigned a certain dimension. For instance, object of admiration might have the values ‘self’ and ‘other’, means of admiration might be assigned the values ‘mirror’, ‘looking directly’ and ‘imagining’, and frequency of admiration might have the dimensions ‘almost never’, ‘occasionally’, ‘often’ and ‘almost all the time’.

Developing concepts, categories and their properties and dimensions is realized by asking questions about and comparing these codes and their features: 1. Asking ‘who’, ‘what’, ‘where’, ‘why’, ‘how’, ‘how many’ questions or questions that probe for specific features of codes (and often help to contextualize these). 2. By asking ‘what if this was NOT the case’ (also called the ‘flip-flop technique’). For instance, we could ask ourselves: ‘Will bodybuilders ONLY look in the mirror to admire themselves (what other motivations might they have to look in the mirror)?’. 3. By making comparisons with context or positions of actors that are very different (also called the ‘far-out comparison’). For instance, we could ask ourselves: ‘What is the difference between a bodybuilder and a ballerina in terms of their motivations for looking in the mirror?’. 4. By making comparisons with context or positions of actors that are only different with respect to a single criterion or a few criteria (also called the ‘close-in comparison’). For instance, we could ask ourselves: ‘What is the difference between a bodybuilder and somebody who started working out in the gym for the first time in terms of their motivations for looking in the mirror?’.

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5. The focus on extreme points of view/standpoints, like ‘this never happened’ or ‘always happened’ (also called ‘waving the red flag’). For instance, we could question our assessment that bodybuilders ALWAYS look in the mirror to admire themselves. By asking such questions and making these comparisons, you are stimulated to look at your data and conclusions in a different way or reconsider and expand on current (perhaps too abstract) interpretations given to the data. The goal of open coding is to reduce a large amount of data to a set of abstract codes that are well defined in terms of their key properties and basic relationships, comparable to a clear description of a set of variables and their basic (hierarchical, causal or other) relationships.

3.3.6 Phase 1 of coding according to Charmaz Charmaz describes the first phase of coding as initial coding, which is in turn defined as: The early process of engaging with and defining data. Initial coding forms the link between collecting data and developing an emergent theory to understand and account for these data. Through coding, you define what is happening in the data and begin to grapple with what it means. (Charmaz, 2014, p. 343) A first key difference between this approach and Strauss and Corbin’s approach is that Charmaz emphasises the need for developing codes that are not too abstract. Instead, she recommends using in vivo codes. Such codes usually refer to terms or statements that ‘everybody knows’ or that reflect a particular groups’ perspective or participants’ actions or concerns. Highlighting such terms and statements helps you to stay close to and explore the meanings attached by respondents to certain topics. Related to this, Charmaz encourages you to code in gerunds or code ‘data as actions’ (p. 116). This serves three purposes: first, it prevents you from typecasting people or incidents or describing them in a superficial way that does not consider their complexity; second, it keeps you from forcing particular theories or concepts on the data, instead developing these more inductively; and third, it allows you to incorporate a stronger sense of agency in your analysis.

Case study 3.8 Initial coding: developing in vivo codes For instance, in her handbook, Charmaz offers the example of a narrative in which the respondent (former patient) offers an elaborate account of a conflict that she experienced with a particular medical doctor. Instead of coding these data using a term like ‘conflict with doctor’ she codes the data in more detail and using gerunds, with codes like ‘accounting for MD’s behaviour’, ‘trying to gain a voice’, ‘remaining unheard’, ‘asserting oneself’, ‘attempting to bargain’ and ‘being misjudged’. In so doing, Charmaz (Continued)

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allows us to see more than just a relationship of conflict between a patient and her doctor, but instead shows how the patient tries to make sense of this situation (there was a conflict because she felt unheard), how she tries to change this situation through various actions, as well as the outcomes of these actions. This might stimulate us to look further into the causes underlying the tension between doctors and their patients, or the strategies used by patients in dealing with a conflict with a doctor, rather than merely establishing conflict. In so doing, she not only offers a more complex picture of what occurs, but also allows other, more inductive concepts such as ‘being heard’ or ‘dealing with conflict’ to take prominence, instead of merely forcing and focusing on conflict. Finally, she encourages us to consider a more active agent by looking at how respondents try to deal with and change their situation instead of merely experiencing it.

A second key difference is that, building further on the initial work of Glaser and Strauss (1967), Charmaz stimulates you to consider a particular set of abstract, theoretical codes as ‘sensitizing concepts’ in interpreting the data; codes that are, in her case, developed by Blumer (1998 [1969]) and related to a constructivist (symbolic interactionist) paradigm. This means that you can draw inspiration in interpreting your data by thinking of particular theoretical concepts that are used in a constructivist paradigm, such as ‘meanings/definitions’, ‘processes’, ‘career’, ‘work’, ‘negotiation’, ‘strategies’, ‘learning/socialization’, etc. However, although Charmaz encourages you to be inspired by particular, more abstract theoretical concepts from the literature in this initial phase of coding, this does not mean that she suggests using such abstract concepts at this early stage in the coding process. This is illustrated in the example above, which shows a clear interest in coding ‘strategies’ that actors employ, but that are not coded in such an abstract manner. Her advice to use in vivo codes and gerunds in the process of initial coding also fits with a constructivist approach, as it stimulates you to explore the meanings inductively and look for expressions of individual agency. Although Charmaz focuses on sensitizing concepts that stem from a constructivist approach, she encourages you to consider sensitizing concepts from other philosophical paradigms. For instance, if you are a critical researcher, you could adopt concepts such as ‘privilege’, ‘exploitation’, ‘resistance’, ‘ideology’ and ‘power’ to interpret your data in a way that fits such a theoretical lens. In sum, Strauss and Corbin’s more post-positivistic approach to coding emphasizes the need to develop abstract variables, their inter-relations and related values (concepts, categories, properties and dimensions) early on. There is little consideration for existing literature in this approach and theoretical sensitivity is obtained by asking particular questions and making comparisons. In contrast, Charmaz’s more constructivist approach emphasizes the need to develop codes that are not abstract but instead close to the meaning presented in the data and with a focus on actions (i.e., what people do). Here, the literature is considered an important source of inspiration, by offering particular sensitizing concepts that stimulate you to interpret your data in different ways (and hence, enhance your theoretical sensitivity).

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It seems that both approaches try to compensate for the theoretical implications of coding data in a particular way. As Strauss and Corbin force you to think in an abstract way about your data early on, they stimulate you to think away from such interpretations by asking particular questions and making specific comparisons (hence, to move away from preconceived, abstract interpretations). In contrast, Charmaz tells you to interpret the data in vivo, or close to the interpretations that seem to emerge from the data itself. However, to make such interpretations theoretically relevant, she wants you to be inspired by abstract theoretical (sensitizing) concepts (hence, to move towards preconceived, abstract interpretations).

Reducing and integration of codes The second phase of coding involves both a process of further data reduction and expansion. At this stage your attention will be directed more towards the list of codes developed through the first phase and less so towards the actual data (transcripts, fieldnotes, etc.). A further step towards building an explanation or understanding of a phenomenon is realized by developing new, more abstract codes, sometimes called ‘categories’ (see Strauss and Corbin above), ‘themes’, ‘concepts’ or ‘focused codes’ (see Charmaz, below). These abstract codes are developed by comparing and investigating the relationships between the codes developed in the first phase with the aim to integrate these codes with each other, where appropriate. At the same time, you engage in a process of data (code-) reduction by leaving out codes that appear to have little theoretical value for the developing theory.

3.3.7 Phase 2 of coding according to Strauss and Corbin The process of integrating codes and in so doing developing more abstract codes begins in the open coding phase of Strauss and Corbin, but takes central stage in the second, axial coding phase, which they describe as: (Axial coding is) a set of procedures whereby data are put back together in new ways after open coding, by making connections between categories. This is done by utilizing a coding paradigm involving conditions, context, action/interactional strategies and consequences. (Strauss & Corbin, 1990, p. 96) Whereas the open coding phase focuses on the identification and initial development of abstract categories, axial coding takes these abstract categories as a starting point and aims to further develop these in terms of very specific features. Hence, while open coding is more ‘open’ or inductive in that you develop concepts and categories based on what emerges from the data as important, axial coding is more closed or deductive in that abstract categories are further developed in terms of a specific set of characteristics, which are described in their coding paradigm. The importance of Strauss and Corbin’s coding paradigm in this phase of the analysis cannot be over-emphasized and effectively forces a structure or focus on the analysis, with the purpose of developing an explanatory model for every (key) abstract category that was identified and initially developed through the open coding phase. In practice,

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this means that each category developed in the open coding phase becomes a phenomenon, which you can then try to explain by describing their causal conditions that lead to the occurrence or development of this phenomenon, the actions, interactions or strategies that actors devise to manage or respond to a certain phenomenon, which occur under a specific set of structural conditions that facilitate or constrain certain strategies within a certain context and lead to certain consequences or outcomes. According to Strauss and Corbin, all these features of their coding paradigm should be treated as subcategories of the main categories (or phenomena) that are central in this analysis. This means that causal conditions and consequences, among other things, should all be described in terms of their own properties and dimensions (or variables and values).

Case study 3.9 Axial coding: developing categories through the coding paradigm For instance, we observe bodybuilders in a gym and notice that they are predominantly involved with ‘training’, ‘teaching’, ‘socializing’ and ‘admiring’ (all abstract categories). Each of these categories are treated as phenomena, for which we develop an explanatory model by describing their causal conditions, actions, structural conditions, context and outcomes. So, we could look to explain the social phenomenon of ‘admiring’ in a gym by looking at what causes bodybuilders to engage in this form of social behaviour, how they respond to this through their own behaviour and how these actions are informed by structural features in a specific context and the outcomes of such actions. All these subcategories should be described in terms of their key properties and dimensions. For instance, we could identify the following causal factors (properties or variables or the subcategory ‘causal conditions’) in explaining bodybuilders’ involvement with ‘admiring’, such as: muscle development; centrality of being a bodybuilder for the person’s identity; self-esteem; gym culture; participation in competitions; and a supportive peer group. All these properties can then be explained in terms of their dimensions (or values). Muscle development could, for instance, be categorized as below average, average or above average. Some properties can be relevant for different categories. Gym culture, for example, might constitute both a causal factor or an element of the social context. As ‘admiring’ referred to two different objects (admiring self and admiring others), this analysis could be done separately for both (sub-)phenomena and compared.

Several conclusions can be drawn from doing this type of data analysis. First, it stimulates you to develop complex models for explaining phenomena and to do so by developing a clear set of variables and values and a set of relationships between these variables and an overarching dependent variable (category). This approach fits well with a key goal of post-positivistic research: trying to develop a picture of reality that is as accurate as possible.

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Second – and this relates to sampling – although such an analysis reveals certain patterns in your data, it will most certainly show gaps for which you do not have (sufficient) information to fully describe the identified (sub-)categories as prescribed by the coding paradigm. As a result, such an analysis should stimulate you to collect/analyse further, but with a clearer idea of what information is missing (and what to look for). The same strategies are used to realize this phase of the analysis as the previous one, but now with a different purpose. First, the same kinds of question are asked and comparisons are made as with the open coding phase, but now they focus more on the relational features (causality, facilitating, inhibiting relationships), as these take a more central place in the coding paradigm. Second, we go back to the original data to validate our hypotheses about certain variables, values and their relationship, and in so doing might modify our hypothesis and/or develop new ones (as this might stimulate the development of new variables, values and relationships). In checking our data again, we also try to identify relationships between variables belonging to a particular category (as described in the example of gym culture) and variables that seem to operate in relationship to different categories (dependent variables). The latter is important in developing insight into which categories and subcategories will eventually become central in your analysis in the third and last stage of coding. The analytical tools that are recommended for this step vary, such as drawing models, writing memos and developing coding trees, all of which aim to help you to better identify relationships between (sub-) categories. Hence, while you go back to the data at this stage, you do so with very specific questions in mind; questions that follow from ‘filling in’ your coding paradigm with your initial set of categories.

3.3.8 Phase 2 of coding according to Charmaz Charmaz (2014) defines the second phase of coding as ‘focused coding’, in which you use ‘the most significant or frequent initial codes to sort, synthesize, integrate, and organize large amounts of data’ (p. 113). Typically, this means that you investigate patterns not in the raw data but in the list of codes that were developed through initial coding. Such focused codes are identified or constructed by asking questions about the codes that you developed, such as: a. Which codes reoccur? b. Which codes seem to suggest a significant insight or finding? c. Which ones seem to relate to each other in a particular way, and how and why do they relate to each other? In addition, Charmaz recommends strongly that you write memos or accounts in which you reflect on the meaning, theoretical importance and connections between in vivo and more abstract codes. This and question b) illustrate that Charmaz wants you to decide what to focus on, unlike Strauss and Corbin, where the focus is primarily directed by a coding framework. As a result, it is likely that your focus will be directed to particular

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codes that have some relevance to your research topic or questions. At the same time, Charmaz stimulates you to look for intriguing codes that appear as theoretically interesting in some way, even if they are not connected to your research topic or questions. Often this results in the creation of new, more abstract codes or concepts and relationships between these. Hence, the process of focused coding usually results in considerable data reduction but also in data expansion, as some codes cease to be important while others merge together into newly constructed, usually more abstract codes. Although the focus lies on the list of the codes developed during the open phase of coding, you will often go back to the raw data in the focused phase of coding to check your interpretation of the data in light of developing more abstract codes and also to re-interpret data, often with a clearer focus.

Case study 3.10 Focused coding: identifying and developing more abstract codes For instance, in her research on the effects of serious illnesses on people’s identities, Charmaz identified/constructed the more abstract code ‘losing a valued self’ after noticing respondents talk about how their illnesses at some point affected the way they looked at themselves. In particular, she was intrigued by a patient who lost her ability to sing at a professional level due to surgery who claimed ‘my voice was gone, so I was gone’. Given her training as a social psychologist and her interest in identity and its relationship to illness, she started to re-read her codes in relation to this particular, more abstract code and as a result constructed other, related codes, such as ‘regaining a valued self’ and ‘disruption (instead of loss) of self’, suggesting that people cannot simply lose their identity, but also experience a disruption and/or actively reclaim and reconstruct identities after suffering from serious illnesses. Hence, instead of seeing ‘losing a valued self’ as an outcome, she identified and conceptualized a range of stages that illustrate an active agent (in line with her affinity with a symbolic interactionist approach).

This example illustrates that in developing more abstract codes Charmaz allows you much more freedom in terms of deciding where to focus; a focus that is informed by your theoretical interests and recurring or striking findings that emerge from your analysis. In sharp contrast, Strauss and Corbin structure your analysis much more through their coding paradigm, which tells you to develop an explanatory model, focused on a particular dependent variable. Both approaches suggest using tools that fit their theoretical interest: Charmaz is primarily interested in developing an understanding and propagates the use of writing memos, while Strauss and Corbin are primarily interested in explaining phenomena and as a result advocate the use of developing visual representations of relationships between categories.

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Building a focused theory In the final phase of coding, you decide on a focus or research question for which you want to develop a theory. In GT you will only stop collecting and analysing data when you have developed a comprehensive explanation or understanding of a particular phenomenon (reaching ‘theoretical saturation’). This means that you have a sophisticated insight into what structures (explain/help to understand) a particular phenomenon. To achieve this you will go back to your original data and your developing coding framework and sometimes even collect additional data. However, these additional data-collection and analysis (coding) efforts will be very focused on a particular theme or research question. In other words, you will have a clearer idea of what kind of information you need and, as a result, you might decide that some chunks of data and parts of your coding framework are less important than others (and can be left out all together). So, while this stage will lead to a further expansion of your theoretical model/coding framework, imposing focus will also lead to a narrowing down of what categories and data more generally are relevant (and result in considerable data and coding reduction).

3.3.9 Phase 3 of coding according to Strauss and Corbin Selective coding is the final phase of coding for Strauss and Corbin which they define as: The process of selecting the core category, systematically relating it to other categories, validating those relationships, and filling in categories that need further refinement and development. (Strauss & Corbin, 1990, p. 116) In order to develop a theory from various, well-developed categories, you need to create what Strauss and Corbin call a ‘storyline’ or a general description of what your theory will be about and what it aims to explain. Although at this stage of the analysis it is often possible to highlight very different potential storylines, ideally you will prefer a storyline for which you have rich data, that focuses on a topic that is theoretically not fully explained and a story that you are interested in developing. In practice, this comes down to identifying a particular category as your core category or phenomenon. Often, this category may have already been developed in your axial coding phase, or it might be a new category if existing ones do not fully grasp its meaning. Strauss and Corbin advise us to focus on only one core category at a time, as involving any more will become too difficult a task to perform. The next challenge is to describe this core category in terms of its properties and dimensions (if you have not done so already), which could be interpreted as offering a rich description of your dependent variable. Afterwards, you will apply the coding framework to this core category but involve all other categories that you developed in the axial phase too, so that they (and their subcategories) can also play a role as causal conditions, actions/ interactions, etc., in explaining your core category. In other words, you explore how

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your previously identified and developed core categories (in the axial phase) might play a role in developing an explanation or theory for the single, core category that you have selected in the final phase as the central category of your analysis/story. To further develop and validate your coding framework, you are expected to go back to the original data and often even collect additional data, but now in a much more focused way, with a clear view of what kind of information you need.

Case study 3.11 Selective coding: developing a theory of a phenomenon For instance, we observe bodybuilders in a gym and notice that they are predominantly involved with ‘training’, ‘teaching’, ‘socializing’ and ‘admiring’ (all abstract categories). After developing an explanatory model for each of these categories (by applying the coding paradigm to each in the axial phase), we might notice that we have very rich data about the category ‘admiring’, and that this is not only a topic that we find interesting, but there seems to be little known in the literature about ‘the role of admiring as a social activity in gyms’. Taking this topic as our storyline, we first define our core category, which we can do fairly quickly given that we have already worked the category ‘admiring’ in detail through the open and axial coding phase. However, we might also realize that we need to further develop this category: although we identified the properties ‘object of admiration’, ‘means of admiration’ and ‘frequency of admiration’ and assigned dimensions to each, we might need to work out the property ‘role of admiring’ itself further (what do we mean by this, what kinds of roles emerge from the data?). Once we have further developed our core category to a point where we feel that all relevant properties and dimensions have been described, we might move to the next phase, in which we apply our coding paradigm to this core category and try to fill in all other subcategories of the coding paradigm. However, at this stage we do not only look at how we developed the coding paradigm for the category ‘admiring’ during the axial phase, we also consider all other categories identified in the axial phase. This means that, for example, we consider how ‘training’, ‘teaching’ and ‘socializing’ all play a role in developing an explanation of the role of admiring as a social activity in the gym. Each of these could play a role as causal conditions, actions/interactions, intervening conditions and context and/or eventual consequences. For instance, bodybuilders who ‘teach others’ might receive more admiration, but also boost their own self-esteem through sharing of knowledge (which might in turn result in self-admiration). Exploring relationships between these categories means that we develop initial hypotheses, which requires us to go back to the data to validate these (or collect additional data). However, it will help that we have developed each of these categories using the coding framework in the axial phase and because we now have a very specific focus: explaining the role of admiring as a social activity in the gym. This also means that in the process of integrating all the other categories with this core category, we will probably abandon certain categories (even if they are well developed) because they do not appear as important in

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explaining our core category ‘the role of admiring as a social activity in the gym’. At the same time, going back to the data and thinking about the relationship between these categories and our core category, might develop new properties and dimensions (and perhaps even new categories). We will continue to go back and forth in the data until we feel that we have fully developed the coding paradigm in relation to our core category. Or, put differently, we will stop our analysis once we know what the roles are of admiring in the gym, what causes this, how actors respond or act to this, under which conditions and with what kinds of outcome.

Various conclusions can be drawn from this example. First, it shows how in GT the focus of your analysis shifts as you continue to analyse your data. Our decision to focus on ‘the role of admiring as a social activity’ might be more straightforward once we have finished the axial coding phase, it is unlikely that we would have predicted this at the start of our research. As we have rich data on this topic and little is known about it, it makes sense theoretically to write about it. This means that you must demonstrate willingness to abandon pre-established research questions and instead move to new or more particular research questions as the coding framework takes shape and choices are made on what to focus on. Strauss and Corbin recommend similar tools for this phase of analysis as those used in the axial phase of coding (asking questions, making comparisons, developing coding trees, memos and diagrams). However, we would like to illustrate the usefulness of three particular strategies advocated by other GT inspired authors (e.g., Miles, Huberman, & Saldaña, 2020; Mortelmans, 2013) who, like Strauss and Corbin, depart from a more post-positivistic approach and aim to develop explanatory models, such as: 1) the use of comparative matrices; 2) the development of typologies; and 3) relational models.

3.3.10 Comparative matrices Given that in a selective phase of coding we are in a position to choose a particular category as our most important (the one that we want to explain) and that we simultaneously want to relate this to other categories that play a role in explaining this, we could list these categories next to each other and explore whether any patterns emerge when we ‘pour’ our data in this list. This can be achieved by applying the following steps: 1. Make a table with two columns, in which the first column contains the (fake) names of the respondents/sources of data that have interesting information about your core category. 2. In the top cell of the second column, write the name of the core category that you want to explain.

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3. For each respondent/source of data in column 1, try to fill in all the relevant, available information about your category in column 2 (leave the cell blank if there is no information). 4. Add additional (sub-)categories as new columns, all of which should, to some extent, be connected with the key variable (and perhaps also with each other). 5. Try to fill in all relevant information for each listed respondent/source of data for these additional categories (leave cells blank if there is no information). 6. Try to discover patterns in the matrix (between the additional categories and between the core and additional categories).

Case study 3.12 Selective coding: developing comparative matrices For instance, Stevens (2006) conducted research on racism in secondary schools and developed a detailed coding structure, consisting of various well-developed categories based on fieldnotes of ethnographic observations and interviews with students. One of the themes on which he collected rich data concerns students’ responses to racism; which is also a topic on which he could find little research. More specifically, Stevens decided to explain why students respond to racism from teachers in specific (and sometimes very different) ways (this is his ‘storyline’). He selected ‘students’ responses to racism’ as his core category and wants to develop a theory that explains variability in students’ responses to racism. To help his efforts in integrating his coding framework with this particular core category, he decides to develop a comparative matrix. First, he assigns the core variable to the first column and then lists all the respondents (using pseudonyms) in his data set who have something interesting to say about this topic.

Response to racism Sadi Tupas Turkish Sisters Spawn Erdy and Shakur

Figure 3.2  Developing comparative matrices – step 1 Although he has developed the category ‘response to racism’ well in a more axial phase of coding, he retrieves all the relevant data from these respondents/cases to double check whether he can describe each of them in relation to this core category. Simply put, this means that he wants to offer at least an accurate description of how each of them responded to or would respond to racism from teachers, according to the information provided in the interviews and through observation.

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Interview with Sadi PS: 

‘Sadi, do you think that there are teachers who will treat you differently because you are of Turkish descent?’ SADI: ‘Yes, but that depends, you know. When, for example, there is a racist teacher, then you have to avoid that they … You know, you have to avoid that they are in the right, you don’t have to do anything wrong, you have to be calm, follow the lesson, you don’t have to give them a chance to say something to you.’ PS:   ‘Yeah, yeah, you shouldn’t give them a reason to say …’ SADI: ‘Yeah.’

Interview with Spawn, Tupas and Erdy SPAWN: ‘You know, our problem is that our Dutch is not very good, I mean not as good as the teacher’s Dutch. And he (teacher of Dutch and English) is going to find it easy to defend himself against us, with his Dutch, with his own vocabulary (Erdy agrees). So, he really can make us look like fools … But I am just going to try to avoid him next year, I mean, I am just going to try to go to another tutor group, so … So somewhere where another teacher teaches Dutch.’ TUPAS:  ‘But come on, that’s not true! I mean he says that his Dutch is not good enough, but look he is able to speak Dutch very well! And me too, and him, in fact all of us! …’ … TUPAS:  ‘But I am not afraid when he [the teacher of Dutch and English] will say something! I have the right to do that if he says something … I … I insult him back if he insults me, and if he starts a fight, then I will start to fight him! … Grr, such an asshole, if he says something to me I have the right to say something back, as long as he starts, I have the right to insult him back. And then they can’t throw me out of school or give me an exclusion or something like that, not even a detention, because otherwise I will bring charges against them!’ … SPAWN:  ‘[when teachers are racist] That’s just … These are just issues where you think “well yeah, I don’t want to study anymore later on if that continues like that, if teachers are like that, I really don’t feel like going anymore, that is just pure discouraging”, you understand?!’ … TUPAS:   ‘We are all just people, isn’t it? There is not a single difference between us! If you cut us open, we will all bleed to death, so that’s just all the same! … but if they are going to insult us like that, then I am going to be discouraged, I mean that’s the truth! I am going to say like: “Come on man, if I have to continue like that, what does it matter? I am not going to continue study like that for six or seven years, that’s not going to work!”’ (Continued)

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Interview with Shakur SHAKUR: ‘… And for example, when you confront a teacher with the fact that he is racist, he will never admit that you are right. Two years ago I had a teacher here at school, [name of teacher]. There was this Belgian guy who tried to change a tyre but he couldn’t do it very well and he was very slow and I asked if I could try it, so that it would go a bit faster. And yeah, I start to change the tyre, work quickly and laugh a bit, and he says “over here you don’t try to be a showman, because I don’t like that, and definitely not from a Turk!” So yeah, that was a purely racist remark and I tell him ‘for a start I am not a Turk, and secondly, why do you use that, why do you say that?!” And yeah, he starts to avoid the question and I just told him “yeah, it’s okay!” So yeah … it really doesn’t interest me who’s racist, they can be racist if they want to, as long as they don’t touch my wallet [others laugh]. … SHAKUR: ‘I think it is a bit normal that some Belgians and teachers are racist, because a foreigner from Morocco or Turkey comes to Belgium, starts to work, drives a beautiful car and is dressed nicely. And some Belgians who have lived here for their whole life cannot afford those things and start to think of how all that is possible, in their own country … and that’s how they become racist. But there is not a lot you can do when someone is racist. There is no point in saying “why are you racist?” to someone who’s racist, because someone who’s racist will always hate foreigners.’ … SHAKUR: ‘There are foreigners who are very quick in saying “you are a racist, you are a racist”. There are like some teachers or pupils who insult you sometimes, who laugh with you, yeah, foreigners are quick to say that they are racist, but it can just be that they, you know, hate you, but not ALL the foreigners …’

Observations with Turkish Sisters After observing the Turkish Sisters [a group of more deviant friends who share a Turkish migration history] for weeks conducting formal interviews and many informal conversations, I noticed that they are critical of many teachers, in particular their form tutor, who they accuse of being racist. They argue that their form tutor and some other teachers punish them too harshly or frequently compared to other students. However, they also admit to not paying much attention to what happens in class and instead prefer to be involved in modelling their hair and doing make-up [with each other, during class time], which they see as much more relevant considering that they want to open a beauty saloon together after finishing school. When reprimanded in the class by teachers, they sometimes insult the teacher for being unfair or racist and almost always challenge them in some way or another [verbal or non-verbal]. Most of the teachers do not pay much attention to them, unless they make too much noise and in so doing disturb the classroom.

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Based on these interviews and observations, Stevens made the following summary of how these students responded/would respond to racism from teachers.

Response to racism Sadi

Exemplary conformity

Tupas

Calculated resistance

Turkish Sisters

Overt resistance

Spawn

Avoiding racism

Erdy and Shakur

Downplaying racism

Figure 3.3  Developing comparative matrices – step 2

Now that the core category is ‘filled in’ for each of the respondents with meaningful information about this topic, we can proceed to the next stage of analysis: identify the categories that seem to explain the observed variability in students’ responses to racism. This first step of the analysis also serves an additional purpose: to establish that there is variability in how students respond to racism. This is necessary in order to proceed to the next step, as it would be difficult to develop explanations for differences in how students respond to racism, if there are no differences between students in how they respond to racism. Based on further analysis of this data, Stevens concluded that the following variables seem to play a role in explaining how students respond to (alleged) incidents of teacher racism: 1.

Who must change in order to deal with this situation (or whether they feel that they have to change themselves or whether the teacher has to change). 2. How sensitive they are to (teacher) racism. 3. Whether they see the teacher’s racism as a barrier to realizing their goals in life. 4. Whether they believe they have the resources to challenge the teacher. 5. Whether they pursue school-related goals similar to those emphasized by their teachers. Having identified these additional categories, he now includes these in his matrix and tries to describe how each response rates for each category, leaving cells empty where he cannot find information. (Continued)

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Response to racism

Change teacher or self

Sensitivity to racism

Racism a barrier to goals

Resources to fight racism

School related goals

Sadi

Exemplary conformity

Self

Low

Yes

Yes

Yes

Tupas

Calculated resistance

Teacher

High

Yes

Yes

Yes

Turkish Sisters

Overt resistance

Teacher

High

Yes

Yes

No

Spawn

Avoiding racism

Teacher

High

Yes

No

Yes

Erdy and Shakur

Downplaying racism

Self

Low

No

No

No

Figure 3.4  Developing comparative matrices – step 3 Now, we can start checking for emerging patterns in our data. For instance, we can see that all the students who think that the ‘teacher’ should change in dealing with racist incidents all seem to be more sensitive to such incidents. We can also establish that only those who do not share the same school-related goals as the teacher (namely the Turkish Sisters) opt for the more extreme form of overt resistance (or overtly challenging the teacher). Furthermore, having the resources (e.g., certain verbal language skills) to challenge the teacher also seems to increase the likelihood of actually challenging teachers. In terms of how young people respond to teacher racism, we have noticed that they can opt for either confronting strategies (overt or calculated resistance) or non-confronting responses, such as avoiding or downplaying racism or conforming to the teacher.

3.3.11 Relational models Developing a matrix or writing memos can also stimulate the development of relational models in which relations between the core category and other categories are graphically presented. Depending on the level of sophistication, such relationships can vary (e.g., causal, mediating, moderating, being part of). The key goal of such models is to present key aspects of the explanatory model that you are developing.

Case study 3.13 Selective coding: developing relational models For instance, and building on the previous example of Stevens (2006), we could focus on the factors that make young people challenge racist teachers; as this kind of strategy seems to be associated with both better mental health outcomes and potentially

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more severe forms of punishment, such as receiving a school exclusion. Going back to our matrix, we might argue that students’ decision to challenge their teachers is informed by various interplaying factors, including the extent of overlap between their educational goals and those of the teacher, the resources that they think they have to challenge teachers and their sensitivity to racism.

Shared goals

Confronting teacher

Resources

Sensitivity to racism

Figure 3.5  A relational model on how young people challenge racist teachers We could also further hypothesize complex relationships between these factors. For example, we might argue that students who are very sensitive to teacher racism, will only challenge teachers if they think they can, or have the resources to do so, as they might also be very sensitive to the consequences of such actions. Or we could argue that students with resources to challenge teachers, will only use them if they are very sensitive to teacher racism.

3.3.12 Typologies A typology is a classification of a phenomenon over some (but not all) key characteristics of that phenomenon. They capture the essential features of a social phenomenon and can be used as ‘yardsticks’ or ‘templates’ in analysing these social phenomena. A key feature of a typology is that all cases can be assigned to one and only one type. Every time you develop an ideal type you define some (sub-)categories as the axes around which your typology will be made. The axes you identify cannot be random, but must have (sub)categories that are significantly influential in explaining the observed variability in your sample regarding a particular core category.

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Case study 3.14 Selective coding: developing typologies For instance, building further on the last example, we might conclude that students’ decisions to challenge teachers depends on two key characteristics: their sensitivity to racism, which we might also interpret as a moral disposition against racism; and the extent to which they think that teacher racism constitutes a barrier to realizing future goals, which might be interpreted as a more instrumental opposition to racism. Making these assumptions might lead us to develop the following typology, in which we then try to put every respondent/case in our sample that has relevant information in relation to these two characteristics. Racism as a barrier to goals Racism as a moral problem

YES

NO

YES

CRUSADERS

IDEALISTS

Overt resistance, Calculated resistance, Avoiding racism (Turkish Sisters, Tupas, Spawn) NO

DIPLOMATS

INDEPENDENTS

Exemplary conformity

Downplaying racism

(Sadi)

(Erdy and Shakur)

Figure 3.6  Typology on decision-making of students when challenging racist teachers You can continue with the development of this typology by giving meaningful names to each category; names that preferably capture distinctive features of the cases/ respondents that are grouped together. In developing typologies, you can also discover that there are certain ‘empty’ cells – or particular configurations for which you could not (yet) find any cases. For instance, the absence of ‘idealists’ in our sample might stimulate us to look for such cases, and/or to develop explanations for why we do not seem to have anyone with this particular profile.

The examples discussed here do not lead to fully developed theories. Instead, they illustrate how the development of a theory can be aided by using matrices, relational models and typologies. At best, these examples help us to develop particular working hypotheses that need to be checked further with the existing (and additional) data. Only if we can account for all the variability in our core category, we can conclude that our theory is sufficiently developed. Finally, while these tools are presented here as part of the selective phase of coding as developed by Strauss and Corbin, they can also be used in other (more constructivist)

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approaches to GT or coding more generally. However, given their focus on developing an explanatory model, made up of relationships between variables, they seem to fit better with the logic of GT as described by Strauss and Corbin. These tools increase focus and reduce variability and complexity; in other words, they help to emphasize and make explicit what you consider essential forces driving the phenomenon under investigation but at the expense of offering complexity and depth. As the focus is often determined by the richness of your data and analysis, your storyline might (and in GT often does) end up focusing on a phenomenon that was not originally identified as meaningful. This illustrates a key feature of GT analysis: the focus of your theory and ultimately what you try to explain (or your research question) develops over the course of your research and often takes you in a direction unanticipated at the start of the research.

3.3.13 Phase 3 of coding according to Charmaz In a third phase of coding, focused codes can be further integrated into a single framework by developing or applying more abstract, theoretical codes. Such a form of theoretical coding can make theory more integrated and connected to other theories, but it can also stimulate you to highlight particular parts of reality at the expense of others. Charmaz refers here to Glaser’s use of ‘coding families’ (Glaser, 1978), or more abstract theoretical concepts that can stimulate you to integrate more substantive codes together into a single framework and to look beyond concepts and theories already firmly established in your mind. Glaser developed various coding families, such as: a. ‘Six Cs’: context, causes, contingencies, consequences, covariances and conditions. b. Identity-Self: self-image, self-concept, self-worth, self-evaluation, identity, social worth, self-realization, transformation of self, conversions of identity. c. Cultural: social norms, social values, social belief, social sentiment. Other coding families focus on: process; degree; dimension; type; strategy; interactions. Hence, theoretical codes are highly abstract concepts, ideas and perspectives that you import from other research to help with the integration of particular focused codes (Thornberg & Charmaz, 2014). Note that the ‘Six Cs’ focus on categories that are very similar to those that are central to Strauss and Corbin’s coding paradigm. The key difference is that Glaser does not impose these codes on you, but rather sees them as inspirational, in that they can stimulate you to look at your codes in a different way and simultaneously stimulate more abstract, theoretical thinking. Similarly, Charmaz considers such theoretical concepts as ‘sensitizing concepts’ that guide but do not determine your analysis. In other words, you should not blindly apply existing concepts to your data in the process of making your analysis more focused and/or drawing patterns between your codes. Only if a theoretical concept seems to emerge from the data as useful in making sense of the data, should that concept be applied (and perhaps modified so that it better fits the data). In addition, and this constitutes an important difference between Charmaz on the one hand and Strauss and Corbin on the other, Charmaz does not think that it is necessary

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that GT analysis must reach this type of theoretical coding in order for it to be called GT. If focused coding results in more than just description and leads to an increased understanding of particular social phenomena, then this can be considered a valid theoretical goal, even if no theoretical integration is achieved between the various focused codes that were developed. For Charmaz, to move from description to understanding you must ‘raise categories to concepts’ (p. 247). While these concepts serve as core variables and hold explanatory and predictive power for more post-positivistic GT, ‘for constructivists, theoretical concepts serve as interpretative frames and offer an abstract understanding of relationships. Theoretical concepts subsume lesser categories with ease and by comparison hold more significance, account for more data, and often make crucial processes more evident’ (pp. 247–8). In other words, although Charmaz does not expect you to develop a full understanding of a phenomenon, that considers all possible variations of a phenomenon (including all possible deviant cases), she expects you to develop concepts that offer some abstract understanding of phenomena.

Case study 3.15 Theoretical coding: from focused codes to theoretical concepts For instance, remember how Charmaz developed the focused code ‘losing a valued self’ (Charmaz, 2014). The analysis did not aim to explicitly link this focused code to higher level theoretical codes as such a level of theoretical integration was deemed unnecessary: ‘In my analysis of losing and regaining a valued self, I made no explicit attempt to integrate my focused codes through theoretical coding’ (pp. 150–1). In other words, although Charmaz acknowledges that her focused code could be linked to higher level, more abstract and theoretical codes like ‘identity’, ‘self’ and ‘coping’, she feels that the focused code by itself offers sufficient additional, theoretical knowledge to her field of interest. By focusing on developing an abstract understanding of phenomena (by developing focused codes), she also encourages you to aim higher than developing mere descriptions of phenomena. Hence, the question ‘how?’, if addressed well, almost naturally leads to addressing the question ‘why?’, as the analysis focuses on action as situated in context.

Furthermore, in this phase, abductive reasoning constitutes a key mechanism through which theoretical coding occurs (Thornberg & Charmaz, 2014).

Case study 3.16 Theoretical coding: applying abductive, deductive and inductive reasoning For instance, Charmaz could ask herself how her concepts of losing and regaining a valued self relate to each other and how this relationship can be understood: when do

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we lose our self and when do we regain it? How does one state develop into the other? In so doing, Charmaz aims to further develop a more integrated theory. Based on her knowledge of social-psychological theories about identity development and her knowledge of the data that she collected, she could start developing certain working hypotheses about how both notions of self relate to each other and develop. Developing these working hypotheses, based on a consideration of all the possible knowledge she has and considers useful, is an example of abduction. Developing certain predictions based on these hypotheses is called deduction and developing or testing hypothesis based on observations (her data) is called induction. Although these three forms of reasoning are employed throughout the analysis process, you will rely more on abductive reasoning in the final (theoretical) stage of coding, as informed guesses are made about how to explain certain phenomena (here, the relationship between two abstract codes), based on a consideration of all the relevant knowledge you have. Therefore, this is also a stage where you are advised to go back to the literature to draw further inspiration in developing working hypotheses.

In the following figure, a brief overview of the differences in GT approaches of Strauss and Corbin on the one hand and Charmaz on the other hand are presented.

Table 3.2  Comparing two approaches to GT coding Corbin and Strauss

Charmaz

Phase 1: Reducing text into meaningful codes Open coding

Development of ‘concepts’ (abstract short codes)

Initial coding

Comparison of concepts into categories, described in terms of ‘properties’ and ‘dimensions’ (values)

Start from ‘in vivo codes’/‘codes from participants’ Formulate ‘data as actions’ Use of ‘sensitizing concepts’

Phase 2: Reducing and integration of codes Axial coding

Development of abstract categories, in terms of specific set of characteristics (i.e., ‘coding paradigm’) Open code becomes ‘phenomenon’: • causal conditions; • actions, interactions or strategies of actors; • structural conditions; • context; and • consequences/ outcomes.

Focused coding

Use of most significant or frequent initial codes Ask questions about the codes Memo writing Development of more abstract codes and relationship between these

(Continued)

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Table 3.2  (Continued) Phase 3: Building a focused theory Selective coding

Selection core category, systematically relating it to other categories, validating those relationships and filling in categories that need further development Development of ‘storyline’ or general description of theory Apply coding framework to core category and involve other categories

Theoretical coding

Integration of focused codes in theoretical framework and develop more abstract theoretical codes Use of Glaser’s ‘coding families’ (e.g., 6 Cs, identity-self, cultural, etc.) This phase is not a crucial part of GT

Go back to original data/ additional data collection Use of tools: comparative matrices, relational models, typologies

3.4 Conclusion and discussion GT is a family of approaches that share the goal of developing an explanation or (partial) understanding of a phenomenon by using theoretical sampling, a constant comparative approach and particular coding strategies to reach theoretical saturation. Although induction is certainly emphasized in the beginning stages of data analysis (by coding data in an open way and being critical of existing research), induction and deduction are employed throughout, and abduction in particular becomes important in the latter phases of data analysis where you try to develop a more abstract explanatory model or theoretical understanding of a particular phenomenon. We have seen that different philosophical paradigms can be applied to GT, leading to somewhat different views on how coding should proceed, what the end product (or theory) should look like (and the related research questions that should be pursued) and how you define your own role in the production of knowledge. Although used widely, key authors in the field argue that GT is also wildly misunderstood and often applied incorrectly. With this chapter we hope to clarify some of the key features of GT and how this data analysis approach differs from related, but different approaches, so that you can better judge whether you are employing or could employ GT. Evaluating GT in general, and considering our own experiences in conducting GT, makes us aware of both the potential as well as the challenges associated with this approach. GT offers a very effective approach to data analysis if the goal is to develop a thorough and theoretically relevant (i.e., building on existing knowledge) understanding or explanation of a particular phenomenon; a phenomenon that also emerges as important through the process of data analysis and is therefore not necessarily recognized as such before the start of the research.

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The challenge, however, is that ‘orthodox GT’, or GT that follows the procedures and principles highlighted above as closely as possible, is quite demanding. Put simply, if you want to do GT well, you should have: 1) very strong analytical skills; 2) respondents that are willing and able to offer rich data, irrespective of your interview skills and questions; 3) strong interviewing skills – if this method of data collection is used, which is often the case and sometimes even advised; 4) considerable time to conduct the research. In addition, you have to accept that there is: 5) considerable uncertainty about both the focus of the research and the time frame required to reach theoretical saturation; 6) an almost default acceptance, if not expectation, that the focus of your research will change as you progress, in a direction that cannot be predicted; 7) you need a considerable volume of data, assuming that theoretical saturation often requires additional waves of rich data; and 8) the need to engage with often very diverse bodies of literature over time, as the inductive process of analysis will direct you to different bodies of relevant literature. It is difficult to see how students who write a bachelor’s or master’s thesis can fulfil all these requirements. Indeed, the time frame and experience of junior researchers makes it almost impossible to reach the standards imposed by an orthodox interpretation of GT. However, perhaps you can relax these principles and approach GT in a more pragmatic way; for example, by acknowledging that only two waves of data collection and analysis can be conducted on a sample of twenty respondents and that, as a result, theoretical saturation will only be reached to some extent. It is important then that you stress what kinds of GT principles you adopted in your research; which ones you relaxed and your motivations for doing so; and what kinds of limitation might be associated with these choices. In addition, you could ‘jump’ from a GT approach to other, related approaches, such as thematic analysis (see Chapter 10) or the generic inductive qualitative model, if this fits your research questions, interests and context more. For instance, you could start with a GT approach, but decide after an initial round of data analysis that a more thematic analysis-inspired approach would be more suitable to what you want to achieve. Again, motivating why you make such decisions and reflecting on the implications of doing so will strengthen your case for approaching your data analysis in such a way. In this sense, Charmaz’s constructivist approach seems more attractive compared to Strauss and Corbin’s approach, given that she relaxes the goal to build an explanatory model out of the analysis. In addition, her use of ‘coding families’ throughout the coding process allows researchers with different theoretical interests and philosophical assumptions to engage with GT. In contrast, Strauss and Corbin expect you to apply a specific conceptual framework (coding paradigm) in analysing data, with the goal to develop an explanatory model of a particular phenomenon. As a result, their approach seems more suited for those who start from a post-positivistic approach and set their eyes on developing a dense theoretical explanation that accounts for the variability in relationship to a particular phenomenon. As a final note, we would like to emphasize again that in writing this chapter we could not properly account for all the different versions of GT and relevant discussions between

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key authors in this field. In so doing, we necessarily offer only a partial reading of what constitutes the GT family. As a result, interested readers should be encouraged to read further into this rich body of literature, to explore additional approaches to GT that are perhaps closer to what they need and want to apply.

3.5 Summary checklist In this chapter you learned about the key features that set GT apart from other approaches to qualitative data analysis. In addition, you should now understand the key differences between two highly influential approaches to GT and their implications for setting up your own GT research design.

3.6 Doing grounded theory yourself In this exercise we will stimulate you to think about what is required to design a GT study for a master’s thesis.

3.6.1 Assignment Imagine that your friend and fellow student Jasmine asks you for advice on how to develop a GT design to study the following topic: ‘female bouncers and conflict’, studied from a more constructivist approach. In helping your peer, please focus on all the main features of a GT analysis (use of literature, sampling, constant comparative approach, coding, theoretical saturation, theoretical end product) and describe how she should address these in relationship to her proposed study. Please also consider that your friend is doing this as part of a master’s thesis, which means that you should consider the limited resources at her disposal.

3.6.2 Model response Given her interest in a constructivist approach, I would recommend using Charmaz’s CGT. This approach strongly recommends that you start with a thorough literature review on this topic. This should result in a critical synthesis of what has been studied in this field, including mapping of different topics or research questions, theories and concepts that are used and what seems to be missing. The conclusion might be that there is very little research that explores differences between male and female bouncers regarding the kinds of conflict that they experience and how they manage them. These initial research questions fit well with a constructivist approach (focus on actors’ experiences and strategies) and can guide but not determine the focus of her research, in that we know that GT almost always results in changes in terms of focus.

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In the next phase, Jasmine should think of an appropriate, basic sample. Given her focus on gender inequalities, your friend might sample four male and four female bouncers as an initial sample. She will collect and analyse interview data from these eight respondents and adopt a constant-comparative approach in which she compares all the data that emerges from these interviews, not only by comparing men with women or by focusing on her initial research questions. In analysing the data, she will first code the data as much as possible in vivo and by using gerunds, and consider sensitizing concepts that are important for a constructivist approach and/or which emerge as relevant from her previous literature review (initial coding). In a second phase, her focus will shift more to the codes that she developed and, by writing memos, she will explore recurring themes, or codes/themes that appear as theoretically relevant and think of relationships between her codes (focused coding). Out of this analysis, she might develop an interest in the importance of ‘masculine-feminine identities’ in that her respondents frequently talk about how they must behave more as a man or woman in responding to particular incidents. As a result, she will read additional literature about such identities as applied in these or relevant contexts (e.g., the performance of gendered identities in household conflicts). In thinking about her next sample, she considers that her research now focuses on how bouncers adopt different gendered identities in different situations of conflict. Her second and final sample consists of different people who can help her to get more insight into her research topic: a female and male bouncer who are considered as very effective by their peers in solving conflicts; a female bouncer who describes herself as a feminist; a nightclub owner who opposes women being bouncers; and a nightclub owner who is strongly in favour of having female bouncers. In addition, she will interview (using an adapted interview questionnaire that now focuses more on the issues that she is interested in) two respondents of her previous sample again, as she could not obtain information on these key issues with them during the first interview. She will analyse these data in a similar way as in the first round, but compare it form the start with the emerging theory as developed at the end of the first wave of data collection and analysis and more focused on her topic of performing gendered identities. The result is that her analysis is more focused on the theme that she identified as important after the first wave. At the same time, she will consider linking existing, more abstract concepts and theories derived from the literature to develop a more thorough understanding of how gender identities are performed in such contexts and will employ increasingly more abductive forms of reasoning in developing a deeper understanding of her topic of interest (theoretical coding). Although she might reach theoretical saturation, it is likely that she will not be able to develop a full theory or thorough understanding of her theme of interest, and that particular questions remain unresolved. However, as long as she adds to our understanding of how male and female bouncers use gendered identities in their work, she fulfils the end product as prescribed by CGT. For instance, she might develop different types of

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‘gender-identity adaptation’ that bouncers must make to be successful and explore how this relates to socialization processes at the job (which would fit well with a constructivist approach). In her conclusions she emphasizes where she added to our understanding of how gender identities are performed in such contexts and which questions warrant further investigation and how (and with whom/what kinds of data) these could be investigated further.

3.7 Recommended reading Charmaz, K. (2014). Constructing grounded theory. Thousand Oaks, CA: SAGE. Strauss, A. and Corbin, J. (1990). Basics of qualitative research: grounded theory procedures and techniques. Thousand Oaks, CA: SAGE. Both textbooks are discussed extensively in this chapter, but the original books offer much more detail and discussion – an absolute must for those who intend on applying them in their own research. Clarke, A. (2005). Situational analysis: grounded theory after the postmodern turn. Thousand Oaks, CA: SAGE. Offers a postmodern approach to GT and therefore a useful alternative approach to GT to those discussed here. Bryant, A. and Charmaz, K. (eds) (2007). The SAGE handbook of grounded theory. Thousand Oaks, CA: SAGE. Offers a collection of valuable essays covering a broad range of issues related to GT written by experts in the field.

3.8 References Apramian, T., Cristancho, S., Watling, C. and Lingard, L. (2017). (Re)Grounding grounded theory: a close reading of four schools. Qualitative Research Psychology, 17(4), 359–76. Birks, M., Hoare, K. and Mills, J. (2019). Grounded theory: the FAQs. International Journal of Qualitative Methods, 18, 1–7. Blumer, H. (1998 [1969]). Symbolic interactionism. Berkeley, CA: University of California Press. Charmaz, K. (2006). Constructing grounded theory (1st ed.). Thousand Oaks, CA: SAGE. Charmaz, K. (2014). Constructing grounded theory (2nd ed.). Thousand Oaks, CA: SAGE. Clarke, A. (2005). Situational analysis: grounded theory after the postmodern turn. Thousand Oaks, CA: SAGE.

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Dey, I. (2004). Grounded theory. In C. Seale, G. Gobo, J. F. Gubrium and D. Silverman (eds), Qualitative research practice (pp. 80–93). London: SAGE. Dunne, C. (2011). The place of the literature review in grounded theory research. International Journal of Social Research Methodology, 14(2), 111–24. Glaser, B. G. (1978). Theoretical sensitivity. Mill Valley: Sociology Press. Glaser, B. G. and Strauss, A. (1967). The discovery of grounded theory: strategies for qualitative research. New Brunswick: Aldine Transaction. Henwood, K. and Pidgean, N. (2006). Grounded theory. In G. M. Breakwell, S. Hammond, C. Fife-Shaw and J. A. Smith (eds), Research methods in psychology (pp. 342–65). Thousand Oaks: SAGE. Hood, J. (2007). Orthodoxy vs. power: the defining traits of grounded theory. In A. Bryant and K. Charmaz (eds), The SAGE handbook of grounded theory (pp. 151–64). Thousand Oaks, CA: SAGE. Maxwell, J. A. (2005). Qualitative research design: an interactive approach. Thousand Oaks, CA: SAGE. McMenamin, I. (2006). Process and text: teaching students to review the literature. PS: Political Science and Politics, 39(1), 133–5. Miles, M. B., Huberman, M. A. and Saldaña, J. (2020). Qualitative data analysis. a methods sourcebook. London: SAGE. Mortelmans, D. (2013). Handboek Kwalitatieve Onderzoeksmethoden. Leuven: Acco. Stevens, P. A. J. (2006). An ethnography of teacher racism and discrimination in Flemish and English classrooms with Turkish secondary school pupils (PhD). Coventry: Warwick University. Strauss, A. and Corbin, J. (1990). Basics of qualitative research: grounded theory procedures and techniques. Thousand Oaks, CA: SAGE. Strauss, A. and Corbin, J. (1998). Basics of qualitative research: grounded theory procedures and techniques. Newbury Park: SAGE. Thornberg, R. (2012). Informed grounded theory. Scandinavian Journal of Educational Research, 55(1), 1–17. Thornberg, R. and Charmaz, K. (2014). Grounded theory and theoretical coding. In U. Flick (ed.), The SAGE handbook of qualitative data analysis (pp. 153–69). Thousand Oaks, CA: SAGE.

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4 Narrative Analysis: Analysing ‘Small Stories’ in Social Sciences Peter A. J. Stevens

Narrative analysis (NA) constitutes a very broad and rich family of data analysis approaches, employed and developed in an equally broad range of disciplinary fields. However, all approaches stimulate us to take storytelling seriously, both as a source of data and as a way of presenting research findings. In addition, NA invites us to look not only at what people say in their stories about themselves and their social reality, and what we can learn from this, but also at how people tell stories (or the structural features of storytelling), the context in which they do and their motivations for doing so.

4.1 Chapter objectives In this chapter you will: • • • • • •

develop an understanding of key, related building blocks in NA, such as discourses, narratives and stories; develop a comprehensive picture of the different genres of NA, such as oral history analysis, life history analysis and analysis of small stories; understand how small stories can be analysed, looking at what is being said, how (or the structure of stories), in which contexts and for what purposes; practise your own skills in setting up an NA study through an exercise at the end of the chapter; understand how you can develop an NA research design (through an in-book exercise) and code data using an NA approach (through an exercise); and know where to find additional reading on this topic.

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4.2 Key features, debates and historical developments Before describing and contextualizing NA, it is important to first clarify what researchers mean when they talk about ‘narratives’ and related concepts such as ‘discourses’ and ‘stories’.

4.2.1 Defining the building blocks: discourses, narratives and stories Discourses, narratives and stories are often used interchangeably in social sciences and simultaneously given different (often implicit) meanings. These three concepts share certain characteristics. First, they all refer to ways in which people present and make sense of social reality or certain aspects (e.g., themselves, others, things that happened, could happen or are ongoing). Second, they imply that people use language to present social reality in a particular way. Third, by using discourses, narratives and stories, people do not merely present aspects of reality in a certain way, they also construct or make reality in a particular way. Fourth, they assume that the act of presenting reality in a particular way is purposeful, in that it serves certain personal and/or group interests, tied to our positions in various social hierarchies, groups and/or organizations. Fifth, they consider the importance of micro, meso and macro contexts in producing or using particular discourses, narratives or stories. Sixth, and related to the fifth point, researchers should be aware and reflective about their own role in producing and interpreting discourses, narratives or stories. Finally, these particular presentations of reality can be found in different kinds of text, including oral or written monologues, images or communication between actors. What then is the difference between discourses on the one hand and narratives and stories on the other? Discourses refer to particular presentations of reality, based on a set of statements that, taken together, present an aspect of reality in a particular way. A narrative ‘is a presentation of an event or series of events’ (Abbott, 2008, p. 13), or a presentation of something that has happened, is (still) happening or will happen. Stories are made up of narratives (sometimes called ‘narrative events’ or ‘narrative discourses’) that are organized in a coherent or consequential and chronological or temporal ordering. In addition, the specific way in which narratives are organized in a story must convey a rupture from a stable state, so there must be a crisis moment where ‘things change’. Finally, stories are told by a narrator to an audience for particular purposes. For instance, we could explore how refugees are presented in newspapers and find that they are presented in certain newspapers as ‘opportunistic’, ‘violent’ and ‘criminal’. Taken together, these statements form a very negative discourse of refugees; a discourse that could label ‘refugees as dangerous’. However, in other newspapers, we might find a different discourse, where refugees are presented as ‘honest’, ‘hard working’ and ‘skilled’, which could be labelled as a ‘refugees as asset’ discourse. We could then watch a documentary on refugees that shows a couple in a particular country that want to flee because of war. The couple interviewed are highly educated people

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who lost their secure, well-paid jobs and fear for their and their children’s lives in their wartorn country. As a result, they want to flee their country to build a better life for themselves and their children abroad. The documentary follows the family from the moment they intend to flee until their arrival in a country that they consider as safe, to their formal application as refugees and eventually their success in finding high-status jobs. Both the newspapers and the documentary offer presentations of refugees (who are they and why do they move?), but in the documentary such a presentation is structured around a sequence of inter-related events that are linked to each other into a coherent whole (war causes fleeing, fleeing causes process of applying for refugee status) and time (there is a beginning, middle and end). The documentary (story) shows different moments of ‘rupture’: the war, the fleeing from one to another country, the formal approval of refugee status and the finding of good jobs. This example also illustrates the difference between a narrative and a story. Statements like ‘I lost my job and fear for our lives because of the war’, ‘we decided to flee’ and ‘we applied for refugee status’, are all narratives: they present something that happened or an event. However, it is their integration into a coherent and temporal whole that turns them into a story. How does this story(telling) relate to the use of discourses? We could argue that the documentary relies on, uses and, as a result, reinforces the discourse of ‘refugees as asset’, but does so in a particular way (through storytelling). In this chapter, I will look at how we can analyse stories narrated by the people we study and what we can learn from such an analysis. However, before describing the steps involved in conducting such an analysis, we will look at how NA developed historically, how storytelling is used in NA and the different genres that can be identified in NA.

4.2.2 Historical roots and development of narrative analysis Researchers have long paid attention to people’s stories about themselves and their social environment in developing an understanding of reality. Between the 1930s and 1960s, scholars associated with the Chicago school tradition in sociology made use of stories narrated by particular categories of people, such as immigrants, homosexuals, drug users and the homeless, to develop accurate pictures of how these marginalized people lived their lives in a society that generally regards them as deviant (Riessman, 2008). From the 1960s onwards, this more post-positivistic use of narratives came under fire as researchers focused more on how people construct different stories about themselves (identities) and their social environment in interaction with others and in particular contexts (for a discussion of different philosophical traditions, see Chapter 1). Informed by constructivist and critical theory, stories were not necessarily seen as accurate pictures of reality, but rather as presentations of a socially constructed reality, in which the positions of respondents in terms of (lack of) power and privilege inform the kinds of story that they do and do not tell (Cigdem, 2011; Riessman, 2008). NA developed exponentially from the 1980s onwards, stimulated by feminist and psychological research, the recognition of developments in socio-linguistic research on

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the structure of conversations and storytelling (aided by the use of technological developments related to the recording of conversations) and the development of postmodern theory, which emphasized agency and consciousness over (Marxist) structural theory and sub-consciousness (Cigdem, 2011; Riessman, 2008). In analysing the use of ‘narrative analysis’ in contributions included in the Social Science Citation Index (see Figure 4.1), we can see a gradual increase of the number of publications that refer explicitly to ‘narrative analysis’ in their abstract over time, with a significant jump from 2015 onwards. Analysis of the disciplinary categories in which NA is used shows that it is primarily employed in the fields of educational research, public health, communication sciences, linguistics, history, literature, sociology and psychology.

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Figure 4.1  Number of publications that refer to NA in abstract over time in Social Science Citation Index (SSCI)

4.2.3 Interpreting, making and telling stories In telling stories, we make sense of our own experiences, of events that we have witnessed. We do this not only to understand what happens around us, but also to convince an audience that our interpretations of these experiences or events are important and ‘true’. As narrative researchers, we take a more suspicious approach to what people tell us. We do not assume that people’s stories reflect reality, but instead present a particular version of reality, narrated in a particular way through storytelling for specific purposes. As a result, we pay attention not only to what people say in telling stories, but also how

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they tell their stories, for what purposes and in what kind of context, including the audiences to whom they present their stories. In addition, narrative researchers do not see themselves as detached observers of stories that are ‘out there’, but often consider themselves as co-constructors and sometimes as the main participants in the development of stories. As a result, the role of the researcher in co-structing data is often critically evaluated in, or considered as an essential part of, doing narrative analysis. Finally, some narrative researchers use storytelling as a way of presenting their research findings, going against established, dominant ways of presenting research in social sciences (e.g., writing a title, abstract, literature review, methods, findings, conclusion and discussion). They do so because they believe that storytelling offers them a more powerful tool to connect with and convince their audience of the importance and ‘truth’ of their interpretation and presentation of reality, just like we do every day in telling our stories to people around us. Considering the various angles from which stories are analysed, the critical role of the researcher in developing stories and the use of storytelling as a way of presenting research findings, we should perhaps not be surprised to find many different approaches to NA. The following paragraphs offer an overview of different ‘genres’ of narrative analysis, based mainly on Kim’s (2016) insightful discussion.

4.2.4 Genres of narrative analysis Kim (2016) makes a distinction between three genres of NA: autobiographical, biographical and arts-based NA. In autobiographical narrative research, the researcher takes themself as the subject of research, analysing their own story. In autobiography the storyteller analyses and presents their own personal life story, presenting aspects of reality (often focused on personal identities), telling their own story in the I-form. Auto-ethnography shifts the focus from the individual to the society, in which the narrative researcher ‘systematically analyzes personal experience to illuminate broader cultural, social, and political issues’ (Kim, 2016, p. 301). Biographical narrative research (BNR) tells us stories about other people, focusing on how they make sense of their lived experiences and perspectives through storytelling, including their past, present and future (Denzin, 1989). There are a number of different genres of BNR. The Bildungsroman approach focuses on a (single) person’s personal growth and identity development. It focuses on an inner or spiritual growth of an individual, on tensions between the ideal and the reality and the importance of context. The life story or life history looks at how individuals tell their own life as honestly as possible and in terms of what they remember and want to disclose to the researcher. The focus is on the life as a whole (not specific moments) but ‘a complete biographical picture’ (Kim, 2016, p. 133) and control of the storytelling is firmly in the hands of the storyteller. There is also a strong recognition of the importance of (personal, micro, meso and macro) context. Oral history focuses on the analysis of stories told by groups

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or communities, based on shared and inter-generationally transferred memories, particularly those stories expressed by marginalized groups in society. However, unlike in life history, the power of storytelling resides more with the researcher, who crafts (ideal typical) oral histories from the stories told by respondents. A particular and more recent type of BNR focuses on small stories (Kim, 2016), which are defined as ‘a gamut of underrepresented narrative activities, such as tellings of ongoing events, future or hypothetical events, and shared (known) events, but it also captures allusions to (previous) tellings, deferrals of tellings, and refusals to tell’ (Bamberg & Georgakopoulou, 2008, p. 381). It is argued that such everyday stories are often ignored in life story or biographical and autobiographical research, which favours a focus on ‘big stories’, derived from in-depth interviews or analyses of other, large volumes of text (Kim, 2016). Proponents of small story research argue that we can learn a lot from analysing such stories, in particular how people construct identities in interaction with others and in particular contexts (Bamberg, 2004; Bamberg & Georgakopoulou, 2008; Georgakopoulou, 2006). In this chapter we will focus on this particular type of narrative research. Arts-based narrative research considers art as both a source of data that (partially) tells stories and as a way in which analysis of stories can be presented. Literary-based narrative enquiry refers to the use of fiction in presenting narrative analysis. Instead of using standard, academic modes of presenting analyses, which prioritize the presentation of ‘facts’, often in a highly prescriptive structure, results of analysis are presented using fictional presentation formats, like a short story or novel. In so doing, findings are presented in a more accessible and appealing way, fusing ‘facts’ with invented characters and plotlines. It is argued that such a presentation format allows for the communication of affect/feelings that emerged from the analysis more appropriately than in more traditional reporting formats. Similarly, visual-based narrative enquiry relies on various forms of art as narratives/stories or as a format to present narrative analysis. The kinds of art form that are included vary greatly, including (archival) photos, drawings and other non-photographic art forms, music, photo-voice or digital storytelling. The latter refers to ‘a three- to five-minute visual narrative that synthesizes photo images, artwork, video, audio recordings of voice and music, and text to create compelling accounts of lived experience’ (Gubrium, 2009, p. 150). This overview shows the rich variety in terms of how narrative analysis is approached in the social sciences; a diversity of approaches that relate to the following questions: • •

•

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Who controls the analysis of narratives or stories? Is it primarily the researcher, the researched or both who analyse reality through storytelling? What is the primary source of data? The researcher’s own experiences or stories or those of one respondent, many respondents – including (or not) the researcher – different groups of respondents and/or other forms of text? What is the scope of the story in terms of space? Is it a story about an individual, incident, group or society?

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To what kind of time frame or period does the story relate? Is it a story about a whole life span or a specific moment?

In focusing on small stories, this chapter discusses only a particular type of NA, one that puts the researcher in control of the analysis, using stories narrated by respondents, stories that are relatively small and focused on particular incidents or experiences that relate to a specific moment in time.

4.3 Doing narrative analysis step by step Although Bamberg and Georgakopoulou (Bamberg, 2004; Bamberg & Georgakopoulou, 2008; Georgakopoulou, 2006) introduced the term ‘small stories’ to NA, the analysis of the stories that I present in this chapter deviates from their approach. They analyse small stories more from a conversation-analysis point of view, in which an analysis of the content of stories is linked to the immediate conversational context (or ‘interactive engagements’) and the larger (institutional) context of a research interview. Instead, I apply a broader and more general set of lenses, borrowed from different approaches to doing NA, focusing on the content of stories, its structure (actors, plot and rhetoric) and the broader social context in which they are developed. The analysis approach discussed in this chapter supplements rather than opposes their approach to the data analysis of small stories and simultaneously illustrates typical ways in which stories are analysed within the broader NA family. In the following sections, I will describe how you can analyse short, focused stories by relying on data collected from research that I carried out in secondary schools in the Republic of Cyprus on the relationship between nationalism and ethnic prejudice (Stevens, 2016; Stevens et al., 2016; Stevens, Charalambous, Tempriou, Mesaritou & Spyrou, 2014). In what follows, I will briefly describe the context in which these stories were collected. I will return to and expand on the importance of this particular context later in the following sections, when exploring how a consideration of context can be integrated in narrative analysis of small stories.

Case study 4.1 ‘Spitting images of reality’: context Cyprus is a divided community in the Mediterranean Sea, with a mainly Turkish-speaking northern part and a Greek-speaking southern part. The two regions, including the capital city of Nicosia, are divided by the Green Line, a demilitarized zone that is controlled by a United Nations peace-keeping force. I conducted research in two private secondary schools located in the mainly Greek-speaking part of Cyprus: Green Lane and Red (Continued)

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Brick (all names of schools and respondents in this case study are pseudonyms). Both schools welcome Turkish Cypriot (TC) and Greek Cypriot (GC) students. However, while Green Lane can be considered a relatively large school, Red Brick is by comparison small. In addition, while most students at Green Lane see themselves as Greek Cypriot, Red Brick is made up of a greater diversity of ethnic minority groups, none of which constitutes a majority group in school. I collected qualitative interview data, a limited number of classroom and playground observation data and survey data from the students and some of their teachers in these two schools. While interviewing students (sometimes interviews with one participant, sometimes with two), some students spontaneously narrated a story of an incident that happened in Green Lane a few years ago. As the story kept popping up in subsequent interviews, I began asking about it in subsequent interviews when the students did not mention it spontaneously. What struck me was that students in both schools were aware of the story, but gave very different accounts of ‘what happened’. Although there were many different versions of this story in the two schools where I interviewed students, the key components could be summarized as follows: Erdem and Costas are both enrolled in Green Lane. Erdem sees himself as a TC and Costas as a GC. Erdem was accused by Costas and other GC students of spitting on a necklace of Costas; a necklace that symbolizes a religious (Christian Orthodox) cross. This ‘spitting incident’ caused a lot of tension in school between GCs and TCs and resulted in an attack by a group of masked outsiders, who came into Green Lane school several days after the incident took place, to target and beat TCs with sticks. In analysing these stories, I came to realize that TCs and GCs gave very different accounts of ‘what happened’, which stimulated me to compare their stories; given that these stories focus on issues that were of theoretical interest to me (ethnic identities and in-out group relationships). It is important to note that the targeted attack by outsiders in the school, and the incident more generally, attracted considerable media attention at that time (as I later discovered). None of the students interviewed consider themselves as first-person witnesses as they did not observe the actual incident between Costas and Erdem and were not personally targeted in the attacks afterwards. After conducting my interviews with the students at these two schools, I managed to track down the two protagonists of the story and they (and their parents) agreed that I could interview them alone and separately to discuss their take on what happened. Although I can reflect on their accounts in a general way, they asked me not to present data from their interviews as part of my presentation of the analysis.

In what follows you will read two short stories, one that illustrates well how GCs narrated the spitting incident and one that illustrates well how TCs told this story. I will use these two discrete, focused stories to show how small stories can be analysed in terms of content, structure, context and intentions.

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Case study 4.2 ‘Spitting images of reality’: typical example of how TCs narrated the spitting incident Peter:  ‘A couple of years ago there was an incident that happened there between a Turkish …’ Yilmaz:   ‘[laughs] Yeah that guy is my friend.’ Peter:   ‘Can you tell me what happened?’ Yilmaz:  ‘I think Greek Cypriots they were always looking for a chance to fight and this was their chance, just a little thing, a little spark [laughs] you know just a little spark causes a fire something like that. I remember, he was looking at a cross, he was like this, HE SNEEZED and the [GC] guy thought he spat on the cross and he called the [GC] group and they started fighting with the Turkish Cypriots and they locked the Turkish Cypriots in a room and they beat them up and there was one older guy, I think he was 13 and he tried to protect them and my cousin was in the room because it was four years ago I think?’ (Interview Red Brick)

Case study 4.3 ‘Spitting images of reality’: typical example of how GCs narrated the spitting incident Argyro:  ‘I believe that because the teachers make it so that “if they [GCs] say to you [TCs] anything: come immediately and tell me”, Turkish Cypriots, for very small things they go [to the teacher] and then [the teachers says]: “why did you do this?”’ […] Lougia:  ‘Well most of the time it’s about the same thing: a Greek Cypriot says something just for the Turkish Cypriots to hear like “Cyprus is Greek” and the Turkish Cypriots gets offended.’ Peter:   ‘Do you think they should get offended or not?’ Lougia:   ‘That Cyprus is Greek? No.’ Argyro:  ‘And I think the other thing that some years before somebody wore a cross and someone else tried to take it out.’ Lougia:  ‘It was a Greek Cypriot who was wearing a cross, they were going for exams, and the Greek Cypriot just kissed the cross and the Turkish Cypriot just spat at it and the Greek Cypriot again kissed the cross and the Turkish Cypriot spat at it again and it happened a third time and then the whole thing started and in the school were people running and throwing and …’ (Continued)

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Argyro:  ‘Before in our school there were images of Christianity [icons] in every class, but they removed them.’ Lougia:  ‘Yes and that year a new rule came in that you are not allowed to wear a cross at the school.’ (Interview Green Lane)

4.3.1 Content/themes Stories are often analysed in terms of their content or what they tell us about how people see themselves or the world around them. In theory, we could analyse stories in terms of their content by using different, established approaches to qualitative data analysis, such as qualitative content analysis (QCA) (see Chapter 8), thematic analysis (TA) (see Chapter 10) or grounded theory (GT) (see Chapter 3). However, key writers in the field of NA prefer an approach to data analysis that resembles more of a TA approach, even using the term ‘thematic analysis’ (Riessman, 2008) or describing ‘themes’ as the end product of the analysis process (Kim, 2016). Like in a TA approach, the analysis of stories in terms of their content is primarily aimed at (contextual) description and less at developing a deeper explanation or understanding of a particular phenomenon. Although the sample and the focus of the analysis can change, they often remain the same and are formulated before data is collected and interpreted. Finally, analysis of content can be both inductive and deductive. These features explain why NA focused on the content of stories, resembles more a TA and is explicitly described as different from GT (Riessman, 2008). It makes sense to focus on themes in analysing the content of stories, as people narrate stories often spontaneously around certain, general, abstract themes, such as: relationships between people (conflict, friendship, intimate), processes of inclusion/ exclusion (including identities), closeness/distance, the idea of a career and the past as an explanation of the present (Gibbs, 2007). As a result, if your research topic focuses on one of these themes, it is likely that respondents will talk about these themes through storytelling, which in turn allows you to apply NA as a way of analysing the data. Considering the many genres of NA, an analysis for themes can refer to both an extended biographical account or to an analysis of bounded (often short) segments of interview or other forms of text (Riessman, 2008). For example, Williams (1984) explores how people make sense of their chronic illnesses, through analysis of their stories of their ‘careers’ as patients. She finds that her respondents often make sense of their illness in a different way (i.e., by providing political, religious or everyday understandings) compared to how such illnesses are understood from the medical

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profession (e.g., as inherited problems). Here, different themes refer to different ways in which patients make sense of being ill. In another example of NA, Ewick and Silbey (2003) instead use relatively short sections of texts from respondents where they describe how they resist authority in the organizations in which they work. Here, the NA develops themes (or types) that present particular ways in which respondents can resist oppressive systems, such as ‘masquerading’, ‘disrupting hierarchy’, ‘foot dragging’, ‘rule literalness’ and ‘colonizing space’. Although these themes are developed from very different types of data, they overlap well with what TA considers as themes; namely, specific patterns of meaning generated or detected in the data that either describe and organize one’s observations and/or interpret aspects of the phenomenon under study (see Chapter 10). When we look at how we should develop such themes from interpreting text, authors within this field do not refer to established textbooks of TA in describing the steps of analysis or strategies that you should adopt (e.g., Boyatzis, 1998; Braun & Clarke, 2021). Instead, they advise us to apply principles related to the analysis of qualitative data more generally, similar to what Hood (2007) calls ‘the Generic Inductive Qualitative Model’ (GIQM) (see Chapter 3), or what I call in the introduction of this handbook, a more ‘pragmatic’ approach to qualitative data analysis. For instance, Kim (2016) refers explicitly to Creswell (2007) and recommends you to first develop codes, which means that you should identify concepts from your raw data through multiple coding practice. Based on these codes, categories are developed, by linking codes to create a unit or category. From these categories, you should look for patterns, by identifying repeated units (called patterns) from categories. Eventually, this leads to the development of themes, which represent similar patterns. Such a form of TA can be conducted in a more inductive, deductive or combined approach, like in TA more generally. So, while an analysis of content in NA has a lot in common with TA in terms of the end product it aims to develop, it is less specific in terms of how this needs to be accomplished. Which themes are then discussed in the spitting incident stories? A first theme that emerges strongly from both sets of stories is conflict: a conflict between two protagonists (a GC boy and a TC boy) and two social groups (GCs and TCs). In addition, in the GC version of the incident, we can also detect a conflict between the (GC) students and their school/teachers. This confirms that people tell stories easily when talking about relationships between actors (Gibbs, 2007). Other related sub-themes seem to emerge from these stories, such as the theme of feeling ‘excluded’, ‘threatened’ and ‘attacked’; themes that share a strong emotional, affective dimension. This shows that both sets of storytellers feel remarkably similar, in that they present themselves as ‘victims of exclusion’, which, like conflict, could be considered as a master theme that can be found in all stories about the spitting incident. Although an analysis of the content of these stories tells us that these young people experience conflict between actors and groups in their school and society, and feel

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mistreated, as a reader we instinctively feel that we need to know more to make sense of these stories. A logical question that follows such an analysis of content is ‘who is doing what to whom?’, or the actors that are presented in the stories, and their respective roles, which relates to a first, structural dimension of storytelling.

4.3.2 Structure A structural analysis of stories looks at how people tell stories. This approach to narrative analysis relies heavily on socio-linguistic research, which aims to identify recurring features in how people use narratives or, more specifically, tell stories (e.g., Herman & Vervaeck, 2005; Polkinghorne, 1995). I will discuss three particular structural features of stories that are often the focus of NA: an analysis of the actors, plot and the rhetorical tools used.

Actors A focus on the actors in a story and the roles they play, stimulates us to see a story as staged or a conscious effort to communicate particular knowledge and feelings by identifying protagonists who do something to other protagonists, like in a play or film. The way we use actors and assign particular roles is therefore not random, and often serves particular interests. Furthermore, the way in which we use actors in storytelling often follows generally accepted scripts or ‘Masterplots’ (Abbott, 2008), so that the audience will more easily understand and interpret the story in the way that the narrator wants them to (see also subsection below on rhetorical tools). The most basic dyad of actors and roles found in almost any story is that of the ‘hero’ and the ‘villain’. In using these two archetypical roles in telling stories, we convey explicitly who is ‘right’ or ‘good’, and who is ‘wrong’ or ‘evil’. TA sometimes relies on more sophisticated (deductive) frameworks for analysing actors and their roles in stories. De Keere and Elchardus (2011) for example, rely on Greimas’ Actantial Analysis (Greimas, 1966) to make sense of how Dutch-speaking and French-speaking Belgians see each other, through storytelling of incidents in which they interacted with each other. This type of analysis makes a distinction between four categories of actors, each with a particular role in the story: The opponent: whoever opposes the subject to realize their goal. The subject: central figure or protagonist of the story. He/she wants something to happen. The object: what the subject wants to happen/have (their goal). The helper: whoever helps the subject to realize their goal.

