Studying Situational Interaction: Explaining Behaviour By Analysing Person-Environment Convergence [1st ed.] 9783030461935, 9783030461942

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SPRINGER BRIEFS IN CRIMINOLOGY

Beth Hardie

Studying Situational Interaction Explaining Behaviour By Analysing Person-Environment Convergence 123

SpringerBriefs in Criminology

SpringerBriefs in Criminology present concise summaries of cutting edge research across the fields of Criminology and Criminal Justice. It publishes small but impactful volumes of between 50-125 pages, with a clearly defined focus. The series covers a broad range of Criminology research from experimental design and methods, to brief reports and regional studies, to policy-related applications. The scope of the series spans the whole field of Criminology and Criminal Justice, with an aim to be on the leading edge and continue to advance research. The series will be international and cross-disciplinary, including a broad array of topics, including juvenile delinquency, policing, crime prevention, terrorism research, crime and place, quantitative methods, experimental research in criminology, research design and analysis, forensic science, crime prevention, victimology, criminal justice systems, psychology of law, and explanations for criminal behavior. SpringerBriefs in Criminology will be of interest to a broad range of researchers and practitioners working in Criminology and Criminal Justice Research and in related academic fields such as Sociology, Psychology, Public Health, Economics and Political Science.

More information about this series at http://www.springer.com/series/10159

Beth Hardie

Studying Situational Interaction Explaining Behaviour By Analysing Person-­Environment Convergence

Beth Hardie Institute of Criminology University of Cambridge Cambridge, UK

ISSN 2192-8533     ISSN 2192-8541 (electronic) SpringerBriefs in Criminology ISBN 978-3-030-46193-5    ISBN 978-3-030-46194-2 (eBook) https://doi.org/10.1007/978-3-030-46194-2 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Publication of this SpringerBrief volume coincides with increasing recognition of the importance of integration of individual and environmental perspectives in the explanation of crime. This is most apparent in the growing popularity of Situational Action Theory (SAT) and empirical tests of its fundamental statements about the interaction of individuals and environments in crime causation. The approach, methods and concepts that are the focus of this volume are relatively new to criminology and represent a major departure from classical research methods in the social sciences. Like with any major shift in thinking and challenge to the status quo, misunderstandings and misspecifications have arisen, and inappropriate applications and evaluations of theory and method are the result. This volume aims to bridge the gap between strong theory about causal situational interaction in crime and the appropriate methods for empirically testing proposed situational mechanisms. It is underwritten by the principle that research should be driven by theory and served by method. The ultimate aim of this volume is not to critique existing attempts to study situational interaction, but to clear the ground for future methodological advances and empirical discoveries in the study of individual-­ environment interaction in behavioural outcomes, including acts of crime. Based at the Institute of Criminology, University of Cambridge, The Peterborough Adolescent and Young Adult Development Study (PADS+) is the home of Situational Action Theory and the Space-Time Budget methodology. As a PADS+ researcher, I have worked intensively with the concepts and methods involved in studying situational interaction in criminology for 15 years. I recognise the range of challenges in this small but swiftly growing field, but also the opportunities it presents for our increased understanding of human action. I wanted to share this enthusiasm and knowledge. This volume provides comprehensive accessible guidance to those studying human behaviour with an interactive worldview, specifying the ways in which the interactive approach dictates research design, operationalisation of concepts, data collection, analytical method and interpretation of results. SAT is a general theory of action (including acts of crime) that is rooted in such an interactive worldview, and this volume aims to clarify the family of procedures required to empirically test v

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Preface

such an interactive theory. I hope that the highly specified evaluations, considerations and solutions make this volume an indispensable resource for researchers using any kind of data who are wishing to analyse person-environment interaction in behaviours (acts such as crime) and their distribution across people or places. The discussions in this volume are borne of but fully supersede my 2017 doctoral thesis, except in cases where pages of the thesis are specifically referenced. Cambridge, UK

Beth Hardie

Acknowledgements

Thank you to my long-time colleagues P-O Wikström and Kyle Treiber for many discussions over the years, which led me to delve into the specifics of these issues and the implications for our and others’ research. I am grateful to Lieven Pauwels, Jenni Barton-Crosby and Clemens Kroneberg for constructive feedback on previous formulations of the ideas in Chaps. 1 and 2. Thanks also go to Gabriela Roman, Harald Beier, Helmut Hirtenlehner, Dietrich Oberwittler and Richard Mann for sharing specific technical expertise and discussing with me some of the technical distinctions I draw in Chaps. 3 and 4. I also appreciate useful last-minute discussions with Alberto Chrysoulakis and the proof-reading efforts of Marilyn Hardie.

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Contents

1 Why and How Criminology Must Integrate Individuals and Environments����������������������������������������������������������������������������������     1 People, Places, and Acts of Crime ����������������������������������������������������������     2 The Problematic Dichotomy of Criminology��������������������������������������     2 Aggregations of Crime and Acts of Crime: The Problematic Level of Explanation����������������������������������������������������������������������������     3 The Problematic Dichotomised Study of Crime Events����������������������     5 Why Integrate People and Places to Explain Action?��������������������������     8 Person-Environment Integration: How?��������������������������������������������������     8 The Additive and Interactive Worldviews��������������������������������������������     8 Approaches to Criminological Research ��������������������������������������������    10 Interaction, Explanation, and Prevention��������������������������������������������    12 Summary: Advocating an Analytic Criminology of Person-­Environment Interaction in Acts of Crime for Effective Prevention������������������������������������������������������������������������������������������������    13 References������������������������������������������������������������������������������������������������    15 2 Integrating Individuals and Environments: A Situational Approach to Studying Action����������������������������������������������������������������    23 Lacking an Integrative Model of Action��������������������������������������������������    23 Studying Dependency������������������������������������������������������������������������������    26 Integrative Models of Acts of Crime��������������������������������������������������������    28 Situational Action Theory and the Situational Model��������������������������    30 ‘Situation’, ‘Environment’, and ‘Setting’��������������������������������������������    31 Studying Convergence: New Approach, New Methods��������������������������    35 Exposure����������������������������������������������������������������������������������������������    37 Summary����������������������������������������������������������������������������������������������    40 Appendix: Clarity of Definitions and Concepts: ‘Situation’, ‘Environment’, and ‘Setting’ ������������������������������������������������������������������    41 Conceptual Ambiguity in Psychology ������������������������������������������������    41

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Conceptual Conflation in Criminology������������������������������������������������    42 ‘Situation’ in Criminology: Historical Context of a Misnomer����������    43 References������������������������������������������������������������������������������������������������    45 3 Evidencing Situational Interaction Without Situation-Level Exposure Data����������������������������������������������������������������������������������������    53 Making Do Without Situation-Level Exposure Data ������������������������������    54 Interaction (Dependence) in Regression Models ������������������������������������    57 Solving the Problematic Distribution of Crime ��������������������������������������    59 Non-linear Models ������������������������������������������������������������������������������    59 Transformation of the Dependent Variable������������������������������������������    62 OLS Regression Plus Safeguards��������������������������������������������������������    63 Reliability and Interpretation ������������������������������������������������������������������    64 Significance������������������������������������������������������������������������������������������    66 Comparing Groups to Access Meaning ����������������������������������������������    67 Conclusion ����������������������������������������������������������������������������������������������    72 References������������������������������������������������������������������������������������������������    74 4 Collecting and Analysing Situation-Level Exposure Data: Clarifying Appropriate Analysis of Person-Environment Convergence to Explain Action������������������������������������������������������������    79 Designing Situational Research ��������������������������������������������������������������    80 Collecting Situation-Level Exposure Data����������������������������������������������    82 Space-Time Budgets����������������������������������������������������������������������������    82 Randomised Scenarios ������������������������������������������������������������������������    85 Future Methodological Avenues����������������������������������������������������������    85 Analysing Situation-Level Exposure Data����������������������������������������������    86 Additive Analysis��������������������������������������������������������������������������������    88 Situational Analysis ����������������������������������������������������������������������������    90 Evaluating Approaches to Analysis of Situation-Level Exposure Data for Appropriate Study of Situational Interaction������������������������    95 Studying Situational Interaction: Conclusion and Next Steps����������������    97 Approach����������������������������������������������������������������������������������������������    97 Data Collection������������������������������������������������������������������������������������    98 Data Analysis ��������������������������������������������������������������������������������������    99 Interactionist Fundamentalism: Any Room for Compromise on the Interactive Approach? ��������������������������������������������������������������   100 Coda ����������������������������������������������������������������������������������������������������   101 References������������������������������������������������������������������������������������������������   102

List of Figures

Fig. 2.1 Person, setting, and situation in SAT������������������������������������������������   31

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List of Tables

Table 1.1 Approaches to criminological research������������������������������������������   10 Table 1.2 Integrating people and places: worldview implications for approach and outcome��������������������������������������������������������������   13 Table 1.3 Classification of some research types and traditions by approach and unit of analysis����������������������������������������������������   15 Table 2.1 Integrating people and places: worldview implications for theory, method, and findings����������������������������������������������������   36 Table 4.1 Integrating people and places: worldview implications for theory, method, and findings (replication of Table 2.1)������������������������������   81

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About the Author

Beth Hardie  is a Research Associate at the Institute of Criminology, a member of the Centre for Analytic Criminology, and Research Manager of the Peterborough Adolescent and Young Adult Development Study (PADS+) at the University of Cambridge. Her work is grounded in an analytical approach (guided by Situational Action Theory; SAT) that integrates individually and environmentally focused explanations of human behaviour, including crime. She has a particular and critical interest in the data collection and analytical methodology required for the analysis of situational interaction (the interaction between people and settings) in action.

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Chapter 1

Why and How Criminology Must Integrate Individuals and Environments

Abstract  This chapter describes the problems and solutions that justify the need for appropriate analysis of the convergence of people in environments to explain action. Thus, it justifies the research approach and methods that are described and explained in this volume. Criminology is plagued by the fragmentation of environmental and psychological approaches. Limited attempts at integration display fundamentally different, incompatible approaches to the question of how to integrate individuals and environments in behavioural research. Furthermore, criminology has traditionally studied the factors related to concentrations of crime in either people or places, but not the factors implicated in an explanation of acts of crime (behaviour). Welcomed event-focused approaches are hindered by not identifying an action process that specifies the precise mechanism by which (how) both individual and environmental factors are relevant to acts of crime. This chapter justifies the need to consider features of both individuals and environments in the study of the causes of acts of crime, as opposed to studying factors related to aggregates of crime in either individuals or environments. It details why an interactive, rather than additive, worldview is essential to research that is best able to inform effective crime prevention policy and practice. The two key questions addressed are therefore why and how criminology should integrate individuals and environments to explain crime. Understanding the answers to these questions is crucial to appreciation of appropriate analysis of situational interaction. If researchers do not see the issues raised in this chapter as problematic and fundamental, they will fail to see the value of the solution. Since the solution is the research approach and methods that are the main topic of this volume, this chapter justifies the volume.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 B. Hardie, Studying Situational Interaction, SpringerBriefs in Criminology, https://doi.org/10.1007/978-3-030-46194-2_1

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People, Places, and Acts of Crime The Problematic Dichotomy of Criminology Over decades, criminological research has repeatedly revealed two facts about crime. First, we know that the distribution of crime varies across people and places. Some people offend a lot, and some do not offend at all; some environments or contexts are crime hotspots, and some places and contexts are relatively crime-free. Second, research also shows that various features of people and places relate to crime. Particular features of people make them more likely to be crime-prone (Ellis, Farrington, & Hoskin, 2019; Farrington, 1992; Jolliffe, Farrington, Piquero, Loeber, & Hill, 2017), and particular features and conditions of places make them more likely to be crime-prone (Andresen, 2019; Bruinsma & Johnson, 2018b; P. Wilcox & Cullen, 2018).1 However, here criminology has become stuck in two parallel ruts. The discipline dichotomises crime research into psychological and environmental tracks that rarely converge, both of which tend to discover correlates that predict, rather than testing mechanisms that explain, crime (see also Wikström, 2011; Wikström, Oberwittler, Treiber, & Hardie, 2012, pp. 3–6; Wikström & Treiber, 2017). Academics from a range of fields have long recognised the need to integrate individual and environmental approaches to explaining behaviour (e.g. Anastasi, 1958; Belsky & Pluess, 2009; Bronfenbrenner, 1979; Carmichael, 1925; Cronbach, 1957; Darwin, 1859; Dodge & Rutter, 2011; Ekehammar, 1974; Esser & Kroneberg, 2015; Fuentes, 2016; Kantor, 1924; Lewin, 1936; Overton, 1973; Piaget, 1971; Rutter, 1989; Sullivan, 1953). This is also true of criminologists. In 1940, Walter Reckless stated that criminology is ‘not a unified body of knowledge but rather a reservoir of diverse insights and, to a large extent, of unintegrated conclusions’ (1940, p. 1). In 1950, Sheldon and Eleanor Glueck stated that ‘proponents of various theories of causation still too often insist that the truth is to be found only in their own special fields of study’ (Glueck & Glueck, 1950, p. 4). This ‘major theoretical issue’ was referred to by Donald West and David Farrington nearly half a century ago as ‘the controversy between the psychological and sociological orientations’ (1973, p.  200) (see also Ohlin, 1970; Rodman & Grams, 1967). These researchers went on to stress the importance of a multicausal, cross-disciplinary theoretical approach for understanding human behaviour including crime. Criminologists have continued to state the need to integrate individual (micro) and environmental (macro) approaches (Farrington, Sampson, & Wikström, 1993;

1  Wikström defines a place as a ‘geographic location and its immediate environment, which includes other people present, the activities going on and its physical layout’ (Wikström, 2019 p.266) (i.e. the social and physical environment of a particular geographical location). Environmental effects (both physical and social) are often termed ‘situational’ (Bottoms & Wiles, 2002; Wilcox & Cullen, 2018); however, the terms ‘environment’ and ‘situation’ are crucially different and are delineated in Chap. 2.

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Jensen & Akers, 2003; Le Blanc, 1997; Liska, Krohn, & Messner, 1989; Reiss, 1986; Tittle, 1995; Tonry, Ohlin, & Farrington, 1991; Wikström, 2005, 2017; Wikström et al., 2012; Wikström, Clarke, & McCord, 1995; Wikström & Treiber, 2016). However, despite repeated calls for integration of the individual and environmental approaches to the study of crime, little progress has been made (Bunge, 2006; Wikström et al., 2012; Wikström & Sampson, 2006) until recently.

 ggregations of Crime and Acts of Crime: The Problematic A Level of Explanation The first major hindrance to progress has been the level at which researchers study crime. Traditionally, research aims to study either the factors related to criminality or the development of crime-prone people and their criminal careers, or the factors related to spatial clusters of crime or the emergence of criminogenic environments and places and their stability and change, but not the factors related to an explanation of acts of crime (behaviour). Criminologists studying the role of the environment in crime causation aim to explain, or more typically to predict, the spatial and sometimes temporal distribution of crime (e.g. Andresen, 2019; Bottoms & Wiles, 2002; Bruinsma & Johnson, 2018a). They do not aim to understand the act of crime itself. For environmental criminology, the outcome of interest is an area- or place-level aggregate of acts of crime, whereby studies aim to discover factors that contribute to a concentration of events. Early research focused on neighbourhood structural characteristics such as residential segregation (Shaw & McKay, 1969). Later research studied the effect of those structural characteristics on social disorganisation (Bursik & Grasmick, 1993; Kornhauser, 1978) and collective efficacy (Sampson, Raudenbush, & Earls, 1997) or on land use and the effect of resultant activity patterns on opportunity (Brantingham & Brantingham, 1984).2 Such traditional research continues to inspire new studies of the spatial distribution of crime (Bruinsma & Johnson, 2018b), whilst study of the concentration of crime at micro-places has grown (Weisburd et  al., 2016). Explaining environment-level aggregates rather than acts of crime means that studies of the role of the environment in crime causation are able to neglect the role of individual differences in the experience and effect of environments (Wikström,

2  The Brantinghams argue that environmental criminology requires an understanding of the coming together of offenders, criminal targets, and time- and place-relevant laws (Brantingham & Brantingham, 1991). This definition of ‘environmental criminology’ is often understandably interpreted as integrating person and environment. However, the Brantinghams do not go on to specify a model of action that details how person-environment interaction results in acts of crime; instead they posit factors that predict spatial concentrations of crime (Brantingham & Brantingham, 1984; Brantingham & Brantingham, 1993).

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2007c), and therefore environmental criminology is currently a criminology without people (Hardie & Wikström, in press).3 Traditionally, psychologists studying crime aim to explain, or more commonly, predict, criminality. The focus is on which features of individuals make them more likely to offend, or offend more frequently (Ellis et  al., 2019; Farrington, 1992; Jolliffe et al., 2017). Thus, the outcome of interest is an aggregation of crimes within an individual, rather than acts of crime. The study of criminality and the distribution of crime among a population can take account of context, for example, by drawing on the work of Bronfenbrenner (1977; Bronfenbrenner & Morris, 1998). However, this research is conducted at the level of the individual, whereby environments play a role as contexts of development. Approaches can be simple and limited. For example, traditional developmental perspectives understand distributions (such as the age-crime curve) as the result of a sequence of maturational change over the life span that is generalisable across individuals, and the role of social contexts are seen as a static part of the sequence (for discussion, see Elder, Shanahan, Damon, & Lerner, 2006; Sampson & Laub, 1997; Treiber, 2017). Some more complex individual-­level criminological research integrates environmental exposure into the longitudinal study of the dynamics of individual change, such as true life-course criminology (Treiber, 2013, 2017). The life-course perspective studies the cumulative effects on individuals’ lives of exposure to features of social contexts (Elder, 1998). For example, Sampson and Laub’s life-course approach shows how transitions that arise from life events can interact with existing behavioural trajectories to generate turning points that can impact on offending behaviour (Sampson & Laub, 1993, 1997). Thus, the role of the environment becomes more dynamic in a life-­ course approach, such that exposure to certain features of contexts over time may change an individual’s criminality. Life-course criminology therefore explores the dynamic role of environments in the distribution of crime among a population and in changes in such concentrations (i.e. the criminal careers of individuals). Such an approach is extremely difficult to study empirically (Sampson & Laub, 1997) and is rarely fully and appropriately evidenced (Treiber, 2017); for exceptions, see Wikström, Treiber, and Roman (forthcoming) and Chrysoulakis (2020). Ultimately, however, even these complex individual-level explanations do not study the role of context in acts of crime, which means that they cannot by themselves account for an 3  Some newer and innovative research studies that might appear to integrate people into environmental criminology arguably still fall short. For example, there is increasing research interest in the role of ambient (as opposed to resident) populations in spatial distributions of crime (e.g. Felson & Boivin, 2015; Hanaoka, 2018; Malleson & Andresen, 2016); however, with the exception of analysis by Wikström et al. (2012, pp. 311–319), there is little consideration of the nature of the varying features of those people who are present, in part due to the limitations of the kinds of ‘big data’ most often used (Hardie & Wikström, in press; see further Chap. 4). Another example is Summers and Guerette’s attempt to consider individuals’ perception of, interactions with, and exposure to environments (Summers & Guerette, 2018). However, they fail to address the content of individual variation (i.e. relevant features of people); therefore, neglecting that differing perceptions of and interactions with environments mean that when it comes to explaining an act of crime, individuals differ in their susceptibility to environmental effects.

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individual’s differential behaviour across different settings. The focus on criminality means that the dynamic role of environments as contexts of action is not addressed.4 Traditionally, research primarily studied aggregations or concentrations of crime in either people or places, but not acts of crime themselves. However, it is problematic to explain an aggregate of actions (Coleman, 1986, 1990; Oberwittler & Wikström, 2009; Wikström, 2007c). Therefore, in order to progress, research should study concentrations of acts of crime in people or places by studying the causes of acts of crime from which the aggregation is comprised.5,6

The Problematic Dichotomised Study of Crime Events In contrast to the study of concentrations of crime in areas or people, the opportunity and control approaches have drawn welcome attention to the crime event. Influential examples are the Routine Activity (Cohen & Felson, 1979) and Situational Crime Prevention (Clarke, 1980) approaches and the focus on self-control in Gottfredson and Hirschi’s General Theory of Crime (1990). However, whilst they each acknowledge the importance of the ‘other’ factor (and even their interaction), these approaches still primarily focus on either environmental or individual factors.7 Acknowledging or even encompassing both individual and environmental factors does not equate to integrating them. Crime events are acts. Neither the Routine Activity and Situational

4  The major exception is the Peterborough Adolescent and Young Adult Development Study (PADS+) which was specifically designed to empirically study Situational Action Theory (Treiber, 2017) and is able to empirically study both the developmental effects of exposure to contexts (Wikström et al., forthcoming) and also the effect of features of contexts on action (Beier, 2018; Hardie, 2019; Wikström et  al., 2012; Wikström, Ceccato, Hardie, & Treiber, 2010; Wikström, Mann, & Hardie, 2018). 5  Within the framework that is fundamental to this volume (SAT), a few studies have explained crime concentrations in people (criminality, e.g. Wikström et al., forthcoming) and places (hotspots, e.g. Wikström et al., 2012, pp. 311–319) by first explaining acts of crime, which then allows analysis of how and why they become aggregated. 6  See Sutherland and Cressey (1970, pp. 73–74) and Manski (1978) for related discussion of the level of study. 7  Efforts have been made to integrate control and opportunity theories of crime (e.g. Felson, 1986; P. Wilcox, Land, & Hunt, 2003). Such a project is conceivable because of a shared assumption of self-interested human behaviour, which implies that actors aim to maximise pleasure and minimise pain. This rational choice perspective determines that the decision to commit an act of crime results from a cognitive calculation which determines that the benefits of an act of crime outweigh the costs. Opportunity and controls are therefore fundamental to this choice, which is rational in the sense that it is self-interested. This assumption of the nature of human action is problematic because it ignores that human nature is not compelled by self-interest alone (Barton-Crosby, forthcoming; Wikström, 2005, 2006). Thus, opportunity and control perspectives, and attempts to integrate them, fall foul of this problematic assumption of the nature of human action. For a discussion, see Hardie (2017, pp. 134–138).