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However, actors and roles can be identified equally in a more inductive way, in which case you explore the kinds of actors that are put on stage in a particular story and what kinds of role they play. In applying a more inductive approach to an analysis of actors in the spitting incident stories we can reach the following conclusions. First, GCs and TCs will identify the same individual (Erdem and Costas) and group protagonists (TCs and GCs) in narrating this story, but will assign them exactly opposite roles: while TCs portray Costas and GCs as the challenger/aggressor and Erdem and TCs as victims, GCs identify the same actors in exactly the opposite roles. This suggests that Erdem and Costas, and their relationship as it unfolds in the spitting incident, mirrors and becomes symbolic of TCs and GCs and the relationship between them. Second, GCs will in addition identify the school and the teachers as important actors who take the side of the TCs, who can be described as ‘traitors’ or ‘helpers’, using Greimas’ analytical framework. Third, in the TCs’ story, other actors are sometimes included who seem to reinforce the roles of the GC and TC protagonists; in this example, the narrator’s cousin, who acts as a witness and probably also a victim and an ‘older guy’, who takes the role of a ‘protector’ or ‘helper’, using Greimas’ analytical framework.

Plot Although the word ‘plot’ is used in different ways, it often refers to the order in which events are arranged in a story (Abbott, 2008). There are many approaches to how stories can be analysed in terms of their plot (for overviews, see Kim, 2016; Mishler, 1995). In this chapter, I will describe and apply Labov and Waletzky’s ideal type of plot (Labov & Waletzky, 1967), as it constitutes one of the most influential approaches to analysing the plot of a story, in particular for short, focused stories that are the topic of interest in this chapter. According to Labov and Waletzky, a fully formed narrative is typically made up of six elements, which are not necessarily present in all stories and often in varying sequences: Abstract: summary and/or point of the story. Orientation: the time, place, characters and situation. Complicating action: the event sequence or plot, usually with a crisis or turning point. Evaluation: the narrator steps back from describing the action to give a meaning and communicate emotions, reflecting the ‘soul’ of the narrative. Resolution: the outcome of the plot. Coda: ending the story and bringing action back to the present.

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Labov and Waletzky’s model allows you to reconstruct the structure of a story from the ‘told’ story (Kim, 2016). In so doing, you can identify theoretically important structural elements of the stories, such as the ‘point’ or evaluation of the stories. It also allows you to compare stories between theoretically interesting groups in terms of how the story is constructed, and what you can say about motivations for telling the story in a particular way. Other approaches of analysing the plot of a story reorder the storyline to demonstrate how the story should have been told if it was told in a chronological order (Mishler, 1995). Yet another approach listens to the narrator’s tone of voice (when does the storyteller change their voice?) to break up the story into segments (called ‘stanzas’), which are then interpreted by the researcher in terms of the themes that are discussed in each stanza (Gee, 1991). The latter approach links a structural analysis of stories with a focus on content or themes. Each approach looks at how stories are told in order to learn something about what is told and/or why. Let us now apply Labov and Waletzky’s ideal type of plot to the way that our storytellers presented the spitting incident.

Case study 4.4 ‘Spitting images of reality’: plot of TCs’ storytelling

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Peter:   ‘A couple of years ago there was an incident that happened there between a Turkish …’

ABSTRACT (P)

Yilmaz:  ‘[laughs] Yeah that guy is my friend.’

ORIENTATION (Y)

Peter:   ‘Can you tell me what happened?’

ABSTRACT (P)

Yilmaz:  ‘I think Greek Cypriots they were always looking for a chance to fight and this was their chance, just a little thing, a little spark [laughs] you know just a little spark causes a fire something like that. I remember, he was looking at a cross, he was like this, HE SNEEZED and the [GC] guy thought he spat on the cross and he called the [GC] group and they started fighting with the Turkish Cypriots and they locked the Turkish Cypriots in a room and they beat them up and there was one older guy, I think he was 13 and he tried to protect them and my cousin was in the room because it was four years ago I think?’ (Interview Red Brick)

EVALUATION (Y)

ORIENTATION (P)

EVALUATION (Y) EVALUATION (Y) COMPLICATING ACTION (Y) COMPLICATING ACTION (Y) COMPLICATING ACTION (Y) COMPLICATING ACTION (Y) COMPLICATING ACTION (Y) COMPLICATING ACTION (Y) COMPLICATING ACTION (Y) ORIENTATION (Y)

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Case study 4.5 ‘Spitting images of reality’: plot of GCs’ storytelling Argyro:  ‘I believe that because the teachers make it so that “if they [GCs] say to you [TCs] anything: come immediately and tell me”, Turkish Cypriots, for very small things they go [to the teacher] and then [the teachers says]: “why did you do this?”’

EVALUATION (A) COMPLICATING ACTION (A) COMPLICATING ACTION (A) COMPLICATING ACTION (A) COMPLICATING ACTION (A)

     […] Lougia:  ‘Well most of the time it’s about the same thing: a Greek Cypriot says something just for the Turkish Cypriots to hear like “Cyprus is Greek” and the Turkish Cypriots gets offended.’ Peter:  

‘Do you think they should get offended or not?’

Lougia:   ‘That Cyprus is Greek? No.’ Argyro:  ‘And I think the other thing that some years before somebody wore a cross and someone else tried to take it out.’ Lougia:  ‘It was a Greek Cypriot who was wearing a cross, they were going for exams, and the Greek Cypriot just kissed the cross and the Turkish Cypriot just spat on it and the Greek Cypriot again kissed the cross and the Turkish Cypriot spat at it again and it happened a third time and then the whole thing started and in the school were people running and throwing and …’ Argyro:  ‘Before in our school there were images of Christianity [icons] in every class, but they removed them.’ Lougia:  ‘Yes and that year a new rule came in that you are not allowed to wear a cross at the school.’      (Interview Green Lane)

ABSTRACT (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) EVALUATION (P) EVALUATION (L) ABSTRACT (A) ABSTRACT (A) ABSTRACT (A) COMPLICATING ACTION (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) COMPLICATING ACTION (L) RESOLUTION (A) RESOLUTION (A) RESOLUTION (L) RESOLUTION (L)

By analysing the structure of this story in relation to its plot, we can make the following conclusions. First, TCs did not spontaneously talk about this story but had to be asked about it by the author. In contrast, GCs were more likely to spontaneously bring up and describe the incident. Second, GCs seemed to tell this story as part of a broader narrative/ story in which they criticized the school and their teachers. Argyro and Lougia’s account of the spitting incident is preceded by their discussion of another, brief story where they criticize TCs for reporting incidents of discrimination that should not be perceived as

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such and for teachers to stimulate TCs to report such incidents. They follow up their account of the spitting incident by narrating two very short stories, which I labelled as ‘resolution’ as they are only one sentence long and seem to function more as a description of what happened as a result of the spitting incident. In these final sentences they criticize their school for not embracing a Christian Orthodox identity by not allowing expressions of key Orthodox Catholic symbols (icons and crosses). Other GCs also talked spontaneously about the spitting incident as part of a broader discussion or narrative in which they criticized school policies and teachers for not recognizing the Christian Orthodox identity of the school and for not allowing GCs to express their opinions about TCs. As a result, for the GCs who narrated this story, the spitting incident seems relevant in relationship to a more general story about their relationship with their teachers in school. It perhaps symbolizes their feeling of being threatened in terms of their religious identity in ‘their’ school; a threat that is carried out by TCs and their teachers’ behaviour. The evaluation part of a story, which presents the ‘point’ of the story for the narrator is often considered as the most important part of a story. Looking at these parts in these two stories we see a similar pattern emerging: the spitting incident shows the feeling of threat that TCs experience at the hands of GCs, who seek confrontation and at the same time the threat that GCs experience in expressing their religious identity. The analysis of these stories according to Labov and Waletzky’s structural model allowed us to link these stories to other stories and to isolate what appeared to be the central meaning of these stories for the young people who shared them with me. In the following section, we will see how these storytellers used particular rhetorical features to convince me, the listener, of the truth of their take on what happened.

Rhetorical tools Telling stories constitutes in itself a powerful rhetorical tool as people are more likely to believe that something happened when you tell them what happened through a story (Abbott, 2008). But there are more specific tools that we can use as storytellers to convince our audience that we are telling them ‘the truth’. People sometimes tell a story in a way that resembles typical ways of storytelling, that are part of the storyteller’s and audience’s culture and that follow a stable and therefore predictable sequence of events. In the scientific literature, these standardized and socially accepted forms of narrating stories are often referred to as ‘genres’ or ‘Masterplots’ (Abbott, 2008). Gibbs (2007), for instance, makes a distinction between four types of dramaturgical genre that most of us will be familiar with: • •

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Romance: the hero of the story must overcome certain challenges to reach their goals and emerge as the winner. Comedy: the goal is to restore the social order and the hero must show that they have the necessary (social) skills to overcome the challenges to this order.

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• •

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Tragedy: the hero is ultimately defeated by the forces of evil and/or is excluded by society. Satire: a cynical perspective of the social hegemony.

When we tell stories using these predictable storylines or genres, we know that our audience will recognize and interpret them more easily. We also know that in so doing we will evoke particular emotions (such as sadness, fear or anger in the case of tragedy, or happiness and relief in the case of a romance); emotions that can be attached to particular heroes or villains that feature in the story. While the use of genres refers to the skeletal form of our story as a whole, storytellers can use a broad range of more specific rhetorical tools to convey particular interpretations about what happened. The following are only a few of the many rhetorical tools that people can use in telling stories (Abbott, 2008; Riessman, 2008; van Dijk, 1999): using ‘direct speech’ (or citing ‘real’ dialogue); making expressive sounds (e.g., ‘aah!’ or ‘oh!’); repetition; verb tense performativity (changing from past to present tense in key moments of the story); stepping out of the story to address the audience directly; use of metaphors, exaggeration, minimizing, leaving certain events of topics out, the ranking that we put in listing issues, emphasizing words; talking more or less slowly; changing topics; nominalization (change verbs into nouns, such as ‘the firing of 100 employees’ instead of ‘X fired 100 employees’ – see Chapter 2 on critical discourse analysis). What kinds of rhetorical tools do our TC and GC storytellers use in narrating their version of the spitting event? Looking at Yilmaz’s account, we can notice how he laughs out loud just before narrating the story. Other TCs also tried to ‘humorize’ the story by laughing about it or ridiculing it. In so doing, they effectively undermine the ‘seriousness’ of the event or downplay its significance. In sharp contrast, the GCs narrated their version of the spitting event in a very serious tone, and in so doing intentionally highlight its relevance and significance. Even the presentation of the actual spitting event by TCs is narrated in a way that resembles a ‘comedy’ as genre or master narrative. In Yilmaz’s account, Erdem did not spit but ‘sneeze’, which was wrongly interpreted as spitting. Other TC respondents used similar strategies, arguing for instance that Erdem did not spit on the cross but simply is a person who ‘spits when he talks’ or ‘was thirsty after playing football and just spat on the ground’. All these versions of the actual spitting incident present a situation where the harmless actions of Erdem were wrongly interpreted as intentional spitting on a cross, a chaotic situation which the hero of the story, Erdem, had to try to solve. Although these TCs’ stories resemble the plot line of a comedy, they also have elements in common with a tragedy; as Erdem is portrayed as a hero who must fight the unfair accusations of GCs (the villains in this story), which he does not seem to successfully manage. In comparing both stories, we can identify two related but opposite rhetorical tools used by our storytellers. While the TCs’ accounts of the spitting incident cover the retaliation event in depth, where outsiders entered the school to hit TCs, this is often missing or minimized in the GCs’ accounts of the incident. This is clearly illustrated in the two example

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stories: while Erdem spends almost half of his presentation of the spitting incident talking about the actual retaliation event, offering a detailed account of who did what to whom, in Lougia’s and Argyro’s account this is reduced to the sentence: ‘the whole thing started and in the school were people running and throwing and …’. The result is that GCs are portrayed in a more negative way in the TCs’ accounts compared to the GCs’ accounts of this story. This shows us that we should pay attention not only to what is said in stories but also to what is not said, discussed in the margins or minimized. Some final rhetorical tools can be identified in analysing both stories. First, Yilmaz presents Erdem as his ‘friend’ and claims that his cousin was present at the retaliation scene. The involvement of trustworthy ‘eyewitnesses’ can be used to make a story more believable. In this case, both actors have first-hand information about what happened, and considering they are a friend and a relative, they could be considered as reliable sources. He also uses a metaphor, by comparing GCs’ actions as ‘a little spark causes a fire’, which is the same as saying that GCs use random incidents to cause trouble; but in a way that sticks more clearly and longer in the mind of the listener. Both stories also rely on exaggeration. In Yilmaz’s story this seems to be used only once, when he claims that ‘they [GCs] were always looking for a chance to fight’. This presentation of reality does not overlap with my observations and interviews of GCs, many of which had a positive view of TCs and were critical of the way some GCs treated TCs. Irrespective of how accurate Yilmaz’s account is, it helps to develop a stronger image of GCs as oppressive against TCs. The GC girls seem to use this rhetorical tool more often. By saying that TCs were told by their teachers to ‘immediately’ report incidents of discrimination and arguing that there were Christian Orthodox icons in ‘every class’, they seem to offer a description of the school that was not supported by other interviews from teachers and (GC) students. Regardless of the accuracy of these claims, such descriptions strengthen the image of schoolteachers as strong protectors of TCs’ interests and strongly opposed to emphasizing a Christian Orthodox school identity. However, the most obvious example of exaggeration can be found in how Lougia presents the key spitting incident, as she argues that Erdem spat three times on Costa’s cross, in between which Costa kissed the cross. Kissing a cross several times, which has spit on it from somebody else sounds unpleasant and far-fetched and certainly does not follow the storyline of the spitting incident as presented to me by the actual protagonists. This suggests that Lougia exaggerated to amplify the image of Costa as a religious, devout and calm, non-confrontational person and Erdem as an aggressive bully who disrespects the religious identity of Costa repeatedly.

4.3.3 Context People do not tell stories in a social vacuum but are stimulated to do so in relation to the social contexts in which they find themselves. Understanding what people are trying to say when they tell us a story and why they do so, requires you to reflect on the significance of these stories for the social contexts in which they are constructed.

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Cyprus is an important context to consider in interpreting these stories. The political and military conflict between the Greek and Turkish Cypriot communities and the resulting invasion and occupation of the Northern part of Cyprus by Turkey in 1974, created a political context in which polarization and mutual feelings of threat and exclusion could develop. These feelings are an essential part of the spitting incident as it was narrated to me by these young people, with clear boundaries between ‘us’ and ‘them’ and ‘good’ and ‘bad’; boundaries that represent how they experience GCs and TCs and their relationships in the context of Cyprus more generally. In a way, the actions of the protagonists in the story represent all that is good about their in-group and all that is bad about the out-group. The school context also seems key in understanding these young people’s stories about the spitting incident. The incident took place in Green Lane, so it makes sense that particularly students from that school narrated the incident. In addition, the criticism towards the school and teachers that we find in GCs’ stories all relate to GCs’ perceptions of Green Lane. Analysis of the data collected from students and teachers from Green Lane and Red Brick shows that relationships between GCs and TCs were more strained and polarized in Green Lane compared to Red Brick (Stevens, 2016). This could in turn be explained by different school features, such as the ethnic composition of the school population, with Green Lane characterized by an overwhelming majority of GCs and a small group of TCs, compared to the smaller and ethnically more heterogeneous school population in Red Brick. In addition, the anti-bullying policies in Green Lane (which targeted mainly GC harassment of TC students and which were applied inconsistently) and the politicized nature of the school-governing bodies (which resulted in school policies being interpreted by students as politically motivated) helped to develop a school context where TCs felt more excluded than GCs, and GCs felt in turn threatened by school policies and the presence of TCs in ‘their school’ (Stevens, 2016). In a way, the spitting incident symbolizes what is wrong with the school for many of the GCs interviewed, while for TCs it symbolized how GCs treat them in their school and Cyprus more generally. Finally, doing NA means that you have to consider your own role in the production of knowledge and, in relation to narratives, in how and when particular stories are told. My presentation as an ‘ethnic outsider’ probably helped me in different ways. First, it allowed me to come across as ‘ignorant’ and ‘neutral’ for both GC and TC students. It is likely that both the content and structure of these stories would have been different if these students were interviewed by people that they might have considered as belonging (more) to either the GC or TC group.

4.3.4 Purposes A key assumption from which NA starts, is that people tell stories for particular reasons (Franzosi, 1998; Kim, 2016; Riessman, 2008). This is important to consider, as it means that people do not necessarily represent ‘the truth’, but rather ‘their truth’ or what they can and want to share about what happened. Although it is impossible for us as researchers to look in people’s minds and ‘see’ their motivations for telling stories,

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people give us clues about the ‘why’ of their storytelling, both through ‘what’ they say and ‘how’ they tell their stories. Based on the above analysis, the purpose of telling the spitting incident is perhaps the clearest for the GC students: they often initiated the storytelling, as part of a more general narrative in which they criticized their school and teachers for taking the side of TCs and for not allowing GCs to express their national and religious identity. Through their storytelling, in terms of content, actors/roles and use of rhetoric, they tell us something about how they want me to see them and GCs more generally: as devout religious ‘Greeks’ who experience continuous threats from TCs in expressing their identity; a threat that is supported by the actions of their school and teachers. The latter also illustrates how they want me to look at TCs and their school/teachers more generally. TCs in contrast did not volunteer to tell this story and when asked to do so, they told the story in such a way that TCs were seen as victims of GC aggression. In so doing, they try to paint a favourable picture of themselves and simultaneously present GCs in a negative way. These motivations for telling stories overlap well with recurring or typical reasons why people tell stories, which are based on the functions that storytelling can offer the narrator. More specifically, Gibbs (2007) identifies six typical functions of storytelling: 1) to convey information; 2) to fulfil psychological needs; 3) to convey a collective point of view; 4) to convince; 5) to present a positive image of the storyteller or increase their credibility; and 6) to transfer certain experiences (e.g., from generation to generation). In applying these functions to the spitting incident case study, we could argue that GCs tell this story in order to share information (to show the importance and threatened nature of their religious identity), to present a collective point of view (to present TCs as threatening and their school/teachers as biased against GCs) and to present a positive image (that GCs are victims and well behaved). For TCs the spitting incdient appears to help them to present a collective point of view (to present GCs as threatening to TCs) and to present a positive self-image (that TCs are victims and well behaved). The particular representations of GCs and TCs that these students construct through storytelling, overlap with how TCs and GCs are represented by the media, school curricula and the parents of these children (Stevens, 2016). This shows the importance of context in shaping storytelling and how a specific incident, narrated by individuals, in fact represents shared ‘collective points of view’ about their world. A very interesting conclusion that I could make as a researcher, after listening to these versions of the spitting incident, including those of the protagonists, is that I remain unsure about what actually happened. However, I feel more confident in knowing how two different groups presented this incident, what kind of image they want to present of themselves and others, how they do this and what might motivate them to do so.

4.4 Conclusion and discussion NA constitutes a very diverse family of qualitative data analysis approaches that all take seriously the analysis of stories and the use of storytelling in presenting data. In this

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chapter we focused on a particular type of NA: the analysis of ‘small stories’, which can be narrated through formal interviews and informal (online, text, face-to-face) conversations. We showed that analysis of such short, focused stories can tell us much about how people present themselves and others and that such representations are not ‘objective’ but selective and purposeful and relate to particular interests and a context in which these stories are told. Taking this more suspicious approach to storytelling, we look not only at what people say but at how, and how both features of storytelling tell us something about their motivations for presenting events, experiences and incidents in a particular way. Given that stories are often told spontaneously or easily in relation to particular themes (e.g., relationships between people, developments over time and movement through space), it makes sense to use NA when studying particular research questions that relate to these themes and stimulate research participants to tell stories by asking particular questions. Although NA can be used from different philosophical approaches, its emphasis on the social construction of reality and how this relates to context and more specifically, the position that people take and the interests that they have, make it fit more naturally with research inspired by more constructivist and critical epistemologies.

4.5 Summary checklist In this chapter you were briefly introduced to the key concepts and assumptions that characterize NA and the range of approaches that can be classified under its broad umbrella. Afterwards, we focused on the analysis of a particular kind of story: small, focused stories, narrated by particular individuals to you, the audience, for particular purposes. Based on the analysis of a how a ‘spitting incident’ was narrated by Greek and Cypriot secondary-school children, we illustrated how an analysis of such ‘small stories’ in terms of content, structure (actors, plot and rhetoric) and context, can tell us much about how people present themselves and others and their motivations for doing so.

4.6 Doing narrative analysis yourself On 6 January 2021, a group of demonstrators stormed the US Capitol to disrupt and delay the Electoral College vote count and to pressure Congress and then-Vice President Mike Pence to overturn the election of Joe Biden in favour of Donald Trump. Ravinder (Rav) is a political science student who wants to describe for his master’s thesis how fellow students with either a conservative or democratic political preference present the Capitol attack and what their purposes are for doing so. He wants to explore how political science students, who are taught to be critical researchers themselves, are informed by their ideology in presenting events that are highly politically in nature. He asks your advice on how to do this.

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Given his focus on describing how people present certain events, and their motivations for doing this in a particular way, you can advise Rav to use NA and apply this to a set of small stories about this event, as narrated by different categories of students. Given the limited time and resources that Rav can fall back on as a student, he could conduct about thirty interviews, fifteen with self-proclaimed conservative higher education students and fifteen with self-proclaimed democrat students. Thirty interviews is a high number for a master’s thesis, but given that they are expected to be very short and focus mainly on this particular event, this could be a reasonable goal, which could be lowered should it become too time-consuming (e.g., eight or ten interviews for each group). Rav would ask his participants simply ‘what happened?’ and stimulate them to describe the event in as detailed a manner as possible, from the beginning to the end, perhaps even using a timeline to help respondents in narrating this incident chronologically. Considering Labov and Waletzky’s plot, Rav could probe his storytellers to give an ‘evaluation’ or the point of a story for the narrator, as this relates directly to their motivation for telling this event in this particular way. He could do this by asking, for instance, ‘what can we learn from this incident?’. Given his interest in the ‘why’ of these presentations, Rav should also focus on the social context in which these people tell stories. He could ask them about their information sources, ‘where did you get really interesting information about this story?’, again using the timeline to ask where they obtained what kinds of information and when. After transcribing these interviews, Rav could start reading the stories based on a digital or hardcopy version of the transcripts. First, Rav could focus on the content of the stories: what kinds of themes can be identified or developed in reading these stories? Do they overlap or are there differences between the two sub-samples? He could develop themes inductively, from the data itself, and/or use themes that he borrowed from the literature. He could identify themes quickly, such as Boyatzis’ approach to TA; develop them more gradually through coding, such as Brown and Clark’s approach to TA (see Chapter 10); or apply a more general approach to coding in qualitative research (see Chapter 1 and Maxwell, 2005), knowing that NA does not expect researchers to employ a particular, strict set of guidelines in analysing the content of stories. Afterwards, Rav could focus on the structure of these stories and start, for instance, with two stories narrated by the self-proclaimed conservative students and two stories told by democrats. These four stories could be selected because they appear to represent good examples of how stories are told by these two groups of students or simply because they appear to be rich in terms of content (in that they offer us a lot of information on his research questions or on how they present this event and their motivations for doing so). Rav could then focus on the actors and their roles, the plot as described by Labov and Waletzky and the rhetorical tools used by these narrators to present their version of this incident. Such an analysis will highlight differences and similarities in how this incident is told by these four students. Given his interest in how ideological dispositions colour the way that we present reality, he would focus in particular on the differences between these two sub-samples. After highlighting these, he would then analyse all other stories collected

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and compare these with his initial analyses to see where these stories add to his developing understanding of how this incident is presented by these two groups of students. A final, key dimension of his NA should focus on the context in which they narrate this story. Rav has already identified ‘ideology’ as a key, personal characteristic that might inform the way in which these political science students tell this story, but his interviews might highlight others, such as underlying emotions (e.g., fear and frustration) or values (e.g., freedom and patriotism) and, related to this, the points of view expressed by certain media, family and friends or student organizations, among others. Having insight into the content, structure and context of storytelling, Rav should be in a good position to write a synthesis of how these two groups of students present this event and what their particular motivations might be for doing so. It would be very interesting if Rav could later present his findings to fellow students as part of a political science class titled ‘Can and should political science students present reality in a nuanced way?’.

4.7 Recommended reading Bamberg, M. and Georgakopoulou, A. (2008). Small stories as a new perspective in narrative and identity analysis. Text & Talk, 28(3), 377–96. These two authors should be credited for putting the analysis of ‘small stories’ on the NA map. This contribution offers a convincing narrative of why we should take small stories seriously in social sciences, as well as a detailed approach of how to analyse such stories, written from a conversation-analysis point of view. Franzosi, R. (1998). Narrative analysis: or why (and how) sociologists should be interested in narrative. Annual Review of Sociology, 24, 517–54. In this classic contribution, Franzosi shows how much sociologists can learn from analysing stories by focusing on a very small story narrated by an interview participant. It is very useful in defining and applying key concepts and features of NA, combining a linguistic and sociological approach to data analysis. Kim, J.-H. (2016). Understanding narrative inquiry. Thousand Oaks, CA: SAGE. This book offers a highly accessible overview of how NA has been applied in key disciplines, the philosophical underpinnings and genres of NA. In addition, it gives insightful tips on how to design a NA study and the different approaches to NA that you can apply. Riessman, C. K. (2008). Narrative methods for the human sciences. Thousand Oaks, CA: SAGE. In this classic handbook on NA, Riessman illustrates different ways of doing NA, focusing both on ‘big’ and ‘small’ stories and on analysing stories as data and using storytelling as a way of presenting data. Throughout she gives many examples of studies that illustrate particular approaches, making this a very accessible and broad introduction to the field.

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4.8 References Abbott, H. P. (2008). The Cambridge introduction to narrative. Cambridge: Cambridge University Press. Bamberg, M. (2004). Talk, small stories, and adolescent identities. Human Development, 47, 366–9. Bamberg, M. and Georgakopoulou, A. (2008). Small stories as a new perspective in narrative and identity analysis. Text & Talk, 28(3), 377–96. Boyatzis, R. E. (1998). Transforming qualitative information: thematic analysis and code development. Thousand Oaks, CA: SAGE. Braun, V. and Clarke, V. (2021). Thematic analysis: a practical guide. London: SAGE. Cigdem, E. (2011). Narrative analysis approaches. In N. Frost (ed.), Qualitative research methods in psychology: combining core approaches (pp. 92–118). Maidenhead: Open University Press. Creswell, J. (2007). Qualitative inquiry and research design: chooisng among five approaches. Thousand Oaks, CA: SAGE. de Keere, K. and Elchardus, M. (2011). Narrating linguistic conflict: a storytelling analysis of the language conflict in Belgium. Journal of Multilingual and Multicultural Development, 32(3), 221–34. Denzin, N. K. (1989). Interpretatibe biography. Newbury Park, CA: SAGE. Ewick, P. and Silbey, S. (2003). Narrating social structure: stories of resistance to legal authority. Amercian Journal of Sociology, 108, 1328–72. Franzosi, R. (1998). Narrative analysis: or why (and how) sociologists should be interested in narrative. Annual Review of Sociology, 24, 517–54. Gee, J. P. (1991). A linguistic approach to narrative. Journal of Narrative and Life History, 1, 15–39. Georgakopoulou, A. (2006). Thinking big with small stories in narrative and identity research. Narrative Inquiry, 16(1), 122–30. Gibbs, G. (2007). Analyzing qualitative data. Thousand Oaks, CA: SAGE. Greimas, A. J. (1966). Structural semantics: an attempt at a method. Lincoln, NE: University of Nebraska Press. Gubrium, A. (2009). Digital storytelling: an emergent method for health promotion research and practice. Health Promotion Practice, 10(2), 186–91. Herman, L. and Vervaeck, B. (2005). Handbook of narrative analysis. Lincoln, NE: University of Nebraska Press. Hood, J. (2007). Orthodoxy vs. Power: the defining traits of grounded theory. In A. Bryant and K. Charmaz (eds), The SAGE Handbook of Grounded Theory (pp. 151–64). Thousand Oaks, CA: SAGE. Kim, J.-H. (2016). Understanding narrative inquiry. Thousand Oaks, CA: SAGE. Labov, W. and Waletzky, J. (1967). Narrative analysis: oral versions of personal experience. In J. Helm (ed.), Essays on the verbal and visual arts (pp. 12–44). Seattle, WA: Amercian Ethnological Society/University of Washington Press.

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Maxwell, J. A. (2005). Qualitative research design: an interactive approach. Thousand Oaks, CA: SAGE. Mishler, E. G. (1995). Models of narrative analysis: a typology. Journal of Narrative and Life History, 5, 87–123. Polkinghorne, D. E. (1995). Narrative configuration as qualitative analysis. In J. A. Hatch and R. Wisniewski (eds), Life History and Narrative (pp. 5–25). London: Falmer Press. Riessman, C. K. (2008). Narrative methods for the human sciences. Thousand Oaks, CA: SAGE. Stevens, P. A. J. (2016). Ethnicity and racism in Cyprus: national pride and prejudice? Basingstoke: Palgrave. Stevens, P. A. J., Charalambous, P., Mesaritou, E., Van Praag, L., Vervaet, R., D’hondt, F. and Van Houtte, M. (2016). Minority students’ responses to racism: the case of Cyprus. British Journal of Educational Studies, 64(1), 77–95. Stevens, P. A. J., Charalambous, P., Tempriou, A., Mesaritou, E. and Spyrou, S. (2014). Testing the relationship between nationalism and racism: Greek-Cypriot students’ national/ethnic identities and attitudes to ethnic out-groups. Journal of Ethnic and Migration Studies, 40(11), 1736–57. van Dijk, T. (1999). Elite discourse and racism. Thousand Oaks, CA: SAGE. Williams, G. (1984). The genesis of chronic illness: narrative re-construction. Sociology of Health and Ilnness, 6, 175–200.

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5 NVivo: An Introduction to Textual Qualitative Data Analysis with Software Charlotte Maene

5.1 Chapter objectives NVivo is a software that makes qualitative and mixed-method research more efficient, manageable and understandable. This chapter offers a detailed guide for the (mainly individual) usage of basic coding and analysis techniques that will help you in different phases of your data analysis. The focus of the chapter is on the analysis of written text documents (e.g., transcripts of interviews, focus groups and news articles, etc.). Understanding how to use software adequately for your research design will enhance your competences as a researcher tremendously. This chapter will not provide you with a complete overview of all the functionalities of NVivo. In this chapter you will learn how to use NVivo for: •

•

•

Structuring qualitative data: this helps you to organize your data in a comprehensible way. It ensures that you can keep an overview of all the data and data analysis that was involved in the project. Sufficient attention to structuring your NVivo project is good practice and data management. Working in an orderly manner runs like a thread through the chapter. Coding of textual data in a reliable manner: this section of the chapter focuses both on deductive and inductive coding techniques (for more information on induction and deduction, see Chapter 1). The chapter also provides tips and tricks to stay on track with your coding work as you learn how to clean up your coding and retrieve a Cronbach’s Alpha to understand your coding consistency. In this section, we explicitly refer to cooperation with fellow researchers. Lastly, this section shows you what you can know about your data at this point in the analysis. More advanced data analysis: this section focuses on the identification of relationships among codes and cases by providing examples of different NVivo analysis techniques (e.g., the creation of matrices and the running of queries). Project items that are related will be collected in sets.

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5.2 Introduction Nowadays, it is common to use software to analyse scientific data. This is the case for both qualitative and quantitative research. Although it is time consuming to organize data files correctly, the overall time that you save at the end of the process is considerable. This is the first important argument in favour of employing software for qualitative data analysis. Importantly, software usage does not automatically increase the quality of the analysis. It can support you to work systematically and in an orderly manner and therefore helps you avoid errors due to inattentiveness. However, you must always proceed with caution and reason according to the guidelines of a particular data analysis method (see, e.g., qualitative content analysis (Chapter 8), thematic analysis (Chapter 10) and discourse analysis (Chapter 2)). These approaches provide the guidelines for scientific procedure and will help you to achieve quality in data processing. Decisions on how to conduct the data analysis should therefore always be based on the approach/method used and cannot be informed by software in itself. The use of software merely facilitates your execution of a certain methodology. You must also adopt a critical and open attitude in order to ask informative questions about the data. This can – among other things – be achieved by analysing scientific literature and approaching the data with a broad and open mind. Qualitative data is particularly suited to describe, understand and explain phenomena, but not to quantify them as a goal in itself. Asking theoretically informed and open-ended questions is therefore important. Sometimes methods aimed at description are confused with a superficial form of data analysis. This is certainly not the case. Working with qualitative data requires a commitment to look at and reflect on the data in detail. Software provides the tools to study a phenomenon in depth and breadth. Data analysis is a delicate matter and must therefore be conducted thoroughly. As a researcher, you must constantly dare to change your perspective towards your data.

5.2.1 Installing the latest version of NVivo This chapter is written for the usage of NVivo (2020, release 1) with Windows. Differences might occur for Mac users. For additional support and tutorials, visit the official software website: • •

For Windows users: https://help-nv.qsrinternational.com/20/win/Content/welcome.htm For Mac users: https://help-nv.qsrinternational.com/20/mac/Content/welcome.htm

In 2020, a new version of NVivo was released to replace NVivo 12. The latest version of the software has a refreshed interface, but no major changes have occurred in the layout of the software. QSR International has changed some terminology – for example, the word ‘node’ has been replaced with the word ‘code’ (for an overview of all the changes in terminology, consult https://help-nv.qsrinternational.com/20/win/Content/aboutNVivo/terminology-changes.htm).

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The biggest changes that have occurred between NVivo 12 and NVivo (2020, release 1) are in the installation packages for initial purchasers. While previous versions made a difference between NVivoPro and NVivoPlus, QSR International now guides the purchaser towards the right choice with a selection menu. As a purchaser you must indicate whether: • • • • •

you are using the software as an academic (i.e., in an educational setting), a non-academic (i.e., commercial and non-profit usage) or a student; you will use the software as an individual or as an organization; you are using Windows or Mac; you want more than one copy of the software; and you are interested in additional modules. This is an important choice. You can choose between two modules: transcription (which offers automated transcription services) and collaboration cloud (up to five individuals can cooperate in one project).

You need purchase the software only once. This means that if you have an older version of the software installed on your computer (e.g., NVivo 11 or NVivo 12), you can purchase an upgrade. The additional modules must be renewed annually and will therefore not be included in your upgrade. If you are using an enterprise licence, you should contact IT support from your organization to request more information about the NVivo version and add-on modules.

5.2.2 A note on the methodology of the chapter This chapter uses screenshots to help visualize the interface and menu of the software. These are taken from the sample project ‘Mixed methods: wellbeing in the older women’s network’. This sample project can be downloaded for free on the official NVivo website. The main colour of the NVivo interface is blue. To focalize your attention on certain elements in the interface, boxes, numbers and arrows are shown with thicker black lines. These will not be present when using the software. For example:

1.

Next to the use of screenshots, road maps are presented to navigate through the different menus in the software. These road maps can be recognized by the text between the square brackets. The text in these square brackets is divided by ‘>’ greater-than signs. This stands for one single (left) click of the computer mouse. A double sign means a double click. For example, the road map for opening the NVivo software: [NVivo icon >>].

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5.2.3 Getting acquainted with the NVivo interface Let’s get started! It is time to open the NVivo software: [NVivo icon >>]. When you double click on the NVivo icon the following interface will appear (see the screenshot in Figure 5.1). This is not yet the ‘workspace’ but simply the start-up of the software that offers you some choices. Let us take some time to scan this screenshot. In the middle of the screen, you find the option to open a brand new project (see box 1). On the left, you will find options to open projects that already exist on your computer (see – for example – box 2). To open the sample project that will be used in this chapter, you will have to download the sample project ‘Mixed methods: wellbeing in older women’s network (OWN)’: [Go to box 3 ‘More Sample Projects’ > the NVivo website will open in your internet browser > Download ‘Wellbeing OWN’ > Save project on your computer > Open other project (box 2) > Select project > Open].

1

3.

2.

Figure 5.1  NVivo – start-up

To illustrate how the interface of the software appears when a new project is created, we continue with box 1 ‘New Project’: [New Project >]. A pop-up screen appears. You must give your new project a name (Project title) and also indicate the file’s location on your computer: [Browse >] (see Figure 5.2). It is optional to change the language setting. When you click ‘Next’, you continue to the next step in which you can select back-up options. Finish by clicking ‘Create project’ (see Figure 5.3).

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Figure 5.2  NVivo – start-up – new project 1

Figure 5.3  NVivo – start-up – new project 2

As you can see on the screenshot below (see Figure 5.4), the user interface of NVivo is divided in three zones. In zone 1, you find the overview of, and access to, all the project items that you will collect in your NVivo project during your data analysis. If you look more closely, you can see that it has three subdivisions:

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•

Import: deals with all the data you will upload into NVivo or connect to your NVivo project. Organize: collects all the NVivo tools that prepare your data for in-depth analysis. For example, it captures all codes that you will assign to your data and offers some important tools for summarizing and synthesizing your coding. Explore: contains all the querries and visualizations that you will use in a later

•

•

phase of your data analysis.

2.

1.

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Figure 5.4  NVivo – interface

In zone 2, you can find a menu bar that will help you navigate the user interface. It avoids the use of keyboard shortcuts. Zone 1 and zone 2 are created to intuitively match each other. For example, when you click on the tab ‘Import’ (in zone 2/the menu bar), nearly every sub-menu that you find there will add documents of various formats in zone 1 – Import. The tab ‘Create’ (zone 2) helps you to organize your project (see zone 1 – Organize). Lastly, the tab ‘Explore’ in the menu bar, will offer you querries and visualizations that you will be able to store in zone 1 – Explore. Zone 3 will always offer you a list view of each project item in zone 1. For example, you can see on the screenshot above (see Figure 5.4), that ‘Files’ is selected in zone 1 (it appears dark grey), but zone 3 appears empty since it is a new project. If any files have been uploaded into this new NVivo project, the list view of the files would appear in zone 3. Every double click on an item in that list will open a detail view on the right of your screen. The screenshot below illustrates this visually: [Open Wellbeing OWN > Files > Interviews >> Acacia V].

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[>>]

Figure 5.5  NVivo – detail view

5.3 Organizing your NVivo project A necessary step before importing the research data into an NVivo project is to reflect on how the data and other research material is organized. Whether it is your first time conducting qualitative research or whether you are an experienced researcher, you probably already have different types of file stored on your computer that are (in)directly relevant for the data analysis. You might already have a descriptive data matrix, raw audio files, transcribed interviews, reflective field notes, doodles, journal articles or a finished written theoretical framework stored on your computer. Well, it is desirable to import all these files in an orderly manner into your NVivo project. At a later stage of your data processing, that would allow you to connect all these files together. In addition, importing them in NVivo makes them immediately accessible while analysing your data. However, it might become clear that adding all these documents together in the standard folder ‘Files’ might give you some problems in the long run. Indeed, data management is important not only to work that is well organized but also because it makes your data accessible for other researchers.

5.3.1 Creating folders One of the most important tools for good data management in NVivo is creating folders (zone 1) in the Project items. The names and structure of the (sub)folders should be informed by the unique design features of your research (e.g., multiple types of data, cycles of data collection, respondent group, etc.). We start by creating a folder: [Select ‘Files’ > Menu bar ‘Create’ > Folder > A pop-up screen appears > Name the new folder > OK] (see Figure 5.6).

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Figure 5.6  NVivo – create folder

In the sample project ‘Wellbeing OWN’, a subfolder was created for the folder ‘Interviews’ – which is not a standard folder of the software. To create this subfolder, you must do the following: [Unfold files > Select ‘Interviews’ > Menu bar ‘Create’ > Folder > A pop-up screen appears]. If you skip selecting the parent folder ‘Interviews’, the new folder appears in ‘Files’ instead of being a subfolder of ‘Interviews’.

Figure 5.7  NVivo – illustration of newly added folder under ‘Files’ In the screenshot above (see Figure 5.7), you can see that the sample project contains seven subfolders that were added by the researcher who developed this sample project. The folder ‘Interviews’ even has a subfolder that was created as an example for this chapter. As we just created folders for ‘Files’, we can also create folders for ‘Codes’. The sample project ‘Wellbeing OWN’ has three subfolders in ‘Codes’: auto codes; Melaleuca 2017 survey and Melaleuca 2018 survey. By clicking on the main folder ‘Codes’, you can see that a coding structure is made that contains data from all the sources in the NVivo project. As an example, we are going to replace that coding structure into a subfolder of

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‘Codes’: [> Codes > Create > Folder > A pop-up screen appears > name the new folder > OK]. We repeated this road map twice further and also added the folders ‘Codes Group Discussions’ and ‘Codes Interviews’ – this could be useful should you want to analyse these data sources from a different angle. In the screenshot below you can see the result (see Figure 5.8).

Figure 5.8  NVivo – illustration of newly added folder under ’Codes’

5.3.2 The right mouse click It is useful to make use of the right mouse click early on when using NVivo, as it is the easiest way to make changes to project items such as renaming, deleting, copying, cutting or pasting them. When performing a right mouse click on a folder (in the example on ‘Malaleuca 2017 survey’), a menu appears (see Figure 5.9).

Right-mouse-click-menu

Figure 5.9  NVivo – right mouse click menu

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This menu offers many interesting options. Most importantly, it allows you to change the name of the folder by selecting ‘Folder properties’. When clicking ‘New Folder’ a subfolder will be created in the folder in which you performed a right mouse click. By clicking ‘New Code’, you add a code in the selected folder. The ‘Cut’, ‘Copy’ and ‘Delete’ function is as in other programs, allowing you to easily relocate codes or documents from one folder to the other. In the screenshot below (see Figure 5.10), all the codes in the standard folder ‘Codes’ are selected and cut: [Press Ctrl > Select code-by-code > Right mouse click > Cut]. Next, go to the folder of destination to paste (in the example, ‘Codes all sources together’) and use again a right mouse click to paste the codes in their new destination (see Figure 5.11).

Right-mouse-click

Figure 5.10  NVivo – cut-copy-paste – 1

Right-mouse-click

Figure 5.11  NVivo – cut-copy-paste – 2

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5.3.3 Import data Different types of file can be imported into your NVivo project (see Figure 5.12). In this chapter, we discuss in detail the import of data from ‘NCapture’, ‘Files’ and ‘Classifications’ (see 5.3.4). Table 5.1 gives you a quick walk through the other options that are not discussed in detail.

Figure 5.12  NVivo – menu bar options under ’Import’ Table 5.1  The import of file formats into NVivo Project

This button allows you to merge your NVivo project with another NVivo project. You can merge all the content or make a selection to exclude/include content. NVivo also offers you the option to create a new merged project or paste the transferred content into the existing (open) project. → Useful when you are working in a team.

Survey

This button allows you to import Excel, .cvs text files, Qualtircs and SurveyMonkey datasets into NVivo. The data needs to be organized in rows and columns. The datasets can be coded in NVivo, but they cannot be edited. → Useful when you are conducting mixed method research.

Bibliography

You can import references from EndNote, Zotero, RefWorks or Mendeley. It is important to first save your references as a RIS or XML file on your computer. → Useful when you are conducting a literature review.

Notes and email

With this button, data can be imported from Memos, Evernote, OneNote and Outlook. When you import emails into NVivo, they will be saved as PDF files. They cannot be edited after import. Interestingly, NVivo will create a Case (see 5.3.4.) for both the sender and recipient and a Relationship (see 5.5.4.) between them to indicate that these people are related to each other. → Useful when you are interested in social relations and communication.

Codebook

In order to import a codebook, it needs to be saved as an .xml format. This file format is also an option when exporting the codebook from your NVivo project. → Useful when you are working deductively.

Reports

In order to import a report, it needs to be saved in one of the following formats: .txt, .xls, .xlsx, .xml. → Useful when you want to take into account research findings from other studies.

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Import of webpage using NCapture NCapture allows you to import webpages into the NVivo project with the hyperlinks of the website still intact. This allows you to code the content of the webpage in NVivo and simultaneously open hyperlinks to new webpages if deemed necessary. Subsequently, these webpages can also be captured with NCapture and entered in the NVivo project. As a first step, it is necessary to install the extension NCapture in your web browser (see Figure 5.13).

Figure 5.13  NCapture – illustration from Google Chrome – 1

Figure 5.14  NCapture from Google Chrome – 2

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Next, surf to a webpage that you want to capture for analysis in NVivo. For example, the Google search results of the topic ‘Senior women’s social groups’. In the top righthand corner of your screen, you open the installed extensions for your web browser (see also the screenshot shown in Figure 5.14): [> Icon Extensions > NCapture > A pop-up screen appears]. In this case we select ‘Web page as PDF’, fill in the name of the webpage that we want to capture. Optionally, you can add a description and assign a code to the entire document. It can take a while before a webpage is captured! It is saved as a download on your computer. You can now transfer it to a location of your choice. Go back to NVivo to import the webpage: [Menu bar ‘Import’ > NCapture > A pop-up screen appears]. Browse the location on your computer where you saved the captured webpages. Select the files you want to import and click Import. Double click on the name of the capture in the Detail view to open the webpage in NVivo (see screenshot below, Figure 5.15). It is possible to code content and use hyperlinks within the webpage.

Figure 5.15  NVivo – illustration of imported NCapture web content Although NCapture is a very useful tool that allows easier access to the analysis of online content, not every webpage is captured nicely with the tool provided by QSR International. If the layout of a webpage is not captured well, it is advisable to use ‘Print/Save to PDF’ as a detour. That PDF will be imported with another function: [Menu bar > Import > Files].

Import of data files To import ‘Files’ not listed in Table 5.1, use the button ‘Files’ in the menu bar ‘Import’. These can be audio files, pictures, PDF documents, text documents and videos. PowerPoint presentations can only be imported as PDF files. NVivo allows you to import a very wide range of data formats into your NVivo project and is compatible with the formatting styles of Word. The screenshot below (see Figure 5.16) illustrates the process of data import with ‘Files’: [Select the destination folder > Menu bar ‘Import’ > Browse > select files (use Ctrl-tab to import multiple files at the same time) > Import].

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2. 4. 1..

3..

Figure 5.16  NVivo – import of data files NVivo (Release 1) allows now for the integration of Word, Excel and Outlook (only for Windows) directly into the NVivo software. This makes the programs more compatible and allows for more efficient workflows. To be able to use NVivo Integration, you must install the add-ins both in Word, Excel or Outlook and NVivo. For more information on sending and receiving documents between these programs, consult the support website of QSR International. So far, we have organized data in a logic and accessible folder structure and touched upon the fact that it is advisable to do the same for the organization of Codes. We have also learned that NVivo allows for the import of many different types of file format, of which some have their own import menu. Ultimately, which files you are going to import in your NVivo project will depend on the scope and goal of your research (e.g., a literature review brings different files than an ethnographic field study). It is likely that your NVivo file is going to keep on growing during the research process, data management is thus important not only at the very start of your research but is continuously present throughout the research process. Before the coding of the data can begin, one last important step must be taken to organize your NVivo project that will allow you to analyse your data to its full potential: the creation of cases and their descriptive characteristics (see 5.3.4).

5.3.4 Create case classifications Every qualitative research analysis deals with units that carry certain characteristics. Depending on the methodology that you are using, these units are sometimes referred to as ‘units of analysis’ or ‘data observation units’. Sometimes researchers refer to the characteristics of these units as ‘variables’ or ‘attributes’. For example, if you are conducting in-depth interviews, these units are ‘individuals’ that have certain demographic characteristics such as age, gender, profession and civil status. These people might also have characteristics that are theoretically relevant for your research question. Certainly, they can also be taken into account. The interviewee can be defined in NVivo as a case, while the demographic and theoretical information can be stored in NVivo as Attributes. Each case can then be linked to its own data (e.g., interview transcript, pictures, sociogram), which

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will allow the cases’ information to be used later on in the data analysis. This can be useful if you are interested in comparing groups of people with each other based on some of these attributes. Of course, people are not the only units that can be defined. Depending on the focus of the research, units can be organizations, book information, focus groups, families, etc. This chapter will show you step by step how to create a workable case classification in your NVivo Project by using the example of individuals as interviewees. Case classifications that hold the descriptive matrix of the cases (‘units’) in your NVivo project can be found in the list of project items on the left of your screen: [Organize > Case Classifications]. In a new NVivo project, that NVivo space will be empty. As you can see in the sample project ‘Wellbeing OWN’, two case classifications have been made: interviewees and survey respondent. Both classifications appear ‘folded’ in the Detail view. With a single click on the ‘plus sign’ next to the classification, you can unfold the attributes. Double clicking on the name of the classification will open the descriptive matrix on the right side of your Detail view (see also the screenshot below, Figure 5.17).

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Unfolded view

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Folded view

Figure 5.17  NVivo – illustration of case classification’s location – list view

[>>] attributes cases

Figure 5.18  NVivo – opened case classification in detail view

There are two ways to make these case classifications in NVivo. First, if you have not yet made a descriptive matrix in Excel or SPSS, it is more interesting to directly create a case classification in NVivo. In order to create a case classification that you can use in your data analysis, you must follow several steps:

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• •

Step 1: create a new case classification and define its attributes. Step 2: create cases and assign their attribute values.

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Step 3: attach each case to its own data (e.g., an interview transcript).

Second, if you have already made a descriptive matrix of your data in Excel or SPSS, you can use the ‘Classification Sheet wizard’ found in the menu bar under ‘Import’ 2.

Create a case classification in NVivo Step 1: create a new case classification and define its attributes Go to: [Case classification > Menu bar ‘Create’ > Case classification > A pop-up screen appears]. A menu appears that allows you to choose a new classification or a predefined classification from NVivo. In this example, we choose a new classification that is called ‘Respondent’. Of course, you can choose the name yourself, afterwards you click OK (see also the screenshot below, Figure 5.19).

Figure 5.19  NVivo create case classification As you can see in the Detail view of the case classifications, there is not yet a plus sign next to the name of ‘Respondent’ (see Figure 5.20), because no attributes have yet to be assigned. In order to do so: [> >Respondent ~ name of the Case classification > Menu bar ‘Classification’ > New attribute > A pop-up screen will appear].

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Figure 5.20  NVivo – illustration of both empty (i.e., respondent) and filled-in case classifications

The pop-up screen has two tabs (see screenshot below, Figure 5.21). The first ‘general’ tab allows you to name the attribute. It can be very useful to also give a description to the attribute, for instance, when you are working with multiple people on the same project, you would like everyone to use the case classification in the same way. For certain characteristics this might be very straightforward, but for others, like ‘profession’ or ‘level of education’, it might not be so clear. You must also indicate what type of attribute you want to create. The options are: text; integer; decimal; date/time; date; time; and Boolean. Categorical attributes are text attributes. Attributes that represent a yes or no answer, are Boolean attributes. For all other types of attribute, other options must be picked. In those cases, the attribute will involve numbers. If you decided on which type of attribute you are dealing with, you change to the second tab ‘Values’. ‘Unassigned’ and ‘Not applicable’ are standard values within NVivo. You can add values by clicking on the ‘Add’ button. You can also change the order of the values. When finished, you click OK.

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Figure 5.21  NVivo – adding a new attribute – 1

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Figure 5.22  NVivo – adding a new attribute – 2

In the screenshot below (see Figure 5.23), you can see that we repeated this procedure three times for the case classification ‘Respondent’: see the added attributes of sex, year of birth and civil status. On the left of the detail view, a plus sign appeared next to the case classification, which confirms that we added content. The basis for the descriptive matrix is now ready, as we set the columns. In the next step, the rows – representing cases – will be added. These will appear in the empty zone that is box 1 on the screenshot (see Figure 5.23).

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Newly added attributes

[>>]

Figure 5.23  NVivo – illustration of case classification without cases

Step 2: create cases and assign their attribute values To work in an orderly fashion, it is good practice to create an analogous folder in Cases that matches the name of the case classification you created earlier. In this way,

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it remains clear to you and others which information belongs together in your NVivo project. To do so, follow this road map: [Cases > Menu bar ‘Create’ > Folder > A pop-up screen appears to name the new folder > OK]. In this example, the case folder is called ‘Respondents’ (see screenshot below, Figure 5.24). Although ‘Respondents’ is selected, it appears empty in the detail view. To add cases, do as follows: [> Respondents > Menu bar ‘Create’ > Case > A pop-up screen appears]. The pop-up screen has two tabs. The first ‘general’ tab allows you to name the case (see Figure 5.24). Again, choose a name that is meaningful and easily recognizable vis-à-vis the related data sources imported in your NVivo project. The second tab allows you to assign the attribute values for a specific case, after selecting the case classification to which it should be linked (see also the screenshots below, Figure 5.25). For example, imagine a respondent (alias Caroline) has kept a photo-diary for one month and two interviews were conducted with Caroline – one at the beginning of the data collection and one at the end. It is likely that all the data will have a reference to the alias ‘Caroline’ in their name. Therefore, it is logical to call the case ‘Caroline’ as well. The consistent use of names and references will serve you well as the NVivo project – and therefore data analysis – becomes more complex.

1.

2.

Figure 5.24  NVivo – create case

To fill in the information for categorical attributes, you can make use of the arrow pointing down on the right-hand side of the table on the pop-up menu. For numerical attributes, you can type in the cell or select a value that already occurred before by also using the down-pointing arrow on the right (see Figure 5.26). When finished, click OK.

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Figure 5.25  NVivo – assigning new case to existing classification

Figure 5.26  NVivo – filling in case classification for new case

Repeat this process for all the cases that are relevant for your research question. As you are doing this, you will see that the list of cases grows. Another way to see if things are going well is to open the case classification in the detail view: [Case classifications > Respondent >> In the Detail view, the descriptive matrix appears]. After assigning attribute values to your cases, you can now see them in the rows of your descriptive data matrix (see Figure 5.27). By itself the data matrix offers already valuable information.

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However, it becomes a tool to use for further analysis when its cases are linked to the data in your project (see step 3 below).

Figure 5.27  NVivo – illustration of filled-in case classification

Step 3: attach each case to its own data This final step is important because it links your data to your descriptive data matrix. Use the project list on the left to navigate to the data that you want to link to the cases and therefore also to the case classification (see screenshot below, Figure 5.28). The entire data source then gets linked to its corresponding case. Note that in this example, InterviewExample1 and InterviewExample2 have had no codes or references assigned yet. This can be recognized by the zeros in the detail view. In this example, these are the necessary steps to establish the link: [Files > Interviews > Interviews Phase 1 > Interview example 1 > Go to Menu bar ‘Home’ > Code > A pop-up screen appears > Use little arrow to unfold ‘Cases’ > Use little arrow to unfold ‘Respondents’ > Interview example 1 > Code selection > a warning will appear > Confirm].

Figure 5.28  Nvivo – adding cases to data

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Figure 5.29  NVivo – warning ’coding entire document’

To check that you did this successfully, you can see that you added a code and a reference to the internal data source of your project.

Figure 5.30  NVivo – illustration successfully coded case to data

Import case classification sheet It is highly likely that you made a descriptive data matrix at the start of your data collection as you were gathering information on your respondents. To save time, it is a good idea to make this descriptive data matrix in Microsoft Excel so that it can be imported into your NVivo project. Before you start with the import in NVivo, there are a couple of things you might want to check in the Excel file first. The first cell of the Excel sheet (cell A1) should contain the name of the case classification you want to use in NVivo, for example ‘Respondent’. In the following columns – more specifically B1, C1, D1, etc. – type in the names of the attributes you want to create in NVivo. In the rows – starting from A2, A3, A4, etc. – fill in the name of the cases you want to create in NVivo. For example, these can be the anonymized names of your respondents. In each of these rows you can now add the attribute values for the respondent. Keep the layout simple. In Microsoft Excel, it could look like Figure 5.31.

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Figure 5.31  Microsoft Excel spreadsheet (for more information, see microsoft. com) – import classification sheet (used with permission from Microsoft.) Save and close the Excel file before you start its import in NVivo. The case classification sheet can be imported in NVivo: [Menu bar ‘Import’ > Classifications > A pop-up menu appears > Import classifications sheets > A pop-up screen appears named ‘Import classification sheet wizard’]. There are the four steps you need to follow. First, use the button ‘Browse’ to select the file on your computer. Click ‘Next’ (see Figure 5.32).

Figure 5.32  NVivo – import classification sheet – Wizard step 1

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Second, choose the classification type you would like to import (see Figure 5.33). In this example, it is a case classification. Importantly, it is also a completely new classification, therefore we do not want to replace any existing classifications in the NVivo project. Make sure that the boxes ‘Update the classification of existing items’ and ‘Replace attribute values of existing files or cases’ are not selected. The only box that needs to be tapped is ‘Create new attributes if they do not exist’. Go to the following step by clicking ‘Next’.

Figure 5.33  NVivo – import classification sheet – Wizard step 2 Third, it is necessary to indicate how the names of the cases are presented in the Excel file (see Figure 5.34). In the example, the cases are represented by names. We would like to add them as new cases to the NVivo project, since these cases do not yet exist. Therefore, we tab the box ‘Create new cases if they do not exist’. Click ‘Next’ to go to the final step of the wizard. Fourth, this last step of the wizard is about the import of numbers into the NVivo project: how are dates, times, decimals defined in your Excel format? Double check how you dealt with the separator of decimals. According to Anglo-Saxon tradition, the decimal point (.) is used as a separator, while other countries – which Microsoft Excel also accepts – use the comma (,). If this is the case, that box needs to be changed in this step of the wizard. Press ‘Finish’ to import the classification sheet in NVivo. The case classification automatically opens in NVivo (see screenshot below, Figure 5.35).

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Figure 5.34  NVivo – import classification sheet – Wizard step 3

Figure 5.35  NVivo – import classification sheet – Wizard step 4

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Figure 5.36  NVivo – illustration of imported classification sheet

Figure 5.37  NVivo – illustration of imported classification sheet without link to imported data

The only thing that is left is to attach each case to its internal data source. This is similar to step 3 of the previous section: [Files > Select appropriate folder > Select internal data source > Go to Menu bar ‘Home’ > Code > A pop-up screen appears > Use little arrow to unfold ‘Cases’ > Use little arrow to unfold the appropriate case classification > Select case name > Code selection > a warning will appear > Confirm].

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5.4 Coding the data This section will guide you step by step in coding your raw data in NVivo. Qualitative researchers agree that this is the most important step of the whole data analysis process. The ways in which you will code your raw data has repercussions for both the continuation of your analysis and the quality of your findings. Before you start coding, it is therefore good to reflect on a) the approach towards the data: deductive versus inductive coding; and b) the coding process in itself as a systematic activity. Depending on the methodological frame, more guidelines and procedures can apply to this coding process, such as rules about the level of abstraction, whether to assign double codes or apply the logics of a coding scheme. These elements are out of the scope of this chapter, which focuses mainly on the technical execution in NVivo. However, some general remarks about coding raw data are summed up below. First, a code that is assigned to raw data should stay close enough to what the fragment is about; it must be concrete enough to recognize what the data in that code is specifically about. However, a code assigned to raw data must also be reusable when a similar fragment pops up. Therefore, you need to learn how to recognize when data fragments are about the same thing. Second, coding creates more depth to your data. It is a process that makes the material more insightful and meaningful compared to the same raw data without any codes assigned to it. To create this depth through coding, codes cannot only be a list of topics that have been discussed. As a researcher, you should constantly ask questions of your data: What is happening here? Who is involved? What did my respondent do? What did they feel? What did they think? To whom were they responding? When did things change? These questions will lead to a totally different and more insightful description of the phenomenon than had you only paid attention to the ‘topics’ present within the data. It is this second and more active approach to coding that will allow you to develop a rich description and eventually leads to a more in-depth continuation of the analysis that allows you to comprehend the phenomenon. In sum, NVivo supports you in your coding process by offering an accessible and organized workspace, but it is what you do that makes or fails the analysis of the data. In what follows, we first address deductive coding, then inductive coding and afterwards the usage of memos to reflect and summarize your codes. In addition, tips and tricks are offered on how to work in an orderly manner and perform quality checks to your coding structure. Lastly, this section discusses what kinds of conclusion you can draw about the data at this stage of the data processing.

5.4.1 Deductive coding When it comes to deductive coding, you already have a fixed coding scheme that you want to apply to the data. These a priori codes can be based on a theoretical literature review. Alternatively, you can apply a coding scheme that has already been used by other

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researchers. Either way, the first step is to make the codes in NVivo and then go to the data and assign them – if applicable. In NVivo, you go to: [Project items ‘Codes’ > Select a folder, for example ‘Codes interviews’ > Menu bar ‘Create’ > Code > A pop-up screen appears ‘New code’] (see Figure 5.38). This same pop-up screen can be accessed by going to: [Project items ‘Codes’ > Select a folder, for example ‘Codes interviews’ > Right mouse click > A pop-up menu appears > New code]. These steps are illustrated in the screenshot below (Figure 5.38). As an example, we recreate a part of the original coding scheme of the sample project ‘Wellbeing OWN’. We repeat this process for all the deductive codes that we want to apply to our data.

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Figure 5.38  NVivo – create code via menu bar

As you can see in the pop-up screen ‘New code’, you can give a name to the new code and you can also add a description. This can be useful later on to help you to remember what the codes were about and how they should be applied to the data. It enhances the reliability of the data and makes the codes reusable over a longer period or by multiple researchers. The new codes will appear in the detail view in the middle of the screen as a non-hierarchical list. However, it is typical for researchers to cluster codes (‘child codes’) within others (‘parent codes’). To create this hierarchy, a drag-and-drop approach can be used: [one selects the child code and drags it on the left side of the list to the parent code and releases the selection (‘the drop’)] (see Figure 5.39).

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Figure 5.39  NVivo – illustration of drag-and-drop technique to hierarchically organize codes Another approach to create child codes is to create the parent code first and subsequently use the pop-up menu after a right mouse click to make a new code within that parent code. The same right mouse click menu can be used to move around codes that have been misplaced by cutting and pasting them in another place in codes. It is also a useful menu if you want the change the name and the description of the code under ‘Code properties’. Once the deductive codes are created, you can start coding the imported data: [Go to project items ‘Files’ > Select a folder, for example ‘Interviews’ > Choose a file >> Data source opens on the right side of the Detail view]. After following these steps, the file you want to code will be open on the right (see ‘Step 1’ on the screenshot, Figure 5.40), however, the codes you just created will no longer be visible in your detail view on the left. Instead, you will see the list of data files (e.g., interview transcripts). This is not terribly useful while coding. It is more practical to see your data on the right side of your detail view and your list of codes on the left hand. To achieve this layout in your NVivo screen: [Go to: Project items ‘Codes’ > Select the folder in which your deductive codes are > The list appears on the left in your Detail view]. This is represented by ‘Step 2’ in the screenshot below (Figure 5.40). Now everything is set to start coding. Note that a new tab appeared in the menu bar on top of your NVivo screen ‘Document’. In this zone you will find many helpful tools while coding documents – most of the options you find there will be discussed in this chapter. To code a fragment of text, you must select it. Those lines will appear shaded in black. Continue as follows: [Select the text > Menu bar ‘Document’ > Code > A pop-up screen appears]. This pop-up screen can also be accessed by using a right mouse click on the selected text. In that pop-up screen you can navigate all the project items under the subdivision ‘Organize’, such as your codes, cases and classifications. In this example, you can navigate to the codes under ‘Codes interviews’ and select ‘… ageing’. Subsequently, you can confirm your selection using the button at the bottom of the screen. This is illustrated on the screenshot for Figure 5.41.

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Step 1

Step 1 Step 2

Step 2

Figure 5.40  NVivo – setting up codes and data in detail view

Figure 5.41  NVivo – coding via menu bar After selecting the code you wish to connect to your text fragment and confirming it, you can see the proof of your coding in the detail view: one new reference is added to the count in the table (see Figure 5.42). References are the amount of data fragments that

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are linked to the code, while ‘files’ refers to the amount of files that are connected to the code. Usually, the amount of references is higher than the amount of files; there can be multiple references to one code in a file.

Figure 5.42  NVivo – illustration succesful coding via menu bar To make the coding more efficient, it is also possible to use a drag-and-drop approach: select the fragment you want to code and keep the left mouse button pressed while dragging towards the list of codes on the left of your detail view. Once you are above the code you wish the use, release the left mouse button and a pop-up notification appears to indicate that you selected the fragment at that code. You can also confirm whether you have done this successfully by checking the count of the references of that code.

5.4.2 Inductive coding With inductive coding you develop a code starting from the raw data without relying on preconceived concepts or theory. Obviously, research is not conducted in a theoretical vacuum, but you use – in the first place – the raw data as the guiding principle to develop code names. In this section, we review the technical steps for inductive coding in NVivo. First, open the internal data file that you would like to code: [Project items ‘Data’ > Files > select a folder and/or file >> Internal data file opens in Detail view]. Next, read carefully through the material. When you encounter a data fragment that is meaningful within your research topic, take a moment to reflect on what the fragment is about. In the example in the screenshot below (Figure 5.43), the interviewee is responding to the question ‘What sort of work did you do?’. In the sample project, a strategy of the coding scheme was to label conversation topics. Following this line of reasoning, the answer of the respondent can be labelled as ‘… work’. This will become a child code from the parent code ‘Talking about …’. However, as it was pointed out in the beginning of this section, coding needs to allow you to develop a deep understanding of the data. Therefore, it could be argued that labelling the answer ‘Just cleaning, to fit in the hours, because I had one daughter still at school’ as ‘… work’ is not a good code. The respondent

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says much more with this one sentence. One could argue that this fragment pinpoints at choices the respondent made regarding her work-life balance. Instead of coding the fragment ‘Talking about work’ one could also code the fragment as ‘decisions about work-life balance’. As becomes obvious, coding of data – especially in an inductive way – is a very conscious and labour-intensive process that calls for a critical attitude from the researcher. As a researcher you need to keep track of the decisions that you are making with regard to your data and be able to support those with arguments.

Figure 5.43  NVivo – inductive coding via insert bar

Once you have decided on how to code the data fragment, you select it. The text becomes shaded black. Afterwards, you simply type in the name of your new code in the bar at the bottom of your NVivo screen and press enter. You have now created a new code. Especially when you start with a non-existing coding scheme, the inductive coding is very straightforward as a technical process. Every new code is added to the list of codes, without any hierarchy, which allows you to organize and structure the codes later.

Figure 5.44  NVivo – set location for inductive coding – 1

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However, there is one caveat. Especially if you are working in a folder within your project items ‘Codes’, you need to clearly indicate the destination of your new code in a smaller bar on the left bottom side of your detail view. To change to location of your new code, click on the icon with the three dots (see circle on screenshot above, Figure 5.43). A pop-up screen appears in which you can select the location of the new code (see screenshot shown in Figure 5.44 and also Figure 5.45). Again, this step is necessary only if you immediately want to add the new code to an already existing coding structure.

Figure 5.45  NVivo – set location for inductive coding – 2 Once the coding structure starts to develop bottom-up, it might be that you want to re-use a code for a different data fragment in another file. The most efficient way to do so is to make use of the drag-and-drop method described earlier (see 5.4.1). If the inductive coding started with a clean slate, the end result is a long list of unstructured codes that are directly linked to the data. An important, subsequent conceptual step is to organize these codes in a hierarchical structure. You scan and read through the list of the codes and decide which codes belong together. These codes are grouped as child codes under a parent code that you give an appropriate, more all-encompassing name. This parent code is added to your list by using the menu bar. You drag and drop the child codes on the left side of the list to its parent code. The parent code will appear empty in the frequency table (zero files and zero references). To collect all the data fragments of child codes under one ‘post hoc’ parent code – which avoids having an empty code in your coding structure – you can use the function ‘Aggregate coding from children’: [Select an empty parent code in the coding structure > Right mouse click > Aggregate coding from children]. All the files and data fragments of the child codes are now summed up in the parent code (see visual presentation in the screenshots below, Figures 5.46, 5.47 and 5.48). This can also be used for non-empty parent codes or when using a deductive coding structure.

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In total 98 references/data fragments

Figure 5.46  NVivo – aggregate coding from children – 1

Figure 5.47  NVivo – aggregate coding from children – 2

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Figure 5.48  NVivo – aggregate coding from children – 3 When one inductive code fits under another inductive code, it is not necessary to create a new parent code. Some research approaches or methods (such as Qualitative Content Analysis, see Chapter 8) consider this organizing and structuring of inductive codes as a complete separate step in the data analysis process.

5.4.3 Writing memos Writing memos constitutes a well-known tool in qualitative research used to reflect on and stimulate the process of qualitative data analysis (QDA). Memos are notes that can contain many different types of thoughts and information that pass through your head while coding your data. You do not start developing memos after all the coding is done, but you start doing so while coding and analysing the data. For example, memos can be used as a diary in which you capture your thoughts and feelings while interpreting the data; this can range from frustrations about certain elements of the data, to a stricter report style of the steps taken to make certain decisions regarding your data. For instance, it is very common to encounter a piece of data that you find very meaningful but difficult to code because it seems that you cannot contain the essence of the fragment or because a lot of different codes could apply. In this case, it might be good to reflect and write down your interpretations of this piece of text. In so doing, you will make more informed decisions about your coding and allow yourself to go back to your rationale behind making the coding decisions when necessary. As a result, using memos enhances the trustworthiness of the QDA. Another situation that is best captured by a memo is that moment during the coding of your data in which you have the feeling that you are finally starting to understand what is going on. The same is true for the opposite situation; the feeling you get when you are coding a data source that seems to completely diverge from the pattern or process that you thought you were discovering before – it is good practice in qualitative research to also reflect on this in a memo. More generally, it is always important to remain sensitive

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to people’s direct and broader environments when reading and coding data, such as their relationships, work environment, family network or other organizations that they are involved in: who are the most important people in your respondents’ daily lives? About whom do they talk spontaneously during the interview? At what stage in their life have meaningful changes occurred regarding their social conditions? Keeping the broader picture in mind while coding your raw data will help you to understand your data at a deeper level. And writing memos will help you to do so. Memos are project items that are collected on the left side of your NVivo project under the subdivision ‘Notes’. To create a new memo, follow these steps: [Go to Menu bar ‘Create’ > Memo > pop-up screen appears to give the Memo title and if applicable attribute values > OK > Memo opens in Detail view and saves automatically]. It is possible to assign codes to memos and link them to only one project item, such as an internal data source or code. This appears as the symbol of a chain on the right of the memo’s name (see Figure 5.49). Memos can be exported to Microsoft Word by using the right mouse click pop-up menu.

Figure 5.49  NVivo – illustration of visual clue to linked memo

Project items with a memo link also show the same chain symbol on the right of the item’s name. By using the pop-up menu of the right mouse click, it is possible to open the linked memo (see Figure 5.50). It will open on the right side of the detail view. Follow the same procedure for other project items that you want to link to another memo. In that case, the button ‘Memo link’ will offer the choices: ‘create new memo link’ or ‘link to existing memo’.

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Figure 5.50  NVivo – opening memo via right mouse click

5.4.4 Revising the coding structure Once you have coded a sub-selection or all your material, it is necessary to perform a quality check on the codes assigned to the raw data. This involves a couple of steps: a) re-reading through your interviews and codes and evaluate whether a data fragment fits the assigned code(s) or not; b) if necessary ‘uncode’ data fragments from certain codes; and c) perform a coding consistency check and subsequently refine the codes and their description. This will make the codes more transparent and enhances the reliability of the coding process.

Re-reading the coded files and codes It is possible to re-read the coded data in two ways. First, by opening the code through a double click. This gives an overview of all the data fragments of all the linked internal files within the code. Second, by opening an internal file and checking all the codes assigned to it. To review the coded internal files in an uncluttered way, it is very helpful to make use of coding stripes and coding highlights. The former opens an overview of all the codes assigned to one fragment of text on the right side of the screen. This allows you to see the exact codes related to the data while reading through the material. The latter shades the coded fragments in yellow. You can choose to shade all the coded data or make a sub-selection. To make use of these tools, do the following: [>> Open an internal file in the Detail view> Go to the Menu bar ‘Document’ > Coding Stripes > All]. The same steps can be followed to make use of the Highlights (see Figure 5.51).

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Figure 5.51  NVivo – setting up coding stripes and highlights If you want to pay attention to one specific code, it might be helpful to assign a colour to the coding stripe of that code (see Figure 5.52). You do this by: [Navigating to the project items ‘Codes’ > Open the coding structure > Select the code and use a right mouse click > A pop-up menu appears > Colour > Select a colour]. The coding stripe will then appear in the selected colour. For the visual representation of this process, see the screenshot below (Figure 5.52).

Right-mouse -click

Figure 5.52  NVivo – colouring for coding stripes

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‘Uncode’ data fragments To uncode a data fragment, it is necessary to select it. The selected text appears in dark blue. Afterwards, use a right mouse click to open a pop-up menu. In this menu, you click on ‘Uncode’. A pop-up menu appears if the selected text fragment has multiple codes attached to it. As you can see from the coding stripes on the screenshot below (Figure 5.53 and 5.54), this is the case for the data fragment in this example. For clarity, the blue coding stripe – and therefore the code ‘OWN involvement’ – will be removed/uncoded.

Right-mouse -click

Figure 5.53  NVivo – uncode via right mouse click – 1

Figure 5.54  NVivo – uncode via right mouse click – 2

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As a proof of the uncoding, the blue stripe will be removed after uncoding (see screenshot below, Figure 5.55). Also, another check consists of navigating to the code structure and finding one ‘reference’ fewer in the table for the code involved. It is also possible to uncode data fragments while reading them from the detail view of a code then from an internal data source: [Go to project items ‘Codes’ > Open correct Folder >> Code of interest opens in Detail view].

Figure 5.55  NVivo – illustration of successful uncoding

Coding consistency check Coding in a consistent way means that you should always code the same piece of text in the same way. Obtaining coding consistency is considered an important goal for various qualitative analysis approaches or methods, such as for qualitative content analysis (see Chapter 8) and thematic analysis (see Chapter 10), that link this to the trustworthiness of your analysis. A first important step in coding consistently is to re-read all your codes and check if all the data fragments fit with your codes’ descriptions and names. A second important step is to perform a coding consistency check. This can be especially useful when multiple people are applying the same codes to the same or different data independently from each other. As you can imagine, it can very easily happen that you interpret one code slightly differently from your fellow researcher and consequently two different types of data fragment end up under the same code. This is undesirable for the transparency and reliability of the overall analysis. However, not all QDA approaches attach the same importance to coding consistency. To practically perform a coding consistency check, you (and a fellow researcher) should pick several internal (uncoded) data sources (i.e., trial data) and separately apply a fixed set of codes. If you work alone, it can be useful to allow some time to pass between

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the double coding. When you are working alone, you can also change your initials when opening your NVivo project and then code data with different initials from the first time you coded the same data source. This allows you to check your own coding consistency as an individual researcher. Afterwards, by using a ‘Coding comparison query’ in NVivo, it is possible to check for coder agreement and request the Kappa coefficient. This is also referred to as ‘inter-rater reliability’. The percentage of agreement – as the name suggests – only takes into account how much of the data was both coded and not coded in agreement between you and your fellow researcher. With this approach, you still run the risk that some data has been coded by you but not by your collaborator, a fact which is not always reflected by the percentage of agreement. Furthermore, since this indicator does not take into account which data has been coded, it might still be that the amount of coded data between the researchers matches well, but without indicating to which data fragments they actually relate. This can also be problematic. To counter these two problems, a more preferable indictor for coding consistency is the Kappa coefficient. This indicator takes into account whether the agreement between the researchers might have occurred by chance. The coefficient ranges from 0 (agreement completely by chance) to 1 (agreement completely by intent). As the Kappa coefficient is an expression of chance vs. intent, it avoids the previously described pitfalls of the indicator ‘percentage of agreement’; the Kappa coefficient is not going to be high if researchers coded an equal amount of data, but assigned different codes to them, while the percentage of agreement would be high. Instead, data fragments that were coded to the same code by the researchers and are the same size will yield a high Kappa coefficient – which is desirable, as it shows that the codes are assigned to the data based on a systematic and intentional process. To check for coding consistency: [Go to the Menu bar ‘Explore’ > Queries > Coding Comparison Query > A pop-up screen appears] (see Figure 5.56).