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Crime Prevention traditions nor the Self-Control Theory specifies an action process that would identify precisely how both individual and environmental factors are relevant (Bouhana, 2013; Hardie, 2017; Wikström, 2010, 2019; Wikström et al., 2012; Wikström & Treiber, 2007, 2016); and this remains true of newer theoretical formulations (e.g. Gottfredson & Hirschi, 2019). These perspectives focus on features of either individuals or environments, at most only acknowledging that variation in the ‘other’ factor may be relevant for crime events. Such acknowledgment falls short of specifying the content of the relevant difference (see, e.g. footnote 3). Without a specified action process that identifies which features are relevant and how, the content and nature of the ‘other’ factor variation are neglected. These approaches cannot therefore be considered integrative. Therefore, empirical tests of the principles of the Routine Activity and Situational Crime Prevention perspectives and Self-Control Theory cannot address person-­environment integration. They instead go on to study either the environment or the individual in isolation. Moreover, the further these empirical studies are from the original formulation of these perspectives, the more likely it seems that they isolate individual or environmental factors. Routine Activity Theory and Unstructured Socialising Theory Routine Activity Theory (RAT; Cohen & Felson, 1979) assumes a motivated offender when that actor is in the presence of a suitable target and in the absence of a capable guardian. This is not to say that RAT precludes the possibility of individual differences. For example, Felson’s (1995) attempted integration of RAT with Hirschi’s social bonding theory in principle allows for the relevance of individual differences at least in terms of parent-child relations. However, individual differences and their interaction with the immediate environment are generally ignored by RAT in favour of differences in routine activities and criminogeneity when predicting the likelihood of acts of crime (see further Wikström, 2019; Wikström & Treiber, 2016). Osgood, Wilson, O’Malley, Bachman, and Johnston (1996) extended RAT to propose that ‘hanging out’ (unstructured and unsupervised socialising) is criminogenic for most adolescents, whether they are otherwise prone to crime or not (see also Haynie and Osgood 2005). Whilst RAT is merely disinterested in individual differences, the approach of Osgood et al. (1996) expressly ‘does not invoke individual characteristics’ (p. 640). Osgood et al. (1996) actually ‘do not assume that everyone is equally receptive to the temptations of situations conducive to deviance’ (p. 639), but ‘neither do we [they] assume that exposure to them is relevant only to a small group of “motivated offenders”’ (p. 639). However, ultimately, Osgood et al. state that motivation is in the setting (and not the individual). After Briar and Piliavin (1965), Osgood et al. call it ‘situational motivation’, but this conception of motivation is not truly situational as it doesn’t differ for different people in the same environments (see Chap. 2 regarding the distinction between environment and situation). This distinction is missed by Wilcox and Cullen (Wilcox & Cullen, 2018), who instead present Osgood et al.’s (1996) ‘unstructured socialising theory’ as an integrative approach. Osgood et al.’s approach means that those testing its propositions explicitly reject the

People, Places, and Acts of Crime

7

relevance of individual factors. As a result, Augustyn and McGloin (2013) observe that ‘how and why the degree of susceptibility to temptations offered by informal socializing varies has received minimal attention’ (p.  118). Furthermore, tests of Osgood et al.’s extension of RAT explicitly hold individuals constant even when the available data does not necessarily constrain analysis (Bernasco, Ruiter, Bruinsma, Pauwels, & Weerman, 2013; de Jong, Bernasco, & Lammers, 2019). Situational Crime Prevention The development of the Situational Crime Prevention (SCP) approach was a reaction against the perceived extreme psychological focus on individual ‘dispositions’ in criminology at the time. Thus, SCP was concerned with controlling behaviour by manipulating physical opportunities for offending and increasing chances of apprehension (Clarke, 1980, 1983). SCP does mention how an individual’s strength of motivation, mood and emotion, moral judgements about behaviours, perception of opportunities and risks, and capabilities would matter for offending. Thus, Clarke’s statements about ‘situations’ include features of individuals. In fact, Clarke (1980, 1983) references an obscure pamphlet by Ohlin (1970) that advocates a field theory approach by which to organise personal, social, cultural, and economic features of the circumstances surrounding acts of crime, terming such an approach ‘situational’. These links to integrative Lewinian Field Theory, which argues that behaviour results from person-environment interactions (see Chap. 2), suggest that, at least at the outset, Clarke’s Situational Crime Prevention had the potential to mean more than just the manipulation of physical features of environments. However, Clarke gave no detail as to the ways by which individual differences may alter the perception of physical opportunities and the effects of them on behaviour. He goes on to say of SCP that ‘an individual’s current circumstances and the immediate features of the setting are given considerably more explanatory significance than in ‘dispositional’ theories’ (Clarke, 1980, p. 139). Reviews of empirical studies undertaken within the principles of SCP (Clarke, 1997; Felson & Boba, 2010; Guerette & Clarke, 2009; Guerette, Johnson, & Bowers, 2016) reflect the state of research over the years. I argue that such reviews reveal that empirical studies go on to focus even more exclusively on what Clarke calls ‘the management, design, or manipulation of the immediate environment’ (1983, p. 225) than the original SCP statements. Self-Control Theory Gottfredson and Hirschi’s Self-Control Theory (SCT; 1990) ignores or underplays the impact of differential motivation, domain-specific self-control ability, and differential response to features and conditions of environments on crime causation (Burt, 2019; Hay & Forrest, 2008; Tittle & Botchkovar, 2005; Wikström & Treiber, 2007). Hay and Forrest (2008) observe that although the original presentation of SCT led some researchers to interpret the theory to predict that the effects of

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self-­control are conditional on the supply of criminal opportunities, Gottfredson and Hirschi (2003) themselves rejected this interpretation. Ultimately, the writings of Gottfredson and Hirschi encourage empirical study of the link between self-control and crime without reference to physical and social environments. Researchers have taken on this task in droves. Reviews show that there are many hundreds of studies that test the link between self-control and crime (Pratt & Cullen, 2000; Vazsonyi, Mikuška, & Kelley, 2017), whilst studies of the differential effects of self-control on behaviour in response to variable environmental conditions are much rarer within criminology (Burt, 2019; Hay & Forrest, 2008; Tittle & Botchkovar, 2005).

Why Integrate People and Places to Explain Action? Most theories focus primarily on the effects of either individual or environmental differences on crime, paying only lip service to integration of these research orientations. By neglecting the mechanisms by which individual and environmental factors integrate and are mutually relevant, these approaches encourage continued fragmentation because empirical tests and future theoretical developments go on to theorise and study one or the other. However, not only do crime propensity and criminogeneity each have a strong bearing on concentrations of crime in people or places, respectively, they are also important features of people and places that are both highly relevant to an integrated explanation of acts of crime. Crimes are acts. People are the source of their behaviour: people act, not environments. Thus, an act requires an actor (with particular characteristics), but actors are always acting in an environmental context. Actors are always exposed to the features and conditions of their environment. To understand the causes of acts of crime (which can then also explain the unequal distributions of crime across people and places) requires simultaneous attention to the criminogenic features of people and places. Until recently in criminology, there generally lacked consensus or even discussion over how the integration of people- and place-oriented perspectives can be achieved in the study of acts of crime. The remainder of this chapter focuses on the ‘how’ of person-environment integration and also the evaluation of methods of integration in terms of crime prevention.

Person-Environment Integration: How? The Additive and Interactive Worldviews The fundamentals of how a person sees the world will determine how they conceive the nature of certain elements of it and how they theorise about the processes that take place, the questions they ask, and the procedures and methods by which they

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attempt to answer those questions. In short, our approach to research is determined by our worldview. In behavioural research that considers both individuals and environments, the different approaches to integration are rooted (usually implicitly) in one of two worldviews. These worldviews assume that the integration of individual and environment components is either additive or interactive (Overton, 1973). Although these worldviews have been distinguished using various (and oftentimes overlapping) terminology throughout the history of the philosophy of science, this discussion focuses on their respectively additive or interactive natures. In short, an additive worldview determines theories based on conceptions of the whole being predictable from and decomposable into its parts. Causality is thus unidirectional (chainlike). Functions are mediators. Resultant research questions are about which factors have influence, and relatively how much influence they have, and are therefore concerned with prediction. In contrast, an interactive worldview understands individuals as active beings and causes as functional and that the whole gives meaning to its parts. This conception means that there can be no one-way causality and no independent causes (see further Bunge, 1963). This raises questions about how and why components interact; such questions are concerned with explanation. These worldviews are summarised in Table 1.2. How can we evaluate these different approaches to person-environment integration? The prevention of crime is of crucial importance to most stakeholders in a society8 (for discussion, see Hardie, 2017, pp. 25–28). However, crime prevention strategies often lack substantive content because professionals rarely agree on the best approach, resulting in inconsistent, poorly evidenced, and uncoordinated action (Blomberg, Mestre, & Mann, 2013; Hardie, forthcoming; Reckless, 1940; Reiss, 1991; Sherman, Farrington, Welsh, & MacKenzie, 2002; Wikström, 2007a; Wikström & Torstensson, 1999; Wilcox & Hirschfield, 2007). A major reason for this is that the discipline of criminology currently fails to adequately fulfil its responsibility to effectively inform policy (Hardie, forthcoming; Manski, 2013; Rein & Winship, 1999; Sampson, Winship, & Knight, 2013; Tittle, 2004; Wikström, 2007a). Therefore, a crucial evaluative criterion for criminological research, including research that integrates people and environments, should be that it effectively informs policy and practice, but also informs effective policy and practice. The additive and interactive worldviews represent a paradigm clash of the kind described by Kuhn (1962) and are fundamentally incompatible (Overton & Reese, 1973). This means that the approaches to criminological research conceived within these irreconcilable worldviews cannot be comparatively evaluated at the level of empirical testing because ‘there is no such thing as a crucial test among divergent assumptions’ (Overton, 1973, p. 84). Worldviews are pre-theoretical. However, the debate over which worldview is most appropriate for the study of crime can be settled based on the effectiveness for prevention of the research approach it bears.

8  Society, crime prevention, and criminological research are discussed from a Western perspective in this volume.

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Table 1.1  Approaches to criminological research 1 2 3

Aim Describe Explain Intervene

Research question Who, where, when Why, how What works

Approach Prediction Causation Prevention

Worldview Additive Interactive –

Approaches to Criminological Research Approaches to improving the policy relevance of criminology vary depending on the criminologist’s interpretation of this challenge. Wikström and Treiber (2017) identify three distinct approaches to criminological research which differ in the role they play and their suitability for informing the most effective policy and practice (summarised in Table  1.1; see also  Hardie, forthcoming). Approach 3 involves assessing what works in crime prevention – studies are concerned only with describing the effectiveness of intervention policy and practice rather than with the phenomena of crime itself. This prevention approach will therefore not be discussed further in this volume. Approaches 1 and 2 study crime in order to inform prevention policy and practice. Approach 1 involves describing the characteristics of who is at risk of committing crime and where and when places are at risk for being crime ‘hot spots’ – these studies are concerned with prediction. Approach 2 involves explaining why and how crime happens – these studies are concerned with causation. Whether a criminologist takes research Approach 1 or 2 is determined by their worldview. The prediction approach is rooted in the additive worldview, and the causation approach stems from an interactive worldview. Prediction and Explanation Successful translation of research findings into effective crime prevention policy is hindered by the widespread conflation of prediction (correlation and description; Approach 1) and causation (explanation; Approach 2) within the criminological discipline (Bunge, 2006;  Hardie, forthcoming; Wikström, 2007b, 2011, 2019; Wikström & Treiber, 2013, 2017). The distinction between prediction and explanation is often ignored or confused in academic endeavour, statistics, and modern applications more broadly (Bunge, 2004; Dawid, Faigman, & Fienberg, 2013; Psillos, 2002); however, this distinction is not new (Holland, 1986; Mill, 1843). Essentially, study of the ‘effects of causes’ aims to detect and describe risk factors that predict who is more likely to commit crime and where and when crime is most likely to occur, but without consideration of theory and mechanism. Risk factors are factors that are significantly associated with and precede the outcome (Kraemer et  al., 1997). Traditionally, statistical regression methods were used to help reduce the number of risk factors for crime by ruling out those correlates that

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were mediated by another correlate (e.g. West & Farrington, 1973). This approach has remained largely the same in criminology, though arguably the techniques for identifying ‘causal risk factors’ for crime have become more refined and technical over the years (e.g. Murray, Farrington, & Eisner, 2009; Walters, 2019). However, the concept of risk factors is flawed and misleading because factors identified as ‘causal risk factors’ often do not or cannot causally influence the outcome (Katz, 1988; Wikström, 2019). A vast array of criminological research has shown that thousands of variables correlate significantly with crime (Ellis et  al., 2019; Farrington, 1992), but statistical significance doesn’t always indicate real-world significance (Wikström & Treiber, 2017).9 Many factors that covary with crime are not causally relevant because they are markers (symptoms, indicators) (Farrington, 2000; Rutter, 2003; Wikström, 2007b, 2011, 2019), or are even just attributes (fixed markers) (Wikström, 2011, 2014), and attributes cannot be causes because they have no causal efficacy (Bunge, 2001; Holland, 1986). Even proponents of the risk factor approach acknowledge that ‘causal risk-factors are not necessarily ‘the cause’ of a particular outcome’ (Kraemer, Lowe, & Kupfer, 2005, p. 27). Thus, a risk factor approach to criminological research establishes that a factor may be important, but not the specifics of how or why that factor is causally relevant. Description of a predictor and the mechanisms by which it causes crime are often very vague. By contrast, a ‘causes of effects’ approach is concerned with explicating mechanisms that link cause and effect. Thus, this explanatory approach answers ‘how’ and ‘why’ questions. Mechanisms are plausible reasons why a putative cause brings about an effect; and because they are for the most part unobservable, they are usually represented by ‘guesses’ about how a process occurs (Bunge, 2001; VanderWeele, 2009). A guiding theoretical framework is therefore crucial to this causation approach because it provides a system of plausible processes by which social action links cause and effect. This minimises the number of suggested and correlated risk factors to those factors that have explanatory power in a cause-­ oriented prevention strategy and further, from among these, can sort the causes from the causes of the causes (Wikström, 2007a, 2011, 2014, 2019). Thus, theory is not a list of statistically relevant factors; mechanisms cannot be entered into a statistical analysis but instead make sense of statistical relationships. Thus, an empirical search for mechanisms does not involve identifying mediating variables, but instead aims to gain evidence that the theoretically proposed, plausible, and often unobservable mechanism is at work (see further Bunge, 2004; Coleman, 1986; Elster, 2007; Gerring, 2008; Hedström & Ylikoski, 2010; Keuschnigg, Lovsjö, & Hedström, 2017; Mahoney, 2001; Mayntz, 2004; Wikström, 2014, 2019; Wikström & Sampson, 2006).

9  An explosion in applications of new ‘big data’ sources to the study of crime will contribute to a continuation of this situation; many such studies are conducted by computational scientists who are mostly data-driven non-criminologists who identify correlates (particularly of spatial concentrations) rather than study causes of crime (see Snaphaan & Hardyns, 2019).

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Interaction, Explanation, and Prevention Whilst criminology has found many consistent correlates of crime, the discipline does not yet agree on the causes of crime (Blomberg et al., 2013; Farrington, 1988, 2003; Sampson et al., 2013; Tittle, 2004; Wikström, 2007a). Science proceeds on the basis that altering causes is the only way to effect a change in an outcome. The prediction approach cannot scientifically address the problem of crime prevention (Hardie, forthcoming;  Wikström & Treiber, 2017). Crime prevention policy and practice devised to address risk factors identified by a prediction approach to criminological research represents a compromise between the information that is needed right now and what is available. This ‘making do’ can be inadequate, unethical, or even dangerous (Hardie, forthcoming). In contrast to the risk factor approach, a mechanistic approach allows research to uncover the causes of behaviour that, if changed, will result in a behavioural change (Bunge, 2004; Wikström, 2019). Thus, this explanatory, causation approach is the only one that can explain the specifics of how crime prevention endeavours can most effectively change behaviour (Hardie, forthcoming).10 The most effective way to understand how we can prevent crime is to explain crime behaviour rather than be able to predict it (Bunge, 2006; Hardie, forthcoming;  Morgan & Hough, 2007; Wikström, 2007b, 2011, 2017, 2019; Wikström & Treiber, 2013). The explanatory ability of research undertaken within the additive and interactive approaches is what distinguishes the effectiveness of these two opposing worldviews for the integration of the individual and environmental approaches to crime (summarised in Table 1.2). An additive approach raises research questions about which factors have influence and how much relative influence each factor has. The question of how integration occurs is left unasked.11 As when in isolation, even when in sum, individual and environmental approaches can only discover factors which predict human action (such as crime). In contrast, explanation of the specifics of how people and environments are linked in behavioural outcomes requires an interactive worldview that is predicated on the understanding that the individual and environmental component 10  This corresponds to a realist position (Wikström, 2007b), which states that ‘capable successful intervention into the social realm likely necessitates, and always stands to benefit from, explicit social ontological reasoning’ (Lawson, 2019, p. 4), where ontology means ‘investigation into the nature, basic constitution and modes of being of stuff, of all phenomena’ (Lawson, 2019, p. 21). 11  There follows an illustration of the difference between the additive and interactive approaches to the integration of individual and environmental insights. Reckless (1940) rightly argued that consideration of individual differences in response to exposure was lacking from the prominent theories of crime of the time, which focused on environmental factors. However, he asks ‘what is the difference between those who are and those who are not affected? What sorts of individuals are they who succumb and what sorts are they who remain immune?’ (Reckless, 1940, p. 444). By asking what the individual differences are, as opposed to how (by what specific mechanisms) those differences mean that the individual responds to the environment differently, Reckless demonstrates an additive approach to the integration of person- and environment- oriented understandings of the causes of crime.

Summary: Advocating an Analytic Criminology of Person-­Environment Interaction…

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Table 1.2  Integrating people and places: worldview implications for approach and outcome Worldview Causality Research questions Concept of integration Example approach to integration Functions evidenced Contribution to knowledge Contribution to behaviour-change policy

Additive Unidirectional, chainlike ‘What’, ‘which’, ‘how much’ Additive Cumulative risk factors Moderating variables Prediction Not causal

Interactive Dependent, interactional ‘How’, ‘why’ Interactive SAT’s integrative situational model Causal mechanisms Explanation Causal

This table is developed in Chap. 2 to include implications for conceptualisation and methods for data collection and analysis

contributions to behaviour cannot be decomposed. This is because the interactive approach raises questions about how component parts interact (including what specific conditions of components are relevant and which processes are involved; why questions) and these how and why questions are concerned with explanation (Bunge, 1963, 2004, 2006; Elster, 2007; Gerring, 2008; Hedström & Ylikoski, 2010; Mahoney, 2001; Mayntz, 2004; Wikström, 2011). Such ‘how’ questions require specific theory and particular empirical investigation (Anastasi, 1958; Overton, 1973). Proponents of ‘analytic criminology’ therefore seek to answer to these how and related why questions by taking an explanatory, mechanistic approach to the study of crime and to crime prevention (Hardie, forthcoming; Pauwels, Ponsaers, & Svensson, 2009; Proctor & Niemeyer, 2019; Treiber, 2017; Wikström, 2006, 2017; Wikström & Treiber, 2013), which is epitomised by Situational Action Theory (Wikström, 2004, 2010, 2019).

 ummary: Advocating an Analytic Criminology S of Person-­Environment Interaction in Acts of Crime for Effective Prevention To explain action is the most effective way to understand how we can change behaviour. Explaining acts of crime is crucial to knowledge about causal mechanisms that must underpin the most effective approach to crime prevention. To explain crime, we cannot continue to study contextual effects on spatial crime concentrations and individual developmental effects on individuals’ criminality in isolation. We must integrate insights from these dichotomised traditions in an explanation of acts of crime. This chapter identifies and evaluates two existing approaches to integrating people and places in criminology. These approaches are defined by the worldview and

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conception of interaction that frames them. To explain action, we must address causal questions about person-environment interaction in acts of crime. Such causal questions are not asked by an additive approach but are fundamental to the interactive approach. Crucially, we cannot integrate individual- and environment-­oriented approaches by summing up risk factors because this only addresses questions that allow us to predict effects. Such an additive approach does not allow us to explain how features of people and places interact to cause acts of crime. Disciplines such as criminology experience trends in terms of the dominant research approaches of the era. Influential theories and research approaches are developed and generate research interest in response to their academic and research context. By taking this context into account when reading back through historical developments in the field, we can observe that new theories and approaches are often strong reactions to the current status quo, and they often direct the attention of the field in an opposing direction. In criminology, these changes have been swift and the switches in direction can be extreme. These developments are important to allow a relatively young discipline to explore all its relevant facets. However, as the discipline matures, integration of these facets is necessary to allow the pendulum to settle. As advocated by analytic criminology (Hardie, 2017, pp.  49–54; Pauwels et  al., 2009; Proctor & Niemeyer, 2019; Treiber, 2017; Wikström, 2006, 2017; Wikström & Treiber, 2013), strong integrative theory becomes crucial, swiftly followed by rigorous empirical testing using appropriate data collection and analysis methods. In a very basic way, Table  1.3 illustrates how some research traditions fundamentally relate to each other by pulling together the discussions in this chapter about the level of analysis, worldview, and research approach. This volume aims to bridge the gap between strong theory and the appropriate methods for empirical testing. Chapter 2 argues that to study person-environment interaction in order to explain crime, we need an integrative model of action that specifies precisely how the criminogenic features of individuals and environments interact to result in an act of crime. Chapter 2 goes on to argue that Situational Action Theory (SAT) provides such a model. A growing interest in SAT and empirical tests of its integrative hypotheses (Hirtenlehner & Reinecke, 2018; Pauwels, Svensson, & Hirtenlehner, 2018) is testament to a developing appreciation of the need for interactive integration of people- and place-oriented explanations of crime.12 The rest of the volume is therefore dedicated to providing guidance to those wishing to empirically test situational hypotheses such as those of SAT. Chapter 2 concludes by outlining the implications of SAT for the kinds of new methods that are required to study situational interaction. One major challenge to the study of situational interaction is the difficulty of collecting appropriate data, so Chap. 3 collates and evaluates some methods for analysing other more common kinds of data in order that analysis of such data can still contribute to knowledge about person-­ environment interaction in crime. Chapter 4 specifies in detail the appropriate data  Not all parts of SAT’s situational model have received equal empirical attention. For example, the role of motivation in the situational process is particularly under-researched (for discussion, see Barton-Crosby, 2018; Barton-Crosby & Hirtenlehner, 2020).

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Table 1.3  Classification of some research types and traditions by approach and unit of analysis Unit of analysis (level) AGGREGATE of crime (individual or environment) ACT of crime (event)

Worldview Additive (dichotomised) Environmental criminology Developmental criminology Situational Crime Prevention Routine Activity Theory Self-Control Theory Unstructured Socialising Theory

Interactive (integrated) Dependence (Chap. 3) Convergence (Chap. 4)

collection and data analysis methods, considering also practical challenges to scientific precision. This chapter addressed how and why we should integrate person and environment in the study of acts of crime, and the approach and methods described in the rest of this volume provide guidance for those wishing to take this empirical approach.

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Chapter 2

Integrating Individuals and Environments: A Situational Approach to Studying Action

Abstract  This chapter describes how adequate study of person-environment interaction requires an integrative model of action that explains precisely how the criminogenic features of individuals and environments interact to result in crime. It presents the situational model of Situational Action Theory (SAT) as the only suitable model of acts of crime. The SAT framework has implications for an integrative analytical approach to data collection and analysis that places situational interaction at the heart of research into the causes of behaviour. In outlining these implications, this chapter clarifies the inter-related concepts of interaction and situation; distinguishes dependence from convergence in order to avoid the ecological fallacy; and defines setting, environment, and situation as distinct concepts to improve empirical testing and interpretation of theory and findings. In order to provide guidance to those wishing to empirically test the situational hypotheses of SAT, the chapter concludes by stipulating the implications of SAT for the family of methodological procedures that are appropriate for the study of situational interaction in acts of crime. Primarily, this involves the collection of event-level situational data that captures the convergence of people in environments.

Lacking an Integrative Model of Action Chapter 1 of this volume justified the need to consider both individuals and environments in the study of acts of crime, as opposed to studying aggregates of crime in either individuals or environments. It went on to detail why an interactive, rather than an additive, worldview is essential to underpin research that is best able to inform effective crime prevention policy and practice. The idea of person-­environment interaction is not new to theories of human action. In 1936, Kurt Lewin’s influential ‘Principles of Topological Psychology’ represented a departure from traditional explanations of behaviour that were rooted in the past, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 B. Hardie, Studying Situational Interaction, SpringerBriefs in Criminology, https://doi.org/10.1007/978-3-030-46194-2_2

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giving a new importance to a person’s momentary interaction with the features and circumstances of their environment (Wolf, 1986).1 Lewin very clearly specified that a situation emerges from the momentary person-environment interaction and that this interaction explains action: If one represents behaviour…by B and the whole situation including the person by S, then B may be treated as a function of S: B = f(S). (Lewin, 1936, p. 12)

Whilst this recognition of person-environment interaction in human behaviour is an important founding principle, it cannot alone guide successful research in this topic. This chapter explains the importance of a highly specified integrative model for the development of concepts and research methods that are appropriate for the study of person-environment interaction in behaviour (such as acts of crime). Building on Lewinian principles, ecological discussions of person-environment relations boomed in the late 1960s (Jordan, 1972; Mischel, 1977). Lewin’s theory particularly resonated with some personality researchers who were reacting against fragmentation in their field. Interactional psychology was a collective response to extreme polarisation of approaches in behavioural psychology, and it aimed to scientifically study the complex interplay of persons and environments in determining behaviour (Ekehammar, 1974; Magnusson & Endler, 1977a; Mischel, 2004). By the late 1970s, interactional psychology had challenged the divisions in the field and put the study of personality ‘at the crossroads’ (Magnusson & Endler, 1977b).2 However, various theoretical, methodological, or empirical attempts to integrate individuals and environments have typically struggled to adequately explain behaviour. This chapter argues that this is because they lack a theoretical model of person-­ environment interaction. Whilst they drew attention to the importance of person-environment interaction for human behaviour, Lewin (1936) and others (e.g. Barker, 1968; Kantor, 1924; Murray, 1938) did not provide an adequate ecological model of action. They failed to sufficiently specify the details of both the situational processes (mechanisms) involved in the relationship between individuals and their environment and how that interaction results in a particular behavioural outcome. Barker himself acknowledged that without a model of person-environment interaction ‘we have to be content with probabilistic predictions across the person-environment boundary on the basis of empirical correlations’ (Barker, 1968, p. 159). Without an adequate ecological model to guide the research to explain rather than predict, interactional psychology and other integrative projects have ultimately failed to explicate and evidence the specifics of how person-environment interaction occurs (Ekehammar, 1  For a discussion of Lewinian theory (later known as Field Theory) and its considerable impact, see Burnes and Cooke (2013); Ekehammar (1974); Jones (1985); Kihlstrom (2013); and Stivers and Wheelan (1986). 2  Concurrently, Urie Bronfenbrenner was reacting against similar divisions in developmental psychology, which ignored the importance of developmental contexts. His ecological systems theory (Bronfenbrenner, 1979) and later bioecological model (Bronfenbrenner, 2005) stress the importance of individual-environment interaction for explaining human development. This volume is focused on behaviour (action) and therefore will not discuss this work further.