Figure 5.56  NVivo – opening coding comparison query In this pop-up screen, you need to make several choices (see Figure 5.57). The first choice has to do with who’s coding work you want to compare. This can be two users (or two different initials of the same users) or multiple users. The second choice concerns which codes you want to compare. This can range from the whole coding structure to a sub-selection or even one code. To keep the example simple, one code ‘Talking about well-being’ was selected. Third, you need to choose the scope of the comparisons.

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This has to do with the amount of internal data sources you want to involve – for example, all the data sources that were double coded during the trial coding. In this example, the interview that was double coded was the one of ‘Acacia A’ from the sample project. Four, you need to choose the indicators that you want NVivo to display; the percentage of agreement or Kappa coefficient. In the example, both were selected. To proceed, it is necessary to click ‘Run’ instead of OK.

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Figure 5.57  NVivo – coding comparison set-up When you click ‘Run’, the results of the query will open in the detail view. The results are organized as a table. The rows capture each comparison that NVivo had to conduct. In this example, it was only one: one code from one file. As a result, we get a table with one row. If the query request consisted of three codes from one file, the query result would display three rows. If the usage of those three codes had been compared in three different internal data sources, it would be nine rows/comparisons displayed. This shows that the coding comparison query can be very extensive and therefore the more complex the request is, the longer it will take for the query to be processed. The columns display the information that is generated by the query. From left to right, it shows: the name of the code, the file name, file location, file size (for text files, number of characters), Kappa coefficient, percentage of agreement, A and B, Not A and Not B, percentage of disagreement, A and Not B, B and Not A. The focus here is on the Kappa coefficient and the

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percentage of agreement. As pointed out before – and as it becomes apparent in the screenshot below (Figure 5.58) – the Kappa coefficient is a much more conservative indicator of coding consistency then the percentage of agreement. Generally, a Kappa coefficient around 0.7 is considered good.

Figure 5.58  NVivo – illustration of Kappa and percentage of agreement Of course, the check for coding consistency does not stop with the display of the Kappa coefficient. The next step is to open the data source that is part of the comparison and to actively look for those moments of disagreement between researchers (see Figure 5.59). It is possible to open the internal data source by double clicking on the file

Figure 5.59  NVivo – illustration of data coded by two different users

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name in the table. To aid yourself in looking for disagreement, it is helpful to select the coding stripes by user: [Menu Bar ‘Document’ > Coding Stripes > Selected items > Users > Select Users > OK]. It is also possible to display the code in which you are interested and display the users who applied this code in this data source as a ‘sub-stripe’. You can do this by opening the coding stripe on the right side of the detail view and subsequently perform a right mouse click on the coding stripe. A pop-up menu appears: [Select More Sub-stripes > Select Project Items > Select applicable users > OK] (see also screenshot above, Figure 5.59). Now, you can read through the document comparing the coded fragments by different researchers. This initiates a new round of revisions to the codes and coding structure that were assigned to the data. This will lead to additional coding or uncoding of certain data fragments. It can also be very useful to adapt the names of the codes and their description based on the results of the coding consistency: [Go to ‘Codes’ > Right mouse click on applicable code name > A pop-up menu appears > Code properties > Add name/description > OK]. This aids the transparency of the coding structure that will be used later on for more advanced analysis.

5.4.5 What do you know about your data so far? At this point, you have created a reliable coding structure that contains appropriate code names and descriptions that have been checked for their quality and fit with the data. In qualitative content analysis (see Chapter 8), the development of such extended coding structure can be the end of the data analysis in itself – sometimes followed up by a limited quantification of the coding structure. It might also be useful to export the coding structure from NVivo. In other QDA methodologies, the creation of the coding structure is only the beginning of the data analysis. In that case, you will want to familiarize yourself with the data at a more conceptual level. To get an overview on how the data looks from a helicopter perspective, a hierarchy chart might be a helpful tool.

Exporting the coding structure from NVivo To export the coding structure from NVivo, follow these steps: [Go to project items ‘Codes’ > Select the folder that contains the coding structure you want to extract > Right mouse click > A pop-up menu appears > Export codebook]. When pressing the button ‘Export Codebook’ a pop-up screen appears that allows you to select the codes that you want to export. Two tabs are interesting in this case: choose whether to tab the box a) ‘automatically select all subfolders’; or b) ‘include number of files and references’. You can also choose between different file formats by using the bottom ‘Browse’: .doxc, .xlsx, .qdc. Only the

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last one will allow you to import the coding structure into another NVivo project. The coding structure appears as the screenshot below (Figure 5.61 and 5.62) in a Microsoft Word file (.doxc) or Microsoft Excel file (.xlsx).

Figure 5.60  NVivo – export codebook

Figure 5.61  Microsoft Word document (for more information, see Microsoft.com) – illustration of exported codebook (used with permission from Microsoft)

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Figure 5.62  Microsoft Excel spreadsheet (for more information, see mircosoft. com) – illustration of exported codebook (used with permission from Microsof)

The hierarchy chart A hierarchy chart is a visual representation of the coding structure; the child codes will appear nested in parent codes and the bigger a code appears on the hierarchy chart, the more coding references and files it contains. It is possible to zoom in on codes by clicking on the parent code. It is possible to navigate from one code to the other. This is a tool that is mainly useful for getting acquainted with your own coding structure, especially when the coding structure is complex and extensive. To open the hierarchy chart, follow these steps: [Menu bar ‘Explore’ > Hierarchy Chart > A pop-up menu appears named ‘Hierarchy chart wizard’]. There are two steps in the wizard (see Figure 5.63).

Figure 5.63  NVivo – opening hierarchy chart via menu bar In the first step (see Figure 5.64), it is necessary to choose what needs to be visualized. In this example, we want to visualize the coding structure, therefore the box ‘Codes’ is tapped.

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Figure 5.64  NVivo – hierarchy chart – Wizard step 1

The second step has to do with the scope of the visualization (see Figure 5.65). You can choose to compare all the codes or make a sub-selection of codes. In the latter case, choose ‘Selected items’ and navigate in the codes and select the desired selection. You must make the same choices for the data that needs to be considered for the visualization. You can include all the data or make a sub-selection. To create the visualization, click ‘Finish’. The hierarchy chart will appear automatically in the detail view.

Figure 5.65  NVivo – hierarchy chart – Wizard step 2

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In this example below (Figure 5.66 and 5.67), it is apparent that most of the coding has been done on respondents’ involvement in the ‘Old Women’s Network involvement’ and ‘Social connection’ – as these codes appear the biggest. It is also possible to zoom in on the codes by single clicking on it. By waving over the codes, NVivo shows how many references are included in the code – both directly and indirectly through aggregation.

Figure 5.66  NVivo – illustration of hierarchy chart

Figure 5.67  NVivo – illustration of hierarchy chart – zoom

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It is possible to change the colour and layout of the hierarchy chart by using the special menu ‘Hierarchy Chart’ in the menu bar. By waving over the buttons, little text balloons pop up explaining the function of each option. It is also possible to use a right mouse click to ‘View the references’ (‘data fragments’) in each code in a new tab of the detail view or ‘Export the hierarchy chart’ (see Figure 5.68).

Right-mouse-click

Figure 5.68  NVivo – opening data from within hierarchy chart

5.5 Continued data analysis In the next section, some interesting NVivo tools are discussed that can help you with a more in-depth analysis of the qualitative data. These tools are discussed independently from any QDA methodology. The purpose is to illustrate their functionalities for your overall NVivo project and analysis strategy. The focus is on the application of a) a framework matrix; b) a coding query; c) a matrix coding query; d) defining and coding at relationships; and e) collecting findings in a static set.

5.5.1 Framework matrices Framework matrices are very good tools to both summarize codes and discover patterns or relationships between cases and codes. Usually, during the coding phase, you start to recognize patterns in the data that will be captured in memos. However, it is also necessary to actually establish the evidence and therefore to check whether it

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is correct. A framework matrix allows you to select a couple of cases and codes from a broad amount of information to focus on in-depth. The cases will constitute the rows of the matrix. You could, for example, first select cases that seem to share similar experiences concerning certain elements of the research. This should be something that is important for your research question. For the sample project, this could be the way respondents dealt with depression and their resilience towards it. The columns collect the codes that are important in understanding the process under focus, for example, the codes related to depression, resilience, but also respondents’ social connections. Again, the selection of the codes should be informed by the research question, theoretically important reflections captured by memos and/or existing theory. It should be clear that you should be able to support the organization of the framework matrix with good arguments. In the cells you can synthesize the coded information of a specific respondent. As it becomes clear, you can make multiple framework matrices. A matrix can also be used to collect information of other cases that seemed to diverge – information that later can be brought together in a matrix to compare both patterns.

Figure 5.69  NVivo – create framework matrix via menu bar

Framework matrices are saved under the Project items ‘Notes’ (see Figure 5.69). To create a new framework matrix: [Go to the Menu bar ‘Create’ > Framework Matrix > A pop-up screen will appear]. In the pop-up screen you first must add a title to the framework matrix and optionally also a description. Do not click OK yet at this point. It is necessary to also fill in the two other tabs in this screen; namely, the rows and the columns of the matrix. To navigate from the tab ‘General’ to the ‘Rows’ or ‘Columns’ tab, it suffices to click on their name. In the tab ‘Rows’ you first need to select which cases you would like to include in the framework matrix and, optionally, you can choose to also select a list of attribute values that you want to display for each of these respondents (see Figures 5.70 to 5.73). Afterwards, do not click OK yet, click the tab ‘Columns’.

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Figure 5.70  NVivo – set-up framework matrix – step 1

1.

2.

Figure 5.71  NVivo – set-up framework matrix – step 2

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Select the cases

Figure 5.72  NVivo – set-up framework matrix – step 2a – select cases

Figure 5.73  NVivo – set-up framework matrix – step 2b – select attributes

In the tab ‘Columns’, you can make a selection of the codes that you would like to include in the framework matrix (see Figures 5.74 to 5.77). Afterwards, click OK and the framework matrix will open automatically in the detail view.

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Example selected attribute values

Figure 5.74  NVivo – set-up framework matrix – illustration of step 2

Figure 5.75  NVivo – set-up framework matrix – illustration of step 3

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Figure 5.76  NVivo – set-up framework matrix – step 3 – selecting theme codes

Example selected codes

Figure 5.77  NVivo – set-up framework matrix – illustration of step 3 When the framework matrix opens in detail view, you can see the table itself on the left side. The name of the cases appears in rows. If you select the attribute values, they are displayed under the name of the case. The orange shading (see grey shading in Figure 5.78) shows which cell is selected. On the right side of the detail view, the internal data source

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of that case opens automatically. It is possible to drag the division of the framework matrix and internal data source to the left and right to change the proportion between the two views. You can make use of coding stripes or highlights to read the data more easily. You can type in each cell simply by clicking on it with your mouse. The interesting thing is that NVivo automatically saves the information the first time you write it. In case the same cell is reused in another framework matrix, it will already be filled. By using a right mouse click, you can delete, export and print the framework matrix.

Figure 5.78  NVivo – illustration of opened framework matrix in detail view

Findings based on the framework matrix could be summarized in a (linked) memo, relations are defined and the data which supports them are recoded (see 5.5.4). All these related project items that provide evidence for an important finding in your data can be collected in a static set (see 5.5.5) – to work on in an orderly manner.

5.5.2 Coding query The coding query is one of the most powerful analysis tools in NVivo and offers you the opportunity to investigate targeted questions and ideas about the data. Importantly – and differently from the framework matrix – a coding query can only subtract information in the NVivo project that has been coded. Information from internal data sources without codes are invisible for this NVivo tool. The coding query not only allows searching for information from codes and cases, it also allows for making complex combinations between the two. It helps to discover relationships in your data – with the focus on the substantive coherence and meaning rather than on the quantifiable relationships between codes and cases (see also 5.5.3, matrix coding query). To open a coding query:

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[Go to Menu bar ‘Explore’ > Queries > Coding > Query settings will open in the Detail view]. It takes some practice to teach yourself how to use this tool efficiently as NVivo offers a lot of different tabs and submenus to choose from (see Table 5.2 and screenshot below in Figure 5.79). In the following, three very useful coding query searches are explained. For every coding query, you can choose to save the query criteria and/or save the query results. This will be stored in your project item list on the left (Explore > Queries). To work in an organized way, you can also create folders for this subdivision.

1. 2. 3.

4.

Figure 5.79  NVivo – coding query general layout

Table 5.2  Options regarding coding queries in NVivo Reference

Explanation

Options

1.

This option defines where you want to look for the information.

• Files and externals: all the information linked to the NVivo project will be searched. • Selected items: used to limit the search to certain specific internal data sources within the NVivo project. • Selected folders: used to limit the search to one or more specific folders.

2.

The coding query includes, by default, only information when ‘All’ the search criteria are met. If you select ‘Any’, the search criteria are less strict.

• All • Any

3.

This option makes you choose which coded content you are looking for, which can be information from codes or cases.

• All selected codes or cases: will render a result from one or more codes and cases that were selected based on an AND operator, which means that both must be met. • Any selected codes or cases: will render a result from one or more codes and cases based on an OR operator, which means that either one of the selected items can be found. This will lead to broader results. • Any case where: this option allows you to search for information that has a certain attribute value linked to it.

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Reference

Explanation

Options

4.

This options allows the scope of the result to be broadened – it does not change the scope of the search – to include data in the immediate context of the coded content.

• Narrow context: adds five words at the beginning and the end of the coded content. • Broad context: the paragraph of which the coded content is part is displayed in the query result. This can be useful for information that was coded at the word level. • Custom context: as a user you select how much additional information you want to add to the coded content of the query search. • Entire source: this option will display the entire internal data source.

Coding query with codes The coding query that focuses on codes is useful to answer: a) descriptive questions, such as ‘What did people say about their involvement in the Old Women’s Network?’ (example 1) (see Figure 5.80); or b) to analyse in-depth the coherence between two codes, for example, ‘How do people relate their mental well-being to their physical health?’ (example 2). As you can see, the second question is a more complex question than the first one.

Figure 5.80  NVivo – set-up coding query with codes

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To request information from one or more codes, you simply use the button with three dots that you find on it. A pop-up screen allows you to select the information. In this example, the scope of the search is set to all the files connected to the NVivo project. The query will also use a Boolean AND operator, as ‘all’ the criteria of the search need to be met. Lastly, the scope of the result is not broadened, which means that only the coded information will appear as a result without additional context. In the pop-up screen in which you select the coded information it could be interesting – depending on the question you want to answer – to tap the box ‘Automatically select descendant codes’. Of course, you should remember whether information from child codes is already aggregated to the parent code. For example, in the screenshot ‘OWN involvement’ is selected (Figure 5.80). If the information from the child codes is aggregated, it will render a different query result than when the information is not aggregated and ‘OWN involvement’ contains unique information. If you click ‘Run query’ (see Figure 5.81), the results will be displayed in the lower part of your screen. It is possible to scroll the results with the right bar. At the bottom of the NVivo screen, you can see how many files and references are included in the query result.

Figure 5.81  NVivo – coding query with codes – run query

To answer more complex questions, a new search criterion can be added to the query. This is done by clicking on the little arrow next to the plus sign on the right (see screenshot below, Figure 5.82). In the example, we choose the option ‘Near’. This creates an additional line to the query criteria. Now, you would need to select in which proximity the two codes need to occur. There are four possible options that are explained in Table 5.3.

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Table 5.3  Coding query search criteria for ‘Near’ information Options

Explanation

Overlapping

The selected codes have common parts (‘overlap’) but they do not necessarily coincide.

In custom context

This option allows you to choose the proximity of the codes yourself.

In same scope item

With this option, the two codes need to occur in the same project items (e.g., a transcript).

In same coding reference

The selected codes occur in the same fragment.

Figure 5.82  NVivo – coding query with codes – addition of conditions

In the example, the option ‘Overlapping’ is selected. This allows us to understand whether respondents talked simultaneously about mental wellbeing and physical health, and if so, how these themes are related to each other. This can lead to the creation of a new relationship to which the proof of the related concepts is coded (see 5.5.4). Alternatively, you can create a new ‘higher order’ theme or concept in your data by coding certain elements of the query result to a new code; for instance, holistic mind-body conception. This is a theoretical process that is directly related to the findings of your research. To code information in the query result, you must use the same process as the inductive open coding: [Select the text fragment > It appears shaded in black > Select on the bottom of your screen the location of your new code > Type in the name of the code in the input bar on the bottom right > Enter > A pop-up balloon appears as proof that content was coded]. As a follow-up, interpretations and thoughts on the findings can be captured in a memo, which can be linked to either the new codes or the query result.

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Figure 5. 83  NVivo – illustration of new code creation based on query result

Coding query with attribute values As information can be accessed from codes, it is also useful to focus on data related to characteristics of the respondents in the research – as people speak from different positions, which can be of theoretical importance in the understanding of patterns and variation in the data. To be able to consult the information from an attribute and information of its data unit together, you must have properly established the link between the two by linking the case to both the case classification (which contains ‘Attributes’) and the internal data source (which contains ‘The information’) (see also 5.3.4). This type of coding query answers questions such as: ‘What did women who are volunteers in the Old Women Network talk about?’. By adding criteria to the query, it is possible to narrow this focus down to other attribute values; for example, the years of experience in the organization or how many hours a week they attend OWN activities. The screenshots below (Figures 5.84 and 5.85) illustrate how these queries look in NVivo. Again, it is possible to continue the analysis based on these results with new codes, relationships and/or memos.

Figure 5.84  NVivo – illustration of coding query with attributes

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Figure 5.85  NVivo – coding query with attributes – addition of conditions

Combination between code and case Finally, a last important application of the coding query is when information from specific coded content is combined with information from attributes. This allows very precise questions about the coded data to be answered; for example, ‘What did women with a good health status (= attribute value) say about ageing (= code)?’. The screenshot below (Figure 5.86) illustrates how the query criteria for this question were defined. Again, it is possible to add more complexity by involving multiple codes (in various proximities of one another) and taking into account more attribute values.

Figure 5.86  NVivo – coding query with codes and attributes

5.5.3 Matrix coding query Whereas the coding query (see 5.5.2) focuses on the in-depth interpretation of relationships between codes and/or attribute values, the matrix coding query allows you to search for patterns in your data on a more superficial level based on the quantification of coding references. This can be very helpful to understand whether patterns emerge between different types of respondent and important codes (see example 1). Another useful application of the matrix coding query regards detecting overlap among a lot of codes at the same time, which can be the basis of more in-depth analysis afterwards (see example 2). Lastly, a matrix coding query can also help to compare codes among cases that appear to be very different from each other based on your initial experiences with the data (see example 3).

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These three applications can help you to better understand the data on a larger scale. It is a different approach from the coding query, which tries to understand the data on a very deep and targeted level. To create a matrix coding query, follow these steps: [Go to Menu bar ‘Explore’ > Matrix Coding > Query settings will open in the Detail view]. First, the scope of the query search needs to be defined. Second, the information for the ‘Rows’ can be selected by using the ‘Plus’ sign on the bottom left. Third, the ‘Plus’ sign on the bottom right can be used to select the information for the columns. Before running the query, you need to choose whether to save the query criteria. After running the query, it is possible to save the results in your NVivo project.

Matrix coding query with attribute values and codes The example below (Figure 5.87) shows whether there is a difference between volunteers and members in how much they talked about their involvement in the OWN and how they perceive social connections. The table includes two attribute values in the rows, and two codes in the columns. These can be easily selected by following the steps in the pop-up menus. By clicking ‘Run query’, a table will appear in the lower part of the detail view. The numbers in the table represent the amount of coding references for each type of respondent. The difference between the two groups that catches the eye is that volunteers talked much more about social connections than members of OWN. This could potentially be an important pattern in the data and become a research finding. Yet, you should remain attentive: the fact that volunteers and members have talked equally about their involvement in the OWN does not mean that they did not highlight different elements of that involvement. By double-clicking on a cell in the table, the coded references will open in a new tab in the detail view. This allows you to read the content that is hidden behind these quantifications.

Figure 5.87  NVivo – general illustration of matrix coding query

Matrix coding query with codes In this example codes are compared with each other with regard to the amount of overlapping coding references they contain. This allows you to discover relationships between

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codes in your data. An important option that must be checked is the ‘And’ button (see screenshot below, Figure 5.88) which makes sure that the data fragment should be coded in both codes to be counted in the cell. The cells on the diagonal present the total amount of references for each code displayed. Under or above the diagonal you can compare the code with others included in the table. For example, cell F4 shows the overlap between the code ‘Relaxed, less stressed’ and ‘Connection with nature’. Again, by double-clicking on the cell, the data references related to both codes will open in a new tab in the detail view. In the example below (Figure 5.88), the cells are shaded. This makes it easier to spot potential relationships between them. The tab ‘Matrix’ in the menu bar also offers some

Figure 5.88  NVivo – matrix coding query with codes – illustration of matrix menu bar

Figure 5.89  NVivo – matrix coding query with cases and codes

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other interesting options such as whether the cells should represent the amount of coded references, files or cases included in those codes. You can also ask to present percentages instead of absolute values.

Matrix coding query with cases and codes A last example for which a matrix coding query can be useful consists in comparing cases in relation to the coding structure. This can be helpful to gain more insight in diverging cases compared to a typical case in your research data. The cases are placed in the columns, while the codes are placed in the rows (see Figure 5.89).

5.5.4 Assigning relationships Relationships between project items become clear by conducting continued analysis on the qualitative data; for example, by writing memos, synthesizing data through framework matrices (see 5.5.1), or interpreting coherences through coding queries (see 5.5.2). Relationships are saved in your NVivo project under the subdivision ‘Codes’ in your project items list. It is possible to assign relationships between cases and between codes. Three steps are necessary to work with this tool: first, it is necessary to create the relationship type (if it does not exist yet); second, the two project items are connected to each other; and third, the data fragments that provide the evidence of this relationship are coded with the relationship.

Step 1: creating the relationship type To create a new relationship type, follow these steps: [Select ‘Relationship Types’ in the project items list > Menu bar ‘Create’ > Relationship > A pop-up screen appears]. In the pop-up screen, four fields need to be filled in: the starting point of the relationship (‘From’), the relationship type, the ending point of the relationship (‘To’), and, optionally, a colour can be assigned. In this step, we focus on the creation of a new relationship type. As it becomes clear now, it is possible to re-use relationship types for re-occurring relationships between project items. Relationship types can be everything that expresses a connection between two elements. For cases, this can be employer-employee relationships, marriages, friendships, etc. For codes, this might relate to more conceptual dynamics such as causality, associations, mutual influences, etc. These types of relationship can be visualized in NVivo with three different types of line: a straight line (—) or associative relation; an arrow (→) or one-way relation; a double arrow (↔) or symmetrical relation. To create a new relationship type, you must type in a name and select one of these three options to represent the relationship visually (see Figure 5.90). To finish, you click OK. It is possible to re-use relationship types for newly created relationships.

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Figure 5.90  NVivo – create relationship type via menu bar

Step 2: define the relationship between project items To establish a link between two project items, it is necessary to fill in the boxes of the pop-up screen that appear after following these steps: [Select ‘Relationships’ in the project items list > Menu bar ‘Create’ > Relationship]. Existing relationship types can be re-used or a new relationship type can be made. Optionally, a colour can be assigned to the relationship. By clicking ‘OK’ the relationship is added to ‘Relationships’ under ‘Codes’ in your project items list on the left of your screen. The coded content of the relationship will automatically open on the right side of your Detail view. If the relationship is new, this will be an empty zone (see screenshots below, Figures 5.91 and 5.92).

Figure 5.91  NVivo – define relationships

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Figure 5.92  NVivo – illustration of empty relationship in detail view

To code the evidence of the relationship, it is necessary to open the project item(s) that show(s) the proof of this relationship and assign the data to the relationship. This can be done in a similar way as the inductive coding procedure, by using the input bar at the bottom of the screen (as illustrated below, Figure 5.93).

Figure 5.93  NVivo – illustration of coding via relationships

Double clicking on the relationship would then show the coded data at that relationship in the detail view on the right.

[>>]

Figure 5.94  NVivo – illustration of coded relationship in detail view

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5.5.5 Collect findings through static sets Due to the data analysis process in NVivo, a lot of different project items that lead to the findings of the study are created: memos, matrices, transcripts, case classifications and queries, among others. Therefore, it might be a good idea to cluster together those project items that are related. This will help you to easily navigate the broad amount of information that is stored in your project. For example, one of the main findings of the study could be the link between well-being and health. A static set that collects all the project items related to this finding is therefore created. Project items that are related to other findings should be collected in another set. To create a static set: [Go to Menu bar ‘Create’ > Static Set > A pop-up screen appears]. You simply have to give the set a title and click ‘OK’ (see Figure 5.95). The set will automatically open in the detail view. It appears empty.

Figure 5.95  NVivo – create static set via menu bar

Figure 5.96  NVivo – illustration of empty static set and right mouse click to add items

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To add project items to the set, it suffices to use a right mouse click and choose the option ‘Add Static Set Members’ (see Figure 5.96). A pop-up menu will appear to navigate all the project items that can be added to the set (see Figure 5.97). After having made a selection, you click ‘OK’. The items will be collected in set in the detail view. Adding project items to a set functions as a copying of these items, which means that the project items do not disappear from their original locations in your project items. For example, a transcript can be both included in a static set and a folder in files. This is the case for every project item added to the set.

Figure 5.97  NVivo – illustration of filled static set

5.6 Summary checklist In this chapter you have learned to organize a NVivo project before importing data files. Subsequently, you have learned to code both inductively and deductively. You also know how to structure codes hierarchically and perform an intercoder reliability check. You have a basic understanding of more advanced analysis tools in NVivo and can also know their strengths and limitations. All in all, you can make informed decisions on how to use NVivo for your QDA.

5.7 Using NVivo yourself Along with this chapter comes an exercise on the usage of NVivo that deals with all the practical tools that were discussed in this chapter. The exercise that is provided uses the sample project ‘Wellbeing in Older Womens’ Network’ and can be downloaded via https://help-nv.qsrinternational.com/20/win/Content/about-nvivo/explore-sampleproject.htm. In total, NVivo provides users with five sample projects that are all wellsuited to explore and train your skills before you have to apply data analysis techniques on ‘real’ research data. These sample projects can be accessed with the previous link. If you did not acquire NVivo yet, you can download a demo version of NVivo here: www.qsrinternational.com/nvivo-qualitative-data-analysis-software/try-nvivo or buy the

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software on the website. Different packages are available for students, researchers or entrepreneurs. NVivo is available for both Windows and Mac users. This chapter dealt with the use of NVivo for textual data analysis, but the application of NVivo is very broad. You can analyse videos, audio, pictures, focus groups, etc., with NVivo. For a complete course or a training programme that is adapted to your needs, you can contact the NVivo Academy. An overview of the NVivo training material can be found here: www.qsrinternational.com/nvivo-qualitative-data-analysis-software/ support-services/customer-hub/nvivo-academy/nvivo-academy.

5.8 Recommended reading Friese, S. (2022). Role and impact of CAQDAS Software for designs in qualitative research. In U. Flick (ed.), The SAGE handbook of qualitative research design (pp. 307–26). London: SAGE. This chapter provides an overview on how different QDA software programs handle data design and analysis, more specifically the application of MAXQDA, Atlas.ti and NVivo are discussed. This chapter can be interesting to read before you acquire a certain software program as it provides the user with the strengths of each software for specific analysis goals or methodologies. O’Neill, M. M., Booth, S. R. and Lamb, J. T. (2018). Using NVivo™ for literature reviews: the eight-step pedagogy (N7+1). Qualitative Report [an online journal dedicated to qualitative research since 1990], 23(13), 21–39. This journal article can help you to conduct a systematic literature review using NVivo 11. The authors provide a method that consists of eight steps, which makes their approach more applicable to older and newer versions of – for example – NVivo. Lysaght, Z. and Cherry, G. (2022). Using SPSS and NVivo to conduct mixed-methods analysis collaboratively online: challenges, opportunities, and lessons learned. In SAGE Research Methods Cases. https://dx.doi.org/10.4135/9781529604023. NVivo has been developing its software to improve opportunities for mixed-method analysis. This article focuses on the experience of two researchers who have used NVivo and SPSS to conduct mixed-method research. The sample project ‘Wellbeing in Older Womens’ Network’ can also be used to train your mixed-method competences in NVivo. Cabrera, G. A. (2018). The use of computer applications in qualitative research: a review. Asia Pacific Journal of Academic Research in Social Sciences, 3, 35–42. This research article will help you to critically reflect on the use of computer assisted QDA more generally (and therefore not only NVivo), as it provides a more critical evaluation of its usage and implications for the field of QDA in general.

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6 Process Tracing: Making Single Case Studies Transparent and Convincing Ferdi De Ville, Niels Gheyle, Yf Reykers and Thijs Van de Graaf

6.1 Chapter objectives Process tracing is a single case-study method that allows students to use within-case evidence to explain a specific outcome that they are interested in and/or to test a theory for some general phenomenon or relationship in the social world. The method offers the tools to either test the explanatory value of a given causal theory or to develop a causal theory. The unique advantage of process tracing is that it enables the transparent and convincing development of a causal explanation, translate this explanation into expected empirical observations, and critically assess the explanatory value of empirical observations. These features of process tracing significantly increase the confidence that students can have in their case study work. Process tracing is a method that is increasingly used in qualitative social science research, especially in the fields of comparative politics, international relations and political economy. After having read this chapter, students should be able to: • • • • •

understand and discuss the objectives and key features of process tracing; outline a basic process-tracing research design; develop a causal mechanism for some outcome of interest; operationalize a causal mechanism into expected observations and tests; and critically assess empirical observations.

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6.2 Key features, debates and historical development Social science students and researchers frequently conduct single case studies. Quite often, this follows from an intrinsic interest in a particular event or evolution that they want to study in more detail. Alternatively, students might be drawn towards single case studies because of a fascination for a particular theory, whose explanatory value they want to test in a certain context. Whatever the primary motivation (empirics driven or theory driven), this interest to study single cases should be nurtured, given the rich amount of detail and complexity of the social world such case studies can lay bare. Yet case study analysts should also be aware of the criticism other researchers may have on the value of single cases for social science. This chapter explains how process tracing is a single case study method that enables researchers to think more systematically about their case and in particular the causal process underlying it. A case is therefore, in this chapter, understood as any sort of unit ‘in which a given causal relationship plays out’ (Beach & Pedersen, 2016, p. 5). Whether this is a country, an individual or a particular policy at a given time and location is hence determined by the theoretical claim that the researcher is interested in. Doing a single case study can be challenging. Ill-designed single case studies often amount to no more than a story that chronologically narrates a number of supposedly connected events. But describing what happened in a certain case is not equal to explaining why something happened, although that is the goal of most social science research and differentiates our discipline from historical studies, or even newspaper articles. How can social science students avoid their work being read as just some narrative, where critical readers might argue that the facts have been ‘cherry picked’, rather than as a robust social science analysis? How can they move from describing particular events towards explaining them in a way that speaks to an audience not necessarily interested in their particular case? How can they know what type of observations or evidence to select in the first place? And what can a single case tell us about a general pattern that may exist? These are all difficult questions that students working with a single case often struggle with. For some authors, qualitative, single case analysis remains a second-best research strategy, to be undertaken only when quantitative, large-N research is not feasible. This position was espoused most prominently in the influential textbook Designing Social Inquiry by King, Keohane and Verba (often referred to as ‘KKV’), published in 1994. Firmly rooted in the idea that social science should mimic the natural sciences and their use of experiments and statistics as much as possible, these authors famously argued that ‘nothing whatsoever can be learned about the causes of the dependent variable without taking into account other instances when the dependent variable takes on other values’ (1994: 129). Good social science, they argue, means collecting (a lot of) data for cases that differ in time and/or across space, and then evaluating which factors vary together.

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However, there are good reasons why we should not dismiss single case studies (see also Rohlfing, 2012). First, they often imply very rich empirical accounts that are better able to capture ‘real world’ complexity and detail than statistical analyses. To deny single case studies a role in social science would therefore overlook much of its potential. Moreover, the statistical, ‘large-N’ methods also have their own shortcomings. Most importantly, many quantitative studies can measure only correlative instead of causal relationships, and even when they establish causation, they are often incapable of laying bare the causal pathway that runs from the independent variable(s) to the dependent variable. Statistical analyses assume that if two variables appear together frequently then both are causally related (this is called ‘probabilistic causality’, see Beach & Pedersen, 2013: 26; King et al., 1994: 81–2, 89). Statistical analysis has, for example, validated one of the famous theories of political science: that democracies do not go to war with each other (the ‘democratic peace theory’). However, even when controlling for potential confounding variables, statistical analyses can never really prove that it is actually ‘democracy’ that causally explains ‘peace’, and not some other important determinant that might have been overlooked (see Rosato, 2003). More importantly, these quantitative analyses cannot explain how democracy produces peaceful conflict resolution. Delving into a single case enables us to say much more about the ‘on-the-ground’ causality, rather than looking for it in average relationships. This is where process tracing comes in as a method that can give single case studies a stronger foothold thanks to two specific advantages. First, process tracing allows for a more rigorous analysis of a single case, by thinking more systematically about the nature of a case and the (theoretically inspired) causal relationship explaining an outcome, and by forcing students to reflect more explicitly in advance on which observations they expect to find. More specifically, process tracing forces the analyst to engage in a priori reflection about: i) the population to which her case belongs; ii) a theoretically inspired causal mechanism that is able to explain the outcome of interest; iii) the evidence she expects to find if this causal mechanism has indeed produced the outcome; and iv) the explanatory value of finding (or not finding) this evidence. Second, a central and distinct feature of process tracing that makes the method uniquely suited to explain outcomes is the focus on ‘causal mechanisms’ (CM). The concept of a causal mechanism will be introduced in detail in section 6.3.3, but for now we can define it broadly as ‘the causal process linking a cause with an outcome’. It allows the analyst to argue how a cause leads to an outcome in the case of interest. This shifts our focus from ‘cause-effect relationships’ (X -> Y, as in statistical or comparative case studies) to ‘causal mechanisms’ linking cause(s) with an outcome (X -> CM -> Y). Such a focus on mechanisms should not lead one to believe that this method is only useful for developing and testing rationalistic explanations of the outcome of a case. On the contrary, process tracing has been used in (soft)

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constructivist settings as well, where the power of ideas, norms or values has a distinct place in causal mechanisms (see, e.g., Checkel, 2006; Norman, 2015). What is crucial, however, is that process-tracing scholars always aim to explain outcomes in the world, rather than interpret or problematise reality. These two strengths of process tracing match the empirics-first or theory-first motivation to engage with single cases, introduced in the opening paragraph of this section. While in practice process tracing always involves a link between theory and empirics (regardless of the primary motivation), this ‘entry point’ still matters for the logic of case selection and the sequence of steps in performing a process-tracing study. In empirics-first process tracing, the primary aim is to explain the outcome of a pre-selected case. In theory-first process tracing, the goal is to test, build or revise theory through the strategic selection of a single case to update our theoretical understanding. While process-tracing textbooks are usually written from a theory-first motivation, we focus on the logic of process tracing when one starts from an empirics-first motivation. This choice is based on our experience with counselling students on research papers, which made it abundantly clear that in most cases their research project starts from an intrinsic interest in better understanding a fascinating outcome, rather than in testing or refining some theory. It has become a norm in the social sciences to discourage such an empirics-first approach. Students are often expected to make first and foremost a theoretical contribution. We believe this intrinsic empirical motivation is not something that should be discouraged, particularly because not all students aspire to have an academic career. Students should learn how to conduct sound research, which includes teaching them how to perform empirically motivated single case study research rigorously. Partly in response to the KKV criticism, qualitatively orientated social scientists have begun to develop process tracing as a method to do case-study research more rigorously and systematically. In recent years, process tracing has not only been gradually developed methodologically but it has also been used more frequently in empirical studies across disciplines (see Figure 6.1), further refining its methodological foundations along the way. The application of the method spans different fields: of the publications included in Web of Science at the time of writing (April 2021) that mention process tracing, over half are situated within political science, international relations and public administration, around 20% in psychology and 12% in economics and management studies, with the remainder assumed by disciplines varying from ecological studies to medicine. In political science, it was especially the work by Andrew Bennett together with Alexander George (2005) and Jeffrey Checkel (2015), who gave a big push in outlining the systematic logic of causal explanation using single case studies. In recent years, the method has been developed in an even more sophisticated and rigorous manner by Derek Beach and Rasmus Brun Pedersen (2013, 2019).

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Figure 6.1  Inclusion of term ‘process-tracing’ or ‘process tracing’ in ‘topic’ in Web of Science publications In this chapter, we are heavily indebted to the work of Beach and Pedersen, although we intentionally deviate from their stricter interpretation of and prescriptions for process tracing (and perhaps in a way they would not necessarily agree with). Our primary aim is pedagogical: we want to explain how some of the key features of process tracing help elevate case studies to a higher level of causal explanation so that students can write better papers and can withstand traditional criticisms on single case studies. To put it differently, we hope that this chapter will enable many students to do decent process tracing, rather than helping a few students to do excellent process tracing. Above all, we believe it is this ambition of a priori reflection about population, causal mechanisms and empirical expectations that forces case-study scholars to be much more transparent about their work, which is the main added value of process tracing that leads to better qualitative case-based research. In the next section, we explain how process tracing, following an empirics-first logic, is done step by step.

6.3 Doing process tracing step by step As we wrote in the introduction, process tracing invites the analyst to critically reflect on their case, and not only describe what happened (often in a type of descriptive narrative). It offers tools to confidently explain why and how something happened, without cherry

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picking empirical observations that ‘accidentally’ match their theoretical expectations. The main objective of process tracing is to evaluate if there is sufficient evidence to conclude that a theoretical relationship connects a cause X to an outcome Y with the help of a particular causal mechanism CM. In that sense, it offers the unique opportunity to move beyond simply stating ‘X is the cause of Y’, rather showing ‘how X has led to Y’ in the case of interest. Doing so compels a researcher to reflect a priori about what she should see or find if a certain theoretical mechanism is present. By making a theoretical causal mechanism explicit, they are then able to purposefully look for (reliable) evidence and make an honest and transparent evaluation about whether we should update our trust in this causal mechanism upwards or downwards, and consequently, if we can be confident that our explanation for our case outcome is relevant (without necessarily being the only explanation). This might sound complicated at first, so let us break down the different steps that are usually involved in setting up and performing a process-tracing study. We will introduce these steps below and further elaborate on them in the following subsections.

Case of what?

Literature review

Conceptualization of causal process

Possible generalization

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Figure 6.2  Scheme of the different steps in a process-tracing study The first step in this quest for more systematized single case studies requires thinking more thoroughly about which population the case you are interested in is an instance of. This means making theoretical abstraction of the specific outcome that you want to explain. Say, for example, that you are interested in explaining why a US-Chinese trade war erupted in 2018 (cfr. Liu & Woo, 2018). The ‘theoretically abstract’ case we are interested in here is a ‘trade war’, since there are many other trade wars one can think of (such as the ‘Opium wars’, the ‘Banana wars’ or the ‘Bra wars’; no we have not made these up, these are actual past trade wars). Likewise, you can be interested in why Belgium sent fighter jets to Syria and Iraq in the fight against ISIL (cfr. Pedersen & Reykers, 2020), but the more abstract population of which this case is an instance of is ‘participation in military interventions by small countries’, as Belgium is certainly not the only small country to join such an intervention. Thinking about the question ‘what is my case a case of?’ will not only allow you to identify the relevant literature to review, as we explain below, but also enables you to convince someone else why they should care about your research, even if they are not particularly interested in your specific case. Once you have been able to situate your specific case in a broader (more abstract) population, you should be able to tap into the existing literature and find out what

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prior research has already found out about the key causes and explanations for a particular phenomenon. Your interest may have been triggered by a specific event (e.g., recent US-Chinese tariffs or Belgium’s F-16 flights over Middle Eastern countries) but making theoretical abstraction of your specific case should direct you to prior studies on the same topic, where you will find existing explanations of trade wars (when and why do countries engage in a trade war?) or the causes and explanations for why (small) countries participate in military missions. Often you will find that many other studies and books on this (or a very similar) topic already exist. One of the most important tasks of the process tracer at the initial stage of the research project is therefore to review this literature and filter out the key causes and explanations. In most cases, you will be able to draw on existing literature about the causes of your outcome, instead of having to invent these from scratch yourself. You will then also be able to specify your research problem into a proper research question, either asking how the outcome of interest can be explained or asking how the identified cause has contributed to the outcome of interest. Depending on the state of the literature, there may already exist (implicit or explicit) causal explanations to apply in your case study. If not, you will have to translate existing theories into a causal mechanism, which is the next step in the process. After acquainting yourself with the literature on the more abstract phenomenon of which your outcome of interest is a case, it is time for the next, and crucial, step: the conceptualization of the causal process. In practice, there are two aspects of your research design that you will need to conceptualize in more detail and in accordance with process-tracing rules. The first entails the specific conceptualization of X and Y, the cause and the outcome. While you might have been trained to think in terms of independent and dependent variables, process tracing does not talk about these elements as variables. Section 6.3.2 explains how you can conceptualize cause(s) and outcome to fit process-tracing research. The second element that needs detailed conceptualization is the very core of process tracing: the causal mechanism that elaborates how a cause and an outcome are theoretically linked to each other. Section 6.3.3 takes up this question. The following step implies transforming the causal mechanism into something that we can measure; in other words, operationalization of the causal mechanism. Every part of the causal mechanism that we have theorized and conceptualized is translated into (a set of) evidence that we expect to find if our mechanism works in practice. In other words, if the mechanism that we are testing is indeed present, what ‘fingerprints’ should it have left behind? In this part, some more advanced concepts like ‘Bayesian thinking’ and the necessity and sufficiency of ‘propositions’ and ‘observations’ come to the fore, which we make more digestible in section 6.3.4. In the following step, it is time to collect observations and evaluate our mechanism. This requires us to look closely at the reliability of what we found, and whether observations strengthen or weaken (parts of) our causal explanation. After we have assembled all evidence, it is also important to reflect on how to aggregate this to make a final evaluation of our belief in the mechanism. This important ‘collecting and evaluating evidence’ step is tackled in section 6.3.5.

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The final step is a tricky one for single case study research: generalization. This entails the question of whether, based on our findings, we can say something about other cases beyond the one that we studied. Single case studies have only limited generalization potential. However, process tracing offers opportunities to generalize beyond the studied case by efficient follow-up process-tracing studies of carefully selected other cases, or by combining the method with other, covariational methods. We briefly discuss the limits and opportunities of process tracing in the conclusion.

6.3.1 Connecting to the literature Reflecting on the more abstract phenomenon of which your specific case is an instance directs you towards theoretical literature. Indeed, what differentiates (positivist) social science from historical research, is that social scientists have the ambition to find systematic explanations for the outcome of similar cases.1 In other words, we cannot do without theory, which is a combination of hypotheses or assumptions stipulating an association between two variables or concepts, and a causal logic that explains the connection between both (cfr. Rohlfing, 2012; Rosato, 2003). It is this ambition to look for theoretical explanations that tilts social science beyond the ambition of only describing what happened, to also explaining why and how something occurred. As Patrick Jackson (2016, p. 702) usefully defined the latter types of questions: ‘answers to “why” questions establish the parameters of causation, and answers to “how” questions fill in the specific details and add some local colour.’ When delving into the literature, it is very likely that you will discover that other authors have already thought about, theorized and tested the relevance of a certain cause and/or causal logic for the outcome that you are interested in. To find that out, however, you need to take a step back and disconnect for a moment from the case you are so interested in.

Case study 6.1 Low inequality in the Netherlands: looking in the literature for potential explanations You might be interested in explaining inequality in the Netherlands. You wonder why the Netherlands has a relatively low level of inequality. The literature review can make you acquainted with statistical research that has shown that the level of redistribution in democracies is strongly correlated with the electoral system that a country has, whereby countries with proportional systems (cause) redistribute income significantly more (outcome) than countries with majoritarian systems (cfr. Iversen & Soskice, 2006). Moreover,

This does not imply that we need to look for something like universal laws (e.g., Newton’s law of gravity) that govern the social world. The tradition of process tracing (with its focus on mechanisms) aims to relate the explanation of single cases to so-called ‘middle-range theories’; theories that we only expect to function in a bounded set of contexts. 1

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you may find that a causal logic for this relationship that researchers have developed is that politicians in proportional electoral systems have the incentive to build a broad coalition of voters, and therefore to develop social redistribution mechanisms that benefit a large share of the population. Politicians in majoritarian systems (especially with small districts) on the other hand will rather focus on a small share of the electorate, namely those swing voters (in swing districts) that are crucial in winning them the majority, and will therefore rather promise more targeted benefits to this group. This knowledge allows you to reformulate your research problem into a proper research question with a clear causal logic: ‘how does a proportional electoral system lead to the development of an egalitarian welfare state?’ In this context, you can build a causal mechanism that translates this explanation for large welfare states in countries with a proportional electoral system and test it for the Netherlands. This allows you to test if, and how well, this statistical correlation and theoretical causal logic holds up in the real world. In social scientific research, however, it is not uncommon that social and political outcomes have different and intertwining causes and explanations, all contributing to an outcome of interest. In other words, there might be different causes that explain the outcome. In the example above, inequality (Y) is explained by the proportional electoral system (X1) via the incentive for politicians to cater to broad electoral demands (CM1). Yet other researchers might have established that the ideology of the governing majority (X2) or the openness of the economy (X3) is associated with the level of redistribution in a country (Y). These different causes do not even have to be competing but might reinforce each other. Furthermore, another author could agree that a proportional electoral system (X1) is indeed the main cause, but may propose a different causal logic, less focused on the incentives of politicians, but more on the link of parties in proportional representation systems with civil society organizations (in so-called ‘pillars’) that lobby for social protection (CM2). In sum, different causes and/or causal explanations may be available in the literature.

A productive strategy for the process tracer is to create a mind map of all the different existing theoretical explanations for a phenomenon, and subsequently to choose the most plausible causal explanation that they want to further investigate for their case at hand (this could also be a combination of factors mentioned in the literature that you think are causally linked). Following the example above, one may want to evaluate whether we should increase our confidence in an explanation for the expansion of the Dutch welfare system that stresses the importance of the ‘pillars’ pressing for social reform in proportional systems (X1, CM2). Of course, you can quickly see that there is an inevitable degree of interpretation at work here since the process tracer needs to decide which causal explanation to apply in her case. This choice can be informed by several considerations. Maybe you already have some insight in your case, and you have a ‘hunch’ that a certain explanation could have large explanatory value. Or you want to do the exact opposite: you want to see whether a less common causal explanation can also contribute to an explanation of the case. This choice therefore depends on: i) the state of the art; ii) your empirical knowledge before starting the actual case study; and iii) the specific puzzle that you want to address.

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A different situation arises when there is only very limited theoretical literature to draw from, or when you have a strong intuition about a causal logic that is not immediately covered by the literature (or you haven’t found it yet). If this is the case, you will have to engage in an imaginative process of linking potentially relevant theoretical insight with exploratory empirical research. It means that you will have to think about which cause and/or what kind of causal mechanism might be involved in a good explanation for the outcome. In short, it comes down to asking yourself the question: what might be the underlying cause X for my outcome Y? An answer to this question will be informed by theoretical intuition that you possess and the preliminary empirical insights in your case. This resembles a pragmatist strategy of abduction and is an inherently creative process (cfr. Friedrichs and Kratochwil, 2009). It is therefore difficult to provide a detailed script on how to approach this situation, but some guidelines may be helpful. First, guided by existing scholarship on the phenomenon, you can start collecting observations and draft a raw ‘empirical narrative’ of what happened in this case. Second, this narrative can be summarized in a sketch (this can be taken literally) where the most important events, or critical junctures, in the case are highlighted. Third, you can try to look for systematic patterns in this sketch that correspond to existing theories. If in your sketch on the US-China trade war, you found that presidential thinking on how trade deficits weaken the United States’ geopolitical power seem to be important, you can relate this to existing constructivist and geo-economic scholarship. Most of the steps sketched above are written from the perspective of an empirics-first motivation to do process tracing, where you start from an intrinsic interest in a certain case. Making abstraction from this case leads you to a certain literature from which you can develop a causal mechanism to explain your case. In other process-tracing articles or textbooks (especially those inspired by Beach and Pedersen), you will find a distinction between several sub-variants of process tracing: the theory-testing, theory-building and theory-revising variants. While the steps taken in these variants are similar to those outlined here, there is one key difference. In the variants of Beach and Pedersen (2019), the main objective of process tracing is to contribute to theory. To allow the researcher to contribute to theory as effectively as possible, purposeful case selection is key. Hence, in these variants, the case to be studied is a function of the theoretical goal, rather than the starting point of the research project.2 While many (under)graduate thesis supervisors think about good science in such a theory-first manner, we believe that this does not reflect how most social science research (especially by undergraduate and graduate students) unfolds in practice. We think that good single case studies through the process tracing logic can also start from a pre-selected case based on intrinsic interest in this case (which does not exclude the possibility of making a theoretical contribution). The exception to this is what Beach and Pedersen (2013, 2016, 2019) label ‘explaining outcome’ process tracing. This variant of process tracing is generally seen as ‘driven by a strong interest in accounting for a particular interesting and/or historically important outcome’, where it is added that this outcome ‘is not viewed as a “case of” something’ (Beach & Pedersen, 2016, pp. 308–9). Hence, the researcher is expected to have no ambition to generalize and the case is considered highly unique or historical. Yet, this is not what is generally expected from (under)graduate social science students. Moreover, many students might think that their case is unique while, in fact, a thorough literature study will show them it is actually part of a wider phenomenon or case population. 2

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6.3.2 Conceptualizing outcome and cause Once you have delved into the literature on the more abstract phenomenon of which your case of interest is an instance, you should conceptualize the outcome and cause in your research design. The outcome is the issue you want to explain, the cause is where your explanation starts.

Case study 6.2 Introduction of unemployment benefits in the Netherlands: conceptualizing cause and outcome Giving a small twist to the example we used above, let us assume that starting from an interest in the development of the welfare state of the Netherlands, you decide that you want to explain the introduction of unemployment benefits in the country in 1949, a key moment in the development of its welfare state that helps account for its high level of redistribution. After having read literature on the genesis of unemployment benefits in Western countries, you realize that many countries seem to have introduced unemployment benefits after the Second World War. What is more, you have come across statistical research that found a significant relationship between the ‘end of war’ (X) and the ‘introduction of unemployment insurance’ (Y) (Obinger & Schmitt, 2019). These studies have even postulated a (so far untested) causal logic for this relationship, namely that governments wanted to appease potentially revolutionary returned (and sometimes still armed) veterans after the war (CM) by introducing unemployment benefits. To systematize your case study into the introduction of unemployment benefits in the Netherlands in 1949 after the end of the Second World War and to make your study more generalizable (or at least useful for other researchers) you need to conceptualize this question or relationship. This means providing a more abstract description of outcome and cause, while defining their boundaries and key characteristics. In process tracing, we do not conceive of outcome and cause as variables. We do not assume that a higher ‘score’ on the cause (e.g., a higher number of deaths during a war) will result in an increase or decrease of the score of the outcome (e.g., a higher increase in the replacement level of unemployment benefits). We rather treat them as ‘concepts’ that are either absent or present in a case.

Good conceptualization in process tracing means that our conceptualization needs to adhere to three standards: a positive pole, mechanistic and specific definition. First, process tracing only defines concepts on their ‘positive pole’. If you want to explain the introduction of unemployment benefits in the Netherlands after the Second World War, you are interested in the relationship between the ending of wars and the introduction of unemployment insurance. You do not want to (and cannot) say anything about the effect of a long-term peaceful period on the potential reduction or elimination of unemployment benefits. This is also called a ‘set theoretical’ approach

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to concepts and cases where one makes ‘asymmetric causal claims’ that run in only one direction (cfr. Marx, Rihoux & Ragin, 2014; Rihoux & Ragin, 2008). It means that we define clear properties that a case should have to be a member of a set (in our example, a set of countries that have experienced a war and have introduced unemployment benefits), and subsequently we assume that a certain theoretical relationship applies only to this set of cases. Second, our concepts also need to be defined in a ‘mechanistic’ style. This means that causes need to be defined in a way that they can trigger a causal mechanism, while outcomes need to be defined in a manner that they can be produced by the preceding causal mechanism. We need to think about ‘causally relevant attributes’ of our concepts that can trigger, or be produced by, a causal mechanism. In our example, it is insufficiently clear how ‘war’ could trigger a causal mechanism that would result in the introduction of unemployment benefits. By defining as a key characteristic of ‘war’ that it has destroyed a significant part of the productive capacity of a country (leading to high unemployment at the end of war), we can begin to see how this can start a causal mechanism. Defining a concept in this way also (further) narrows the population of cases in which we expect our mechanism to be present (in this case to those where the war has led to severe material destruction). Third, we also want to define our concepts in a relatively specific way. As process tracing aims to explain what happened in a certain case (and possibly from there also wants to generalize findings to comparable cases) we should select relatively detailed causal attributes for our concepts. Again, we will not assume that any military conflict (such as the 1969 Football War between El Salvador and Honduras that lasted 100 hours) will result in the introduction of unemployment benefits. We would delineate our concept to ‘total wars’, by specifying minimum duration, number of casualties, etc. We would also not want to define our outcome too broadly, as any establishment or amendment to unemployment benefits, but narrow it down, for example, to the first introduction of statutory compulsory unemployment insurance.

6.3.3 Conceptualizing the causal process Central to process tracing is our focus on how one or more causes are linked to an outcome in a case that we want to explain. In other research designs focused on the covariation between causes and effects, this causal link is ‘black boxed’. In process tracing, we are explicitly studying this link, which we call a ‘causal mechanism’. While many different definitions can be found, a causal mechanism is nowadays primarily understood as ‘a theory of a system of interlocking parts that transmits causal forces between a cause (or a set of causes) and an outcome’ (Beach, 2016, p. 3). Beach and Pedersen have usefully conceptualized these parts as being composed of entities that engage in activities, whereby ‘entities can be understood as the factors (actors, organizations or structures) engaging in activities, where the activities are the producers of change or what transmits causal forces through a mechanism’ (Beach, 2016, p. 3., italics in original). Figure 6.3 below provides a template of how a simple causal mechanism composed of two parts looks like

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schematically. As can be seen, entities should be denoted as nouns and activities as verbs. In this way, activities are what transmits causal energy throughout the mechanism, from cause(s) to outcome. Part 1

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Figure 6.3  Template of a causal mechanism as system. Based on Beach, 2018, p. 6; Beach and Pedersen, 2019 In the methodological literature on process tracing, there is much debate about what a mechanism actually is. Roughly speaking, we could argue that these are understood in two ways, either as detailed interlocking ‘systems’, or more as ‘minimalist’ schemes. For students, it is less important to fully grasp the underlying philosophical understanding of these different approaches (see Beach & Pedersen, 2019, Chapter 2), than to understand what they can do for them, and how their research benefits from engaging in either of these two broad variants. The key difference between both is that in a ‘systems approach’, the level of detail of the theorized mechanism is much higher, and is characterized by so-called ‘productive continuity’, meaning that each part of the mechanism ‘logically leads to the next part, and there are no large logical holes in the causal story’ (Beach, 2016, p. 4). A minimalist understanding, on the other hand, looks more like a rough draft of a mechanism: it is more abstract, less demanding in terms of continuity between the parts, and sometimes even reduced to a ‘one liner’. It is important to stress from the beginning, however, that neither approach is inherently better than the other, but that your research objective and ambition, as well as your time and resource constraints, will determine which approach suits you best. Let us turn to some everyday examples to see when and why you could choose either a system or a minimalist understanding. First, take the example of starting a Mercedes Benz vehicle in 2020. This is no common car. The automatic key switch, the advanced technological components of different parts of that car, and the specific Mercedes Benz technology about igniting an engine, all make this a very specific car. Process tracing the steps of igniting a Mercedes Benz in a systems understanding of mechanisms will therefore specify all these very detailed steps, making sure that every step logically leads to the next one. In the end, we know how a Mercedes Benz is ignited. However, if we understand this ignition process of a 2020 Mercedes, do we then also know how a Russian Lada

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from 1963 works? Probably not, as these types of car, the technology behind it and the brand-specific features will differ significantly. Simply following the steps of the mechanism set up for the Mercedes will not bring you very far. What this shows is that a ‘systems’ understanding limits the ability of your mechanism to ‘travel’ across cases. The mechanism that we built for this car is very specific to the case at hand; so specific often, that it only works for a very small population of cases (here, Mercedes-like cars from post-2016, for example). Yet this is where the ‘minimalist’ understanding differs. We could also elaborate our mechanism in more abstract terms, moving away from all the specific and detailed know-how of how Mercedes cars work, to end up with a more general mechanism of how ‘cars’ work. We could say, for example, that ‘a car’ (not specified) whose ‘switch’ is turned on (be it with a key or an automatic button) needs three abstract intermediate steps: burning fuel, energy conversion, mechanical work. These are not specific to a particular car (every non-electric car works this way), nor do they necessarily have this ‘productive continuity’ characteristic (why is it logical that burning fuel leads to energy conversion?). We may not know the exact link between all these parts, but that is not problematic as we are interested in establishing a relatively abstract mechanism that does the trick in explaining how many cars start. ‘Switch -> ignition -> start’. Such a mechanism would apply to both the Mercedes and the Lada, as both follow this abstract mechanism. Even though this is a ‘minimal’ approach, it has taught us a little bit more about the causal logic that leads to starting a car than a traditional correlational study would have offered us (which would be limited to switch -> start).

Case study 6.3 Belgium’s winning goal against Japan at the 2018 World Cup A slightly patriotic example can further elucidate this. We presume everyone can still visualize the perfect last-minute counter-attack goal that Belgium’s national football team scored against Japan in the 2018 World Cup (if you can’t, go to YouTube and watch it, preferably with the Titanic soundtrack; if you do, go to YouTube and watch it again). Let us imagine that you are a football coach eager to learn, and you want to train your players in perfecting the counter-attack (cfr. Sarmento et al., 2014). How do we analyse and represent this goal? A systems approach to a mechanism resembling that goal would logically show each detailed step of that counter: Courtois (goalkeeper) catches the corner and distributes it to De Bruyne (central midfielder), who passes it wide to Meunier (right winger), who low-crosses it for Lukaku (centre-forward), who purposefully steps over the ball, for Chadli (left winger) to finish. Glorious. Studying the goal from this perspective teaches us something about this goal, in this context, by this team, and might make us an expert in counter-attacks by the Red Devils (the nick-name of the Belgian football team). It is a perfect explanation for this outcome. Yet it is no easy template to use in every other game, as it cannot be reproduced in the same way in any situation. A good football coach would therefore also look at the more generalizable

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dynamics behind this goal, either producing a one-liner mechanism calling it ‘counter’ (which is perhaps too vague) or producing two or three relatively abstract steps so that his team can implement his own (less epic) version of this in the future: goalkeeper instantly restarts an attack, open play to left or right flank, immediate switch play to centre, finish.

As might become clear, process tracing in a systems-understanding means that ‘a lot can be said about a little’ (Reykers & Beach, 2017). By analogy, a minimalist understanding would mean that we can ‘say little about a lot’, in the sense that we at least have some idea about the general causal logic underlying a social phenomenon, without being too specific. As mentioned above, the choice for either of these depends on your research question, your ambition to speak to a wider audience, and the state of the literature. If you enter a literature field where very little is known about underlying mechanisms, you might want to start with a more general, minimalistic mechanism that is not well-suited to explain specific cases but does the trick for now. Beach and Pedersen (2019) argue that this might be specifically useful at the very beginning of a research project (to ‘probe’ if our hunch is plausible) or at the very end, when we aim to test the reach of the mechanism (see section 6.4 conclusions). On the other hand, you can simply be primarily interested in explaining your specific case, without much consideration for generalization, which would steer you towards a systems understanding. All in all, it is important to repeat that neither is inherently ‘better’. Even the minimalist understanding forces you to think more seriously about underlying causal processes than you would have done otherwise. Let us return to the example of explaining the introduction of unemployment benefits in the Netherlands in 1949. Herbert Obinger and Carina Schmitt in their article ‘Total war and the emergence of unemployment insurance in western countries’ (2019) used regression analysis to demonstrate that a nation’s exposure to the horrors of war enabled the introduction of unemployment insurance in the post-war years. In their article, they make some suggestions about the causal logic underlying this relationship. But their research design does not allow them to test this causal logic. You could then decide to conceptualize their suggestions as a systemic causal mechanism that can be tested in your case, the Netherlands. Have difficulties finding work

Demand social protection

Makes social concessions to guarantee social peace

Results in End of total war with severe material destruction

Veterans returning to an economy with a weak labour market

“Undeserved” unemployed

Government

Introduction of mandatory unemployment benefits

Figure 6.4  A systemic causal mechanism linking total war with unemployment benefits

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Alternatively, a minimalist understanding would either be a further abstraction or partial understanding of the steps in-between, or it can even be a so-called ‘one-liner’ (see Figure 6.5). If we have little clue about which of the different plausible theoretical causal processes might explain an outcome in a case that we are interested in or we are unsure how plausible our hypothesized causal explanation actually is, it might be useful not to unpack the causal logic as a systemic mechanism just yet. Rather, it might be more efficient to perform (relatively superficial) plausibility probes to see if we find any evidence for one or more causal explanations. This means that we will be limited in explaining how a mechanism operates, but it would, in our example, increase our confidence that pressure from veterans has some explanatory power for the introduction of unemployment benefits after war. Hence, we might use the plausibility probe to decide if it is worthwhile to further unpack our causal mechanism as a system and trace it intensively at a later stage. End of total war with severe material destruction

Fear of government of armed ressurection by veterans

Introduction of mandatory unemployment benefits

Figure 6.5  A minimalist causal mechanism linking total war with unemployment benefits Two final remarks have to be made about this important section on causal mechanisms. First, causal mechanisms come in many forms and can be made much more complex than in the examples given above. They can have many more steps than just two or three. Moreover, they do not need to be linear but can involve feedback loops. And they can be multi-causal, where, for example, one cause triggers the mechanism but another cause later on in the process is necessary to keep the mechanism going. Furthermore, they can also be conceptualized at different levels of analysis: from macro-level evolutions that span decades (e.g., how globalization has contributed to welfare state expansion in small states) to micro-level mechanisms (e.g., how minister-portfolios have been distributed during a cabinet formation). Important to keep in mind is that complex mechanisms are not necessarily better or worse than simple linear mechanisms, but that the former are less likely to travel across cases. Mechanisms are therefore flexible tools that can be moulded to your objectives and ambition. Second, causal mechanisms are conceptualized to only operate under so-called ‘contextual conditions’ that we assume need to be present. These contextual conditions differ from causes because they only enable a causal mechanism to operate, but they don’t trigger the causal mechanism. In the example of the link between the end of war and the introduction of unemployment benefits, ‘sufficient fiscal and institutional capacity of the state’ can be considered a contextual condition, because we can assume it needs to be present to allow states to introduce unemployment benefits, but it does not trigger the mechanism leading to the initiation. In the example of the Mercedes Benz above, an important contextual condition might be that it is not freezing cold. If this

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condition is not present (i.e., it is freezing cold) our mechanism might not work. Yet ‘being sufficiently warm’ does not trigger the ignition process. It is merely, but importantly, an enabling factor.

6.3.4 Operationalization of the causal process Once you have developed a theoretical causal mechanism that might explain how a cause is linked with an outcome, the next question is: how do you test the plausibility of this explanation? How do you operationalize this causal mechanism, or, put in simpler wording: how do you make it measurable? The first step in operationalization here is making ‘propositions’ about which evidence we can expect to find if our causal explanation has indeed been at work in our case. If the explanation for the introduction of unemployment benefits in the Netherlands in 1949 is that the government wanted to appease returned unemployed veterans, what evidence, or ‘empirical fingerprints’, would we expect to find? One proposition could be that we should find evidence that ‘the share of veterans without a job was high in the first post-war years’. A second proposition could be that we would expect that ‘veterans made vocal demands for unemployment benefits’; for example, through pamphlets or public rallies. Third, we could expect to find some evidence that ‘policymakers were informed about the demands of veterans’ and that ‘they were concerned about the threat that these veterans posed for social stability or the survival of the cabinet’. Operationalization does not stop with thinking and making explicit which evidence you expect to find. It also involves reflecting in advance about the meaning of finding or not finding evidence. To make ‘inferences’ about the plausibility of an explanation based on (not) finding evidence, Beach and Pedersen (2019) have in a very useful way applied informal Bayesian reasoning to process tracing. In line with Bayes’ theorem, evidence helps us with updating our confidence in a certain explanation, or, at a more detailed level, a part of an explanation (see also Bennett, 2015). According to this logic, when assessing the value of evidence, we need to take three things into account: i) how much or how extensively has the causal theory already been proven before you started your study (i.e., your prior confidence in the explanatory value of a theory); ii) how strongly does your evidence strengthen your belief in the causal theory (i.e., the theoretical importance of evidence); and iii) how credible are your pieces of evidence (the empirical trust we have in our evidence). Prior confidence is important because it affects the significance of the evidence we find. If the literature already has high confidence in a certain explanation, finding some weak additional confirming evidence that supports this explanation does not update our knowledge much. But, if in this case we find some disconfirming evidence, this has a stronger effect on the existing knowledge or confidence that we have in this explanation. If we take the example of unemployment benefits again, we could state that the prior confidence in the explanation of pressure from unemployed veterans was quite low, as most existing studies have so far only uncovered correlations between the end of

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war and the introduction of unemployment benefits. This means that (even relatively weak) evidence supporting the ‘veteran pressure’ explanation will significantly update our confidence in this explanation (we know that we must take it more seriously than thought before this study). But if someone had already conducted a study into the introduction of unemployment benefits and tested the above suggested explanation of government appeasement of potentially revolutionary veterans, only strong confirming evidence would further increase our confidence in the theory, as we can assume that this explanation will play an important role. Another way of thinking about this is in terms of the originality of your research. If you explain something that has already been studied extensively (e.g., the European Union’s weak foreign policy response to human rights abuses) and where there is a large consensus in the literature about the explanation (e.g., the unanimity requirement in the Council leading to least common denominator negotiating dynamics), you will need to come up with strong new evidence to make a meaningful contribution to the literature. As you probably realize, this is why a thorough literature study is essential for conducting good process tracing. Besides evaluating predicted evidence in relation to prior knowledge, we also need to theoretically evaluate it in relation to our proposed explanation. This is a crucial step for avoiding criticism of cherry picking, and one which is relevant for all sorts of case study (and an often-overlooked asset of recent developments in the process-tracing literature). Many process-tracing textbooks, including Beach and Pedersen (2019), use the concepts ‘unique’ and ‘certain’ to evaluate the importance of propositions and evidence, thereby building on the work of Van Evera (1997, pp. 31–4). Yet, in our experience, students often have difficulty grasping the exact meaning of these concepts. We therefore think it is beneficial to stick to the more common and straightforward terms ‘sufficient’ and ‘necessary’. This implies that you should think about each predicted evidence if it is theoretically necessary for your explanation that you should find it (not finding this predicted evidence would strongly weaken your explanation), and if finding evidence for this proposition is theoretically sufficient for your explanation (finding this predicted evidence would strongly validate your explanation). To understand this better, the analogy to a murder case has been made in an enlightening manner (McCarty, 2021). If we assume that a suspect has killed a victim with a gun, one necessary proposition for our explanation is that the suspect’s whereabouts match the place of the murder. If our suspect can prove that they were not around the area where the murder took place, they have an alibi. However, finding evidence that they were in the area does not prove their guilt. On the other hand, we can assume that were we to find a recording of a security camera showing our suspect shooting the victim, this would be sufficient for our proposition that our suspect has killed the victim. At the same time, one can also prove guilt without having the video footage (e.g., if there were several eyewitness testimonies), which means that it is not necessary evidence. Combining the ‘necessary’ and ‘sufficient’ dimensions of propositions about expected evidence, and openly reflecting upon them in advance, comes with two key benefits. One benefit is that we guard ourselves against criticism of cherry picking facts. As highlighted in the introduction, single case study scholars (and qualitative researchers more generally)

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often face questions such as ‘how have you selected the presented facts?’, ‘how can we be sure that the given quote is actual evidence?’, or ‘can you prove that you have not just picked the ripe cherries, which prove your theoretical claim?’. With the process-tracing toolbox, for each theoretical proposition, you can specify the kind of observations that you would need to find to prove your point and arrive at a diagram with four quadrants of types of test that differentiate the extent to which they allow us to update our confidence in the proposition. We use the ‘murder mystery’ metaphors described by McCarty (2021) as we agree with him that they are more intuitive than other terms for these tests: •

•

•

•

Motive and opportunity (neither necessary, nor sufficient): this is the weakest test one can perform. It means pursuing predicted evidence that is neither theoretically necessary nor sufficient but adds probability to your explanation. For example, a minister stating in a newspaper interview that his government thinks introducing unemployment benefits is required to preserve social peace can be seen as neither necessary nor sufficient. The motivation for a decision does not need to be publicly communicated (it could have been a motivation for ministers even if they did not explicitly state this), while this quote alone does not prove that our causal mechanism explains our outcome of interests (e.g., we must also show that this is due to the mobilization by war veterans). A single ‘motive and opportunity’ test updates our confidence in a proposition only very limitedly, but a combination of such tests may update our confidence more significantly. Alibi: an alibi test (necessary but not sufficient) can eliminate our explanation but cannot prove it. Passing a single alibi test does not greatly increase our confidence in a proposition, but if several of these tests are passed, we can increase our confidence somewhat. An alibi test for our ‘appeasement of veterans’ explanation of unemployment benefits would be that a government should only have seriously discussed the introduction of unemployment benefits after the war. If we found that the government-in-exile had already drafted plans to initiate unemployment benefits during the war, this seriously undermines our causal explanation. Finding this evidence is necessary to prove our point, but in itself it is insufficient. Smoking gun: A smoking-gun test, to the contrary, is sufficient but not necessary. Finding predicted evidence of this type seriously increases our confidence in our proposition, but not finding this predicted evidence does not lower our confidence. A smoking-gun test in our example would be evidence of a meeting between the minister of social affairs and representatives of war veterans in the days before the decision to introduce unemployment benefits. Not finding this evidence, however, does not mean that your proposition is to be refuted. DNA: Finally, DNA tests are the strongest test types, as, if available, they are both necessary and sufficient. Not passing a DNA test would disqualify our explanation, while passing it would strongly confirm it. However, in the social sciences, it is difficult to think of any evidence that meets this criterium. In your research, you will therefore likely depend more on a combination of slightly weaker tests, which together can lead to a strong confirmation of your propositions and overall causal theory.

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Another, more practical benefit of reflecting upon the necessity and sufficiency of your evidence in advance is that it facilitates your data collection process. It allows you to evaluate your data and observations along the way, and in that way serves as a yardstick to answer the question ‘how much data do I (still) need to gather?’.

Sufficiency

Necessity Low

High

Low

“Motive and opportunity”

“Alibi”

High

“Smoking gun”

“DNA”

Figure 6.6  Four types of process-tracing test. Based on McCarty (2021) If your goal is to test an already theorized causal mechanism (see section 6.3.1), you need to think beforehand which evidence left by the activities in (part of) a causal mechanism can be expected to be found. This predicted evidence for propositions will be formulated based on a combination of theoretical insights and existing knowledge about the case at hand. But is it realistic to assume that all evidence can simply be found? Not finding evidence does not necessarily falsify our proposition. It is always possible that the evidence is present but we have been unable to observe it. Likewise, in some research projects, large chunks of empirical data are collected before a causal mechanism has been (completely) developed. Here, you need to think afterwards whether what is found is also evidence from which credible inferences can be drawn. In both instances, you need to justify clearly why a certain inference is (not) drawn from a piece of evidence.

6.3.5 Collecting and evaluating evidence After having conceptualized and operationalized a causal mechanism, the third big step in process-tracing research is collecting and evaluating evidence. Evidence in process tracing can come from various forms of data: statistics, official documents, archives, memoires, interview transcripts or secondary literature. As should be clear, process tracing is not a data collection method in itself and does not rely on a single type of data. Every type of data that helps updating our confidence in a proposition is relevant. Through the operationalization of the causal mechanism explained in the previous section, you know what predicted observations to look for. But what if you have found or not found these predicted observations? Can you then immediately draw conclusions about the confidence you can have in your explanation?

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The answer is no. You cannot naively assume that making or not making an observation equals finding or not finding evidence. Indeed, a crucial question you need to ask is: does the presence or absence of an observation imply the presence or absence of evidence? Just like you need to think about the theoretical necessity and sufficiency of propositions as explained in the previous subsection, you must reflect upon the empirical necessity and sufficiency of observations. In other words, you need to ask if there might be an alternative empirical explanation why you have made an observation (than the fact that the evidence exists) or why you have not made an observation (than the fact that the evidence does not exist). Put differently, you should be attentive for, respectively, ‘false positives’ and ‘false negatives’. An example of a false positive – where you think you have observed evidence while it is not present – might be when you derive an observation from an interview but your interviewee had a motive not to tell you the truth. An example of a false negative might be where you have not observed expected evidence of a meeting between a policymaker and a lobbyist while the meeting has actually happened, because the meeting was deliberately kept out of the records. To minimize the risk of making false positives or false negatives, you need to carefully assess the reliability of a source. This you can do by thinking about the role of your source, its proximity to the actual activity you are seeking evidence of, and the credibility of the source in relation to the activity you are tracing. Concerning the role, you need to ask if and how your source was involved in the activity you are tracing, and if they should have known of this activity. Moreover, you need to ask how proximate your source was to the activity – that is, how many steps removed from the action were they. This links to the well-known division between primary and secondary sources. Usually, a primary source should have a better, first-hand knowledge of the activity you are interested in and should therefore be considered more reliable than a secondary source, who might have received or transmitted information with some distortion. Finally, you should assess the reliability of your source. This means asking the question whether your source could have any motives to deceive you. For example, could your source be inclined to minimize their role in an activity (or, the other way around, to brag about their part)? Or could they be inclined to shelter someone else in the story you are studying? Moreover, you cannot only increase your trust in evidence by carefully assessing individual observations. You can also boost your confidence by looking for different observations for the same (predicted) evidence. This is also called ‘triangulation’. The more different observations you find for some (predicted) evidence, the more certain you can be about the actual presence (or absence) of this evidence. However, this general rule comes with two caveats. First, these different observations must be independent from each other. If an interviewee confirms an observation that you have made from the minutes of a meeting, this can increase your confidence in evidence only if this interviewee did not get their information from the minutes themself. Second, there are ‘diminishing returns’ in finding extra observations for evidence. Making a third independent observation about evidence adds more additional confidence in the existence of evidence than making a tenth observation.

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In sum, the key message about collecting and evaluating evidence is that the detailed a priori conceptualization and operationalization that is required in process tracing goes hand in hand with a more thorough evaluation of evidence. Not all observations are evidence, neither is all evidence equal. Being aware of this before, during and after collecting evidence is a crucial prerequisite for good process tracing, and transparently reflecting upon this is essential for presenting a sound single case study.