Lacking an Integrative Model of Action

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1974; Hardie, 2017, pp.  67–73; Jordan, 1972; Kihlstrom, 2013; Magnusson & Endler, 1977b; Mischel, 1977, 2004). Psychologists embarking on new interaction research took the traditional approach of their discipline. In traditional psychology, the person is the level of data collection and analysis, and the discipline traditionally has an additive worldview and resultant research approach. The additive paradigm demands independent data about components, such as questionnaire or psychometric data about individuals (or in the case of environments, systematic social observation, or ecometric data about locations and areas) (Chap. 1). Statistical models developed within the additive paradigm understand events as functions of such independent elements. Such regression methods were often developed within psychology, and they dominate the discipline. A well-specified integrative ecological model of action would have provided interactional psychologists with testable implications that would not have been satisfied by traditional additive methods and individual-level data. Researchers would have been incentivised to develop new methods to collect integrative data at the level of actions and develop a new analytical approach (e.g. Chap. 4).3 However, without such guidance, the interactional psychologists relied on the research methods they knew. This means they applied additive methods to the study of interactive questions, all at the level of the person rather than the act. In practice, measures of exposure to features of environments are captured at the individual level (i.e. ‘individual exposure data’; see below); then additive regression methods traditionally compare the magnitude of the person-exposure multiplicative interaction term variance to the magnitude of the main effects of the component parts (Chap. 3). Thus, in traditional studies of person-environment ‘interaction’, individual and environmental risk factors are treated as separate components by regression models; thus, this work is not equivalent to the study of person-environment ‘interaction’ as defined by Lewin. Furthermore, the unit of research is traditionally the person and not the action (event/behaviour). In these two ways, the interactional psychology research methods were mismatched to the research question they aimed to address. At the heart of this mismatch are different conceptions of ‘interaction’.4 Lewin’s (1936) concept of ‘interaction’ is inherently interactive and refers to mutual 3  This missed opportunity in interactional psychology research was exacerbated by the poor conceptualisation of the role of perception and the associated conflation of the concepts of environment and situation (see appendix). 4  Various meanings of the term ‘interaction’ have been (often implicitly) applied when theorising about or empirically studying individual-environment interaction (e.g. Barker, 1968; Ekehammar, 1974; Kantor, 1924; Lewin, 1936; Mischel, 1977; Overton & Reese, 1973; Wikström, Oberwittler, Treiber, & Hardie, 2012; Wikström & Treiber, 2016). Olweus (1977) classified these meanings into four kinds. The first meaning refers to a general sense in which individuals and environments ‘combine’ or ‘connect’ in evoking behaviour, but does not specify the nature of this interaction. The second meaning is reciprocal action, also known as ‘transaction’ or ‘dynamic interaction’, and this relates more to ongoing and even long-term processes of change and development. These first two meanings of ‘interaction’ are not relevant for the discussion in this volume due to the (respectively) general and specific sense of them. Olweus (1977) states that the third and fourth meanings

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interdependency (i.e. inseparability of individual and environment). I term this situational interaction. Another conception of ‘interaction’ is grounded in an additive worldview and is concerned with the relative contribution of independent elements to an outcome, as assessed by regression models. This kind of interaction can therefore be called statistical interaction. The particular conception of interaction that underpins the additive approach (statistical interaction) is incompatible with that implicated in the interactive approach (situational interaction) (Olweus, 1977). However, these two conceptions of ‘interaction’ are often conflated in the criminological literature when attempts are made to interactively integrate individual and environmental approaches in the study of acts of crime, using methods and procedures that originate in the additive tradition. This mismatch has resulted in the application of inappropriate data collection and analysis methods to the study of person-environment interaction in crime causation. The additive approach amounts to the study of statistical dependency between independent measures of features of environments and persons. This dependency effect is assessed at the level of people or environments which is mismatched to the level of the research question (i.e. behaviour; acts of crime).

Studying Dependency Statistical interaction captures a dependency effect. Statistical dependence is a particular kind of relation between features of units under study (Wermuth & Cox, 2005). Most research that aims to study person-environment interaction in action outcomes actually studies dependency in acts that have been aggregated. In such criminological studies of dependency, the units under study are often people, though in environmental studies of crime the units under study are areas (see Chap. 3 for examples). In interaction studies, statistical dependency means that the effect of one independent factor on crime depends on the magnitude of another (Ai & Norton, 2003; Friedrich, 1982). For example, when the units under study are persons, person-­environment dependency (as evidenced by statistical interaction) means that the effect of the selected features of the person on their aggregate of crimes (e.g. frequency) is dependent on the magnitude of their exposure to selected features of places (and vice versa5). Such dependency affords statements about individuals and the differential effect of exposure on their aggregated actions (e.g. criminality).6 of ‘interaction’ are incompatible due to the irreconcilable differences between the interactive or additive worldview in which they are rooted and the structure of the data that they demand. I term these kinds of interaction ‘situational’ and ‘statistical’, and it is these two conceptions of ‘interaction’ that are discussed in this volume. 5  Statistical interaction is two-way; neither factor is the moderator (Chap. 3). 6  Or, when environments are the units under study, person-environment dependency affords statements about the role of the exposure of particular kinds of environments to particular kinds of individuals in crime hotspots (which are aggregations of acts of crime at the environment level).

Studying Dependency

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Such statements may be consistent with individual-level effects that would be expected as a result of particular interactive processes (such as those proposed by the situational model of SAT); therefore Chap. 3 of this volume reviews various methods for assessing person-environment statistical interaction (dependence) in independent individual-level data. However, evidence of statistical interaction in aggregated data requires an assumption of co-occurrence at the individual or environment component level, in order to draw concrete conclusions about the process of person-environment interaction and its effect on action. This assumption of co-occurrence is an example of an ecological fallacy. An ecological fallacy occurs when inferences about the nature of units are deduced from inferences about the group to which those units belong. The term was coined by Selvin (1958), but refers to a paradox that was demonstrated in a seminal paper by Robinson (1950), whose observations led him to conclude that ‘an ecological correlation is almost certainly not equal to its corresponding individual correlation’ (1950, p. 357). Just as the ecological fallacy is inherent in analyses which interpret regressions on environment or geographic aggregates as predictive relations on the level of individuals, so the ecological fallacy can equally occur when inferences about the outcome of situations (person-environment convergences) are deduced from inferences about the person or environment which experienced those situations. Thus, interpreting regressions on exposure data collected or aggregated to the individual  or area in order to draw conclusions at the level of person-environment interactions represents a fallacy. For example, Wikström et al. (2012) acknowledge that statistical interactions revealed in data captured at or aggregated to the individual level ‘do not demonstrate that a particular person (with a particular crime propensity) is actually in a particular setting (with particular criminogenic features) when he or she commits an act of crime’ (p. 407; emphasis added). Regressions on, and statistical dependence between, individual or environment level aggregates are not truly predictive of relations on the level of situations (person-environment interactions).7 Furthermore, the ecological fallacy is a particular problem for the study of person-­environment interaction. Hammond (1973) built on Robinson’s (1950) arguments to establish that some ecological inferences are valid, but showed that due to aggregation bias, the ecological fallacy is a particular problem when the independent variable has a contextual effect. Since contextual effects are the object of study and therefore absolutely to be expected in studies of the effects of person-­ environment interaction on behaviour such as acts of crime, ecological inferences are entirely inappropriate for these kinds of studies. Thus, findings from analysis of non-situational data may actually obscure evidence of situational processes. For example, individual-level data relating to a particular individual who offends frequently but who is only sometimes exposed to a particular setting would imply a weak setting effect on crime outcome; yet situational exposure data about time

7  Statistical dependency between features captured at the level of situations is less problematic for evidencing situational-level processes (see Chap. 4).

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spent by that same individual may instead clarify and strengthen the setting effect by determining that actually the individual only offends when they are exposed to that particular setting, even though this exposure is rare. There is an even more fundamental reason why the study of dependence in this manner is not fully appropriate for understanding person-environment interaction in acts of crime. Statistical dependence implies only unidirectional causality and linearity rather than a causal interaction effect (Edwards, 2009; Haar & Wikström, 2010) and is unable to help us answer why questions (Ekehammar, 1974; Magnusson & Endler, 1977b; Mischel, 1973; Overton, 1973; Overton & Reese, 1973). Statistical interaction can be difficult to interpret (Chap. 3), and discovery of dependency immediately raises questions about the processes that explain the statistical relationships between variables, thus making substantive theory important for the study of interaction (Olweus, 1977). A guiding theoretical framework provides a system of plausible mechanisms (processes) by which cause and effect are linked. Studying these usually unobservable mechanisms requires evidence that is consistent with their existence; they are not factors to be entered into a statistical analysis. Thus, a theoretical model that specifies the mechanism that explicates person-environment interaction in acts of crime is essential for addressing causal questions of how and why (see further Chap. 1; Bunge, 2006).

Integrative Models of Acts of Crime Half a century ago, Lloyd Ohlin wrote a US government pamphlet entitled ‘A situational approach to delinquency prevention’ in which he stated that to better direct prevention programmes, criminologists ‘need a systematic way to relate the wide variety of factors and conditions which produce delinquent events’ (Ohlin, 1970, p. 1). Eschewing a multiple factor approach, Ohlin advocated ‘some type of field theory approach to order the concepts and findings from studies on the personal, social, cultural, and economic dimensions of the circumstances surrounding delinquent acts’ (Ohlin, 1970, p. 1). The pamphlet does not reference Lewin by name, but ‘field theory’ refers to the Lewinian Gestalt perspective that states that individuals as social actors embedded in their immediate social environments (i.e. situations) are the root of behaviour (e.g. Lewin, 1936). Ohlin’s recommendation extends to suggest that a ‘situational schema for understanding delinquent acts… is the most fruitful approach’ to delinquency prevention (Ohlin, 1970, p. 1), with the implication that such an interactive theoretical framework (i.e. model) would organise the many and varied risk factors for crime and allow ordered study of person-­ environment interaction in crime causation.8 Unfortunately, Ohlin’s specific suggestion did not ignite the discipline to develop and test integrative models of acts of

8  Gibbons (1971) also advocates an integrative model for the study of crime causation, but the outline he provides belies an additive approach.

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crime. His pamphlet has been referenced only a handful of times, each time back in the 1970s and early 1980s,9 and he is chiefly remembered for other works and innovations (Fox, 2009).10 Criminology still does not generally acknowledge the central importance of person-environment interaction for explaining acts of crime. Few have taken up the challenge of specifying an integrative model of action that details the mechanisms by which they interact (Bunge, 2006; Wikström, 2019a; Wikström et al., 2012; Wikström & Sampson, 2006). Wikström argued that his Situational Action Theory (SAT) provided the first substantively specified integration of individual and environmental theories of crime (Wikström, 2005) and recently stated that ‘few main criminological theories outside Situational Action Theory take the role of the person-environment interaction seriously in their analyses of crime causation’ (Wikström, 2019a, p. 265). Criminological theories that do attempt some integration of personal and environmental factors very rarely detail how their interplay affects acts of crime. SAT is one exception and is the focus of this volume. There follows discussion of one other exception11 and one relevant theory developed within the sociological discipline. One notable example of a theory that attempts to provide a model by which to integrate psychological and sociological explanations of crime is the Social Schematic Theory of crime (SST; Simons & Burt, 2011; Simons, Burt, Barr, Lei, & Stewart, 2014). SST is conceptually less specific than SAT. SST’s concept of ‘criminogenic situational definitions’ conflates the concepts of motivation, perception, and choice, which are conceptually delineated clearly by SAT.  The measure of ‘criminogenic situational definitions’ used by Simons et al. captures both opportunity present in the setting and the individual’s perception of action alternatives, but cannot separate them, e.g. ‘When you are out and about, how often do you encounter situations where you become aware that there is an opportunity to help yourself at some sucker’s expense?’ (2014, p. 670). A further example of SST not making distinctions that are clear in SAT is that the ‘criminogenic schema’ component of SST does not conceptually separate the constituent elements of poor self-control (‘immediate gratification’) and weak morality (‘disengagement from conventional norms’) (Simons et al., 2014, p. 669). In contrast, the specific, distinct, and even interacting roles of these individual features have been demonstrated in empirical studies that explicitly test or evidence SAT (Antonaccio & Tittle, 2008; Hirtenlehner 9  Interestingly, initial presentations of the Situational Crime Prevention approach (SCP; Clarke, 1980, 1983; Mayhew, Clarke, Sturman, & Hough, 1976) referenced Ohlin’s pamphlet and similarly argued that an adequate theory of crime needed to include individual development and historical factors, but also circumstances and features of environments that are proximal in time and space to the crime event. Unfortunately, SCP didn’t go on to develop a model and also the approach became increasingly focused on environmental factors and neglected individual differences (see further, Chap. 1 and appendix to this chapter; also Bouhana, 2013). 10  Though see Steffensmeier and Ulmer (2015), who lament the ‘roads not taken’ in criminology, including the opportunity to build on elements of Ohlin’s work with Richard Cloward. 11  Other notable work in this regard includes Thomas (2019); McGloin, Sullivan, and Kennedy (2011); and Schulz (2016), though the concepts and mechanisms are less well specified and certainly less comprehensively tested than SAT.

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& Kunz, 2016; Ivert, Andersson, Svensson, Pauwels, & Torstensson Levander, 2018; Pauwels, 2018; Svensson, Pauwels, & Weerman, 2010; Wikström et al., 2012; Wikström & Svensson, 2010). Further, according to SST, these ‘criminogenic schemas’ include ‘hostile views of relationships’ (Simons et al., 2014, p. 669), which is also a distinct element of SAT’s conception of the action process (Barton-Crosby, 2018; Barton-Crosby & Hirtenlehner, 2020). Additionally, compared to SST, empirical evidence for SAT provides a more convincing and specific account of the (conditional) relevance of both internal and external controls (self-control and deterrence) (e.g. Hirtenlehner, 2019; Hirtenlehner & Hardie, 2016; Hirtenlehner, Pauwels, & Mesko, 2014; Kroneberg, Heintze, & Mehlkop, 2010; Schepers & Reinecke, 2018; Svensson, 2013; Svensson et  al., 2010; Wikström, 2008; Wikström & Svensson, 2010; Wikström & Treiber, 2007; Wikström, Tseloni, & Karlis, 2011; Zimmerman, Botchkovar, Antonaccio, & Hughes, 2015). Outside criminology, the well-specified Model of Frame Selection (MFS; Esser, 2009; Esser & Kroneberg, 2015; Kroneberg, 2014) is an integrative model of action that has been applied to acts of crime and moral rule-breaking (Beier, 2018; Kroneberg et al., 2010). Although there is no specific comparison of these recent theories in the literature, Treiber (2017a) does discuss MFS’s ‘script selection’ with reference to SAT’s model of criminal decision-making. Ultimately, MFS and SAT have generally very similar implications for many research questions and have even been treated as a single theoretical framework (e.g. Beier, 2018). The differences between MFS and SAT may be drawn upon usefully when working within either framework. SAT has been described as a theory that is simultaneously broad and deep: ‘It is broad in the sense that it successfully integrates person-oriented and environment-­ oriented explanations of criminal conduct. It is deep in the sense that it illuminates the mechanism that brings about criminal behaviour’ (Hirtenlehner & Reinecke, 2018, p. 3). Crucially in terms of its suitability as a guiding theoretical framework, SAT is arguably currently more elaborately specified than any other theory that attempts to integrate individual and environmental explanations of crime. Probably as a result of its clear and testable implications, SAT has been more extensively applied and empirically tested within criminology than MFS and SST and has also been tested in a wider range of contexts and nations, using more innovative methodology (Hirtenlehner & Reinecke, 2018; Pauwels, Svensson, & Hirtenlehner, 2018; Wikström, 2014).

Situational Action Theory and the Situational Model Wikström’s SAT shares Lewin’s conceptions of situation and interaction. A crucial proposition of SAT is that characteristics of individuals and features of settings alone (or in sum) are not sufficient to cause action (see also Bunge, 2006); it is only through their interaction that they are activated. SAT’s PEA hypothesis stresses that personenvironment interaction (mutual dependency) is inherent in situations, and it is

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particular situations that give rise to particular acts (such as acts of crime). The PEA hypothesis (Person x Environment = Action) gives rise to SAT’s situational model of action. Where Lewin did not specify a model of situational interaction, the situational model of SAT provides a precise, empirically testable model that proposes the mechanisms by which personal and environmental factors interact to determine action in response to motivations. The situational model also stresses the importance of features of persons and settings for the content of situations (Fig. 2.1).12 SAT’s situational model explains why crime events happen. It states that the particular combination of an individual (and their traits and state) and their immediate environment (and its characteristics and state) give rise to particular motivations and perceptions of action alternatives (including crime), from among which individuals choose and form an intention to act (Treiber, 2017b; Wikström, 2006, 2014, 2017, 2019a; Wikström & Treiber, 2016). Thus, the stated causal mechanism for crime is the two-step perception-choice process. Wikström writes that ‘what connects a person to the immediate environment is perception (the information we get from our senses) and what connects a person to their action is choices (our intentions to act in one way or another)’ (2019a, p. 265). SAT’s situational model thusly specifies the action mechanism that integrates person and environment.

‘Situation’, ‘Environment’, and ‘Setting’ Situations, environments, settings, and contexts are not conceptually the same. The fundamental implications of the differences between these concepts for the kind of theory, measurement, and analysis that is appropriate for their study are key to this volume. SAT is precise and clear in delineating these concepts (see Fig. 2.1). Within the framework of SAT, the environment is defined as all that is external to human Fig. 2.1  Person, setting, and situation in SAT

PERSON

SETTING

SITUATION Propensities

Motivation & Perception of action alternatives

Environmental Inducements

 The relative concepts depicted in Fig. 2.1 are also displayed in Wikström (2019a) and Wikström, Mann, and Hardie (2018).

12

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beings, where the social environment includes social relations and events and the physical environment consists of non-human objects and their relations (Wikström, 2007). For SAT, a setting is defined as the features of the environment that at any given moment an individual can access with their senses and is therefore the part of the environment to which an individual is directly exposed and reacts (Wikström, 2006, 2010). Exposure is fundamental to and inherent in the concept of a setting (different kinds of measures of exposure are described below). This means that whilst an environment exists independently of actors, a setting is defined by the presence of an actor. Features of settings to which individuals are exposed and react include objects, relations, circumstances, and conditions which can include supervisors, time of day, and any media present. Features to which the person is exposed are the only features capable of influencing a persons’ actions and development (Wikström, 2006).13 Thus, as described above, SAT states that action results from situations which arise from person-environment interaction (PxE = A). This is in line with a Lewinian definition of a situation. Lewin’s (1936) eq. B = f(S) determined that behaviour (B) is a function of (f) situations (S), and the definition went on to specify that situations comprise individual persons (P) in environments (E), whilst individuals are distinct from environments. ‘In psychology one can begin to describe the whole situation by roughly distinguishing the person (P) and his environment (E)’ (Lewin, 1936, p.  12). Thus, the equivalent Lewinian equation is B = f(P,E). SAT’s situational model further specifies that it is the convergence of particular characteristics of individuals and particular features of settings that give rise to particular situations, which result in particular actions. The concepts of situation, environment, and setting are not clearly delineated in the interactional psychology work that built on Lewinian principles, and they are traditionally conflated in criminology. Most works in interactional psychology refer to person-situation interaction, rather than person-environment interaction as Lewin originally intended. For criminologists other than those working within the framework of SAT, situational factors are environmental features (external to individuals) that represent opportunities for crime (for examples, see appendix). The spatial and temporal element of such opportunities is what makes such features relevant, at or near the moment and location of crime events. This necessary proximity to the crime event is the reason why many perspectives and studies of crime events don’t use the word ‘environmental’ to describe features of environments that are relevant for acts of crime. Instead, they inaccurately apply the term ‘situational’ for this purpose in place of a more appropriate term, such as ‘setting’. The appendix of this chapter traces how and why these concepts came to be conflated in these disciplines, providing specific examples of inappropriate terminology usage. These examples contextualise the importance of using appropriate terminology in the study of person-­environment interaction, which is described below.  SAT defines a place as a geographic location and its immediate environment, which is sometimes used interchangeably with the more specific concept of setting (Wikström, 2019a, p. 266). A geographical location is distinct from a ‘functional place’, such as a school, house, shop, or park (Wikström et al., 2012, p. 73).

13

Integrative Models of Acts of Crime

33

Whilst features of environments are linked to a crime event (act) by being proximal in time and space, they are also simultaneously linked to the offender (actor) because the actor is experiencing those features at the time/place of their act. It is this mutual dependency of an actor and the environment in which an act takes place that most criminological perspectives neglect. Mutual dependency may not always be explicitly neglected in initial presentations, but is usually implicitly neglected in the specifics of definitions, concepts, methods, emphasis, and therefore later work founded in these perspectives (for specific examples, see appendix). In particular and crucially, even when they acknowledge the presence of an actor, and maybe even that such actors vary, theories and perspectives neglect the nature and content of the individuals’ characteristics and features. Therefore they also neglect how those characteristics impact upon the individual’s perception of and response to the parts of the environment (and its features and characteristics) to which they are exposed. The lack of a model that addresses such how questions is therefore at the root of the neglect of the content (specific features) of people in environmental explanations of crime. This neglect means that when most criminologists refer to situational features, they mean features only of environments, specifically features of environments that are relevant at or near the moment and location of crime.14 Perception is the cognitive representation formed by an individual in response to their environment. As such, processes of perception are themselves interactive processes. As recognised by SAT’s situational model of action, differential perception of circumstances and features of environments is crucial to motivation and the action process. It is the process of perception that links a person (and their features) to the features of their environment (Wikström, 2019b). Perception is a process that is under-specified, neglected, or conflated in most theories of human behaviour, particularly in criminology (see appendix), but is central to Situational Action Theory. So, situations always comprise a person interacting with an environment. An environment still exists when a person leaves it, whereas a situation is dependent on both the environment and the individual exposed to it (perceiving it, experiencing it, responding to it, and acting in it). Hence, ‘the same environment represents different situations for different people’ (Wikström & Treiber, 2013, p. 323). The process of perception and concept of exposure are thus crucial to person-environment interaction. The opportunity perspective or psychological perspectives have no adequate model with which to address perception and exposure. As a result, researchers in these traditions conflate concepts and imprecisely refer to (features of) situations when they mean (features of) environments to which individuals are exposed that are perceived and responded to by actors (see appendix). Thus in criminology, ‘situation’ is widely used to refer only to the environment in which a person acts,  The reverse is also true of psychological explanations of crime. Whilst interactional psychology acknowledged the importance of environments for action, there was no model for specifically how particular features of individuals and particular features of environments interact to result in particular actions. Studies were therefore able to neglect the particular features of environments and instead focused on the differential response of individuals with particular features to environments in general.