6.4 Conclusion and discussion In this chapter, we have argued that process tracing is a method that enables students to do better single case studies by being transparent and reflecting critically about every decision in the research process before, during and after the study. It has been the great merit of several methodological scholars, and Beach and Pedersen (2019) in particular, to develop process tracing in such a way that it responds to most methodological criticisms on single case studies. However, a risk of their strict prescriptions for process tracing is that it becomes very hard to meet them, especially for undergraduate students. In this chapter we adopted a more pragmatic approach towards process tracing and have argued that there is considerable value in observing the process-tracing rule book in spirit rather than to the letter. This means that students can apply the process-tracing logic in a pragmatic fashion by at least observing the following steps: linking a case with a certain literature and theory; translating a theory to testable causal propositions; and evaluating the theoretical and empirical value of evidence. In concluding this chapter, we also want to address two disadvantages of the method. The first is that process tracing only allows you to draw conclusions about the one case under scrutiny. Indeed, as we have already mentioned in section 6.4, process tracing only allows you to make a judgement about the significance of one (set of) cause(s) and one mechanism for the outcome Y in your selected case. It does not tell you much about the value of other causes and causal mechanisms, nor does it allow you to make strong claims about the application of this mechanism in another case. Sometimes this is not a disadvantage, as we may be interested in only having one good explanation for our single case. At other times, we may also be interested in ‘generalization’. Process tracing in its strict interpretation, as proposed by Beach and Pedersen, has little ambition to generalize. Yet, at the same time, researchers are often confronted with the question of how generalizable their explanation is, which might be a frustrating question in a peer-review process or during an oral thesis defence. In fact, it is probably also one of the most commonly given comments by (under)graduate thesis supervisors: ‘what can we learn from this case?’. Nowadays, two main approaches exist that attempt to move towards generalization. The first is called the ‘snowballing outward’ strategy to test the boundaries of the mechanism’s application (Beach & Pedersen, 2019, pp. 129 ff.). Given the practical limitations and demands of process tracing, it is nearly impossible to test the mechanism in all similar cases that you can think of. Rather, this approach implies strategically selecting other cases, starting with similar cases (where you know in advance that cause, outcome and contextual conditions are present), and then step by step moving towards ‘deviant’ cases (where the

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cause is present, but the outcome is not, or the other way around). Doing so might result in finding that the mechanism works in every context (homogeneity), in specific sub-sets (heterogeneity) or only worked in the analysed case (idiosyncrasy). Second, it is also becoming increasingly popular to link process tracing with other set-theoretical methods, especially qualitative content analysis (QCA), in so-called ‘mixed method’ designs (e.g., Beach & Rohlfing, 2018). The idea here is that the mechanism of a process-tracing exercise might grant valuable insights into which determinants we have to include in larger-N QCA exercises, or alternatively, that we use the outcome of QCA to investigate mechanistic causality in a specific case. Although both approaches might not be attainable for (under)graduate students in their research project, actively reflecting on these future research avenues will cover you against those generalization critiques. A second disadvantage is that process tracing is time and resource consuming. Process tracing demands that you have a good knowledge of both the literature and your empirical case at hand. It demands a lot of thought into conceptualizing cause, outcome and causal mechanism, as well as the operationalization of that mechanism. What adds to the challenge is that these efforts also open your research to more criticism, as you are explicit and transparent about the theoretical and methodological choices that you have made. All that takes place even before you can begin to delve into the evidence, for which the standards have also been raised and require thorough reflection. Based on these considerations, it is easy to think: is this all worth it? In our view, process tracing gives you the tools and mindset to think more critically and systematically about cases, more so than you would if you simply described the narrative of a case. That is a key advantage that we should harness. We would, therefore, encourage every student or researcher to follow these steps as well as possible, but not be discouraged when it is not always perfectly possible. The fact that you stop and reflect at several points during your research and think critically about questions such as: ‘what is this a case of?’; ‘what can be the theoretical relationship underlying it?’; ‘what would be good evidence?’; ‘is my evidence reliable?’ is incredibly valuable, even if you start from a pre-selected case of interest. If we want process tracing (and the wider ambition of more critical thinking about explanatory case studies) to become widely dispersed and shared, we must be flexible and give due credit where it is due: for studies that think critically and systematically about the causal explanations for some outcome or phenomenon, and in doing so provide a level of transparency that is extremely valuable in good, qualitative, social science. If process tracing leads us in that direction, it is well worth the effort.

6.5 Summary checklist After having read this chapter, the reader should be able to: • •

understand the different perspectives on measuring causality taken by process tracing compared to statistical and other covariational analyses; translate existing research into a process-tracing design, which requires transforming your initial research interest into a question focused on how the outcome

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of interest can be explained or how the identified cause has contributed to the outcome of interest; conceptualize a cause and outcome by means of a positive pole, mechanistic and specific definition, and conceptualize the causal process by unpacking the causal black box into a series of interlocking parts; make propositions about which predicted evidence should be found if a causal mechanism has explanatory value, informed by an assessment of prior confidence in the theory; argue what type of tests these propositions imply for our confidence in the causal mechanism, based on an assessment of theoretical relevance of the predicted evidence; assess the empirical trust that we can have in observations, by judging their empirical necessity and sufficiency, the reliability of the sources and the level of triangulation; and explain the key advantages and disadvantages of the method.

•

•

•

•

•

6.6 Doing process tracing yourself 6.6.1 Assignment As we explained in this chapter, a process-tracing analysis often starts with you encountering some specific outcome or general phenomenon in the social world that puzzles you, and about which you want to find an answer to the question: ‘how did this happen?’. For the exercise below, we cannot start from your personal issue of interest, as that would require individual mentoring. Instead, we will use an existing published case study that has not explicitly used process tracing but that allows you to apply the process-tracing toolbox. Imagine that, based on your participation in or fascination with Youth4Climate, you are interested in the question ‘how can citizen groups affect public policies?’. During your literature review, you came across the article ‘Public opinion and interest group influence: How citizen groups derailed the Anti-Counterfeiting Trade Agreement’ by Andreas Dür and Gemma Mateo published in 2014 in the Journal of European Public Policy (Dür & Mateao, 2014). In this article, the authors aim to explain how ‘resource-poor’ citizen groups ‘against all odds’ succeeded to derail ACTA, a proposed international agreement that would set international standards for the protection of intellectual property (do go ahead and read the paper, it’s interesting!). The authors do not use process tracing explicitly. But we argue that their research can be translated into a process-tracing design. Actually, we claim that their argument would work even better when presented as a process-tracing study. After reading the article, try to translate the authors’ theory to a causal mechanism based on the template below. Fill the rectangles with steps in the causal mechanism consisting of entities that engage in activities. Try also to find a contextual condition that needs to be present for the causal mechanism to work.

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Citizen group lobbying

203

Policy change

Contextual condition

Figure 6.7  Empty causal mechanism linking citizen group lobbying with policy change

6.6.2 Model response There is no single correct solution to this exercise. You might phrase the different steps slightly differently from how we do in the example solution below. Other citizen groups join the bandwagon Citizen group lobbying

Government responds to public demands

The public’s interest increases

Policy change

Business groups remain silent

Contextual condition: the issue needs to be one that is able to arouse emotions and to which groups can present an easy solution

Figure 6.8  Model response for causal mechanism linking citizen group lobbying with policy change This figure translates Dür and Mateo’s ‘conjectures’ to steps in a causal mechanism. We argue that conceptualizing their theory as a causal mechanism improves their research design. This causal mechanism recognizes the ‘sequential’ nature of their conjectures, with which we mean that they happen at different points in time, where one step leads to the next. The causal mechanism starts with citizen group lobbying as the

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cause (X). A (few) citizen group(s) concerned about an issue engage(s) in outside lobbying to protest against, or campaign for, a certain issue. This sets in motion the causal mechanism (CM). In step 1, citizen group lobbying increases the salience of an issue and increases the public’s interest in the cause the citizen group is campaigning for. This leads to step 2, which actually consists of two paths. On the one hand, ‘the initial success of a campaign in terms of mobilizing the public leads to a bandwagon effect’ (p. 1204), whereby other public interest groups join the campaign as they want ‘to address issues that are dear to their broad base of potential supporters and/or members’. On the other hand, initial success ‘deters counteractive lobbying’ because business groups ‘may decide that it is better not to have their own name associated with a highly unpopular stance’ (p. 1204). In the third step, interest groups provide information about the public opinion on an issue to policymakers. Because many groups have joined the side of the initial citizen group(s), while the adversaries (business groups) remain silent, the signal that governments receive is that public opinion is firmly on the side of the initial citizen group(s). To maintain or increase their legitimacy, the government will change its policy stance in the direction of the citizen groups’ position (outcome, Y). A contextual condition for this causal mechanism that Dür and Mateo mention is that the issue needs to be one ‘that arouse[s] emotions and for which groups can present an easy solution’ (p. 1200).

6.7 Recommended reading Beach, D. and Pedersen, R. B. (2019). Process-tracing methods: foundations and guidelines (2nd edition). Ann Arbor, MI: University of Michigan Press. In this second edition of their textbook on process tracing, the authors give a very convincing introduction to the method. They discuss the metatheoretical foundations of the method and introduce the method step by step. Our discussion in this chapter is much indebted to Beach and Pedersen’s book. Students that want to immerse themselves more in the method are strongly advised to read the book itself. Bennett, A. and Checkel, J. T. (2015). Process tracing: from metaphor to analytical tool. Cambridge: Cambridge University Press. This volume is, alongside the aforementioned book by Beach and Pedersen, considered one of the standard works on process tracing. The authors discuss the philosophical roots of process tracing, discuss applications in a range of social science subfields (comparative politics, EU studies, Cold War history, etc.) and sketch an agenda for future process tracing research. Pedersen, R. B. and Reykers, Y. (2020). Show them the flag: status ambitions and recognition in small state coalition warfare. European Security, 29(1), 16–32.

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In this research article, the authors delve into the puzzling outcome of small countries contributing forcefully to US-led military interventions. Their literature review shows that many scholars have found proof of a desire for recognition and status ambitions as a potential explanation for this outcome, but how these ambitions actually make a small country decide to put its military troops at risk has so far remained a black box. They apply process tracing to develop a causal mechanism based on this status theory and test it on the case of Belgium’s participation in the military intervention in Iraq and Syria. The authors follow several (not all) of the steps presented in this chapter, both in the development and in the testing of their causal mechanism. Winward, M. (2021). Intelligence capacity and mass violence: evidence from Indonesia. Comparative Political Studies, 54(3–4), 553–84. This research article asks what explains regional variation in the frequency and form of mass categorical violence. Based on a thorough review of the literature on civil war violence, the author develops a theory about how low intelligence capacity can lead to more frequent state violence and higher rates of killing. This causal theory is then tested on the case of Central Java during the 1965–6 Indonesian Killings. Worth paying attention to when reading this article is how the author has developed a causal mechanism (clearly consisting of agents that engage in activities) and how he uses a variety of data sources (including archival data, official documents and interviews). Particularly noteworthy is how he reports on his evidence in the supplementary material added on the journal’s webpage. Centre for Development Impact. (2015). Applying process tracing in five steps. CDI Practice Paper 10. This paper shows how process tracing is used more and more outside academia strictu sensu; for example, for assessing the impact of international development interventions. Public administration and non-governmental organizations are increasingly required to demonstrate the positive effects of their policies or activities. Because statistical studies have limitations in terms of demonstrating causal impact as discussed in this chapter, they turn more and more to process tracing as a method for impact assessment. This paper discusses how process tracing can contribute to impact evaluation based on two applications.

6.8 References Beach, D. (2016). It’s all about mechanisms: what process-tracing case studies should be tracing. New Political Economy, 21(5), 462–72. Beach, D. (2018). Process tracing methods. In C. Wagemann, A. Goerres and M. Siewert (eds), Handbuch Methoden der Politikwissenschaft. Springer Reference Sozialwissenschaften. Wiesbaden: Springer. https://doi.org/10.1007/978-3-658-16937-4_43-1.

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Beach, D. and Pedersen, R. B. (2013). Process-tracing methods: foundations and guidelines. Ann Arbor, MI: University of Michigan Press. Beach, D. and Pedersen, R. B. (2016). Causal case study methods: foundations and guidelines for comparing, matching, and tracing. Ann Arbor, MI: University of Michigan Press. Beach, D. and Pedersen, R. B. (2019). Process-tracing methods: foundations and guidelines (2nd edition). Ann Arbor, MI: University of Michigan Press. Beach, D. and Rohlfing, I. (2018). Integrating cross-case analyses and process tracing in set-theoretic research: strategies and parameters of debate. Sociological Methods & Research, 47(1), 3–36. Bennett, A. (2015). Disciplining our conjectures: systematizing process tracing with Bayesian analysis. In A. Bennett and J. T. Checkel (eds), Process tracing: from metaphor to analytic tool. Cambridge: Cambridge University Press. Bennett, A. and Checkel, J. T. (2015). Process tracing: from metaphor to analytical tool. Cambridge: Cambridge University Press. Checkel, J. T. (2006). Tracing causal mechanisms. International Studies Review, 8(2), 362–70. Dür, A. and Mateo, G. (2014). Public opinion and interest group influence: how citizen groups derailed the Anti-Counterfeiting Trade Agreement. Journal of European Public Policy, 21(8), 1199–217. Friedrichs, J. and Kratochwil, F. (2009). On acting and knowing: how pragmatism can advance international relations research and methodology. International Organization, 63(4), 701–31. George, A. L. and Bennett, A. (2005). Case studies and theory development in the social sciences. Cambridge, MA: MIT Press. Iversen, T. and Soskice, D. (2006). Electoral institutions and the politics of coalitions: why some democracies redistribute more than others. American Political Science Review, 100(2), 165–81. Jackson, P. T. (2016). Causal claims and causal explanation in international studies. Journal of International Relations and Development, 20, 689–716. King, G., Keohane, R. O. and Verba, S. (1994). Designing social inquiry: scientific inference in qualitative research. Princeton, NJ: Princeton University Press. Liu, T. & Woo, W. T. (2018). Understanding the U.S.-China trade war. China Economic Journal, 11(3), 319–40. Marx, A., Rihoux, B. and Ragin, C. (2014). The origins, development, and application of qualitative comparative analysis: the first 25 years. European Political Science Review, 6(1), 115–42. McCarty, T. W. (2021). Methods can be murder: a metaphorical framework for teaching research design. Journal of Political Science Education, 17(4), 623–40. Norman, L. (2015). Interpretive process-tracing and causal explanations. Qualitative and Multi-Method Research, 13(2), 4–9. Obinger, H. and Schmitt, C. (2019). Total war and the emergence of unemployment insurance in western countries. Journal of European Public Policy, 27(12), 1879–901.

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Pedersen, R. B. and Reykers, Y. (2020). Show them the flag: status ambitions and recognition in small state coalition warfare. European Security, 29(1), 16–32. Reykers, Y. and Beach, D. (2017). Process-tracing as a tool to analyse discretion. In T. Delreux and J. Adriaensen (eds), The Principal Agent Model and the European Union (pp. 255–82). London: Palgrave. Rihoux, B. and Ragin, C. C. (2008). Configuration comparative methods: qualitative comparative analysis and related techniques. London: SAGE. Rohlfing, I. (2012). Case studies and causal inference: an integrative framework. London: Palgrave. Rosato, S. (2003). The flawed logic of democratic peace. American Political Science Review, 97(4), 585–602. Sarmento, H., Anguera, M. T., Pereira, A., Marques, A., Campaniço, J. and Leitao, J. (2014). Patterns of play in the counterattack of elite football teams: a mixed-method approach. International Journal of Performance Analysis in Sport, 14, 411–27. Van Evera, S. (1997). Guide to methods for students of political science. Ithaca, NY: Cornell University Press.

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7 Qualitative Comparative Analysis: A Qualitative Method for Uncovering Complex Causal Relations Tim Haesebrouck

Why are radical right parties successful in some countries but not in others? Why does the United Nations Security Council respond strongly to some humanitarian crises, while it does not react to others? Why did democracy survive in some European countries during the interwar period, while it broke down in others? As a (social) scientist, you will often be confronted with a puzzling variation in theoretically, empirically or substantively interesting phenomena. In 1987, Charles Ragin introduced a tool that can help you explain such puzzling variation: qualitative comparative analysis (QComA). QComA allows you to systematically compare an intermediate to large number of cases and identify the differences between cases in which an effect occurred and cases in which the same effect did not occur. Hereby, QComA can help you uncover complex causal relationships and find what combinations of conditions consistently result in a specific outcome.

7.1 Chapter objectives In this chapter, you will be introduced to the original ‘crisp set’ version of QComA (which works with binary data). More specifically, you will learn how to use this crisp set version to uncover complex causal relations. • •

The chapter starts by introducing the main features of QComA and discussing the type of complex causal relations that you can uncover by applying it. Subsequently, you will learn how to apply QComA in your research. Using a real-life example, the chapter walks you through the main steps in a study that applies QComA.

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•

The chapter finishes with an exercise on how to develop a QComA research design, after which an annotated bibliography will guide you in selecting additional readings on QComA.

7.2 Key features, debates and historical developments QComA was first introduced by the American sociologist Charles Ragin in the 1987 book The Comparative Method. In the decades following publication, QComA became increasingly popular among social scientists. An analysis of the Web of Science Core Collection database shows there are 738 publications that include ‘qualitative comparative analysis’ and ‘QComA’ in ‘topic’. Strikingly, more than 65% of QComA publications were published during the past five years, and 90% were published during the past ten years, which is indicative of the increasing popularity of the method. The majority of QComA publications can be found in the disciplines of political sciences (19%), business (19%), sociology (12%), interdisciplinary social sciences (10%) and public administration (8%).

140 120 100 80 60 40 20

2001

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

0

Figure 7.1  Inclusion of term ‘QComA’ and ‘qualitative comparative analysis’ in ‘topic’ in Web of Science core collection over time Over the years, QComA was subject to numerous innovations, of which the introduction of the fuzzy set version stands out as the most important (Ragin, 2000). As QComA’s popularity increased, different approaches to the method have emerged, accompanied

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by (sometimes heated) debates on the appropriate standards of good practice in QComA. It goes beyond the scope of this chapter to discuss all the different variants of QComA and the different approaches to the method (Thomann & Maggetti, 2020). Therefore, you will be introduced to only the original crisp set version of QComA and will learn how this method can be used to uncover causal relations as defined by regularity theories of causation. In terms of ontology and epistemology (see also Chapter 1), this chapter hereby positions itself within a neo-positivist understanding of science, which assumes that social phenomena are governed by observable regularities (Mello, 2021). However, it is important to note that QComA has been linked to other metatheoretical perspectives, such as critical realism (Haesebrouck & Thomann, 2021; Rutten, 2021). The remainder of this section will introduce the main features of QComA, after which you will learn more about the main objective that can be pursued when applying QComA: uncovering complex causal relations.

7.2.1 Main features of QComA QComA was originally developed by Ragin (1987: 83–5) as a mid-way between qualitative and quantitative approaches to comparative research. QComA indeed embodies some characteristics generally associated with quantitative approaches. In line with quantitative approaches, QComA is based on rigorous and replicable analytical techniques. Moreover, it allows you to compare ‘more than a few cases’, arrive at parsimonious explanations, and produce (modest) generalizations (Ragin, 1987, pp. 83–4; Rihoux, 2003, pp. 352–6; Rihoux & Marx, 2013, p. 186). Nevertheless, the first letter in the acronym stands for qualitative and there is a broad consensus that QComA belongs more to the qualitative research tradition. Each case is examined as a complex whole, as a specific combination of an outcome (the phenomenon under investigation) and conditions (the factors that are expected to explain the outcome). Moreover, the cases never entirely disappear during the analysis and it is always possible to identify the specific cases that correspond to the combinations of conditions. QComA also requires a close affinity with the cases to adequately perform several crucial steps in the analysis. Most importantly, in-depth case knowledge is very important when dichotomizing the conditions and outcome and when solving the contradictory configurations. Furthermore, in the crucial interpretation phase, the results of a QComA analysis and the specific paths to the (absence of) the outcome must always be related back to the specific cases. In line with more traditional qualitative research methods, QComA adopts a ‘causes of effects’ approach to explanation and aims to explain the presence of specific outcomes in the cases under investigation (Mahoney & Goertz, 2006, p. 230; Oana et al., 2021, p. 7). This contrasts with the ‘effects-of-causes’ approach, which is followed in quantitative research and seeks to ‘estimate the average effect of one or more causes across a population of cases’ (Mahoney & Goertz, 2006, p. 231). The difference between both approaches can be illustrated by the specific way in which qualitative and quantitative researchers would rephrase the general question ‘what causes the electoral success of

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radical right parties?’. Qualitative researchers would translate this general question into the more specific question: ‘what explains the success of radical right parties in one or two specific elections?’. In contrast, quantitative researchers would rather focus on questions like: ‘what is the average effect of unemployment on the electoral success of radical right parties?’. In line with qualitative research, the objective of QComA is explaining why an outcome occurred in specific cases. However, QComA can examine a larger number of cases (generally ten to fifty) than more traditional qualitative methods. QComA is particularly suited for examining divergence puzzles, in which cases that look very similar display a puzzling variation in the outcome of interest (Day & Koivu, 2019). QComA can best be applied to a research puzzle that follows the following structure: ‘Why has outcome X occurred in some cases, but not in others?’. For example, a researcher that applies QComA would translate the general question ‘what causes the electoral success of radical right parties?’ into ‘why were radical right parties successful in some Western democracies, but not in others?’. The results of QComA will, then, show which combinations of conditions consistently result in the success of radical right parties and which combinations result in the absence of success. QComA will allow you to explain why an outcome occurred (or did not occur) in the cases under investigation and, hereby, helps researchers draw causal inferences. The specific understanding of causation that underlies QComA is discussed in further depth in the next subsection. At this point, it suffices to introduce one of the key features of the method: QComA is particularly apt at capturing a complex form of causal relations that is generally captured under the expression ‘multiple conjunctural causation’. This form of causal complexity has two dimensions: conjunctural causation and equifinality. •

•

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Conjunctural causation implies that a given condition plays only a causal role in combination with other conditions (Oana et al., 2021). In other words, conjunctural causation indicates that the impact of a causal condition usually depends on the presence or absence of other conditions. For example, studying is causally relevant for passing an exam, but studying will not always result in passing. There can be many instances in which students who studied did not pass an exam, but this does not imply that studying is not causally related to passing. The impact of studying on passing might depend on whether other conditions are present or absent. For example, studying might only result in passing in combination with being smart or attending the classes. Equifinality or multiple causation implies that there can be several paths towards the same outcome. Outcomes can have a plurality of causes: different combinations of conditions can lead to the same outcome. The combination of studying and being smart might, for example, constitute a first path towards passing an exam. However, there might also be a second path towards passing, in which it does not matter whether a student is smart. For example, students that studied and had some luck with the exam questions might also pass the exam, even if they are not very smart.

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Figure 7.2 provides an example of a multiple conjunctural causation, which includes two causal paths towards the outcome ‘passing the exam’. The first path combines studying with being smart, the second path combines studying with having some luck with the exam questions.

Smart

Study

Pass Exam

Luck with Questions

Causal relation

Figure 7.2  Multiple conjunctural causation QComA can, thus, be used to examine why an outcome occurred in some cases but not in others and, thereby, find out which combinations of conditions lead to an outcome. More specifically, the method allows you to systematically compare an intermediate to large number of cases by applying two analytical tools that are introduced in the second section: truth tables and logical minimization. However, QComA is more than a set of analytical techniques and also refers to a broader research approach. An important feature of QComA as a research approach is that research is considered an iterative process of going back and forth between theoretical ideas and empirical evidence. QComA proceeds in five main stages, but researchers regularly need to reconsider decisions made at earlier stages of their research based on information gained in later stages (cf. section 7.3 Doing qualitative comparative analyis step by step).

7.2.2 Causal inferences and qualitative comparative analysis QComA allows researchers to explain why an outcome occurred in some cases but not in others. More specifically, QComA allows the uncovering of complex causal relations by systematically comparing an intermediate to large number of cases and identifying the differences between cases in which the phenomenon of interest (or outcome) occurred and cases in which the same outcome did not occur. In experimental studies, scientists also use comparisons to find causal relations

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(Ragin & Rihoux, 2009, p. xvii). For example, we know that dropping the temperature of water below 0°C causes water to freeze because researchers have compared the state of water below 0°C with the state of water above 0°C. Structurally comparing real-life cases constitutes a ‘crude substitute’ for such experiments, which are rarely possible in the social sciences (Ragin & Rihoux, 2009, p. xviii). ‘Causal’ and ‘causation’ are often treated as ‘dirty’ words in observational (i.e., nonexperimental) studies, mainly because of the many pitfalls and challenges from drawing causal conclusions for observational data (Hernán, 2018). However, causation seems too important for causal inference not to be at least one of the objectives of social science research. Causation can be considered ‘the most fundamental connection in the universe’ (Mumford & Anjum, 2013). It links actions to consequences, it is the basis of explanation, prediction and strategic intervention in the world around us. In the words of the eighteenth-century philosopher John Hume: ‘causation is the cement of universe’ (Mackie, 1974). QComA can help you draw causal conclusions from observational data by offering tools that will allow you to systematically compare empirical cases. Methodological publications on QComA consistently emphazise QComA’s ability to uncover complex causal relations as one of the method’s main strenghts and distinctive charachteristics (Ragin, 1987, pp. 23–33; Rihoux, 2013, p. 239). Strikingly, however, textbooks on QComA do not generally provide a clear defintion of causation or make clear which types of evidence confirm (or disconfirm) a causal argument (Rohlfing & Zuber, 2019, p. 2). This is unfortunate, given that a basic understanding of the type of causal relations that can be established with QComA is necessary for understanding the goals that can be pursued with QComA, grasping how the analytical tools used in QComA can help achieve these goals and correctly interpreting the results of QComA. To this day, a multitude of plausible theories of causation exist, many of which consider causes as difference-makers of their effects (Baumgartner, 2020). Difference-making means that a condition is ‘a cause when its presence or absence makes a difference to the presence or absence of the effect’ (Rohlfing & Zuber, 2019, p. 6). Given that QComA is geared towards identifying such difference-makers, it can best be underpinned by a difference-making theory of causation. More specifically, QComA can be underpinned by a regularity theory of causation, which suggests that causal relations meet three criteria: (1) the effect must regularly follow the cause; (2) the cause must temporarly precede the effect; and (3) the cause and the effect must be proximate in time and space (spatio-temporal contiguity) (Baumgartner, 2006; Psillos, 2009). The modern regularity theory of causation that can underpin QComA builds on John Mackie’s INUS-theory of causation (for the most recent version of this theory, cf. Baumgartner & Falk, 2019; Mackie, 1965, 1974). Necessity and sufficiency are crucial concepts in this theory, which assume that the same cause is always accompanied by the same effect (1) and that an effect only occurs if a cause is present (2) (Graßhoff & May, 2001, p. 85). The first principle suggests that causes are sufficient for their effects, the second that causes are necessary. In order to understand regularity theories of causation, it is important to be familiar with the concepts of sufficient and necessary conditions:

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•

•

215

A condition is sufficient for an outcome if the outcome is always present when the condition is present. In other words, the condition can never occur without the outcome. Studying is, for example, sufficient for passing an exam if every student who studied for the exam passed the exam. A condition is necessary for an outcome if this condition is always present when the outcome is present. In other words, the outcome can never occur without the condition. Studying is, for example, necessary for passing an exam if every student who passed the exam studied for the exam.

As captured by the expression ‘multiple conjunctural causation’, causal relations are generally more complex than one condition being sufficient and/or necessary for an effect. The complexity of causal relations has two dimensions. In line with the notion of conjunctural causation, conditions are rarely sufficient on their own, but only in combination with other conditions. Causes are, thus, sufficient combinations of conditions rather than individual conditions. In line with the notion of equifinality or multiple causation, there is rarely only one combination of conditions that is sufficient for an outcome and there are only very few conditions that are part of every path towards the outcome. In consequence, rather than single conditions, the disjunction of all sufficient combinations is necessary for an outcome. Regularity theories of causation suggest that causally relevant conditions are part of sufficient combinations (e.g., studying and being smart) and that the disjunction of these sufficient conjunctions is necessary for the outcome (e.g., every student that passed the exam has studied and either had luck with the questions or is smart). However, not all conditions that are part of a sufficient combination are causally relevant and not all sufficient combinations contain causally relevant conditions. Causally relevant conditions must meet two criteria: they must be indispensable parts of sufficient combinations (1) that are themselves indispensable in a necessary disjunction (2). The first criterion is the most relevant for the remainder of this chapter: a condition is only causally relevant if it is an indispensable or non-redundant part of a sufficient combination. More specifically, causally relevant conditions are part of a sufficient combination that would no longer be sufficient (i.e., consistently result in the outcome) in the absence of the condition. Studying is indispensable in the sufficient combination smart and study if smart students that do not study do not pass. Nonredundancy is essential for identifying causally relevant conditions because sufficient conditions can be conjunctively supplemented with arbitrary factors without altering their status as sufficient conditions (Baumgartner, 2015, p. 842). In consequence, every single feature of cases that display the outcome is part of a sufficient combination for this outcome. For example, if the combination of being smart and studying is sufficient for passing an exam, all smart students that studied will pass the exam. However, these students have a lot of other characteristics: they might have blue eyes, large feet, long hair. All these conditions are part of at least one sufficient combination for the outcome, but are not causally relevant for passing an exam.

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Conditions that are indispensable parts of sufficient combinations that are indispensable parts of a necessary disjunction that meet this criterion are difference-makers for the outcome: if a condition is indispensable in a sufficient combination that is indispensable in a necessary disjunction, this condition will be the only difference between cases of the presence of the outcome and cases of its absence (Baumgartner & Ambühl, 2020). QComA applies analytical techniques that can identify which combinations of conditions are sufficient for an outcome, and remove redundant conditions from sufficient combinations. Hereby, QComA allows researchers to identify difference-makers. To sum up, QComA allows you to systematically compare cases to identify the differences between cases in which the outcome occurred and cases in which it did not occur. Hereby, QComA allows researchers to uncover complex causal relations. More specifically, QComA results in solutions that: (1) represent multiple sufficient combinations of conditions for an outcome (i.e., multiple conjunctural causation); (2) include conditions that are indispensable parts of sufficient combinations that are indispensable parts of necessary disjuctions; and (3) can be interpreted as combinations of difference-makers for an outcome.

7.3 Doing qualitiative comparative analysis step by step QComA proceeds in five main stages. However, research with QComA follows an iterative logic. In QComA, you must regularly reconsider decisions made at earlier stages of your research based on information gained in later stages. The first step in a QComA is developing a research design (1). More specifically, you will need to formulate an appropriate research question, select relevant conditions and specify the case selection. Subsequently, you will need to operationalize and dichotomize your conditions and outcome (2). The third step consists of the construction of a (preliminary) truth table and solving the so-called ‘contradictory configurations’ (3). This generally requires reconsidering the previous two steps. In the fourth step, logical minimization is used to produce solutions that can be interpreted as complex causal relations (4). The last step of QComA consists of the interpretation of the results (5). A study that applies QComA does not usually stop after the first results are produced. The first solution that you arrive at generally will not provide a plausible explanation for the outcome of interest and you will need to reconsider earlier steps of your study. Figure 7.3 summarizes the five main stages and shows at which stages you might want to reconsider prior steps.

7.3.1 Research design The first step you need to take when you apply QComA is not specific to the method: the development of an appropriate research design. This step consists of the formulation of a research question, selecting conditions that can be relevant for explaining the outcome and specifying the case selection.

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1. Research Design

2. Operationalization & Dichotomization

3. Truth Table

Contradictory Configurations

4. Logical Minimization

5. Interpretation

Implausible Explanation

Iterative Step

Figure 7.3  Main steps in QComA As argued in the first section, QComA can help you explain why an outcome occurs in some cases, but not in others. The method is particularly suited for examining ‘divergence puzzles’. QComA can best be applied to a research question that follows the following structure: ‘Why has outcome X occurred in some cases, but not in others?’. QComA has, for example, been applied to examine the following research questions: • • •

Under which conditions did introduction of women’s suffrage occur before the First World War (early), and when only after the Second World War (late)? (Palm, 2013). Why do some [Green] parties – and not others – ultimately gain access to governmental participation? (Rihoux, 2006). Over the past two decades, the United Nations Security Council has responded more strongly to some humanitarian crises than to others. This variation in Security Council action raises the important question of what factors motivate United Nations intervention? (Binder, 2015).

Each of these research questions aims to explain why an outcome (early introduction of women’s suffrage, government participation, strong United Nations Security Council response) occurred in some cases but not in others.

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QComA only constitutes an appropriate methodological choice for solving divergence puzzles if a sufficiently large number of cases is available for empirical research. In-depth case studies should be preferred if the number of cases is small (i.e., below eight cases) or if the outcome does not vary among the available cases (i.e., only one or two positive or negative cases is available for empirical research) (cf. Beach & Pedersen, 2016 on case-study methods). To avoid the impact of the included conditions being confounded by non-included causal conditions, the cases should share a sufficiently large number of background characteristics (Berg-Schlosser & De Meur, 2009). These similarities are generally referred to as ‘scope conditions’, conditions that might be causally relevant, but are constant in the population under investigation (Ragin, 2000, pp. 43–63). Moreover, given that QComA is particularly apt at uncovering a complex form of causation, QComA can best be used if a researcher has theoretically or substantively founded expectations that different (multiple) combinations (conjunction) of conditions produce the outcome of interest (Schneider & Wagemann, 2012). Once an appropriate research question has been formulated, you must select the cases that will be included in the analysis. Case selection is strongly dependent on the research question, which determines the scope of the population from which cases can be selected. QComA is often applied to a research situation in which the population of interest only includes an intermediate (ten to fifty) number of cases. If cases are selected from a larger population, case selection should be guided by the research question and the theoretical framework. More specifically, researchers should aim to achieve enough variation for each of the included conditions and the outcome. As a general rule, Rihoux and De Meur (2009) suggest that each condition and the outcome should be present and absent in at least one-third of the cases. However, this will not always be possible in reallife research, in which the values of causally relevant conditions do not always display such a pattern of variation. Next to selecting cases, you also need to select plausible explanatory conditions (Berg-Schlosser & De Meur, 2009). Conditions are usually selected from existing theories, after conducting a thorough literature review. Researchers should aim to arrive at a good balance between the number of cases and the number of conditions, mainly to keep the number of logical remainders to a minimum and avoid overly complicated solutions. With an intermediate number of cases (ten to fifty), it is common practice to include four to seven conditions in the analysis. However, a comprehensive literature review will generally result in a much higher number of possible explanatory conditions. There are several strategies to deal with such an abundance of potential explanatory conditions (Berg-Schlosser & De Meur, 2009; Mello, 2021). First, conditions that do not vary between the cases should not be included in the analysis. Second, other research methods (e.g., the MSDO/MDSO procedure or statistical techniques) can be used to find which conditions have most explanatory potential (Haesebrouck, 2017a). Third, the researcher can test several different models to inductively arrive at the model that provides the best explanation for the variation in the outcome. Fourth, if a rich body of theoretical and substantive knowledge is available, it might be possible to select

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conditions that can be combined in a theoretical model that suggests there are several combinations of conditions that lead to the same outcome (Amenta & Poulsen, 1994). The goal of the QComA is, then, to test and refine such a multi-causal theoretical model.

Case study 7.1 QComA: military participation in the Libya intervention From here onwards, a real-life example will be used to illustrate the main steps of QComA. The example is based on a study on burden sharing during the 2011 military intervention in Libya, which focuses on the following research question: ‘Why did some states participate in the Libya operation while others did not?’ (Haesebrouck, 2017b, 2019b). Cases were selected that share enough background characteristics to allow for meaningful comparison. Most importantly, the selected countries were all established democracies, which have comparable decision-making procedures on the use of force. Additionally, NATO members that do not have fighter jets were excluded from the analysis (given that the main component of the operation was an air mission). Lastly, several cases were excluded because of data restrictions (i.e., the lack of comparable data on public opinion). Conditions were selected that can be combined in a theoretical model that expects multiple combinations of causally relevant conditions to lead to ‘military participation’. First, collective action theory expects that small states will have few incentives to contribute to collective military efforts. In consequence, only states with substantial military capabilities were expected to have an incentive to participate. Whether or not military powerful states actually participated was expected to depend on domestic conditions. Countries were expected to participate in the operation if either the general public supported military participation or if the government was not responsive to popular opinion because elections were far away and parliament was not involved in the deployment decision. Figure 7.4 summarizes these expectations. Military Participation

Public Support Military Power No Parliamentary Veto Power

No Proximate Elections

Figure 7.4  Theoretical framework

7.3.2 Operationalization and dichotomization Dichotomization, or the assignment of a score of 1 or 0 to the cases on the conditions and outcome, is of utmost importance in QComA. In QComA, you will need to carefully

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justify the dichotomization of cases on substantive and/or theoretical grounds (Rihoux & De Meur, 2009; Schneider & Wagemann, 2012). This requires a clear definition of how the conditions and outcome can be observed empirically, after which researchers can empirically examine whether the conditions and the outcome are present in each case. The dichotomization of the outcome and the conditions can draw on qualitative information, gained from, for example, interviews, documents or historical archives, or on quantitative indicators (Mello, 2021, p. 86). More specifically, dichotomization can be based on qualitative criteria or on a combination of a quantitative indicator with a dichotomization threshold. Dichotomization thresholds are ideally based on accepted scientific knowledge (Schneider & Wagemann, 2012, p. 32). If this is not possible, researchers can resort to a mechanical cut off point, like the mean or median. It is important to avoid ‘artificial’ cut off points, which assign different values to cases that have very similar values on the raw data. Cases just above or below the threshold should be carefully examined, to assess whether the threshold is fixed at an appropriate value. In the study on the Libya operation, the outcome, ‘military participation’ and one condition, ‘parliamentary veto’, were dichotomized based on qualitative criteria. The dichotomization of the other three conditions, ‘nearby elections’, ‘military power’ and ‘public support’, was based on a quantitative indicator and a dichotomization threshold. Table 7.1 presents the raw and binary data. •

Military Participation (MP): cases are assigned a score of 1 on the outcome if they participated in the air campaign.

•

Parliamentary Veto (PV): cases are assigned a score of 1 on this condition if Parliament had a veto on the decision to participate in the military operation.

•

Military Capabilities (MC): operationalized as the average military spending in the twenty years prior to the operation. The dichotomization threshold was fixed at 20,000, in the large gap in the raw data between Italy and Turkey. Hereby, the five NATO members that are considered military powerful in the academic literature (the United States, the United Kingdom, France, Germany and Italy) were assigned a score of 1 on military power.

•

Proximate Elections (PE): was operationalized as the days until the next general election. The dichotomization threshold was fixed at one year (365 days), given that literature generally suggests that governments become more responsive in election years.

•

Public Support (PS): operationalized as the percentage of the general population that supported the military operation. The dichotomization threshold was fixed at 50%, assigning NATO members in which over half of the population supported the operation a score of 1.

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Table 7.1  Raw and binary data MP

PV

crisp

crisp

raw

crisp

raw

crisp

raw

crisp

caw

crisp

Belgium

1

0

5277

0

1184

0

70

1

1879

0

Bulgaria

0

0

979

0

841

0

46

0

939

1

France

1

0

54027

1

458

0

58

1

931

1

Germany

0

1

44114

1

925

0

37

0

1569

0

Italy

1

1

32748

1

758

0

47

0

446

1

Netherlands

1

1

10133

0

1180

0

65

1

2003

0

Norway

1

1

4589

0

912

0

70

1

2777

0

Poland

0

0

5368

0

206

1

35

0

1786

0

Portugal

0

0

3687

0

207

1

57

1

1727

0

Romania

0

0

2214

0

624

0

39

0

1215

0

Slovakia

0

1

1163

0

1183

0

30

0

1663

0

Sweden

1

1

5715

0

1282

0

69

1

2471

0

Turkey

0

1

13983

0

87

1

23

0

569

1

United Kingdom

1

0

49596

1

1511

0

53

1

2156

0

United States

1

0

531831

1

596

0

59

1

6640

0

Case

MC

PE

PS

TH

MP: Military Participation; PV: Parliamentary Veto; MC: Military Capabilities; PE: Proximate Elections; PS: Public Support; TH: Threat. Sources: Haesebrouck (2017b, 2019b)

7.3.3 Constructing a truth table and solving contradictory configurations After the cases have been dichotomized, a first truth table can be constructed. A truth table lists all logically possible combinations of conditions. A condition can be either present or absent in a truth table row. A value of 1 indicates that a condition is present, a value of 0 indicates that a condition is absent. The number of possible combinations of conditions and, thus, truth table rows depend on the number of included conditions: if ‘n’ is the number of conditions, there are 2n logical possible combinations of conditions. Table 7.2 presents the truth table of the example on the Libya operation. Each row corresponds to a specific combination of conditions. Row 1, for example, corresponds to the combination in which ‘military capabilities’ and ‘proximate elections’ are absent and ‘public support’ and ‘parliamentary veto’ are present. Four conditions are included in the study, resulting in sixteen possible combinations of conditions and, therefore, sixteen truth table rows.

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Table 7.2  Truth table Conditions Row

Outcome

MC

PE

PS

PV

MP

 1

0

0

1

1

1

Belgium, Netherlands, Norway, Sweden

Cases

 2

1

0

1

0

1

France, United Kingdom, United States

 3

0

0

0

0

0

Bulgaria, Romania, Slovakia

 4

0

1

0

0

0

Poland

 5

0

1

0

1

0

Turkey

 6

0

1

1

0

0

Portugal

 7

1

0

0

1

C

Germany (0), Italy(1)

 8

1

1

1

1

R

/

 9

1

1

0

1

R

/

10

1

1

1

0

R

/

11

1

1

0

0

R

/

12

1

0

0

0

R

/

13

1

0

1

1

R

/

14

0

0

1

0

R

/

15

0

0

0

1

R

/

16

0

1

1

1

R

/

MP: Military Participation; PV: Parliamentary Veto; MC: Military Capabilities; PE: Proximate Elections; PS: Public Support.

Each case is assigned to the truth table row that corresponds to the combination of conditions that characterizes this case. For example, Belgium is included in row 1 of the truth table because it is a case in which ‘military capabilities’ and ‘proximate elections’ are absent and ‘public support’ and ‘parliamentary veto’ are present. Nine of the sixteen rows in the truth table are empty. Such empty truth table rows are called logical remainders: combinations of conditions that do not correspond to empirical cases. In general, such logical remainders cannot be avoided in empirical research. Because the diversity of the available cases is naturally limited, there will not always be an empirical case for every combination of conditions (Berg-Schlosser & De Meur, 2009, p. 27). However, logical remainders have negative consequences for the conclusions that can be drawn from QComA. More specifically, because of logical remainders, it is not always possible to determine for every condition whether it is causally relevant for the outcome (cf. section 7.3.4 Conservative, parsimonious and intermediate solution). An outcome value is assigned to every truth table row that does not correspond to a logical remainder. A value of 1 is attributed to truth table rows that only include cases in which the outcome is present and, thus, correspond to sufficient combinations for the outcome. The outcome, military participation, is present in all the cases included in row 1 and 2, which are therefore assigned a score of 1 in the outcome column. Rows that only

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include cases of the absence of the outcome are assigned a value of 0, which indicates that they are sufficient for the absence of the outcome. The outcome was absent in all cases included in rows 3 to 6, which are therefore assigned a score of 0 in the outcome column. Next to logical remainders and rows that are sufficient for the outcome or the outcome’s absence, truth tables can also include contradictory configurations: truth table rows that both include cases of the outcome’s presence and cases of the outcome’s absence. These truth table rows cannot be assigned a score of 1, given that they are not sufficient for the outcome. However, they can also not be assigned a score of 0, because they are not sufficient for the outcome’s absence. Contradictory rows are assigned a C in the outcome column. The truth table presented in Table 7.2 includes one contradictory configuration: row 1 includes Italy, which participated in the operation, and Germany, which did not participate in the operation. Researchers will almost always be confronted with contradictory truth table rows in the course of their research. In real-life research, the number of contradictory configurations can (and probably will) be much higher than in this textbook example. Such contradictions offer valuable information on the pattern of variation of the outcome and the explanatory power of the included conditions. More specifically, contradictory configurations indicate that causally relevant conditions are not included in the study, that not all background conditions are shared among the cases or that the outcome and/or conditions should be measured differently. Although it will not always be possible to arrive at a truth table without contradictory rows, researchers should attempt to resolve as many contradictions as possible. At this point, it is important to re-iterate that research with QComA follows an iterative logic. Contradictory configurations provide clues on what aspects of a study could be redesigned to arrive at a better explanation for the variation in the outcome. More specifically, a thorough look at the cases in contradictory truth table rows can help decide on an appropriate strategy to resolve contradictory configurations. Some of the cases that are included in contradictory rows might not share the same scope conditions as the other cases, which can be a reason to re-specify the population and exclude some of the cases from the study. Contradictory configurations can also indicate that an important condition is missing in the theoretical model and can be resolved by adding or replacing a condition. Lastly, contradictory configurations can indicate that a condition or the outcome was not adequately conceptualized, operationalized or dichotomized. If this is the case, researchers might want to reconsider the operationalization of their conditions or the dichotomization threshold. The two most commonly used strategies for solving contradictory configurations are reconsidering the operationalization or dichotomization of the conditions or outcome (1) and replacing or adding conditions to the theoretical model (2). Both strategies can be used to solve the contradictory configuration in the truth table. First, the contradictory configuration can be solved by changing the dichotomization threshold of the condition ‘public support’. One important difference between

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Germany and Italy is that the German public was far more negative towards military participation than the Italian public. More specifically, while Germany had a value of 37% on ‘public support’, Italy had a value of 47%, which is only just below the dichotomization threshold. By dropping the threshold from 50% to 45%, in the significant gap between Bulgaria and Romania, Italy receives a score of 1 on ‘public support’ and is located in a different row from Germany. This results in the truth table presented in Table 7.3, which does not include contradictory configurations. However, there do not seem to be good reasons for dropping the dichotomization threshold of ‘public support’ below 50%. Moreover, as will be argued in the last subsection of this chapter, the results with the new threshold do not seem substantively meaningful and would provide reasons for the researcher to reiterate the research cycle.

Table 7.3  Truth table Libya with new threshold ‘public support’ Row

MC

PE

PS

PV

MP

Cases

 1

0

0

1

1

1

Belgium, Netherlands, Norway, Sweden

 2

1

0

1

0

1

France, United Kingdom, United States

 3

1

0

1

1

1

Italy

 4

0

0

0

0

0

Romania, Slovakia

 5

0

0

1

0

0

Bulgaria

 6

0

1

0

0

0

Poland

 7

0

1

0

1

0

Turkey

 8

0

1

1

0

0

Portugal

 9

1

0

0

1

0

Germany

10

1

1

1

1

R

/

11

1

1

0

1

R

/

12

1

1

1

0

R

/

13

1

1

0

0

R

/

14

1

0

0

0

R

/

15

0

0

0

1

R

/

16

0

1

1

1

R

/

MP: Military Participation; PV: Parliamentary Veto; MC: Military Capabilities; PE: Proximate Elections; PS: Public Support.

Second, the contradictory configuration can also be resolved by adding a condition to the truth table. More specifically, researchers confronted with this contradiction can examine whether there are differences between Italy and Germany that are not included in the theoretical model. Because of Italy’s geographical location, Libya constituted a greater threat to Italy than to Germany. Given that this might explain why Italy participated in the operation, a new condition was added to the study: ‘threat’. This condition was operationalized as the distance between the cases and Libya. The threshold was fixed at 1,000 km, in

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the significant gap between Bulgaria and Romania. The resulting truth table is presented in Table 7.4, in which all contradictory configurations were resolved. This truth table will be used in the remainder of the chapter, except when explicitly stated otherwise.

Table 7.4  Truth table Libya with ‘threat’ Row

MC

TH

PE

PS

PV

MP

 1

0

0

0

1

1

1

Cases Belgium, Netherlands, Norway, Sweden

 2

1

0

0

1

0

1

United Kingdom, United States

 3

1

1

0

1

0

1

France

 4

1

1

0

0

1

1

Italy

 5

0

0

0

0

0

0

Romania, Slovakia

 6

0

0

1

0

0

0

Poland

 7

0

0

1

1

0

0

Portugal

 8

0

1

0

0

0

0

Bulgaria

 9

0

1

1

0

1

0

Turkey

10

1

0

0

0

1

0

Germany

11

1

0

0

0

0

R

/

12

1

1

0

0

0

R

/

13

1

0

1

0

0

R

/

14

0

1

1

0

0

R

/

15

1

1

1

0

0

R

/

16

0

0

0

1

0

R

/

17

0

1

0

1

0

R

/

18

1

0

1

1

0

R

/

19

0

1

1

1

0

R

/

20

1

1

1

1

0

R

/

21

0

0

0

0

1

R

/

22

0

1

0

0

1

R

/

23

0

0

1

0

1

R

/

24

1

0

1

0

1

R

/

25

1

1

1

0

1

R

/

26

1

0

0

1

1

R

/

27

0

1

0

1

1

R

/

28

1

1

0

1

1

R

/

29

0

0

1

1

1

R

/

30

1

0

1

1

1

R

/

31

0

1

1

1

1

R

/

32

1

1

1

1

1

R

/

MP: Military Participation; PV: Parliamentary Veto; MC: Military Capabilities; PE: Proximate Elections; PS: Public Support; TH: Threat.

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7.3.4 Conservative, parsimonious and intermediate solution Next to truth tables, QComA builds on another analytical tool: logical or Boolean minimization. While truth tables are used to find the combinations that are sufficient for the (absence of) the outcome, logical minimization allows the elimination of redundant parts of these sufficient combinations. Hereby, logical minimization allows the identification of the conditions that are difference-makers for the outcome and, according to regularity theories of causation, are causally relevant (cf. section 7.2.2 Causal inferences and qualitative comparative analysis). Logical minimization builds on a language that follows the conventions of Boolean Algebra and set theory and can be used to express sufficient and necessary conditions: • • • • •

[~] indicates that a condition or outcome is absent; [*] represents logical AND, which indicates the conjunction of two conditions; [+] represents logical OR, which indicates the disjunction of two (combinations of) conditions; → indicates that a (conjunction of) condition(s) is sufficient for an outcome; ← indicates that a (disjunction of) condition(s) is necessary for an outcome; and

•

↔ indicates a necessary disjunction of sufficient conjuncts.

The truth table indicates that there are four sufficient combinations for the outcome. Together, these combinations constitute a necessary disjunction for the outcome, given that the outcome occurs only if one of these combinations is present. This necessary disjunction of sufficient combinations can be expressed as follows: ~MC*~TH*~PE*PS*PV + MC*~TH*~PE*PS*~PV + MC*TH*~PE*PS*~PV + MC*TH*~PE*~PS*PV ↔ MP This formula constitutes a (very complex) description of the cases in which the outcome is present, but cannot be causally interpreted because it includes conditions that are not indispensable for the sufficiency of the solution. For example, TH is not indispensable in the third disjunct (MC*TH*~PE*PS*~PV) given that the second disjunct (MC*~TH*~PE*PS*~PV) shows that MC*~PE*PS*~PV is sufficient in the absence of TH. Causally interpreting a necessary disjunction of sufficient combinations requires eliminating redundant conditions from sufficient combinations. Removing redundancies from logical expressions can be achieved with logical minimization. In QComA, this is achieved by applying the following principle: ‘if two sufficient combinations only differ in one condition, with this condition being present in one combination and absent in the other, then the condition that distinguishes the two combinations can be considered irrelevant and removed from the combination’ (Ragin, 1987, p. 93). In our example, the sufficient combinations in truth table row 2 (MC*~TH*~PE*PS*~PV) and truth table row 3 (MC*TH*~PE*PS*~PV) are identical except for the condition TH. MC*~PE*PS*~PV is sufficient for the outcome, irrespective of whether TH is present. TH is, thus, redundant and can be eliminated from the combination.

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Logical minimization can be used to remove redundant conditions from sufficient combinations and, hereby, arrive at a less complex solution. If the data is characterized by limited diversity (i.e., if there are logical remainders), three different solution types can result from applying minimization to a truth table. Depending on the remainders included in the minimization process, the following solutions can be produced: the complex or conservative solution, the parsimonious solution and the intermediate solution. These different QComA solutions have complementary strengths for analysing limited diverse data. The conservative solution results if no logical remainders are included in the minimization process. This solution can, thus, be produced by applying the minimization rule to truth table rows that include cases and correspond to sufficient combinations. In our example, two of the four truth table rows that are sufficient for the outcome only differ on one condition: row 2 and row 3. The truth table can, thus, be minimized as follows: MC*~TH*~PE*PS*~PV (row 2) and MC*TH*~PE*PS*~PV (row 3) = MC Resulting in the following solution: ~MC*~TH*~PE*PS*PV + MC*~PE*PS*~PV + MC*TH*~PE*~PS*PV ↔ MP It is not possible to remove other conditions if only truth table rows with empirical cases that are sufficient for the outcome are included in logical minimization. This ‘conservative’ solution corresponds to a necessary disjunction of sufficient combinations. However, it includes conditions for which the data does not provide evidence that they are actually causally relevant (or difference-makers) for the outcome. For example, the truth table contains no evidence that ~MC is an indispensable part of the combination ~MC*~TH*~PE*PS*PV. More specifically, there are no cases that correspond to the combination MC*~TH*~PE*PS*PV (cf. row 26 of the truth table is a remainder). In consequence, it is not clear whether ~TH*~PE*PS*PV is sufficient without ~MC. In other words, it is not clear whether ~MC is an indispensable part of this combination and, thus, causally relevant. If a truth table contains logical remainders, the conservative solution can still include conditions that are not causally relevant. A second solution type is the parsimonious solution, which, if logical remainders are included, results in logical minimization. More specifically, the parsimonious solution results if remainders that can be used to remove conditions from the solution are included in the minimization process. In our truth table, there were twenty-two logical remainders. Ten of these logical remainders can be used to arrive at a less complex solution (it is very complicated to identify the remainders that can be used for arriving at a less complex solution, so software is usually used to produce the parsimonious solution):

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• • • • • • • • •

MC*TH*~PE*~PS*~PV (row 12) MC*TH*PE*~PS*~PV (row 15) ~MC*~TH*~PE*PS*~PV (row 16) ~MC*TH*~PE*PS*~PV (row 17) MC*TH*PE*PS*~PV (row 20) MC*TH*PE*~PS*PV (row 25) MC*~TH*~PE*PS*PV (row 26) ~MC*TH*~PE*PS*PV (row 27) MC*TH*~PE*PS*PV (row 28)

•

MC*TH*PE*PS*PV (row 32)

If these remainders are included in logical minimization, the following solution results: ~PE*PS + MC*TH ↔ MP This ‘parsimonious’ solution only includes conditions for which the data actually provides evidence that they are indispensable parts of their sufficient combination and, thus, causally relevant. Row 5, for example, shows that PE is not sufficient for the outcome by itself, but only in combination with PS. In other words, this row shows that PS is indispensable for the sufficiency of ~PE*PS. This is the case for every condition in the parsimonious solution. The parsimonious solution also has an important downside: by including logical remainders in logical minimization, conditions were removed from the solution for which the data does not provide evidence that they are irrelevant. For example, the second disjunct suggests that MC*TH is sufficient for military participation, irrespective of whether ~PE is present. However, there is not a single case in which MC*TH are combined with PE. In consequence, there is no evidence that ~PE is not relevant for the outcome. Nevertheless, the parsimonious solution suggests that MC*TH will result in the outcome irrespective of whether ~PE is present. The conservative and parsimonious solution, thus, have complementary strengths and weaknesses. The conservative solution results if only sufficient combinations that correspond to empirical cases are included in the minimization process. This solution only removes conditions for which the data provides evidence that they are not relevant but might include irrelevant conditions. The parsimonious solution results from including sufficient rows with empirical cases and logical remainders in logical minimization. This solution is guaranteed to only include conditions for which the data provides evidence that they are relevant, but researchers risk missing relevant conditions when focusing on this solution. If there are no logical remainders, these solutions will be identical. A truth table with logical remainders confronts researchers with a dilemma. Either they can interpret the parsimonious solution and risk missing relevant conditions, or they can interpret the conservative solution, which can include irrelevant conditions. A third solution type was developed to mitigate both the risk of excluding relevant conditions and including irrelevant conditions: the intermediate solution. This solution

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builds on theoretically derived expectations that conditions will contribute to the occurrence of the outcome when they are present rather than absent (or vice versa) (Schneider & Wagemann, 2012, p. 324). In the example, theoretical and substantive knowledge suggests that some conditions are unlikely to contribute to the occurrence of the outcome when they are absent, while other conditions are unlikely to contribute to the outcome when they are present. More specifically, prior knowledge suggests the following directional expectations: • •

the absence of military capabilities, threats and public support are unlikely to be part of the cause of military participation; and the presence of proximate elections or a parliamentary veto are unlikely to be part of the cause of military participation.

If these directional expectations are correct, some logical remainders can be expected to be more likely to be sufficient for the outcome than sufficient combinations that correspond to empirical cases. The assumption that such logical remainders are sufficient for the outcome is a so-called ‘easy counterfactual’. For example, the logical remainder represented in row 12 (MC*TH*~PE*~PS*~PV) includes the absence of parliamentary veto (~PV), while the sufficient combination presented in row 4 (MC*TH*~PE*~PS*PV) includes the presence of parliamentary veto (PV). Given that we expect the absence rather than the presence of this condition to result in military participation, the logical remainder represented in row 12 is more likely to be sufficient for this outcome than the sufficient combination presented in row 4. Therefore, the assumption that the remainder that corresponds to row 12 is sufficient for the outcome is an easy counterfactual. Six of the ten logical remainders used to produce the parsimonious solution correspond to easy counterfactuals (which can be identified with the software): • • • • •

MC*TH*~PE*~PS*~PV (row 12) ~MC*~TH*~PE*PS*~PV (row 16) ~MC*TH*~PE*PS*~PV (row 17) MC*~TH*~PE*PS*PV (row 26) ~MC*TH*~PE*PS*PV (row 27)

•

MC*TH*~PE*PS*PV (row 28)

If only these easy counterfactuals are included in the minimization process, the following intermediate solution results: ~PE*PS + ~PE*MC*TH ↔ MP At the time of writing, the intermediate solution is the preferred solution in QComA (Baumgartner, 2015; Schneider & Wagemann, 2012). However, while this solution was originally introduced to mitigate the problems caused by limited diversity, the intermediate solution has the same problems as the conservative and parsimonious solution.

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Like the conservative solution, it might include conditions that are not relevant; like the parsimonious solution, it might have removed conditions that are actually relevant. The intermediate solution includes conditions for which there is no empirical evidence that they are relevant. For example, there is no empirical evidence that ~PE is an indispensable part of the combination ~PE*MC*TH. There is not a single case in which MC*TH is combined with PE. In consequence, there is no evidence that ~PE is relevant if MC and TH are present. The main reason why this condition is in the intermediate solution is that prior expectations suggested that its presence would have a positive impact on the outcome. However, conflating such theoretical expectations with empirical evidence seems a textbook case of confirmation bias. Conversely, there is no evidence that all conditions that are not included in the intermediate solution are not relevant, only that it would very strongly contradict prior knowledge if these are relevant. Nevertheless, whether or not conditions that were removed to arrive at the intermediate solution are actually irrelevant depends on whether directional expectations are correct. There is an ongoing controversy about which solution should be at the centre of substantive interpretation (Baumgartner & Thiem, 2020; Dus¸a, 2019). Publications on the issue have generally argued in favour of focusing on one solution. However, it seems a better practice to build on all three solutions for drawing conclusions in empirical research (Haesebrouck, 2022). The parsimonious solution is capable of identifying causally relevant conditions, the conservative solution of removing irrelevant conditions. Next to the conditions for which the data provides evidence that they are relevant or contextually irrelevant, there will be conditions for which the data neither suggests that they are relevant or irrelevant. In line with the procedure for crafting the intermediate solution, it might be interesting to make clear for which of these ambiguous conditions it is not plausible that they are relevant. To sum up, when applying QComA, researchers will generally be confronted with logical remainders: logically possible combinations of conditions for which no empirical instances are included in the analysis. Three different solutions can be produced from a truth table with logical remainders: 1. The conservative solution results if no logical remainders are included in logical minimization. No relevant conditions will be removed during minimization, but the solution might include irrelevant conditions. 2. The parsimonious solution results if logical remainders are included in minimization to arrive at a less complex solution. This solution does not include irrelevant conditions, but relevant conditions might have been removed during minimization. 3. The intermediate solution is based on prior theoretical expectations. Conditions are included in this solution if there is evidence that they are relevant or if there is no evidence that they are irrelevant and they are in line with theoretical expectations. Conditions for which there is no empirical evidence that they are relevant and that do not correspond to directional expectations are not

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included in this solution. Irrelevant conditions might be included in this solution because they correspond to prior expectations, while some relevant conditions might have been removed because they do not correspond to theoretical expectations.

7.3.5 Interpretation and re-iteration of the research cycle Arriving at minimal solutions is not the ultimate goal of QComA. Instead, the results must be related back to theoretical expectations, the cases and, most importantly, the general research question. First, you need to assess whether the solutions provide a plausible explanation for the pattern of variation in the outcome. In applied research, the results of a first analysis of the truth table might be completely at odds with theoretical expectations, contradict common sense or be inherently inconsistent, with both the absence and presence of some of the included conditions being linked to the outcome. The parsimonious solution provides the best point of departure, given that it identifies the conditions for which the data provides evidence that they are causally relevant. For many conditions, the body of existing knowledge provides good reasons to expect their presence rather than absence to be linked to the outcome. If the parsimonious solution contradicts such expectations, researchers should assess whether the results of the QComA are actually plausible and, if not, reiterate the research cycle and reconsider previous steps. For example, the following parsimonious solution results from truth table 8 (which results if ‘threat’ was not included and the dichotomization of public opinion was changed): MC*PS + PS*PV ↔ MP This solution includes the presence of parliamentary veto power, which contradicts prior directional expectations. In the absence of a plausible explanation for why the presence of this condition is linked to military participation, researchers should reiterate previous steps of their study. If the solutions (and especially the parsimonious solution) provide a plausible explanation for the variation in the outcome, they can be subjected to further interpretation. As argued above, there is an ongoing debate on which QComA solution should be located at the centre of substantive interpretation. Given that each of the three QComA solutions provide relevant information, combining them allows us to gain as much information as possible on the causal structure behind the phenomenon under investigation. The parsimonious solution is capable of identifying causally relevant conditions, the conservative solution of identifying conditions that are not relevant in the context of the study. Next to the conditions for which the data provides evidence that they are relevant or irrelevant, there will be conditions for which the data neither suggests that they are relevant or irrelevant. In line with the procedure for crafting the intermediate solution, it might be interesting to make clear for which of these ambiguous conditions it

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is not plausible that they are relevant in the context of the research. Combining the three solutions can be accomplished in a summary table, which shows for which conditions the data provides evidence that they are relevant, for which conditions there is evidence that they are contextually irrelevant and for which conditions there is no such evidence.

Table 7.5  Presentation QComA results Ambiguous Relevant

Plausible

MC*TH

~PE

~PE*PS

/

Implausible

Irrelevant

~PS*PV

/

~MC*~TH*PV

/

Cases Italy (France) Belgium, Netherlands, Norway (France, United Kingdom, United States)

/

MC*~PV

TH/~TH

France, United Kingdom, United States (Belgium, Netherlands, Norway)

MP: Military Participation; PV: Parliamentary Veto; MC: Military Capabilities; PE: Proximate Elections; PS: Public Support; TH: Threat. Cases between brackets are covered only by the parsimonious and intermediate solutions.

The results suggest that there is strong evidence that the combination of public support with the absence of proximate elections and the combination of military capabilities with threats are causally relevant for military participation. This partially confirms the theoretical model presented in Figure 7.4. On the one hand, it shows that the pattern of participation in the Libya operation was the result of a complex interplay between domestic-level and international-level conditions. However, it suggests that the conditions interact in a different way from that expected in the model. Moreover, in contrast to prior expectations, the results do not show that parliamentary veto power is relevant for the pattern of military participation. The results can also be related back to the cases. For example, researchers could have a look at the case of Italy to see whether its participation in the Libya operation can actually be explained by the combination of military capabilities and threats. The solutions also leave open some important avenues for further research. Most importantly, it is unclear whether or not the absence of prior elections is an indispensable for military participation if both military capabilities and threats are present. To sum up, a study with QComA roughly proceeds in five main steps: 1. research design – formulation of a research question, selection of cases and conditions; 2. operationalization and dichotomization of outcome and conditions; 3. building a truth table and solving contradictory configurations; 4. producing the conservative, parsimonious and intermediate solution; and 5. interpretation of the results and re-iterations of the research cycle.

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However, QComA is an iterative research approach. Throughout their research, researchers must frequently reconsider previous steps to arrive at a better result.

7.4 Conclusion and discussion This chapter provided a concise introduction to QComA, a qualitative method with some characteristics of quantitative methods that can help you explain why an outcome of interest occurred in some cases but not in others. More specifically, the chapter discussed some of the main features of QComA, discussed the type of causal relations that can be uncovered with the method and the main steps that a study with QComA follows. Evidently, one chapter cannot provide an exhaustive discussion of an entire method, nor can it do justice to the variety of approaches to QComA or the rich methodological debates on the standards of good practice to be applied (Thomann & Maggetti, 2020). Moreover, this chapter focused on the original crisp set variant of QComA and did not discuss its more complicated fuzzy set version, which allows for assigning scores between 0 and 1 and, thereby, taking into account the degree to which the conditions and outcome are present in the cases (Ragin, 2000). Although far less popular than fuzzy set or crisp set QComA, two other variants of QComA must also be mentioned here because they can be an appropriate methodological choice in a variety of research situations: multi-value QComA and two-step QComA (Haesebrouck, 2015, 2019a). If you are interested in these other QComA variants, the recommended reading in section 7.7 can guide you in selecting additional reading on QComA.

7.5 Summary checklist In this chapter, you learned about the key features of QComA and the type of causal relations that can be uncovered with QComA. Subsequently, you were introduced to the main steps of a QComA study.

7.6 Doing qualitative comparative analysis yourself In this exercise we will stimulate you to think about how to create a research design for a study that applies QComA in a master’s thesis.

7.6.1 Assignment Imagine that your friend and fellow student, Jos, asks you for advice on how to create a research design for a study in which he wants to apply QComA to examine the electoral success of political parties. In assisting your peer, please focus on all the main features of a research design for QComA (research question, case selection and condition selection)

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and propose how he would address them in relation to his proposed topic. Please also consider that your friend is doing this as part of a master’s thesis, which means that you must consider the limited resources at his disposal.

7.6.2 Model response Jos should first develop a research question about his topic that is suited for QComA. The method is particularly suited for examining ‘divergence puzzles’ and can be best applied to a research question that follows the following structure: ‘why has outcome X occurred in some cases, but not in others?’. Jos could formulate a research question that follows the general structure: ‘why are some parties successful in elections, while other are not?’. Given that only a limited number of conditions (three to seven) can be included in QComA, a study that applies QComA should focus on cases that share a sufficiently large number of background characteristics. Therefore, Jos should formulate a research question that clearly demarcates the scope of the study. Jos could, for example, focus on the electoral success of a particular party family, on democracies with similar electoral systems and/or on a well-defined time frame. Moreover, he should also take into account that a sufficiently large number of cases must be available for his study and that the outcome needs to vary among the available cases. At least eight (but preferably more) cases should be available. The outcome should be present in at least three (but preferably more) cases and absent in at least three (but preferably more) cases. Taken these criteria into account, Jos could focus on the following research question: why were some Green parties successful in national elections in West European democracies between 2015 and 2020, while others were not? Jos should also justify why he uses QComA for examining his research question. QComA is particularly apt at uncovering complex causal relations, so Jos could argue that there are reasons to expect that there are different combinations of conditions that lead to the electoral success of Green parties. Once an appropriate research question has been formulated, Jos must select the cases that will be included in the analysis. It is probably possible to include all Green parties that participate in national elections in West European democracies between 2015 and 2020. However, if Jos is faced with time constraints and/or conditions that are challenging or time-consuming to operationalize, he could focus on a smaller number of cases. If this is the case, case selection should be guided by the research question and the theoretical framework: Jos should aim to achieve enough variation on each of the included conditions and the outcome. If possible, the conditions and the outcome should be present and absent in at least one-third of the cases. Next to selecting the cases, Jos must also select plausible explanatory conditions. More specifically, Jos should select conditions that could potentially explain why Green parties are successful in some countries but not in others. I would recommend Jos to start with a thorough literature review and select four to seven conditions

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from the literature. Given the popularity of his topic, it is possible that his literature review will result in a much higher number of possible explanatory conditions. I would advise Jos to include only conditions that vary among his cases. In addition, he could use other research methods to find out which conditions have most explanatory potential or focus on the conditions for which previous studies provide strong evidence that they are causally relevant. Alternatively, he could test several different models, each including different combinations of conditions and, thereby, inductively arrive at the model that provides the best explanation for the variation in the outcome. Lastly, he could also select the condition that can be combined in a theoretical model that suggests that there are several combinations of conditions that lead to electoral success. In conclusion, I would emphasise that a study with QComA follows an iterative logic, so that Jos will probably have to reconsider several of the decisions that he made in his research design after producing a first truth table or a first solution.

7.7 Recommended reading Mello, P. A. (2021). Qualitative comparative analysis: an introduction to research design and application. Washington, DC: Georgetown University Press. Oana, I.-E., Schneider, C. Q. and Thomann, E. (2021). Qualitative comparative analysis (QCA) using R: a gentle introduction. Cambridge: Cambridge University Press. These two comprehensive recent textbooks provide a good starting point if you want to learn how to apply QComA in your research. Ragin, C. C. and Rihoux, B. (2009). Configurational comparative methods: qualitative comparative analysis (QCA) and related techniques. London: SAGE. This edited volume provides a concise and very hands-on introduction to QComA. Ragin, C. C. (2008). Redesigning social inquiry: fuzzy sets and beyond. Chicago, IL: University of Chicago Press. Schneider, C. Q. and Wagemann, C. (2012). Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis (QCA). Cambridge: Cambridge University Press. These books provide a more comprehensive discussion of QComA and set-theoretic methods. Ragin, C. C. (1987). The comparative method: moving beyond qualitative and quantitative strategies. Berkeley, CA: University of California Press. Ragin, C. C. (2000). Fuzzy-set social science. Chicago, IL: University of Chicago Press. These seminal works written by Ragin’s are still relevant for researchers that want to fully understand QComA and its development throughout the years.

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Lastly, several software packages are available if you want to apply QComA in your research (an overview of the available software for QComA is provided at http://compasss.org/software/). The two most popular packages are fsQCA and Tosmana, which both rely on a ‘point-and-click’ style graphical user interface. However, several R packages are becoming increasingly popular among QComA users (Dus¸a, 2018; Oana et al., 2018). Although it takes a bit longer to master these packages, learning how to use them is well worth the time and effort given their many advantages over the more traditional software (Oana et al., 2021, p. 22).

7.8 References Amenta, E. and Poulsen, J. D. (1994). Where to begin: a survey of five approaches to selecting independent variables for qualitative comparative analysis. Sociological Methods & Research, 23(1), 22–53. Baumgartner, M. (2006). Complex causal structures: extensions of a regularity theory of causation. PhD thesis, Bern: University of Bern. Baumgartner, M. (2015). Parsimony and causality. Quality & Quantity, 49(2), 839–56. Baumgartner, M. (2020). Causation. In D. Berg-Schlosser, B. Badie and L. Morlino (eds), The SAGE handbook of political science (pp. 305–21). London: SAGE. Baumgartner, M. and Ambühl, M. (2020). Causal modeling with multi-value and fuzzyset coincidence analysis. Political Science Research and Methods, 8(3), 526–42. Baumgartner, M. and Falk, C. (2019). Boolean difference-making: a modern regularity theory of causation. The British Journal for the Philosophy of Science. doi:10.1093/bjps/ axz047. Baumgartner, M. and Thiem, A. (2020). Often trusted but never (properly) tested: evaluating qualitative comparative analysis. Sociological Methods & Research, 49(2), 279–311. Beach, D. and Pedersen, R. B. (2016). Causal case study methods: foundations and guidelines for comparing, matching, and tracing. Ann Arbor, MI: University of Michigan Press. Berg-Schlosser, D. and De Meur, G. (2009). Comparative research design: case and variable selection. In B. Rihoux and C. C. Ragin (eds), Configurational comparative methods: qualitative comparative analysis (QCA) and related techniques (pp. 19–32). London: SAGE. Binder, M. (2015). Paths to intervention: what explains the UN’s selective response to humanitarian crises? Journal of Peace Research, 52(6), 712–26. Day, C. and Koivu, K. L. (2019). Finding the question: a puzzle-based approach to the logic of discovery. Journal of Political Science Education, 15(3), 377–86. DuŞa, A. (2018). QCA with R: a comprehensive resource. Cham: Springer. DuŞa, A. (2019). Critical tension: sufficiency and parsimony in QCA. Sociological Methods & Research. doi.org/10.1177/0049124119882456. Graßhoff, G. and May, M. (2001). Causal regularities. In W. Spohn, M. Ledwig and M. Esfeld (eds), Current issues in causation (pp. 85–114). Hardehausen: Mentis.

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Haesebrouck, T. (2015). The added value of multi-value qualitative comparative analysis. Paper presented at the Forum Qualitative Sozialforschung/Forum: Qualitative Social Research. Haesebrouck, T. (2017a). EU member state participation in military operations: a configurational comparative analysis. Cambridge Review of International Affairs, 30(2–3), 137–59. Haesebrouck, T. (2017b). NATO burden sharing in Libya: a fuzzy set qualitative comparative analysis. Journal of Conflict Resolution, 61(10), 2235–61. Haesebrouck, T. (2019a). An alternative update of the two-step QCA procedure. Quality & Quantity, 53(6), 2765–80. Haesebrouck, T. (2019b). Who follows whom? A coincidence analysis of military action, public opinion and threats. Journal of Peace Research, 56(6), 753–66. Haesebrouck, T. (2022). Relevant, irrelevant, or ambiguous? Towards a new interpretation of QCA’s solution types. Sociological Methods & Research. doi.org/10.1177/00491241211036153. Haesebrouck, T. and Thomann, E. (2021). Introduction: causation, inferences, and solution types in configurational comparative methods. Quality & Quantity. doi.org/10.1007/s11135-021-01209-4. Hernán, M. A. (2018). The C-word: scientific euphemisms do not improve causal inference from observational data. American Journal of Public Health, 108(5), 616–9. Mackie, J. (1965). Causes and conditions. American Philosophical Quarterly, 2(4), 245–64. Mackie, J. (1974). The cement of the universe: a study of causation. Oxford: Oxford University Press. Mahoney, J. and Goertz, G. (2006). A tale of two cultures: contrasting quantitative and qualitative research. Political Analysis, 227–49. Mello, P. A. (2021). Qualitative comparative analysis: an introduction to research design and application. Washington, DC: Georgetown University Press. Mumford, S. and Anjum, R. L. (2013). Causation: a very short introduction. Oxford: Oxford University Press. Oana, I.-E., Medzihorsky, J., Quaranta, M., Schneider, C. Q. and Oana, M. I.-E. (2018). Package ‘SetMethods’. Oana, I.-E., Schneider, C. Q. and Thomann, E. (2021). Qualitative Comparative analysis (QCA) using R: a gentle introduction. Cambridge: Cambridge University Press. Palm, T. (2013). Embedded in social cleavages: an explanation of the variation in timing of women’s suffrage. Scandinavian Political Studies, 36(1), 1–22. Psillos, S. (2009). Regularity theories. In H. Beebee, C. Hitchcock and M. Peter (eds), The Oxford handbook of causation. Oxford: Oxford University Press. Ragin, C. (1987). The comparative method: moving beyond qualitative and quantitative strategies. Berkeley, CA: University of California Press. Ragin, C. (2000). Fuzzy-set social science. Chicago, IL: University of Chicago Press. Ragin, C. and Rihoux, B. (2009). Configurational comparative methods: qualitative comparative analysis (QCA) and related techniques. London: SAGE. Rihoux, B. (2003). Bridging the gap between the qualitative and quantitative worlds? A retrospective and prospective view on qualitative comparative analysis. Field Methods, 15(4), 351–65. doi:10.1177/1525822x03257690.

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Rihoux, B. (2006). Governmental participation and the organizational adaptation of Green parties: on access, slack, overload and distress. European Journal of Political Research, 45, S69–S98. Rihoux, B. (2013). Qualitative comparative analysis (QCA), anno 2013: reframing the comparative method’s seminal statements. Swiss Political Science Review, 19(2), 233–45. Rihoux, B. and De Meur, G. (2009). Crisp-set qualitative comparative analysis (csQCA). In C. Ragin and B. Rihoux (eds), Configurational comparative methods: qualitative comparative analysis (QCA) and related techniques (vol. 51, pp. 33–68). London: SAGE. Rihoux, B. and Marx, A. (2013). QCA, 25 years after ‘The Comparative Method’: mapping, challenges, and innovations—mini-symposium. Political Research Quarterly, 66(1),167–235. Rohlfing, I. and Zuber, C. I. (2019). Check your truth conditions! Clarifying the relationship between theories of causation and social science methods for causal inference. Sociological Methods & Research. doi:10.1177/0049124119826156. Rutten, R. (2021). Uncertainty, possibility, and causal power in QCA. Sociological Methods & Research, 00491241211031268. doi:10.1177/00491241211031268. Schneider, C. Q. and Wagemann, C. (2012). Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis (QCA). Cambridge: Cambridge University Press. Thomann, E. and Maggetti, M. (2020). Designing research with qualitative comparative analysis (QCA): approaches, challenges, and tools. Sociological Methods & Research, 49(2), 356–86.

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8 Qualitative Content Analysis: A Practical Introduction Charlotte Maene

You collected qualitative data about a phenomenon – you might feel a bit overwhelmed – so the next thing you would like to know is, ‘How do I come to a complex overview of all this information?’ In this case, qualitative content analysis (QCA) might be the right approach for you! QCA is a method of data analysis applied by researchers from many different scientific fields. It enables you to identify categories present within textual data by following a systematic coding process that leads to a thorough contextual description (Hsieh & Shannon, 2005) and limited quantification of the research phenomenon (Mayring, 2000). As a result, QCA can also be the stepping stone you need to ask more complex questions about your data as it will help you to describe a phenomenon in all its main dimensions and characteristics.

8.1 Chapter objectives Your main tool as a QCA researcher is the development of a coding scheme preceded by rigorous code development. Hence, this will be the central focus of this chapter. It will help you to execute a QCA by going through the process step by step. More specifically, we will talk about: • • • •

the application and origin of QCA; its basic principles; inductive code development (or ‘Conventional QCA’); and deductive code development (or ‘Directed QCA’).

At the end of this chapter, you will have all the tools you need to apply QCA by yourself; a clear strategy on how codes develop from raw data and you can take into account the requirements of a coding frame.

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8.2 Key features, debates and historical development 8.2.1 Application and origin of QCA Nowadays, QCA is a data analysis technique applied by a wide variety of scientific fields such as nursing and other healthcare sciences (40%), educational research (14%) and interdisciplinary social sciences and psychology (11%). These disciplines together collect 2,697 publications in Web of Science in 2020. Although QCA has its origin in the communication sciences, it is applied to a lesser extent, with only 2.3% of all the publications in Web of Science. Overall, the application of QCA has drastically increased in the past twenty years (see Figure 8.1).

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QCA has its roots in the positivistic paradigm (Graneheim et al., 2017) and originates from a research methodology termed ‘content analysis’, coined by the communication scientist Berelson (1952, p. 18) as a ‘research technique for the objective, systematic and quantitative description of manifest content’. Although content analysis as a methodological tool is still used today, from its beginnings it has been under attack from researchers who dismiss it as ‘simplistic’ and ‘superficial’ (Mayring, 2015). In 1952, Kracauer coined the term ‘qualitative content analysis’ in an essay in which he states that an overemphasis on quantifications reduces the accuracy of the analysis.

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In 1983, Philippe Mayring, as a psychologist, published the first handbook on QCA, Grundlagen und Techniken qualitativer Inhaltsanalyse. Over the course of his career as a qualitative methodologist, Mayring shifted his approach towards QCA from a qualitative research method to a hybrid research method (Schreier et al., 2019). Overall, Mayrings’ account of QCA stays true to its historical scientific underpinnings as a (post-)positivistic and highly objective research methodology. The contributions of Hsieh and Shannon (2005), Graneheim and Lundman (2004) and Elo and Kyngäs (2008) are considered cornerstones of QCA. Although both methodological streams (Germanic versus Anglo-international) developed QCA in a similar fashion, there are some interesting differences to be found – mainly regarding the rules and guidelines concerning the inductive/conventional approach in QCA. Differences between the deductive/direct approach seem negligible.

8.2.2 The basic principles of qualitative content analysis First, the central analytical tool of the QCA method that captures manifest or latent meaning present in the data (Burla et al., 2008) is the development of a coding frame (Schreier, 2014). Your competence to develop a fitting coding frame is vital, since the ‘success’ (i.e., its trustworthiness) (Mayring, 2015) of the analysis depends on it (Hsieh & Shannon, 2005). Category development is a rigorous balancing act. On the one hand, you must choose between labelling beyond the specifics of a concrete passage, and thereby develop more abstract categories that apply to different specific passages that capture the same meaning. On the other hand, you must stay close enough to the original raw material to allow for a nuanced and rich description of the research phenomenon (Schreier, 2014). As a result, coding frames can (easily) consist of more than 100 codes representing main and subcategories. There are different approaches to develop these categories that are the analytical constructs of the study: •

•

You can make use of previous research results and existing theory (White & Marsh, 2006) in the development and application of the coding frame – referred to as a ‘deductive’ or ‘directed’ QCA (Assarroudi et al., 2018). You can also develop the categories in a completely bottom-up way from the raw textual data itself – this is the most popular approach within QCA as an analytic method and is therefore often referred to as ‘conventional’ QCA (Assarroudi et al., 2018) or ‘inductive’ QCA (Mayring, 2000).

Second, QCA is a strict systematic and rule-bound procedure (Mayring, 2000). Since the development of a coding frame constitutes the core of the method, the process on how to develop these categories is elaborately explained in all theoretical and empirical contributions. Depending on the source/author, between seven (Hsieh & Shannon, 2005; Schreier, 2014) and sixteen steps (Assarroudi et al., 2018) are described. However, it always comes down to the same sequence and logic with the main steps being:

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• • • •

defining the research question; selecting/collecting the sample; developing definitions/abstraction of the categories; starting the initial coding followed by an evaluation of the coding frame, modification of the coding frame and the main coding phase; and

•

reporting the results.

QCA is an iterative process (Schreier, 2014). Although this systematic procedure should be followed at all times, it can occur during qualitative research that changes have to be made to the research question, scope of the study, abstraction level of the coding and definitions of the categories, among other things. Therefore, you might ‘literally’ have to take a few steps back and re-orientate the analysis. Every adaptation of the coding frame requires a repetition of the trial coding. It is a test of the quality and transparency of the definitions of each category by performing a double coding which should lead to a similar application of the coding frame (Burla et al., 2008). This trial coding should always be documented in the methodology of the study (Mayring, 2015). There are nuanced differences between the procedure for a directed or conventional qualitative content analysis, which will be explained in detail below. At this point, it is important to stress that this sequence of steps – as well as every decision made during the analysis of the data – should be based on a systematic process (Mayring, 2015) as it differentiates QCA as a scientific instrument from layperson techniques for textual data analysis. You must therefore inform yourself about QCA before starting the analysis and follow the necessary training. Third, QCA results in a contextual description of the research phenomenon. At the start of the research you should develop a very clear idea on what and/or which aspect(s) of the phenomenon you want to talk about at the end of the analysis. This is necessary to sufficiently take into account the context of the text and to achieve a ‘rich’ description of the research phenomenon. The types of code that will be generated – especially in a conventional QCA – depend greatly on the proposed research questions and their focus. If you want to focus on ‘the perception’ of a research participant, which elements of the perception will you consider? Is the focus on the respondents’ thoughts, opinions, emotions or past experiences in relation to the research phenomenon? It is important that you know the scope of the description, since it might be needed to develop multiple coding frames to capture all elements of someone’s perception (Mayring, 2000). The immediate context of the text should also be considered – how was the textual data generated? Was it self-generated or does it rely on available source? (see Schreier, 2014) – and questions such as ‘who is talking?’ and ‘to whom is one talking?’ need to be answered (Mayring, 2015). The focus on ‘description’ of textual data also brings limitations to what is possible with QCA as an analytical instrument (Schreier, 2014). QCA does not describe any tools to question the text itself. It is therefore not suited to engage in a critical analysis of textual data. If this is your interest, however, it might be useful to read Chapter 2 to become more familiar with discourse analysis. QCA is also not suited for theory building, since it does not elaborate in-depth on relationships between

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research participants or the developed categories. If it is your goal to generate new theoretical insights, a grounded theory approach might be better suited (see Chapter 3). QCA is not particularly suited to opening up textual data but rather to reducing it (you assign textual data to a category system: ‘the coding frame’). The analytical tools of a thematic analysis (explained in detail in Chapter 10) might help you to find in-depth relationships among texts and cases. Fourth, it is not uncommon – yet sometimes contested – to include quantifications into the results of a QCA. This quantification will be less elaborate than in a study applying QCA, and could include frequencies, cross tabulations, statistical measures of bivariate association. The main goal of using quantifications in a QCA should be to reinforce the utility of the coding frame and to direct future research into interesting paths for more in-depth analysis. For example, a cross tabulation between the cases of the study and the developed categories can show that the characteristics of the research phenomenon occur similarly among cases and adds weight to the quality of the coding frame as an instrument that captures the general and shared content of the research phenomenon beyond the individual experiences or the particularity of the text. Furthermore, it could help you to discover relationships between categories on the one hand and types of text on the other. As a result of these advantages, some authors (Mayring, 2015; White & Marsh, 2006) are not shy about including quantifications in the research report. This strand of research places QCA within the mixed methods paradigm or refers to it as a ‘hybrid approach’ (Schreier et al., 2019). However, not all QCA practitioners perceive QCA as a hybrid method and argue for a ‘qualitative turn’ in QCA. This recent movement is supported by the Anglo-international literature (Schreier et al., 2019). Three viewpoints are worth mentioning. First, Kuckartz (2014; 2019) proposes analysing the textual data beyond the development of categories into a coding frame by adding case comparisons into the results of the QCA study. By doing this, attention is drawn to the sampling units that constitute the base of the research results and different types of case could be identified. This is referred to as ‘type building text analysis’ (Schreier et al., 2019). Second, Schreier (2016) suggests using ‘category’ as intended by grounded theory researchers as a more ‘abstract’ analytical tool that can be described in a fixed set of characteristics such as the causal conditions and/or consequences of the category and the way people interact with the discovered abstract category (see Chapter 3 on grounded theory). Clearly, the use of the word ‘category’ has a drastically different meaning from the one thus far applied by QCA researchers. Third, there is a stream of researchers who want to narrow the gap between QCA and thematic analysis by changing the procedure of inductive code development via, for example, allowing overlap between codes (Elo & Kyngäs, 2008; Graneheim & Lundman, 2004) and exploring paths for subsequent analysis of the coding frame to discover ‘(meta)themes’ (Graneheim et al., 2017) (see also Chapter 10 on thematic analysis). The researchers who strive for the more ‘qualitative’ turn in QCA do not mention the use of quantifications in reporting the research results. Regardless of whether you are a proponent or an opponent of the use of quantifications, the newly proposed methodological paths within QCA do not touch upon the previously mentioned characteristics of the QCA method.

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Overall, the research process that underpins a QCA is divided in two phases: the preparation phase and the analysis phase. The former deals with all the decisions that you have to make before the analysis of the data can start. The latter entails all the steps you need to take to develop categories, both in the case of a conventional QCA and a directed QCA. These two phases will be addressed in detail below.

8.3 Doing qualitative content analysis step by step 8.3.1 Preparation phase Before diving into ‘category development’ – which is the core task of a QCA – it is necessary to reflect on four preliminary steps: defining the research question; collecting the sample; defining the units of analysis; and familiarizing yourself with the data. Both conventional and directed approaches to QCA have these research tasks in common (Elo & Kygnäs, 2008). First – and unlike, for example, a grounded theory method – the research question is clearly defined beforehand. When developing a research question, you must consider the potential and the limits of a QCA. QCA is highly suited to map phenomena in depth and can help in finding preliminary relationships between categories and types of text/respondent. This allows for theoretical comparisons between texts. However, QCA cannot assist in discovering explanations or revealing processes into ‘how’ a phenomenon ‘is’ the way it is. Causal explanations and ‘why’ questions are therefore not suited for QCA. Although QCA has its roots in quantitative methodology, closed questions are not suited since the goal is to offer a rich description that adds theoretical relevance to the research field. Open questions that aim at describing a phenomenon in its core characteristics are perfect for QCA. For instance, Kygnäs and colleagues (2019) contend that QCA is often used to investigate human experiences and perspectives in everyday life and therefore propose examples of good research questions such as: a) what is the meaning of disease for adolescents who have Type 1 diabetes? b) How do these adolescents take care of themselves? c) Which factors affect the way they take care of themselves? These research questions were answered by conducting interviews with youngsters who have Type 1 diabetes. Interviews are not the only way that QCA can be organized. Pelto-Piri and colleagues (2014) gave psychiatric staff the task of keeping a diary on how they addressed ethical issues while dealing with patients. The analysis gave a unique insight into the (reflexive) practices of the staff and showed a need for more ethical leadership support. Again, Schreier (2014) discusses Shannon’s (1954) study on newspaper cartoons to show that QCA can also be conducted on (semi)visual materials. To give further inspiration on what type of questions can be answered by a QCA, the research questions of Shannon’s study are given: a) Who are Annie’s (the protagonist of the newspaper cartoons) friends and opponents? b) Who of the opponents gets injured or killed? c) What are the goals that Annie and her friends approve of? d) How do the characters suggest to reach these

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goals? This specific QCA showed that the cartoonist clearly favoured a conservative middle-class ideal, which was conveyed via the illustrations and text. The research questions are informative for the focus of the analysis on manifest or latent content. This is mainly an important consideration before we start the coding process (see further, category development on p.253). Second, once a research question has been developed, you need to set up a sampling strategy. It is common in QCA to work with a purposive sample that selects those texts that are theoretically relevant (Assarroudi et al., 2018). Unlike other qualitative methodologies, it is not uncommon in a QCA that the sample is heterogeneous; to include different types of text on the same topic enhances the chances to come to a rich description of the research phenomenon. You need to plan this step in the research process carefully in order to collect a sample that is suited for academic research but also realistic in relating to the overall time span of the research. Category development is an iterative process that is time consuming. This sets a limit to the overall sample size. It is good practice to compare your decisions with those made in other studies. Rigorously selecting the material on which the analysis will be based increases the overall trustworthiness of the investigation. The criterion of transferability is especially important in this regard (White & Marsh, 2006). In qualitative research, the goal is to discuss whether the findings are applicable in other contexts and what the theoretical implications are if this is (not) the case. It could be argued that it is the main goal of a directed QCA to answer these questions by applying an already existing coding frame into a new context. The goal of the research should not be to make generalizations. However, if it is the aim of the research to continue with the data in a quantitative way, it might be better to consider taking a random sample (Mayring, 2000). Once you carefully reflected on all these decisions, the data collection can start. There can be all sorts of data collection units involved. Taking the example of Pelto-Piri and colleagues (2014), the data collection units are diaries, while in the research of Shannon (1954) the data collection units are the cartoons of a daily newspaper. If you are conducting interviews, you might want to make the transcriptions as fast as possible after an interview is conducted and do so in great detail (Assarroudi et al., 2018). Especially if the aim of the research is to analyse latent content, sighs, laughter and pauses should be included into the transcript (Elo & Kygnäs, 2008). It is also necessary to include sufficient context information with the textual data, since the aim of a content analysis is to contextualize research findings (Schreier, 2014). Third, you need to make a decision regarding the ‘chunking’ of the data into units of analysis. A unit of analysis is the textual context in which you will develop categories. These units need to be small enough so they become meaningful entities but big enough to be analysed as texts in themselves (Graneheim & Lundman, 2004). For example, consider again the study of Pelto-Piri and colleagues (2014) who uses psychiatric staff diaries as data collection units. Those diaries as a whole would be too big to understand the everyday experiences of staff members. It is therefore much more useful to analyse the diary day by day. In the study of Pelto-Piri and colleagues (2014) the unit of analysis

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is the separate daily contributions to the diary for each staff member. Similarly, if you conduct focus groups, you might be interested in what each respondent said and not so interested in the focus group as a whole. The unit of analysis would then become what each respondent said, rather than the transcript of the whole focus group. While a data collection unit is a practical choice in the research process, the unit of analysis is based on a theoretical choice related to the research question (Assarroudi et al., 2018). The units of analysis are the basis on which you will report the results of the study. This also means that once the data collection unit is assigned to smaller units of analysis, you do not go back to that bigger context to interpret the results of the analysis. Elo and Kyngäs (2008) state that a unit of analysis can even consist of sentences, words or a portion of a page. That being said, it is also common practice to use a whole transcript as a unit of analysis. In such cases, the data collection unit and the unit of analysis do not differ in size. The process of dividing data into units of analysis is called segmentation (Schreier, 2014). Lastly, before you can start with the coding process – which is the main phase in QCA – you need to become familiar with the data. It is recommended to read through the data and let it soak in, before starting to make adjustments to the text. As Elo and Kygnäs (2008) suggest, this step is necessary for you to gain theoretical insights from the textual material and to go beyond a superficial description of the text. These authors also propose useful guiding questions to help you gain a deeper understanding of your data: Who is telling? Where is this happening? When did it happen? What is happening? Why is it happening? Once you have taken the time to read the textual material with these questions in mind and a clearer idea is formed of the complex and related meanings of the material, you are ready to start the development or application of the categories (Assarroudi et al., 2018).

8.3.2 Analysis phase During the preparation phase described above, you make the data ready for the analysis and become familiar with the general structure of the text. Kygnäs and colleagues (2019) recommend that you read through the data several times before you start the analysis. They especially stress this in case you are interested in the inductive approach. During the data analysis phase, you perform tasks that will lead to the development or application of a coding frame. Importantly, and regardless of the adopted approach, the coding frame must meet six requirements: 1) Exhaustiveness (see Schreier, 2012). According to this requirement, each unit of coding must be assigned a label and the label needs to be taken up in the coding frame. In this way, you avoid potentially relevant material being overlooked and all data is considered for analysis. When going through the process of creating different levels of category, it might be necessary to work with residual categories. It is, however, recommended to limit this practice to a minimum and rather reflect on the theoretical relevance of each coding unit.

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2) Transparency (see Kyngäs et al., 2019). This requirement refers to the obvious link between the open code and the coding unit on which the code is based. Creating open codes that are not transparent has as a consequence that the connection to the raw data is lost at a too early a stage in the analysis process. Consequently, the development of the coding frame risks becoming too abstract and too interpretative. 3) Brevity (see Kyngäs et al., 2019). An open code should not be too long. Ideally, the coding unit can be captured in one word. An open code of three words is the very maximum, since it otherwise indicates that the ‘core content’ of the coding unit is not captured. A device that can help you to bridge between the coding unit and the open code is a condensed description of the coding unit. It is an intermediate step that helps you to capture the content of the coding unit. 4) Mutual exclusiveness (see Schreier, 2012; 2014). This requirement entails that one coding unit can be applied to only one subcategory of a main category. This means that coding units can be double coded only if those codes apply to different main categories, but that the coding units cannot re-occur under one main category. In a more practical sense, this shows that QCA acknowledges that sentences can consist of multiple meanings and content, but that it is better for theoretical clarity to keep them separate from each other. By doing so, two qualities of the coding frame are achieved: each main category in the coding frame can be described in a unidimensional way, as it describes clearly what the text did and did not discuss regarding that topic (see also the sixth requirement); and relationships between main categories of the data can be established since subcategories can be linked to each other through this double coding. 5) Saturation (see Schreier, 2012). Each subcategory in the coding frame needs to be used at least once. It goes without saying that this is necessary since the codes and category are developed in a data-driven way when you apply inductive QCA. However, a category can remain empty during a deductive QCA. That would be a highly relevant theoretical finding in itself. 6) Uni-dimensionality (see Schreier, 2012; 2014). There cannot be re-occurring categories in the overall coding frame. This makes the coding frame more parsimonious. Especially when software is employed to conduct the analysis, data from different types of respondent or relationships within the coding frame should be accessed through different analytical instruments. Below, diverse approaches to QCA will be discussed. Since ‘conventional’ (inductive) QCA is the most common, it will be discussed first. Subsequently, the directed approach of QCA, as well as the (rather limited) summative approach, will be addressed.

Conventional qualitative content analysis Conventional QCA is also referred to as ‘inductive’, ‘data-driven’ or ‘text-driven’ QCA (Graneheim et al., 2017) and is used mainly as an explorative technique with the

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aim of developing a more theoretical understanding of a research phenomenon (Assarroudi et al., 2018). Conventional QCA is most applicable when the research tradition is still very young or findings are fragmented and inconsistent (Schreier, 2014). Studies using conventional QCA propose research questions such as ‘how do the very old experience loneliness?’, put forward by Graneheim and Lundman (2010) in their study on the ways that old people deal with loneliness, or ‘which emotions are associated with classroom learning for high and low achievers?’, answered by Mayring’s (2015) by comparing whether there was a difference between low and high achievers in the emotions that they reported in their diary. Some challenges come with this inductive approach. First, we must be self-conscious about our preconceived ideas towards the research topic but also about the way that we, as human beings, perceive everyday events in our direct social environment. Reflecting on this might help us to overcome bias during the open coding process (Graneheim et al., 2017). A second challenge of working inductively with qualitative data is to overcome a ‘mere’ description of what has been said in the data. We must preserve the theoretical nature of qualitative research and keep in mind that scientific labour needs to result in a deep understanding of the research phenomenon (Graneheim et al., 2017). The ruleguided nature of QCA can help us to avoid these pitfalls.

Initial (open) coding During the preparation phase, you decide on the ‘size’ of the units of analysis, which can be text fragments (e.g., a book chapter, a newspaper article, thematic elements of an interview) or an entire text as a whole. The unit of analysis must allow for a meaningful analysis of the text within a context. However, selecting the unit of analysis in QCA is not enough. QCA methodologists state that it is also necessary to divide the units of analysis into ‘coding units’ (Schreier, 2014) or ‘units of meaning’ (Graneheim & Lundman, 2004) (for an illustration see Case Study 8.1). This entails a segmentation of the text into pieces, which will allow you to code the material consistently (Schreier, 2014). In practice, it involves deciding whether whole paragraphs are going to be coded or if the material will be coded sentence by sentence. It might also happen that some people decide to code at the word level. The choice has to be made theoretically, in the sense that you take into account the research questions and eventually the type of follow-up analysis that might be required or wanted (Mayring, 2014). This segmentation of the textual data is especially useful when multiple coders will work on the same data or you know beforehand that there will be a considerable break between a first and second round of coding. In addition, we need to decide on the main categories of the analysis. Indeed, main categories can be known beforehand, since those are the dimensions on which you want to focus during the analysis (e.g., feelings/emotions and behavioural decisions) (Schreier, 2012). It is important in QCA to know these main categories before open codes are assigned to the material for two reasons: a) double-coding of a coding unit is allowed only if the fragment is assigned to different main categories; and b) it makes you sensitive

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to what type of open codes you want to generate. The following case study draws on a qualitative study (Maene, 2018) that analysed eighteen in-depth interviews with divorced mothers whose children had contact with a stepmother on a regular basis in the family of their father. The goal of the study was to understand which gatekeeping strategies existed between these two family units and what specific role the mother-stepmother dyad played in these family relations.

Case study 8.1 Maternal gatekeeping towards stepmothers: coding units The following fragment is part of an interview with Kimberly (a divorced mother) on ‘New formed families and perceptions on step motherhood’. The research questions of this study were: ‘How do mothers define step motherhood?’, ‘What strategies do mothers employ to deal/cope with the presence of a stepmother?’ and ‘What social interactions occur between (step) parents in new formed families?’. Based on these specific research interests we can identify three main categories that will be important in understanding the phenomenon: 1) definitions and perceptions; 2) emotional and behavioural interventions; and 3) communication/process dynamics. In the following fragment, the coding units are indicated by //coding unit//. As becomes clear, the strategy of the researcher was to code sentence by sentence. This intermediate step of assigning coding units is useful, as it remains clear how the text has been read during the coding process – even if some time passes. Another advantage is that it will enhance the coder reliability (see further) and/or the cooperation among researchers on the same text. I 

Your son’s stepmother had a different approach from you in educating children. Can you describe to me how your approach differed from hers? RES //She was much more strict than me.// My son would ask her for candy and simultaneously walk to the kitchen cabinet to take them, but that was not allowed there.// Instead, if he’s with me, he can take a candy if he wants, there is no need to beg.// Another example is about proper punishment. //So, if he would do something wrong at school, she used to punish him all evening for that. //While, if my son was with me, I would get angry with him for a bit but then I would let it go. //Sometimes his stepmother prohibited him from watching TV all evening or he would have to go to bed right after dinner; for me, that does not work. //When my son was with her, he constantly had the feeling he was being punished for something. //Another problem was the difference she made between her children and my son. //If his half-brother was naughty, it was never a problem. //Her children were used to getting everything and my son nothing.// I repeatedly asked her to stop making a difference like that, then she would claim that she loved my son as well. //

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The data is then completely ready to be analysed. However, it is better not to go through this process for all the data at once, but to select a subsample based on a diversity principle (Schreier, 2014) (see Case Study 8.2). You select texts that are either internally heterogeneous based on the topics that occur in those texts or focuses on texts that are externally heterogeneous based on the type of text or research participant that it reflects. Applying this diversity principle means that the coding frame will reflect the variety of the data, which leads to a better fit. Accordingly, you start to assign open codes to at least 10% of the material.

Case study 8.2 Maternal gatekeeping towards stepmothers: the diversity principle Table 8.1  Sample description Name respondent

Year of the divorce

Custody arrangement

Kimberly

2011

Mother full custody

Veronica

2009

Fifty-fifty mother/father

Hortensia

2002

Father four days a month

Marie

2013

Fifty-fifty mother/father

Sophia

2009

Father four days a month

Table 8.1 is a shortened version of the full sample that consisted of eighteen interviews with divorced mothers. A small majority of the respondents had agreed with their ex to organize the custody fifty-fifty, in which they take care of their kids part time. Practically, they organized that the kids go one week to their fathers’ house and the next week to their mothers’ house. In 2006, Belgian law stated that this custody and residency arrangement would be preferred by court over all other solutions. We could say that it is considered ‘the norm’. Before 2006, custody was automatically assigned to the mother, with a residency arrangement in which fathers would take care of the children during the weekend or every other weekend. Other arrangements, for example, in which children after a divorce would stay solely with their mother or father, would be considered exceptional. Now, if we were to select a subsample of these eighteen interviews to develop initial open codes and categories (see further), we would be interested both in the ‘rule’ and the ‘exception’, as a practical translation of the ‘diversity principle’. Therefore, we select the interview of Kimberly and Veronica as the subsample. Preferably, we would involve a third interview – for example of Hortensia – as that respondent would represent a custody arrangement according to the ‘old norm’. If we choose to work with three interviews in the subsample instead of two, our sample consists of more than 10% of the material. This shows that it is a rule that is not written in stone, but is a guiding principle that can help you not to lose yourself in the overwhelming amount of data. Theoretical considerations (e.g., the variation in custody arrangements) are equally important as quantifiable rules.

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After you decided on a starting point for the analysis, read through that text again and assigns open codes to the text. By doing this, you select the data that is relevant to answering the research question, and that data that will no longer be considered in the analysis. In this sense, open coding can be considered a form of data reduction (Kygnäs et al., 2019). Data that is not captured by open codes becomes invisible to you in the following analysis on the data. Therefore, the assignment of open codes is a very important analytical step during the analysis. You should take sufficient time to reflect on the data before labelling the coding units. In light of this, Saldana (2015) refuses to refer to open coding as data reduction, but rather approaches it as adding value to the text since you perform analytical labour that represents the way the text has been read. These open codes must stay close to the direct ‘content’ of the raw data to allow transparency (for an illustration see Case Study 8.3). It is recommended not to give any interpretations, especially if the focus is on manifest content analysis. At this point it is necessary to clearly state what types of content/code can be recognized in texts. If you do not reflect on which type of content you want to capture, it might lead to a coding frame that brings together a lot of different types of content but without any clear analytical and theoretical motivation (Saldana, 2015). The research question and the main categories of the coding frame help you to decide. It is possible to code multiple types of content, but it is wise to do so consciously and systematically (e.g., by assigning the different types of content to different main categories). First, you can perform descriptive coding by summarizing the basic topic of the coding unit. Second, it is possible to use process codes that capture the action that is taking place in the data. You can do so by using gerunds (words that end with -ing, such as frowning, reading, yelling, eating). These process codes can range from simple everyday activities to more conceptual actions. Third, there are emotion codes to capture the emotions (implicitly or explicitly) expressed by the participant or emotions that are (implicitly or explicitly) referred to in the text. With (implicit) emotion coding, it can happen that these emotions are your interpretation. It is necessary to transcribe non-verbal signals next to the spoken language of the research participant, both to assign process codes and emotion codes in the case of interview transcriptions. Fourth, values coding refers to the labelling of textual data based on the values, attitudes, worldview presented in the coding unit. Other interesting codes to apply to textual data are, for example, versus codes that point to the contradictions and conflict between two elements (e.g., individuals, groups, processes). These coding strategies are only a limited selection from Saldana’s (2015) manual on coding. Open codes can be re-used to capture different data fragments, but it is not unusual for a high amount of open codes to be generated in this phase. This practice can be particularly useful when the material is complex (multilayered) with one coding unit belonging to more than one main category or when the coding units are rather long. If coding units have to be ‘broken down’ into smaller pieces to assign codes, it usually means that the coding units are too big. In such a case, you risk the coding becoming more inconsistent.

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Case study 8.3 Maternal gatekeeping towards stepmothers: open coding In this example, we continue the interview fragment of Kimberly (see example 1). Earlier, we divided the interview fragment into coding units. These coding units are presented below. For each coding unit we develop an open code. Sometimes we still use a brief description rather than an open code, as it is not yet clear how we can capture the essence of the shorter fragment. These longer codes should be reduced in length or name in a next step (see further; category development). Remember, double coding in QCA is allowed only if coding units will be assigned to different main categories. The main categories of this research were about ‘perceptions of the mother’, ‘emotions and behaviour’ and ‘communication’. Therefore, keep in mind that if you want to assign two or more open codes to a coding unit, each open code should capture a different essence of the fragment. Applying different types of code (e.g., an emotion code or process code) might help you to understand the value of each coding unit. ‘She was much more strict than me.’

• •

Open code: stepmother being strict (process code) Open code: stepmother ‘more’ strict than mother (descriptive code)

‘My son would ask her for candy and simultaneously walk to the kitchen cabinet to take them, but that was not allowed there.’

•

Open code: stepmother not allowing candy (process code)

‘Instead, if he’s with me, he can take a candy if he wants, there is no need to beg.’

•

Open code: Contrary to stepmother, no begging needed (versus code)

‘Another example is about proper punishment.’

•

Open code: Contrary to stepmother, on ‘proper’ punishment (versus code)

‘So, if he would do something wrong at school, she used to punish him all evening for that.’

•

Open code: stepmother punishing all evening for mischief at school (process code)

‘While, if my son was with me, I would get angry with him for a bit but then I would let it go.’

•

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Open code: Contrary to stepmother, getting angry and letting it go (versus code)

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‘Sometimes his stepmother prohibited him from watching TV all evening or he would have to go to bed right after dinner; for me, that does not work.’

• •

Open code: stepmother prohibiting TV and bedtime after dinner (process code) Open code: disagreement on/annoyed by prohibiting TV and bedtime after dinner (emotion code)

‘When my son was with her, he constantly had the feeling he was being punished for something.’

•

Open code: son constantly feels punished (descriptive code)

‘Another problem was the difference she made between her children and my son.’

• • •

Open code: Stepmother differing treatment of step/biological children (process code) Open code: Frustration with stepmother about different treatment step/ biological children (emotion code) Open code: Different treatment of stepmother step/biological children is a problem (descriptive code)

(Sub)Category development After open codes are generated, they need to be grouped into (sub)categories, that can subsequently be grouped together in main categories (for an illustration see Case Study 8.4). As a result, a hierarchical structure of categories emerges. A category groups codes that share a common characteristic. If needed – and to protect the conceptual transparency of the coding frame – this can be double-checked in the raw data (Kyngäs et al., 2019), which allows you to compare or contrast them with other categories within the coding frame. This process shows that ‘category development’/development of the coding frame is both an empirical and conceptual/theoretical challenge (Elo & Kyngäs, 2008). It can happen that during this clustering of open codes, not every open code can be assigned to a category and that you find yourself with some residual open codes that do not belong together. This is usually a sign that saturation has not been reached as you are left with unidentifiable categories in the data. Further data should be collected to reach saturation. This is, however, not always possible and therefore, you have two options: a) create a residual group of open codes; or b) exclude these open codes from the analysis. In both cases, it is good practice to mention this in the data report by arguing what those codes did represent and why they were omitted from the analysis (Kyngäs et al., 2019). The more heterogeneous

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the research sample, the more likely it is that saturation will not be reached during the research project. It is also important that you take into account the difference between the ‘abstraction’ and ‘interpretation’ level of the coding frame under development. Abstraction refers to the various levels and their basic structure within the coding frame, while interpretation refers to the extent the codes, sub-themes and main themes try to catch the data’s latent meaning (Graneheim et al., 2017). If you decide to focus on manifest content, you should be attentive to the way in which labels are named. In the case of manifest content QCA, these labels should represent the shared content of the lower situated codes or (sub)categories and not be an interpretation of what is ‘meant’ with those content. In manifest QCA, the categories should be low in interpretation level, but can vary greatly in abstraction level. A manifest QCA analysis can be highly multidimensional with several main categories that identify a range of subcategories, and at the lowest abstraction level the open codes that were assigned to the units of coding (Graneheim et al., 2017). You should make sure that the degree of abstraction and interpretation is consistent throughout the coding frame, with codes on the same level having the same degree of interpretation. It is very difficult to develop a coding frame, and it takes up a lot of time to do it properly. You need to reflect constantly about the data and the decisions being made in the data coding process. To capture this process, you can document everything in research memos (White & Marsh, 2006). The result of this step is the first version of the coding frame (see the example below in Case study 8.5).

Case study 8.4 Maternal gatekeeping towards stepmothers: category development After assigning codes to the interviews in our subsample, the next important task is to cluster the open codes into subcategories and eventually (main) categories. The research question and research theme are taken into account in this process. You also make open codes more concise and/or reduce/merge open codes and/or rename them if this is deemed necessary. Consequently, a hierarchical structure starts to emerge (see Table 8.2). For simplicity, only fragments that related to ‘the stepmother’ in the new formed family were selected. If you coded a whole interview, you would get much more information (e.g., about the relationship with the mothers’ ex, the children, etc.). These raw data fragments are presented here for transparency. When you do this step yourself, it is unnecessary to line up the raw data fragments, since you will be very familiar with your data at this point. Once you clustered all your open codes into (sub)categories,

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you organize them in a hierarchical tree structure (or a coding frame, see Case study 8.5).

Table 8.2  Overview raw data to open code and (sub)category assignment Respondent

Data fragment

Open code

Subcategory

Category

Veronica

In the beginning, her role was to mediate between me and my ex and also between my ex and my son.

Stepmother mediating role between parents and children

Actions of the stepmother

Behaviours of (step)parents

You know, in the Relief that past, when he stepmother used to have stuff arrived that he did not understand (for example, related to paperwork, taxes and administration) he still used to call me to help him out. [Phff], the moment that she arrived, that part of my life was gone [sounds relieved].

Attitudes towards the stepmother

Perception of the stepmother

And second, I am happy because she can also side for my son during arguments.

Stepmother sides with children

Actions of the stepmother

Behaviours of (step)parents

I like that about her; she is a mother. She is a mother.

Happy that stepmother is a mother

Attitudes towards the stepmother

Perception of the stepmother

I am always going to greet her.

Greeting the stepmother

Communications with stepmother

(Step)Parental communications

She is a mother, she gets it.

‘Mothers’ get it

Cognitions about motherhood (general)

Definitions about family roles (Continued)

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Table 8.2  (Continued) Kimberly

She was much more strict than me.

Stepmother ‘more’ strict than mother

Cognitions about the stepmother (specific)

Perception of the stepmother

So, if he would do something wrong at school, she used to punish him all evening for that.

Stepmother punishing all evening for mischief at school

Actions of the stepmother

Behaviours of (step)parents

Another problem was the difference she made between her children and my son.

Different treatment of stepmother step/biological children is a problem

Cognitions about the stepmother (specific)

Perception of the stepmother

Texting – but always as if we were in a fight

Heated texting with stepmother

Communications with the stepmother

(Step)Parental communications

Case study 8.5 Maternal gatekeeping towards stepmothers: coding frame In Table 8.3, a shortened version of a coding frame is given that potentially could have been developed from the initial open coding of two interviews. This gives you an overview on which codes and categories are developing. This first version of the coding frame helps you to reflect on the consistency of the interpretation and abstraction level of the codes. For example, the categories received ‘neutral’ names (see, for instance, ‘attitudes towards the stepmother’), the content that is captured by this category represents however all ‘positive’ emotions of mothers towards stepmothers, while the category ‘specific cognitions’ towards the stepmother captures all ‘negative’ ideas on behaviours of stepmothers. We could argue that these category names are therefore too general and that it would suit the data well to opt for names such as ‘positive attitudes towards’ the stepmother. It is to be expected that later on you might encounter ‘negative attitudes’ towards the stepmother too. Breaking this up in two codes adds complexity – higher abstraction level – as new levels are added to the coding frame; however, they might be a better interpretation on the shared content under each category.

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Table 8.3  Shortened example coding frame Category

Subcategory

Open code

Behaviours of (step) parents

Actions of the stepmother

Mediating role Siding with stepchildren Punishing all evening

Actions of the father

Disowning children Investing in ‘quality time’

Perception of the stepmother

Attitudes towards stepmother →‘Positive attitudes towards stepmother’ Specific cognitions stepmother →‘Specific negative cognitions about stepmother’

(Step)Parental communication

Communication stepmother

Relieved at arrival of stepmother Happy she’s a mother Problem with different treatment More strict Greeting each other Heated texting Practical texting

Communication father

Custody lawyer meeting Arguing over the phone Unanswered phone calls

Definition family of roles

General cognitions on stepmother

Mothers ‘get it’

Cognitions on motherhood

Need to protect children They’re ‘my’ children

The process described above is applicable in cases where the coding frame is built up from manifest content (Gruneheim & Lundman, 2004). In the case of latent content which is the interpretation of what the unit of coding means, the use of ‘theme’ and ‘sub-theme’ is preferred. However, some researchers appear to use the words ‘theme’ and ‘category’ interchangeably (Graneheim et al., 2017). Also note that the usage of ‘themes’ in the context of a QCA is not the same as how researchers in thematic analysis use it (see also Chapter 10). Others use the word ‘concept’ instead of ‘category’, for example when the goal of the study is to develop a theory (Elo & Kygnäs, 2008). Again, some scholars seem to use the word ‘code’ for everything that is present in the coding frame that is not raw data. However, leading QCA scholars do not accept this.

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Making all these distinctions explicit is very important (Assarroudi et al., 2018; Hsieh & Shannon, 2005). Using words in a consistent way and avoiding changing the terminology during the report is paramount. It is also good practice to explain and define the analytical tools applied throughout the text to make it very clear for the reader what the abstraction and interpretation level of the presented coding frame is.

The coding scheme Once the preliminary coding frame is developed, a coding scheme that explains to other users how the coding frame should be applied to the data has to be specified (Hsieh & Shannon, 2005; Schreier, 2014) (for an illustration see Case Study 8.6). This coding scheme is also an important step of the analysis in QCA since it offers a definition of each category within the coding frame. These category definitions consist of a series of elements. The definition of the category clearly states when the given category is applicable and of which characteristics it constitutes. You describe clear links when the category is applicable to the data and provide examples of coding units or open codes in the category. Finally, to avoid overlap and confusion with other categories in the coding frame, it might be useful to also add decision rules to further explain the application of the coding frame. This task not only brings the analysis to a higher conceptual level, but also aids the trustworthiness of the overall analysis. The development of the coding scheme is therefore an important core task – next to the development of the coding frame – in QCA.

Case study 8.6 Maternal gatekeeping towards stepmothers: coding scheme Although the coding scheme is the result of inductive open code development, as researchers we look at the social reality from within a discipline. This scientific discipline gives direction to which category names we assign to open codes, how we structure phenomena and subsequently how we define the reality under observation. The example in this chapter is drawn from a micro-sociological study with strong links to social psychology. Therefore, if you want to work in a truly ‘inductive’ manner it is important to reflect on your own frame of reference when developing and defining these categories. Try to challenge yourself; would other categories and definitions be possible based on the open codes you generated? What makes your approach defendable? At this point, the analysis is still ongoing and therefore you can still change and rethink your work. This will become considerably more difficult once the coding frame and coding scheme are applied to all the data. In fact, then it is not supposed to change anymore.

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Description

This category includes all the subcategories involving the observed behaviours of parents in which the (step) children are directly involved.

A category that is labelled as ‘perception’ can contain subcategories that relate to cognition and attitudes of the respondent as every category in here captures mental processes that are not directly observable unless made explicit by the respondent. In this specific case, it is about the stepmother involved in the family of the father’s children.

Although communication is strictly speaking ‘behaviour’, this category in the coding frame is reserved for ‘social interactions’ in which the mother (respondent) is directly involved with another (step)parent.

This category involves all the subcategories that capture expressions of the respondents about ‘roles’ in the extended family. A description of a role by the respondents is a statement about a comprehensive pattern of behaviours and attitudes of people in a certain position. It gives meaning to the social reality and makes positions within the family identifiable for the respondent.

Category

Behaviours of (step)parents

Perception of the stepmother

(Step)Parental communication

Definition of family roles

Table 8.4  Shortened example coding scheme

Subcategories are divided by parent position

Subcategories are divided by parent position

A specific cognition of the respondent is a mental process that gives information on the mother’s ideas, beliefs and how she observes and processes social information.

Subcategories are divided by parent position.

Subcategory description

By splitting behavioural categories from perceptional categories, coding units that contain information about both can be placed in both by assigning a double open code to the coding unit. One open code then captures the behavioural dimension of the fragment/coding unit, while the other open code captures the perceptional element. The coding units need to get another name at all times!

Applicability

A mother protects children

Arguing over the phone

More strict

Siding with stepchildren

Examples

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The trial coding During the trial coding, you select new data to evaluate the first version of the coding frame. The material you select should cover all different types of data and/or all data sources (see also ‘diversity principle’) (Schreier, 2014). During the trial coding, you and/or a fellow researcher perform two rounds of coding on the same material. Before the coding starts, you divide the data into units of coding and become familiar with the coding frame and the coding scheme. All the coders should have a clear idea of the codes that are taken up in the coding frame. If the coding frame is quite extensive, it might be best to divide the coding frame into pieces (e.g., by main category) and apply it into phases on the data; otherwise, trial coders might forget to use meaningful pieces. This is needed to correctly apply the open codes and the categories developed in the coding frame. Within the literature on QCA, this is referred to as ‘coder training’ (White & Marsh, 2006). If you work alone, it is best to leave a period of at least ten days in between the first and second round of coding (Schreier, 2014). If two independent coders perform the trial coding, it can be done simultaneously. If the coding scheme is straightforward and the coding frame not overly complex, the coding units end up twice in the same category or open code. If the difference in coding between the first and the second round is small, it indicates that the quality of the coding frame and the corresponding coding scheme is high. To improve the quality of the coding frame, it is necessary to first focus attention on the coding units that were assigned to completely different open codes/categories and increase the conceptual/theoretical transparency of those codes or categories. It might be that some categories are redundant or that it is unclear in which instance one or the other has to be applied. We might also find that the coding frame needs to be elaborated. QCA researchers agree that dialogue is absolutely necessary and the most useful technique to improve the quality of the coding frame (Elo & Kyngäs, 2008; Mayring, 2015; Schreier, 2014). If you are conducting the study by yourself, it is wise to consult an expert panel or supervisor to discuss key conflicts in the coding frame. Researchers, especially those who consider QCA as a hybrid technique, propose to use a quantifiable indicator of the coder agreement. For each code it is possible to calculate the amount of overlap between the two coders. This makes it an efficient technique to evaluate to overall quality of the coding frame, but also to track down fast inconsistencies between open codes/categories (Schreier, 2014). If the trial coding is conducted by two coders and the coding frame applies mutually exclusive codes (one-to-one classifications) – which is usually the case in content analysis – Cohen Kappa (Cohen, 1968) can be used as a measure for intercoder reliability (Burla et al., 2008). Since this measure is very popular, it is supported by different software packages such as NVivo (QSR International, see Chapter 5). Cohen’s Kappa is a better measure than the percentage of agreement between codes, since it takes into account that codes might have been assigned by chance. Cohen’s Kappa can range from -1 to +1 (McHugh, 2012). Values lower than zero indicate complete random agreement, if any has occurred. Kappa values

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between 0.40 and 0.60 are considered moderate, while values over 0.80 can be regarded as nearly perfect intercoder reliability. Cohen’s Kappa cannot be used in all instances. If more than one coder is involved in the data analysis process, it is necessary to use Fleiss’ Kappa (Fleiss et al., 1979; Gwet, 2014), which can be calculated with the IBM SPSS statistical software. If the coding frame for nearly all codes made use of double coding (one-to-many classifications instead of one-to-one classification), Cohen’s Kappa cannot be applied. An alternative in this case is to make use of Fuzzy Kappa (Kirilenko & Stepchenkova, 2016). Since the latter is not widely used yet, or supported by commercial software packages, the software to calculate the Fuzzy Kappa can be freely downloaded online (https://sourceforge.net/projects/fuzzy-kappa). Some qualitative researchers are not in favour of the use of coefficients as indicators for coder agreement. They argue that alternative interpretations of a text are always possible (Elo & Kyngäs, 2008). If any changes are made to the coding frame (which is most likely to happen in this phase), the trial coding starts all over again. Between every trial run, it is advised to begin with a clean slate and to ensure that all the data will be eventually considered for the final version of the coding frame. The trial coding makes QCA an iterative process (Mayring, 2015). The data and the coding frame are revised over and again until a consensus is reached. If you work in a team, you should discuss with each other why they do not reach agreement on certain coding units and develop solutions in order to continue with the main analysis of the textual data. Even if you conduct the analysis alone, it is highly recommended to consult a supervisor, fellow students or colleagues to reflect on the most critical and important codes in the coding frame. Enhancing the quality of the coding frame increases the trustworthiness of the overall study (Kyngäs et al., 2019). More specifically, this relates to the criterion of dependability of qualitative data, which means that the analysis and data are stable over time and varying conditions. By taking into account the dependability of your study, you aim to reach consistency throughout the research process.

Main analysis and reporting of results Once you have reached a consensus on the coding frame and no new codes are generated or adapted by trial coding, all the data is coded with the same coding frame. At this point, you cannot make further changes to the coding frame (Schreier, 2014). To conduct the main analysis, all the remaining data must be divided into coding units which are subsequently assigned to an open code/category in the coding frame. This is the least time-consuming task of all steps in the conventional QCA, since the coding frame has already been sufficiently tested. Only Schreier (2014) proposes to double code for at least one-third of the remaining data to ensure the coding stays consistent. Other authors do not mention whether they continue to double code after the trial coding has finished. If all the textual data is coded at least once in a consistent way with the same coding frame, you must then decide how to continue the analysis. One way is to continue by interpreting the overall category system that has been developed and make use of

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existing theory to contextualize your research into previous research findings (Mayring, 2015). It is very important that a full account is given on the research process, category development and that the coding scheme is presented. To establish a clear link between the coding frame and the raw data, authentic citations can be given (Kyngäs et al., 2019). This is especially important to add authenticity to the findings, as it shows the different standpoints and phrasing of the original text. If you carefully select citations from various data sources, you add to the trustworthiness of your study. The goal of this discussion should be the theoretical relevance and conceptual depth of the analysis, since this is the main strength of conventional QCA. If it helps to answer the research question, you can make use of percentages and numbers to stress the relevance of one category compared to the other or to pinpoint the reader to differences among categories (White & Marsh, 2006). If the coding frame is presented in this way, it invites other scholars to validate the coding frame by a deductive QCA approach, or compare the conceptual framework in another research context.

Directed qualitative content analysis Directed content analysis, also referred to as ‘deductive’ or ‘concept-driven’ content analysis, has as its main strength that it can confirm and extend already existing theory (Hsieh & Shannon, 2005). We most often opt to use a deductive approach when we want to compare social contexts with each other in a theoretical way by testing whether certain concepts re-occur in different settings, or to test an existing theory in a completely new context (Kyngäs et al., 2019). As a precondition, a theory and preferably a coding frame to apply to the new textual data should already be developed. Therefore, directed QCA cannot be used for research topics that are yet to be explored. A good example of a research question that is suited for this type of QCA is found in a study on gaming (Bourgonjon, 2015): how do the personal stories of players on how gaming affects their lives – as expressed in popular game forums – reflect cultural rhetorics on gaming? The study aimed at comparing individuals’ personal meanings of video gaming with existing meanings of video gaming in public discourse. To investigate the complexity and diversity in the stories of these gamers, Bourgonjon (2015) used a coding scheme developed by Belfiore and Bennett (2008) in their research on the taxonomy of the impact of art. The researchers make explicit from the very beginning of the study that it was their goal to focus on the positive experiences, since previous research mainly focused on negative impacts of video gaming (Bourgonjon, 2015). Another example of a directed QCA research is the recent study of Wei and Watson (2019) on the application of Watson’s human caring theory in healthcare teams. As a result, the empirical analysis provided real-lived definitions and examples of what is considered ‘human care’ in an interprofessional work setting and thereby affirmed the underpinning theoretical model. However, the explicit use of theory in directed content analysis, poses some challenges. First, we start the research process with an informed but also very clear

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vision on what the goal of the research is. This can lead to bias in both data collection and data analysis, with cases and findings being more likely to be theory supporting than theory subverting (Hsieh & Shannon, 2005). Being so focused on theory, you might overlook the contextual uniqueness of the data or label data that does not fit the theory as irrelevant (Graneheim et al., 2017). To take these challenges into account and simultaneously increase the trustworthiness of the study, you should pay extra attention to the confirmability of the study. This means that extra attention is given while reporting the results to the link between the data and the findings by making use of ‘audit trails’; these are documents that increase the transparency and offer evidence regarding which reflections and steps you took to come to the given results (Kyngäs et al., 2019). In the following, the procedural steps to conduct a directed QCA will be discussed.

Defining the structured or unconstrained categorization matrix Before you can start coding the material, it has to be decided with which codes and categories the material will be approached. Since the (majority) of the codes are defined beforehand, we will refer to it as the ‘categorization matrix’ instead of the coding frame, the term used for conventional qualitative data analysis. A first decision is whether you will make use of a structured or unrestrained matrix (Elo & Kygnäs, 2008). When you opt for a structured categorization matrix, only those data segments that fit into the coding matrix will be used (Assarroudi et al., 2018), while if you choose to work with an unconstrained matrix, some codes can be developed inductively. The latter seems to correspond with what Mayring (2000) named the formative categorization matrix. The choice for either one or the other should be theoretical and take into account the aim of the study (Elo & Kyngäs, 2008). Both in the case of the unconstrained and structured matrix, the codes and categories are developed from theory. Once the matrix is formed, a coding scheme needs to be developed. Similar to the inductive approach, each category gets a definition; an anchor example and decision rules are also proposed to help coders label the data in a consistent way (Mayring, 2015). The main difference between the coding scheme in the deductive or inductive approach is that in the former everything is based on previous research and theory, while in the latter the coding scheme is made after the open coding of the data has started.

Initial coding and exploration Similar to the inductive QCA approach, the categorization matrix needs to be piloted on the data. On the one hand, you are training to apply the categorization matrix to the data (developing sensitivity to recognize concepts in the data, optimizing coding units, etc.). On the other hand, it offers you an evaluation of the fit of the categorization matrix to the data, which informs you on the need for inductive codes. Before actual coding can be done on the data, you should take time to further explore the data through

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initial coding. The first practical step is applying the diversity principle (Schreier, 2014) to the data and selecting texts from different sources or participants. The selected material should allow you to test almost all the categories in the matrix. The material is divided into coding units (Schreier, 2014). In the inductive approach, coding units are selected because you get immersed in the data and explore the data by reading it multiple times. In the deductive QCA approach, the coding units are picked with the lens of the categorization matrix in mind (Assarroudi et al., 2018). Some researchers refer to this task as ‘initial coding’. During this exploration, you begin to understand how you will deal with coding units that do not fit the material. Hsieh and Shannon (2005) propose that all relevant data should be coded, even if it does not fit into the categorization matrix. During the initial coding, you might already write down some inductive open codes to consider. It is important to remember that this procedure is applicable only if an ‘unconstrained’ categorization matrix is used. If a structured categorization matrix is used, no inductive codes will be developed from the data. You should also add examples from the data to the coding scheme (with the deductive codes), to make its application more transparent and easier to use; however, keep a clear overview on which codes were deductive and which inductive and how these developed through the research process (Assarroudi et al., 2018).

Trial coding and revision Once you have established a clear idea on how the categorization matrix can be applied to the data, you can start with coding the material to start the trial coding. If you work alone, the timespan between the second round of coding should be at least ten days (Schreier, 2014). Analyses conducted by multiple coders should apply the categorization matrix independently and already take into account the proposed inductive codes. Schreier (2014) recommends dividing the categorization matrix into smaller parts if it consists of more than forty categories that need to be applied to the data. This can be done efficiently by applying each main category separately. After this process of double coding, you should discuss the differences in the application of the categorization matrix and adapt the frame accordingly. For a more elaborate account on how this should be done, the reader might also look at the discussion on trial coding provided in the conventional QCA section. Overall, it seems that all scholars applying directed QCA agree that the categorization matrix should be adapted (Assarroudi et al., 2018; Hsieh & Shannon, 2005; Mayring, 2015; Schreier, 2014). We should especially reflect on the place of the inductive codes in the categorization matrix. This might lead to the addition of inductive codes in theoretically developed categories, or to create an overall new part in the categorization matrix. For conceptual clarity, it is better to refer to this cluster as ‘generic category’ instead of ‘main category’ (Elo & Kyngäs, 2008). The latter is used in the case of directed QCA to indicate deductive categories. The way we deal with inductive codes, as well as all the other adaptations to the categorization matrix, should be clearly documented.

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Main coding process Once a transparent and applicable categorization matrix has been developed, the main analysis can start. All the data is coded with the same categories (Schreier, 2014). Again, to increase the trustworthiness of the investigation, you can opt to double code part of this material to check the coding consistency. It might happen that some coding units do not fit the categorization matrix. It is useful at this point to keep these data segments separately and later reflect on the meaning of these coding units in relation to the text and its context (Hsieh & Shannon, 2005). At this point, it is important not to change the categorization matrix anymore.

Reporting the results A first requirement in the presentation of the results of a directed QCA is to give an account of the whole research process with special attention to the theoretical development of the categorization matrix and its empirical evolution. This includes a clear account of empirical examples of theoretical categories to establish a link with the raw data and eventually the development of inductive codes (Assarroudi et al., 2018). Second, the main analysis should clearly bridge theory and social reality by presenting an elaborate and insightful discussion of theory supporting and non-supporting evidence (Hsieh & Shannon, 2005). For this purpose, it is not uncommon to use frequencies to highlight how many times certain content appeared in the data compared to other topics. The link with previous research and previous applications of the categorization matrix should be mentioned (Kygnäs et al., 2019). Moreover, it should be clear how the theory can be extended or refined due to the findings of the directed QCA (Hsieh & Shannon, 2005).

8.4 Conclusion and discussion QCA is a qualitative research method that enables you to identify categories present within textual data by following a systematic coding process that leads to a thorough contextual description (Hsieh & Shannon, 2005) and limited quantification of the research phenomenon (Mayring, 2000). Two approaches have been discussed elaborately in this chapter. The first, conventional QCA, is based on an inductive process of category development. It is particularly suited to explore phenomena that are underrepresented in the academic field and can help us to gain theoretical insights. The second, directed QCA, makes use of a deductive research process. The starting point of category development is theory and previous research rather than the textual data in itself. This approach is helpful when we want to test an existing theory or coding frame in a new context. The goal of directed QCA is to give an account of the similarities or differences between two phenomena or to refine a theoretical framework.

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What all these approaches have in common is that they are designed to ask one specific type of research question, namely questions that aim at rich description of the social world. This means that they cannot be used to answer those research questions that aim at understanding an underlying process or development of a phenomenon, nor can they answer questions that want to shed light on in-depth relationships between concepts or cases. To repeat this at the end of this chapter is important, since it makes no sense to opt for QCA if it does not match the overall goal of the study. Nevertheless, for research projects that have a medium-term length, QCA can be a good starting point to understand the research topic. Afterwards, you could opt to re-analyse the data through a different lens by engaging more critically with the data or choosing methodologies that allow for stronger theoretical developments. Another element that all these approaches to QCA have in common is the strict rulebound procedure that must be followed during the research process. In QCA, criteria on trustworthiness are followed with absolute rigour. The main criteria are transferability, confirmability, dependability, credibility and authenticity (Kygnas et al., 2019; Lincoln & Guba, 1986). These criteria are important in all approaches in QCA, although they were only mentioned in specific instances within this chapter. Lastly, one important point of discussion among QCA methodologists is whether the use of quantifications is appropriate to report the coding frame and coding scheme (Schreier et al., 2019). Researchers who call for a more ‘qualitative’ turn in QCA will not report quantifications, while researchers who consider QCA as a hybrid approach will report quantifications and might use the coding frame as a stepping stone for further statistical analysis.

8.5 Summary checklist In this chapter you learned to conduct two different QCA approaches step by step. You now understand what realistic goals for a QCA study are and how this differs from other qualitative data analysis methods. You know the importance of trustworthiness in QCA.

8.6 Doing qualitative content analysis yourself In this exercise we will prompt you to think about what is required to design a QCA study for a master’s thesis.

8.6.1 Assignment Imagine that your friend and fellow student, Tunde, asks you for advice on how to develop a QCA design to study the following topic: ‘female members of political rightwing organizations’. In helping out your peer, please focus on all the main features of a QCA analysis (developing a research question, purposive sample, diversity principle,

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code development, coding scheme development and trial coding), and describe how he would address these in relation to his proposed study. Please also consider that your friend is doing this as part of a master’s thesis, which means that you should consider the limited resources at his disposal.

8.6.2 Model response Tunde should develop a research question about this topic that is suited for a QCA. The answer to the research questions should allow for a rich, in-depth description of the research theme. Subsequently, Tunde should start with an elaborate literature review on the different topics of his research theme, such as gender, political organizations, right-wing extremism. Tunde should be attentive to the social context of both the theoretical and empirical studies. It is important to make the right choice between an inductive or deductive QCA. If his research theme has already been studied empirically in other contexts or a well-established theoretical framework exists for which empirical evidence seems to be lacking, a deductive QCA could be the right approach. This would help Tunde to replicate and further develop theoretical and/or empirical knowledge about female members of right-wing organizations. However, if the literature is very fragmented and empirical research that is similar to his focus is lacking, an explorative inductive QCA is the best choice. This approach would allow Tunde to lay theoretical foundations for this phenomenon. Tunde decides to develop an inductive QCA as he realizes from the literature review that feminism has a very leftist connotation and an overall right-wing point of view on feminism is lacking in the current literature. As a result, Tunde would like to study the following research question: How do female members of right-wing political parties understand feminism? When Tunde has decided on the research questions and which QCA approach is the most appropriate, he should develop a theoretical and purposive sample. Tunde should take into account that the scope of the sample is strictly defined by answering questions such as: What are right-wing organizations? When is an organization considered right wing? If important theoretical differences exist among these types of organization, he might want to take those into account for this sample. It should reflect important theoretical types of right-wing organizations. For example, he could choose to select four women from three different types of right-wing organization. This allows for the analysis to transcend the specific characteristics of one organization in particular. On the other hand, if right-wing organizations are very rare in his societal context, it might be good to interview only female members from one organization. Overall, a minimum of twelve interviews should be reached to have a sufficient rich data set. In QCA, it is the most common to only have one data collection phase. Once Tunde has collected his data, he needs to familiarize himself with the data of his respondents. Afterwards, he selects a subsample of his interviews by using the diversity principle. He does that by selecting interviews that are internally different from each other. He also makes some informed decisions regarding his coding strategy; first,

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he segments the text into coding units (e.g., words, phrases or paragraphs), second, he decides which types of code (and therefore ‘content’) are going to be assigned to coding units. Once Tunde has made these decisions, he can assign codes to the coding units of his subsample. He lists those codes in a separate file. This step is followed by a phase of category development that consists of clustering the open codes together in broader categories. The result is a hierarchical coding structure. To enhance the reliability of these categories and codes, Tunde adds descriptions and application rules. This is called the development of a coding scheme. Before coding all the data with the developed coding scheme, Tunde should conduct a trial coding. To do this correctly, he must select new data. This will push Tunde to increase the validity of his codes and categories. He might have to repeat this process a couple of times until the coding becomes consistent and very transparent. He should pay particular attention to those codes that repeatedly not match the same data. This is an important theoretical phase that leads to enhanced conceptual clarity. When Tunde has a coding scheme that has been trial tested, he must code all his data with this coding scheme. Afterwards, he writes his results in which he thoroughly describes all the choices he has made during his code and category development. He presents the coding scheme with its definition and application rules. Tunde needs to provide examples from the data to support these categories. He reports in an elaborately descriptive way which subcategories emerged from the empirical data with regard to the way in which women in right-wing political organizations talked about feminism and their daily experiences with womanhood. Equally important is to conclude with an in-depth theoretical interpretation of these categories in the coding frame. If Tunde included more than one right-wing organization, he should also present and interpret a table to show whether the applications of the coding scheme differ among those organizations and if possible, provide an associative statistical test to support this finding.

8.7 Recommended reading Schreier, M. (2012). Qualitative Content Analysis in practice. London: SAGE Publications Ltd. This handbook is well-suited if you have collected data through interviews and focus groups and are interested in applying inductive QCA. It does not provide an account of deductive QCA. Kyngäs, H., Mikkonen, K. and Kääriäinen, M. (eds) (2019). The application of content analysis in nursing science research. Cham: Springer International Publishing. This handbook covers both inductive and deductive types of qualitative content analysis but if you are interested in applying QCA in a mixed-method design you might find this book helpful as it goes into detail on the application of statistical interferences as a result of QCA analysis.

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8.8 References Assarroudi, A., Heshmati Nabavi, F., Armat, M. R., Ebadi, A. and Vaismoradi, M. (2018). Directed qualitative content analysis: the description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), 42–55. Belfiore, E. and Bennett, O. (2008). The social impact of the arts. Basingstoke: Palgrave Macmillan. Berelson, B. (1952). Content analysis in communication research. Glencoe, IL: Free Press. Bourgonjon, J. (2015). Video game literacy: social, cultural and educational perspectives (doctoral dissertation). Ghent: Ghent University. Burla, L., Knierim, B., Barth, J., Liewald, K., Duetz, M. and Abel, T. (2008). From text to codings: intercoder reliability assessment in qualitative content analysis. Nursing Research, 57(2), 113–17. Cohen, J. (1968). Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213. Elo, S. and Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–15. Fleiss, J. L., Nee, J. C. and Landis, J. R. (1979). Large sample variance of kappa in the case of different sets of raters. Psychological Bulletin, 86(5), 974. Graneheim, U. H. and Lundman, B. (2004). Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105–12. Graneheim, U. H., & Lundman, B. (2010). Experiences of loneliness among the very old: the Umeå 85+ project. Aging & Mental Health, 14(4), 433–438. Graneheim, U. H., Lindgren, B. M. and Lundman, B. (2017). Methodological challenges in qualitative content analysis: a discussion paper. Nurse Education Today, 56, 29–34. Gwet, K. L. (2014). Intrarater reliability. Wiley StatsRef: Statistics Reference Online. Hsieh, H. F. and Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–88. Kirilenko, A. P. and Stepchenkova, S. (2016). Inter-coder agreement in one-to-many classification: fuzzy kappa. PloS one, 11(3), e0149787. Kuckartz, U. (2014). Qualitative text analysis: A guide to methods, practice & using software. London: SAGE Publications Ltd. https://dx.doi.org/10.4135/9781446288719 Kuckartz, U. (2019). Qualitative content analysis: from Kracauer’s beginnings to today’s challenges. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 20(3). Kyngäs, H., Mikkonen, K. and Kääriäinen, M. (eds) (2019). The application of content analysis in nursing science research. Cham: Springer. Lincoln, Y. S. and Guba, E. G. (1986). But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Directions for Program Evaluation, 1986(30), 73–84. Maene, C. (2017). A qualitative study of trajectories of maternal gatekeeping towards the stepmother. 13th Conference of the European Sociological Association.

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Maene, C. (2018). Moeders over stiefmoederschap: de mythe voorbij? Een kwalitatieve studie over het traject van maternal gatekeeping tegenover de stiefmoeder. Sociologos, 39(2), 107–25. Mayring, P. (2015). Qualitative content analysis: theoretical background and procedures. In Approaches to qualitative research in mathematics education (pp. 365–80). Dordrecht: Springer. Mayring, P. (2000). Qualitative Content Analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 1(2). https://doi.org/10.17169/fqs-1.2.1089 McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276–82. NVivo 12, QSR international, www.qsrinternational.com/nvivo-qualitative-dataanalysis-software/home. Pelto-Piri, V., Engström, K. and Engström, I. (2014). Staffs’ perceptions of the ethical landscape in psychiatric inpatient care: a qualitative content analysis of ethical diaries. Clinical Ethics, 9(1), 45–52. Saldaña, J. (2015). The coding manual for qualitative researchers. Thousand Oaks, CA: SAGE Publications Ltd. Schreier, M. (2012). Qualitative Content Analysis in practice. London: SAGE Publications Ltd. Schreier, M. (2014). Ways of doing qualitative content analysis: disentangling terms and terminologies. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 15(1). Schreier, M. (2016). Kategorien – Codes – Kodieren. Versuch einer Annäherung an diffuse Begrifflichkeiten. Keynote Lecture, Conference ‘Qualitative Inhaltsanalyse – and beyond?’ Weingarten, Germany, 5 October 2016. Schreier, M., Stamann, C., Janssen, M., Dahl, T. and Whittal, A. (2019). Qualitative content analysis: conceptualizations and challenges in research practice – Introduction to the FQS Special Issue Qualitative Content Analysis I. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 20(3). Shannon, L. W. (1954). The opinions of Little Orphan Annie and her friends. Public Opinion Quarterly, 18(2), 169–79. SPSS (2010). SPSS for windows (version 20). www.ibm.com/analytics/ spss-statistics-software?lnk=STW_US_STESCH&lnk2=trial_ SPSS&pexp=def&psrc=none&mhsrc=ibmsearch_a&mhq=spss. Wei, H. and Watson, J. (2019). Healthcare interprofessional team members’ perspectives on human caring: a directed content analysis study. International Hournal of Nursing Sciences, 6(1), 17–23. White, M. D. and Marsh, E. E. (2006). Content analysis: a flexible methodology. Library Trends, 55(1), 22–45.

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9 Textual Analysis: A Practical Introduction to Studying Texts in Media and Cultural Studies Frederik Dhaenens and Sofie Van Bauwel

Even though plenty of academic attention has been given to consumers and audiences of media, it is essential for students and scholars in social sciences and humanities to take the actual content of popular media culture seriously. Why can a film or pop song be considered sexist, racist and/or homophobic? What makes a popular television soap so relatable? How do sitcoms employ humour to tackle societal taboos? Such questions demand an understanding of how popular media content works and, more importantly, how audiences can make sense of them. Even though you can explore meaning-making practices by means of qualitative audience research, this chapter aims to highlight that an understanding of the actual texts is equally important. To understand how people make sense of popular media culture, you need to understand how a popular culture text or group of texts are constructed.

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Demonstrate what makes textual analysis a unique method on its own and how it differentiates from other methods (e.g., discourse analysis, content analysis) that also study media content and media texts. Provide a practical, tangible and comprehensible step-by-step overview of the method.

9.2 Key features, debates and historical development An introduction to textual analysis starts by pointing out that the ‘text’ in ‘textual’ refers to a broad range of media and cultural artefacts. As McKee (2003) points out, a text can

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be more than a document of written words. Books and news articles are evidently texts, but films, fiction and factual television programmes, comics, digital games, online and social media content (e.g., an Instagram picture, a tweet, a TikTok clip, etc.), clothes or graffiti equally qualify as text. Scholars who conduct textual analyses treat these texts as a means by which people make sense of themselves and the societies that they are part of. Formulating the aim of a textual analysis as such reveals the broad applicability of the method within academia. Unsurprisingly, textual analysis has been practised in plenty of disciplines and multidisciplinary fields. In contrast to methods exclusively developed within social sciences, textual analysis is a flexible and open method and can easily be integrated in divergent research traditions. Its flexibility may, however, be its biggest threat. In comparison to methodological literature on post-positivist content analysis or (critical) discourse analysis, only a few authors have explicitly written about textual analysis as a proper research method (see Flick, 2013; Larsen, 2002; McKee, 2003). A reason why textual analysis has been given little attention may have to do with the fact that many fields and disciplines – particularly within humanities – have a different praxeology. Academic articles and books may not present a detailed account of their chosen scientific approach in contrast to many traditions within social sciences. A more pertinent reason, however, is the method’s refusal to follow strict research principles and protocols, to conduct research that is generalizable, or to assume that any researcher should be able to obtain a similar result when using the same research design. Not infrequently has the assumed absence of a research design nudged scholars into questioning the value of textual analysis or implying that a proper textual analysis can be conducted only if it follows the methodological rules of, for instance, a content analysis (Neuendorf, 2016) or a discourse analysis (Gee, 2014; Tannen et al., 2015). Yet, such critiques often come from a misunderstanding of a researcher’s ontological and epistemological position and the methodological conventions that typify a particular research tradition. Instead of relying on fixed procedures, many contemporary scholars who use textual analysis share a common analytic language that is shaped by insights from hermeneutics, semiotics, deconstruction and film analysis using concepts that are used as premises/ shared assumptions. They are familiar with the various modes of how to interpret a text as a cultural artefact that relates back to historical and/or contemporary societies. On the other hand, the freedom that characterizes this method may give way to some scholars to produce equivocal, sloppy, undertheorized and unmotivated readings of popular culture texts. Since the 2000s onwards, scholars have invested in developing textual analysis as a proper method for their own research projects. This can be noted in the way that textual analysis is increasingly mentioned as a method in research articles that examine a diversity of textual material. Looking at the inclusion of the term ‘textual analysis’ in research articles and proceedings papers in the Web of Science Core Collection, we notice from the 2010s onwards a remarkable increase (see Figure 9.1), with a record high number of 513 publications in 2019.

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Figure 9.1  Inclusion of term ‘textual analysis’ in ‘topic’ in Web of Science core collection over time Second, when assessing in which fields textual analysis is used (see Figure 9.2), it can be noted that in articles in the research area of communication in particular, the method is mentioned the most (14%), followed by education research (9%), field of language and linguistics (6%) and sociology (5%). Yet, it should be underscored that not explicitly referencing textual analysis does not mean that the method was not used. For instance, in comparison to the field of communication, the field of cultural studies accounts for only 2% of the articles that use textual analysis, despite the fact that many scholars within cultural studies (indirectly) employ textual analytic approaches (Barker, 2000; McKee, 2003). With this chapter, we challenge the idea that ‘anything goes’ in textual analysis and demonstrate that the method can be of use to analyse the different layers of a text in a semi-structural manner. Moreover, it helps understanding the text profoundly in a specific cultural context. In this chapter, we zoom in on how the method is employed within the field of media and cultural studies. Following Hammer and Kellner (2009), we consider media and cultural studies to be an interdisciplinary field consisting of two research traditions that share similar authors, perspectives, methods and topics. Taking into account that media studies and cultural studies are both informed by constructivist and critical perspectives on theory and research (see Chapter 1), they share the assumption that the study of cultural texts and practices allows us to understand society, in which culture is produced and consumed (Storey, 1997). Culture here is conceptualized in the broadest sense of the word; it refers to ‘the entire range of a society’s arts,

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beliefs, institutions, and communicative practices’ (Grossberg et al., 1992, p. 4). Hall and Jefferson (2006) note that the focus on culture is not tantamount to a cultural deterministic perspective. Rather, media and cultural studies are interested in how media, culture and society relate to one another. It focuses on how media and culture are lived (aka lived experiences) and signified in the everyday life of human beings, it revaluates popular (media) culture by erasing the moral hierarchy between low and high culture and it assumes that audiences can actively negotiate the role and meaning of popular (media) texts (Barker, 2000; Kellner & Durham, 2006). At the same time, we underscore that the method can be used for other projects that assume a constructivist and critical approach and that aim to understand the role and meanings of particular media texts and popular culture artefacts in contemporary society. When planning to explore or examine assumptions regarding the role of media and culture in society, you will be confronted with a large variety of approaches and methods. For scholars within the field of media and cultural studies, there is no proper, practical, ready-to-use fixed methodological toolbox, as many identify themselves as being situated at the crossroads between social sciences and humanities. Unsurprisingly, Grossberg and

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colleagues (1992) refer to the development of a research design in the field as a form of ‘bricolage’. They argue that the choice of methods depends upon a careful consideration of the research questions and the specific context instead of relying on the ‘formalized disciplinary practices of the academy’ (1992, p. 1). This resistance to being disciplined is notable in the range of (predominantly) qualitative methods that are being employed in the field, including textual analysis but encompassing ethnographic studies, oral history, archival research and in-depth interviews as well. However, this does not mean that the framework used for the analysis, which needs to be built every single time, is not structured and standardised as a semi-structured frame. As Barker (2000) points out, plenty of research within media and cultural studies concerns the study of media and culture as texts. Even though the method comes with various definitions, a few have tried to summarize what a textual analysis does. For Larsen (2002, p. 117), ‘[t]extual analysis examines a given object – a text or a group of texts – as closely and systematically as possible in order to answer specific research questions’. The systematic approach does not imply that a textual analysis is just another word for a content analysis. Larsen emphasizes that, in general, the aim of both methods is different. Whereas a content analysis is interested in discerning patterns, trends and relations between the manifest content of a text and an ‘objective’ account of reality, textual analysis is focused on understanding latent meanings of a text from a constructivist perspective. The constructivist approach is particularly underscored in McKee’s definition of textual analysis, which he defines as making ‘an educated guess at some of the most likely interpretations that might be made of that text’ (2003, p. 1). He argues that a text does not hold one truth and, because of the nature of human beings, it is naïve to assume that researchers are able to objectively analyse what a text means and distillate the one and only true meaning of a text. Rather, texts are ambiguous and open and it is up to media and cultural scholars to unravel the possible interpretations of a text. Yet, to avoid forcing a text to mean anything, McKee underscores the importance of looking for a likely interpretation of a text. Although McKee does not articulate it, this could be described as abductive reasoning as texts are produced within specific temporal and spatial contexts, an understanding of these contexts is crucial to see what these texts may signify. For instance, if you want to understand the role of modern manga in contemporary Japanese society, you should take into account the sociocultural and historical context of Japan after the Second World War, which fostered the production of manga (Johnson-Woods, 2010). Such contexts guide the analysts towards a likely interpretation. Further, McKee (2003) emphasizes the importance of an educated position, by which he not only points at the necessity to acquire factual knowledge surrounding the production or consumption of a text, but also at mastering a particular perspective from which a text can be interpreted. Plenty of critical social and cultural theories have guided researchers in their analysis of popular media culture, including neo-Marxist perspectives, postcolonialism, cultural materialism, critical race theory, feminism and queer theory. These particular perspectives provide the researcher with key concepts and insights throughout all phases of the research. The researcher is expected to enquire whether

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and how these concepts and insights are being articulated within a text’s content or imagery. Understanding a text in terms of ideology is essential to researchers who are interested in demonstrating whether a given text reiterates hegemonic power relations. Unsurprisingly, such an approach also reveals how many of these scholars are politically committed and points out that they use their analysis and rhetoric to convince people why a particular text, for instance, reiterates anti-democratic or sexist discourses. Last, an educated position also implies being able to see a text as constructed, meaning that studying how something is done also offers insight into the ‘why’. It is crucial, for instance, to see a film as a montage of scenes that were carefully prepared, directed, filmed, cut and ordered in a particular manner. In other words, a textual analysis will try to get a grip on the different structural and formal layers of a text (which may range from narration, narratives, dialogues, mise-en-scène, settings, cinematography, montage, camera perspectives, sound, and so on) and try to understand their roles in the process of signification and representation. Isolating, for instance, montage may help in understanding how even neutral reports of news events may be shaped to be understood in a dominant way by cutting out certain elements or arranging the scenes in a certain sequence. When doing a textual analysis of, for instance, an audiovisual text, it is important to analyse the different parameters that construct the representation. Different traditions have looked for ways to study these building blocks or parameters. For example, there is semiology, a long tradition of semiotics that analyses the denotations and connotations of a text. Mostly used for print media and more specific advertisements, it offers a way to look at the deeper meaning of the text. This semiotic approach has also been introduced in the tradition of film studies and screen studies to study audiovisual material by Metz’s film semiology (1974). Metz stressed that cinema and audiovisual representations at large are concrete expressions of language and can thereby be studied as a language system with different elements, also referred to as parameters. Metz used the term ‘codes’ to differentiate between these different elements. He differentiated between specific cinematographic codes (such as camera movement, montage, etc.) and non-specific cinematographic codes (such as narrative principles or use of colours). When conceptualizing film as text, this text is seen as constructed by signs, relations between different elements (codes) into a system or structure. A textual analysis is then the study of this system and the different elements. Film studies (see Bordwell & Thompson, 2004) and later television studies (see Fiske, 1987; Lury, 2005) have further distinguished and described the different parameters that constitute these texts. Most overviews differentiate between the parameters narration, mise-en-scène, cinematography and montage. Narration is an umbrella term that refers to narrative elements such as space, time, story, characters, dialogues and themes, as well as the way a story is told. The importance of narration has been thoroughly explored. Whereas film studies invested significantly in understanding the narration or the way a film’s story is told (see Bordwell & Thompson, 2004), work on music videos explored the importance of non-narrativity of a text (see Vernallis, 2004). The parameter mise-en-scène includes the acting, costumes, props, decor and lighting. Cinematography refers to the

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quality of the image (e.g., black and white, colour), the way the characters and settings are framed (e.g., use of close-ups), the way the camera moves or the type of lenses. Lastly, montage refers to the way the different shots are cut and edited. To construct a personal analytic tool, you will need to look for the parameters and elements that are valuable to study in relation to your research questions and the specificities of the medium. Take, for example, television. Fiske (1987) argues that the basic parameters (described as ‘codes’) of television are camerawork (e.g., size of the shot, angle of the shot, type of lens, use of focus), lighting (e.g., low, high, soft, hard), editing (e.g., linear, continuous), sound and music (diegetic or non-diegetic), graphics, mise-en-scène (i.e., all objects in front of the camera), casting, setting and costume, make-up, action, dialogue and ideological codes. All these parameters and elements co-construct the television text as, for example, the size of the shot can influence the meaning of a particular scene (e.g., a long shot creates a distance whereas a close-up can create intimacy). Similarly, the point of view (i.e., position of the camera, which simulates the perspective) can suggest an identification position for the viewer (Creeber, 2006). Yet not all are relevant to, for instance, a study investigating the representation of female news anchors in news programmes. Such a study would most likely focus on camerawork, costume and dialogue. Camerawork would allow us to assess whether female news anchors are represented by shots that sexualize or objectify their bodies; costume and dialogue would allow us to see whether they affirm or challenge gender stereotypical gender codes. Non-diegetic music (e.g., a soundtrack) or types of lens would, on the other hand, prove to be less useful or meaningful. However, for a study that wants to understand the relation between popular music culture and teen-drama series, studying non-diegetic music (music that is not part of the ‘natural’ setting of the story) would then be essential. As such, we underscore that no clear-cut analytical toolbox can be used as a frame for all textual analyses.