14

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irrespective of differential perception and therefore differential effect of environmental features and circumstances on different individuals and their actions (see also Wikström & Treiber, 2013, 2016). The Importance of Conceptual Clarity This conflation of concepts leads to misinterpretations of integrated theory and innovative research. Those who wish to critically evaluate theories that take an integrative approach should consider that criminologists have developed as academic theorists and researchers in the context of our deeply fragmented discipline, or its component fields. Whilst it is of course acceptable for theories to take a different approach and define concepts in different ways, evaluation must be carried out within those stated definitions and that approach or otherwise must address the approach or definition directly. However, the developmental paths of individual criminologists mean that integrative theory is very often interpreted, evaluated, and even empirically tested from within the additive worldview of either of the estranged sociological or psychological domains, which is outside the situational approach of the interactive theory being evaluated. For example, reviews of theoretical approaches in criminology are fragmented such that Wilcox and Cullen (2018) review opportunity theories and Britt and Rocque (2016) review control theories, yet neither review correctly or adequately characterises Situational Action Theory. This mismatch between the approach of the researcher and that of the reviewer is also a problem for evaluation of empirical studies. Empirical situational research is often reviewed and evaluated from within the conceptual constraints of the researcher’s own perspective. As a result, innovative situational research is often erroneously dismissed as ‘old news’ by those whose own concept of situation is conflated. For example, Wilcox and Cullen (2018) conceptually equate environments, settings, and situations, which leads them to overlook key distinctions made by SAT and erroneously equate research into the effects of features of environments (as opportunities) with research into the interaction of individuals and environments in acts of crime. In another example, Trinidad, Vozmediano, and San-Juan (2018) review ‘environmental factors’ in their appraisal of youth crime research in the Routine Activity, Rational Choice, Crime Pattern Theory, and SAT traditions. However, the reviewers conflate the terms ‘environmental’ and ‘situational’ and, relatedly, fundamentally misunderstand SAT. This means that this review combines the opportunity perspectives with SAT all together as ‘situational perspectives’, thus disregarding crucial differences in the empirical tests and research findings between the opportunity approach and SAT. Other reviews misunderstand and misrepresent methodological advances due to the conceptual ambiguity inherent in the perspective of those conducting the review. The Space-Time Budget data collection method was primarily developed to test integrative propositions by collecting ‘situational data’ (see Chap. 4). However, Hardie and Wikström (in press) note that reviews of the method by those working within the opportunity paradigm (e.g. by Hoeben, Bernasco, Weerman, Pauwels, &

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van Halem, 2014; Van Halem, Hoeben, Bernasco, & Ter Bogt, 2016; Weerman, Hoeben, Bernasco, Pauwels, & Bruinsma, 2018) ignore the integrative situational nature of the data and thus the situational empirical tests it facilitates. True situational research (i.e. that which adequately captures and studies the outcome of person-­environment interaction) is less common than the use of the term ‘situational’ in research would suggest. Within a range of approaches there are large numbers of studies that purport to research situational effects when they actually study environmental and setting effects (see further, appendix).15 Although it may seem to some a trivial, semantic issue, the accuracy of the definition and conceptualisation of situations (and thus features and processes that are ‘situational’) is crucial to progress in understanding crime events, particularly in the study of person-environment interaction. To protect against continued fragmentation, the approach of appropriate empirical tests and appraisal of integrative theory and methods must originate within the same interactive worldview as the theory, including the theory’s resultant stated concepts and definitions.16 There is existing adequate terminology for the features of environments that are contiguous with actions and events, for example, ‘settings’. The terminology of ‘situations’ should be reserved for referring to that which arises from the particular content and features of person-environment interaction. This appropriate use of terminology is important because what appear to be subtle conceptual differences have fundamental implications for the kinds of data and analysis required to study situational interaction and test integrative hypotheses such as those arising from the situational model of SAT (Chap. 4). New data collection and analysis methods are required to study the criminogenic convergence of people (with particular features) and environments (with particular features).

Studying Convergence: New Approach, New Methods Person-environment interaction is becoming increasingly studied in criminology (Pauwels et al., 2018) due to a burgeoning analytic approach to the study of crime (Pauwels, Ponsaers, & Svensson, 2009; Proctor & Niemeyer, 2019; Wikström, 2006, 2017; Wikström & Treiber, 2013) which is epitomised by the highly specified theoretical framework of Situational Action Theory. Fully appropriate evidence of person-environment interaction in acts of crime, such as tests of hypotheses arising from SAT’s situational model of action, requires an interactive approach to both the collection and analysis of data. Each worldview determines a particular ‘family of theories’ and a particular ‘family of procedures’ (Overton & Reese, 1973). The study of person-environment interaction dictates a  Thomas (2019) uses situational terminology, but his study includes both features of environments and truly situational features. 16  Critical evaluation of the worldview itself is a different matter that cannot be decided by empirical testing (Chap. 1; Overton, 1973). 15

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particular research approach, research design, data collection, analytical method, and interpretation of results, as determined by the interactive worldview. Building on Table 1.2 in Chap. 1, these are summarised and contrasted with alternative procedures in Table 2.1. As recognised by Overton and Reese, ‘researchers who look for interactions between higher-order mental processes and environment will

Table 2.1  Integrating people and places: worldview implications for theory, method, and findings Worldview Additive THEORY; APPROACH Causality Unidirectional, chainlike Questions ‘What’, ‘which’, ‘how much’ Concept of Additive integration Cumulative risk factors Example approach to integration Meaning of Of the environment, particularly in ‘situational’ relation to opportunity, contiguous with event DATA COLLECTION Data level Individual OR environment

Interactive Dependent, interactional ‘How’, ‘why’ Interactive, situational SAT’s situational model

Arising from the convergence of (features of) a person and an environment

Situation (person-environment convergence; individual IN environment) Independent measures of (i) features, Spatio-temporally linked (dependent) Measures collected at that (ii) generalised exposure to features of measure of individual IN environment PLUS resultant (spatio-temporally the other factor, (iii) aggregated level linked) behavioural outcome behavioural outcome Exposure type ‘Individual-level exposure data’ OR ‘Situation-level exposure data’ ‘environment-level exposure data’ EMPIRICAL ANALYSIS AND FINDINGS Individual OR environment Situation (person-environment Level of convergence; individual IN analysis and environment) conclusion Are the effects of independent person Do rates or probabilities of the act of Analytical interest vary by the convergent and environment factors on the research (person-environment) conditions of aggregated behavioural outcome of question situations? interest dependent on each other? Meaning of Statistical interaction. Situational interaction. ‘interaction’ Dependency Inseparability of individual and environment; convergence Functions Moderating variables Causal mechanisms evidenced Evidence Statistical dependence Situational convergence POTENTIAL CONTRIBUTION To knowledge Prediction Explanation To behaviour-­ Not causal Causal change policy

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seldom use the same research approach as those who look for nothing more complex than covert mediators’ (1973, p. 80). The interactive approach to the study of crime events does not deconstruct situations into independent component parts, but is concerned instead with the analysis of the convergence of (features of) individuals and (features of) the environments and settings they are in that brings about an act of crime (Wikström, Ceccato, Hardie, & Treiber, 2010; Wikström et al., 2018; Wikström & Treiber, 2016). Thus crucially, data collection and analysis must operate at the level of situations, whereby a data unit represents a person-environment convergence and behavioural outcome (Table 2.1).17

Exposure Exposure is a crucial concept in person-environment interaction research. Exposure refers to the convergence of a person (and their circumstances and characteristics) and an environment (and its circumstances and characteristics) in time and space. Thus, settings, which are the parts of the environment which individuals perceive and experience (including circumstances, people present, social relations, and events as well as non-human objects and relations), are crucial to SAT. A test of an integrative model of action requires data on the features and circumstances of people, and the features and circumstances of places, a spatio-temporally relative measure of which people and places are exposed to each other, and a measure of the behavioural outcome.18 There are different levels of exposure data, not all of which meet these requirements and therefore they have different applicability for situational interaction research. Individual-Level Exposure Data When studying person-environment interaction, individual-level exposure data is used in studies of statistical dependency, such as those described in Chap. 3. In traditional individual-level analysis, data and analysis units are people. Thus, crime  It is usually not possible for all features to be measured at the situational level. It is important to address the assumptions involved when using data captured at different levels to represent features of situations. Assumptions regarding some features may be quite acceptable (e.g. relatively stable features such as an individual’s moral rules or the collective efficacy of small areas). It may be more problematic to accept these assumptions for features that are more situationally variable (e.g. parental knowledge, substance use, crime involvement of peers present). This issue is discussed further in Chap. 4. 18  More accurately stated, the study of situational interaction in action requires data that captures the convergent (criminogenic) features and behavioural outcome of a moment in space-time. However, we do not yet have methods that can capture all relevant features of people and places at the level of situations, nor may such methods ever be fully feasible (see further Chap. 4). 17

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outcome data are also person-level aggregates (e.g. officially recorded or self-­ reported frequency), and features of individuals are measured, for example, by means of self-report questionnaires, official records about a person, or psychometric assessment of individuals. Traditional individual-level analyses that address person-environment interaction require generalised individual-level exposure data that captures a total amount of a particular kind of exposure an individual experienced over a certain time period or geographical area. Individual-level exposure data is most commonly collected via generalised questionnaire items such as ‘how much time do you spend per week hanging out with your friends?’ or ‘how often do you spend time in the city centre’. These kinds of measures are often used by research in the opportunity-focused Routine Activity and Unstructured Socialising traditions (e.g. Agnew & Petersen, 1989; Hoeben, Osgood, Siennick, & Weerman, 2020; Osgood, Wilson, O’Malley, Bachman, & Johnston, 1996). Environment-Level Exposure Data In environment-level analysis of environment-level data, data and analysis units are places or geographical areas. Smaller areas are best for these analyses (Gil, 2019; Oberwittler & Wikström, 2009). Crime outcome measures are environment-level aggregations of crime (e.g. area crime rates and hotspots). Features and characteristics of the locations and places are captured using various methods, for example, official data (e.g. census, land use, police), community surveys, or systematic social observation. Previously only vague and sometimes implicit proxies for the people present in environments were used in environment-level research into spatial concentrations of crime, but some studies have recently started to include generalised environment-­ level measures of exposure to people. This is due in part to technological developments that have led to the generation of specific data on human movement that was previously difficult to collect in any meaningful specificity or quantity. For example, GPS-enabled mobile phone and social media data can provide measures of the numbers of people present at a specific location or small area during particular time periods, which can then be related to area crime rates (Felson & Boivin, 2015; Hanaoka, 2018; Malleson & Andresen, 2016; Sampson & Levy, 2020; Song et al., 2019). However, ‘big data’ of this kind is a by-product of human technology usage. It is generated automatically, as opposed to being designed and collected with a research purpose in mind (Hardie & Wikström, in press; Snaphaan & Hardyns, 2019). The many difficulties of using such data for study of situational interaction cannot be discussed here, but the most fundamental limitation is that it typically provides no content relating to the characteristics of the individuals to which the environments are exposed.

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Situation-Level Exposure Data Appropriate study of situational interaction requires situation-level exposure data which captures the behavioural outcome of the convergence of people in environments. Features of people and environments can be measured at the situation level, but more often they are represented by aggregates or generalised measures collected via traditional methods that are applied to the situations (see further Chap. 4). The individual- and environment-level data and act of crime (or other noncrime act) must all be spatio-temporally linked and measure which person (with particular traits and state) is exposed to which environment (with particular traits and state) at a particular time and location (Hardie & Wikström, in press; Wikström et al., 2010, 2018; Wikström & Treiber, 2016). It is also important to capture the behavioural outcome of different kinds of exposure (i.e. whether a crime results from criminogenic convergent conditions but also noncriminogenic convergent conditions), as well as to capture the convergent conditions of different kinds of behaviours (i.e. the convergent conditions of both acts of crime and other noncrime outcomes).19 The few methodologies for appropriately collecting such complex data are described in Chap. 4. A situation-level exposure data unit is the situation and thus captures the behavioural outcome of a particular person-environment interaction. This is the fundamental requirement for the appropriate study of situational interaction.20 In contrast to generalised individual- or environment-level exposure data, situation-level exposure data captures the convergence of people and places by recording either whether or not a particular person (with particular features) was exposed to particular environmental features at various specific (possibly continuous) points in time and space or whether or not a particular place or area (with particular features and conditions) was exposed to particular features of individuals at various specific (possibly continuous) points in time. Situational data can therefore be aggregated to people or places for the purposes of study at those levels (see further Chap. 4). Although there are many individual- or environment-level applications for situation-level data, this volume focuses on the use of situation-level data for the study of situational interaction in action.

 This is the principle that to study and understand the convergent conditions that lead to acts of crime, we must also study the convergent conditions that do not lead to acts of crime and lead to acts other than crime. This principle applies to levels other than situations, e.g. individual-level (to understand offenders, we must also study non-offenders) and environment-level (to understand crime hotspots, we must also study areas of low crime concentration). This is a similar principle to Maslow’s belief that we cannot understand mental illness until we understand mental health (Maslow, 1954). 20  Early interactionist psychologists argued that individual-environment interactions (and not individuals, as had become popular) should form the unit of analysis for psychology (Kantor, 1924; Murray, 1938). As described in this chapter, this was not realised in research that followed due to the continued permeation of an additive worldview in the development of research methods. 19

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Summary Traditionally, within psychology at least, studies of human behaviour have struggled to progress understanding of person-environment interaction. This chapter argues that this has been due to the lack of any model that provides a specified testable mechanism by which individuals and environments interact to result in action. There was a mismatch between the integrative nature of the research question and the additive nature of the traditional approach and methods by which it was addressed. Research attempted to study behaviour using measures and analysis at the level of individuals and not action. These problems ultimately limited the research. Attempts to empirically test a model of situational interaction would have made such problematic mismatches apparent and instead directed researchers to take an interactive approach to data collection and analysis in order to study behaviour at the level of person-environment interactions and resultant actions. Situational Action Theory (SAT) provides such a situational model of action. Tests of this model require an interactive approach to data collection and analysis. Situation-level exposure data facilitates situation-level analysis, so resultant empirical evidence is at the level of the situations in which actions take place. This allows researchers to evaluate situational mechanisms at the level of actions, which is essential for the explanation of acts of crime (see also Chap. 1 and Elster, 2007, p. 36; Sutherland & Cressey, 1970, p. 73). Ultimately, only evidence of convergence can test theories about the specifics of how particular features of individuals and environments interact to result in acts of crime (e.g. SAT’s model of situational interaction). Such empirical tests are crucial to knowledge about causal mechanisms, and knowledge about the causes of crime must underpin the most effective crime prevention policy (Chap. 1). Capturing the outcome of the (spatio-temporally linked) convergence of individuals in environments requires a particular kind of data. Such data requires specialist data collection methodology (e.g. Space-Time Budgets), alongside appropriate methods for analysing such data (Chap. 4). Not all studies of person-­ environment interaction are able to collect such complex and costly data. Therefore, regression analysis of dependence between person and environment factors in data that is not spatio-temporally linked has become increasingly common in criminology since the development of SAT, in order to test data for statistical interaction evidence in support of hypotheses about situational interaction in the process leading to acts of crime (see Table 2.1). Chapter 3 collates and evaluates methods for studying dependency when situational-level data is not available. The analyses of person-environment interaction in individual- or environment-­ level data (dependence) described in Chap. 3 cannot evidence person-environment interaction at the level of situations and their resultant actions (convergence). Robinson’s seminal paper highlighted a paradox but also noted that its effect (the ecological fallacy) was widely unrecognised in 1950 and that researchers regularly substituted individual for ecological correlations ‘tacitly rather than explicitly’ (Robinson, 1950, p. 352). Currently, seventy years on, studies of crime events rarely

Appendix: Clarity of Definitions and Concepts: ‘Situation’, ‘Environment’, and ‘Setting’

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explicitly acknowledge when the level of analysis does not match the event level of the mechanisms implied and conclusions drawn (i.e. that which uses individual- or environment- level data; see also, Wikström et al., 2018). To avoid the ecological fallacy, research must only draw or imply conclusions at the appropriate level or, at the very least, explicitly acknowledge when conclusions are drawn at a different level to that of the analysis (see further Chap. 4). Most ideally, research should aim to collect and analyse data at the appropriate level. Research into person-environment interaction should assess event-level data which captures the convergence of people in environments (situational-level exposure data).21

 ppendix: Clarity of Definitions and Concepts: ‘Situation’, A ‘Environment’, and ‘Setting’ This chapter discussed how situations, environments, settings, and contexts are conceptually different. The fundamental implications of the differences between these concepts for the kind of theory, measurement, and analysis that is appropriate for their study are key to this volume. The main body of this chapter discussed how the appropriate use of terminology is crucial to the study of person-environment interaction. By tracing how concepts came to be confused, and why, this appendix provides fertile additional context for this discussion.

Conceptual Ambiguity in Psychology Most works in interactional psychology, even those founded on and directly referring to Lewinian principles, refer to person-situation interaction rather than person-­ environment interaction as Lewin originally intended (e.g. Cooper & Withey, 2009; Kihlstrom, 2013; Magnusson & Endler, 1977a; Mischel, 2004). I propose that this shift in semantics in the interactionist literature was due to a conflated definition of ‘environment’. It was already long-recognised that a major challenge for the empirical study of person-environment interaction was to accurately and usefully describe environments (Ekehammar, 1974). Although presented from different perspectives in different times, key theorists of person-environment interaction all stated that an important way to classify environments was to distinguish between the physical environment and the psychological environment (e.g. Kantor, 1924; Koffka, 1935; Magnusson, 1971; Murray, 1938; Pervin, 1968), and this idea became central to the interactional literature (Ekehammar, 1974). By

 This volume focuses on situational analysis (of person-environment interaction); however, it is also the case that research into crime events that does not take person-environment interaction into account should also collect data and conduct analyses at the event level.

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naming an individual’s perception of their physical environment the ‘psychological environment’, the literature conflated the distinct concepts of perception and environment. Perception is a cognitive representation formed by an individual in response to their environment. Therefore, perceptual processes are themselves interactive. It was an important development that individuals’ differential perception of the physical environment came to the fore in interactional research. However, perception was implicitly understood as a feature of the environment and not a feature of the interaction itself. This is unfortunate and arguably held the discipline back, because, as recognised by SAT, differential perception of circumstances and features of environments is crucial to motivation and the action process. I argue that implicitly in the interactionist literature, ‘environment’ came to refer to all that is external to persons, whereas ‘situation’ came to refer to the ‘perceptual environment’, which was proximal in time and space to, and therefore most relevant for, action. This is evidenced by this statement by Magnusson and Endler about the focus of the interactionist model of personality being on ‘the ongoing multi-­ directional interaction between an individual and his or her environment, especially the situations in which behaviour occurs’ (1977a, p. 4). Explicitly however, interactional psychology presents the words environment, setting, and situation as being interchangeable (e.g. Argyle, 1977; Ekehammar, 1974; Kihlstrom, 2013; Magnusson & Endler, 1977a; Mischel, 2004; Olweus, 1977).

Conceptual Conflation in Criminology This conflation of ‘environment’ and ‘situation’ in interactional psychology is also evident in criminology. For example, in 1970 Sutherland and Cressey echoed the terminology of the then pioneering interactional psychologists by summarising that some criminologists conclude that ‘the immediate determinants of criminal behaviour lie in the person-situation complex’ (Sutherland & Cressey, 1970, p. 74). Clarke and Cornish observed that psychologists who were disenchanted with personality research (i.e. interactional psychologists) placed emphasis on situational cues and opportunities, and they explicitly stated that this ‘became a primary influence on British studies of situational crime prevention’ (1985, p. 158). For most criminologists, situational factors are usually environmental features (external to individuals) that represent opportunities for crime (Birkbeck & LaFree, 1993; Wikström & Treiber, 2016; Wilcox & Cullen, 2018). The spatial and temporal element of such opportunities is what makes such ‘situational’ features relevant, at or near the moment and location of crime (Briar & Piliavin, 1965; Gibbons, 1971; Sutherland, 1947), thus, in comparison to environmental factors, situational factors have long been defined as being more ‘immediately contiguous with the criminal event’ (Clarke & Cornish, 1983b, p.  46). This necessary proximity to the crime event is the reason why many perspectives and studies of crime events use the term ‘situational’ to describe features of environments that are relevant for acts of crime.

Appendix: Clarity of Definitions and Concepts: ‘Situation’, ‘Environment’, and ‘Setting’

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The main chapter text argues that most perspectives neglect this mutual dependency of (features of) the actor and (features of) the environment of an act. Whilst mutual dependency may not have been explicitly ignored in initial presentations of approaches and perspectives, it is often implicitly neglected in the specifics of definitions, concepts, methods, emphasis, and therefore later work founded in them. This neglect means that when most criminologists refer to situational features, they mean features of environments and, specifically, features of environments that are relevant at or near the moment and location of crime. There follows some discussion of how this came about.

‘Situation’ in Criminology: Historical Context of a Misnomer In the book that builds on the original work of the father of criminology Edwin Sutherland, Sutherland and Cressey refer to an ‘objective situation’ as being important to the extent that it provides an opportunity for crime (Sutherland & Cressey, 1970). The deliberate inclusion of the word ‘objective’ here negates differential perception by an actor (subjectivity); thus, Sutherland and Cressey are really referring to the environment being experienced by the actor (a setting, to use the SAT definition) rather than a situation. Interestingly, they further distinguish that ‘the situation is not exclusive of the person, for the situation which is important is the situation as defined by the person who is involved’ (Sutherland & Cressey, 1970, p. 74). Thus, Sutherland and Cressey’s ‘situation as defined by the person’ is closer to a Lewinian concept of situation and also nods to the relevance of perception.22 However, they go on to present an influential individual-focused ‘genetic explanation of criminal behaviour’ ‘on the assumption that a criminal act occurs when a situation appropriate for it, as defined by the person, is present’ (p75). This starting-­ point assumption subsumes all consideration of perception, thus undermining their prior distinction between ‘objective situations’ (i.e. settings according to SAT) and the ‘situation as defined by the person’ (i.e. similar to situations according to SAT). In this way, criminology mirrored interactional psychology in its conflation of the concepts of perception and environment. As in interactional psychology, the root of the conflation of ‘environment’ and ‘situation’ in criminology seems to be in inadequate handling of the role of perception. Perception is a process that is crucial for the study of person-environment interaction in action (see main chapter text). As discussed in Chap. 1, without a model of action that integrates features of individuals and those of their environments (settings) via the process of perception, researchers in both psychology and criminology were destined to struggle. With no model by which to address perception and exposure in the opportunity perspective, many researchers conflate these

 For this reason, Sutherland is often cited in support of the view that situations consist of more than just the objective opportunities present in the immediate environment (e.g. Thomas, 2019).

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various concepts and imprecisely refer to (features of) situations when they mean (features of) environments or settings that are perceived and responded to by actors. As the father of criminology, Sutherland influenced much that came after. Like Sutherland, studies and perspectives that followed did not reject, and sometimes positively embraced, the possibility of differential perception of environments and settings by individuals. However, the similarity showed no substantive interest in the characteristics and features of individuals that might shape perceptions of settings and the relevance that differential perception might have for acts of crime. Most conspicuously, ‘Situational Crime Prevention’ (SCP; Clarke, 1980; Cornish & Clarke, 2003) refers to intervention measures that target opportunities and conditions in settings and environments as ‘situational’ whilst steadfastly ignoring the individual (Chap. 1). SCP was initially called ‘Physical Crime Prevention’ (Clarke & Cornish, 1983a, p. 235; Mayhew et al., 1976),23 but Clarke’s eventual choice of terminology for the approach was probably influenced by a need to distinguish environmental factors that influence the crime event, from environmental factors that influence the development of the offender and their circumstances, which were involved in the apparently less promising ‘social’ prevention approach (Clarke & Cornish, 1983b, pp.  45–46).24 This has led to an entire branch of criminological research being termed ‘situational’ despite the proponents of this approach openly stating that researchers ‘know little about the offenders’ (Cornish & Clarke, 2008, p. 38). The work of Briar and Piliavin (1965) provides a prominent earlier example of the use of the term ‘situation’ in place of ‘environment’ or ‘setting’ that has had far-­ reaching implications in the discipline. Their influential concept of ‘situational motivation’ refers to short-term motivation ‘experienced by all boys’ (Briar & Piliavin, 1965, p.  36), explicitly disallowing variation between individuals in response to their environment. Therefore, Briar and Piliavin’s ‘situational motivation’ concept could instead be more specifically titled ‘environmental motivation’ or ‘setting motivation’.25 This semantic imprecision is mirrored in the great deal of research within the opportunity perspective that builds specifically on their definition. For example, Osgood et al.’s influential individual-level perspective on crime explicitly situates Briar and Piliavin’s (1965) idea of situational motivation as a central concept (Osgood et al., 1996, p. 638). Thus, their empirical analysis ignores individual differences and focuses solely on features and circumstances of the  Which echoes the terminology of Jeffery’s (1971) approach to ‘crime prevention through environmental design’ 24  Another source of influence may have been Ohlin’s ‘Situational approach to delinquency prevention’ (1970) which Clarke cited in the precursor and initial presentations of SCP (Clarke, 1980, 1983; Mayhew et al., 1976). This is ironic with regard to the impactful miswording of the Lewinian concept of ‘situation’ by the SCP approach because Ohlin’s call for a ‘situational approach’ was rooted in Lewinian Field Theory principles (see further Chap. 2). 25  This would reserve the term ‘situational motivation’ for a motivation concept that is rooted in the interaction of individual and environment. Differential, situational motivation such as this is consistent with the interactive instigators and mechanisms implied by SAT (Barton-Crosby, 2018; Barton-Crosby & Hirtenlehner, 2020). 23

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environments to which individuals are exposed. Osgood et al. refer to their approach as a ‘situational explanation of crime’, when it is a description of environmental inducements to crime (criminogenic features of settings). As a result, a number of criminological studies that are ultimately rooted in the Routine Activity perspective and influenced by Osgood et al. (1996) are titled ‘situational’; however, the analyses either do not analyse individual differences or statistically hold them constant and therefore cannot, by a Lewinian definition, study situational effects (e.g. Anderson & Hughes, 2008; Averdijk & Bernasco, 2014; Bernasco, Ruiter, Bruinsma, Pauwels, & Weerman, 2013; Hoeben & Weerman, 2014; Weerman et al., 2018). Instead they study the independent (of individual differences) effects of environmental features and conditions. Even though opportunity perspectives sometimes acknowledge that individuals may well perceive and respond to opportunities and motivations differently, studies conducted within these perspectives do not account for or study person-­environment interaction (Chap. 1).

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Chapter 3

Evidencing Situational Interaction Without Situation-Level Exposure Data

Abstract  Overall, this SpringerBrief monograph argues that studying the convergence of individuals in environments (situational interaction) is the most appropriate way to study human action, including crime. Despite that being the main argument of the volume, this chapter argues for the continued relevance of evidence of statistical interaction for the study of person-environment (situational) interaction in human behaviour. This is because there are very few studies in any field that can collect the situation-level data about real-world interactions and behavioural outcome that is required for fully appropriate study of situational interaction. In contrast, individual-level exposure data about individuals and environments is much cheaper to collect, but it only allows assessment of statistical interaction (dependence), most commonly using regression methods. This chapter collates and evaluates a range of techniques employed by studies that test the situational model of SAT by assessing person-environment dependency in (most often) individual-level data. In so doing, the chapter highlights the challenges of demonstrating and interpreting dependency, particularly in outcome data that commonly exhibits a problematic distribution (such as crime counts). The chapter concludes that important future directions in the study of statistical interaction are (i) improvements to, and more accurate application of, existing methods, (ii) development and application of new appropriate methods, and (iii) replication in various forms using multiple methods. These future directions are all essential to maintaining and enhancing the utility and relevance of statistical interaction as a complementary element of evidence about the causes of action.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 B. Hardie, Studying Situational Interaction, SpringerBriefs in Criminology, https://doi.org/10.1007/978-3-030-46194-2_3

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3  Evidencing Situational Interaction Without Situation-Level Exposure Data

Making Do Without Situation-Level Exposure Data Chapter 1 argued that studying the convergence of individuals in environments is the most appropriate way to study human action, including crime. Such situational interaction refers to mutual interdependency in action outcomes, i.e. the inseparability of individual and environment. Fully appropriate ‘situational analysis’ of behaviour therefore must identify the particular convergence of (features of) people and (features of) the settings they are in that brings about a particular action, and this analysis requires a specific kind of spatio-temporally linked data (Chaps. 2 and 4; Hardie & Wikström, in press; Wikström, Ceccato, Hardie, & Treiber, 2010; Wikström, Mann, & Hardie, 2018). Such data can be called situation-level exposure data, and records whether or not a person (with particular features) was exposed to various particular environmental features at various specific (possibly continuous) points in time and space, and also records the behavioural outcome of each particular convergence (Chap. 2). There are very few studies in any field that collect truly situation-level data about the content of real-world interactions and resultant behavioural outcomes. Criminological research has not traditionally focused on person-environment interaction in action outcomes (Chap. 1). This means there has not been a demand for situation-level data that captures the convergence of features of individuals and the features of settings to which they are exposed. The collection of precise, rich, and complex situation-level exposure data is also resource intensive. Low demand and high cost means that this specialist data is rare. It is much cheaper and simpler to collect generalised individual- or environment-level exposure data, which captures a total amount of a particular kind of exposure an individual or area experienced over a certain time period, alongside collecting independent counts of crime, aggregated to individuals or areas. Not only is this kind of data collection cheaper, but it can often be more efficient too because these kinds of generalised individual- or environment-level variables have sometimes already been collected by existing studies. The barriers to situation-level data collection mean that many studies of person-environment interaction analyse individual- or environment-level exposure data instead. There is discussion and detail elsewhere in the volume about these different kinds of data (Chap. 2) and collection methods (Chap. 4). However, this solution to the lack of situational data is in itself problematic. Chapter 2 describes that whilst situation-level exposure data can be assessed for evidence of the convergence of features of individuals and features of environments in particular action outcomes, generalised individual- or environment-level exposure data only allows assessment of statistical interaction (dependence) in aggregated outcome measures, most commonly using regression methods. Using independent individual- or environment-level exposure data to draw conclusions about situational interaction requires an assumption of co-occurrence of the exposure and the action. This is problematic because whilst an individual may often be exposed to certain features of environments and also often offend, this does not determine that they were exposed at the time of all, or indeed any, of the offences.

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Ultimately, using regression analysis of exposure data collected at or aggregated to the individual or environment level to draw conclusions at the level of situations represents an ‘ecological fallacy’ (see further Chap. 2). Instead, findings can show statistical dependency at the individual or environment level between independent individual and environmental factors. Discovery of a dependency effect does not evidence causal interaction; instead, it immediately raises questions about why and how factors interact. Such causal questions can only be answered by empirically testing substantive theory about the mechanisms that explain the statistical relationships between variables (see further Chap. 1). This volume argues that to continue to develop research into the causes of actions (such as crime), we must prioritise and improve appropriate methods for both collecting and analysing situation-level exposure data about the behavioural outcomes of particular kinds of person-environment convergences. However, the shortage of situation-level data is likely to continue, and there are also some limitations on the level of appropriate conclusions from situation-level data (see Chap. 4). In the light of this, this chapter argues that clarifying and improving methods by which to demonstrate statistical interaction in individual- or environment-level data remains a worthwhile endeavour in the study of situational interaction in behavioural outcomes. Clarification of methods is necessary because statistical interaction effects are notoriously sensitive and difficult to interpret (particularly when outcomes such as crime frequency are awkwardly distributed). Evaluation of some existing methods used to test the situational model of SAT suggests that it is advisable to draw conclusions from analyses that use a number of methods to replicate findings because the various procedures for demonstrating statistical interaction are imperfect and may show differing results. This requires a range of effective methods, so there is also a need to improve existing methods and develop new ones. Therefore, this chapter discusses some of the challenges and solutions involved in identifying and interpreting statistical interaction, particularly in criminology. Using case studies that aim to test situational interaction as defined by the situational model of SAT, the chapter does not discuss the substantive findings or interpretation. Rather, it evaluates some techniques that have been applied, in an attempt to identify dependence in mostly individual-level data, to the study of situational interaction. Demonstrating statistical interaction of any kind is far from straightforward, particularly in nonexperimental research (Edwards, 2009; Jaccard, Wan, & Turrisi, 1990; Lubinski & Humphreys, 1990; McClelland & Judd, 1993). Usually, data for these analyses is collected at or aggregated to the individual level (i.e. the unit of analysis is the person) and analysed using regression methods. For example, analysis of variance methods such as ANOVA was commonly used in the field of interactional psychology in the 1960s and 1970s to assess individual-level independent data about features of individuals and their exposure to features of environments for evidence of statistical interaction in their aggregated behavioural outcomes (e.g. Magnusson & Endler, 1977). Since the development of Situational Action Theory in the field of criminology, testing individual-level data for evidence in support of situational interaction in the

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process leading to acts of crime is increasingly common, mostly via various regression methods (e.g. Alruwaili, 2019; Barton-Crosby & Hirtenlehner, 2020; Gerstner & Oberwittler, 2018; Hardie, 2019; Hirtenlehner, Pauwels, & Mesko, 2014; Hirtenlehner & Treiber, 2017; Kokkalera, Marshall, & Marshall, 2020; Pauwels, 2018; Schils & Pauwels, 2014; Song & Lee, 2019; Svensson, 2013; Svensson & Pauwels, 2010; Wikström, 2009; Wikström et al., 2010; Wikström & Butterworth, 2006; Wikström, Oberwittler, Treiber, & Hardie, 2012; Wikström & Svensson, 2008; Wikström, Tseloni, & Karlis, 2011).1 Some studies employ hierarchical or multilevel forms of regression to questions relating to situational interaction (Antonaccio, Botchkovar, & Hughes, 2017; Eklund & Fritzell, 2013; Pauwels, 2011; Zimmerman, Botchkovar, Antonaccio, & Hughes, 2015). The methods discussed in this chapter have been used by studies of person-­ environment interaction to analyse mainly individual-level data. Researchers interested in person-environment interaction at the level of environments rarely measure the features of people to which environments are exposed; rather, measures of some social conditions of environments implicitly function as proxies for environment-­ level measures of exposure to particular kinds of people. New data sources (e.g. big data digital traces of human whereabouts) facilitate study of the role of ambient population in spatial crime distribution (e.g. Malleson & Andresen, 2016). Such data improves measures of the exposure of environments to people; however, it cannot capture the features of those people and is therefore not suitable for testing the situational model of SAT (Chap. 4). Wikström et al. (2012, pp. 312–319) present a rare example of appropriate environment-level study of person-environment interaction (see further below). Most studies of person-environment dependency discussed in this chapter analyse individual-level data. Researchers conducting such studies have different skills and capacities. The various data collected or available for analysis have different distributions and structures. Even within the study of person-environment interaction, empirical tests of substantive hypotheses may have different requirements, and there are many analytical techniques to consider for use. There may be some points of accepted best practice that are applicable to some methods, but there is not one correct method, nor necessarily one correct way of applying each particular method. Combined with the challenges of demonstrating and interpreting statistical interaction, this means that the main recommendations of this chapter are that it is wise to use multiple

 These examples include those that test the main exposure x propensity interaction, as well as partial tests, for example, where controls are assessed in place of the moral context or propensity, and include varying operationalisations of key concepts. Schepers and Reinecke (2018) and Pauwels, Svensson, and Hirtenlehner (2018) review studies that use regression to test the morality x self-control interaction relationship posited by SAT; see also more recent examples not covered by the reviews (e.g. Alruwaili, 2019; Craig, 2019; Ishoy & Blackwell, 2018; Ivert, Andersson, Svensson, Pauwels, & Torstensson Levander, 2018). In addition, Trivedi-Bateman (2019) tests SAT-derived hypotheses about the interaction between moral rules and moral emotions, and Serrano-Maíllo (2018) tests a morality x temptation hypothesis. 1

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methods, encourage replication, and conclude with caution. This is particularly the case since there are limits to the conclusions about situational interaction that can be drawn from individual- or environment-level data and analysis.

Interaction (Dependence) in Regression Models Regression, in various forms, is the most common analytical method used for studying interaction (Jaccard et al., 1990). The most common form being for researchers to estimate interaction terms in order to ‘infer how the effect of one independent variable on the dependent variable depends on the magnitude of another independent variable’ (Ai & Norton, 2003, p. 123). A statistical interaction effect thus captures a dependency effect, which occurs when the effects of two independent factors on an outcome are dependent on the other (Friedrich, 1982; Gerstner & Oberwittler, 2018; Wermuth & Cox, 2005). A traditional individual-level analysis of person-environment interaction usually consists of an initial regression model predicting an individual-level behavioural outcome, in which an individual-level measure of a person’s exposure to aspects of criminogenic environments2 and a measure capturing a particular personal characteristic are the independent variables. Interaction is usually assessed by estimating a second model that also includes a multiplicative interaction term (the product of the predictors). An F-ratio interprets the difference in R2 between models with and without the interaction term to determine whether there is statistically significant interaction inherent in the data (Cohen, Cohen, West, & Aiken, 2013; Jaccard et al., 1990; e.g. Wikström & Svensson, 2008). Product terms can be included in linear regression models (see many examples below),3 complex multilevel models (Antonaccio et  al., 2017; Pauwels, 2011), and non-linear models (e.g. Barton-­ Crosby & Hirtenlehner, 2020; Gerstner & Oberwittler, 2018; Hirtenlehner, 2019; Mcneeley & Hoeben, 2017). There are some widely adopted standard procedures for modelling interaction. When product terms are included in regression models, all independent variables should be z-standardised, and model outputs should report unstandardised solution coefficients rather than the standardised Betas (Aiken & West, 1991; Friedrich, 1982). Models containing an interaction term should also include all constitutive elements of that interaction term (Brambor, Clark, & Golder, 2006; Braumoeller, 2004). When three-way interaction is hypothesised, the model should include a  Such measures can be originally measured at the individual level or aggregated from situationlevel data – either way, the unit of analysis is the person. 3  Due to the natural non-normality of product terms, a regression model is no longer linear if it includes a multiplicative interaction term (product terms are not normally distributed even when the variables that constitute them are normally distributed, Edwards, 2009); however, this chapter still refers to such models as ‘linear’ in order to distinguish them from inherently non-linear models. 2

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three-way product term (as well as all regressors and two-way interaction terms). Aiken and West (1991) argue that it can be appropriate to exclude non-significant lower-order (two-way) interaction terms from models of three-way interaction. To be conservative, Hardie (2019) models three-way interaction both including and excluding lower-order interaction terms and reports finding substantively identical results. When dealing with multiple variables and complex processes, estimating progressive models can be helpful for building a narrative. For example, Hardie (2017) presents initial models prior to the introduction of two- and three-way product terms in order to structure discussion about the various elements of the complex three-way interaction that is eventually demonstrated. However, Hardie also acknowledges that presenting outputs from this stepwise process can be misleading statistically. When a later model indicates the existence of interaction, the lower-order effects in earlier models should be disregarded, meaning that to assess even three-way interaction, only one model is required. This is because interpretation of the lower-order effects in initial models is meaningless in the light of higher-order interaction in a subsequent model (Brambor et al., 2006; Edwards, 2009). This statistical argument is wholly consistent with an interactive worldview and SAT, which both argue that it is not fruitful to analyse component parts of an interactive whole (Chap. 1). One of the main aims of this volume is to demonstrate how and why evidence of convergence at the situation level (rather than a demonstration of dependence at the individual or environment level) is the fully appropriate way to evidence the situational hypotheses of SAT.  However, when situation-level data is not available, studying convergence is not possible. Instead, regression modelling of bidirectional statistical interaction in individual- or environment-level data can provide evidence of dependency that is consistent with the convergence posited by the situational model of SAT, which states that neither independent individual nor environment factors but their interaction causes crime. This chapter addresses the two main problems that arise when studying person-environment dependency in crime outcomes: (i) how to deal with the awkward distribution of crime across people and places and (ii) how to confirm and interpret statistical interaction effects. Dealing with these two kinds of problems also involves an interplay between type I and type II error. When studying dependency, type I error refers to incorrectly inferring moderation or falsely assuming a moderation effect, whereas type II error occurs when moderation is incorrectly rejected. Researchers should aim to reduce type I error, of course, but many of the strategies for dealing with the problems inherent in studying statistical dependency, particularly in awkwardly distributed data, risk increasing the chance of type II error. There is therefore a balance to be struck between being sure that interaction is inherent in the data (reducing type I error) and not being so conservative that identifying elusive interaction effects becomes too difficult (not over-increasing type II error). This chapter therefore also discusses the considerations that this balancing act raises for the study of person-­ environment statistical interaction in crime outcomes.

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Solving the Problematic Distribution of Crime One of the biggest problems for assessing statistical interaction within criminology specifically is that the dependent variable is commonly a count of crimes committed by each individual. Crime frequency is a discreet variable that typically has a zero-­ inflated negative binomial distribution, particularly among a representative sample. This is also a relevant problem for studies of the spatial distribution of crime, particularly at the small-area level and over limited time periods. This awkward distribution is problematic in any linear models because they assume a normally distributed outcome (Agresti, 1996; McClendon, 1994; Osgood, Finken, & McMorris, 2002). Some researchers suggest using different crime outcome measures (such as measures of variety, e.g. Bendixen, Endresen, & Olweus, 2003; Sweeten, 2012) and some studies of situational interaction model crime variety (e.g. Barton-Crosby & Hirtenlehner, 2020; Svensson, 2013). However, since alternative measures capture a different aspect of offending or crime distribution (which is not necessarily of theoretical interest), using these avoids the problem rather than solves it. Alternative individual-level crime outcome measures themselves also typically display skewed distributions, which are often driven by large numbers of non-­ offenders who affect most individual-level crime measures. Indeed Svensson (2013) found similar interaction effects in linear models of both crime frequency and crime variety. Many criminologists are therefore faced with the challenge of analysing a discreet dependent variable that displays a zero-inflated negative binomial distribution. Wikström, Treiber, and Roman (forthcoming, Chapter 5) identify two approaches by which researchers commonly attempt to resolve this problem: (1) selecting a regression model that is suitable for the distribution of the dependent variable (i.e. a non-linear model) and (2) transforming the dependent variable to satisfy the assumptions of a linear regression model. Many of the case studies described in this chapter also use a third approach: (3) applying safeguards to mitigate the effects of violating the linear regression model assumption of a normally distributed dependent variable. Specifics and examples of these three approaches are discussed below; many of the tests of situational interaction discussed in this chapter wisely employ more than one of these approaches in an effort to draw robust conclusions about statistical interaction.

Non-linear Models Usually, the use of non-linear models is advised to take account of problematic distribution of the dependent variable (e.g. Gardner, Mulvey, & Shaw, 1995; Hilbe, 2011). Poisson regression is most suitable when the mean and variance of the dependent variable are equal; negative binomial (NB) regression is most suitable when the dependent variable is overdispersed (i.e. the variance is greater than the

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mean). Zero-inflated versions of these models estimate two models, one for the zeros and one for the count part of the dependent crime variable. These zero-inflated Poisson or NB models are often applied in criminology because they are now commonly deemed the most appropriate non-linear models to account for the large proportion of non-offenders (Osgood, 2000; Rydberg & Carkin, 2017; for an exception, see Wikström et al., forthcoming). However, non-linear models are not necessarily advisable for assessing interaction because the addition of product terms to such models does not perform well at capturing moderation relationships (Ai & Norton, 2003; Berry, DeMeritt, & Esarey, 2010; Bowen, 2012; Gerstner & Oberwittler, 2018; Hirtenlehner et  al., 2014; Karaca-Mandic, Norton, & Dowd, 2012; Norton, Wang, & Ai, 2004; Svensson & Oberwittler, 2010). Negative binomial models are inherently multiplicative, so the risk of methodological artefacts is increased when these models also include an additional multiplicative term, because the product term interaction is affected or even cancelled out by the model-inherent interaction (Hirtenlehner, 2019).4 As a result of such complications, Hirtenlehner and Kunz conclude that ‘a statistically significant product term coefficient is neither necessary nor sufficient for claiming interaction in logit or negative binomial models’ (2016, p. 401). Despite these difficulties, some researchers have studied situational interaction using a non-linear framework.5 McNeeley and Hoeben (2017) inadvertently assess person-environment interaction6 by first comparing multiple negative binomial regression models of the relationship between predictor and outcome in groups of participants that are defined by the moderator. Tests for the equality of regression coefficients between sub-groups are rarely appropriate in a non-linear framework. This is because the requirement that the influence of all omitted variables is the same for all sub-groups is unlikely to ever be met (Hirtenlehner, 2019; Hirtenlehner & Kunz, 2016; Karaca-Mandic et al., 2012; Mood, 2010; Williams, 2012). Instead, to confirm and facilitate interpretation of interaction effects, McNeeley and Hoeben (2017) conducted a negative binomial regression including a multiplicative interaction term on their whole sample and then followed a procedure described by Hilbe (2011) to determine 4  Serrano-Maíllo (2018) tests a self-control x temptation interaction using a large range of statistical methods for examining interaction. This study includes the application of a method offered by Bowen (2012) and Leitgöb (2014) that separates the two kinds of interaction in a non-linear framework. This method could perhaps be investigated further and applied to the study of person-environment interaction. 5  In addition to those described below, other studies are listed on page 64. Further, some studies use non-linear models to test other SAT-inspired interaction relationships. For example, Ishoy and Blackwell (2018) use a negative binomial model to test morality x self-control interaction and an additional three-way interaction with gender. They aim to mitigate the difficulties of assessing interaction in non-linear models by stating that their results are ‘substantially similar’ to those from an OLS regression analysis and by plotting interaction graphs as per Aiken and West (1991). 6  McNeeley and Hoeben (2017) do not set out to test the interactions inherent in the situational model of SAT, but their study can be reinterpreted to do so (Hardie & Wikström, in press) and so is included as an example here.

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specifically at which levels of the predictor variables they significantly interact to predict the outcome. This method does indeed facilitate interpretation of interaction effects but is perhaps best suited to categorical or nominal data where the different values of the independent variable(s) (and resultant intersected groups) have substantive meaning. This method also risks type II error due to the potential for substantial loss of variance inherent in the treatment of the independent variables. To inform interpretation of the interaction, McNeeley and Hoeben (2017) also plotted the actual data in an interaction graph (see further below). Censored regression is a class of models in which the dependent variable is censored above or below a certain threshold, for example, Tobit regression. Tobit models are advocated by Osgood et  al. (2002) for use in criminology because they account for the zero-inflated distribution of the dependent variable which results from a large number of non-offenders in most individual-level measures of crime. As when transforming the dependent variable (see below), researchers must consider the theoretical implications of estimating censored models, in particular that non-offenders are particularly important to the explanation of the causes of crime. The logic of SAT dictates that features of non-offenders and their exposure to particular features of settings are highly relevant to estimating a person-­environment interaction effect in offending outcomes. There is a gap in the research literature on including interaction effects in Tobit regression. Due to the partial non-­linearity of the Tobit model, scepticism is warranted over the suitability of Tobit regression for estimating and interpreting interaction effects in crime outcome data. At the very least, Tobit analyses of person-environment interaction in zero-inflated crime outcomes should be supplemented with additional analyses using alternative methods. For example, in their partial test of SAT’s interactive hypotheses, Pauwels, Weerman, Bruinsma, and Bernasco (2011) used a Tobit regression and a square root transformation of the dependent variable. Although their additional analyses were not shown, presumably for reasons of lack of space, the authors report that the findings from both non-square root transformed Tobit models and OLS regression models were ‘very similar’. Greene (2010) argues that whilst assessing interaction can be problematic, it is the process of statistical testing about interaction terms that produces uninformative or misleading results. Greene therefore advocates that interactions can be better understood when analysed (and presented, e.g. graphically) as implications of an already specified and estimated model (see also Brambor et  al., 2006; Williams, 2012). Some recent studies of situational interaction are using an approach outlined by Hilbe (2011; see also Williams, 2012)7 to analyse marginal effects produced by negative binomial regression models to confirm and aid interpretation of interaction within a non-linear framework (Barton-Crosby & Hirtenlehner, 2020; Gerstner &

7  Antonaccio et al. (2017) use related techniques (Buis, 2010; Norton et al., 2004) to aid confirmation and interpretation of person-environment interaction in multilevel overdispersed Poisson models.

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Oberwittler, 2018; Hirtenlehner, 2019).8 Such analysis can be conducted using the ‘margins’ and ‘pwcompare(effects)’ commands in STATA.  In negative binomial models, the marginal effect refers to the effect of a one-unit change in a continuous independent variable on the count change in a predicted dependent variable, when other explanatory factors are fixed at specific values (Hilbe, 2011). To further assess the existence and nature of interaction, these studies test the significance of the difference between marginal effects at various values of the moderator (e.g. mean and +/− 1 standard deviation) by using the z-test proposed by Paternoster, Brame, Mazerolle, and Piquero (1998). Only selected marginal effects can be tested, and therefore the method does not allow for a continuous moderator; however, the marginal effects at representative values (MER) approach seems most fruitful for the further study of situational interaction within a non-linear framework.