9.3 Doing textual analysis step by step 9.3.1 Research questions and selection of cases A decent textual analysis starts from solid but open research questions. When conducting research within the tradition of media and cultural studies, you will probably be interested in understanding what cultural products may tell us of the society, community or subculture in which it was produced. Even though such a research aim may be addressed by audience and reception studies, textual analysis will be considered the key method if the research addresses how specific sociocultural themes or issues are represented in a particular media or cultural product. Since media and cultural studies are informed by constructivist and critical perspectives, it is unlikely to formulate research questions that intend to test hypotheses. Instead of predicting or explaining, your research questions will aim to understand something. Hence, open research questions are most appropriate, as well as anticipating that new questions may come along while writing the study’s literature review or conducting the actual textual analysis. As an illustration, we zoom in on

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a study that aims to understand how music videos represent the bodies of contemporary non-white male popular music artists. Even though this study could focus on several artists, we present you with a study that focuses on one artist: Belgian artist Stromae. In the context of a PhD dissertation, Stromae would have to be one of a series of case studies. In the case of a master’s thesis, a student could focus on one artist alone. The internationally known artist tackles identity politics in his music and music videos. We formulate the following two research questions: (1) How is the body of Stromae represented in his music videos? (2) How do the music videos articulate intersectional gender and racial subject positions and empowerment? The research questions stem from the assessment claim that contemporary music videos have become increasingly important in shaping the identity of an artist. In particular, the visualization of the performer’s body is a key aspect of the industry’s marketing strategies. Media scholars (Railton & Watson, 2011; Wallis, 2011) have demonstrated that many music videos reiterate hegemonic gender ideologies, which means that the videos represent men and women in binary, oppositional and traditional ways and thereby contribute to the preservation of a patriarchal and heteronormative ideology. For instance, they have demonstrated that in the videos, female bodies are objectified and sexualized, whereas male bodies in videos are considered desexualized, displaced or disguised. In other words, by averting to represent male bodies as objects of sexual desire, these videos show men who are in control over their own bodies and able to assert their agency. Unsurprisingly, men are often depicted as active subjects who gaze at the women on display. Additionally, scholars such as Dyer (1997) and hooks (2004) argue that the dimensions of race and ethnicity further organize these bodies into a hierarchy, where white bodies are represented as the norm and superior to all bodies of colour. They also draw our attention to how a popular culture produced by white people has resulted in the creation and circulation of racist stereotypes. For instance, hooks (2004) points to a history of representing black bodies as hypersexual, which resulted from white people’s prejudices and sexual fantasies. Yet, that does not imply that all popular culture texts reiterate these sexist and/or racist discourses. To this end, it is key to explore how contemporary non-white male popular music artists represent their own bodies. It may help us gain insight into how non-white male artists negotiate these dominant discourses on how bodies ‘should’ be represented. Hence, this study is interested in how Stromae represents his own body in his videos and how the representation of a body can contribute to empowerment. Since this form of textual analysis starts from a constructivist epistemology, it will set out to map the most likely meanings of these representational practices. Its aim is not to find the one and only interpretation of a music video, but to demonstrate why particular readings of the work are likely and valid. To illustrate how such a textual analysis into this matter can be conducted, we discuss the analysis of one single video: Papaoutai (2013). Focusing on one video is perfectly possible. A single text can be a popular Hollywood film, but also an underground video artwork or a limited edition of a record by an independent music artist. Media and cultural studies argue that one can study such a single text as long as it is motivated by what the academic

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and social relevance is of studying that single text. For instance, Madonna’s music video Vogue has been studied by plenty of scholars because of the video’s engagement with identity politics and the rarely documented ballroom scene of black and Latino LGBTQ+ people in New York (e.g., Chatzipapatheodoridis, 2017). Some research questions demand a comparative approach or an analysis of diverse cases. For such projects, a selection of various episodes of one or more television series could be chosen, as well as comparing dissimilar cultural texts that nonetheless deal with a similar sociocultural theme.

Case study 9.1 Research questions and sampling: masculinities in superhero television series For instance, you could analyse how masculinities are represented in contemporary superhero television series. Even though it may seem that superheroes have always ruled both the big and small screen, it should be noted that the remarkable increase in televised superheroes only began in the 2000s, with series that wanted to introduce superheroes into the quality tradition that typified this period of television – emphasizing seriality, complex narratives and high production values (e.g., Heroes). It coincided with the creation of the Marvel Cinematic Universe, an umbrella term given to the series of live-action adaptations of Marvel superhero comics all situated within the same universe. With the increase in streaming services, we also noticed an increase in ‘quality’ superhero series – some successful, some failing dramatically. What is nonetheless interesting to note in the recent stream of superhero series is the emphasis on superheroes that are flawed. Yet, more critical scrutiny is needed to fully understand how we should make sense of the return of the superhero to the small screen, and particularly with regard to the embodiment and expression of masculinity. Do we see a reiteration of hegemonic masculine ideals? Or are these programmes able to resist outdated modes of masculinity? Such a study could, for instance, compare several episodes from The Boys, Doom Patrol and The Umbrella Academy, and explore what these flawed superheroes are telling us about masculinity in contemporary Western societies.

It is, however, important to note that you can formulate a research question with a particular text already in mind, as well as draft a project apart from preselected texts. In the end, you must decide whether your research demands a preselection of (a) case(s).

9.3.2 Literature review and theoretical concepts Within media and cultural studies, it is unlikely that you will look at a text as a blank canvas. A scholar has acquired knowledge and experience through various other encounters and is not supposed to ignore those insights when conducting a textual analysis. It is, however, possible to set up a textual analysis with little prior knowledge in order

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to explore a certain theme or issue. Nonetheless, even in such cases, scholars will try to inform themselves to avoid a free and unmotivated reading of a given text. Hence, most textual analyses start from a thorough theoretical framework and literature review in which key concepts and theories related to the sociocultural theme or issue at stake are discussed and conceptualized in order to ease the textual analysis. The conceptualization is especially important as it allows studying complex and abstract concepts in a text.

Case study 9.2 Literature and concepts: negotiating heteronormativity in Love Simon For instance, when studying how heteronormativity is negotiated in Love Simon, dubbed to be the first Hollywood film that revolves around two gay teenagers falling in love with one another, you will need to operationalize to a certain extent how heteronormativity operates and how it can be discerned in a given text. Since heteronormativity refers to an ideological framework that shapes – often in subtle ways – ideas, norms and values on gender and sexuality, a more concrete discussion is needed to be able to see how this film confirms or challenges heteronormative thinking. Such a discussion should, first, describe which aspects of everyday life are co-constructed by heteronormativity (e.g., behaviour, clothing, professions, sex, love). Second, it should include an assessment of how a particular society has made sense of these aspects. Does the society – in which the text is produced – value monogamy? Are women who affirm traditional gender roles valued over women who provoke or challenge gendered behaviour or clothing? Are gay men allowed to express their desire in public? Do we see a hierarchy between gay men who share heteronormative aspirations and gay men who are unwilling and/or unable to conform to heteronormativity? To this end, various articles and authors (e.g., Butler, 1999; Warner, 1999) will have to be consulted in order to come to an understanding of the key concepts.

For our study into the meaning of Stromae’s music video, we started with reading work by scholars on popular music culture and identity politics within the field of media and cultural studies. For a study that revolves around the representation of bodies in music videos, we looked specifically for literature that gave a detailed account of former research into the representation of bodies in music videos and work that helped to shape and fortify our theoretical framework. Among others, we consider the work by Grosz (1994) to be fruitful, as she discusses the concept of corporeality. She argues that people are corporeal – meaning that mind and body should not be seen as two separate phenomena – and underscores that men and women are as corporeal as one another. As such, her work helps to defy myths that men are less corporeal than women and to understand that bodies represented in media (e.g., film, newspaper articles) can produce subject positions. Further, Alcoff’s (2006) conceptualization is informative as she argues that both race and sex are ‘most definitely physical, marked on and through the body, lived as a material

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experience, visible as surface phenomena and determinant of economic and political status’ (p. 102). Like women, non-white subjects are often considered and represented as more corporeal than white bodies. Alcoff’s work also illustrates the importance of looking at the body as inherently intersectional. Weiss (1999) stresses that there is no such thing as the body, as the body is always something more specific: it is marked by a plethora of identities such as class, age, sexuality, ethnicity and so on. Last, besides mapping work on representations of bodies in music videos (e.g., Railton & Watson, 2011; Wallis, 2011), we point out that much research within media studies has focused on the symbolic annihilation or othering of non-white subjects (e.g., Chow, 2008; Fürsich, 2010; Gross, 1995). Even though the mapping and questioning of practices such as symbolic annihilation or othering non-white subjects are pivotal, we like to raise the attention that few scholars have discussed the representations of black male and female bodies as able to produce multiple and changing subject positions and multiple agency. To conclude, the drafting of a theoretical framework and literature review should be done with the general aim of the study in mind. It should help you in refining or revising the guiding research questions and in creating an analytic lens and toolbox to be used when doing the actual textual analysis. If, in later stages of study, the key concepts turn out to be vague, incomplete or irrelevant, it is best to revisit and rework the framework and review.

9.3.3 Preparation of the analysis Even though the focus of a textual analysis is on the text itself, an understanding of contextual elements cannot be dismissed. This phase in the research is reserved for gathering information that helps you understand the sociocultural position of the text(s). Such information is medium- and product-specific and thereby contextual. For instance, when dealing with a television series, it may concern information about the production company and the people who made and/or directed the series, its star actors or actresses, the channels that distribute it, how it is being promoted and marketed, whether it is considered an international or transnational product, for which audience it is made, and so on. This information allows you to relate the meaning that a text is conveying to a sociocultural context in which it is produced and consumed. For instance, a US broadcast television sitcom that targets a teenage audience has a very different sociocultural identity than a US pay-cable fantasy series that targets an adult audience. The context nudges its products into particular identities with relatively stable conventions, expectations, norms and values. Having knowledge of such a context may prevent you from arguing that the sitcom represents sex in a more concealed and implicit way than the fantasy series since such practices of representation are the obvious result of the coinciding of genre, the television distribution context and the intended audience. More relevant statements could be made when comparing similar sitcoms and questioning whether one sitcom is more progressive and emancipatory in its representation of sex than the other, or enquiring how the representation of sex in one particular sitcom relates to the hegemonic norms and values of the country or region in which it was produced or furthermore consumed.

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Case study 9.3 Preparation of analysis: sexual consent in teen drama For example, if you are interested in exploring how teen drama represents sexual consent, you would be expected to contextualize the series’ representational practices by reflecting on its production context and its imagined audience, especially when dealing with series that have quite diverse production contexts. For instance, if you settled on comparing SKAM and Euphoria, you would need to acknowledge that SKAM was produced by the Norwegian public broadcaster NRK, while Euphoria was created for premium television network HBO. Whereas NRK has a public service mandate, HBO is driven by profit. Second, whereas SKAM’s intended audiences were sixteen-year-old Norwegian girls, Euphoria was targeted to young adults across the globe, which can be taken into account when assessing how sex and sexuality is depicted in both series.

Another decision that needs to be made during this phase is the demarcation of the material that you are going to actually study. First, a text consists of multiple discursive layers that help convey particular meanings. A text does not exist out of one meaning but out of different and layered meanings. Second, a text can be a television soap, which implies that a huge amount of episodes can be studied. The process of demarcation is guided by both pragmatic and paradigmatic considerations. First, it is impossible to study everything, which would be hugely time consuming. Second, it is also redundant and irrelevant to many research setups within the field of media and cultural studies. Since the shared intent of these studies is to understand a particular phenomenon rather than to explain, the focus will be on thoroughly understanding a few aspects. Hence, a selection of the texts available for the research needs to be made. In some cases, it may be normal to study a full text such as a film, a record or a graphic novel. Yet, even in these cases, not all the layers will be taken into account. For instance, understanding why a particular film was considered racist or sexist may not per se require an analysis of all characters throughout the film or take into account all the parameters and their numerous characteristics (e.g., sound design, decor or aspect ratio). In some other cases, a selection of texts and/or a selection of sequences or scenes will have to be made. When dealing with a television series, for instance, you will likely study a selection of episodes, which may be chosen randomly or selectively. In some cases, you will have to work selectively, since the theme you are studying (e.g., representation of mourning) is dealt with in only a few episodes. In some other cases, it will be more appropriate to work randomly; for instance, when studying whether soap operas reiterate gender or class stereotypes. For our own case study into Stromae’s Papaoutai, we used this phase to gather information on Stromae’s artist persona. This means that we looked at his musical oeuvre,

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tried to decide what genre his music belongs to, how his other music videos look, what kind of audience his music speaks to. Further, we decided to study only one of the artist’s music videos. Since our focus is on the body of the artist, we took into account a broad range of parameters that are used to represent the body. This included aspects related to narration (e.g., space, time, story, characters), the mise-en-scène (e.g., acting codes, costumes, decor, lightning), cinematography (e.g., framing of the body, camera movement) and sound design (e.g., use of music and lyrics in relation to what is being shown). Last, since we are studying only one small text and focusing on the representation of the body, we did not have to decide whether to select parts of the texts in either a random or selective manner. Consider that an intensive textual analysis of one music video can take up to three hours.

9.3.4 Textual analysis Having decided on which texts and parameters to focus, you proceed by describing the general features of the text(s). This comes down to watching the text for a first time and describing its main characteristics, such as story and dominant genre conventions, in order to get a grip on the object of study. Next, you will try to dissect and deconstruct a text in order to thoroughly interpret it. Since this chapter is mainly concerned with audiovisual material, we illustrate this phase by focusing on the method used on film, television and other material with moving images. As such, the practice of opening up a text is done by recognizing that an audiovisual text consists of various sequences. The concept of sequence has been coined by Metz (1974) who has interpreted it as a narrative entity in which a specific act or event takes place. In contrast to a scene, it does not demand continuity in time and place. It does demand continuity in action. Consequentially, sequences often feature several scenes. Ideally, you start by marking the various sequences in an audiovisual text. Creating a sequence overview allows you to unravel the way an audiovisual text is structured and to continue the research with analysing particular sequences into depth. Even though the method has been developed within the tradition of film analysis (e.g., Aumont, 1997; Bordwell & Thompson, 2004; van Kempen, 1995), it can be applied to other audiovisual media as well. Even a product with a short time span such as a commercial, YouTube video, a vlog or a music video can consist of more than one sequence. Having identified a text’s sequences, the research continues by selecting the sequences considered relevant to the research aim and interpreting how they convey certain meanings. To this end, the preselected parameters will be used. We can thus look, for instance, at mise-en-scène, narrative themes, sound design or camera framing and enquire how and what these parameters signify. There are plenty of ways to organize this, but drafting tables that contain descriptions of each parameter per sequence is common (e.g., van Kempen, 1995). Besides describing what you see, it is also important to preserve some space in the table to interpret the ideological meanings of these sequences. To do so, you will most likely start from the conceptualizations of your key concepts and use these

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to interpret how the sequence represents the issue at stake and whether or not it uses several parameters to do so. For example, you can work with specific themes that can be considered tangible materializations of the key concepts. As such, this phase will result in a series of tables and appendixes that describe and interpret in detail how a television series or a film is representing certain themes or issues. For our analysis of Papaoutai we started by watching the music video and getting a grip on the story being told. The video complements the upbeat pop song that nonetheless recounts how a fatherless boy is trying to understand why his father left him. The video represents that narrative. The boy, however, does imagine his father being around. Yet, to represent an absent presence, Stromae, who plays the role of the father, impersonates the father as a life-sized dummy. The video consists of seven sequences, which we list in a table. For each sequence in this sequence overview, we have provided a short summary of the narrative events. Further, for each sequence we have added how the other aforementioned parameters were used. For instance, during the second sequence (see Table 9.1), the son is looking at his neighbours and is filled with joy at seeing how on one side a mother and her daughter, and on the other side a father and his son, move, dance and act in sync with each other. Besides the narrative elements, parameters of significance here are foremost the acting codes, costumes, décor and sound. We particularly underscore the importance of the choreography in this music video. The way the people in the street dance with each other is a crucial trope throughout this sequence and the video. Even though each dancing pair in the video has a distinct style, they dance in sync with one another. The use of synchronized dancing can be interpreted as an incorporation of Rwandese dance moves into a contemporary context as a way to intersect the story with notions of ethnicity and race and to represent a community in which black culture is hegemonic. Also, the use of sync dancing implies that even though there are many ways of being, there remains a desire to be in sync with a family member in order to be understood, accepted and loved and having an identity or a sense of belonging. This is further emphasized by the costumes, as the use of matching outfits implies a strong bond between the mother and daughter and the father and son respectively. Such readings can be filled out in a separate section in the table or underneath each description of each parameter (see Table 9.1). It is up to the researchers themselves to develop a tool that works best for them. The aim, however, should be to figure out a mode of analysing a text that goes beyond describing a text’s layers. Rather, it should help you to interpret how the relation between the layers help convey particular meanings. In the music video, for instance, the lyrics and the narrative events represented in the video interact with one another. The absent father in the lyrics is represented as a passive, mute, dummy in the video. Further, the tool should also incorporate contextual elements and theoretical concepts to fortify the readings. In this video, knowledge of Stromae’s Rwandese roots helps to contextualize the dancing, while insights into concepts such as corporeality help understand that material bodies can become means to criticize or challenge patriarchy or racism.

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Table 9.1  In-depth interpretation of sequence 2 of Papaoutai Narrative events

Acting

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The boy is in the garden with his father (played by Stromae), washing the car. But since the father is ‘fake’, the activity is unrewarding. The boy then watches a mother and daughter, as well as a father a son, dance and act in sync on the street, which makes him smile.

Whereas Stromae’s character does not move (he stands still, frozen like a statue), the people in the street do move: they dance and act in sync and use expressive gestures; they express happiness and their movements imply understanding, feeling one another. The dance moves link back to Stromae’s Rwandese roots. All actors are black.

Each pair is dressed in a similar manner (including Stromae and son), to emphasize their familial bond.

Suburban environment, but represented to emphasize that it is not a real-life setting, it looks like a fantasy environment. In a way, it looks like a reversal of a 1950s white suburbia, with only black inhabitants.

This sequence corresponds to the song’s bridge and chorus. What is shown corresponds to the lyrics. The bridge represents an outsider’s perspective, asking the child ‘Where is your father?’. The chorus is sung from the son’s perspective: he asks the same question, while observing strong family ties (represented in the dancing scenes).

9.3.5 Discussion of results In the last phase, you must ensure that you are able to respond to the research questions. Even though textual analyses often produce more data than anticipated, you have to make sure that you discuss only those results that are relevant to the research questions. Hence, when writing down the results of a textual analysis – whether in an essay, article or report – you should avoid presenting a ‘chronological’ reading of a text or an all-encompassing overview of all the layers that are meaningful. Instead, organize your reading around the various arguments that you want to make about the text. In the case of Papaoutai, we looked back at our table and noted that our interpretations could be tied to three conclusions. First, we argue that Stromae contributes to challenging the idea that male bodies are less corporeal than female. Stromae is known for his extraordinary dancing and uses his body – both in videos and on stage – as a leverage for sociocultural issues. In this particular video, he even deliberately presents himself as a passive and robotic figure to highlight the absent presence of the father figure and to critique traditional interpretations of masculinity and fatherhood. This critique is underscored in the way that it contrasts the life-sized dummy with the male and female characters who lose themselves in dance and who dare to express themselves. Second, the

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video demonstrates how non-white material bodies in music videos can become political in the sense that they can offer subject positions that advocate empowerment and agency. Third, the video challenges the hegemonic whiteness of contemporary Western society by situating his characters in a neighbourhood where all the characters are black. As such, the video presents black characters as complex and layered and thereby avoids the burden of representation non-white people often experience when they are the only non-white person in a white community.

9.4 Conclusion and discussion Such text-based research is not without its fair share of critique. The most common critic is the fluidity of the interpretations. Because media texts convey contextualised meanings, these meanings are not fixed in time and place and are thereby changeable. This criticism is supported by the fact that no clear and fixed analytical framework can be offered. Many different parameters, codes and particular elements of a text can be analysed. Hence, depending on the decisions and choices made by you, a similar text can be studied by very different analytic methodological research designs. A researcher who wants to use an analytic design drafted by a former researcher working on the same text can do so but the person will have to be sufficiently informed and critical to reorganize and extend the framework with new parameters or codes. Second, this criticism also entails the fact that even when using a semi-structured analytical framework the analysis, as such, is still partly a reception of the researcher who will interpret the parameters. However, we advise not dismissing these interpretations as ‘plucked out of thin air’, but to see that they are based on a thorough theoretical framework and an understanding of the specific context of the text. Further, these in-depth readings do not preclude a quantitative content analysis of the same text or context. Such joint studies may shed a more complex light on the issues that are studied. Similarly, such readings – even though mostly focused on unravelling the preferred readings of a text – cannot say anything about what people actually do with these texts and how they give meaning – as an individual and as an audience – to these texts. Yet again, such criticism can be countered by complementing textual analyses with audience and reception studies that explore media use and consumption of actual audiences. Last, even though the field emphasizes the importance of studying production and consumption of texts as well (Creeber, 2006; Krijnen & Van Bauwel, 2015; Zettl, 2006), scholars within the field do sometimes dismiss the importance of the production context of the media text. To challenge this, textual analytic studies can be fortified by conducting in-depth interviews with professionals who play a key role within the production and distribution of media and culture to provide a detailed account of how, where and when a particular media text is produced and distributed.

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9.5 Summary checklist This chapter introduced you to textual analysis, a qualitative research method that is used within the fields of media and cultural studies to unravel the latent meanings of a popular culture text. The chapter demonstrated how various parameters constitute popular culture texts and how studying these parameters allows you to understand how people are able to give meaning to films, television series or other texts. It also provided you with a step-by-step overview of how to conduct a textual analysis. Last, the chapter also enabled you to differentiate a textual analysis from other methods used to study media content and texts.

9.6 Doing textual analysis yourself 9.6.1 Question The wildly popular sitcom Friends, which ran from 1994 to 2004, is a landmark television show still enjoyed by a young audience today and can be seen as a point of reference for many people. The sitcom focuses on six characters (three white heterosexual women and three white heterosexual men) in their twenties and thirties living in New York City. The series is mostly invested in depicting the friendship between the characters, but sex is a key theme of the sitcom as well, as it plays an important part in the lives of all the characters. The sitcom, a prime-time family genre, has a long tradition of representing storylines that revolve around sex and sexuality (Levine, 2007; Mills, 2009). Yet, the way they deal with sex has been read and interpreted in a paradoxical manner. Some authors argue that the genre is ultimately very conservative – both in style and societal views – and only articulates a positive discourse on sex when sex is experienced within the confines of marriage as part of a heterosexual script (see Attalah, 1984) or in the context of a heterosexual and masculine norm (see Mills, 2005). Other authors (Doty, 1993) contradict these statements and argue that sitcoms have the ability to enable audiences to read against the grain and, for example, represent non-heterosexual sexual practices while using parody to mock the strict heterosexual scripts. So how does the classic sitcom Friends represent sex? We want to suggest a study on the representation of sex in this sitcom (see also Dhaenens & Van Bauwel, 2017). Our research question will be formulated as follows: How is sex represented in the television sitcom Friends? With this exercise, we want you to think about what you will be looking at when you want to conduct a textual analysis and prepare for the analysis. What will you be looking for exactly? What will be your focus? How will you select your data? Which parameters will you use? Put differently, we formulate three objectives: 1. What will you be studying when analysing the representation of sex? How will you operationalize sex?

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2. How will you select your data for the sample? 3. Decide which parameters you will use to analyse how sex is represented and argue why.

9.6.2 Model answer 1. Operationalization When studying sex in representations, we should consider that, especially in primetime television, explicit sexual acts are not represented. We will thus look for representations that deal with sexual practices, activities and desires, rather than taking a broad approach to sexuality or sexual identity. It may thus concern discussions or suggestive or insinuated representations of sex. We therefore study how Friends represents these sex practices, to what discourses these practices are articulated, and what these articulations signify. 2. Sample Such a research set up demands the analysis of a selection of episodes from all ten seasons. Since we focus on sex, we consider a thematic analysis the most appropriate mode of analysis. It thus means that we will not need to analyse everything from each episode and are able to focus on the sequences that depict or deal with sex. As such, we can also include a decent number of episodes. We decide to select twenty-five episodes, which we consider sufficient for the analysis that we have in mind as well as an achievable number for a master’s thesis. Textual analysis can be time consuming and you need to take into account that one twenty-five-minute episode will take around eight hours of analysis. As many episodes qualify to be included in the sample, we decide to select only episodes that feature sex as a pivotal theme. Further, to ensure all seasons are included we can select three episodes out of each of the first five seasons and two episodes out of each of the last five seasons, as sex became a less prominent theme in the latter seasons. 3. Analysis First, we need to gain insight into the ways used to depict sex in the sitcom. Because of the format, explicit sex scenes are rare or absent. Explicit references to sexual practices can be traced in the conversations between characters, where it often acts as both a serious topic or a source of humour. Dialogue (as part of the parameter narration) will be one of the key codes that is central in the analytical toolbox. Second, sexuality is represented not only in a straightforward manner but is also disguised as metaphors and in-jokes, which characterises the genre of the sitcom. Third, sex is also insinuated by means of before and/or after scenes. In these cases, we need to look for mise-en-scène (e.g., setting, décor, acting), sound codes (e.g., sexual sounds) and cinematographic codes such as camera framing (e.g., close-ups that feature fragmented naked parts of bodies) or camera movement (e.g., use of fade out before sex). Also, the montage and editing will be an important parameter in the representation and suggestion of the

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before and/or after sex scenes. Last, the mimicking of sex in scenes that does not represent sex (e.g., playing a game that mimics sex) can also be examined by looking at, for instance, sound codes or acting.

9.7 Recommended reading McKee, A. (2003). Textual analysis: a beginner’s guide. London: SAGE. This handbook is one of the few books that introduces students and scholars to the field of textual analysis. It is a good starting point to dig into the field of textual analysis. Larsen, P. (2002). Mediated fiction. In K. B. Jensen (ed.), A handbook of media and communication research: qualitative and quantitative methods (pp. 117–37). London and New York: Routledge. This chapter provides a description of traditions in the study of representations in fiction including an interesting example of an analysis of the film The Big Sleep with a focus on a shot-to-shot analysis. Bordwell, D. and Thompson, K. (2004). Film art: an introduction (seventh edition). Boston, MA: McGraw-Hill. This introductory book offers the grammar to conduct a film analysis. A very good starting point to begin analysis of audiovisual material and more specifically film. Vernallis, C. (2004). Experiencing music videos: aesthetics and cultural context. New York: Columbia University Press. This handbook provides a great deal of examples of how to analyse the specific format of the music video, with attention paid to the aesthetics of the format and the importance of the editing and the music and lyrics in music videos. Creeber, G. (ed.) (2006). Tele-visions: an introduction to studying television. London: BFI. This introductory handbook gives a good first insight in the study of factual and fictional television genres elaborated with interesting case studies such as a shot-by-shot analysis.

9.8 References Alcoff, L. (2006). Visible identities: race, gender and the self. Oxford: Oxford University Press. Attalah, P. (1984). The unworthy discourse: situation comedy in television. In D. W. Rowland and B. Watkins (eds), Interpreting television: current research perspectives (pp. 222–49). Beverly Hills, CA: SAGE. Aumont, J. (1997). The image. London: BFI. Barker, C. (2000). Cultural studies: theory and practice. London: SAGE.

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Bordwell, D. and Thompson, K. (2004). Film art: an introduction (seventh edition). Boston, MA: McGraw-Hill. Butler, J. (1999). Gender trouble: feminism and the subversion of identity (second edition). London: Routledge. Chatzipapatheodoridis, C. (2017). Strike a pose, forever: the legacy of Vogue and its re-contextualization in contemporary camp performances. European Journal of American Studies, 11(3). https://journals.openedition.org/ejas/11771. Chow, Y. F. (2008). Martial arts films and Dutch-Chinese masculinities. China Information, 22(2), 331–59. Creeber, G. (ed.) (2006). Tele-visions: an introduction to studying television. London: BFI. Dhaenens F. (2017). Reading the Boys of Girls. In M. Nash and I. Whelehan (eds), Reading Lena Dunham’s Girls (p. 121–33). London: Palgrave Macmillan. Dhaenens, F. and Van Bauwel, S. (2017). Sex in sitcoms: unravelling the discourses on sex in Friends. In F. Attwood, D. Egan, B. McNair and C. Smith (eds), Routledge companion to media, sex and sexuality (pp. 300–8). London: Routledge. Doty, A. (1993). Making things perfectly queer: interpreting mass culture. Minneapolis, MN: University of Minnesota Press. Dyer, R. (1997). White: essays on race and culture. Oxon and New York: Routledge. Fiske, J. (1987). Television culture. London and New York: Methuen. Flick, U. (ed.) (2013). The SAGE handbook of qualitative data analysis. London: SAGE. Fürsich, E. (2010). Media and the representation of others. International Social Science Journal, 61(199), 113–30. Gee, J. P. (2014). An introduction to discourse analysis: theory and method (fourth edition). London and New York: Routledge. Gross, L. (1995). Minorities, majorities and the media. In J. Curran and T. Liebes (eds), Media, ritual and identity (pp. 87–102). London and New York: Routledge. Grossberg, L., Nelson, C. and Treichler, P. A. (1992). Cultural studies: an introduction. In L. Grossberg, C. Nelson and P. A. Treichler (eds), Cultural studies (pp. 1–20). New York and London: Routledge. Grosz, E. (1994). Volatile bodies. Bloomington, IN: Indiana University Press. Hall, S. and Jefferson, T. (eds) (2006). Resistance through rituals: youth subcultures in post-war Britain (second edition). Oxon and New York: Routledge. Hammer, R. and Kellner, D. (2009). From communications and media studies through cultural studies. In R. Hammer and D. Kellner (eds), Media/cultural studies: critical approaches (pp. ix–xivii). New York: Peter Lang. hooks, b. (2004). We real cool: black men and masculinity. New York and London: Routledge. Johnson-Woods, T. (ed.) (2010). Manga: an anthology of global and cultural perspectives. New York: Continuum. Kellner, D. M. and Durham, M. G. (2006). Adventures in media and cultural studies: introducing the keyworks. In M. G. Durham and D. M. Kellner (eds), Media and cultural studies: KeyWorks (revised edition, pp. ix–xxxviii)). Malden, MA: Blackwell. Krijnen, T. and Van Bauwel, S. (2015). Gender and media: representing, producing, consuming. London: Routledge.

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Larsen, P. (2002). Mediated fiction. In K. B. Jensen (ed.), A handbook of media and communication research: qualitative and quantitative methods (pp. 117–37). London and New York: Routledge. Levine, E. (2007). Wallowing in sex: the new sexual culture of the 1970s American television. Durham, NC and London: Duke University Press. Lury, K. (2005). Interpreting television. London: Arnold. McKee, A. (2003). Textual analysis: a beginner’s guide. London: SAGE. Metz, C. (1974). Film language: a semiotics of the cinema. New York: Oxford University Press. Mills, B. (2005). Television sitcom. London: BFI. Mills, B. (2009). The sitcom. Edinburgh: Edinburgh University Press. Neuendorf, K. A. (2016). The content analysis guidebook (second edition). London: SAGE. Railton, D. and Watson, P. (2011). Music video and the politics of representation. Edinburgh: Edinburgh University Press. Storey, J. (1997). Cultural studies and the study of popular culture: theories and methods. Edinburgh: Edinburgh University Press. Tannen, D., Hamilton, H. and D. Schiffrin (eds) (2015). The handbook of discourse analysis (second edition). London: Wiley Blackwell. Van Kempen, J. (1995). Geschreven op het scherm: Een methode voor filmanalyse. Utrecht: LOKV. Vernallis, C. (2004). Experiencing music videos: aesthetics and cultural context. New York: Columbia University Press. Wallis, C. (2011). Performing gender: a content analysis of gender display in music videos. Sex Roles, 64(3–4), 160–72. Warner, M. (1999). The trouble with normal. Cambridge, MA: Harvard University Press. Weiss, G. (1999). Body images: embodiments as intercorporeality. New York: Routledge. Zettl, H. (2006). Television production handbook (ninth edition). San Francisco, CA: Thompson: Wadsworth.

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10 Thematic Analysis: An Analytical Method in its Own Right Davide Dusi and Peter A. J. Stevens

Thematic analysis (TA) is a technique for the analysis of qualitative data which allows you to identify or develop patterns of meaning and/or central ideas that unify them (referred to as themes) pertaining to a given set of data. You will then use these themes to organize, describe or interpret (perceptions of) reality (Boyatzis, 1998; Braun & Clarke, 2006, 2021). Once (relationships among) themes are identified, you need to interpret their relevance and implications for the research question you aim to answer. Verbal individual or group interviews (see King et al. 2018), focus group data (see Barbour, 2008) or textual data (written material) are usually at the core of thematic research. Nevertheless, video material and images might also be thematically analysed (Joffe, 2012).

10.1 Chapter objectives Throughout this chapter, you will become acquainted with: • • •

TA’s origin, underlining principles and main features; Richard Boyatzis’ (1998) original method and guidelines to conduct a TA; and Braun and Clarke’s (2006, 2021) popular and influential approach to TA.

These will serve as a step-by-step guide for you to engage in any analysis of qualitative data that might benefit from a thematic lens and expand your range of analytical tools in doing qualitative data analysis. Besides recommended readings, we also offer you two exercises where you can put into practice the guidelines and analytical strategies described in this chapter.

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10.2 Key features, debates and historical development Although many of the principles and procedures of TA are rooted in the old tradition of content analysis (Joffe, 2012; Joffe & Yardley, 2004), its appearance as a method is often traced back to the 1970s and particularly to the scholarship of Gerald Holton. Through his work, Holton (1975) postulated the need for more scholarly awareness regarding ‘themata’ pertaining to specific research topics/fields, and put forward TA as a tool for the historiography of science (see also Holton, 1978). This tool was immediately picked up and praised by scholars in the sociology of science such as Merton (1975), who acknowledged its usefulness to access more implicit, tacit themes and thematic structures in one’s (field of) research (see also Joffe, 2012). Since then, for at least a couple of decades, TA has been variably and inconsistently used (Braun & Clarke, 2014, 2021). It was only at the end of the 1990s that a clear specification of (a more post-positivistic, see also Chapter 1) TA and procedural guidelines were developed and proposed by organizational theorist and social psychologist Richard Boyatzis (1998). TA then became increasingly employed (see Figure 10.1).

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Figure 10.1  Publications listed in Web of Science regarding TA as a method and/or studies employing TA as an analytical technique from 1998 to 2020 In the second half of the 2000s, Braun and Clarke (2006) developed their own, more constructivist approach to TA (see Chapter 1 for more on constructivism); and focused particularly on how to use TA in psychology. Given the popularity of their 2006 paper, and the different interpretations and adaptations of their approach to TA, Braun and Clarke (2021) recently revised and updated their take on TA, which they named ‘Reflexive

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TA’ (RTA). They explicitly distinguish RTA from two other approaches to TA, including Boyatzis’ approach, which they call ‘coding reliability TA’, and what they call ‘codebook TA’. In this chapter, we focus only on Boyatzis’ approach to TA and RTA, due to space limitations, the popularity of both approaches and because they represent versions of TA based on different epistemological assumptions. Since the start of the millennium, TA’s popularity as a qualitative data analysis technique rose considerably and diverse approaches to it resulted (e.g., Braun & Clarke, 2006, 2021; Castleberry & Nolen, 2018; Guest et al. 2012; Joffe, 2012). Due to the popularity of Boyatzis’ method – which spans beyond disciplines’ boundaries – and to that of Braun and Clarke’s (2006, 2012, 2019, 2021) approach, TA is currently employed in a broad range of disciplines. It is used widely in social and health sciences; particularly in psychology and mental health research (Braun & Clarke, 2012; Joffe, 2012; Terry et al., 2017), nursing and health research (Vaismoradi et al., 2013), but also, to name a few, in sociology and anthropology (Guest et al. 2012; Merton, 1975), media studies (Herzog et al. 2019), higher education research (Maguire & Delahunt, 2017), genderqueer studies (Bradford et al., 2019) and multidisciplinary methodological practices such as community-based research (Riger & Sigurvinsdottir, 2016). Notwithstanding its ever-growing popularity, TA has not been immune to critique that often shows a lack of understanding about its potential, variability and flexibility, and, overall, focus on its alleged lack of nuance and interpretative depth (Braun & Clarke, 2014, 2020). In reality, TA provides a robust, systematic framework for coding and analysing qualitative data. It allows you to approach your data from different angles and to assume diverse perspectives during the development of your coding – theory-driven, research-driven, data-driven; or a combination (hybrid) of deductive and inductive approaches (see, e.g., Boyatzis, 1998; Guest et al. 2012; Joffe, 2012 and Chapter 1 of this handbook). TA can be applied in different instances as it is not tied to a particular theoretical approach or connected to specific streams of research (Fereday & Muir-Cochrane, 2006; Joffe, 2012), and its coding techniques may be separate from the theoretical orientation of the research one is conducting (Braun & Clarke, 2006, 2014, 2021). It enables you to conduct robust and sophisticated analyses of qualitative data and to present them in ways that are accessible also for non-expert audiences (Ayres, 2008; Boyatzis, 1998; Braun & Clarke, 2014; Joffe & Yardley, 2004). Certainly, TA cannot serve any possible purpose (Nowell et al., 2017) as well, as it does not allow/aim to formulate thorough explanations of phenomena and/or make theoretical predictions (Newman, 2006). It is therefore particularly useful if you do not need, or aim, to develop a comprehensive understanding or explanation of a phenomenon, but rather aim to contribute to existing theory by describing certain aspects or (re)presentations of reality. More generally, it is highly appropriate when data require some degree of interpretation as well as when you need or want to focus on all aspects of the data. As anticipated above, TA is a technique that allows you to identify or develop patterns of meaning and/or central ideas (that unify them) pertaining to a specific data set and to use them to organize, describe or interpret (perceptions of) reality.

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In other words, if the goal of a researcher is to understand how a phenomenon is understood, experienced or presented, then TA constitutes a highly appropriate method to achieve that goal. Notably, TA is not merely about reporting what a phenomenon is (thought) about, as ‘making sense’ also suggests a desire to develop a deeper understanding of a phenomenon. Indeed, TA often considers the importance of context in describing phenomena, and in so doing offers not just a description of social reality (or how it is presented), but also to some extent an understanding or explanation of why that is the case. All in all, through a TA you might either focus on generating, identifying and/or describing (relationships among) themes, or, by going a little further, on generating metathemes and/or constructs that contribute to theory in a more abstract/formal way. At any rate, the end result of a TA must highlight the most salient constellations of (shared) meanings present in the data set (Braun & Clarke, 2021; Joffe, 2012). Importantly, the ability to thematise meaning is not only necessary to conduct a proper TA, but it also represents a generic, fundamental skill across qualitative work (Holloway & Todres, 2003). Since TA revolves around identifying themes in a data set and detecting relationships among them, the notion of ‘themes’ needs further clarification. As mentioned earlier, themes refer to specific patterns of meaning generated or detected in the data that either describe and organize one’s observations and/or interpret aspects of the phenomenon under study (Boyatzis, 1998; Paré, 2016). Braun and Clarke (2021) make a distinction between ‘topic summaries’ and ‘themes’. While the former is ‘a summary of everything the participants said about a particular topic’ (p. 76), the latter refers to ‘a pattern of shared meaning organised around a central concept’ (p. 77). For instance, in doing research on adults who decide not to have children, a topic summary might be a list or summary of the ‘reasons why they do not want to have children’. This category is different from themes such as ‘compensatory kids’ or ‘it’s making a choice that is important’. While the former presents a range of possible meanings or values around a topic, the latter themes each represent a concept that structures a particular, shared understanding of what it means when adults decide not to have children. Each theme has therefore its own central organizing concept or ‘the (sometimes implicit) idea that unifies meaning in a theme’ (p. 284). According to Braun and Clark, RTA needs to develop themes and not topic summaries. Themes can be identified either directly in the data – manifest level – or as interpretations of the data – latent level (Boyatzis, 1998; see also Bradley et al. 2007). TA usually draws upon both types of theme. For instance, you can identify a set of manifest themes (explicit content), which can then help to access a more latent level (implicit content) of meaning (Joffe, 2012; Vaismoradi & Snelgrove, 2019). Deducting latent meanings that underpin (sets of) manifest themes requires interpretation from your side (Joffe & Yardley, 2004). Themes can be drawn from theoretical ideas that you use in your study (deductive way), from the raw data itself (inductive way), as well as by combining deductive and inductive approaches (Ayres, 2008; Braun & Clarke, 2019; Joffe, 2012). Notably, themes derived from theory allow you to replicate, extend and refute existing studies (see, e.g., the theory-driven code development strategy in Boyatzis, 1998), but prevent them drawing

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on the naturalistically occurring themes that can be developed gradually by interpreting and coding the data itself – which is instead allowed through a more inductive approach (see, e.g., Braun & Clarke, 2006). Scholars often overcome limitations of inductive and deductive approaches by combining them and therefore employing an ‘hybrid’ approach. Put simply, they approach their data set with certain preconceived categories derived from theories – as well as knowledge regarding previous findings in the area under study – while also remaining open to new concepts/themes that they might detect in the raw data. Such a dual deductive/inductive approach for the detection and interpretation of a set of themes in the data set increasingly characterizes high-quality qualitative work in different disciplines (Joffe, 2012). But how do you do this in practice? Below we try to answer this question by providing a step-by-step guide for conducting TA.

10.3 Doing thematic analysis step by step TA is a systematic and transparent form of qualitative analysis. Although focused on themes across a data set, it does not sacrifice depth of analysis. You might even argue that TA forms the implicit basis of much other qualitative work (Joffe, 2012). But how to conduct proper TA in practice? And which version of TA should you employ? In what follows, we will start by describing Boyatzis’ (1998) original method and guidelines to conduct TA. Afterwards, we will also present Braun and Clarke’s (2006, 2019, 2021) widely employed and more popular approach to TA. In the conclusions, we will compare both approaches and highlight their respective strengths and weaknesses.

10.3.1 Boyatzis’ method to conduct a thematic analysis According to Boyatzis (1998), TA consists of seeing, encoding and interpreting patterns of meaning (themes) in the data set. Once identified, you might, for instance, start to investigate or measure the frequency of themes in a specific set of data. Themes may also be clustered in groups. In this case, you will need to understand to what kind of construct a specific group of clustered themes might refer to. Importantly, for Boyatzis, the aim of a TA is not only to identify themes, measure their frequency (e.g., by number of cases), or cluster them in groups, but also to detect relationships among them. You might therefore start by trying to understand if there is a relationship among specific themes and what type of relationship that is. Once you identify a relationship among themes, you formalize this relationship into a meta-theme. If two or more themes are brought together in a meta-theme, you will then aim to understand what that meta-theme is about and interpret how it might help you to further understand the phenomenon that you are studying (see also Paré, 2016). Boyatzis (1998) divides TA in four main stages: 1) seeing themes; 2) encoding themes; 3) codes development; and 4) analysis. Specifically, seeing themes refers to your ability to spot multiple occurrences of an item/topic throughout the data set. In this stage,

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previous understandings/knowledge regarding the topic or phenomena under study derived from empirical or theoretical research might influence you in identifying themes (Strauss & Corbin, 1998). Encoding themes entails instead ascribing the same meaning to the same theme(s) throughout the data set (Vaismoradi & Snelgrove, 2019). You cannot ascribe two different meanings to the same theme detected in, say, two different interviews; nor can you change the meaning of the theme over different phases of the analysis (Boyatzis, 1998). As for codes development, Boyatzis underlines that a good thematic code should contain five elements: i. 

a label (name) – the name should be clear, close to the raw data, and remain the same throughout analysis, interpretation and presentation of the research’s data and results;

ii. 

a definition of what a specific theme concerns;

iii. a description of the conditions under which the theme is likely to occur; iv. inclusion and exclusion criteria – namely, situations including and excluding the code; and v.  examples regarding when to use the code. Boyatzis proposes four different and straightforward strategies for code development: theory-driven, research-driven, data-driven and hybrid approach. As for the theory-driven strategy for code development, the elements of the code (e.g., label, definition, description) are derived from assumptions and hypotheses of a theory. This is a very time efficient way to develop code insofar as the coding framework of a research may be developed before the data collection. All in all, the code is generated from the theory and you select units of analysis from the data set in accordance with that very theory for its application.

Case study 10.1 Factory workers’ motivation to perform: theory-driven strategy For instance, let us assume that you are interested in conducting qualitative research on factory workers’ motivation to perform on the work floor. You decide to employ self-determination theory (see, e.g., Gagné & Deci, 2005) since it provides a multidimensional approach to motivation. You have conducted interviews with factory workers and managers of a specific company. Before you start analysing your interview transcripts, you will develop your codes on the basis of concepts, assumptions and hypothesis of self-determination theory regarding motivation – for example, need for growth; intrinsic motivation; autonomy, competence, sense of belonging and attachment as important elements for growth; and extrinsic motivators and negative feedback as factors that might hinder personal growth. Codes are then applied to the selected units of analysis (i.e., words, sentences, paragraphs from the transcripts) and interpretations about the employed theory are made on the basis of absence or presence of the codes in the data (see Boyatzis, 1998: 33–6; see also Paré, 2016).

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Note that you only code data that seems to fit the description of these codes and ignore all other data. In so doing, you understand/show whether a specific theory proves valuable for your data set, whether it is as general as it claims to be, whether it needs reworking to be applicable to the context in which you collected your data and conducted your research, etc. When it comes to research-driven codes development, you derive/employ codes that have been already developed/employed by other scholars in previous research. Codes may also come from the literature review that you conducted as part of the preparation for your research. Overall, specific codes are borrowed from past research and applied to your units of analysis which are selected based on the research design (Miller & Crabtree, 1999).

Case study 10.2 Students’ experience with discrimination: research-driven strategy For instance, let us imagine that you want to conduct a study on students’ experience with discrimination at school. Assume that this phenomenon has been under-studied in your country; but a lot of empirical research on the topic has already been conducted abroad. Important scholars have conducted qualitative research to investigate the phenomenon and put forward their understanding of it and/or constructs that contribute theoretically to its characterization. If you choose a research-driven approach to code development, you will have to start by using the code book of, say, a very renowned study that you are inspired by and want to ‘replicate’ in your country/context. Of course, before applying this pre-established coding-structure to your (for example) interview transcripts based on interviews you conducted with students, you need to test (check) the compatibility of those codes with your data and their applicability to the content of the interviews. As in the case of the theory-driven strategy (see above), codes are then applied and interpretation about the theory is made based on the absence or presence of codes in the data (see Boyatzis, 1998: 37–41; Paré, 2016).

You can therefore see, for example, whether general propositions put forward by other scholars based on research conducted abroad prove to be valuable in your country and/or whether your study can add something to what has been discovered so far on the topic of your interest (e.g., whether it represents an exemption to a general rule or shows something new about the phenomenon under study). The data-driven strategy allows you to develop codes inductively. To do so, you employ a criterion reference to select a sample of data based on which the code is generated (Boyatzis, 1998). Simply put, you select a characteristic of your sample that, with regard to your research goals, sets your respondents apart the most and is of theoretical relevance. This criterion reference can relate to socio-demographic characteristics (e.g., age, gender, status of employment or level of education might be relevant to understand

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how your respondents relate to a specific phenomenon), but also to characteristics more specific to your field of study, research question and/or the practice/phenomenon that you are investigating. For instance, imagine you are investigating the level of engagement of a group of citizens with different backgrounds in a project designed to develop digital tools to assist volunteering associations present in a specific region to promote and get support for their activities. Although there might be several characteristics that set your respondents apart on a more general level (e.g., age, education, gender), in the case of your research their (level of) digital skills (low digital literate, high digital literate, etc.) might be the best one in helping you to further detect differences among them and their level of engagement. This does not mean, however, that other characteristics might not be taken into account during your analysis at a later stage.

Case study 10.3 Students’ position within higher education systems: data-driven strategy For example, let us say that you have conducted individual interviews with university students to answer the following research question: ‘what kind of role(s) and/or position(s) do university students have in higher education systems and what are their motivations to enrol in universities?’. You do not need to develop codes based on all the conducted interviews. Instead, you employ a criterion reference that might help you create subcategories in your sample and be relevant to your research question (e.g., ‘university disciplines’ – students from different disciplines assumedly have different understandings of what their role and position is within higher education systems). On the basis of this criterion, you select a discrete number of interviews (e.g., three interviews with students from the natural sciences, three with students from the humanities and three from the social sciences) and develop the codes based on them. Each data source (here an interview) pertaining to the selected sample is read a couple of times. In this phase your goal is that of ‘seeing themes’ and ‘encoding themes’. As anticipated, ‘seeing themes’ refers to identifying what themes are discussed in a specific interview. Every time you identify a theme in an interview you list it on a piece of paper or in a Word file until you develop a summary of all the themes that were discussed in that very interview. ‘Encoding themes’, instead, means for Boyatzis (1998) that you are able to ascribe the same or at least a similar meaning to data pertaining to the same theme that you might find in one – or multiple – interviews. Put simply, you are able to consistently recognize the occurrence of a specific theme throughout one or multiple interviews and to call it always with the same name. Importantly, at this point, you still do not attach any label, or code, to the text you read, but you develop summaries of the themes discussed in each interview.

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Afterwards, you need to compare the summaries of each data source to determine thematic similarities within each subsample (e.g., within the interviews conducted among students belonging to the natural sciences). But what does this mean? It means that you need to take the time to look for shared patterns of meaning (themes) in your data – for example, you notice that there are themes that keep on coming back in almost every interview and that your respondents have consistently addressed while answering your questions. Once you have detected the similarities among the summaries of each interview you list them in a summary of the main themes discussed in that subsample. For instance, in the case of your study on students’ role(s) and motivations within higher education systems mentioned above, such a summary might look like a list of themes that your interviewees more or less explicitly talked about, such as: passive consumption of educational material, involvement in students’ unions activities, participation in curricula design, utilitarian approach to higher education (just need a diploma), higher education as personal growth, higher education as a path to the job market, teacher-students partnership in research, etc. Summaries of subsamples are then aggregated and overarching themes – namely, themes dominant across all the subsamples – are identified. These overarching themes are listed and clustered in thematic families and each of them becomes a code. You are actually in the very important stage of code development. This is a crucial phase as you need to assign to each code a definition of what that specific theme concerns, a description of the conditions under which the theme is likely to occur, inclusion and exclusion criteria – situations including and excluding the code, and examples regarding when to use the code. You now have a list of codes that you can apply to your data (Boyatzis, 1998; Paré, 2016). The fourth and last strategy proposed by Boyatzis for codes development entails the employment of a hybrid approach (but see Fereday & Muir-Cochrane, 2006 for a slightly different take on hybrid approaches to code and themes development in TA). This partly overlaps with the data-driven approach to codes development. However, they have a fundamental difference: the hybrid approach consists of a data-driven, inductive approach to code development that does not rely on a criterion reference for data sampling. Since no criterion reference is employed for data sampling, you need to read each data source (e.g., an interview) at the beginning of the analysis. This is the case when your approach to a specific topic, and possibly your research question, do not point in any specific direction or to any specific characteristic of the respondents as relevant to shed light on the phenomenon under study.

Case study 10.4 Citizens’ engagement in the design of a city park: hybrid approach Let us say, for example, that your municipality started a project to design a new city park. The project foresees the collaboration between the municipality and citizens (Continued)

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intended as future users of the city park. You are into citizenship studies and are, in this case, interested in investigating what role citizens actually play in this collaboration. You have conducted interviews with organizers of the project, employees of the municipality, and a number of citizens who took part in it and who were willing to be interviewed. However, your research question and approach neither prescribe nor suggest a specific focus on specific respondents (e.g., you do not want to know what the role of the involved teenagers was versus that of the over sixty-fives). Therefore, to develop your code, you must be as open as possible to what might emerge from the data. Thus, do not sample data based on a criterion reference (e.g., age of your respondents), but take all of them into account to develop your code.

All this makes the hybrid approach the most articulated strategy for code development among those proposed by Boyatzis. Indeed, you need to read and re-read each data source and generate a summary of the main themes pertaining to it. Your goals here are ‘seeing themes’, ‘encoding themes’ and making for each interview a summary (list) of the themes that were discussed by respondents. Afterwards, the generated summaries are compared to determine thematic similarities among the diverse data sources – you look for the main themes your respondents consistently addressed and brought up. At this point, overarching themes – namely, themes present across all the data sources – are listed and clustered in thematic families. Each theme then becomes a code. Even in this case, you need to assign to each code a definition, a description as well as provide inclusion and exclusion criteria and examples regarding when to use the code (see above). Finally, codes are applied to all data (Boyatzis, 1998; Paré, 2016). Importantly, regardless of the codes development strategy you choose, it is paramount that you carefully document and keep track of any choice that you made as well as to proceed through any TA’s stage in a systematic way in order to increase the validity, traceability and verification of the analysis (Nowell et al., 2017). In other words, you might want to keep a ‘diary’ in which you take notes of what you did, when, and why you made certain decisions. Also, while documenting all these steps, you might store the following items in a coherent, accessible way: screenshots and evidence of all the different phases of the analysis, reporting time/date/type of enquiry conducted, reason(s) to take specific steps and the outcomes of these steps. The last step of Boyatzis’ method consists of the actual analysis. In this step you are supposed to code all the data using the coding framework developed in the previous phase. Ideally, the code that you developed remains the same from the moment you start and throughout your analysis. Of course, before you start you might consider checking your code one last time in order to further refine it – for example, to break down some themes (codes) in sub-themes should you realize they are too general/broad. Furthermore, and importantly, although your goal is to code all the data, only data that fits the codes of that framework are assigned to codes. Once you have coded your data, you need to

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combine themes among each other. In other words, you need to bring together two or more themes in a meta-theme. This procedure is a necessary step to move from the initial task of identifying themes-only to the fundamental task of seeking relationships among them. Importantly, you do not bring together random themes, but themes relevant to answer your research question among which you found associations. But how to do this practically? First, we should remind ourselves that in Boyatzis’ approach each (identified) theme becomes/is a code (see above). Once we realize that each code we developed, decided to employ, in the previous steps of our TA is a theme, we just need to look in our data for associations among codes. For instance, you are conducting research on the work-life balance of employees of a firm in your region, particularly in regard to what factors/activities might negatively and positively affect their well-being. After having coded your data (e.g., interviews you have conducted with the employees of the firm) you look at them (either on print, on a file on your PC, etc.) and try to see if certain themes co-occur and if their co-occurrence keeps on manifesting itself in your data. You notice that, say, a theme you named ‘parenting’ keeps on co-occurring together with another theme that you called ‘work overload’ and with one you named ‘increasing stress’. You just found an association among themes. You wonder if you should put them together as relevant for your research. You realize that they are certainly relevant since they have to do with your respondents’ work and free time, with their family and probably with their health. The fact that they keep on co-occurring throughout your data suggests they have a strong relationship. You then look at what type of relationship they have and what that relationship means for your research. You understand, for example, that the work-overload, and the weight of the duties connected to being a parent that some of your respondents endure, negatively affect their well-being since they lead to a strong increase in stress levels. You might now want to put these themes together in a meta-theme called, for example, ‘causes of distress’, and take note of relevant pieces of your data (e.g., sentences of an interview) that you coded with these themes and explicitly show the relationship you found among them. Notably, continuing with your analysis you might find associations (relationships) among a meta-theme and other (meta-)themes. For instance, in this case you might find associations and a strong relationship between the meta-theme ‘causes of distress’ and another theme or meta-theme you called ‘job burnout’. The further you proceed with your analysis, the clearer it will become which themes are related to each other and how this is relevant for your research. Importantly, we have explained this process as though you would do it manually and without the help of any software. Indeed, you are not obliged to use software to analyse your data. However, although this process might work for small amounts of data (e.g., a small number of interviews) it might become unmanageable with larger data sets and lead you to be inaccurate in your analysis, lose overview of your data and, even worse, lose sight of the relevant associations/relationships existing among the themes. Luckily, for the coding and the analysis of any amount of qualitative data, today researchers can employ software such as NVivo or ATLAS.ti. Should you choose to use

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software to analyse the data, you will need first to code them using the software of your choice. Afterwards, you might want to run a matrix query (see Chapter 5 on NVivo) among several codes (themes) relevant to answer your research question. The matrix query will then display if and what coding co-occurrences exist among the themes that you have decided to explore in your data. If any, coding co-occurrences will indicate themes associations (e.g., among ‘parenting’, ‘work overload’ and ‘increasing stress’). The remaining steps are the same as those of the manual approach: themes that co-occur the most will then be brought together in a group/set that will represent a second level construct (meta-theme/meta-code). These can be further combined with other themes, provided that you find a relationship among specific sets of codes and other codes (themes). At any rate, evidence that backs up the identified relationship must be coded (e.g., through a relationship node in NVivo). By conducting a thorough data analysis you might find several relationships among themes, as well as among a theme and a specific set of themes. You should then judge the relevance of the identified relationships for the goals of the conducted research and, specifically, for finding an answer to the research question underpinning your study. Only the most salient relationships will be kept and reported in the study results, while other, less meaningful relationships will be discarded. Below, we introduce a specific approach to TA put forward initially in the field of psychology and that has become increasingly popular in recent years.

10.3.2 Braun and Clarke’s approach to thematic analysis Differently from Boyatzis, Virginia Braun and Victoria Clarke started to develop their own approach to TA by explaining how to use it in psychology (see Braun & Clarke, 2006, 2021). They divide TA’s analytical process into six phases: 1) familiarizing yourself with your data; 2) coding; 3) generating initial themes; 4) developing and reviewing themes; 5) refining, defining and naming themes; and 6) writing up. We will see that while Boyatzis is more detailed when it comes to the possibilities and the steps required for code development, Braun and Clarke focus more on the steps you take in developing a more abstract framework of themes after initial coding. In the preparation phase, and in line with Boyatzis’ approach to TA, RTA starts from specific research questions, even if you apply an inductive form of TA. For instance, Braun and Wilkinson (2003) state that ‘In this paper, we look at women’s talk about the vagina, focusing on a broad range of meanings evident in women’s talk’ (p. 29). As there is no indication that these research questions have developed somehow through analysis of the data, they suggest that in TA, even if conducted from an inductive approach, researchers often start with specific research questions or focus, which then strongly structures the analysis (see also, Fereday and Muir-Cochrane, 2006). Although Braun and Clarke (2021) emphasize that research questions can change over the course of the data analysis process, they equally stress that coding and theme development should be focused continuously on the research questions, which suggests the more stable nature and structuring influence of the latter.

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Braun and Clark initially remained vague in terms of how existing literature should be used, arguing that ‘reading less’ beforehand fits better with a more inductive approach, while ‘reading more’ beforehand better suits a more deductive approach to TA (Braun & Clark, 2006). However, in their 2021 handbook they advocate reading throughout the whole research process and not just mainly at the beginning, as additional bodies of literature might appear important as the analysis progresses. Although Boyatzis does not discuss the role of a literature review in great detail, a thorough review seems an essential first step in the research process, as it helps the researcher to decide which strategy for code development should be used (i.e., whether you should use an existing coding framework or existing concepts from certain theories). As a result, it seems as if the literature review precedes the analysis more in relation to Boyatzis’ approach, while in RTA the analysis informs more when and what will be read. Regarding the employed sampling framework, samples are usefully fixed over the course of the research and described in terms of their key features (size and composition), to highlight their theoretical relevance. However, little attention is paid to how such decisions could inform the process of data analysis. For instance, in their TA of women’s talk about their vagina, Braun and Wilkinson (2003) simply describe their sample in terms of size (fifty-five women), composition (age, education and sexual identity) and employed sampling technique (convenience sampling), but otherwise do not reflect on the theoretical relevance or limitations of this sample. The sample also seems to remain fixed in that it does not change over the course of the research (for a similar example, see Taylor and Ussher, 2001). Notably, if attention is focused on sampling decisions in TA literature, those decisions seem to focus more on the number of cases that should be selected rather than the theoretically relevant properties of such cases (for a discussion on this, see Fugard and Potts, 2015 and Hammersley, 2015). In describing their research question, Braun and Wilkinson offer us an important clue regarding what kinds of topic and research questions TA can handle well. Given the lack of research on women’s perceptions of their vagina, it makes sense to employ a method that allows us to describe how they talk about this. So, research phenomena of which we know very little constitute suitable topics for a TA, which aims to develop a more abstract, descriptive analysis of these phenomena, and that considers the importance of context and relationships between themes. Finally, Boyatzis and Braun and Clark emphasize the need for transcribing data in a consistent way, as an essential step in preparing data for the process of coding. In so doing, they hope to increase the chances that different researchers will code or interpret the data in the same way. According to Braun and Clarke (2006, 2021), in the first phase you should familiarize yourself with your data by reading and interpreting it (e.g., interview transcripts and audio versions of interviews) to get an idea of what the data is about and, while doing so, taking notes of potentially, theoretically meaningful codes. In a second phase, you need to start coding the whole data set. As was the case for Boyatzis’ original method, RTA allows you to develop codes both inductively (identify the codes through interpretation of text) and/or deductively (select codes based on

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existing theory or apply an existing – validated – coding structure). In both approaches, coding can focus on the explicit or surface meaning, and/or on the more conceptual, latent meaning of text. In addition, although Brown and Clark state that the whole data set should be coded, what they seem to imply is that all the data is read and interpreted but only those sections of text are coded that appear relevant to the research questions. Braun and Clarke emphasize that although codes can be derived from existing research or theory, they should not be conceptualized as themes, which are often broader and more closely related to the research questions (Braun & Clarke, 2006, 2021). This points to an important difference between these two approaches to TA when applied more inductively. While Braun and Clark develop themes gradually through coding, Boyatzis develops themes and a related coding framework early on, by developing and comparing summaries of themes that can be identified in text. In pointing out that an inadequate thematic structure might be the result of ‘selective or inadequate coding … or if coding evolved over the dataset and data were not recoded using the final set of codes’ (p. 66), Braun and Clarke (2012) highlight an important characteristic of TA: data should be coded in the same way consistently and changes to how you code should stimulate the researcher to recode the data, using the adapted coding structure. However, while Braun and Clarke emphasize the need for ‘rigorous’, ‘systematic’ and ‘thorough’ coding (Braun & Clarke, 2021), they do not necessarily recommend that researchers should develop a ‘codebook’. The latter involves a detailed account of the hierarchical relationship between codes, the code label or name, the definition of what the code concerns, and a description of how to know when the code occurs (criteria), as well as exemplars and counter examples (e.g., see Boyatzis, 1998). This is often done and considered important in TA to develop more reliable or ‘trustworthy’ thematic structures (Nowell et al., 2017). In a third phase your focus should shift from the data to the codes and you should begin the process of generating initial themes by thinking about connections between codes and how they might relate to more abstract, overarching themes. The goal of this phase (and end product) should be ‘a collection of candidate themes, and sub-themes, and all extracts of data that have been coded in relation to them’ (Braun & Clarke, 2006, p. 20). Good themes stand alone, but they also need to form a coherent whole with the other themes; like pieces of the same puzzle, they should present you with a meaningful and insightful picture of your data (Braun & Clarke, 2012). A key difference between a code and a theme is that the former typically captures a specific or a particular meaning, while the latter describes a broader, shared meaning. Each theme should have a central, organizing concept that describes what a theme is about, or what kind of meaningful concept holds the codes attached to this theme together. Good candidate themes should capture the data attached to it and address the research question. Although good themes relate to the research question, they should not try to answer research questions (or sub-questions), as this could lead to a more superficial analysis of the data and development of summary-type themes. So, good themes are ‘built around a singular central idea or argument, and do not try to be all things to all people (i.e., they are not topic summaries)’ (Braun & Clarke, 2021, p. 95).

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In their research on how women talk about the vagina, Braun and Wilkinson (2003) developed an initial structure of five themes: positive talk; negative talk; neutral talk about the vagina; lack of awareness of the vagina; and difficult topic to talk about. Each of these five themes has in turn one or more codes, which only connect to the overarching theme to which they belong. For instance, the main theme ‘difficult topic to talk about’ consists of two sub-codes: ‘laughter’ and ‘depersonalization’. The main theme ‘negative talk’ consists of three codes: ‘embarrassment’, ‘vulnerability’ and ‘not quite nice’. These codes only connect to their respective themes at this stage of the analysis. Braun and Clark emphasize the usefulness of presenting the development of themes visually, using a thematic map. In a fourth phase you develop and review your themes. Here, you check if the initial themes fit the data by going back to all the data attached to each theme and the full data set (Braun & Clarke, 2006, 2021). You realize this by asking two questions about each theme: do the data connected to this theme belong to this theme? If not, you might want to change a little what you consider as belonging to/not belonging to the theme and/or consider some data as not belonging to this theme. The second question relates to your thematic map: does it reflect well the data as a whole or the different meanings that you identify as a researcher in the data? Good themes are supported by the data and are sufficiently coherent: they have their own focus and boundaries, they do not merge into each other but together tell an overall story that addresses the research question (Braun & Clarke, 2021). In their study on how women talk about the vagina, such a review of their initial themes leads to a further integration of the coding structure (and reduction from five to three main themes): ‘positive talk – vagina as asset’, ‘vaginal awareness’ and ‘negative talk – vagina as liability’. The theme ‘difficult to talk about’ has been removed from the analysis, as it overlaps too strongly (and could be considered as part of) the larger themes of ‘awareness’ and ‘negative talk’. Here, too, the authors illustrate the usefulness of presenting these themes visually, using a thematic map. In a fifth phase you refine, define and name your themes. In this stage you take the step from ‘coding’ to ‘writing output’ and you should be able to describe in text what the themes are about and which (if applicable) sub-themes they contain (which includes finding a name for each theme which represents its content very well). In so doing, it is essential that each theme should: 1) have a clear focus (not talk about too many different issues); 2) relate to the overall research question; and 3) address a different part or element of the analysis, so that themes do not appear as repetitive (although they can build on each other). Here, Braun and Clarke (2006, 2012, 2021) emphasize the importance of ‘writing’ as a key analytical strategy compared to Boyatzis, which might relate to their more constructivist approach. By writing a coherent storyline or synopsis for each theme (‘what story does this theme tell?’ and ‘how does this theme fit into my overall story about the data?’) and by coming up with an appropriate name for each theme, you become more aware of how issues you want to write about within each theme belong together and relate to the overarching theme, and how well the different themes relate to the research question. The outcome should be a more integrated and coherent analysis. For instance, by writing out the storylines of the different themes described

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above, Braun and Wilkinson (2003) identified two overarching themes in women’s talk about their vagina: the vagina as liability, and the vagina as asset. Within each theme, three sub-themes were identified: for liability the sub-themes were ‘nastiness and dirtiness’, ‘anxieties’ and ‘vulnerability’; for asset the sub-themes were ‘satisfaction’, ‘power’ and ‘pleasure’. The outcome of this analysis can be presented again in a thematic map and also finds its way into the eventual title of their contribution. In a final, sixth phase, you produce a report. Here, the authors present strategies that are relevant in relation to how you make the step from doing qualitative data analysis to presenting this analysis in a format accepted by social science disciplines. These strategies are relevant for different qualitative analysis approaches/methods and relate to issues such as how to use citations, how to develop a coherent structure in your writing and how to quantify.

10.4 Conclusion and discussion After having described Boyatzis’ original method for conducting a TA and Braun and Clarke’s more recent RTA, we can now draw some general conclusions on TA as an analytical technique. Specifically, TA focuses on developing a network of interconnected themes (patterns of meaning), or the central ideas that unify them, across a data set. Such themes should be conceptually different from each other, but connected to each other; at least through their relationship with and relevance for an overarching research question. TA is often driven by initial research questions or particular topics that structure the subsequent analysis. It allows researchers to approach their coding in a deductive, inductive, as well as ‘hybrid’ way (dual inductive/deductive approach). The importance of coding in a consistent fashion is paramount for conducting a proper TA. All in all, the purpose of a TA is to develop or identify (relationships among) themes, formalize or interpret these into meta-themes, as well as cluster themes in groups that suggest second-level constructs. These must then be interpreted with regard to their importance for the research question that you aim to answer.

Table 10.1  Key differences and similarities between Boyatzis’ and Braun and Clarke’s approach to TA Boyatzis

Braun & Clarke

Differences •  The researcher tries to describe reality.

• The researcher tries to construct a shared understanding of reality.

• Themes are identified early on by the researcher.

• Themes are developed or generated gradually by the researcher.

• Developing a coding framework is essential.

•  Developing a coding framework is not essential.

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• Exploring associations between or the mutual occurrence of themes is essential.

• Exploring associations between or the mutual occurrence of themes is not essential.

• Visual presentations of your thematic structure are not essential.

• Visual presentations of your thematic structure are essential.

• Writing is considered an essential tool in presenting your findings.

• Writing is considered an essential tool in analysing your data and presenting your findings.

• It is more common to conduct a rigorous literature review at the start, which can then inform your strategy of coding (deductive and/or inductive).

• Your strategy of coding (deductive and/or inductive) and the developing focus of your analysis determines more when and what you read.

• The identification of themes and the development of a coding framework precedes coding.

• The coding of data culminates in the gradual development of themes.

• Replicability of analysis or reliability of coding as essential criterion of quality.

• Quality of analysis demonstrated by moving beyond description or listing and by showing reflective attitude of researcher(s) throughout.

• A theme is a pattern that describes, organizes or interprets parts of reality.

• A theme is a central idea that unifies a particular pattern of meaning (e.g., concepts, ideas, experience, sense-making).

Similarities • The research questions developed at the start of the research rarely change and strongly structure the process of developing themes. • The size and composition of the sample is often decided at the beginning and the sample rarely changes over the course of the research process. • The analysis can focus on making comparisons between theoretically important groups (e.g., gender) from the beginning and throughout the data analysis process. •  Themes can be developed deductively and/or inductively. •  Researcher can apply TA from very different epistemological assumptions. •  Themes must be coherent and not overlapping. •  Themes should describe the data set as a whole and all the data must be analysed in TA. • Although the whole data set is analysed, codes or labels are only attached to text that is considered meaningful in relation to the research questions. • Coding in a systematic (or always in the same) way is essential and increases the quality of the analysis.

Key differences between both approaches stem in part from different epistemological assumptions. Boyatzis’ emphasis on identifying themes, developing and applying a coding framework consistently, the need to look for associations between themes and a choice between specific strategies of data analysis, which are informed by the outcome of a literature review, suggest a stronger adherence to a more post-positivistic philosophy. In contrast, Braun and Clarke’s focus on (gradually) developing themes, their more open or less structured approach to TA, the emphasis they put on interpretation of data instead of description and quantification and the need to develop themes that reflect ‘collective or shared meanings and experiences’, show a stronger influence of a more constructivist

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approach to data analysis. At the same time, both approaches are explicitly open to different epistemological assumptions, but highlight the need to make such assumptions (and developments of) clear throughout the whole research process. The strengths of TA can be summarized in that it constitutes a very flexible approach to data analysis, can be applied to a broad range of social science disciplines, kinds of qualitative data (text, visual, sound, artefacts) and from a more deductive, inductive or combined approach (theory, research, data or hybrid-driven). In addition, by emphasizing the need to code data in a systematic (uniform) way, it increases the reliability of the findings within the sample. Indeed, being systematic and transparent regarding the analysis allows other researchers to trace the process whereby you reached your results and easily judge their value and accuracy (Joffe, 2012).

10.5 Summary checklist Throughout this chapter, you have become acquainted with TA as an analytical technique, its origin, underlining principles and main features. Richard Boyatzis’ (1998) original method – the first to provide a clear specification of TA, as well as to develop procedural guidelines – has been addressed in detail, as well as Braun and Clarke’s (2006, 2019, 2020, 2021) more popular RTA. By doing this, we have provided a valuable stepby-step guide to conduct a TA of various kinds of qualitative data (e.g., verbal individual and/or group interviews, focus groups data, textual data (written material)), as well as visual data (i.e., video material and images). An overview of key issues, strengths and weaknesses pertaining to TA as an analytical technique has also been presented. Below, we provide you with an exercise (and another in the appendix) so that you can get more hands-on experience conducting TA. Finally, we suggest some additional, essential reading to further improve your understanding of TA.