Transformation of the Dependent Variable One alternative to using non-linear models for addressing problematic distribution of the dependent variable is to transform it (most often log, but also square root and reciprocal transformations, or reverse score transformation for negatively skewed data). Firstly, since crime is a rare event, the typically large number of zeros (non-­ offenders) in individual-level data cannot be linearised; thus, for example, log-­ transformed crime frequency outcome measures often still violate the assumptions of OLS linear regression. Further, data transformations may inadvertently throw away precious information (Cohen, 1990) and should not be applied without consideration of substantive theoretical implications (Russell & Dean, 2000; Schils & Pauwels, 2014). Most importantly, transformations of the magnitude required to ‘normalise’ individual- or environment-level crime frequency are particularly problematic when assessing interaction effects. Firstly, notoriously ‘shy’ interaction effects are further weakened by the removal of vital variation via dependent variable transformations. Discovery of a statistically significant statistical interaction term may therefore be particularly impressive in models that predict crime frequency dependent variables that are log transformed (Hardie, 2019; Hirtenlehner & Hardie, 2016; Hirtenlehner & Treiber, 2017; Kokkalera et al., 2020) or square root transformed (Svensson & Pauwels, 2010). An additional strategy is to compare findings from models of both logged and unlogged crime (e.g. Hardie, 2019) or conduct sensitivity analyses of a logged crime outcome to complement unlogged analysis (Barton-Crosby & Hirtenlehner, 2020). Secondly, interaction effects in models of transformed crime outcomes become particularly difficult to interpret (see also Hannon & Knapp, 2003; Svensson & Oberwittler, 2010). Therefore, when modelling transformed crime outcome data, even more care must be taken to appropriately interpret the nature of the apparent interaction (see further below).

8  Serrano-Maíllo (2018) discusses and applies this method alongside a number of other methods to test SAT’s morality x self-control interaction hypothesis.

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OLS Regression Plus Safeguards In order to avoid such problematic transformation of a dependent crime variable, some studies of situational interaction in criminology use an unlogged skewed dependent crime variable in an ordinary least squares (OLS) regression. Some scholars conclude that complexities which arise when studying interaction using other approaches (such as within a non-linear framework) mean that despite violation of model assumptions, OLS linear regression is still the most suitable approach for analysing situational interaction in nonexperimental data (Pauwels et al., 2018; Schils & Pauwels, 2014). OLS regression is relatively robust against heteroscedasticity (see, e.g. Fox, 1991), but it is nevertheless an advisable general strategy to ‘apply several safeguards against the pitfalls of skewness and the resulting violation of assumptions’ (Svensson & Oberwittler, 2010, p. 1008). Studies of SAT’s personenvironment interaction hypotheses are increasingly employing multiple safeguards when analysing skewed crime count data with OLS regression. Such safeguards include using robust methods. Robust methods offer an alternative to transformation of the dependent variable for resolving the problems associated with a non-normally distributed dependent variable. Some researchers compute heteroscedasticity-robust standard errors to correct for the heteroscedasticity of residuals (Hardie, 2019; Hirtenlehner et al., 2014; Svensson & Oberwittler, 2010),9 whilst Hirtenlehner (2019) employs Huber’s M estimator (Huber, 2004) to particularly adjust analyses for a small number of outliers with a very high crime frequency,10 and Barton-Crosby and Hirtenlehner (2020)11 employ clustered robust standard errors to account for the clustering of students in classes. Such robust methods are accessed via various commands in STATA or as macros for use with SPSS, e.g. Darlington and Hayes (2017). Other robust methods such as bootstrapping may also be valuable to the study of situational interaction. Using robust errors means that conclusions about a finding that demonstrates interaction can be more confidently made (reduces type I error); but this does not mean that there can be no confidence in a demonstration of interaction that doesn’t apply robust methods, because employing robust errors is a conservative method that can itself increase type II error. Another way to mitigate the effects of the skewed dependent variable in a linear regression model is to include the quadratic terms of the involved predictors into the model with the multiplicative interaction term (Hardie, 2019; Hirtenlehner et al., 2014; Svensson & Oberwittler, 2010). In criminology, the use of OLS regression to assess interaction in skewed data has been criticised because the variance explained

9  Others also take this approach when testing alternative interaction relationships posited by SAT (e.g. Hirtenlehner & Kunz, 2016). 10  Due to the common extreme spatial clustering of crime (e.g. Wikström et al., 2012, pp. 189–193), Huber’s M estimator may be suitable for the future study of person-environment interaction in environment-level data. 11  The dependent variable in this study is crime variety.

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by a product term may actually be due to curvilinearity rather than moderation (Osgood et al., 2002). Including quadratic terms when necessary reduces the likelihood of incorrectly inferring moderation (type I error) by accounting for the non-­ linear element of the relationship between independent and dependent variables (Lubinski & Humphreys, 1990). Edwards (2009) warns, however, that this strategy may increase the chance of incorrectly rejecting moderation (type II error) if the quadratic terms are correlated with the product term. Finally, in an additional attempt to avoid type I error (which falsely assumes a moderation effect), some studies safeguard their conclusions by specifically comparing the results from their linear OLS models (with violated assumptions) with findings from non-linear analyses of the skewed dependent variable. Some of these non-linear techniques are already outlined above. Examples include research that ran additional confirmatory negative binomial models (Schils & Pauwels, 2014), or Tobit regression models which are designed to deal with potential ‘floor effects’ of left-censured data (e.g. Svensson, 2013; Svensson & Oberwittler, 2010; Svensson & Pauwels, 2010); assessment of interaction using a ‘Wang model’ for highly skewed count data (Wikström et al., 2011); application the Inteff technique to logit models (Hardie, 2017; Hirtenlehner & Hardie, 2016; Hirtenlehner & Treiber, 2017)12; and comparison of marginal effects at representative values within the negative binomial framework (Barton-Crosby & Hirtenlehner, 2020; Hirtenlehner, 2019). All found that the results from the non-linear analyses complemented and confirmed the results from the linear models.

Reliability and Interpretation Despite the many considerations discussed so far, a significant interaction term is not the panacea for the difficulties of demonstrating statistical dependence (Braumoeller, 2004; Edwards, 2009; Greene, 2010). Its absence does not necessarily mean that person-environment interaction is not inherent in the data, and crucially, its presence does not provide guidance as to the nature of the interaction effect. Researchers should be wary of concluding ‘no interaction’ from a single method or model of multiplicative interaction. This is particularly the case if that interaction has already been evidenced using spatio-temporally linked data. Antonaccio et al. (2017) study situational interaction by estimating cross-level interactions in a multilevel model of independent individual-level and neighbourhood-level data. They consider their test of SAT’s situational model ‘exploratory’ because it does not 12  ‘Inteff’ (Ai & Norton, 2003; Norton et al., 2004) provides estimates of the average overall interaction inherent in the data whilst allowing for the confounding of the model-inherent and multiplicative term forms of interaction in the model, but the procedure is not without limitations: it is only available for binary models and  is only able to estimate one two-way interaction at a time. In addition, there is some informal discussion in the statistical community about a potential problem with the calculation of the standard errors of regression coefficients within the Inteff procedure.

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employ the appropriate spatio-temporally linked data and their findings are contrary to studies that do. They conclude that ‘because of the existence of supportive findings or prior research using spatiotemporal data, we suspect that our inconsistent results can be attributed to data limitations’ (Antonaccio et al., 2017, p. 231). Other issues discussed in this chapter should also give pause to researchers who are tempted to conclude that their study provides no evidence of person-­environment interaction. These issues include the use of non-representative or incomplete samples that might limit variation on key variables; conducting only a partial test of interactive hypotheses such that results may obscure interaction effects that might be apparent in a full test; and incomplete or inadequate measurement of any of the theoretical concepts, which might dilute their apparent and potentially interactive effect. When situational interaction is evidenced, rigorous science expects replication in various forms (Yong, 2012). Cross-national comparison is one way to test the generalisability of the situational model of SAT (e.g. Hirtenlehner et al., 2014; Svensson & Pauwels, 2010; Wikström & Svensson, 2008). Initially, most studies that test situational interaction have used data from Western Europe, but increasingly studies are analysing data collected outside Europe (Alruwaili, 2019; Antonaccio et al., 2017; Kokkalera et al., 2020; Parent, Laurier, Guay, & Fredette, 2016); and suitable data exists from Ghana, four cities in Latin America, and two cities in China that has so far only been applied to other research questions but could potentially be used for future analysis of situational interaction (Ma, 2006; Serrano-­Maíllo, 2018; Xun, forthcoming).13 More cross-national comparisons and studies in more varied contexts are needed to add weight to the body of evidence. Pauwels et al. (2018) note an over-representation of youth samples in tests of the situational model of SAT, so demonstrations of situational interaction in more varied samples are needed too. Some studies of situational interaction already focus on specific crime types, e.g. shoplifting (Hirtenlehner & Hardie, 2016; Hirtenlehner & Treiber, 2017) and violent extremism (Schils & Pauwels, 2014), though applications to a broad range of crime types should continue as another important form of replication. Studies of different kinds of data can also help to build a robust body of reliable evidence. Various forms of data may be appropriate, for example, Hardie (2017, pp. 256–259) argues that longitudinal data is not necessary for testing the causal mechanisms of the situational model of SAT (see also Pauwels et al., 2018; Wikström et al., 2018); Gerstner and Oberwittler (2018) test situational interaction using social network data; and Chap. 4 describes situation-level data collection methods such as randomised scenarios and Space-Time Budgets. Finally, as should be clear from this chapter, studies using multiple or at least different analytical methods are particularly crucial when assessing statistical interaction, which can prove problematic especially in crime outcomes. It is a sign of a healthy research area that many of the examples in this chapter make important contributions by replicating, in various ways, previous evidence of situational interaction.

13

 Ghanaian data collected by Justice Tankebe (University of Cambridge) has not yet been analysed.

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Significance The presence of a significant interaction term is sometimes taken directly as evidence of statistical interaction. However, such significant interaction effects are often difficult to identify, particularly in nonexperimental research. This can be for many reasons, as should be becoming clear (see also McClelland & Judd, 1993). Measurement error can be a big problem for the reliability of the product term in regression models even when the reliabilities of the independent variables are adequate. This is a difficult problem to rectify or even quantify.14 The complications described here and many of their solutions make interaction effects shy to reliable demonstration, increasing type II error (incorrectly concluding no interaction). Additionally, any models should take care to avoid ‘overcontrolling’ (the addition of unnecessary covariates to models) because the resultant low power further hampers attempts to demonstrate statistical interaction (Chap. 4; Hirtenlehner & Kunz, 2016; Hirtenlehner et al., 2014; McClelland & Judd, 1993).15 A major hindrance to reliability of product terms is scant variance in the independent variables involved (McClelland & Judd, 1993). This can be a problem for studies that test the situational hypotheses of SAT’s model of action. For example, SAT states that individuals vary in their sensitivity to exposure to features of settings; thus, if only certain kinds of individuals are present in a study sample, the study may find that sensitivity to exposure varies little across individuals and therefore find no interaction effect. This issue is of particular relevance to studies that test person-­ environment interaction hypotheses within a particular non-representative sample, such as substance users or incarcerated offenders (e.g. Gallupe & Baron, 2014; Piquero, Bouffard, Piquero, & Craig, 2016) or students (e.g. Schepers & Reinecke, 2018), since variation in key individual characteristics and their exposure to particular features of settings may be limited and thus person-environment interaction would not necessarily be expected or findings may be distorted. Poor operationalisation of concepts is another source of relative invariance in predictor variables. This is particularly likely to be a problem for concepts that are particularly difficult to measure, such as moral contexts. For example, Schepers and Reinecke (2018) reduce SAT’s concept of moral context to a measure of perceived peer delinquency; thus, key hypothesised causal features of moral contexts may not be captured by this measure. The authors state that this may be a reason for their discrepant findings (Schepers & Reinecke, 2018).

 Edwards (2009) and Lubinski and Humphreys (1990) suggest using the work of Bohrnstedt and Marwell as a starting point. 15  Many commonly employed statistical controls can be avoided on the grounds that they are markers or attributes (Farrington, 2000; Rutter, 2003; Wikström, 2011) and therefore cannot be causal (Bunge, 2001; Holland, 1986). Background characteristics that covary with crime should not just be entered into models as statistical controls because measurement or specification of the actual mechanism is poor or lacking (Pauwels, Ponsaers, & Svensson, 2009). See further Chap. 4. For a discussion on the ‘illusion of statistical control’, see Christenfeld, Sloan, Carroll, and Greenland (2004). 14

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Before concluding that the lack of a significant interaction term is evidence against the interactive hypotheses of the situational model of SAT, researchers should consider whether they captured adequate or appropriate variance in the predictors and conducted appropriate analysis of statistical interaction in a potentially problematic dependent outcome.

Comparing Groups to Access Meaning In addition to issues of reliability, statistical interaction can be difficult to interpret correctly. Such complications serve to highlight that empirical analysis of interaction effects must be guided by a strong and rigorous theoretical model to ensure that it actually tests the specifics of hypotheses as intended and that the results about interactions are meaningful (Greene, 2010; Karaca-Mandic et al., 2012). Some difficulties in interpretation are specific to non-linear models (Ai & Norton, 2003) or are exacerbated in any models including higher-order (e.g. three-way) interactions (Braumoeller, 2004); however, there are pitfalls to be avoided when interpreting statistical interaction in any models. In general, researchers studying statistical interaction cannot rely on usual methods of model interpretation because the coefficients, standard errors, and p values reported in traditional results tables are often substantively uninformative when reported for models of multiplicative interaction (Brambor et  al., 2006; Greene, 2010). Firstly, lower-order regressors should not be interpreted because the meaning of lower-order coefficients in the presence of interaction terms is complex since their effect is conditional on the other when the other is at its mean (because the variables are mean-centred) (Brambor et  al., 2006; Braumoeller, 2004; Edwards, 2009). Further, interpreting the substantive meaning of the coefficient of a two- or three-way interaction term is exceedingly complex and often done badly. For example, Braumoeller says of the political science literature that ‘the focus has been on running, or even flying, when the fundamentals of walking have yet to be made clear. Nowhere is this fact more apparent that in the case of the humble interaction term’ (2004, p.  807); a survey of research employing multiplicative interaction terms found only 10% of political science papers used and interpreted them correctly (Brambor et al., 2006); and economics suffers a similar problem (Drichoutis, 2011). Criminologists looking to evidence situational interaction by studying person-­environment statistical dependency would be wise to take heed of failings in other disciplines and work to improve methods to aid interpretation and then apply them fully and accurately. Group comparison techniques present various ways to assess situational interaction and, specifically, to assess the nature of that interaction. Any group comparison procedure necessitates one variable to take the role of ‘moderator’. Though mathematically identical, moderation is a specific kind of statistical interaction effect that distinguishes between the roles of the two variables involved in the interaction (Edwards, 2009). SAT posits that neither independent individual nor environment

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factors but their convergence causes crime. This means that the situational hypothesis of SAT is that the interaction is bidirectional. In principle, graphs and analysis of individual- environment interaction where either variable is the ‘moderator’ can evidence the situational model of SAT. In practise, however, most presentations and ad hoc probing of interaction effects cited in this chapter depict the effects of exposure to particular environmental features at different levels of individual features.16 The individual characteristic is usually the ‘moderator’. This is true of graphical presentations of situational interaction in both individual- and situation-­level data, including hypothetical scenario data (e.g. Wikström et al., 2012, pp. 156, 352, 389). This is also the case with analytical and graphical comparisons of marginal effects at representative values of the ‘moderator’ (e.g. Gerstner & Oberwittler, 2018) and comparison of regression slopes between groups categorised by the ‘moderator’ (e.g. Wikström et al., 2011). This is because SAT is a theory of action, most often used to explain acts of crime. Since actors are the source of actions, it is intuitive to analyse and display the individual feature as the ‘moderator’. Of course, when the focus of the test of the situational model of SAT is the distribution of crime across environments and settings (e.g. Antonaccio et  al., 2017; Wikström et  al., 2012, p. 315), any analysis or graphical representation of person-environment interaction might favour displaying the environmental variable as the ‘moderator’. Multiple Group Analysis By estimating several regression models predicting the relationship between the independent variable and crime for groups of cases (e.g. people or places) categorised using the moderator, researchers can assess the nature of interaction in their data. For example, Svensson (2013), Wikström and Svensson (2008), and McNeeley and Hoeben (2017) all used this approach to complement models that implied interaction in data from the whole sample in order to explore the nature of the interaction. In addition, Svensson (2013) applied the z-test proposed by Paternoster et al. (1998) to test the equality of the OLS regression coefficients between the groups defined by the moderator. De Buck and Pauwels (2019) also apply this z-test to compare the regression coefficients produced by negative binomial models that estimate effects for three groups categorised by the moderator. Paternoster et al. (1998) state that this test for the equality of the regression coefficients is applicable to all types of regression that yield maximum likelihood estimates. As discussed above, however, it is questionable in a non-linear framework that any test for the equality of coefficients between sub-groups would meet the requirement that the influence of all omitted variables be the same for all groups (Hirtenlehner, 2019; Karaca-Mandic et al., 2012; Mood, 2010; Williams, 2012).

 For an exception, see Kokkalera et al. (2020), which graphs the effects of individual features on predicted crime outcome at three different levels of exposure to particular settings.

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Some less well-known approaches have also been applied in order to compare groups in the study of person-environment interaction. Wikström et al. (2011) estimated a finite mixture Poisson with logit mixing probabilities model (Wang model) to compare the effect of the independent variable on expected crime between groups dichotomised by the moderator. They compared these findings with those from OLS regression models of both the whole sample and of groups categorised by the moderator. Another group classification and comparison method that is relatively unknown in criminology has been applied to the study of situational interaction by Parent et al. (2016). Chi-squared automatic interaction detection is a form of group comparison method that was developed within the data mining approach (CHAID; Breiman, Friedman, Olshen, & Stone, 1998). CHAID is a decision tree method, rooted in adjusted significance testing (Bonferroni testing). There may be some merit in further exploring the applicability of these less common group comparison methods to the study of situational interaction, though they are arguably superseded by newer group comparison techniques, for example, those developed within the structural equation modelling framework (e.g. Liao, 2013). Structural equation modelling (SEM; e.g. Kline, 2016), or path analysis if latent variables are not included, is also a way to build group comparison models that simultaneously allow a test of hypothesised interactions and examination of the nature of any interaction. The multiple group comparison (MGC) method involves testing for the equivalence of a causal structure using SEM. More traditional group comparison methods such as ANOVA are unable to compare the size of the effect within each group, which is necessary to assess interaction. This limitation is solved by SEM with an ad hoc test for the equivalence of structure. This invariance testing strategy allows us to see to what degree an effect is operating equivalently across the groups, where the groups represent a categorisation of the moderator. Assessing the presence of interaction is conducted by assessing the difference in goodness of fit statistics between a baseline model (whereby the regression is estimated freely in each group) and a comparison model (in which the regression coefficients testing the relationship between the predictor and the outcome are constrained to be equal across groups). A significant deterioration in model fit in the comparison model as opposed to the baseline model means the regression coefficient is nonequivalent across the groups; i.e. some interaction/moderation is occurring.17 SEM has the main benefit that it allows for a flexible pattern of relationships, and this flexibility allows for the correction of estimates that may have been biased by relationships of any kind (not just quadratic) between independent variables. These models also control for measurement nonequivalence, and the flexible pattern of

 Byrne (2010) observes that a difficulty with this procedure is that the conclusions based on the change in two goodness of fit statistics (Δχ2 and ΔCFI) can be different, suggesting that simultaneously there is equivalence and nonequivalence across groups (interaction and no interaction) in the same data; however, since the χ2 goodness of fit statistic is so sensitive to sample size, many researchers now effectively ignore it. Therefore, the requirement to ‘choose the approach which we believe is most appropriate for the data under study’ (Byrne, 2010, p. 271) is not problematic, and the correct conclusion is clear. 17

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relationships permitted by SEM or path analysis allows for multiple dependent variables. Conducting this kind of analysis using software such as MPLUS also simplifies the process because it has built-in options to use a robust estimator which corrects coefficients for a skewed dependent variable, or an estimator that accounts for the nature of the dependent variable by running the type of regression that is appropriate for the data. Of course, the complications of correcting a skewed dependent variable still exist despite these measures; however, there is considerable merit in applying this technique to the study of situational interaction. The SEM framework has not yet been used to test situational interaction in individual-­level data, but Barton-Crosby (2018) and Schepers and Reinecke (2018) have both used MGC within the SEM framework to test other interaction relationships posited by SAT. As with all group comparison methods, these analyses require categorisation of the moderator. Such loss of variance might be particularly crucial for the observation of dependence in samples that are already limited. For example, Schepers and Reinecke (2018) acknowledge that their student sampling frame may not ‘encompass individuals with extremely antisocial morals living in absolutely crime encouraging moral contexts’ (pp.  88). Due to the robustness of the OLS regression that underlies the MGC method, Schepers and Reinecke (2018) are satisfied to use a form of SEM that is reliant on maximum likelihood estimation.18 In contrast, Barton-Crosby (2018) corrects for skewness in the dependent variable by using SEM that relies on robust maximum likelihood (MLR). Using MLR serves an equivalent purpose to robust standard errors (Muthén & Muthén, 2017). As discussed above, whilst robust methods are more conservative (reduce type I error), they may make identifying elusive interaction effects more difficult (may increase type II error). Path analysis has, however, been applied to the study of person-environment interaction at the environment level. Wikström et al. (2012, pp. 312–319) analyse very rare data that captures the features of small areas (community survey, land use, and census data), the aggregated exposure of those small areas to people with particular characteristics (Space-Time Budget and questionnaire data), and the area-­ level crime count (official police data). This innovative analysis evidences, at the environment level, the interaction of features of environments and features of individuals in crime hotspots. Despite its complexity, SEM is becoming more accessible. The framework holds much promise for the study of statistical interaction effects, in terms of the group comparison techniques described here but also other techniques for assessing interaction that is afforded by SEM.

 This may also be because they use a crime versatility index as a dependent variable rather than count data; however, the versatility index is still skewed and includes a large proportion of non-offenders.

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Graphical Representation Visual assessment of data can be a very useful descriptive way to understand the nature of difficult to interpret statistical interaction effects. Cox argues that to interpret two-way interaction, ‘it will often be enough to make a qualitative or graphical summary of the appropriate two-way tables of means, with associated standard errors’ (Cox, 1984, p. 15). Interaction between variables in graphs such as these is signified by a gradient of association between one independent variable and the dependent outcome (or predicted outcome) that increases or decreases according to an increase or decrease in the other independent variable. However, such graphs require categorisation of at least one of the independent variables for group comparison (e.g.  Alruwaili, 2019; Hardie, 2019; Hirtenlehner, 2019; Hirtenlehner & Hardie, 2016; Hirtenlehner et al., 2014; Hirtenlehner & Treiber, 2017; Wikström, 2009; Wikström & Butterworth, 2006; Wikström et al., 2010, 2012). Such categorisation can be arbitrary and subjective yet impactful (Hardie, 2019; Wikström et al., 2012, p. 139). When multiple categorised independent variables are intersected, the resultant groups of participants vary in size, particularly if the variables have been categorised to emphasise extremes and if they are correlated (Hardie, 2017, pp. 266–270).19 In very small groups, the outcome variable can become very unstable and therefore unrepresentative, and in some cases, certain groups may need to be excluded from graphs. Presentation of this data is important. For example, the relative scales chosen to depict the data must not mislead the eye. The problem of small groups and therefore the potential effect of outliers and influential cases must be borne in mind at the time of presentation and discussion of interaction graphs in order to avoid misleading audiences. For example, for clarity, Hardie (2019) displays error bars and confidence intervals on graphs of average crime frequencies for participants grouped by intersected categorised person and environment variables. Rather than plotting group mean or median offending, some interaction graphs show instead the simple slopes of the regression at various values of the moderator as advised by Aiken and West (1991) (e.g. Kokkalera et al., 2020; Schils & Pauwels, 2014; Svensson & Pauwels, 2010);20 see also Eklund and Fritzell (2013) for an interaction plot of slopes from a multilevel model. Wikström et al. (2011) use the results of Wang models to plot interaction graphs of the effects of the independent variable on the number of expected offences for groups defined by the mediator. Gerstner and Oberwittler (2018) plot negative binomial model estimates as well as the marginal effects at representative values (MERs). A major benefit of these

 This is due to the relationships between these variables, which are driven by processes of selection and emergence. However, any evidence of the situational process must remain consistent irrespective of group size because SAT states that the same cause (situational process of perception and choice) influences action regardless of the causes of the cause (social processes such as selection and emergence). 20  Wikström and Svensson (2008) and Svensson (2013) calculate and present the slopes but do not plot them. 19

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various visual methods is that they simplify interpretation of the nature of any interaction inherent in data.

Conclusion Studying person-environment interaction in behavioural outcomes requires data that captures the convergence of kinds of people in kinds of settings, but such situational exposure data is complex and costly to collect (Chaps. 2 and 4). Much of the existing (and likely future) individual- and environment-level exposure data is not situational, but still has value for the study of situational interaction. This is because whilst analysis of non-situational exposure data cannot evidence criminogenic person-­environment convergence (Chap. 2), demonstrations of statistical interaction (i.e. evidence of dependence) in individual- or environment-level data can provide evidence that is at least consistent with situational processes proposed by integrative theories of action such as Situational Action Theory. Furthermore, situational analysis of situation-level exposure data (i.e. evidence of convergence) has the limitation that it cannot afford conclusions about individuals and the differential effect of exposure on individuals, only about situations (Chap. 4). Some studies demonstrate dependence in individual- or environment-level data and also convergence in situation-level data to build a comprehensive picture of evidence that is consistent with the model of situational action proposed by SAT (Hardie, 2019; Wikström et  al., 2010, 2012). In this way, evidence of statistical dependency in individual- or environment-level exposure data can find utility as a complement to situation-level evidence, by providing policy-relevant clarification about the effect of situational interaction on relationships at the individual or environment level. Despite this utility, evidencing dependence is not straightforward. This chapter illustrates that many methods are imperfect for assessing statistical interaction effects which are notoriously sensitive, and different procedures may show differing results. Some researchers state that OLS regression is the best way to analyse interaction in skewed count data, such as crime frequency, because it currently provides the most reliable coefficients and the most interpretable results (Pauwels et  al., 2018; Schils & Pauwels, 2014). This chapter highlights how, whilst there are difficulties with using OLS regression to assess interaction in non-normally distributed data, many of the common alternatives are controversial or arguably even more problematic because they can cause problems for interpretation of findings or remove vital variance, or provide results that can be misleading. Many methods used to assess statistical interaction are generally beset by difficulties and limitations, particularly because the distribution of the outcome of interest is problematic. Corrections, procedures, and approaches that resolve one methodological problem often introduce another. These difficulties do not mean that such analyses are incapable of providing evidence of statistical interaction. They just mean that we must be careful when concluding that interaction is not apparent, for example, from analyses that do not find a statistically significant multiplicative interaction term or

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inconsistent evidence of group invariance. Whilst we should take care to avoid incorrectly rejecting a moderation effect (type II error), we should also take measures to avoid falsely assuming a moderation effect (type I error). In addition, statistical interaction (particularly higher-order or three-way interaction) is difficult to interpret correctly, and there are pitfalls to avoid in doing so. Difficulties in studying statistical interaction mean that conclusions about dependency (or a lack thereof) and its meaning should be drawn with caution and remind us that replication analyses are essential. Various forms of replication are important for the reliability of findings and conclusions. This chapter highlights that it is sensible to use multiple tools to assess the same awkwardly distributed crime outcome data for evidence of person-environment dependency in order to build a picture of interaction (or its lack), even when some methods might provide contradictory or unclear results (Hardie, 2017, pp.  273–286; see also, Hirtenlehner & Hardie, 2016).21 To facilitate this multi-method approach, this chapter cites studies of person-­environment interaction in crime outcomes and collates a range of techniques for studying dependency, particularly in awkwardly distributed behaviour, additionally discussing ways to mitigate their methodological pitfalls and flaws. In conclusion, this chapter argues for more robust and accurate use of linear regression methods for assessing dependence, as well as the further development, specialisation, and application of machine learning, SEM, or path analysis techniques and the analysis of marginal effects of non-linear models to the study of person-environment interaction in non-situational data. Researchers may also consider the data and analysis in different ways in order to find, evaluate, and apply methods that have been developed in other disciplines. For example, by thinking about exposure as a treatment effect, analysts may be encouraged to assess the suitability of methods that assess causal effect heterogeneity (Brand & Thomas, 2013) for the study of person-environment dependency. This volume highlights the importance of our worldview for the family of theories and procedures with which we approach research (Chap. 2). This reminds us it is important that the selection of analytical techniques is guided by theoretical considerations and not just statistical arguments (see also Schils & Pauwels, 2014). Furthermore, it is important to remain mindful of the fundamental limitations of particular data for particular research questions, which is a crucial consideration when using non-situational individual- or environment-level exposure data to study situational interaction in acts of crime. This chapter provides guidance for evidencing situational interaction in the absence of situational data. The methods discussed in this chapter stem mainly from the additive paradigm and have been discussed in regard to their application to individual- or environment-level data. Chapter 4 shows how these same additive methods (complete with the difficulties and complexities discussed here) can be gainfully employed by the interactive paradigm, if and when they are applied to interactive, situational data. This is particularly the case for

 A study by Serrano-Maíllo (2018) is also a good example of this approach, but it focuses on a different interaction hypothesis and does not study situational interaction.

21

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group comparison methods, which, when applied to situational data, are able to statistically analyse person-environment interaction without deconstructing the convergent nature of the data.

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Norton, E. C., Wang, H., & Ai, C. (2004). Computing interaction effects and standard errors in logit and Probit models. The Stata Journal: Promoting Communications on Statistics and Stata, 4(2), 154–167. https://doi.org/10.1177/1536867X0400400206 Osgood, D.  W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology, 16(1), 21–43. https://doi.org/10.1023/A:1007521427059 Osgood, D. W., Finken, L. L., & McMorris, B. J. (2002). Analyzing multiple-item measures of crime and deviance II: Tobit regression analysis of transformed scores. Journal of Quantitative Criminology, 18(4), 319–347. Parent, G., Laurier, C., Guay, J.-P., & Fredette, C. (2016). Explaining the frequency and variety of crimes through the interaction of individual and contextual risk factors. Canadian Journal of Criminology and Criminal Justice, 58(4), 465–501. https://doi.org/10.3138/cjccj.2015E11 Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36(4), 859–866. https://doi. org/10.1111/j.1745-9125.1998.tb01268.x Pauwels, L.  J. R. (2011). Adolescent offending and the segregation of poverty in urban neighbourhoods and schools: An assessment of contextual effects from the standpoint of situational action theory. Urban Studies Research, 2011, 1. Pauwels, L. J. R. (2018). Analysing the perception-choice process in Situational Action Theory. A randomised scenario study. European Journal of Criminology, 15(1). Pauwels, L. J. R., Ponsaers, P., & Svensson, R. (2009). Analytical criminology: A style of theorizing and analysing the micro-macro context of acts of crime. Contemporary Issues in the Empirical Study of Crime, 1, 129–140. Pauwels, L.  J. R., Svensson, R., & Hirtenlehner, H. (2018). Testing Situational Action Theory: A narrative review of studies published between 2006 and 2015. European Journal of Criminology, 15(1), 32. Pauwels, L. J. R., Weerman, F., Bruinsma, G., & Bernasco, W. (2011). Perceived sanction risk, individual propensity and adolescent offending: Assessing key findings from the deterrence literature in a Dutch sample. European Journal of Criminology, 8(5), 386–400. https://doi. org/10.1177/1477370811415762 Piquero, A. R., Bouffard, J. A., Piquero, N. L., & Craig, J. M. (2016). Does morality condition the deterrent effect of perceived certainty among incarcerated felons? Crime & Delinquency, 62(1), 3–25. https://doi.org/10.1177/0011128713505484 Russell, C.  J., & Dean, M.  A. (2000). To log or not to log: Bootstrap as an alternative to the parametric estimation of moderation effects in the presence of skewed dependent variables. Organizational Research Methods, 3(2), 166–185. Rutter, M. (2003). Crucial paths from risk indicator to causal mechanism. In B.  B. Lahey, T.  E. Moffitt, & A.  Caspi (Eds.), Causes of conduct disorder and juvenile delinquency. New York: Guilford Press. Rydberg, J., & Carkin, D. M. (2017). Utilizing alternate models for analyzing count outcomes. Crime & Delinquency, 63(1), 61–76. https://doi.org/10.1177/0011128716678848 Schepers, D., & Reinecke, J. (2018). Conditional relevance of controls: A simultaneous test of the influences of self-control and deterrence on criminal behaviour in the context of Situational Action Theory. European Journal of Criminology, 15(1), 77–92. Schils, N., & Pauwels, L. J. R. (2014). Explaining violent extremism for subgroups by gender and immigrant background, using SAT as a framework. Source: Journal of Strategic Security, 7(3), 27–47. https://doi.org/10.2307/26465192 Serrano-Maíllo, A. (2018). Crime contemplation and self-control: A test of Situational Action Theory’s hypothesis about their interaction in crime causation. European Journal of Criminology, 15(1), 93–110. https://doi.org/10.1177/1477370817732193 Song, H., & Lee, S.-S. (2019). Motivations, propensities, and their interplays on online bullying perpetration: A partial test of Situational Action Theory. Crime & Delinquency, 1–22. https:// doi.org/10.1177/0011128719850500

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Svensson, R. (2013). An examination of the interaction between morality and deterrence in offending: A research note. Crime & Delinquency, 61(1), 3–18. Svensson, R., & Oberwittler, D. (2010). It’s not the time they spend, it’s what they do: The interaction between delinquent friends and unstructured routine activity on delinquency: Findings from two countries. Journal of Criminal Justice, 38(5), 1006–1014. Svensson, R., & Pauwels, L.  J. R. (2010). Is a risky lifestyle always “risky”? The interaction between individual propensity and lifestyle risk in adolescent offending: A test in two urban samples. Crime & Delinquency, 56(4), 608–626. Sweeten, G. (2012). Scaling criminal offending. Journal of Quantitative Criminology, 28(3), 533–557. Trivedi-Bateman, N. (2019). The combined roles of moral emotion and moral rules in explaining acts of violence using a Situational Action Theory perspective. Journal of Interpersonal Violence, 088626051985263. https://doi.org/10.1177/0886260519852634 Wermuth, N., & Cox, D. R. (2005). Statistical dependence and independence. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (2nd ed., pp. 4260–4264). New York: Wiley. https://doi.org/10.1002/0470011815.b2a15154 Wikström, P.-O. H. (2009). Crime propensity, criminogenic exposure and crime involvement in early to mid adolescence. Monatsschrift Fur Kriminologie Und Strafrechtsreform, 92(2–3), 253–266. Wikström, P.-O.  H. (2011). Does everything matter? Addressing the problem of causation and explanation in the study of crime. In J. M. McGloin, C. J. Sullivan, & L. W. Kennedy (Eds.), When crime appears: The role of emergence (pp. 53–72). New York: Routledge. Wikström, P.-O. H., & Butterworth, D. (2006). Adolescent crime: Individual differences and lifestyles. Cullompton, England: Willan Publishing. Wikström, P.-O. H., Ceccato, V., Hardie, B., & Treiber, K. (2010). Activity fields and the dynamics of crime: Advancing knowledge about the role of the environment in crime causation. Journal of Quantitative Criminology, 26(1), 55–87. Wikström, P.-O. H., Mann, R., & Hardie, B. (2018). Young people’s differential vulnerability to criminogenic exposure. European Journal of Criminology, 15(1), 10–31. Wikström, P.-O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: The social and situational dynamics of young people’s urban crime. Oxford University Press. Wikström, P.-O. H., & Svensson, R. (2008). Why are English youths more violent than Swedish youths? A comparative study of the role of crime propensity, lifestyles and their interactions in two cities. European Journal of Criminology, 5(3), 309–330. Wikström, P.-O.  H., Treiber, K., & Roman, G. (forthcoming). Character, criminogenic circumstances and criminal careers. Towards a dynamic and developmental life course criminology. Oxford, UK: Oxford University Press. Wikström, P.-O. H., Tseloni, A., & Karlis, D. (2011). Do people comply with the law because they fear getting caught? European Journal of Criminology, 8(5), 401–420. Williams, R. (2012). Using the margins command to estimate and interpret adjusted predictions and marginal effects. Stata Journal, 12(2), 308–331. https://doi.org/10.1177/1536867x1201200209 Xun, X. (forthcoming). Exploring Chinese youth crime under the framework of SAT: a comparison with UK (Doctoral thesis, University of Cambridge). Yong, E. (2012). Replication studies: Bad copy. Nature News, 485(7398), 298–300. Zimmerman, G. M., Botchkovar, E. V., Antonaccio, O., & Hughes, L. A. (2015). Low self-control in “bad” neighborhoods: Assessing the role of context on the relationship between self-control and crime. Justice Quarterly, 32(1), 56–84.

Chapter 4

Collecting and Analysing Situation-Level Exposure Data: Clarifying Appropriate Analysis of Person-Environment Convergence to Explain Action

Abstract  Research should be driven by theory and served by method. Studying person-environment interaction as defined by Situational Action Theory (SAT) requires an interactive approach. Operationalisation of concepts, data collection methods, and analytical techniques must each be consistent with an interactive worldview and the specific implications of the situational model of SAT. Situational action refers to the behavioural outcome of the interaction of an individual (and their features and state) and an environment (and its characteristics and conditions). In order to study situational interaction, data must therefore capture the spatio-­ temporally linked convergence of a particular person in a particular environment at the level of the situation and the resultant action. This requires specialist data collection methods, for example, real-world Space-Time Budgets and randomised experimental hypothetical scenarios. The selection and application of analytical techniques must primarily be guided by interactive theoretical principles in order to retain the convergent properties of the situational data. Appropriate methods include comparative presentations of rates, risk ratios, and machine learning techniques that do not divide or nest component parts of the data. In practice, the various features and states of individuals and environments captured in situational data are rarely all measured at the situational level. Therefore, to complement the interactive situational methods, analytical methods may be applied that can take account of methodological obstacles, such as various regression-based techniques that include some account of measurement error. Suggestions of fruitful areas for improvement, development, and replication are included within these recommendations about the data collection and analytical methods that are appropriate for the study of situational interaction.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 B. Hardie, Studying Situational Interaction, SpringerBriefs in Criminology, https://doi.org/10.1007/978-3-030-46194-2_4

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Designing Situational Research Researchers do not have to hold the same worldview, subscribe to the same conceptual definitions, or test the same mechanisms as each other. However, it is crucial that the specifics of their data collection and analysis are appropriate for the research question they address. Before embarking on any research, researchers must first take time and care to consider and specify the research question, define the concepts and proposed mechanisms involved,1 and select the appropriate unit of study and analysis. If these are each precise and comprehensively specified, the research design that follows can be more accurate and appropriate (see further Hedström, 2005; Pauwels, Ponsaers, & Svensson, 2009). Ideally, these questions, definitions, and considerations are guided by a theoretical framework such that they sit within a broader system of plausible processes (Chap. 1). This is an explanatory, mechanistic, analytical approach to research (see analytic criminology, Hardie, 2017, pp. 49–54; Pauwels et  al., 2009; Proctor & Niemeyer, 2019; Treiber, 2017; Wikström, 2006, 2017; Wikström & Treiber, 2013), which is epitomised by Situational Action Theory (SAT; Wikström, 2004, 2010, 2019) and the PADS+ study (see below). Some of the key concepts implicated in the study of person-environment interaction are misunderstood, conflated, contested, or unfamiliar (Chap. 2). Furthermore, traditionally, most studies (i) lack the guidance of a clear model of how individuals and environments interact in action outcomes and (ii) attempt to study behaviour at the level of either individuals or environments rather than action (Chap. 2). SAT is a strong theory in the sense that it clearly and concisely defines key concepts, highly specifies proposed mechanisms, and generates precise and concise testable implications. Those relevant to the study of situational interaction are outlined in Chap. 2 – note the conceptual distinction between setting, environment, and situation. The specificity of SAT facilitates the rigorous empirical testing of its interactive hypotheses; hence it is increasingly attractive to empirical researchers (Pauwels, Svensson, & Hirtenlehner, 2018).2 SAT is particularly valuable to those studying the behavioural outcome of a person-environment interaction because well-specified models of person-environment interaction in action are very rare (Chap. 2). Chapter 2 culminated in a description of the kind of research design and the situation-­level exposure data required by an appropriate study of person-­environment interaction in acts of crime, as guided by, for example, the empirical implications of the interactive hypotheses of SAT (see Table  4.1).3 This chapter builds on these

1  These should be made explicit. As human beings, all researchers have a personal opinion (theory) about the processes they study. It is unscientific to keep this implicit. The safest way to ensure impartiality of the findings is to deeply consider and explicate the theory, design a rigorous empirical test, and faithfully report the outcome (which may require rejection or refinement of the theory). 2  Some parts of SAT’s situational model have not been examined as much as others, for example, the role of motivation in situational processes (for discussion, see Barton-Crosby, 2018; BartonCrosby & Hirtenlehner, 2020). 3  Table 4.1 is a replication of Table 2.1 from Chap. 2, which itself builds on Table 1.2 in Chap. 1. Therefore, discussion of parts of this table appears in Chaps. 1 and 2 as well as this chapter.

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Table 4.1  Integrating people and places: worldview implications for theory, method, and findings (replication of Table 2.1) Worldview Additive THEORY; APPROACH Causality Unidirectional, chainlike Questions ‘What’, ‘which’, ‘how much’ Concept of Additive integration Cumulative risk factors Example approach to integration Meaning of Of the environment, particularly in ‘situational’ relation to opportunity, contiguous with event DATA COLLECTION Data level Individual OR environment

Interactive Dependent, interactional ‘How’, ‘why’ Interactive, situational SAT’s situational model

Arising from the convergence of (features of) a person and an environment

Situation (person-environment convergence; individual IN environment) Independent measures of (i) features, Spatio-temporally linked (dependent) Measures collected at that (ii) generalised exposure to features of measure of individual IN environment PLUS resultant (spatio-temporally the other factor, (iii) aggregated level linked) behavioural outcome behavioural outcome Exposure type ‘Individual-level exposure data’ OR ‘Situation-level exposure data’ ‘environment-level exposure data’ EMPIRICAL ANALYSIS AND FINDINGS Individual OR environment Situation (person-environment Level of convergence; individual IN analysis and environment) conclusion Are the effects of independent person Do rates or probabilities of the act of Analytical interest vary by the convergent and environment factors on the research (person-environment) conditions of aggregated behavioural outcome of question situations? interest dependent on each other? Meaning of Statistical interaction Situational interaction ‘interaction’ Dependency Inseparability of individual and environment; convergence Functions Moderating variables Causal mechanisms evidenced Evidence Statistical dependence Situational convergence POTENTIAL CONTRIBUTION To knowledge Prediction Explanation To behaviour-­ Not causal Causal change policy

descriptions to detail the technical methodological implications for the appropriate study of person-environment interaction in acts of crime. Appropriate data is the vital feature of studies of situational interaction that can or do evidence convergence rather than dependence (Table 4.1).

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Collecting Situation-Level Exposure Data Data that captures the outcome of the convergence of people in places is uncommon in criminology. It is even more uncommon when it captures both the convergence of features of people and features of places and also the convergent conditions of noncrime outcomes as well as acts of crime, all of which is required for the most appropriate study of situational interaction in acts of crime. Such data is usually expansive, is complex in structure, covers a broad range of variables, and requires innovative data collection methods and instruments. It is also often very time-consuming and costly to collect and use. This is because it requires highly trained specialist staff and involves very intensive data collection, processing, and analysis (Mischel, 2004; Wikström, Oberwittler, Treiber, & Hardie, 2012, pp.  70–78; Wikström, Treiber, & Hardie, 2012). Faced with the practical difficulties of collecting situational data, researchers must be particularly motivated by its unique benefits, which are often undervalued or misunderstood (particularly by those whose methodological and analytical priorities are guided by an additive worldview) (Hardie & Wikström, in press). In criminology to date, two kinds of situational exposure and behavioural response data have been collected by a small number of studies: time and space-­time use data (e.g. Space-Time Budgets; STB) and certain kinds of experimental scenario data.4 The UK-based Peterborough Adolescent and Young Adult Development Study (PADS+) is a large-scale longitudinal study that was specifically designed to provide data capable of testing the core propositions of SAT, including the situational model (Treiber, 2017; Wikström, 2014). As such, PADS+ has led advances in situational methodology in criminology and collects both STB and scenario kinds of situational data alongside various other data types (Wikström, Oberwittler, et al., 2012, Chap. 2; Wikström, Treiber, & Roman, forthcoming, Chap. 2).

Space-Time Budgets Space-Time Budgets (STBs) collect very detailed data from each participant about each hour during a period of their daily lives, including where they were geographically, who they were with, what they were doing, and what place they were at, as well as whether they committed an act of crime or not5. Specially adapted STB

4  It is difficult to search vast literatures for kinds of methodologies and kinds of analyses, especially when these can be applied to various topics and terminology is not consistent. To my knowledge, appropriate studies of person-environment interaction in criminology use only these methods. 5  Wilcox and Cullen (2018) argue for methodological developments that facilitate detailed data about opportunities for crime. The existing STB method does already collect these details; however, the STB is capable of much more because it provides data that can test SAT, which is a theory of situational interaction. SAT cannot be classified as an opportunity theory due to the different fundamental assumptions about human nature that underpin these perspectives (Chap. 1).

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interviews were developed for and introduced to criminology in 2001 by PADS+6 and can make a unique contribution to the study of situational interaction in crime causation (Hardie & Wikström, in press). The PADS+ STB is a specialist methodology for capturing real-life data about the behavioural outcome of the convergence of particular kinds of individuals and particular kinds of environments in time and space (Hardie & Wikström, in press; Wikström, Oberwittler, et al., 2012; Wikström, Treiber, et al., 2012). To be capable of analysing situational interaction in acts of crime, STB data must capture the convergent conditions (i.e. features and state of the individual and features and circumstances of the setting) at the precise moment of the act of crime (or other noncrime acts). Whilst hour units are appropriate to broadly capture convergent conditions when acts of crime are not occurring, the STB data must record the convergent conditions during an act of crime, even if the act only lasted moments (Wikström, Oberwittler, et al., 2012, pp. 73–75). This means that researchers are trained to record the conditions relevant to the majority of each hour, but also the conditions during an act of crime should one occur during any particular hour. PADS+ collects a wide range of further details about those individual participants and the environments in which they spent time using other methods, for example, community surveys, official census and land use data, questionnaires, psychometric assessments, and gathering of genetic material. This individual- or environment-level data is then applied to STB hours according to the identity of the person who spent the hour or the identity of the setting or location in which the hour was spent (Hardie & Wikström, in press; Wikström, Treiber, et al., 2012). Single hour STB data units capture situations, i.e. person-environment interactions (the features of the setting and its circumstances and the characteristics of the person in that setting) and the resultant crime outcome. The data unit and unit of analysis is the situation. This fundamental feature of the STB data structure makes this exposure data situational and affords the analysis of situational interaction. These data units are defined snapshots that may hide real-world variability and are far from a perfect measure of situations. Whilst ideally all data should be captured at the level of situations, this is not currently practical or feasible, and the STB represents an exceptional methodological step forward in capturing evidence of situational processes. The STB method is not just wholly superior to any other for this purpose; it actually currently provides criminology with the only measure of situations (person-environment convergence; exposure) and behavioural outcome that is suitable for the study of situational interaction in real-life acts of crime. The PADS+ STB methodology has been replicated internationally in criminological research projects in the Netherlands (SPAN; Study of Peers, Activities and Neighbourhoods); Sweden (MINDS; Malmö Individual and Neighbourhood Development Study); and Slovenia (SPMAD; Slovenian Study of Parental Monitoring and Adolescent Delinquency; partial replication, no spatial element)

 Initial development and application of the STB method by the Peterborough Youth Study in 2001 (Wikström & Butterworth, 2006) served as a pilot for the longitudinal PADS+, which developed and applied the STB method in 2004. 6

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and with an offender sample at the University of Montreal, Canada.7 Hardie and Wikström (in press) list the different applications of this kind of STB data in criminology so far,8 many of which aggregate data to capture individual-level exposure to features of environments to conduct traditional regression-based analyses of the role of setting factors in individual offending and do not address person-environment interaction (e.g. Hoeben & Weerman, 2014, 2016; Janssen, Eichelsheim, Deković, & Bruinsma, 2016; Weerman, Bernasco, Bruinsma, & Pauwels, 2015).9 STB data is indeed suitable for this purpose because it captures individuals’ time use and exposure to the social and physical environment much more accurately, elaborately, and reliably than traditional generalised individual-level time-use exposure measures (for discussion see Hardie & Wikström, in press). In contrast, when situation-level exposure data is aggregated to areas or places, it becomes environment-level exposure data that is also capable of specifying the exposure of particular places and areas to people with particular features. Combined with traditional environment-­ level measures of the characteristics of places, this data has been used to explain area variations of crime in an innovative analysis of person-environment interaction at the environment level (Wikström, Oberwittler, et al., 2012, pp. 312–319). STB data is more rarely used when it has not been aggregated to people or places. The SPAN study has analysed SPAN STB data at the level of hours (i.e. not aggregated to individuals) to compare the environmental characteristics of settings in which offending, substance use, and victimisation do and do not occur (Averdijk & Bernasco, 2014; Bernasco, Ruiter, Bruinsma, Pauwels, & Weerman, 2013; de Jong, Bernasco, & Lammers, 2019). These analyses explicitly hold ‘individual confounders’ constant and assess the impact of specific setting features on outcomes at the hour level, and therefore they do not assess person-environment interaction. However, as recognised by the PADS+ study, the true strength of STB data (and the justification of its cost) lies in its almost unique ability to facilitate situational analyses. Therefore, only those few studies that analyse STB data for the purpose of studying person-environment interaction in acts of crime feature as case studies in the remainder of this chapter. Hardie and Wikström summarise the STB thus: When used in conjunction with data about individuals and environments, as in the PADS+ design, the STB is uniquely able to capture whether or not a crime happens under the situational convergence of particular conditions, i.e., when a particular individual (with particu7  Adapted versions of the PADS+ STB method have also been applied to qualitative studies of desistance (Farrall, Hunter, Sharpe, & Calverley, 2014, Chap. 6; including cross nationally, Segev, 2020, Chap. 8) and in the field of urban design (Townshend & Roberts, 2013). 8  A very recent publication not covered by Hardie and Wikström’s review used MINDS STB exposure data, aggregated to the individual-level, to test the developmental ecological action (DEA) model (as opposed to the situational PEA model) of SAT (Chrysoulakis, 2020). 9  Often these are explicitly termed studies of ‘situational effects’ but are not truly situational because the theories they test hold the ‘motivated offender’ constant and do not address personenvironment interaction. Instead they are studies of environmental or setting effects, which can include the effects of human and non-human objects, social relations, and events (see Chaps. 1 and 2).