10.6 Doing thematic analysis yourself In this exercise we will encourage you to think about what is required to design and conduct a TA for a master’s thesis.

10.6.1 Assignment Imagine that a classmate of yours, Erica, is interested in studying ‘how on-campus and off-campus higher education students experienced distance online learning activities during the covid-19 pandemic’. Erica asks you for some tips on how to design her research. Importantly, she adds that through the analysis of the data she will collect she will strive to answer her research question in an articulated and sophisticated way. Put simply, she aims to both detect associations among main concepts she might identify in her data set (e.g., to recognise co-occurrences of two concepts in the data) and

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put forward propositions regarding the relationship between these concepts in order to reach a higher level of abstraction in her analysis. Erica knows that her first, more descriptive goal (identifying associations among concepts in the data) can be achieved with a qualitative content analysis (QCA), but she also knows that QCA does not allow her to put forward claims regarding the relationships among those concepts and the meaning of those relationships for her research question. She tells you that her intention is to start from the data themselves (students’ experiences) without totally neglecting existing theory about the topic of her interest. Since you became very familiar with TA, you tell Erica that TA, and particularly Boyatzis’ approach to TA, is well suited to achieve both her goals. Erica, however, does not know TA and asks you to help her to structure both her research and analysis according to this technique. In helping her, please focus on all the main features of a TA analysis (sampling, code development, etc.), and describe how she would address these in relation to her data and research goals. Please also consider that your friend is doing this as part of her master’s thesis, which means she has limited resources at her disposal.

10.6.2 Model response Given her interest in starting the research and analysis from the data, and her interest in describing and finding associations, I would recommend using Boyatzis’ data-driven approach. Erica is interested in the experiences of both on-campus and off-campus students. She does not seem, yet, to make a difference between, say, bachelor and master’s students and/or male and female students. Boyatzis’ data-driven approach is suited to this kind of research because it is a data-driven, inductive approach to code development that relies on a criterion reference for data sampling (in this case off-campus/on-campus students). In other words, during the process of codes development Erica will make use of the attributes on-campus and off-campus to select part of her interviews. She will develop the codes on the basis of those and then applying her codes to all her data. Importantly, you might want to explain to Erica that this approach will allow her to explore, detect and compare other specific differences among students (male/female; bachelor/master’s, etc.) later on during her data analysis. Erica should also be familiar with the main existing theories regarding her topic of interest. For instance, she might consider recent research in secondary education that suggests that students’ social class background informs the kinds of cultural, economic and social capital that they can use in dealing with the challenges of distance learning. In this way, while beginning her analysis in a data-driven manner, she will be able to keep in account existing studies and knowledge developed by established scholars and other researchers in the subsequent phases of her research. The outcome of her work should therefore add and relate to the work that has been conducted in her field of study. First, Erica should think of an appropriate, basic sample. Given her focus on students’ experience of distance learning and on different settings in which students might have followed online courses during the covid-19 pandemic, she might sample twelve students, six who took part in distance learning from their home and six who did it from a

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room/studio that they rent in a university student residence. Afterwards, Erica will have to conduct individual interviews with these twelve respondents and preferably transcribe them. In order to transcribe them in an accurate and consistent way she will use a general set of transcription conventions (e.g., regarding intonation, length of pauses, interrupted speech). In so doing, both verbal and non-verbal cues of each interview will be accurately grasped. Also, each transcribed interview will then become more ‘understandable’ for other researchers or general readers who might want to look at the transcribed data or read interview excerpts. Once the interviews are transcribed, she will focus on code development. She will first collect all her data before starting the process of data analysis. As anticipated, Erica does not need to develop codes based on all the conducted interviews. She will instead employ a criterion reference relevant to her research question (on-campus/off-campus), on the basis of which she will select a discrete amount of interviews (e.g., two/three interviews with off-campus students and two/three with on-campus ones). Your friend will then need to read each data source (interview) pertaining to the selected sample and write a summary of the main themes she identified in the selected interviews. Afterwards, Erica will compare the summaries of these four to six interviews to determine thematic similarities within each subsample (e.g., within the interviews conducted among on-campus students). Summaries of subsamples (on-campus students’ interviews and off-campus students’ interviews) are then aggregated and overarching themes – namely themes present across all the subsamples – are identified. These overarching themes are listed and clustered in thematic families; each of them becomes a code. As anticipated (see Boyatzis’ method description above), for each thematic code Erica will have to develop a label, a definition, a description, as well as to provide inclusion and exclusion criteria and examples of when to use the code. Once Erica has developed her thematic structure, she needs to apply it to her data (the interviews). Once Erica has applied her codes (the themes she identified in the first phase of her analysis) to her data, she will have to look for associations among them. In order to find associations (co-occurrences) among codes (themes) she might want to employ software (e.g., NVivo) to make this task easier. By running queries via the employed software, Erica might find several relationships among themes. She will not need to consider all of them, only those she judges relevant to answer her research question. Relevant relationships will be formalized in meta-themes, second-level constructs, while other, less meaningful relationships will be discarded. At this point, your friend might want either to focus on describing those relationships or on generating constructs that contribute theoretically. She might find, for instance, that a specific theme has a strong relationship with a specific meta-theme or set of themes, or that a meta-theme has a relevant relationship with another meta-theme or a specific cluster of themes. At any rate, Erica will then need to take some time to interpret all these relationships in light of her research question and the goal of her study. Of course, she is not expected to come out with any theory about it. As long as she adds to our understanding of how on-campus and off-campus HE students experienced distance online learning activities during the covid-19 pandemic, she fulfils the end-product as

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prescribed by the TA. It might also be that while investigating students’ experiences, she also finds out relationships between the settings in which these students took part in distance learning and the coping mechanisms that they employed to face what they were experiencing (i.e., being forced to follow courses online while being house/studio-bound because of the pandemic). In the conclusions of her research she must emphasize where she added to our understanding of the phenomenon that she decided to investigate, as well as to suggest which questions warrant further investigation and how (and with whom/what kinds of data) these might be investigated and answered. Overall, it is important that Erica carefully documents and keeps track of any choices that she made, as well as that she proceeds through all the stages of TA in a systematic way. This will increase the validity, traceability and verification of her analysis. If a professor, a friend or any other external evaluator will ask her what she did, how and why, she will be able to account for every single step she took.

10.7 Recommended reading Boyatzis, R. E. (1998). Transforming qualitative information: thematic analysis and code development. London: SAGE. The first clear (post-positivist) specification of TA and a thorough formulation of procedural guidelines to analyse qualitative data with this technique. Starting from a post-positivist perspective to data analysis, this contribution guides the reader to highly systematic coding and analytical phases of qualitative data. A must-read for those interested in employing TA. Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. A much-cited alternative to Boyatzis’ original TA approach. Developed from a constructivist perspective, Braun and Clarke’s method provides greater freedom than Boyatzis’ to the researcher during the analytical phase. Particularly suited for those interested in a slightly more flexible approach to code development and to the discovery of themes in qualitative data sets. Braun, V. and V. Clarke (2021). Thematic analysis: a practical guide. London: SAGE. An updated and expanded discussion of Braun and Clarke’s take on TA, which they name here ‘Reflexive TA’ or ‘RTA’. Offers a very accessible and detailed account of the practical steps that you must take in order to conduct RTA, as well as of the philosophical assumptions that underpin RTA and of how this approach is different to other TA approaches. Fereday, J. and Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5, 1–11.

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A systematic guide to the integration of inductive and deductive TA to interpret raw data and particularly to the combination of data-driven and theory-driven codes. By combining the data-driven inductive approach of Boyatzis (1998) and Crabtree and Miller’s (1999) deductive a priori template of codes, the authors develop their own hybrid approach to data analysis. This is also an interesting read for those who want to know more about the combination and application of TA with and to phenomenology. Joffe, H. (2012). Thematic analysis. In D. Harper and A. R. Thompson (eds), Qualitative methods in mental health and psychotherapy: a guide for students and practitioners (pp. 209–23). Chichester: Wiley. A useful overview of the development of TA as an analytical technique. It provides clear insights on the historical origins and influences of TA, its key epistemological assumptions as well as its application as a method (suitable type of data, research questions, etc.). While focused mainly on the application of TA in mental health studies, it is certainly useful/applicable for/to other fields of study.

10.8 References Ayres, L. (2008). Thematic coding and analysis. In L. M. Given, The SAGE Encyclopedia of qualitative research methods (pp. 867–8). Thousand Oaks, CA: SAGE. Barbour, R. (2008). Doing focus groups. London: SAGE. Boyatzis, R. E. (1998). Transforming qualitative information: thematic analysis and code development. London: SAGE. Bradford, N. J., Rider, G. N., Catalpa, J. M., Morrow, Q. J., Berg, D. R., Spencer, K. G. and McGuire, J. K. (2019). Creating gender: a thematic analysis of genderqueer narratives. International Journal of Transgenderism, 20(2–3), 155–68. Bradley, E. H., Curry, L. A. and Devers, K. J. (2007). Qualitative data analysis for health services research: developing taxonomy, themes, and theory. Health Services Research, 42(4), 1758–72. Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. Braun, V. and Clarke, V. (2012). Thematic Analysis. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf and K. J. Sher (eds), APA handbook of research methods in psychology, vol. 2. Research designs: quantitative, qualitative, neuropsychological, and biological (pp. 57–71). Washington, DC: American Psychological Association. Braun, V. and Clarke, V. (2014). What can ‘thematic analysis’ offer health and wellbeing researchers? International Journal of Qualitative Studies on Health and Well-being, 9. Braun, V. and Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–97. Braun, V. and Clarke, V. (2020). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 1–25.

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Braun, V. and Clarke, V. (2021). Thematic analysis: a practical guide. London: SAGE. Braun, V. and Wilkinson, S. (2003). Liability or asset? Women talk about the vagina. Psychology of Women Section Review, 5(2), 28–42. Castleberry, A. and Nolen, A. (2018). Thematic analysis of qualitative research data: is it as easy as it sounds? Currents in Pharmacy Teaching and Learning, 10(6), 807–15. Fereday, J. and Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. Fugard, A. J. B. and Potts, H. W. W. (2015). Supporting thinking on sample sizes for thematic analyses: a quantitative tool. International Journal of Social Research Methodology, 18(6), 669–84. Gagné, M. and Deci, E. L. (2005). Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331–62. Guest, G., MacQueen, K. M. and Namey, E. E. (2012). Applied thematic analysis. Thousand Oaks, CA: SAGE. Hammersley, M. (2015). Sampling and thematic analysis: a response to Fugard and Potts. International Journal of Social Research Methodology, 18(6), 687–8. Herzog, C., Handke, C. and Hitters, E. (2019). Analyzing talk and text II: thematic analysis. In The Palgrave Handbook of Methods for Media Policy Research (pp. 385–401). Cham: Palgrave Macmillan. Holloway, I. and Todres, L. (2003). The status of method: flexibility, consistency and coherence. Qualitative Research, 3, 345–57. Holton, G. (1975). On the role of thêmata in scientific thought. Science, 188, 328–34. Holton, G. (1978). The scientific imagination: case studies. Cambridge: Cambridge University Press. Joffe, H. (2012). Thematic analysis. In D. Harper and A. R. Thompson (eds), Qualitative methods in mental health and psychotherapy: a guide for students and practitioners (pp. 209–23). Chichester: Wiley. Joffe, H. and Yardley, L. (2004). Content and thematic analysis. Research Methods for Clinical and Health Psychology, 56, 68. King, N., Horrocks, C. and Brooks, J. (2018). Interviews in qualitative research (second ed.). London: SAGE. Maguire, M. and Delahunt, B. (2017). Doing a thematic analysis: a practical, step-bystep guide for learning and teaching scholars. All Ireland Journal of Higher Education, 9(3). Merton, R. K. (1975). Thematic analysis in science: notes on Holton’s concept. Science, 188(4186), 335–8. Miller, W. L. and Crabtree, B. F. (1999). Using codes and code manuals: a template for organizing styles of interpretation. In B. F. Crabtree and W. L. Miller (eds), Doing qualitative research (second ed., pp. 163–77). Thousand Oaks: SAGE. Newman, W. L. (2006). Social research methods: qualitative and quantitative approaches (sixth ed.). Boston, MA: Pearson Education.

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Nowell, L. S., Norris, J. M., White, D. E. and Moules, N. J. (2017). Thematic analysis: striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. Paré, M.-H. (29 February–4 March 2016). Advanced qualitative data analysis [PowerPoint slides]. Bamberg, Germany: 5th ECPR Winter School in Methods and Techniques. Riger, S. and Sigurvinsdottir, R. (2016). Thematic analysis: handbook of methodological approaches to community-based research: qualitative, quantitative, and mixed methods (pp. 33–41). Oxford: Oxford University Press. Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory (second ed.). Thousand Oaks, CA: SAGE. Taylor, G. W., & Ussher, J. M. (2001). Making sense of S&M: A discourse analytic account. Sexualities, 4(3), 293–314. Terry, G., Hayfield, N., Clarke, V. and Braun, V. (2017). Thematic analysis. In: Willig, C. and W. Stainton-Rogers (eds.) The SAGE handbook of qualitative research in psychology (pp. 17–37). London: Sage. Vaismoradi, M. and Snelgrove, S. (2019). Theme in qualitative content analysis and thematic analysis. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 20(3). Vaismoradi, M., Turunen, H. and Bondas, T. (2013). Content analysis and thematic analysis: implications for conducting a qualitative descriptive study. Nursing and Health Sciences, 15(3), 398–405.

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11 Conclusions: Comparing Destinations and Road Maps Peter A. J. Stevens

In the introduction chapter of this handbook we argued that there are many good reasons for adopting a more orthodox approach to qualitative data analysis (QDA), but also that we should do so in a pragmatic way and consider the differences within each approach and the opportunities to change approaches as our research progresses. We also showed how these approaches function as road maps, tailor-made to reach a particular destination or investigate particular research questions (RQs). In addition, we described how these RQs in turn relate to specific philosophical assumptions and related paradigms. The subsequent chapters all provided you with detailed maps that you could follow to reach particular destinations. In the concluding chapter I would like to compare these road maps and their destinations, to see connections between them and where they seem to differ most. At the same time, I want to suggest a particular strategy that you can use to decide for yourself what kinds of RQ and analysis approach you could consider for your own research, assuming that you have at least a general theme of interest. I will do this by applying the approaches discussed in this handbook on one particular research theme: higher education (female) students’ involvement with sex work. I chose this theme as it relates to an actual master’s thesis, written and eventually published by Judith Van Schuylenbergh (2017). More specifically, I want us to imagine that you are Judith’s promotor for her thesis and that she wants to research this particular theme but that she is undecided regarding the kinds of RQ that she wants to study and the data analysis approach that she might use. One way in which we could help Judith is by exploring how different philosophical paradigms might stimulate her to develop particular questions and how these could be studied using a specific (more orthodox) QDA approach. In adopting a more post-positivistic approach, Judith could be interested in explaining, for instance, why students decide to engage with sex work. Here, Judith wants to develop an explanatory model that links particular phenomena as causes, conditions, interacting factors … to the ‘dependent variable’: engaging/not engaging with sex work.

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Qualitative Comparative Analysis (QComA), process tracing (PT) and Post-positivistic grounded theory (PPGT) could all help her to do so. Applying a QComA approach would mean that Judith would look for the most important factors that influence students’ decision to engage with sex work. Applying PT would allow her to do the same, but it could also be employed to discover the underlying mechanisms that explain why certain factors stimulate students’ engagement with sex work. Both QComA and PT would require Judith to conduct a thorough literature review to identify potential explanations for (not) engaging with sex work. This could be combined with initial interviews with some students who are (not) engaged with sex work. PPGT starts from a more open approach by putting what can be found in existing research ‘in brackets’ and instead investigate in a more inductive way, for instance by interviewing a basic sample of higher education (HE) students engaged with sex work, to explore which factors and processes are important in explaining students’ decisions to engage with sex work. PPGT would also expect Judith to change the focus of her research and related research questions over time. For instance, as she progresses with her data analysis and builds a basic theory, she might find that the first experiences of such work are crucial and instead focus on how such first experiences explain continued involvement with sex work. If Judith’s literature review concluded that ‘the first experiences’ are indeed important to explain (continued) engagement with sex work, but that we know little about how this makes a difference, she could decide to immediately focus on this phenomenon using PT, as PT is ideally suited to explain the underlying mechanisms through which variable A influences variable B. However, there are also practical issues to consider: QComA requires a sufficient number of cases and variability in the sample, meaning that Judith would have to find a sufficient number of students who engaged with sex work and a similar sample of students who decided not to (e.g., at least eight to ten of each group). In addition, if the literature suggests for instance that ‘economic motivations are key to engaging with sex work’, Judith might feel that she needs to select students from (for instance) the United Kingdom and Belgium, as higher education is considerably more expensive in the UK compared to Belgium. PT and PPGT are more flexible in terms of sampling and could both start with a small sample of students who are involved with sex work. However, difficulties in finding these participants, might stimulate Judith to focus on students who decided not to participate with sex work, as they should be easier to involve with her research, and develop an explanation for why they do not want to do this type of work. If Judith concludes, after conducting a first review of the literature, that we actually know very little about how students involved in sex work experience their work, she might want to describe what sex work constitutes or means for these students. The verb ‘to constitute’ refers more to a post-positivistic approach, suggesting that we want to find out what ‘doing sex work’ actually is. In contrast, the verb ‘to mean’ refers to a more constructivist approach, as we try to understand how people give meaning to doing sex work. Applying a Qualitative Content Analysis (QCA) approach, Judith would build a coding framework inductively, or perhaps based on existing research on other types of

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sex worker and/or interviews (deductively or combined) with students engaged with sex work. She could conclude, for instance, that the experience of sex work relates to specific topics, such as their working conditions, clients, public opinion, financial rewards, challenges, etc. This coding framework would represent the different aspects that constitute sex work or what it means to be involved with sex work and should be replicable, in that other researchers should come up with the same classification of topics when using the same coding framework on the same sample. If Judith tries to establish correlations between these topics (e.g., between working conditions and challenges) or link them to more abstract concepts (such as ‘coping’), then she is moving her analysis much more in the direction of post-positivistic thematic analysis (PPTA) or a basic form of constructivist grounded theory (CGT) or constructivist thematic analysis (CTA) respectively. Note that the focus of the analysis is on describing the key, constitutive elements of a phenomenon or how it is experienced by a certain category of people, not on developing a deeper understanding or explanation of why that is the case. Adopting a more constructivist approach, Judith might want to gain a deeper insight in what it means to engage with sex work for HE students by focusing on how they develop such an understanding. Hence, developing a deeper understanding means more than describing shared experiences about what it means to be involved in sex work. Instead, Judith wants to understand how these young people developed such a view on sex work over time. Note that in using the verb ‘developing’, Judith assumes that these students are not ‘forced’ to perceive sex work in a particular way, due to external, structural or cultural constraints. Instead, these students take an active role in defining their work, in interaction with the contexts in which they operate. Adopting CGT, which is strongly inspired by symbolic interactionism, Judith could explore how students involved with sex work define such work, and how these definitions changed over time and context and in interaction with significant others (clients, other sex workers, friends). Judith would expect her research focus to change as she progresses with her data analysis and focus increasingly more on specific phenomena that appear meaningful to the respondents but poorly understood in the literature. For instance, after developing a basic theory, Judith finds that interactions with clients play a crucial role in developing particular views about sex work, something which appears to be understudied. As a result, she decides to focus her research on the role of these interactions in developing particular meanings towards sex work and even consider interviewing clients. To make her findings theoretically relevant, she would explore connections with her findings and more abstract concepts and theories, such as (work) socialization and personal and professional identities. Research questions developed from a critical perspective take a more suspicious approach to what people say through interviews, pictures, films, etc. More specifically, they link people’s presentations of reality (including how they present themselves) to their interests, which are in turn linked to the position they take in the social structure. Taking a more critical approach, Judith could conceptualize students’ experiences of sex work as presented through interviews as a purposeful way of presenting themselves and

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social reality, aimed at realizing personal interests (such as developing a positive identity), which in turn relate to the structure in which they operate, or their positions on various (gender, educational and job-status, age, ethnic) hierarchies in society. In taking this more critical lens, Judith would not only investigate how they present themselves and other significant others, but also why they do this and the rhetorical tools that they use to do this. This is exactly the approach that Judith selected for her master’s thesis, and in so doing she showed how these students present a positive image of themselves, both in terms of how they define sex work to Judith the interviewer, as through the way in which they describe themselves through their online ads, aimed at attracting a particular type of customer (‘clean, intimate and respectful’):

Figure 11.1  Avertisement from HE student advertising sex services In this online advertisement, the student/sex worker looks for a particular kind of client: one who seeks a ‘real girlfriend’ experience, ‘mutual respect’ and a ‘real date’ that is ‘more than just an ordinary sex date’. She emphasizes that safe sex is a must and that she also wants to be spoiled. All this portrays an image of someone who is not just providing sex for payment, but someone who wants to be treated as an equal (respect, safety, enjoyment). This shows how this student actively portrays an image of herself that is more positive than how sex workers are often perceived. This more positive image helps her to maintain a positive identity (as somebody who is in control and safe, enjoying their work, dealing with ‘good customers’, getting paid well) in a context where their work is often considered immoral, degrading, abusive and unsafe.

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Judith then links her findings to feminist theory that considers women’s involvement in sex work as (for these women) potentially liberating and gratifying, where women decide to use their body with whom they want to have sex in a way they enjoy, leading also to financial independence; as opposed to the view of sex work as necessarily oppressive to women. However, she rightfully stressed that such an image emerged from analysing data related to women who did not seem to need the money or did not experience any other structural need for engaging in sex work, which is not representative for this population as a whole. The three more critical approaches discussed in this handbook could all be used by Judith. However, opting for one approach over another seems to relate more to the kinds of data that she will be using and whether her focus is more on manifest and/or hidden meanings attached to certain presentations of reality. If she is focusing her analysis primarily on visual data, like the photos that these young women use in advertising themselves to potential clients and the additional textual info that they provide with these photos, then she could opt for textual analysis (TexA). This approach looks at the different structural and formal layers of a text, such as narration, narratives, dialogues, mise-en-scène, settings, cinematography, montage, camera perspectives, sound, and so on, to try to understand how they, in interaction with each other, play a role in presenting a particular version of reality in a more subtle or less straightforward way. If Judith is instead using interview data from interviews with these sex workers, she might want to explore how they present ‘students as sex workers’ and other categories of sex workers using particular discourses or a set of statements that together offer a particular image of reality. Here, the focus can be on both latent and manifest meaning and she could for instance find that these students portray their type of sex work in more positive terms compared to other types of sex work, which can in turn relate to their desire to develop a positive identity in a context where such work is generally looked down upon. If Judith finds that her respondents narrate particular stories of events in which they had to negotiate their professional and work identities to outsiders, she might use narrative analysis of small stories (NASS) to analyse the content and structural features of these ‘short stories’ and what can be learned from these in how they represent themselves and others and the reasons for doing so. These three approaches to QDA all emphasize that the way in which reality is presented by these sex workers is not random, but tied to their particular structural position and related interests and also contribute to the development of a view of reality. Imagining what it could mean to investigate particular RQs and applying appropriate approaches to do so, means that we think of the feasibility or the practical hurdles involved in carrying out your research. For instance, one reason why Judith opted at some point to analyse advertisements posted by students working as sex workers, is that she found it very difficult to find and convince women to participate with her study, which illustrates the stigma that they experience regarding their involvement with this type of work. In contrast to her potential respondents, these advertisements were readily available once she identified key websites that are used by HE students to

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advertise their services. Analysing these adds allowed her to explore how they present themselves to clients and how such ‘image management’ relates to their particular interests, attached to their position as sex workers and students. Interestingly, while it seems logical that Judith would employ a more critical approach, particularly TexA, given her focus on how sex workers present their image in a context of stigma, by using images and written text, she claims to have applied a grounded theory (GT) approach. However, her approach resembles more a pragmatic approach to QDA (see introduction chapter), in which some general principles of QDA are (successfully) used, rather than an application of GT in an orthodox way. This illustrates Hood’s (2007) assertion that sometimes researchers wrongfully claim to use GT, but at the same time that adopting a more pragmatic approach can also lead to high quality output. This example shows you that we use different approaches to QDA for different kinds of end product: if we aim for description, we would rely on QCA, if we aim for explanation we use QComA, PT and PPGT and for developing a deeper understanding we use CGT; with PPTA and CTA taking a more in between position in terms of aiming for description or explanation/understanding. If we focus on understanding how presentations of reality are constituted within and shape a particular context characterized by social inequality, we use our critical approaches (critical discourse analysis (CDA), TexA and NASS). The importance of the literature and particularly the extent to which existing concepts, theories and empirical findings direct your research, also varies considerably between these approaches. This means that if you want to apply specific theoretical concepts or ideas, you are more likely to use more deductive approaches, like QComA, PT and more deductive approaches to QCA and TA. Critical approaches to data analysis, such as CDA, TexA and NASS, also allow you to start your analysis using particular theoretical ideas or concepts. Only in those approaches to QDA that emphasize the need to at least start analysing inductively, like CGT, PPGT and CTA and more inductive approaches to QCA, researchers are strongly advised not to start from pre-established concepts and ideas. At the same time, we saw that PPGT expects researchers to employ a coding paradigm after a first phase of coding, which imposes a prescribed focus on your analysis. We also saw that CGT stimulates you to be sensitive of particular theoretical concepts after an initial phase of coding. This shows that there is no approach to QDA discussed in this handbook that advocates pure induction, as researchers consciously or subconsciously rely to some extent on concepts they have learned in interpreting data. Finally, we noticed that these different approaches to QDA rely not only on deduction and/or induction, but also on abduction. Abductive reasoning means that we use all our knowledge of existing theories/research and our data to make predictions (hypotheses) about specific phenomena and relationships between them. Abduction starts when, as researchers, we are confronted by a frustrating feeling, a feeling of doubt and/or surprise by findings that we cannot explain. In order to resolve this feeling, our mind tries to find answers (or formulate hypotheses) on these questions by ‘absorbing (the greatest possible amount of) environmental data, which are then (albeit subconsciously) interpreted and

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used to arrive at a meaningful conclusion’ (Willig, 2014, p. 127). Importantly, abduction does not allow itself to be limited by theoretical assumptions but aims to develop a theory that explains the unanswered questions. Put differently, abduction is a form of ‘informed guessing’ (Willig, 2014, p. 128), that often occurs spontaneously and in a ‘flash’ while analysing data, and which results in an idea that explains something better that was previously unexplained or unclear. Through abduction, deduction and induction cycles, theories are developed, re-formulated into predictions and tested, showing that all three means of reasoning are used together and in interaction to develop theory. So, abduction leads to the development of hypotheses or ‘the formulation of a rule (or theory) in a proposition’ (Willig, 2014, p. 131), then a prediction is made based on this rule (deduction) which is then verified or falsified by means of observation and induction (looking at the facts to confirm this theory/rule). Or: ‘abduction searches for theories, deduction for predictions, induction for facts’ (Willig, 2014, p. 131). Another difference that can be noted in relationship to QDA approaches relates to the question of whether research questions should remain more or less fixed over the course of your research project or whether they can or should change. While most approaches to QDA, also more pragmatic ones, emphasize that RQs can change over the course of the research process (and often see this as an essential feature of qualitative research more generally), it is remarkable that they are unlikely to do so in most of the orthodox approaches discussed in this handbook. In every approach, bar CGT and PPGT, research questions remain fairly fixed over the whole research process. At the same time, researchers are generally allowed to change RQs if necessary, but in most approaches discussed here, RQs stay the same once data is being analysed in-depth. In sharp contrast, GT approaches expect the researcher to change RQs over the course of the research process and only start with guiding, initial RQs.

11.1 References Hood, J. (2007). Orthodoxy vs. Power: The defining traits of grounded theory. In A. Bryant and K. Charmaz (eds), The SAGE handbook of grounded theory (pp. 151–64). Thousand Oaks, CA: SAGE. Van Schuylenbergh, J. (2017). Identiteit en image management bij studenten werkzaam in de seksindustrie. Ethiek & Maatschappij, 19(1–2), 1–31. Willig, C. (2014). Interpretation and analysis. In U. Flick (ed.), The SAGE handbook of qualitative data analysis (pp. 136–49). Thousand Oaks, CA: SAGE.

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APPENDIX

Chapter 2 Additional exercise: critical discourse analysis The framework of CDA is regularly used to study written texts but should not be limited to that specific interpretation of ‘text’. This exercise focuses on the visual mode of discourse. Take a look at the following picture, which was taken during the US presidential election campaign of 2016 and was published on the homepage of the then candidate Donald Trump’s website. Describe the visual semiotic choices by drawing on the notion of salience. Are there certain features in the composition that are made to stand out, to draw our attention and that foreground certain meanings? What about the use of cultural symbols, colours, focus, foregrounding? What is the underlying meaning and identity articulated by this picture? Keep in mind its specific context and objective as well.

Figure A.1  Analysing a picture through CDA

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An elaborated analysis will again depart from the three dimensions of our model. Here, we briefly summarize the key points. On the text level, the most prominent indicator includes the dominant US flag as a cultural symbol with which Trump would like to identify himself and his discourse. In a similar vein is the consistent use of red and blue in his clothing and promotional material. Given the picture’s status as PR material (cf. discursive practices), the composition of all people portrayed is purposefully and meaningful. It suffices to point out the central, foregrounded and elevated position of Trump in combination with the fact that the audience is literally and figuratively standing behind him and looking up to him as their leader. Finally, the audience members are clearly selected according to a number of socio-economic indicators such as gender, ethnicity and age to discursively construct the image of a president who unites and appeals to all Americans. This bring us to the dimension of social practices by referring to the dominant articulation of a strong collective identity in a sense of the heterogeneous US population being ‘united’ behind their Republican leader Trump who is discursively linked to broader ideas of patriotism and related American values.

Chapter 3 Additional exercise: grounded theory – coding according to a constructivist approach of GT Context: In what follows you will read the typed (transcribed) version of an interview conducted by a student in the context of the course Introduction to Qualitative Research. In this exercise, the student tries a face-to-face qualitative interview to learn more about ‘the causes behind the school choice that parents make for their children’ (research question). Suppose this is a thesis student who sends you his first interview for feedback on the codes found. Exercise: Please do the following with the transcript: 1. First you will apply a form of initial coding by indicating paragraphs/sentences/ words in the text (using the comments function in Word), whereby, in accordance with a more constructivist approach to GT, you mainly indicate codes that carry an action of a particular actor. 2. Then you will code with a more focused approach in the text (by means of the comments function in Word), where you already think about the interrelationships between the codes found and related: structuring mechanisms that shape how school choice is made by this parent. You will indicate such focused codes in bold in your comments, which will include short memos that reflect your thinking on those focused codes. 3. At the end of the text you will write a synthesis (more theoretical coding) of your initial and focused codes in relation to the central research question in an extensive memo and also indicate which new/additional research questions come up and any implications for the sampling.

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Interview transcript

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Coding according to CGT

INTERVIEWER: Hello, I am (interviewer name), I am currently studying sociology at the University of Ghent. As part of our lessons, we intend to conduct an interview and I would like to thank you in advance for participating. PETRA: That’s a pleasure. INTERVIEWER: Okay, before we really get started, just briefly explain. Uhm, yes, the interview itself will take about fifteen minutes twenty minutes. During that time I will ask you to fill in a short questionnaire. Then I will start with a first question and then you can take the time to formulate your opinion and then we will see where the conversation takes us. PETRA: Okay, that’s good. INTERVIEWER: Good. Eh, I should also mention that this conversation is being recorded, but only for further processing so that we can be sure that we can use all the valuable information. PETRA: Ahzo okay, but I shouldn’t worry about that? INTERVIEWER: No, not at all. Uh, the processing will only be watched by my other 2 team members and our accompanying prof. And if you want I can also work anonymously, so that your name is not written anywhere afterwards. PETRA: Ah yes, but that is not a problem at all, if my name is used, you shouldn’t do it anonymously. INTERVIEWER: We will keep that in mind, and then we can start with “the real thing”. So as I said, I have a short questionnaire here, if you’d like to complete that. PETRA: No problem, I see that there is already a pen ready, can I use it? INTERVIEWER: Yes, of course, use it. … INTERVIEWER: Okay, well, that is already one administrative point behind you. PETRA: (laugh)

(Continued)

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(Continued) Interview transcript

Coding according to CGT

INTERVIEWER: Um, we’re actually looking for the definition of a good school, so what you personally think is a good school, plus the elements that played a role in choosing the school for Niels, Robbe and Ward. Now, I have some sort of introductory fill-in-form [note to reader: student presented a list of factors that could be important for the respondent in choosing a particular school], it would be really nice if you could take the time to fill this in. PETRA: Hmm, then we’re going to do that é, and tis what I think? Or what there really is in the school itself? INTERVIEWER: It is about what you find more or less important and not what is present or not at your children’s school. … INTERVIEWER: I see that you think a lot of things are rather important, but most of all great technical support and childcare facilities [i.e. the possibility for the school to take care of the child after school until the parents pick the child up from school], can you explain that a bit further? PETRA: Well, yes, I think it is important to have childcare facilities, because me and my partner are both working and it can sometimes be that our work ends on some days, or there is an extra meeting or something, and I always find it convenient that I do not have to worry about the children because they know if we can’t come immediately that they have to stay in school for a while, so that’s a good thing that there is a possibility, because otherwise we immediately have to find someone to get them and take them home and stuff. INTERVIEWER: Ahzo, and technical support? PETRA: Well, yes, I still remember when my eldest son came home and was suddenly working on our computer and I asked him what he was doing, and he simply replied, “Well, Mommy, I’m using the computer like in the classroom with the Miss [teacher]”, and still remember that I was surprised that they already received such lessons. But then I asked the teacher herself and she gave a whole explanation of what they all do and how the system works (…) and since then I have been a big fan.

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Finds childcare important. Working late can cause stress for parents regarding their children who are waiting for them.

Children will worry less if they can wait longer at school. Other people are called upon immediately if the children cannot stay at school. Memo and more focused code (here we think about underlying relationships between codes): parents ‘don’t want to worry’ about their children if they can’t be picked up right away (the latter also seems to affect the former). ‘Immediately have to find someone‘ also suggests a source of stress / pressure for the parents and third parties. A certain stress prevention seems to guide the choice of school, which raises the question of which stressors parents experience and how they deal with them? Mother also finds technical support important in determining school choice. Mother is surprised that her son learned so much about ICT at school. Mother asks teacher about what her son learns about ICT at school.

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Interview transcript

Coding according to CGT

INTERVIEWER: Do I dare to put the term added value in my mouth?

Mother finds it positive that / additional value in her son being taught ICT at school.

PETRA: Yes, it is definitely an added value, I think it is positive that they have been working on it from an early age, because today you simply cannot do without IT anymore, I think it is important that they start it so early, they already have a good basis and then they can continue on their own. INTERVIEWER: Yes, now you have highlighted two elements here, do you think that these two characteristics lead to a good school? Or are there other characteristics of a good school? PETRA: Euhm (.) Euh gosh, I think that is related to it, if all of that is okay, if all those characteristics that are really important to me are okay, then that is okay for me a good school. Well, that might not be a good school for another person, think it is subjective, and depending on who appeals to you. Because if other people might fill in the list differently, then this is a less good school for those people.

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Memo and more focused code (reflects on underlying relationships between codes): Mother was previously negative about computing but has changed her opinion after being impressed by teacher’s motivation. She sees learning ICT as essential ‘to get a good foundation‘ and then they ‘already have a good basis‘ and can ‘continue on their own‘, something ‘you simply cannot do without‘. The focus seems to be on maximizing skills essential for the child’s later life.

Mother presents her own motivations for choosing a particular school as subjective (not normative and reflecting a personal preference).

INTERVIEWER: Yes, and how important is it to find a good school for your children? PETRA: It is important, because if I think it is a good school, then I also feel comfortable, then I radiate that calmness to the children and then they find the school nice too. INTERVIEWER: Yes, I can understand that. And has the school that you attended [as a child] also influenced the choice of the school where your children go now? PETRA: May I also answer on behalf of Lorenzo, my partner?

Mother is more at ease (‘more comfortable‘) when she chooses a ‘good school‘ and she will also radiate that ‘calmness on her children‘. Memo and more focused code (thinks about underlying relationships between codes): here, the link between the mood of the mother and that of the child is made quite explicitly, and discussed together with school choice: the mother chooses a school based on factors that influence (reduce) stress and avoid tension and provide mental rest to her and her children.

INTERVIEWER: Yes, yes. PETRA: Yeah, okay, well I didn’t go to school here, um there was no possibility [for me] to go to this school, but Lorenzo did go to this school and uh, we live in the same village, em and he recommended this school very much because it was a good school, “from his own experience”, and it turns out to be so. INTERVIEWER: Okay, yeah, and you said you didn’t go to that school yourself, did that play a part in deciding which school to go? Did you have arguments against Lorenzo not to go to that school?

Parents lived in the same village where the father grew up. Father himself attended the school of children and had ‘recommended the school very much‘ because he thought this was a ‘good school‘. Memo and more focused code (thinks about underlying relationships between codes): Here, the mother will fall back on a source of knowledge she trusts: her husband who is not only familiar with the school, but who has also experienced it as good. The father can explain ‘the benefits of going to those grade classes‘ at school and how cool it is. Again there is an underlying uncertainty about the unknown (grade classes, how cool the school is) and the mother

(Continued)

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(Continued) Interview transcript

Coding according to CGT

PETRA: Well yes, well yes, it was also because it was too far that I could not go to school here, but here are now grade classes where the children go to school and I never had grade classes so my husband could explain the benefits of going to those grade classes, so that was really interesting to know, so the feeling that he had at school, that was cool to hear.

who (like asking teachers about ICT) actively searches for information / takes into account information that is more reliable that removes uncertainty.

INTERVIEWER: Okay, and has that been a decisive factor in choosing that school?

PETRA: That was not necessarily a decisive factor now, because if that school would have been ten kilometers further than where we now live, we would not have taken that school.

The choice to live in your own village may also indicate a search for ‘the familiar’. Memo and more focused code (reflects on underlying relationships between codes): the choice to ‘return / settle’ in the familiar environment seems to tackle a lot of stressors: close to grandparents, close to the school, to a known school.

The mother thinks that the distance between the house and the school is the deciding factor, even more important than familiarity and certainty about the quality of the school.

INTERVIEWER: So the distance. PETRA: The distance to school also plays a part, yes, and especially because it is a school in the [our] village, which is what we looked for in particular. INTERVIEWER: Uh yes, uh, what did the children think about the choice of school?

Mother did not involve children’s voice/opinions in deciding on school choice.

PETRA: Yeah, they didn’t really decide on that, yes, as they go to school there, they also get to know those children and now I see that they like that because when they meet someone outside the school, in the village, they think it is great that they know everyone.

Mother finds it more important that they meet local friends rather than have a say about which school they (children) should attend.

INTERVIEWER: Yes, but they don’t actually have a decision themselves PETRA: No, not really no. And for the two youngest it was actually obvious that they also went to the same school [as their older brother], that makes it easy when they sit together, and I also hear from the supervision that they get a lot out of each other’s presence, and they already have friends there so if they had to change now, I think the unknown would scare them off. INTERVIEWER: Yes, ‘the new’ would somehow stop them. And uh, did the school’s choice to recruit certain teachers or not, did that also play a role? PETRA: Wait, the choice of? INTERVIEWER: The choice of the teachers, so the teachers who teach in the school, did that also play a role in your choice for a school?

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Memo and more focused code (reflects on underlying relationships between codes): the ‘unknown would scare them off’. Again, the mother seems to be guided by the ‘known’ and the ‘unknown’ is seen as a source of stress in determining the school choice. The ‘known’ ensures ‘friends’, ‘makes it easy ‘, ‘get a lot of each other’s presence’ or the inverse of ‘having less friends,’ ‘more difficult,’ and ‘not benefiting from each other’s presence.’ School choice is determined by avoiding the unknown as it deters, and seeking a supportive environment in a social sense, which is mainly associated with choosing an environment the mother is familiar with. To (not) deter and be (not) familiar / strange are connected as context features and seem to influence school choice.

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Interview transcript

Coding according to CGT

PETRA: No, because we actually didn’t know which teachers there are. It’s only as they pass a year that we know who is there and that we then hear from other parents how that teacher actually is, because we actually do not know.

The mother did not know about the teacher so she did not consider this as an important factor in deciding on a school.

INTERVIEWER: Okay, yes, and could you please clarify whether you agree or disagree with the following statement and why? So: multiculturalism has a positive influence on the quality of a school. PETRA: Yes, I think that is important because in the beginning there were no children of a different nationality and now every year a number of them are added gradually and I find it interesting because they also hear other things as well yes yes of religion, there was definitely someone who was in Ramadan, and yes, then the children already learn about it in the classroom. INTERVIEWER: Hm PETRA: Euh and jah that they have different thoughts or react in different ways, yes, I think that’s okay, I’m in favour of that.

The mother will define multiculturalism (MC) as a situation where ‘children of different nationalities’ are sitting with her children; where this is ‘important’ because her children can learn something about other religions. Memo and more focused code (reflects on underlying relationships between codes): it does not go into more detail, but the mother suggests that MC is a situation of limited exposure to diversity, both in terms of number of students (’gradually a number’) and with regard to the way in which this diversity is put at the centre (’they also hear other things as well’).

INTERVIEWER: Okay, so uh, if I understand everything, there are certain factors such as distance to the school, accessibility by car that helped to determine your choice, and of course also the background of Lorenzo who also went to school there. PETRA: Yes, and that it is actually located in the village, that we actually wanted the children to know the other children of the village. Village school was actually the first choice.

The mother chooses a village school because the children’s friends in school will be those of the village.

INTERVIEWER: A, yes? PETRA: Yes, if we went to live in a different village with a different school, I think we would have chosen the village school. INTERVIEWER: Also the village school yes. PETRA: Yes, well of course if it looks like a good school to us, on the basis of the factors already mentioned. And now, that the parents-in-law also live right across school who can provide childcare, is of course a nice bonus. INTERVIEWER: Yes, that seems ideal to me, of course. Well, then I would like to thank you again for this interview, and I can continue working with the data. PETRA: Thank you very much.

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Theoretical coding looks for relationships between codes and, as part of that, for underlying structuring mechanisms that seem to structure behaviour with regard to ’school choice’. For the choice of school, this parent seems to choose primarily what feels as ‘familiar’. Typically, that choice removes a lot of negative feelings / emotions, such as feeling uncomfortable, afraid, or worried. Numerous stressors have been mentioned, such as having to round up people to get the children out of school, having children wait for their parents, not sending children to the same school, sending the children to a distant school, or to a school where the friends in school don’t live nearby, or sending them to a school that is not fun or not ‘known’. The preference for a social environment characterized by close relationships between people who know and support each other well and who live close together reinforces the feeling of ’trust’, both in terms of the nature of the relationships between people (they trust each other ) and in terms of ’being familiar with’ that environment (or not being confronted with changes). The way in which the parent talks about multiculturalism at school also seems to indicate that it is important that ‘diversity’ can complement the ‘familiar’: ‘they also hear other things as well yes’. The mother’s fear for the unknown is also illustrated by her reaction (‘feeling surprised’) to her son being taught ICT. She seems suspicious and will collect information to alleviate that suspicion for that particular kind of ‘unknown’. The voice of the children themselves is also not trusted: neither in choosing a school, nor in indicating the usefulness / importance of ICT. In summary, you can say that the unknown scares this mother and that she opts for what she knows and for a context that retains this known and gives little reason for change. It would be interesting to investigate 1) how the fear of the unknown arises, 2) which stressors play a role in other parents and 3) how it is used to stimulate school choice (alternative, additional research questions). We could describe this mother as an (American) robin, who keeps returning to the same region where she hatched herself For illustration, browse for instance: ‘American Robin National geographic Kids’ and look for images. Additional analyses can identify other types of parents (or use typological analysis), and (underlying) essential structuring characteristics that characterize parents in determining a school choice (e.g. here ’fear of the unknown’ as a driving emotion and ’return to your own nest’ as a solution). With regard to the sample, we can search for parents who do not return to their own nest in terms of school choice (if they are not already included in the basic sample). Some notes on this type of analysis: • The focus in coding on ’what actors do’ automatically interprets the data from the premise that people are not passive actors, but that they consciously shape their social environment by taking meaningful actions (in accordance with the focus of a constructivist, symbolic interactionist perspective). A focus on ‘what actors do’ also ensures that the meaning you assign to codes remains close to the meaning (or at least the wording) contained in the text.

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• •

•

•

•

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Applying a constructivist perspective, also ensures a focus on interactions and how they shape meaning, such as the role of the father, the son and the teacher who, in interaction with the mother, shape the choice for a particular school. A constructivist perspective also has an eye for strategies, e.g. what the mother does to gather more information about issues she finds important. From a constructivist perspective, the meanings that actors give to central concepts are naturally also examined. Here the mother seems to see a ‘good school’ as a ‘trusted school in a trusted environment’. As a researcher you can further unpack the meanings of ‘trust’ (e.g. as ‘what you already know / what you are familiar with’, what ‘you trust’ / ‘do not question’, etc.). The importance of writing memos in analysing relationships between the codes and identifying key structuring features is also highlighted in this example: they constitute focused bits of analysis that help you in developing an understanding of underlying patterns and meanings. This analysis ‘calls’ for a comparative analysis: the addition of another case for which we compare all insights with this case. That is the intention of further analyses, in which theoretical saturation is only achieved if you have built a theory that allows you to understand (from a constructivist approach to GT) or explain (from a post-positivist approach to GT) a particular phenomenon that is theoretically important to you (e.g. understand why parents choose a particular school, or understand why they are afraid for the unknown, or understand why they choose a trusted / less trusted school). The interview piece was chosen because it has rich data regarding the research question. When you encode an interview in its entirety, you don’t always find so much data that seems directly relevant to your research questions. For the first interviews it is recommended that you also code all data (also data that might appear as less relevant), because it makes you aware of the themes about which you collect information. If you collect information on less relevant topics, you can decide to adjust your interview questions. On the other hand, you can also collect information about themes that seem relevant, but that you did not anticipate. You can then focus more on this in the further analyses.

Chapter 4 Additional exercise: narrative analysis Context: Red (pseudonym) is an Irish-British young man (sixteen years old) who spontaneously narrated a story about him bullying a fellow classmate in the past. The data used in this example is part of the author’s PhD research (Stevens, 2006) and has not been previously used or published in any format. Analyse this story by looking for:

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1. structure and genre of the story; 2. rhetorical devices (e.g., metaphors); 3. possible motivations for telling this story.

RED: ‘I still got a bully in me like I -- I’m not a bully, [...] I didn’t go up to someone punching the face. My bullying was like, this kid called Nico for example, who’s like a proper geek. He was like, really -- quite weird. He wasn’t weird -- obviously if you talk to him, you will think he was weird. He cared about things, so for example, if I said to him, I have a better computer than you, he would go, [makes sound as if he gets angry]

Abstract and orientation

So, I got him mad. [...] because he cared about something so much. Like if the teacher [said you’re work is not good]. [he] would rip the paper or break his pen in half.

Resolution

So, it was more kind of to get a reaction I used to do. Like, I wouldn’t be bullying like kind of — everyone was hitting him and taking his money and all that. Me, I would just — I was the worse because and I would just sit there in the classroom, just […]’ I: ‘Just what?’

Abstract and orientation

RED: ‘Just making the soul deteriorate bit by bit and saying all things --’ I: ‘Yeah. It’s like mostly psychological’ RED: ‘Yeah. I knew I was going to psychologically tear him apart. I’ll say things like “My computer... my computer is better than yours!”. He’ll say, you might have a Pentium 4 but I have Pentium 5. He’ll go, Pentium 600, Pentium 700.’

Complicating action

And then I did the worst thing I ever did. Do you want to hear about it? This is the worst thing, I actually felt bad about it. I regret this the most (serious).

Abstract and evaluation

I articulate quite well, especially to teachers. So, what I did was, he’s a geek. So, he has one of them pens, we click: different colors, a four color pen. It was really nice so... he went to the toilet and I went inside to steal it out of his pencil case.

Orientation

And he was like — but every so often, I’ll flash him the pen and show it to him and say -- and then he kept on telling teachers. It’s so funny but really sad. I was laughing about it, but [he would say] “he’s got the pen!”. I would go “Nico no, I haven’t!”. And they would go, give us that. [...] he’s winding them up the whole day. After school, there was a massive [...] so his mum came [...] but I was walking home, he saw me at the bus stop I said [“witch this] Nico”, I picked up his pen, pointed a gun at him, stepped in front of him. He said, “No...!°, and started crying... And I felt bad about it now. Looking back and I felt bad. But at the time, when everyone’s egging you on and just laughing about it...

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(Main) Complicating action

This is the first period [or first lesson of the day], now. The whole day, he was looking for it and he kept on accusing me taking his pen. When the teachers came to me, I would say, I didn’t have his pen. I’ll say, “He’s just lying! He just assumes it’s me! It wasn’t me!”, I just kept on...

Evaluation

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He was the weakest, ain’t it. He was the weakest of the group so he got —’ I: ‘And so, people jumped on that?’

Vocabularies of motive

RED: ‘Yeah, but people who are weak — but no, he was mentally weak as well. So, I didn’t [...] because I was bored. Because what I would do, I would finish all the work quickly. Like in 10 minutes, I’d finish the whole lesson’s work, literally. Like in year 7, I was whizzing everyone. So, what I would do is I was just sitting there bored and [...] fun. Wind him up, this guy who’s some geek. Just wind him up. Yet, there was all the geeks, but they didn’t react in the same way he reacted.

Vocabularies of motive

Like he had a little string in his trousers and someone pulled a belt on him and he pulled [...] string and [...] string on his trousers. He was a weird kid and weirdness gets picked up like that’.

Figure A.2  Analysis of a small story according to its plot

Synthesis With regard to the motivations for sharing this story we can see that Red tries to justify his bullying to some extent, by stating that: a) everyone was bullying Nico; b) Nico provoked this because he was so ‘weird’, c) Red was bored in class because he was smarter than the rest. Rhetorically, he mainly uses exaggerations, stating that ‘everyone’ was bullying Nico and that Red was smarter than ‘everyone’ in the class. On the other hand, he will also use strong language/words to describe his impact on Nico: it was ‘the worst’ thing he did, and he had ‘torn Nico psychologically’. This story follows the tragedy genre : the hero (Nico) is defeated by the forces of evil (Red) and rejected by society (school). We could apply Greimas’ Actantial Analysis in analysing the actors and their roles in this story: you can see Red as the object, which has Nico as its subject, and where the other students are Red’s helpers. The opponent in this story is perhaps Red’s guilt, which in the end makes him realize that he was not allowed to pursue his goal (bullying Nico). In this story, Red comes to see himself as a negative character. The purpose (function) of the story seems to be that Red wants to cry out on the one hand (satisfy psychological needs), and on the other hand justify his behaviour (to display a positive image) and perhaps also to structure his own self-image (‘I’m a bully, but I regret what I’ve done so I don’t want to be a bully anymore’). The theme addressed here is that of relationships between people, especially between the bully and the bullied.

Reference Stevens, P. A. J. (2006). An ethnography of teacher racism and discrimination in Flemish and English classrooms with Turkish secondary school pupils. (PhD). Coventry: Warwick University.

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Chapter 5 Additional exercise: NVivo This exercise consists of different tasks that gradually build up in their complexity and this build-up corresponds with the logic of the NVivo chapter. First, you will practise making folders in the sample project ‘Mixed methods: wellbeing in older Women’s network (OWN)’. To open the sample project that will be used in this exercise, you will have to download the sample project ‘Mixed methods: wellbeing in older Women’s network (OWN)’: [Go to ‘More Sample Projects’ > the NVivo website will open in your internet browser > Download ‘Wellbeing OWN’ > Save project on your computer].

Task 1: Opening a saved NVivo project and creating a folder Double click on the NVivo icon to start up the software. Enter the initials of your name. Now click on the left side of the NVivo interface ‘Open other project’ and select on your computer the sample project (see also Figure 5.2). Go to the section ‘Coding’ in the blue project items list on the left of the NVivo interface. Create a new folder under ‘Codes’, you can name this folder, for example, ‘Task 1’. If you need help to perform this task, also read 5.3.1 in the NVivo chapter.

Task 2: Practising with cases and the case classification Go to the pre-existing case classification ‘Interviewees’ and try to create a ‘trial case’ (see also section 5.3.4, step 2). Fill in the attribute values for the case classification ‘Interviewees’. Open the case classification in the detail view to double-check whether you managed to add a trial case. If you managed to do this successfully, you need to perform the last step and correctly link your trial case to an internal data source (see also section 5.3.4, step 3). Go to the folder ‘Interview’ under ‘Files’. Select an interview that is already imported and code the entire document with your trial case. Did you get the warning that you were sure you wanted to code an entire document? Good! That means you did it right. As a final check you can navigate to your case and see whether a code is assigned.

Task 3: Practising with inductive and deductive coding Open the interview of Acacia. You can assign inductive codes to Acacia’s interview by typing them in the bar on the bottom of your screen. However, make sure that you are entering these new codes directly in the correct folder (the folder you created in Task 1). If you are not sure how you can do this easily, also read section 5.4.2. in the book chapter again. When you have finished coding Acacia’s interview, go to the folder ‘Task 1’ under Codes and rename it ‘Task 3’. You can do this by using your right mouse click (see also section 5.3.2). The codes that you generated inductively are not hierarchically structured, try to create ‘parent’ and ‘child’ codes by using a drag-and-drop technique.

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Now open another interview. On the right side of your detail view you want to see the transcript, on the left side of your detail view you want to see the codes in the folder ‘Task 3’. Practise your deductive coding by dragging data fragments into the existing coding structure on the left. To help you, you can re-read section 5.4.1 on deductive coding.

Task 4: Practising with a matrix coding query that combines codes The task that you are going to perform now is slightly more advanced than the example discussed in the chapter. It is explained step by step what you need to do. First read section 5.3 on the matrix coding query in the book chapter. Before creating the actual matrix coding query, go to the case classification ‘Interviewees’ and check which members are involved in OWN as ‘volunteers’. Write down their names. Do the same for the women who are just a ‘member’ of the organization. Now use the menu bar to create the matrix coding query (see also section 5.5.3). You are going to create two matrix coding queries that combine codes with each other but for two groups of respondents separately. Before filling in the rows and columns of the matrix, limit the scope of the search to only those transcripts that belong to women who are volunteers. Use the bottom ‘Selected items’ to do this. Now you add the codes of the coding structure in both the rows and the columns which will help you to detect overlap. Save the results of the query as ‘Task 4 – volunteers’. Repeat this process but now only for the women who are members of the OWN. Save the query results as ‘Task 4 – members’. Double check by navigating the project list items on the left to see whether you have successfully saved both query results.

Task 5: interpreting query results by writing memos Create three new memos (see also section 5.4.3) and name them as follows: Task 5 – Volunteers; Task 5 – Members; Task 5 Comparison Volunteers and Members. Navigate the project items to open the queries you created in Task 4. Now you can: a) interpret for both respondent groups separately which double coding patterns emerged; and b) compare each group of respondents with each other. You can detect double coding by scanning the cells of the matrix above or below the diagonal. Also remember that you can access the coding fragments by double clicking on each cell of the matrix. This allows you to synthesize and interpret the data more in-depth instead of solely using the quantifications in the matrix. Write your findings in their corresponding memos. Close all three memos.

Task 6: collecting project items and findings in a static set Create a static set (‘Task 6 – Analysis related to volunteers’) by using the menu bar (see also section 5.5.5) in which you want to collect project items that are related to your analysis of volunteers – you might also want to add the interviews of the volunteers here so you can easily access them when needed. You have already created two project items that are related to volunteers: a matrix coding query (see Task 4) and a memo (see Task 5).

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You can add these two items and, if you want, the interviews to the static set by using a right mouse click. As you continue your analysis in NVivo, you can now add queries and memos that relate to this group of respondents here, which allows you to work in an orderly manner and easily navigate important findings of your data analysis.

Chapter 6 Additional exercise: process tracing Exercise two: theoretical and empirical evaluation The second exercise is about theoretically and empirically evaluating the evidence that Dür and Matteo (2014) present. As explained in Chapter 6, in process tracing we translate (steps in) a causal mechanism to ‘propositions’ about empirical fingerprints that we expect to find (this step is what we call ‘operationalization’). And operationalization includes thinking what finding or not finding this predicted evidence means for the confidence in our theory. The theory here is that citizen group lobbying (X) derailed ACTA (Y) through ‘bandwagoning and silencing’ (CM). We can make the following propositions for the three steps in this causal mechanism: 1. 2. 3. 4.

the public salience of ACTA will increase; the number of citizen groups active on ACTA will increase; business groups will refrain from taking positions on ACTA; and MEPs will change their position and justify this as being responsive to citizen concerns.

Task 1: Explain for each of these propositions which types of test (motive and opportunity, alibi, smoking gun or DNA) they represent. Task 2.2: Identify for each proposition one observation that Dür and Matteo present for these propositions. Evaluate the empirical confidence we can have in them.

Example solution Theoretical relevance of propositions 1. The public salience of ACTA will increase. This can be called an ‘alibi’ test. This proposition is theoretically necessary for the explanation. If public salience for ACTA remains low, some other explanation than the ‘bandwagonning’ mechanism proposed by Dür and Mateo will have been responsible for derailing ACTA. However, finding evidence for this proposition is not sufficient for our explanation.

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2. The number of citizen groups active on ACTA will increase. This test can also be seen as an ‘alibi’ test. This proposition is theoretically necessary for the explanation. If the number of citizen groups stayed low, some other explanation than the ‘bandwagonning’ mechanism proposed by Dür and Mateo will have been responsible for derailing ACTA. However, finding evidence for this proposition is not sufficient for our explanation. 3. Business groups will refrain from taking positions on ACTA. This might be qualified as a ‘motive and opportunity’ test. Finding this would add to our confidence in the explanation somewhat, but might be seen as neither necessary nor sufficient. Even if we do not find that business groups actively refrained from taking positions, the lack of large-scale opposition might still have added to producing the mechanism. But finding purposeful passivity is not sufficient for confirming our mechanism. There might be other explanations why business groups have remained silent, like lack of interest in the issue. 4. MEPs will justify their opposition to ACTA as being responsive to citizen cocerns. This proposition might be called a ‘smoking gun’ test. We do not need to find MEPs justifying their opposition by explicitly referring to the anti-ACTA campaign. They might have done so without making public declarations about it, perhaps not to offend their allies in the business world. On the other hand, if we do find evidence for this proposition, it strongly confirms the causal mechanism.

Finding and evaluating evidence 5. The public salience of ACTA will increase. A first strong piece of evidence that Dür and Mateo present for this proposition is based on Google search data. This shows that ACTA hardly featured as a search term before the public campaign started, and increased when the campaign was at its peak. This evidence can be seen as reliable. A second strong piece of evidence is based on the number of media articles on ACTA in LexisNexis, compared to the number of articles on climate change. This also shows a strong increase at the time of the peak of the campaign. Also, this evidence can be seen as reliable. 6. The number of citizen groups active on ACTA will increase. The evidence that Dür and Mateo present for this step is based on interview material. They give quotes like ‘it was the resonance that ACTA had with the public and the resulting opportunities to present us and our demands that made the topic one of very

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high importance’. One could question the reliability of an individual quote like this. A citizen group representative might want to brag about her impact on the derailing of ACTA. However, the evidence becomes more reliable as the number of interviewees making the same case increases and, especially, because interviewees from the other side of the conflict also confirm this. This is what we called ‘triangulation’ in the chapter. 7. Business groups will refrain from taking positions on ACTA. The evidence that the authors present for this proposition is also based on interview accounts. They cite a respondent from the business side who said that ‘a difficult situation in which we did not want to give a bad image to the public opinion’. Again, one should be careful with relying too much on such claims. Here, a respondent from the losing side might have an incentive to deceive us by minimizing their efforts, because she does not want to admit that their strategy was ineffective, for example. 8. MEPs will change their position and justify this as being responsive to citizen concerns. Here, the authors mainly provide ‘account’ evidence by citing public claims from (key) MEPs that justify their opposition to ACTA by referring to the public concerns. For example, the authors quote Martin Schulz, at that time president of the European Parliament, who explained the opposition of Parliament by referring to ‘the existence of European public opinion that transcends national borders’. However, we should again be empirically critical of these claims. Politicians have an incentive to justify their policies by referring to public concerns, while other motives (in this case for example, lobbying by protectionist business groups) might really lie at the root of their position.

Reference Dür, A. and Mateo, G. (2014). Public opinion and interest group influence: how citizen groups derailed the Anti-Counterfeiting Trade Agreement. Journal of European Public Policy, 21(8), 1199–217. DOI: 10.1080/13501763.2014.900893.

Chapter 7 Additional exercise: qualitative comparative analysis Assignment Table 1 presents the truth table of a fictional study that examines under what conditions students pass an exam. The study includes four conditions: being smart (SMART), studying (STUDY), attending the classes (CLASS) and cheating (CHEAT). Based on prior theoretical expectations, we expect that the presence rather than the absence of these

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conditions could be causally relevant for the outcome. The truth table has sixteen (or twenty-four) rows. Three rows (1–3) correspond to sufficient combinations for passing, five rows correspond to sufficient combinations for not passing (4–9). The other rows correspond to logical remainders.

Truth table fictional study ROW

SMART

STUDY

CLASS

CHEAT

PASS

 1

1

1

1

0

1

Cases A,B,C,D

 2

1

1

0

0

1

E,F,G,H

 3

1

0

1

1

1

I, J

 4

1

0

0

0

0

K,L

 5

1

0

1

0

0

M,N,O

 6

0

0

0

1

0

P

 7

0

0

0

0

0

Q,R,S,T

 8

0

0

1

1

0

U

 9

1

1

1

1

R

/

10

1

1

0

1

R

/

11

1

0

0

1

R

/

12

0

1

0

0

R

/

13

0

1

1

0

R

/

14

0

1

1

1

R

/

15

0

1

0

1

R

/

16

0

0

1

0

R

/

The parsimonious solution of this truth table is: • STUDY+SMART*CHEAT ↔ PASS The intermediate solution of the truth table is: •

SMART*STUDY + SMART *CLASS*CHEAT ↔ PASS

Fulfil the following assignments: 1. Produce the conservative solution for PASS by applying logical minimization. 2. Interpret the solutions. For which conditions is there evidence that they are causally relevant in a specific combination? For which conditions is there evidence that they are not causally relevant in a specific combination? For which conditions does the truth table not make clear whether or not they are relevant? Which of these ‘ambiguous’ conditions are very unlikely to be relevant? You can draw a table like Table 7.5 presented in the main text to answer this question.

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Model answer 1. Conservative solution and logical minimization. The conservative solution is produced by applying logical minimization to truth table rows that include cases and correspond to sufficient combinations. In our example, three truth table rows correspond to sufficient combinations. 1. SMART*STUDY*CLASS*~CHEAT → PASS 2. SMART*STUDY*~CLASS*~CHEAT → PASS 3. SMART*~STUDY*CLASS*CHEAT → PASS Row 1 and 2 can be minimized to: SMART*STUDY*~CHEAT → PASS. Further reduction is not possible, so the following solution corresponds to the conservative solution: SMART*STUDY**~CHEAT + SMART*~STUDY*CLASS*CHEAT ↔ PASS 4. Interpret the solutions Ambiguous Relevant

Plausible

Implausible

Irrelevant

1

STUDY

SMART

~CHEAT

CLASS/~CLASS

2

SMART*CHEAT

CLASS

~STUDY

/

Chapter 8 Additional exercise: QCA This exercise consists of different tasks that gradually build up in their complexity and this build-up corresponds (mainly) with the logic of the QCA chapter. The main goal of this exercise is not to guide you through the whole QCA process but rather to give you some extra training in the development and evaluation of the coding frame. First, you will practise assigning inductive codes to the interview example 1. The interview is conducted with a divorced mother and generally covers the organization of the newly formed family from the moment that step-parents became involved. Before you dive into this inductive coding, you should re-read all the steps explained in the Chapter 8 section ‘Conventional qualitative content analysis’ (p. 247). Second, you will practise hierarchically structuring the inductive codes that you developed. You might need to create subcategories. Once you have this version of a coding frame, try to deductively assign these codes to the interview example 2. To help you with this task, you can also re-read the Chapter 8 section ‘Initial (open) coding’ (p. 248). Lastly,

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check whether your developed coding frame meets the requirements of a QCA analysis. Use the checklist listed under section 8.3.2. Reflect for yourself which changes would have to be made to strengthen your coding frame. The above is now explained step by step: Task 1: Read through interview example 1. Try to answer the following two questions: 1) what would be appropriate coding units for this interview? 2) what types of content are present within this data? Task 2: Read the interview again but now you try to assign open codes to the interview. Try to be as consistent as possible in the length of the fragments (i.e., the coding units) and also the types of content that you are coding. Remember that a coding unit can be assigned two codes or more, as long as you try to capture different content related to that coding unit. Task 3: Create a hierarchical structure and subcategories by clustering the open codes that have a common characteristic (e.g., the type of content they bring together). List for each subcategory all the open codes (i.e., categories) under it. Sometimes it might be necessary to make the coding frame that you are developing more parsimonious by merging codes together or renaming them. Task 4: Try to apply this first version of your coding frame to interview example 2. You could also re-use this coding frame on interview example 1. This allows you to answer two questions: 1) Is your coding frame a good representation of the different types of content present in your data? 2) Have you managed to use your coding frame in a consistent way? Task 5: Evaluate the coding frame by using the six requirements for a QCA coding frame and develop a coding scheme (i.e., try to define each (sub)category in your coding frame in a transparent manner that illustrates its applicability).

Interview example 1 RES It would be much easier if my ex would accept that I have a new husband. And also that he has a new wife. I understand his anger with me for what I did, but what I do not understand is why he is so hateful towards the children? It has been two years since the divorce and we both found someone new. Until this day, I keep on calling his new wife – my children’s stepmother – instead of him. I 

Truly?

RES Yes, yes, especially about the children, we are able to talk to each other pretty well. My ex remains angry with me, even his new wife says so. That is very strange for me to hear that from her. I 

How do you mean?

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RES Well, his new wife says stuff like: ‘Veronica you should know that you were the love of his life and you hurt him so badly.’ I used to think that’s true but still … I mean – he started a new relationship in the meantime. I wonder; ‘Is he happy?’ The baseline is, my ex is bitter. I 

Uh ok, and about which matters related to the children do you and your ex talk?

RES None. Only when we really really really have to talk to each other. I 

What are the situations in which you really really really have to talk to one another?

RES My son always gets himself into trouble. It happened that for one month he did not go to his father anymore because the situation was escalating. My son has a very difficult character, so I do not blame my ex. Actually, I blame them both. They just do not get along. The last phone call I got from my ex was: ‘I have to choose, my son or my new wife, because she is going to leave me if that boy keeps on creating a mess. I think he should stay with you.’ For one month my son did not go, but then it solved itself. I 

And besides this example, in which situations would go talk to your ex?

RES Well, I will not talk to my ex anymore without the new partners there. If my ex’s new wife is not there, he always calls me names and I do not want that. If his new wife is there, the conversation is more civilized. If my new husband is there, my ex is sitting there like a loser. In those cases, he just does not talk at all. There is no point that I ask stuff of my ex, he simply does not do it. Before he met his new wife, I had to take up all the tasks by myself. I 

And could you tell me more about those conversations with your ex, his new wife and you? How do those go?

RES In the beginning, her role was to mediate between me and my ex and also between my ex and my son. The fact that he met his new wife, made me happy for two reasons. First, I got rid of my ex for a lot of stuff that she now does for him. He also became less bothering towards me. You know, in the past, when he used to have stuff that he did not understand (for example related to paperwork, taxes and administration) he was still used to call me to help him out. [Phff], the moment that she arrived, that part of my life was gone [sounds relieved]. And second, I am happy because she can also side with my son during arguments. She does not necessarily side with my ex. I like that about her; she is a mother. She is a mother. She also has a child of her own and she understands. I 

She is a mother. And how did your children deal with the arrival of a stepmother in the family of your ex?

RES Look, she hurt me only once – in my opinion and that was when she openly said: ‘your little boy is just like a son to me’. Now, I guess I can understand, that was completely in the beginning and my son was leaning more towards her than towards me. I 

That is a thing she told you or something that you –

RES Yes, yes yes. And I never showed her, but it hurts a great deal. I keep on hearing it in my head. And you know what? The thing is that she is not always nice towards our daughter.

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345

How do you mean?

RES She has a daughter of her own, so I guess she does not need mine. My son, she welcomed him with open arms, but my daughter, not so much. She gets bitchy. She told my daughter once that she should stop ‘bothering’ her father, just because she dropped in at a moment that was not ‘his week’. Now, they arrived at a point where they just ignore each other. Yeah, I believe that is what they do, when I hear my daughter talk about her. I also hear though that my daughter is cranky towards her stepmother, because she believes she does not help out enough, compared to what I was doing when I was still married to her father. […] I 

So overall, how would you describe the relationship you have with the stepmother of your children?

RES If I meet that woman in the supermarket I am going to say hi. And she is going to say hi. And I wish we can continue like this for ever because there will be a time – for example, when one of the children is going to get married – that I want to be able to be at the party with everyone, all together. I do not want my children to be in the position that they have to think or feel like they have to choose. I want us all to be there for them. Therefore, I am always going to greet her. She is going to get a good morning and a good evening – a ‘how-you-doing’. I don’t mind talking to her. I 

  Ok …

RES Just, for our children, it can never be a stumbling block on their path.

Interview example 2 I 

OK, so if I understand correctly – in that period – your son stayed with his father during the week and stayed with you during the weekend?

RES Yes, indeed. But at a certain point, my son had two half-brothers in the family of his father. That was after his father had gotten married to his new partner. From that moment onwards, the situation started to escalate. Every Friday when I went to pick up my son at his fathers’ house, and suddenly, there she was (the stepmother) instead of his father outside of the door. Waiting. I had never seen her before. But you know what? She immediately wanted to intervene in how I should raise my children. I 

Truly? Can you explain me in what way?

RES Of course. She said: ‘Your son is allowed to do everything he wants when he is with you, but here with us – we set rules.’ She said stuff like that. As a response I said: ‘You don’t know anything yet, I have been raising my son for the last four years – I know my son.’ My son is a kid that needs to play a lot outside, and well, at his fathers’ house, that was not allowed. They always punished him – well ‘she’ always did because his stepmother was educating him instead of his father. I 

How did you feel about that?

RES At times, it was very, very difficult. Often I was thinking: ‘I am going to strangle that woman’. But yeah. We texted a lot with each other, my sons’ stepmother and me. We were always going back and forth. Texting – but always as if we were in a fight.

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Always as if you were in a fight?

RES Yes, arguing all the time. And if I went to pick up my son, always we were arguing. Until one day, I decided to end it. It is not healthy for a child. I even noticed that on Sunday when I wanted to bring him back to his fathers’ house, he was always crying: ‘Mama, I want to stay with you’. At a certain point he said that his stepmother sometimes hit him. So … I 

And what did you do then? You said you decided to end it, like change the situation?

RES No, things did not change immediately. First, I went to talk to my ex. We discussed how our son must have felt staying with his father during the week. I brought up that it seemed weird to me that he wetted his bed every night there, but not when he stayed with me. It was exactly because of that conversation that I realized that something was really off. So the weekend after, I talked to my son about the bedwetting and out of the blue he said: ‘Mama, I would like to live with you, I like staying here better.’ From that moment onwards, he started to be naughty. You know, he started to be mischievous at his fathers’ house. For example, he started to pee his pants not only during the night, but sometimes just like that; where he was standing at the point that he felt like peeing. When his father asked him: ‘What are you doing?’ He was used to answer: ‘I am doing this just because I want to be with my mother.’ In sum, he was doing it on purpose. It kept on going like that, until one day, I got a phone call. His father told me: ‘I can’t do it anymore, he needs to go.’ I remember answering: ‘Go, what do you mean with go?’ I also warned my ex that if he was serious in sending his son away, he could not change his mind after two days. His father repeated that he really wanted to stay with me and not with him and that they were desperate. He asked me to go to pick him immediately. I said that was not an option since I was working. I first wanted to meet with our lawyers. I wanted to have it on paper that it was ok that my son was coming with me with the consent of his father – so that legally everything would be in order. In the end, his father said that he only wanted to take care of his son four weeks a year – for the holidays – and one weekend per month. I remember the day as if it was yesterday; I went to pick my son up at five in the afternoon. There he was – standing outside on the driveway with his schoolbag in his hand all alone. They did not give the clothes, then – I probably would not have wanted the clothes. We went shopping and I bought him a whole new wardrobe that day. I 

 Yeah …

RES But you know what? I also said to his father this: ‘Do you realize what you are deciding upon? One weekend a month? You are going to lose your connection with him completely.’ A couple of months later we went to court with the whole situation and the judge decided I should have full custody. […] I 

Your son’s stepmother had a different approach from you in educating children. Can you describe to me how your approach differed from hers?

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RES She was much more strict than me. My son would ask her for candy and simultaneously walk to the kitchen cabinet to take them, but that was not allowed there. Instead, if he’s with me, he can take a candy if he wants, there is no need to beg. Another example is about proper punishment. So, if he would do something wrong at school, she used to punish him all evening for that. While, if my son was with me, I would get angry with him for a bit but then I would let it go. Sometimes his stepmother prohibited him from watching TV all evening or he would have to go to bed right after dinner. For me, that does not work. When my son was with her, he constantly had the feeling he was being punished for something. Another problem was the difference she made between her children and my son. If his half-brother was naughty, it was never a problem. Her children were used to getting everything and my son nothing. I repeatedly asked her to stop making a difference like that, then she would claim that she loved my son as well. […] I 

And which role do you expect from a stepmother?

RES Well, I think that she should not be completely responsible for the education of my child. But I also think that she should not make any difference between my son and hers. I believe that would have prevented a lot of misery. I think that for my son, that was the most painful, that he felt he was treated differently. For example, when the second half-brother was born, my son was not allowed to hold his new little brother. The older half-brother of him could hold the baby, but my son couldn’t. I think that must have hurt him. He was so proud to become ‘older brother’ but she did not acknowledge him. It even hurt my feelings when he arrived at my house and told me these stories. She should have engaged him more: ‘Would you like to help me change the diaper of your little brother?’ You know, things like that. Little things that – that changes a lot. It creates a bond.

Model answer This model answer illustrates tasks 1 to 3 from the online exercise. It is based on interview example 2 and consists of different tasks that gradually build up in their complexity and this build-up corresponds (mainly) with the logic of the QCA chapter. The main goal of this model answer is not to guide you through the whole QCA process but rather to give you some extra examples in the development of an inductive coding frame. The model answer shows how codes are created inductively and corresponds with section 3.2.1 in the book. First, inductive codes are assigned to the interview. The interview is conducted with a divorced mother and generally covered the organization of the newly formed family from the moment step parents were involved. This interview is

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similar to interview example 1 that you can use for practice. Second, the list of inductive codes will be organized hierarchically by creating subcategories. This whole procedure is explained step-by-step.

Task 1: Inductive coding strategy Instruction: Read the interview. Try to answer the following two questions: 1) what would be appropriate coding units for this interview? 2) what types of content are present within this data? Answer: I want to code the interview as much as possible sentence by sentence. These are my coding units. I am interested in understanding: what happened in these families from the moment they became ‘newly formed’ and what their communication strategies were. I am also interested in understanding the drivers for the mothers’ behaviour in this process. More practically, I decided to read the interview multiple times. First I wanted to focus on the actions taking place in the newly formed family: who is doing what according to the mother? Who are the different family members that occur in the story of the mother? While I was reading the interview, I was also reflecting on what I was reading. It occurred to me that during this interview the mother described more ‘general’ actions of the family members involved (for example: son likes to play with stepbrothers/sisters), but also actions that lead to a very drastic family change (for example: sons starts bedwetting). I think that theoretically, it will be important to separate these two. I wrote this thought down in another document. The second time I read the interview, I tried to pay attention to the emotions and thoughts the mother explicitly refers to while explaining the events in her family. Another reflection that came to my head was the fact that for this mother, her son took a central place. I also wrote that down.

Task 2: Inductive open coding of the interview Instruction: Read the interview again but now try to assign open codes to the interview. Try to be as consistent as possible in the length of the fragments (i.e. the coding units) and also the types of content you are coding. Remember that a coding unit can be assigned two codes or more, as long as you try to capture different content related to that coding unit. Answer: Some of the open codes that I assigned to the interview are still quite long (see below). I will not worry about that yet. In the next phase, while I am structuring the open codes in a first version of the coding frame, I will try to make the codes shorter (more parsimonious). It is possible that I will not use all the codes that I assigned during this first open coding. I also realize that normally you design a first version of the coding frame based on the coding of at least two interviews (by using the diversity principle). In that situation, similar but differently formulated codes from the two interviews could already be merged together under one label/open code.

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My notes/reflections during the open coding of this interview: General actions of (step)parents in newly formed family Actions ‘directly’ related to important changes/events in newly formed family Mother references (a lot) to feelings of her children I wrote these things down because I do not understand yet whether these topics are unique to this interview or will be something that will be more common through all the interviews. That is why I want to remember these three things about this specific interview.

Open inductive coding in the interview Transcript

Codes (and brief description)

Interviewer: OK, so if I understand correctly, in that period your son stayed with his father during the week and stayed with you during the weekend? Respondent: Yes, indeed. But at a certain point, my son had two half-brothers in the family of his father. That was after his father had gotten married to his new partner.

This information does not have to be coded necessarily, it could also be stored in a descriptive data matrix or sociogram of the newly formed family.

From that moment onwards, the situation started to escalate.

Escalation starts when father remarries

Every Friday when I went to pick up my son at his fathers’ house,

Mother picks up son on Fridays.

, and suddenly, there she was (the stepmother) instead of his father outside of the door. Waiting. I had never seen her before

Surprise about circumstances first meeting of stepmother in the driveway

But you know what? She immediately wanted to intervene in how I should raise my children.

Stepmother intervenes in child raising

Stepmother waits for mother in driveway

Interviewer: Truly? Can you explain me in what way? Respondent: She said: “Your son is allowed to do everything he wants when he is with you, but here with us – we set rules.”

Stepmother sets rules for (step)son

As a response I said: “You don’t know anything yet, I have been raising my son for the last four years – I know my son.”

Mother as expert tells stepmother off

My son is a kid that needs to play a lot outside, and well, and at his fathers’ house, that was not allowed.

Son not allowed to play outside at fathers’ house.

(Continued)

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(Continued) Transcript

Codes (and brief description)

They always punished him – well ‘she’ always did because his stepmother was educating him instead of his father.

Stepmother more involved than father in raising kids

Interviewer: How did you feel about that? Respondent: At times, it was very, very difficult.

Mother experiences custody arrangement as difficult

Often I was thinking: “I am going to strangle that woman”.

Mother has bothered/annoyed thoughts about the stepmother

We texted a lot with each other, my sons’ stepmother and me. We were always going back and forth. Texting – but always as if we were in a fight.

Heated texting between mother and stepmother

Interviewer: Always as if you were in a fight? Respondent: Yes, arguing all the time. And if I went to pick up my son, always we were arguing.

Mother and stepmother argue all the time

Until one day, I decided to end it. It is not healthy for a child.

Mother feels she decided she initiated the ‘end’ of the arguing

I even noticed that on Sunday when I wanted to bring him back to his fathers’ house, he was always crying:

Sons cries when he goes back to his father

he was always crying: “Mama, I want to stay with you”.

Son wants to stay with mother

At a certain point he said that his stepmother sometimes hit him. So…

Son says stepmother hits him

Interviewer: And what did you do then? You said you decided to end it, like change the situation? Respondent: No, things did not change immediately.

Change does not occur right away

First, I went to talk to my ex.

Mother talks to father about problems

We discussed how our son must have felt staying with his father during the week. I brought up that it seemed weird to me that he wetted his bed every night there, but not when he stayed with me.

Son only bed wets when staying with father

It was exactly because of that conversation that I realized that something was really off.

Mother realizes something is off due to bedwetting son

So the weekend after, I talked to my son about the bedwetting and out of the blue he said: “Mama, I would like to live with you, I like staying here better.”

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Transcript

Codes (and brief description)

From that moment onwards, he started to be naughty. You know, he started to be mischievous at his fathers’ house.

Son is naughty at fathers’ house

351

For example, he started to pee his pants not only during the night, but sometimes just like that; where he was standing at the point that he felt like peeing. When his father asked him: “What are you doing?” He was used to answer: “I am doing this just because I want to be with my mother.” In sum, he was doing it on purpose.

Son purposely wets his pants

It kept on going like that, until one day, I got a phone call. His father told me: “I can’t do it anymore, he needs to go.”

Father decides ‘son must go’

I remember answering: “Go, what do you mean with go?” I also warned my ex that if he was serious in sending his son away, he could not change his mind after two days. His father repeated that he really wanted to stay with me and not with him and that they were desperate

Father and stepmother feel desperate by behaviour of the son

He asked me to go to pick him immediately. I said that was not an option since I was working. I first wanted to meet with our lawyers. I wanted to have it on paper that it was ok that my son was coming with me with the consent of his father – so that legally everything would be in order.

Mother organizes the custody change legally

In the end, his father said that he only wanted to take care of his son four weeks a year – for the holidays – and one weekend per month

Fathers choice for new custody arrangement; four weeks a year, one weekend each month

I remember the day as if it was yesterday; I went to pick my son up at five in the afternoon.

Mother has very vivid memory of the day of the big change

There he was – standing outside on the driveway with his schoolbag in his hand all alone.

Day of the ‘big’ change; sons stands alone on the driveway

They did not give the clothes, then – I probably would not have wanted the clothes. We went shopping and I bought him a whole new wardrobe that day.

Day of the big change, son gets a new wardrobe

Interviewer: Yeah… Respondent: But you know what? I also said to his father this: “Do you realize what you are deciding upon? One weekend a month?

Mother double checks father’s decisions with him

(Continued)

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(Continued) Transcript

Codes (and brief description)

You are going to lose your connection with him completely.”

Mother concerned that father and son are going to lose their bond

A couple of months later we went to court with the whole situation and the judge decided I should have full custody.

Judge gives mother full custody

[…] Interviewer: Your son’s stepmother had a different approach than you in educating children. Can you describe to me how your approach differed from hers? Respondent: She was much more strict than me

Stepmother is more strict than mother

My son would ask her for candy and simultaneously walk to the kitchen cabinet to take them, but that was not allowed there.

Son cannot get candy when he wants to Vs. no need to beg

Instead, if he’s with me, he can take a candy if he wants, there is no need to beg. Another example is about proper punishment.

Stepmother different approach on punishment

So, if he would do something wrong at school, she used to punish him all evening for that.

Punished all evening for mischief at school Vs. angry for a little bit

While, if my son was with me, I would get angry with him for a bit but then I would let it go. Sometimes his stepmother prohibited him from watching TV all evening or he would have to go to bed right after dinner. For me, that does not work. When my son was with her, he constantly had the feeling he was being punished for something.

Son feels punished all the time

Another problem was the difference she made between her children and my son.

Stepmother differentiates between step- Vs. biological children.

If his half-brother was naughty, it was never a problem. Her children were used to get everything and my son nothing. I repeatedly asked her to stop making a difference like that,

Mother asked stepmother to stop making the difference

then she would claim that she loved my son as well.

Stepmother claims she loves stepson

[…] Interviewer: And which role do you expect from a stepmother? Well, I think that she should not be completely responsible for the education of my child.

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Stepmother only partially responsible for education of child

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Transcript

Codes (and brief description)

But I also think that she should not make any difference between my son and hers.

Stepmother cannot differentiate between stepand biological children

353

I believe that would have prevented a lot of misery. I think that for my son, that was the most painful, that he felt he was treated differently.

Different treatment is most painful

For example, when the second half brother was born, my son was not allowed to hold his new little brother.

Stepson not allowed to hold half brother

The older half-brother of him could hold the baby, but my son couldn’t. I think that must have hurt him. He was so proud to become ‘older brother’ but she did not acknowledge him.

Lack of acknowledgement for son

It even hurt my feelings, when he arrived at my house and told me these stories.

Mother is hurt by experiences of son

She should have engaged him more: “Would you like to help me change the diaper of your little brother?” You know, things like that. Little things that – that changes a lot. It creates a bond.

Task 3: Structuring the open codes and subcategory development Instruction: Create a hierarchical structure and subcategories by clustering the open codes that have a common characteristic (for example: the type of content they bring together). List for each subcategory all the open codes (i.e. categories) under it. Sometimes it might be necessary to make the coding frame that you are developing more parsimonious by merging codes together or renaming them. Answer: Before I start structuring the open codes I assigned to this interview, I re-read the list of all the open codes I generated independently from their immediate link to the interview. While I am reading these codes, I use different fonts for fragments that seem to refer to a similar kind of content: emotion, direct communications, actions by different family members, etc. After I organised the open codes according to different fonts, I am going to change their order and group all the fragments with the same font together and read them again. I reflect on different questions: do these fragments truly belong together? Can they be kept together or should I create subgroups? Why do these fragments go together/what do they have in common? How can I name these open codes to capture their content in a transparent and concrete way? List of open codes with visualization of subcategories (can be colour, font, …) Escalation starts when father remarries Mother picks up son on Fridays

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Stepmother waits for mother in driveway Surprise about circumstances first meeting of stepmother in the driveway Stepmother intervenes in child raising Stepmother sets rules for(step)son Mother as expert tells stepmother off Son not allowed to play outside at fathers’ house Stepmother more involved than father in raising kids Mother experiences custody arrangement as difficult Mother has bothered/annoyed thoughts about the stepmother Heated texting between mother and stepmother Mother and stepmother argue all the time Mother feels she decided she initiated the ‘end’ of the arguing Sons cries when he goes back to his father Son wants to stay with mother Son says stepmother hits him Change does not occur right away Mother talks to father about problems Son only bed wets when staying with father Mother realizes something is off due to bedwetting son Son is naughty at fathers’ house Son purposely wets his pants Father decides ‘son must go’ Father and stepmother feel desperate by behaviour of the son Mother organizes the custody change legally Father’s choice for new custody arrangement: four weeks a year, one weekend each month Mother has very vivid memory of the day of the big change Day of the ‘big’ change; sons stands alone on the driveway Day of the big change, son gets a new wardrobe Mother double checks father’s decisions with him Mother concerned that father and son are going to lose their bond

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Judge gives mother full custody Stepmother is more strict than mother Son cannot take candy when he wants to Vs. no need to beg Stepmother different approach on punishment Punished all evening for mischief at school Vs. angry for a little bit Son feels punished all the time Stepmother differentiates between step- Vs. biological children Mother asked stepmother to stop making the difference Stepmother claims she loves stepson Stepmother only partially responsible for education of child Stepmother cannot differentiate between step- and biological children Different treatment is most painful Stepson not allowed to hold half brother Lack of acknowledgement for son Mother is hurt by experiences of son

Table of open codes by initial subdivision, with reflections on how to generate subcategories from them Subdivision

Reflection + working ‘titles’

Roman black

Family organization?

These codes all seem to deal directly with the events that lead to the change of the custody arrangement, which is an important change in the organization of the newly formed family. However, it seems needed – since other interviewees also had these drastic changes – to create some more subdivisions that will make it more transparent, concrete and applicable to different interviews. Escalation starts when father remarries.

The event that initiated the change.

Mother feels she decided she initiated the ‘end’ of the arguing .

This also seems related to the initiation of the change.

Change does not occur right away. Mother realizes something is off due to bedwetting son.

This seemed to have been the ‘Red flag event’.

Father decides ‘son must go’.

This was the critical event that – de facto – changed the situation.

Father and stepmother feel desperate by behaviour of the son. (Continued)

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(Continued) Mother organizes the custody change legally. Fathers choice for new custody arrangement: four weeks a year, one weekend each month.

The outcome.

Mother has very vivid memory of the day of the big change.

Memories about the day of the actual change.

Day of the ‘big’ change; sons stands alone on the driveway. Day of the big change, son gets a new wardrobe. Judge gives mother full custody.

The outcome.

Underlined

Communication?

This seems to be about direct communication between the stepmother and the mother Mother as expert tells stepmother off Heated texting between mother and stepmother Mother and stepmother argue all the time Mother asked stepmother to stop making the difference Italic black

Feelings?

This seems about to be about the feelings of the mother on things that happened in the newly formed family. One open code however is about her thoughts. Should that be a different category? Lack of acknowledgement for son

Is this not a cognition instead of a feeling?

Mother is hurt by experiences of son Different treatment is most painful Mother concerned that father and son are going to lose their bond Mother experiences custody arrangement as difficult Mother has bothered/annoyed thoughts about the stepmother Surprise about circumstances first meeting of stepmother in the driveway Bold black

Perceptions about involvement?

What is immediately apparent from these codes is that the stepmother is involved in parenting the (step)children. However, some codes are descriptive, other codes clearly indicate (the versus codes) an opposition with the approach of the mother. Thus, these two need to be dealt with separately. -  General involvement of the stepmother -  Involvement for which the mother indicates she disagrees This could be a good starting point in the first version of the coding frame.

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Stepmother intervenes in child raising Stepmother sets rules for(step)son Stepmother more involved than father in raising kids Stepmother is more strict than mother Son cannot take candy when he wants to Vs. no need to beg Stepmother different approach on punishment Punished all evening for mischief at school Vs. angry for a little bit Stepmother claims she loves stepson

This does not seem to fit here. What to do with this code?

Roman grey

Behaviour of children – mothers’ perspective?

These codes relate to the experiences of the children in the newly formed family –from the perspective of the mother. There are references to specific behaviours of the son, for example bed wetting and crying. There also seem to be codes that have more to do with prohibitions such as: not allowed to hold half-brother or not allowed to play outside. These last two behaviours were also things the mother did not agree upon. Thus, do they fit better in a subcategory of the ‘Arial’ codes? (Step)son not allowed to hold half brother Son feels punished all the time Son is naughty at fathers’ house Son purposely wets his pants Son only bed wets when staying with father Sons cries when he goes back to his father Son wants to stay with mother Son says stepmother hits him Son not allowed to play outside at fathers’ house

Is this not better fitted under the ‘Arial’ category? Communication with ex/father

Italic grey Communications between the mother and the father Mother talks to father about problems Mother double checks father’s decisions with him Bold grey

Cognitions about stepmotherhood? (Continued)

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(Continued) Mother general ideas about step mothering Stepmother only partially responsible for education of child Stepmother cannot differentiate between stepand biological children

Bringing the open codes and subcategories together in the first version of the coding frame Below I present a first version of a coding frame. This is – by far – not a finished coding frame, but something that will keep on changing during the trial coding. The goal is to create a coding frame that fits all the interviews that were conducted with mothers on their experiences with their newly formed family and their relationship with the stepmother in particular. The trial coding that follows is an iterative process in which you keep on changing the coding frame until it empirically fits the data. 1. The occurrence of drastic / important change i. The initiation • Escalation starts when father remarries. • Mother feels she decided she initiated the ‘end’ of the arguing. • Change does not occur right away. ii. The red flag event • Mother realizes something is off due to bedwetting son. iii. The critical event • Father decides ‘son must go’. • Father and stepmother feel desperate by behaviour of the son. iv. The moment of the actual change • Mother has very vivid memory of the day of the big change. • Day of the ‘big’ change; sons stands alone on the driveway. v.

• Day of the big change, son gets a new wardrobe. The outcome • Mother organizes the custody change legally. • Father’s choice for new custody arrangement: four weeks a year, one weekend each month.

• Judge gives mother full custody. 2. Direct communication between the mother and stepmother • Mother as expert tells stepmother off. • Heated texting between mother and stepmother. • Mother and stepmother argue all the time. •

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Mother asked stepmother to stop making the difference.

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3. Experiences of the mother i.

Emotions of the mother •

Lack of acknowledgement for son.

•

Mother is hurt by experiences of son.

•

Different treatment is most painful.

•

Mother concerned that father and son are going to lose their bond.

•

Mother experiences custody arrangement as difficult.

ii. Thoughts of the mother •

Mother has bothered/annoyed thoughts about the stepmother.

•

Surprise about circumstances first meeting of stepmother in the driveway.

4. The stepmothers’ involvement in the newly formed family i.

Parenting actions of the stepmother •

Stepmother sets rules for(step)son.

•

Stepmother more involved than father in raising kids.

ii. Condoned actions of the stepmother by the mother. iii. Stepmother is more strict than mother •

Son cannot take candy when he wants to Vs. no need to beg.

•

Son not allowed to play outside at fathers’ house.

iv. Stepmother different approach on punishment

v.

•

Punished all evening for mischief at school Vs. angry for a little bit.

•

Son says stepmother hits him.

Different treatment between step- and biological children •

(Step)son not allowed to hold half brother.

vi. Responses from children to presence of the stepmother •

Son feels punished all the time.

•

Son is naughty at fathers’ house.

•

Son purposely wets his pants.

•

Son only bed wets when staying with father.

•

Sons cries when he goes back to his father.

•

Son wants to stay with mother.

5. Direct communication between mother and the father •

Mother talks to father about problems.

•

Mother double checks father’s decisions with him.

6. Mothers’ general ideas on ‘step mothering’ •

Stepmother only partially responsible for education of child.

•

Stepmother cannot differentiate between step- and biological children.

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Chapter 9 Additional exercise: textual analysis Question Contemporary television series Girls (HBO, 2012–17) is known for its complex representations of femininities and womanhood. Over the course of six seasons, it explored the lives of four white middle-class women living in Brooklyn. Yet, often overlooked are the series’ male characters. Even though they take on the role of secondary characters, the four ‘boys’ are equally busy constructing their self-identity, setting out work and life goals and handling several intimate relationships. Since many of these acts and practices relate to gender, we want to propose a study that examines the way this series represents masculinities (see also Dhaenens, 2017). In contemporary Western societies, the privileged status of men and patriarchy has been repeatedly questioned. Within academia, such challenges came from feminist scholars and social scholars working within the field of men and masculinities. An important scholar within the latter field is R. W. Connell. In her pivotal work Masculinities (2005), she conceptualized masculinities as multiple and in relation to a hegemonic masculinity. Eric Anderson (2012), however, demonstrates that AngloAmerican societies no longer construct one masculinity as hegemonic. He sees lesser explicit homophobia and a more equal distribution of power among men and argues that these societies feature men who embody an orthodox masculinity alongside men who embody an inclusive masculinity. As television fiction often reflects ongoing debates in society, it is relevant to enquire how a television series such as Girls engages with these debates. Does it reiterate the idea of a hegemonic masculinity or does it represent men as embodying an inclusive masculinity? With this exercise, we want you to contribute to such a project by conducting a textual analysis of one episode. Even though this study normally requires the examination of multiple episodes, we want you to practise how to interpret a single text. We chose ‘Boys’ (episode 6, season 2), and we ask you to focus on only two of the series’ male characters: Ray and Adam. We formulate five objectives: 1. What are the main theoretical concepts you plan to use? How will you operationalize them? 2. Decide which parameters you will use to analyse how masculinity is represented in this episode and argue why. 3. Prepare a sequence overview. 4. Conduct a textual analysis of the relevant sequences by which you study how masculinities are represented. 5. Based on this episode, what are your main conclusions? Does this episode represent men as embodying a hegemonic masculinity and/or do they embody an inclusive masculinity?

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Model answer Operationalization We will use the theories of hegemonic masculinity and inclusive masculinity. Such theories are too complex and abstract to examine immediately and demand a more tangible interpretation. Even though there is no one way to interpret a theory, the authors who coined the concepts have provided detailed conceptualizations. Hegemonic masculinity (Connell, 1995): • A certain masculinity becomes hegemonic within a gender order by infusing its gendered norms and values into institutions, culture and individuals. For example, in contemporary Western societies, men are expected to be strong, rational, orientated towards participating in public spheres and able to restrain their emotions. Further, they are expected to be heterosexual and act, behave, talk or walk in ways that are considered traditionally masculine. • The position grants men an unquestioned superior position over women and men who do not embody or fail to embody that certain masculinity. • There are many masculinities and they all stand in hierarchical relation to one another. As such, there are men who embody the hegemonic ideal, men who aspire the ideal and men who fail or refuse to embody that ideal. Inclusive masculinity (Anderson, 2012): • • •

In contemporary Western societies, there is a decrease of homohysteria (homophobia, femphobia, heterosexuality as norm). This results in the coexistence of orthodox masculinities (that are no longer hegemonic) and inclusive masculinities. Inclusive masculinities are embodied by men who demonstrate physical and emotional homosocial behaviour and who do not engage in sexism and homophobia.

Selection of parameters There are quite a few parameters that may be of relevance here, but the essential ones are the story and narrative themes (which can be studied by means of a narrative analysis), acting (which can be studied by means of an analysis of the mise-en-scène) or framing (which can be studied by means of a cinematographic analysis). Describing and interpreting the story and main themes will help us gain insight into the actions, goals, desires and feelings of the male characters. Second, a focus on acting will help us read the way the characters embody masculinity. The way they talk, act, move and interact with one another, what they are wearing will help us understand what kind of masculinity the characters embody and/or aspire to. Last, the way the camera frames the male characters may help us read whether they are considered by the camera as superior, neutral or inferior to the other characters (e.g., by analysing the angle of the camera).

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Sequence overview We start by pointing out that there may be various ways to divide an episode into sequences. What we propose here is one way to do so. It, however, remains important to identify why you consider a sequence to be a singular unit. Rely on Metz’s formulation of sequences and scenes to identify the sequences. Further, we underscore that a sequence overview may be quite elaborate. If you provide a detailed discussion of each parameter you plan to study, you may end up with plenty of information. Similarly, even an episode that lasts less than half an hour, may be turned into a sequence overview of a few pages. To this end, we only provide a brief and simplified version of the sequence overview.

Table 2  Sequence overview of ‘Boys’ (season 2, episode 6) Sequence

Location

Summary of the sequence’s narrative

1. Hannah’s book deal

Restaurant

Hannah is meeting with a publisher. They close a book deal. She has one month to finish an e-book.

2. Jonathan fires Soojin

Jonathan’s apartment

Marnie and Jonathan are in bed, both are naked. Soojin, Jonathan’s assistant, enters the bedroom. Jonathan and Soojin act all normal. However, after finding out that she took a scoop of his ice cream, he fires her. He then asks Marnie whether she wants to host the party Soojin was planning to host. She agrees.

3. Ray wants his book back

Coffee shop

Shoshanna is trying to persuade Ray to follow a seminar on entrepreneurship. He is not interested. Hannah arrives at the coffee shop to start her shift. Ray wants his copy of Little Women back from her. She says she left it at Adam’s. Adam is Hannah’s ex-boyfriend. She tells Ray that he has to get it himself because she is afraid of Adam.

4. Adam asks Ray to help him return a stolen dog.

Adam’s apartment

Ray knocks on Adam’s door. Adam is angry to hear Ray is connected to Hannah, but lets him in nonetheless. Ray tells Adam the reason of his visit and it turns out the book is in the bathroom. In the bathroom, Ray stumbles onto a wild dog. Ray finds out Adam stole the dog out of concern for the animal’s wellbeing. Ray convinces Adam to return the dog to the owner. Adam wants Ray to accompany him on the trip. Ray hesitantly agrees to do so.

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Sequence

Location

Summary of the sequence’s narrative

5. Marnie’s trying out outfits

Marnie’s apartment

Marnie is trying out outfits in front of Shoshanna. While Marnie is happy to host a fancy party for her ‘boyfriend’, Shoshanna is complaining about her boyfriend (Ray) and how he does not want to spend money on dates.

6. Ray and Adam take the ferry

Ferry terminal and the ferry

The sequence consists of scenes in which Ray is rambling about Staten Island. Halfway through the trip, they start talking about girls. They agree that relations with older and younger women are the best.

7. Hannah tries to write her e-book

Hannah’s apartment

Hannah is trying to write her e-book. Jessa, who is staying at her apartment, tells her the book will not matter. Hannah points out her depression but tells her she can stay as long as she wants.

8. Ray and Adam talk and argue about Hannah

Various streets on Staten Island

While walking, Ray asks Adam about his past relationship with Hannah. Adam uses various metaphors to describe how happy he is without her. However, when Ray tells him he never understood why Adam would want to be together with someone like Hannah, Adam starts to get angry and defends Hannah. Ray, a bit upset, tries to apologize by changing his ideas about Hannah but Adam is no longer reasonable and wonders whether Ray wants to have sex with Hannah. Adam then mocks Ray’s relationship with Shoshanna. The men start pushing one another. Adam runs off and leaves Ray behind with the dog.

9. Hannah does not feel at ease

Jonathan’s apartment

Hannah does not feel at home at the party with fancy hipsters and artists. After failing to have a conversation with Marnie, who is the hostess and apparently friends with everyone, Hannah decides to leave.

10. Ray argues with the daughter of the dog’s owner

A street on Staten Island

Ray accidentally runs into the daughter of the man who owns the dog. She does not want anything to do with the dog and runs off. Ray is persistent but she does not care, threatens to use a pistol. She uses gay and ethnic slur and wants him to back off. He does not follow her, is startled.

11. Jonathan makes clear to Marnie that they are not a couple.

Jonathan’s apartment

While choosing wine bottles, Jonathan is thanking Marnie for her work at the party. He, however, makes clear that they are not a couple. She starts to cry. He tries to comfort her but then finds out that she never really loved him but only the idea of him.

12. Hannah and Marnie talk on the phone

Hannah’s apartment and a metro stop

Hannah calls Marnie and they chat. They both are lying to one another. They both feel bad but pretend that they are well. (Continued)

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(Continued) Sequence

Location

Summary of the sequence’s narrative

13. Ray and the dog are watching the evening fall

Along the shore of Staten Island

Ray and the dog are watching Manhattan from the shore. He talks to the dog, says even the dog thinks he’s pathetic: ‘You think I’m a kike. I’m not even that. I’m nothing.’ Ray starts to cry. The dog seems to comfort him.

Interpretation of relevant sequences As masculinity is embodied and articulated in practically every scene the men are in, we consider the sequences 3, 4, 6, 8, 10 and 13 of relevance. Even though you could consider scenes in which characters are talking about men or masculinities in general, we do not include these here as the focus is on Ray and Adam’s masculinities. Ideally, you provide for each sequence a more in-depth analysis of the sequence. For this illustration, we will only provide a more detailed analysis of sequence 4.

Table 3  In-depth interpretation of sequence 4 of ‘Boys’ (season 2, episode 6) Narrative themes

Acting

Framing

Masculinities

Ambitions in life, male bonding, ethics (e.g., in relation to stealing a dog), personal interests (Ray’s copy of Little Women).

In accordance with the series’ use of comedy, the words and gestures are slightly exaggerated. Ray is represented more shy and timid than normal, feels intimidated by Adam. Adam is insensitive but more angry than usual. He nonetheless tries to connect with Ray.

Neutral framing (no high or low angles)

Attempts at homosocial intimacy (male bonding, asking help from another man), reiteration of the idea of hegemonic masculinity (e.g., Adam’s woodcraft, his way of addressing Ray), alternative masculinity (e.g., Ray’s affection to the book Little Women)

If you continue doing this for all relevant sequences, you will find yourself collecting multiple interpretations of masculinity. Starting from that, you will be able to see certain trends or aspects return that may help us understand what kind of masculinities are being represented.

Main conclusions In this episode, we see how the series represent, on the one hand, men who do not embody a hegemonic masculinity. On their own, they each challenge the idea of a traditional or orthodox masculinity. They both negotiate multiple discourses of masculinity.

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Adam can be patriarchal but at the same time emotionally open and respectful to his ex-girlfriend. Ray desires to be a traditional man but fails to do so: he lacks ambition, has desires and interest that do not conform with traditional masculinity (e.g., his book Little Women), and he assumes an inferior position to Adam as he seems intimidated by Adam’s masculinity. Furthermore, both men try to connect with one another. This attempt at homosocial intimacy implies a presence of inclusive masculinity. Halfway through the episode they even talk with one another in an open and equal manner, challenging the idea that men should be private about their own feelings. On the other hand, the series also shows how hegemonic masculinity has not vanished. Even though neither Adam nor Ray embodies a hegemonic masculinity, they refrain from aggressive and protective behaviour as well as outdated ideas about masculine desire at a moment when both men fail to understand each other’s different perspectives on relationships. In the end, they have not won anything by evoking hegemonic masculine behaviour. This is emphasized in the last scene in which Ray is crying and referring to himself as nothing. The series thus seems to imply that men in the series – and in Western society at large – already embody inclusive masculinities but have not yet succeeded in seeing it that way.

Reference Anderson, E. (2012). Shifting Masculinities in Anglo-American Countries. Masculinities and Social Change, 1(1): 40–60. Connell, R.W. (2005). Masculinities. Berkeley: University of California Press. Dhaenens F. (2017). Reading the Boys of Girls (p. 121–33). In M. Nash and I. Whelehan (eds). Reading Lena Dunham’s Girls. London: Palgrave Macmillan.

Chapter 10 Additional exercise: thematic analysis This exercise will help you familiarize yourself with the steps of Braun and Clarke’s TA approach (particularly steps 1 to 5). We provide you with excerpts from two interviews. These interviews are about participation of individuals in the development of digital tools (e.g., a smartphone app) to tackle societal issues. As a researcher, you are interested in people’s motivations in taking part in this kind of initiative as well as their role and contribution to the overall endeavour. Below, we show you how to approach in a straightforward way Braun and Clarke’s steps. We do this with regard to interview 1. Afterwards, you can try yourself with interview 2. Follow the steps below while analysing the interview: Step 1: Familiarize yourself with the data to get an idea of what the data is about and take notes of potentially theoretical meaningful codes. Step 2: Generate initial codes through the interpretation of text (inductively): label raw data.

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Step 3: Shift your focus from the data to the codes and start searching for themes (sub-themes, candidate themes, etc.). Step 4: Review the themes; avoid overlap among them. While doing so ask yourself: does data belong to the theme and does it reflect the data as a whole? Step 5: Define the themes through writing about them (describe them and elaborate on the sub-themes if applicable).

Interview 1 transcript I: 

What do you think about this initiative?

RES: … I think it is a very interesting initiative, very vibrant and it attracts people … they all have the social engagement and these are people who feel like they want to do something about problems in the society and … ehm … and it’s great to see all these people together and they have all these different backgrounds and they complete each other with their skills and knowledge … and the synergy and the energy that rises from that it’s super interesting and it’s really nice to see how in a very short time … also because of the methods that are used and the facilitation that has been done … how this initiative arises. However I expected there was going to be some sections to tell you something about research that has been done on social problems here in Flanders … because that’s what we want to do … something about it … and now I am very curious to see how it goes on and … I can see how it can turn into something great and it could also go totally wrong … eh eh … so … It is good to see that they have hired someone for coaching the participants … I:  … ok, and why did you take part in this initiative? RES: … ehm … different reasons … I was very curious to see how this initiative was going to work, who was going to be there, how many people would end up there … eh … what kind of facilitation do they do … and I was interested to learn; to learn about the methods that they were using … ehm … and to see how participation can happen in a more … in people their free time … like … participation that I know is ehm … is stakeholder participation and youth participation and this time I saw how adults can be involved outside of any work circumstance … of course there were people there who were there for work … but there was also people there who just had an interest and they came there in their own time to see what they could mean to the project … besides that I was also there for networking, to see if I could meet anyone and see if there are people that are interested to meet for my business and that proved to be right … I:  

What did you think about the whole process when it started, how did you feel about it?

RES: I do remember that I didn’t expect there to be many people and I was very much amazed for how many people were there … I also expected there to be a lot of young people and not so many adults but I think in most situations it was the other way around … so that really surprised me … I:  

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… Why didn’t you expect so many people?

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RES: … cos I don’t know Flanders so much as a place where people are very much engaged with the problems of other people … and I thought there was going to be many people who have problems themselves … not a lot [of people] there just to help … I:   And what does this have to do with the idea of combining digital innovation and to employ the design of digital tools to solve problems? RES: … I think if you say the word digital innovation it sounds very good and people are very interested and everybody sees there is a world of opportunities … but … to make it … to make it into something real we don’t know how, most people are still in the dark of what a digital solution could be for problems … and it seems unreal to think that there could be a digital solution to certain problems … and of course people want to know what could that be, what they can learn, and I want to be part of that … it seems like … I don’t know … something sci-fi and interesting … I: 

… so do you think that attracts more people to participate?

RES: … Definitely you get a lot of smart people who are into digital web design … eh … whatever you call it … I don’t even know the word … and I don’t think they would be attracted to be there if that wasn’t the goal … to find digital solutions … what is key about participation is you look for the skills and the people that you want to involve … and you look for their skills and you look for how you can use those skills and if you make it public that you start a project where you need people with digital skills cos you look for digital solutions … then it’s great cos people feel that they will have a role … for me … I don’t know anything about digital stuff and it was all more vague … I wasn’t really sure what I was going to do there, but of course if you have a background already you know you contribute and people feel good when they can contribute. I think you need to be very clear on the skills that you want and then people with that skill will feel they can mean something … and then you need social commitment of people … I think that’s a problem … you ask people to be involved for a year without any payment whatsoever … and then you need people that are really eager to learn and that are idealistic … and that also limits your group of course … I: 

… In what sense idealistic? …

RES: … Idealistic in a way that I think they think they mean something to change the world … you have to be idealistic to believe that and if you don’t believe that I don’t know how you can be motivated to take part in that … I: 

… I wonder if all participants managed to effectively contribute to their own group’s project …

RES: … well you have people with different roles there, you have people there who work as guidance, there are people that just have good ideas … maybe some of them are co-producers, co-creators, and then some of them have a role as a coach in their own group … I think it is very interesting to think about what to call it cos there are implications in the form of participation or whatever that it’s been organized here … and those implications or consequences are not always good … so … but what is great and it cannot be denied is that people who would not meet in another ways are put together to build something … for a cause that they want

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to do something for and without each other it wouldn’t work, they need each other … but what it isn’t facilitated is to have different skills in each group; and this ended up in being the case only in some groups … I know in my group there was not a good balance to make it work … maybe the methods that were being used eventually led to a good mix in other groups, but I am not sure about it.

Model answer based on interview 1 Step 1: Familiarize yourself with the data to get an idea of what the data is about and take notes of potentially theoretical meaningful codes. •

Potentially meaningful codes: { Social engagement { To do something for society { Complementary skills & knowledge { Facilitation { Participants’ expectations { Curiosity { Learning { ‘Leisure time’ participation { Networking { Business { Digital opportunities { Interest in digital solution { Doubts solving problems with digital solutions { Digital literacy { Contribution thanks to digital skills { Idealism { Different roles { Forms of participation { Implications and consequences of participation {

Need for heterogeneity in skills

Step 2: Generate initial codes through the interpretation of text (inductively): label raw data Interview 1 Transcript

Initial codes

Interviewer: What do you think about this initiative?

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Initial codes

Respondent: …I think it is a very interesting initiative, very vibrant and it attracts people…they all have the social engagement and these are people who feel like they want to do something about problems in the society and…ehm…and it’s great to see all these people together

To do something for society

and they have all these different backgrounds and they complete each other with their skills and knowledge… and the synergy and the energy that rises from that it’s super interesting

Complementary skills and knowledge

and it’s really nice to see how in a very short time…also because of the methods that are used and the facilitation that has been done…how this initiative arises.

Initiative’s short timeframe

However I expected there was going to be some sections to tell you something about research that has been done on social problems here in Flanders…because that’s what we want to do…something about it…and now I am very curious to see how it goes on and…I can see how it can turn into something great

Expecting more information regarding social problems

and it could also go totally wrong…eh eh…so…

Expecting a bad outcome

It is good to see that they have hired someone for coaching the participants…

Professional coaching/support

Expecting a good outcome

Interviewer: …ok, and why did you take part in this initiative? Respondent: ..ehm…different reasons…I was very curious to see how this initiative was going to work, who was going to be there, how many people would end up there…

Curiosity as motivation to participate

eh…what kind of facilitation do they do…

Curiosity as motivation to participate

…and I was interested to learn; to learn about the methods that they were using…ehm…

Eagerness to learn as motivation to participate

and to see how participation can happen in a more…in people their free time…like…

Curiosity as motivation

…participation that I know is ehm…is stakeholder participation and youth participation and this time I saw how adults can be involved outside of any work circumstance…

to participate ‘Leisure time’ participation

of course there were people there who were there for work…

Work as motivation to participate

but there was also people there who just had an interest and…

Interest in socially relevant projects

they came there in their own time… to see what they could mean to the project…

‘Leisure time’ participation

besides that I was also there for networking, to see if I could meet anyone and see if there are people that are interested to meet for my business and that proved to be right…

To do something for society Networking as motivation to participate Work as motivation to participate (Continued)

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(Continued) Transcript

Initial codes

Interviewer: What did you think about the whole process when it started, how did you feel about it? Respondent: I do remember that I didn’t expect there to be many people…

Expecting few participants

and I was very much amazed for how many people were there…

Surprise for high number of participants

I also expected there to be a lot of young people and not so many adults but I think in most situations it was the other way around…so that really surprised me…

Expecting younger participants

Interviewer: Why didn’t you expect so many people? Respondent: …cos I don’t know Flanders so much as a place where people are very much engaged with the problems of other people…and I thought there was going to be many people who have problems themselves…not a lot [of people] there just to help…

Expecting more people with problems to participate

Interviewer: And what does this have to do with the idea of combining digital innovation and to employ the design of digital tools to solve problems? Respondent: …I think if you say the word digital innovation it sounds very good and people are very interested and everybody sees there is a world of opportunities…

Digital innovation as positive

but…to make it…to make it into something real we don’t know how, most people are still in the dark of what a digital solution could be for problems…and it seems unreal to think that there could be a digital solution to certain problems…

Doubt about digital solutions to solve problems

and of course people want to know what could that be, what they can learn, and I want to be part of that…

Curiosity in digital possibilities as motivation to participate

it seems like…I don’t know…something sci-fi and interesting…

Digital solutions as interesting but fictional

Digital opportunities

Interviewer: So what do you think that attracts more people to participate?

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Initial codes

Respondent: Definitely you get a lot of smart people who are into digital web design…eh…whatever you call it…I don’t even know the word…and I don’t think they would be attracted to be there if that wasn’t the goal…to find digital solutions…what is key about participation is you look for the skills and the people that you want to involve…and you look for their skills and you look for how you can use those skills and if you make it public that you start a project where you need people with digital skills cos you look for digital solutions…then it’s great cos people feel that they will have a role…for me…I don’t know anything about digital stuff and it was all more vague…I wasn’t really sure what I was going to do there…

Interest in digital solutions as motivation to participate

but of course if you have a background already you know you contribute…

Digital background guarantees contribution

and people feel good when they can contribute…

Contributing as trigger of positive feelings

I think you need to be very clear on the skills that you want and then people with that skill will feel they can mean something…

Interest in digital solutions as motivation to participate Skills as key for participation Participants’ recruitment based on skills

Uncertainty of one’s own role based on lack of digital skills

Participants recruitment based on skills

and then you need social commitment of people…I think that’s a problem…

Finding socially committed people as problem

you ask people to be involved for a year without any payment whatsoever…

‘Leisure time’ participation

and then you need people that are really eager to learn and that are idealistic…and that also limits your group of course..

Eagerness to learn as motivation to participate Finding idealistic people as challenging

Interviewer: In what sense idealistic? Respondent: Idealistic in a way that I think they think they mean something to change the world…you have to be idealistic to believe that and if you don’t believe that I don’t know how you can be motivated to take part in that…

Being idealistic as motivation to participate

Interviewer: I wonder if all participants managed to effectively contribute to their own group’s project…? (Continued)

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(Continued) Transcript

Initial codes

Respondent: well you have people with different roles there, you have people there who work as guidance,

Guidance role

there are people that just have good ideas…maybe some of them are co-producers, co-creators…

People with just ideas

and then some of them have a role as a coach in their own group…I think it is very interesting to think about what to call it cos there are implications in the form of participation or whatever that it’s been organized here…and those implications or consequences are not always good…so… but what is great and it cannot be denied is that people who would not meet in another ways are put together to build something…

Coaching role

for a cause that they want to do something for…

To do something for society

and without each other it wouldn’t work, they need each other…

Complementary skills and knowledge

but what it isn’t facilitated is to have different skills in each group; and this ended up in being the case only in some groups…I know in my group there was not a good balance to make it work…maybe the methods that were being used eventually led to a good mix in other groups, but I am not sure about it.

Lack of facilitation in balancing groups’ skills

Collaborative roles

People connect through the initiative

Lack of complementary skills

Step 3: Shift your focus from the data to the codes and start searching for themes (sub-themes, candidate themes)

• To do something for society • Interest in socially relevant projects • • • • • • •

Curiosity as motivation to participate Eagerness to learn as a motivation to participate Work as a motivation to participate Networking as a motivation to participate Curiosity in digital possibilities as motivation to participate Being idealistic as motivation to participate Interest in digital solutions as motivations to participate

• Complementary skills and knowledge • Lack of complementary skills • • • •

social engagement

participants’ diverse skills

Skills as key for participation Participant recruitment based on skills Uncertainty of one’s own role re lack of digital skills Digital backgrounds guarantees contribution

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heterogeneous motivations

importance of digital skills to contribute

factors influencing outcome

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• • • • • • •

Expecting younger participants Expecting people with problems to participate Surprise for high number of participants Expecting more information re social problems Expecting a good outcome Expecting a bad outcome Expecting few participants

• Digital innovation as positive • Digital opportunities

mixed and unmet expectations

positive re digital solutions

• Doubts about digital solutions to solve problems • Digital solutions as interesting but fictional • Finding socially committed people as problem • Finding idealistic people as challenging • ‘Leisure time’ participation • • • •

Guidance role People with just ideas Collaborative role Coaching role

373

attitudes re digital solutions sceptical re digital solutions

recruitment issues

participants’ role

• Professional coaching/support • Lack of facilitation in balancing groups’ skills

received support

For each identified theme, we kept a list of the codes that led us to it. Note that the codes people connect through the initiative' and `initiative's short timeframe' were not used.

Step 4: Review the themes: avoid overlap among themes. While doing so ask yourself: does data belong to the theme and does it reflect the data as a whole? Reviewed themes relevant for research interest: • •

•

• • • •

heterogeneous motivations + social engagement as a sub-theme factors influencing initiative’s outcome + participants’ diverse skills and importance of digital skills as sub-themes (from this last sub-theme we remove the wording ‘to contribute’ from its name because the digital skills of participants have to do with much more than being able to effectively participate) the theme ‘mixed and unmet expectations’ has been initially changed into ‘expectations meet reality’ since it describes how the actual initiative differed from what the participants expected and had in mind at its very outset attitudes regarding digital solutions + positive attitudes re digital solutions and sceptical attitudes re digital solutions as sub-themes participants’ role received support the theme ‘recruitment issues’ has been discarded because deemed not relevant with regard to our research interest.

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Step 5: Define the themes through writing about them (describe them and elaborate on the sub-themes if applicable). •

•

•

•

•

•

heterogeneous motivations: this theme refers to the many and diverse motivations that led participants to take part in the initiative. It is of particular importance for the research since motivations are at the core of our interests. Very relevant is the sub-theme ‘social engagement’. Although participants might refer to it as one possible driver for participation or as formally the main driver to take part in a socially relevant initiative, the theme heterogeneous motivations allows us to see that the social engagement might not be the actual main driver of participants; or perhaps not a motivation at all if you think about motivations such as networking, business and the like. factors influencing initiative’s outcome: this theme is paramount in order to understand what might lead to good (successful) or bad (unsuccessful) outcomes of initiatives like the one that has been investigated. Of particular interest for our research are the subthemes ‘participants’ diverse skills’ and ‘importance of digital skills’. These two sub-themes allow us in particular to detect the importance of participants’ profile and skills for the final outcome of the initiative. They also inform us regarding different possible levels/degrees of contribution to the initiative pertaining to different levels of digital skills the involved individuals might possess. These might also have an influence in hierarchies within the groups, in the quality of the final digital products that are developed, in the actual level of involvement in the project and in the level of satisfaction in one’s own contribution to tackle societal issues. expectations meet reality: is an important theme because it provides us with a further window on people’s motivations to participate. These motivations might to be read between the lines of participants’ claims regarding their expectations and how they were satisfied or disappointed when those were (not) met. attitudes regarding digital solutions: this theme is relevant because it helps us to have an overview of the kind of mindsets and attitudes of the individuals that took part in the initiatives and carried out specific roles. What is interesting about the two sub-themes (‘positive attitudes re digital solutions’ and ‘sceptical attitudes re digital solutions’) is that they might point to positive attitudes as further drivers/ motivations to participate, but also to the level of digital skills of specific participants (high-digital-literate). On the other hand, when it comes to, say, sceptical attitudes, this might point to initial hesitation in embracing the initiative and its goals and to the employment of other reasons as drivers to participate. participants’ role: this theme is fundamental to have an overview of individuals’ role(s). It is particularly useful because it also allows a distinction to be made between those roles that are assumed by participants themselves and those that are assigned to them according to their skills, motivations, attitude, etc. received support: the theme refers to the overall help participants were promised or actually offered by the organizers of the initiative. It is an important theme because good support or lack thereof may strongly influence the level of contribution of specific types of participants who cannot carry out digital tools development on their own. This theme might also point to specific types of participants to which the support of the organizers was actually offered and unveil how it boosted, influenced and perhaps shaped in specific ways their contribution

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375

Interview 2 I: … what kind of participants did you meet through the initiative? Could you describe them? RES:  … their backgrounds were really different…unemployed, researchers, government, experts, a bit of everything. I:

… and why were they there in your opinion?..

RES: … that’s a difficult question…everybody had multiple reasons to be there. I think they were all just interested in generating ideas and thinking about social issues, because they are…they are kind of in their neighbourhood, in the environment in which they live and it’s close to their home context, what I do remember now is that there was one guy who left the session after about 15 or 20 minutes looking kind of angry because he expected to actually be able to get a solution for a specific problem out of one session. I think he had very different expectations than just brainstorming about issues; he really thought this was much more concrete like they would solve his issues and not some general issues in society … but he was kind of the only one actually. I: … ok, and going back to the various participants you met, what about the fact that some didn’t have digital skills? What kind of influence did this have on their participation? RES: … I think they made it pretty clear actually that you didn’t really need a background in digital skills, but I would have also made sure that there were people with digital skills so if you didn’t have any you would not quit cos there were anyway other people who could do it with you or for you … actually the organizers promised there would be “experts” to help us but there weren’t any. A member of our team also like just learned digital tools development by himself so actually he needed that support but he didn’t get it and he himself tried to give support to us so … that was kind of a shame indeed, I can understand that if you’re in a group where there’s nobody with digital familiarity or who has really a passion for it and wants to spend a lot of time on it, like we did, that than you would drop out … it is a major issue.. I: … and what do you think could be a solution to include those people who have skills but not digital skills in these kind of initiatives? RES: … if they ask right away who already has digital solutions in mind then you are not gonna put up your hand if you don’t have a digital solution … ehm so, I don’t know, how do you solve it? ... it’s co-creation process management and creativity management … you have to keep things as open as possible and still go towards the goal because on the other hand the purpose of the all trajectory was digital solutions so … that’s one side but it’s kind of a shame that they only focused on the digital ones and not on the other ones … ehm … like in our project the digital part isn’t the first solution, it comes after, it’s mainly…it’s also kind the offline solution which is much more important. We just took the digital solution in it because we want to continue under this initiative … I think every solution can have a digital aspect even if it’s just a new methodology to guide people who

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are vulnerable … so maybe the digital part should be communicated and seen as something really loose not like “it has to be an application” or to create a website, it can also just be a medium to spread your solution around…maybe they have, they put the expectation of the digital part too high, maybe.. I: … and what do you think about the phase in which you developed your own project?.. RES: … for us it was really interesting to have six months to just think about the project. You had a clear goal and you had a good incentive to try your best, but the support we got in itself was very poor actually, you probably heard that from other teams too … I think we saw the coach once a month for one hour and the sessions on themselves were ok actually, we did have some input and he gave some concrete advice but he soon realized that our idea didn’t really fit into that digital picture so … it was only once a month and only one hour and lot of things which we were promised like the digital support, the learning support and regular coaching, intensive coaching, all those kind of stuff which I already told you … we didn’t get the support … I: … how did you solve it then? How would you improve that process? RES: … we met some other experts but that was through our own network. I think it could have been done better with a mix of on the one hand digital, individual coaches, so you have time with maybe one expert in business modelling, strategy and another who is expert in digital skills, who can support in developing your website or your application or something like that…that on the one hand…and on the other hand also like group sessions and group workshops…it would have been really nice if we had also like a group coaching session, a kind of workshop, but also like getting to know each other, other groups, helping each other ehm… maybe even coaching each other.. I: … ok, and did you and your group spend/invest your own money in the project you developed while taking part in this initiative? RES: … yeah, we spent some of our own money to develop our project/idea, one thousand euros. I: … you talked about your idea and its development, but what did you do, if anything, to protect it while you developed as part of an open project?.. RES: … there are ways to protect general ideas but the thing is…for technology the ways to protect ideas are very strong, because technology is clearly defined, ideas aren’t, and there’re some … I think with a certain amount of euros you can like protect an idea, but I don’t know how concrete it should be…ehm...but we didn’t do anything cos even if people would have known our idea they did not know how we wanted to develop it. I: … I wonder of what one needs to be aware when they decide not to protect their idea in an initiative like this(?)... RES: … well, I think, there is a good side to it and there is a bad side to it, the bad side is yeah, it’s ideas that other people co-created and they generated a bit and it’s not

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377

nice if somebody picks it up and goes away with it and just takes all the credits for themselves, also for the organizers it’s not nice because it was an idea generated in their sessions and it should be going through the co-creation, through the all trajectory, on the other hand if there is an idea, it is good and even a professional organization is immediately able to pull it out on a societal level it’s actually pretty good, but even in this case there were indeed ideas that professional organizations thought of as “hey, this is good, I’m gonna develop it” and they can do it more quickly because they are already bigger. For general society it is kind of ok that these things happen, on the other hand … yeah … you miss the sharing collaborating aspect, which is also important for society … it’s kind of double, there’s a win somewhere, the fact that it actually gets developed, and on the other hand there’s yeah … it’s just sad …

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INDEX

Note: Page numbers followed by ‘f’ indicate figures; those followed by ‘t’ indicate tables. abductive reasoning, 74–75, 322–323 abstract code, 59, 61 abstraction, 254 actors, 94 actual wording, 55 advertising sex services, 320f aggregation, 36 Alcoff, L., 280–281 alibi test, 197 anonymization, 36 articulation, concept of, 23–26, 24f arts-based narrative research, 88 assigning relationships, 171–174, 172–174f audiovisual parameters, 276, 283 authenticity, 262 autobiographical narrative research, 87 axial coding, 59–61, 75t Bamberg, M., 89 Barker, C., 275 Beach, D., 182–183, 188, 190, 193, 195, 196, 200 Belfiore, E., 262 Belgium’s winning goal against Japan, at World Cup (2018), 192–193 Bennett, A., 182 Bennett, O., 262 big stories, 88 Bildungsroman approach, 87 biographical narrative research (BNR), 87–88 Blumer, H., 58 Bourgonjon, J., 262 Boyatzis method for thematic analysis analysis, 302–304 Braun and Clarke’s approach vs., 308–309t codes development, 298–302 definition of theme, 298 description of theme, 298 encoding, 297–298 examples, 298 inclusion and exclusion criteria of theme, 298 label of theme, 298

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seeing, 297–298 stages of themes, 297–304 thematic code, 298 Boyatzis, R., 294–295, 297–304, 308–309t Boys, Doom Patrol, The, 279 Braun and Clarke’s approach to thematic analysis, 304–305 Boyatzis method vs., 308–309t coding whole data set phase, 305–306 developing and reviewing themes phase, 307 familiarizing with data phase, 305 generating initial themes phase, 306–307 refining, defining and naming themes phase, 307–308 report producing phase, 308 Braun, V., 294–296, 304–308, 308–309t brevity, 247 bridge theory, 265 camerawork, 277 case classifications, 122–124, 123f creating, 124–130, 124–130f import sheet, 130–134, 131–134f case selection, 218 categorization matrix, 263 category development, 56, 241, 245, 254–256, 353 causal inferences, 213–216 causal mechanism (CM), 181–184 conceptualization of, 185, 195–198 linking citizen group lobbying with policy change, 203f, 204f linking total war with unemployment benefits, 193f, 194f operationalization of, 185, 190–195 as system, template of, 191f causal relation. see qualitative comparative analysis (QComA) cause-effect relationships, 181, 211 Charmaz, K., 44–77 Checkel, J., 182 Chouliaraki, L., 26, 27 Christian Orthodox identity, 98

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380

QUALITATIVE DATA ANALYSIS

cinematography, 276–277 city park, citizens’ engagement in design of, 301–302 Clarke, V., 294–296, 304–308, 308–309t close-in comparison, 56 coder training, 260 codes development, 297–298 data-driven strategy for, 299–301 hybrid approach strategy for, 301–304 research-driven, 299 theory-driven strategy, 298 coding axial, 59–61 Charmaz phases, 57–59, 61–63 consistency, 109, 148–152, 149–151f, 265 constructivist approach and, 326–333 deductive, 135–139, 136–139f families, 73 focused, 61–63 in grounded theory, 53–65, 73–75, 75–76t inductive, 139–143, 140–143f initial, 57–59 meaningful codes, reducing of text into, 54–55 open, 55–57 paradigm, 59–60 reliability TA, 295 selective, 63–73 Strauss and Corbin phases, 55–57, 59–61, 63–65 structure, revising, 145–152, 146–151f theoretical, 73–76 two rounds of, 260 units, 248–249, 264 writing memos, 143–144, 144–145f ‘coding entire document’ warning, 130f coding frame, 243, 256–257 development of, 241 in-depth theoretical interpretation of categories in, 268 quality of, 261 coding query, 163–169, 164–169f with attribute values, 168, 168–169f with codes, 165–167, 165–166f combination between code and case, 169, 169f options regarding in NVivo, 164–165t search criteria for ‘Near’ information, 167t see also matrix coding query coding scheme, 258, 259t, 263 adding examples from data to, 264 with definition and application rules, 268 development of, 268 coding structure, 116, 141 coded files and codes, 145–146, 146f coding consistency check, 148–152 exporting from NVivo, 152–153, 153–154f hierarchy chart, 154–157, 154–157f revising, 145–152 ‘uncode’ data fragments, 147–148, 147f, 148f

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Cohen Kappa, 260–261 collectivization, 35 comedy, 98 comparative matrices, 65–70 concept-driven content analysis. see directed qualitative content analysis concepts, open coding and, 56 confirmability, 263 conjunctural causation, 212 conservative solution, 227, 230 conspiracy theory, 20 constant-comparative approach, 47–48, 79 constructivist approach, 11, 14t, 318–319 coding according to, 326–333 media and cultural studies, 273–275, 277, 278 narrative analysis, 85 constructivist grounded theory (CGT), 50, 318 constructivist thematic analysis (CTA), 318 content analysis, 240 narrative analysis, 92–94 context, 19–21, 100–101 contextual uniqueness of data, 263 continued data analysis assigning relationships, 171–174, 172–174f coding query, 163–169, 164–169f, 164–165t, 167t framework matrices, 157–163, 158–163f matrix coding query, 169–171, 170–171f static sets, collect findings through, 175–176, 175–176f conventional qualitative content analysis, 241, 247–248 coping, 319 Corbin, J., 44 costume and dialogue, 277 credibility, 199 Creswell, J., 93 critical approach, 12, 14t, 319–322 media and cultural studies, 273–275, 277, 278 critical discourse analysis (CDA), 17, 325–326 analysing newspaper articles, 38 articulation, 23–26, 24f broader context and brief history, 19–21 concepts and principles, 22–26 Fairclough’s three dimensions of analysis, 26–29 ideology, 23 key features, debates and historical development, 18–19 phase of text analysis, 32–36 phases of research design, 29–31 power, 22–23 self-analysis, 37–40 step by step analysis, 26–36 summary checklist, 37 critical discourse moments, 30

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INDEX

critical race theory, 275 critical stance, 17, 19, 21, 22, 37, 52 critical theory, 85 Cronbach’s Alpha, 109 cultural materialism, 275 cultural studies. see media and cultural studies cyclical process of research, 2, 26, 29, 31 dancing, 284–285 data choice of, 30–31 collection units, 245 files, importing of, 121–122, 122f management, 109 transcription, 31, 305 data analysis, 30 writing for, 2 see also continued data analysis; qualitative data analysis (QDA) data-driven QCA. see conventional qualitative content analysis data-driven strategy for code development, 299–301 de Keere, K., 94 de Lange, R., 26 De Meur, G., 218 deduction, 55, 322–323 deductive coding, 109, 135–139, 136–139f deductive content analysis. see directed qualitative content analysis deductive reasoning, 8, 74–75, 322 democratic peace theory, 181 dependability of qualitative data, 261 description, aim for, 322 descriptive matrix of the cases (units), 123 Designing Social Inquiry (King, Keohane and Verba), 180 Dey, I., 45 dialogue, 277 dichotomization thresholds changing, 223–224 qualitative comparative analysis, 219–220, 221t difference-making theory of causation, 214, 216 dimensions, open coding and, 56 directed qualitative content analysis, 241, 262–263 discourse, 22, 23, 84 analysis, 19–20, 23, 242 articulating, 23–26 categories, 18 definitions of, 18–19 narrative, 84 refugees, of, 84–85 social constructionist approaches to, 19, 20, 21 theory, 19 see also critical discourse analysis (CDA)

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381

Discovery of Grounded Theory, The (1967), 44 discrimination, students’ experience with, 299 discursive devices (DD), 27, 30, 31 anonymization and aggregation, 36 functionalization, 35 impersonalization, 35 individualization and collectivization, 35 lexical choices, 32–33 modality, 34–35 nominalization, 33–34 selection of, 39 transitivity, 33 visual semiotic choices, 36 discursive practices, 27–28, 39 discursive psychology, 19 divergence puzzles, 212, 217 diversity principle, 250, 260, 264 DNA tests, 197 documentary. see stories Dunne, C., 52 Dür, A., 202–204 Dyer, R., 278 educated position, importance of, 275–276 ‘effects-of-causes’ approach, 211 Elchardus, M., 94 Elo, S., 241, 246 empirical narrative, 188 empirics-first process tracing, 182, 188 encoding themes, 297–298 Engström, I., 244, 245 Engström, K., 244, 245 epistemological questions, 9–10 equifinality/multiple causation, 212 Euphoria, 282 evidence, collecting and evaluating, 198–200 Ewick, P., 94 exhaustiveness, 246 experience, research questions and, 9 explanatory models, developing, 50 explorative technique, 247–248 factory workers’ motivation, 298 Fairclough’s three dimensions of analysis, 26–29 false negatives, 199 false positives, 199 far-out comparison, 56 feminism, 275 feminist theory, 321 film studies, 276–277 Fiske, J., 277 Fleiss’ Kappa, 261 flip-flop technique, 56 focused coding, 61–63, 75t, 79 focused theory, 63 folders, creating, 115–117, 116f, 117f Foucault, M., 22 framework matrices, 157–163, 158–163f

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382

QUALITATIVE DATA ANALYSIS

Friends (television show), 287–288 functionalization, 35, 39 Fuzzy Kappa, 261 generalization, 186, 200–201 generic inductive qualitative model (GIQM), 45–46, 93 genres, 98–99 Georgakopoulou, A., 89 George, A., 182 Gibbs, G., 98, 102 Glaser, B. G., 8, 44–77 Görgös, R., 10–11 Graneheim, U. H., 241, 248 Greimas’ Actantial Analysis, 94 Grossberg, L., 274 Grosz, E., 280 grounded theory (GT), 4 approach, 43, 243, 322 coding, 53–65, 73–75, 75–76t, 326–333 comparative matrices, 65–70 informed, 52 key features/debates/historical developments, 44–46 relational models, 70–71 self-analysis, 78–80 step by step analysis, 46–76 summary checklist, 78 theoretical sampling, 46–49 theory in, 49–51 typologies, 71–73 use of literature in, 51–53 Grundlagen und Techniken qualitativer Inhaltsanalyse (Mayring), 241 guiding questions, 246 Hall, S., 273 Hammer, R., 272 HBO, 282 heterogeneous sample, 245 heteronormative ideology, 278 heteronormativity, negotiating, 280 hierarchy chart, 154–157, 154–157f Holton, G., 294 Hood, J., 45–46, 48, 322 hooks, b., 278 Hsieh, H. F., 241, 264 human caring theory, 262–263 human experiences and perspectives, 244 humanities, 271–272, 274 hybrid approach, 243, 301–304 ideology defined, 23 patriarchal and heteronormative, 278 impersonalization, 35 importing data, 119f, 119–122, 119t data files, 121–122, 122f webpage using NCapture, 120–121, 120–121f

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in vivo codes, 57 individualization, 35 induction, 9, 54, 322–323 inductive coding, 109, 139–143, 140–143f inductive open codes, 264 inductive QCA. see conventional qualitative content analysis inductive reasoning, 8, 74–75, 322 inequality, 186–187 informed grounded theory, 52 initial coding, 57–59, 75t, 79, 248–253 developing in vivo codes, 57–58 and exploration, 263–264 interdiscursivity, 28, 39 intermediate solution, 228–231 interpretation, 254 intertextuality, 23, 28 iterative process, 213, 242, 261 Jackson, P., 186 Japan, Belgium’s winning goal against, 192–193 Jefferson, T., 273 Kappa coefficient, 149–151 Kellner, D., 272 Kim, J.-H., 87, 93 knowledge, research questions and, 9 Kress, G., 24 Kuckartz, U., 243 Kyngäs, H., 241, 246 Labov, W., 95–96, 98, 104 Laclau, E., 19 ‘large-N’ methods, 181 lexical choices, 32–33, 39 Libya intervention, military participation in. see military participation in Libya intervention literary-based narrative enquiry, 88 literature review, 305 grounded theory, 51–53, 78 process-tracing, 184 logical minimization, 226 logical remainders, 222, 230 Love Simon, negotiating heteronormativity in, 280 Lundman, B., 241, 248 Machin, D., 22, 26, 32, 36 manifest articulation, 24 manifest content QCA, 254 Marvel Cinematic Universe, 279 (Marxist) structural theory, 86 masculinities, superhero television series, 279 Masterplots, 94, 98 Mateo, G., 202–204 maternal gatekeeping towards stepmothers category development, 254–256 coding frame, 256–257

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INDEX

coding units, 249 diversity principle, 250 open coding, 252–253 matrix coding query, 169–170 with attribute values and codes, 170, 170f with cases and codes, 171 with codes, 170–171, 171f Maxwell, J. A., 45 Mayr, A., 22, 26, 32, 36 Mayring, P., 241, 248, 263 McCarty, T. W., 197, 198f McKee, A., 271–272, 275 meaningful entities, 245 media and cultural studies. see textual analysis (TexA) memo and more focused code, 328–331 memos, writing, 61, 143–144, 144–145f Mercedes Benz technology, 191–192 Merton, R. K., 294 meta-theme, 297, 303 methodological questions, 10 Metz, Ch., 276, 283 military participation in Libya intervention, 219 contradictory configurations, solving, 221–225, 222t, 224t, 225t military capabilities, 220–225 operationalization and dichotomization, 219–220, 221t proximate elections, 220–225 public support, 220–225 truth table, constructing, 221–225, 222t, 224t, 225t mise-en-scène parameter, 276, 288 mixed-method designs, 10, 201 modality, 34–35, 39 montage, 277 Montesano Montessori, N., 26 motivation, factory workers’, 298 motive and opportunity test, 197 Mouffe, C., 19 multi-causal theoretical model, 219 multimodal analysis, 27 multiple conjunctural causation, 212–215, 213f ‘murder mystery’ metaphors, 197–198 music videos, 276, 278, 283–284 Madonna’s, 279 non-white material bodies in, 286 representation of bodies in, 280–281 Stromae’s, 280 see also Papaoutai (2013) mutual exclusiveness, 247 narrative analysis (NA), 83, 276, 333–335 content/themes, 92–94 context, 100–101 discourses, narratives and stories, 84–85 genres of, 87–89 historical roots and development of, 85–86, 86f

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383

interpreting, making and telling stories, 86–87 key features, debates and historical developments, 84–89 purposes, 101–102 self-analysis, 103–105 step by step analysis, 89–102 structural analysis, 94–100 summary checklist, 103 narrative analysis of small stories (NASS), 321 narrative discourses, 84 narrative events, 84 NCapture, 120–121, 120–121f necessary conditions, 215–216, 226 Nelson, C., 275 neo-Marxist perspectives, 275 newspaper articles, analysis, 38 nominalization, 33–34, 39 NRK Norwegian public broadcaster, 282 NVivo Integration, 122 NVivo software, 14, 109–110, 336–338 acquainted with interface, 112–115 coding raw data in, 135–157 continued data analysis, 157–176 detail view, 115f exporting from, 152–153, 153–154f installing latest version of, 110–111 interface, 114f self-analysis, 176–177 start-up, 112f, 113f summary checklist, 176 see also organizing NVivo project Obinger, H., 193 ontological questions, 9 open coding, 55–57, 75t, 251–253 developing categories, 56 developing concepts, 55 inductive, 264 organizing NVivo project case classifications, 122–134, 123–134f creating folders, 115–117, 116f, 117f data files, data files, 121–122, 122f importing data, 119f, 119–122, 119t mouse, right click, 117–118, 117f, 118f NCapture, 120–121, 120f, 121f Orthodox Catholic symbols, 98 outcome and cause, conceptualizing, 189–190 outcome value, 222 Papaoutai (2013), 278–279, 282–286, 285t parenting styles constant-comparative method, 47–48 kind of theory, 50–51 sampling, 47 theoretical saturation, 48 use of literature, 53 parsimonious solution, 227–228, 230 patriarchal ideology, 278

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384

QUALITATIVE DATA ANALYSIS

patterns of meaning (themes), 295–297. see also themes Pedersen, R. B., 182–183, 188, 190, 193, 195, 196, 200 Pelto-Piri, V., 244, 245 personal interests, to study research questions, 9 phenomenon, theory of, 64–65 philosophical assumptions, in research, 9–15, 317 philosophical tradition, 13, 14t. see also specific entries plausibility probes, 194 plausible explanatory conditions, 218, 234 plot, 95–98 popular media culture, 271, 275 positivistic approach, 10 post-positivist approach, 10–11, 14t, 317 post-positivistic grounded theory (PPGT), 50–51, 54, 318. see also grounded theory (GT) post-positivistic thematic analysis (PPTA), 318. see also thematic analysis (TA) postcolonialism, 275 postmodern theory, 86 power, interpretation of, 22–23 pragmatic approaches, 2 preparation phase, 304 probabilistic causality, 181 process tracing (PT), 179, 318, 338–340 advantages of, 181–182 causal process, conceptualizing, 190–195 causal process, operationalization of, 195–198 collecting and evaluating evidence, 198–200 connecting to literature, 186–188 disadvantages of, 200–201 key features, debates and historical development, 180–183 outcome and cause, conceptualizing, 189–190 self-analysis, 202–204 step by step analysis, 183–186, 184f summary checklist, 201–202 productive continuity, 191, 192 properties, open coding and, 56 proximity, 199 purposes, narrative analysis and, 101–102 purposive sample, 245 qualitative comparative analysis (QComA), 209–210, 318, 340–342 causal inferences and, 213–216 conservative, parsimonious and intermediate solution, 226–231 features of, 211–213 interpretation and re-iteration of research cycle, 231–233 key features, debates and historical developments, 210–216, 210f

13_STEVENS_INDEX.indd 384

main steps, 217f multiple conjunctural causation, 213f operationalization and dichotomization, 219–220, 221t research design, 216–219 self-analysis, 233–235 step by step analysis, 216–233 summary checklist, 233 truth table and solving contradictory configurations, 221–225, 222t, 224t, 225t qualitative content analysis (QCA), 92, 239, 240, 311, 318, 342–359 analysis phase, 246–265 application and origin of, 240–241 basic principles of, 241–244 citations of, 240f in contextual description, 242 conventional, 241 deductive/directed, 241 iterative process, 242 key features, debates and historical development, 240–244 manifest content, 254 preparation phase, 244–246 quantification, 239, 243 self-analysis, 266–268 step by step analysis, 244–265 summary checklist, 266 as systematic and rule-bound procedure, 241 qualitative data analysis (QDA), 1–2 dependability of, 261 foundations for choosing approaches, 4–15 orthodox approaches to, 3–4, 317 principles for, 2–3 research questions, 4–15 structuring, 109 quality, defined, 1 quantifiable indicator, 260 queer theory, 275 racism, in education constructivist approach, 11 critical research, 12 post-positivist research, 10–11 Ragin, C., 209, 210, 211 reflexive TA (RTA), 294–295. see also constructivist thematic analysis (CTA) reflexivity, 26 refugees, discourse of, 84–85 regularity theory of causation, 214–215 relational models, 70–71 representation of reality, 18, 22, 25, 27, 30, 50 research-driven codes development, 299 research problem, choice of, 29–30 research process, 262, 265 research question (RQ), 2, 4–5, 244–245, 304, 317, 323

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INDEX

from critical perspective, 319–320 focus, scope, coherence and feasibility, 5–7, 6t, 7t formulating, 30 literature, 7–9 personal interests, knowledge and experiences, 9 philosophical paradigms, 9–15 qualitative comparative analysis, 217, 234 superhero television series, 279 theoretical choice related to, 246 research theme (RT), 5, 317 rhetorical tools, 98–100 Richardson, J., 25, 26, 27, 32 right mouse click, use of, 117–118, 117f, 118f rigorous balancing act, 241 Rihoux, B., 218 romance, 98 Rwandese dance, 284 sampling choice of, 30–31 framework, 305 size, 245 strategy, 245 superhero television series, 279 theoretical, 46–49 satire, 99 saturation, 247, 253 Schiffrin, D., 18 Schmitt, C., 193 Schreier, M., 243, 261, 264 Schuman, H., 26 scope conditions, 218 seeing themes, 297–298 segmentation, 246 selective coding, 63–65, 76t comparative matrices, 66 relational models, 70–71 theory of phenomenon, 64 typologies, 72 self-determination theory, 298 sensitizing concepts, 8, 58 ‘set theoretical’ approach, 189–190 sex work advertising, 320f students’ involvement with, 317–323 sexual consent, in teen drama, 282 Shannon, L. W., 244–245 Shannon, S. E., 241, 264 Silbey, S., 94 SKAM, 282 small stories, 88–90, 103–104 smoking-gun test, 197 ‘snowballing outward’ strategy, 200 social constructionist approaches, 19–22, 30 social practices, 26–29, 31, 32, 37, 40 social reality, 265 Social Science Citation Index, 86, 86f social sciences, 271–272, 274

13_STEVENS_INDEX.indd 385

385

‘spitting images of reality’ context, 89–90 Greek Cypriots narrating, 91–92, 97 plot of storytelling, 96–97 Turkish Cypriots narrating, 91, 96 static sets, collect findings through, 175–176, 175–176f stepmothers, maternal gatekeeping towards, 249 category development, 254–256 coding frame, 256–257 coding units, 249 diversity principle, 250 open coding, 252–253 Stevens, P. A. J., 10–11 stories, 84 big, 88 content/themes, 92–94 context, 100–101 genres of, 87–89 interpreting, making and telling, 86–87 purposes, 101–102 small, 88–90, 103–104 structural analysis, 94–100 see also narrative analysis (NA) storytelling, 86–87, 102 Strauss, A., 8, 44–77 street dance, 284 structural analysis, narrative analysis, 94–100 structural theory (Marxist), 86 structured categorization matrix, 263 students experience with discrimination, 299 involvement with sex work, 317–323 position within higher education systems, 300 (sub)category development, 253–258 sufficient conditions, 215–216, 226 synchronized dancing, 284 systematic coding process, 239 systems approach, 191 teen drama, sexual consent in, 282 television basic parameters of, 277 series, superhero, 279 studies, 276–277 text-driven QCA. see conventional qualitative content analysis textual analysis (TexA), 283–286, 321, 322, 360–365 defined, 275 key features, debates and historical developments, 271–277 literature review and theoretical concepts, 279–281 phase of, 32–36 preparation of analysis, 281–283 recommendation of reading, 289

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386

QUALITATIVE DATA ANALYSIS

research questions and selection of cases, 277–279 self-analysis, 287–289 step by step analysis, 277–286 summary checklist, 287 type building, 243 textual data, coding of, 109 thematic analysis (TA), 92, 243, 293, 365–374 Boyatzis’ method, 297–304, 308–309t Braun and Clarke’s approach, 304–308, 308–309t coding reliability, 295 coding whole data set phase, 305–306 definition, 298 description of, 298 developing and reviewing themes phase, 307 examples, 298 familiarizing with data phase, 305 generating initial themes phase, 306–307 identification of, 296 inclusion and exclusion criteria of, 298 key features, debates and historical developments, 294–297 label of, 298 preparation phase, 304 recommendation of reading, 313–314 refining, defining and naming themes phase, 307–308 Reflexive, 294–295 research questions, 304 self-analysis, 310–313 stages of, 297–304 step by step analysis, 297–308 summary checklist, 310 from theoretical ideas, 296–297 thematic code, 298 thematic maps, 307, 308 thematic structure, 294, 306, 312 themes, 92–94 defined, 296, 298 description of, 298 developing and reviewing, 307 encoding, 297–298 generating initial, 306–307 inclusion and exclusion criteria of, 298 label of, 298 meta-theme, 297, 303 refining, defining and naming, 307–308 seeing, 297–298 stages of, 297–304 thematic code, 298 see also thematic analysis (TA) theoretical coding, 73–76, 79 theoretical sampling, 46–49 theoretical saturation, 48, 63, 79 theoretical sensitivity, 51 theory-driven strategy for code development, 298 theory-first motivation, 182, 188 theory of phenomenon, 64–65

13_STEVENS_INDEX.indd 386

Thornberg, R., 52 threat, 224, 225t three dimensions of analysis, Fairclough’s, 26–29 three-step coding process, 2 tragedy, 99 transferability, 245 transitivity, 33, 39 transparency, 247 Treichler, P. A., 275 trial coding, 242, 260–261, 264, 268 triangulation, 199 trustworthiness, 258 truth table constructing, 221–225, 222t public support, 224t threat, 225t type building text analysis, 243 typologies, 71–73 Umbrella Academy, The, 279 ‘uncode’ data fragments, 147–148, 147f, 148f unconstrained matrix, 263 unemployment benefits, in the Netherlands causal process, conceptualizing, 190–195 causal process, operationalization of, 195–198 collecting and evaluating evidence, 198–200 connecting to literature, 186–188 outcome and cause, conceptualizing, 189–190 uni-dimensionality, 247 unit of analysis, 245–246 units of meaning, 248 Van Dijk, T.A., 21, 29, 36 Van Schuylenbergh, J., 317–322 visual-based narrative enquiry, 88 visual semiotic choices, 36 Vogue (music video), 279 Waletzky, J., 95–96, 98, 104 Watson’s human caring theory, 262–263 ‘waving the red flag’, 57 Web of Science Core Collection database critical discourse analysis, 20–21, 21f grounded theory, 44, 44f process-tracing, 182, 183f qualitative comparative analysis, 210, 210f qualitative content analysis, 240, 240f textual analysis, 272, 273f thematic analysis, 294, 294f webpage, importing of, 120–121, 120–121f Weiss, G., 281 ‘whatever works’ approach, 1 Wilkinson, S., 304–305, 307 Williams, G., 93 work-life balance, of employees, 303 writing for data analysis, 2 memos, 61, 143–144, 144–145f

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