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lar characteristics measured by other PADS+ instruments such as questionnaires) is in a particular setting (with particular characteristics and features captured by the STB, PADS+ community surveys and other data) at a particular time, place, and spatial location. Each STB hour (data unit) captures a behavioural outcome of a situation, about which a combination of PADS+ data affords a great deal, and this data uniquely facilitates a true social ecology of crime. (Hardie & Wikström, in press)

Randomised Scenarios Randomised hypothetical scenarios (also known as factorial surveys or vignettes) present a method for experimentally manipulating features and circumstances of hypothetical environments and recording particular individuals’ hypothetical behavioural responses (Auspurg & Hinz, 2015; Pauwels, 2018a; Rossi & Nock, 1982; Wallander, 2009; Wikström, Oberwittler, et al., 2012, Chap. 8). Scenarios therefore allow for the experimental exposure of particular individuals (with particular features captured using, for example, traditional questionnaire methods) to experimentally manipulated features of hypothetical environments. Each data unit captures the behavioural outcome (hypothetical scenario response) of convergence of a person in an environment (hypothetical scenario). Thus, scenario data allows for situational analysis and is simpler and cheaper to collect than STB data. However, it has rarely been applied to the study of situational processes and only captures hypothetical behavioural outcomes (Wikström, Oberwittler, et al., 2012). Scenario studies that capture situational interaction and test the situational model of SAT are included as case studies in the remainder of this chapter.

Future Methodological Avenues Methodological options for collecting situation-level exposure data are limited. A range of disciplines have considered and developed innovative methodologies that capture individuals’ and environments’ exposure to one another (e.g. Alessandretti, Sapiezynski, Sekara, Lehmann, & Baronchelli, 2018; Basta, Richmond, & Wiebe, 2010; Browning & Soller, 2014; Chaix et al., 2012; Chataway, Hart, Coomber, & Bond, 2017; Engström & Kronkvist, 2018b; Gesler & Albert, 2000; González, Hidalgo, & Barabási, 2008; Humphreys, Panter, Sahlqvist, Goodman, & Ogilvie, 2016; Perchoux, Chaix, Cummins, & Kestens, 2013; Ruiter & Bernasco, 2018; Sampson & Levy, 2020; Schönfelder & Axhausen, 2003). Some features of these methods hold some promise for the study of situational interaction, though most are currently limited by not capturing data about the features of the people or environments involved, and the data collected by these studies has been or will be applied to other research questions. Most of these discussions, methodologies, and studies are otherwise concerned with the effects of generalised exposure and not even the differential effect of exposure.

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Some methodological discussions are specifically relevant to the STB methodology and criminology. Some researchers argue that the relatively short reference period of the STB is problematically short, suggesting technological solutions to reduce the research burden (e.g. GPS, online volunteered geographic information, big data, crowdsourcing) (Solymosi & Bowers, 2018; Van Halem, Hoeben, Bernasco, & Ter Bogt, 2016). However, whilst innovative technological methods have benefits for environmental-level studies of crime (Sampson & Levy, 2020; Snaphaan & Hardyns, 2019; Solymosi & Bowers, 2018; Van Gelder & Van Daele, 2014), they are limited to collecting certain kinds of data (Bernasco, 2019; Hardie & Wikström, in press; Snaphaan & Hardyns, 2019). This means they are not always capable of collecting appropriate data for the study of situational interaction. Some studies’ technological and methodological advances may have potential to complement methods like the PADS+ STB (e.g. the Space-Time Adolescent Risk Study, see Basta et al., (2010), Dong et al. (2019); the Adolescent Health and Development in Context study, see Browning, Calder, Cooksey, Ford, and Kwan (2014) and Browning and Soller (2014); and STUNDA, a smartphone application developed at the University of Malmö, see Engström and Kronkvist (2018a, 2018b)). These studies currently do not aim to analyse situational interaction in behaviour outcomes. Ultimately, at the time of writing, there are no readily available technological shortcuts to capturing spatio-temporally referenced data on the behavioural outcome of the convergence of particular features of people in particular kinds of settings. It is likely that innovative applications of technology will continue to impact upon methodological advancements in the study of situational interaction, such as facilitating cheaper and more nuanced and detailed measures of real-world social contexts and individuals’ responses to them. Methods that allow more features of people and places to be captured at the level of situations would be particularly welcome. However, enthusiasm for the data benefits of technological advances must not obscure the crucial features of situation-level exposure data that makes it capable of testing situational hypotheses about action. Some methodological innovations show great promise in principle (e.g. Engström & Kronkvist, 2018a, 2018b), but would require careful development if they were to be applied to the study of situational interaction in acts of crime. Experimentally, it is not hard to envisage that immersive virtual reality technologies (delivered via wearable technology such as Samsung Gear or Oculus Rift) will allow research participants to explore and respond to environments that are built, manipulated, and augmented using technologies of animation, motion capture, and 3D recording.

Analysing Situation-Level Exposure Data This discussion about the analysis of situational data centres around nine publications. Eight of these are the only English-language publications that use situation-­ level STB or scenario data to assess SAT’s model of situational interaction in the

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process leading to acts of rule-breaking.10 Beier (2018), Hardie (2019), Wikström, Ceccato, Hardie, and Treiber (2010), and Wikström, Mann, and Hardie (2018) use PADS+ STB data; Haar and Wikström (2010) use PADS+ scenario data; Wikström, Oberwittler, et al. (2012) use both STB and scenario data from PADS+; Pauwels (2018a) replicates the PADS+ scenario methodology; and Eifler (2016) uses a novel scenario design.11 In addition, Schulz (2016) does not explicitly attempt to test the situational model of SAT, but replicates PADS+ situational scenarios to assess a related moderation relationship and is methodologically relevant to this discussion. The remainder of this chapter describes and evaluates the different methods and inherent assumptions of these nine publications, using them as case studies to demonstrate minimal and optimal features of studies that test a plausible theory of situational action using situation-level exposure data (the findings of these studies are not substantively discussed). In traditional datasets such as those discussed in Chap. 3, data units refer to people, or environments. In contrast, situational data is structured such that each data unit refers to a situation (the spatio-temporally linked convergence of particular characteristics of individuals with particular characteristics of settings) and behavioural outcome (Chap. 2). Thus, person-environment interaction is captured within situation-level exposure data. Although they analyse situational data, the analytical approach of these nine studies can be broadly divided into techniques that are rooted in the additive paradigm and techniques that align with the principles of the interactive paradigm (Table 4.1). These two analytical approaches and some of their techniques are each described below and then comparatively evaluated with regard to methodological and theoretical considerations.

 Unless they directly test SAT, it is not straightforward to search for these kinds of studies. In addition, there follows some examples that demonstrate that it not straightforward to determine which studies meet the criteria for inclusion as case studies. (1) There are additional studies presented in languages other than English that are likely to be relevant, but are not discussed here because they are inaccessible to the author who is limited to English language (e.g. factorial survey studies by Beier, 2016; Eifler, 2015). (2) Wepsäläinen (2016) and Alruwaili (2019) both analyse data from scenarios that replicate the PADS+ scenarios in order to test SAT, but Wepsäläinen does not address situational interaction, and the situational analysis of scenario data by Alruwaili consists only of a graph of association split by the moderator, so neither are included as case studies. (3) Innovative studies by Bachman, Paternoster, and Ward (1992) and Thomas (2019) use scenario data from the USA to study interaction relationships. Both studies present findings that could be reinterpreted as being consistent with the situational model of SAT. These studies are not included here for reasons of parsimony and space because the concepts, measures, and data used differ in too many ways from the approach under discussion. However, for the study of situational interaction, the analytical methodologies of these studies warrant future attention. 11  Despite explicitly aiming to test the perception-choice process proposed by SAT, Eifler (2016) defines and operationalises key concepts differently to the theory. This study is included here because it is methodologcally interesting and provides evidence that is relevant to the testing of SAT’s situational model. 10

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Additive Analysis The development of regression methods is rooted in the additive paradigm (Chap. 3). Regression-based methods for analysing statistical interaction require independent variables in order to assess dependency between them in their effect on the outcome. This means that these methods deconstruct the situation-level exposure data into its component parts; similarly, any multilevel methods necessarily nest data within either individuals or environments. As discussed in Chap. 2, these additive analytical methods introduce an assumption of convergence at the level of analysis. Using statistical interaction between features of people and features of environments assessed at the individual or environmental level to evidence situation-­ level processes therefore falls foul of an ecological fallacy. This is because of the mismatch between the level of analysis and level of conclusion. Crucially however, when analysing situation-level data about action, in which person-environment convergence is inherent, it is likely that this assumption of convergence is unproblematic. This is because the assumption of convergence is made at the situational level. Situation-level data captures the convergent conditions at the moment of the act under study. Scenario data captures conditions at the moment of the hypothetical action under study, and STB data should be designed to do so (see STB section above). This means that the assumption of convergence (required when using dependence to evidence situational processes) is correct in situational data. Therefore, additive analysis of statistical interaction in situational data for the purposes of studying situational processes does not fall foul of the ecological fallacy. Chapter 2 distinguished between evidence of dependence and evidence of convergence, stating that only convergence can evidence situational interaction. However, the relevance of this distinction for the study of situational interaction is related to the specificity of the convergence captured by the data being analysed; individual- or environment-level data does not capture convergence, whereas situation-­level data does. Referring to Table 4.1, this all means that when data collection is undertaken within the interactive paradigm (i.e. situational data), the empirical findings that result from an additive analytical approach can evidence situational interaction and therefore have relevance for research questions and an overall contribution that is within the interactive paradigm. Four of the nine case studies take an additive approach to the analysis of situation-­ level exposure data for evidencing situational interaction in behavioural (or hypothetical behavioural) outcomes (Beier, 2018; Eifler, 2016; Pauwels, 2018a; Schulz, 2016).12 Chapter 3 reviewed various problems and solutions involved when studying statistical interaction using regression methods. Many of these methodological  Wikström, Oberwittler, et al. (2012, pp. 390–393) and Alruwaili (2019) use logistic regression to analyse situation-level scenario data; however, their analysis does not fully constitute a test of statistical interaction, so they are not included here. Both go on to assess convergence, though Alruwaili’s minimal assessment is not included in the examples below. Pauwels also conducted a similar study to test the morality x self-control interaction posited by SAT (Pauwels, 2018b).

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considerations are relevant to the application of these additive methods to situation-­ level data. They will not be repeated here. Eifler (2016), Pauwels (2018a) and Schulz (2016) analyse situational data collected in three European scenario studies using regression-based analytical models. Schulz (2016) and Pauwels (2018a) mitigate the challenges of modelling non-linear statistical interactions (see further Chap. 3) by estimating interaction in linear probability models (LPM).13 Their unit of study is hypothetical scenarios which capture the outcome of particular person-environment convergences in time and space, meaning that the assumption of convergence is met. In order to best model the two-­ step situational perception-choice process of SAT, Eifler (2016) uses a two-part model (Manning, Duan, & Rogers, 1987) involving robust probit models and a multiple group comparison component. The regression elements of this study mean it is classified here as an additive analysis of situational data, but group comparison analysis has relevance for conducting situational, as opposed to additive, analysis of situational data (see below). This mixed regression and group comparison analysis applied by Eifler (2016) warrants future investigation for the study of situational interaction. Using real-world PADS+ STB data, Beier (2018) also applies linear probability modelling to study situational interaction. Since single STB hours are captured as part of an STB interview, these LPM are multilevel. One level of the model nests the hours within the person spending those hours, which means that at this level of the model, it infers that the person and environment conditions of interest co-occurred. This inference of convergence is made at the hour (situation) level. Since the STB records the exact convergent conditions at the time of an act of crime (or not), this inference of convergence is indeed correct. Another one of the nine case studies conducts an analysis that is not regression-based, but still evidences dependency. Haar and Wikström (2010) contribute a Rasch model analysis of the PADS+ scenario data. Like Beier’s multilevel LPM, the Rasch model is multilevel and nests scenarios within individuals which means it evidences dependency at the scenario (situational) level. These studies all use additive (mostly regression) methods to provide evidence of dependency that is (by virtue of the convergent nature of the STB or scenario data) equivalent to evidence of the differential effects of convergence. Most of these publications graphically display the comparative probabilities predicted by their models. By including comparative graphical presentations of actual crime rates

In addition, other studies use additive methods to analyse situation-level data about crime and substance use outcomes (e.g. Bernasco et al. (2013) and de Jong et al. (2019); see also Averdijk and Bernasco (2014) and Ruiter and Bernasco (2018) for victimisation outcomes). However, as discussed earlier in this chapter, these studies assess environmental and setting effects independently of individual effects, rather than individual-environment (situational) interaction, and so these studies are not relevant here. Other studies collect situational data and test interaction hypotheses of SAT using additive methods, but do not test SAT’s situational hypothesis (e.g. Craig, 2019). 13  Pauwels (2018a) also assesses statistical interaction in a logistic regression model, for completeness of his replication of previous scenario analysis (Wikström, Oberwittler, et al., 2012, Chap. 8).

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calculated from the situational data (see further below) in addition to or rather than model effects, true convergence could also have been assessed (and likely demonstrated due to the presence of dependence at the situation level). It is likely that the authors did not include demonstrations of convergence for reasons of space.14 Statistical analysis is usually valued over basic but powerful descriptive data presentations, for example, by the peer review process. When space is at a premium, this makes some sense in the case of traditional analyses of situational interaction (e.g. regression-based analyses of dependence in individual-level data; see Chap. 3). However, in the case of situation-level data, the accuracy and specificity of evidence and conclusions of convergence afforded by simple analytical methods (see below) are equally important to present than ‘more rigorous’ statistical analysis. These rare papers make important methodological and empirical contributions to the study of situational processes. By virtue of the convergent nature of the situation-­ level data they analyse, their demonstrations of dependency between features of individuals and environments in outcomes are made at the level of situations. Unlike studies which evidence dependency at a level that is different to the level of the convergence under study, these studies do not fall foul of the ecological fallacy. The considerations of analytical approaches to the study of statistical dependence and their future development discussed in Chap. 3 are therefore particularly relevant to the study of situational interaction when these additive methods are applied to situation-­level data. In particular, structural equation modelling (SEM) techniques, which allow analyses to be more flexible so as not to impose any particular form on relationships, may be appropriately adapted and applied (e.g. latent class models, Reinecke, 2010).

Situational Analysis Only four English-language publications in criminology use both data and an analytical approach that are situational to conduct studies of criminogenic convergence that test the situational model of SAT (Hardie, 2019; Wikström et al., 2010, 2018; Wikström, Oberwittler, et  al., 2012, pp.  347–363 and 388–393).15 All use (either hypothetical or real-world or both) situational-level exposure data collected by PADS+, meaning that appropriate replication studies using other situational data are much needed. The complexity of studying situational interaction lies in the collection, management, and structure of situation-level exposure data and not necessarily in the analysis. Situational data captures the specific person-environment convergent conditions  In Dutch-language versions of his paper, Pauwels does display descriptive graphs that evidence convergence (2016a, 2016b), but these were removed from the English-language version (2018a) for reasons of space (personal communication). 15  Hardie’s (2019) situational findings were first presented in an earlier thesis (Hardie, 2017, pp. 295–303). 14

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of both acts of crime and other (noncrime) acts. In order to evidence situational interaction in the process leading to behavioural outcomes such as crime, analyses of situational data must demonstrate that the likelihood of an act of crime varies by particular convergent conditions of interest that characterise situations. This allows specific statements about the convergent conditions under which crime is more or less likely and thus evidences causal situational interaction. The basic analytical requirement amounts to the comparison of groups: Does the rate of crime in one group of situations differ from another? Many of the developments in analytical methods for comparing groups that are discussed in Chap. 3 are directly relevant here (see also, perhaps, Eifler, 2016, discussed above). Alternatively, this group comparison can be adequately achieved using a relatively simple analytical method. Descriptive Methods The following group comparison method is the simplest effective situational analysis: (i) Categorise situational data units based on the intersection of (categorical or post hoc categorised) measures of theoretically relevant features of people and environments. (ii) Calculate rates16 of crime events for each category of situation whereby: rate = n events/(n events + n non-events) (iii) Comparatively present or analyse those rates (e.g. graphically, risk ratios, or significance test). Three of the four existing situational studies utilise this descriptive method and calculate crime rates for different convergent conditions (Hardie, 2019; Wikström et al., 2010; Wikström, Oberwittler, et al., 2012, pp. 347–363 and 388–393). The number of data units that fall into each category of situation can differ sometimes drastically (see Hardie, 2017, pp. 299–300). This is due to processes of selection, which influence relationships between variables (ibid, pp.  266–270). Such processes of selection affect real-world STB data, whereas exposure is experimentally controlled in scenario studies, usually via a randomisation process (see, e.g. Wikström, Oberwittler, et  al., 2012, pp.  378). Therefore, unstable data can be a problem for analysis and presentation of behavioural outcomes of rare situations in STB data. Situational analysis must demonstrate whether or not the behavioural outcomes of the different kinds of situations are different, irrespective of different group sizes. This is because SAT states that the same process (the situational process of perception and choice) causes action regardless of the causes of the cause (e.g. social processes of selection and emergence).

 The term ‘rate’ is used rather than ‘probability’ to distinguish between a directly calculated rate and a probability that is estimated by a statistical model.

16

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This analytical method which compares rates neutralises the main potential problem of different group sizes. Crucially, it prevents conditions under which a lot of time is spent and many, but proportionally fewer, crimes are committed, from overshadowing conditions under which a modest number of crimes take place but not much time is spent (Hardie, 2017, pp. 228; Wikström, Oberwittler, et al., 2012). This is possible because as well as capturing the convergent conditions of acts of crime, the situational data also captures noncrime hours and hours that are not exposed to the conditions of interest (Chap. 2). This method requires categorisation of the situational data. Categorisation always involves some loss of variance and relies on often arbitrary cut-points (Chap. 3). However, modelling the distribution of rates can be complex (see below). This descriptive comparison method does not allow for statistical control of covariates; however, this is not necessarily problematic. Controls are often unnecessarily applied to inferential statistical equations according to somewhat unfounded convention. The kinds of variables that are commonly included as statistical controls (e.g. attributes) cannot be causal; they are usually entered into equations without theoretical reason but because they are associated with the mechanism at work which is not itself measured (Bunge, 2001; Christenfeld, Sloan, Carroll, & Greenland, 2004; Hardie, 2017; Holland, 1986; Pauwels et  al., 2009; Wikström, 2010, 2011). The inclusion of unnecessary controls can actually damage the predictive power of models. For example, during analysis of situational STB data, Wikström et al. (2018) showed that day and time have no predictive value for crime once causally relevant features are accounted for (because time itself cannot be causal but is instead a marker of criminogenic features) and actually found that their inclusion significantly decreased the predictive power of the model due to over-­ fitting. Analyses that include such control variables (e.g. Bernasco et al., 2013) may therefore underestimate the efficacy of causally relevant variables. Comparing Rates: Magnitude and Significance This group comparison method allows studies to go on to demonstrate any difference between the rate of crime under different conditions. It is the magnitude of this difference that is most meaningful in explaining crime and drawing conclusions that are relevant for prevention and intervention (see also Beier, 2018). Hardie (2019) using STB data and Wikström, Oberwittler, et al. (2012, Fig. 8.5) using scenario data present visually powerful graphs of rates to depict the nature of the situational interaction. Note that these are not to be confused with graphs that show predicted probabilities resulting from statistical models (e.g. Eifler, 2016; Haar & Wikström, 2010; Pauwels, 2018a). Hardie (2019) and Wikström, Oberwittler, et al. (2012) use risk ratios (i.e. exposed rate/unexposed rate) to calculate the size of the difference between two rates of STB crime under different convergent conditions. A risk ratio (RR; also called relative risk) is not the same as an odds ratio (OR), despite terminology being used interchangeably, including by some of the studies cited here. The

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denominator is ‘all exposures’ and ‘non-event exposures’, respectively, for odds and risk ratios, though actually odds ratios approximate risk ratios when quantifying rare conditions (Hilbe, 2011). This relative risk method is easy to interpret, for example, the crime rate under one set of conditions might be 15 times that of under another set of conditions. It is also possible to assess the significance of the difference between the rates of crime under different conditions. Significance, however, is a question of existence rather than magnitude. Significance testing captures whether the difference observed is substantively important in the sense that it is likely to reoccur when replicated. Significance testing is increasingly criticised, particularly when applied inappropriately (Cohen, 1990; Gelman, Skardhamar, & Aaltonen, 2017; Good & Hardin, 2012; Selvin, 1957; Ziliak & McCloskey, 2008). The merits of significance testing are particularly questionable for analyses of copious STB data because a large n results in even small differences being statistically significant (Cohen, 1990). Lowering the (arbitrary) significance threshold neutralises the effect of a large n. Whilst appropriate in disciplines such as physics, this solution adds further arbitrariness in social science because most data about people, contexts, and behaviour is not accurate to an appropriate measurement level. Despite arguable arbitrariness, to appease convention Hardie (2019) applies a method to test the statistical significance of various apparent differences in rates of crime under differing convergent conditions using a two-proportion z-test (Agresti, 2002; Fleiss, Levin, & Paik, 2003).17 The z-test statistic is a z-score defined by z = (p1 - p2)/SE, where p1 is the proportion (i.e. rate) from sample 1 (i.e. situation 1), p2 is the proportion from sample 2, and SE is the standard error of the sampling distribution. Since z-scores are normally distributed, the z-test statistic has an associated probability which can be assessed against a (albeit arbitrary) significance level. In a further complication, calculating the difference (and testing the significance) between rates of crime under different conditions is problematic when assessing as rare a phenomenon as crime. For example, doubt is cast on the z-test statistic and associated p value when any expected cell count drops below five (or even 10). For a relevant discussion regarding the chi2 test, see Howell (2012, pp.  151–152). Calculating magnitude and significance of differences is even more problematic when expected cell counts are zero. For example, using STB data, Wikström, Treiber, and Hardie (2012) report that commonly no crimes occurred under conditions when certain features of people and features of environments converged, in spite of a large number of hours spent under those conditions during which crimes

 This method was first applied to this purpose by Hardie (2017, pp. 301–303). Wikström et al. (2010, STB data) and Wikström, Oberwittler, et al. (2012, scenario data) test significance by applying a X2 test; however, this z-test is more appropriate because it compares proportions. Hardie observes that some ratio calculators used in medicine give confidence intervals; however, they are usually too wide to be helpful (Hardie, 2017, p. 229). 17

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could have occurred. Risk ratios are impossible to calculate when either probability is zero because the calculation involves dividing one by the other.18 Reliably demonstrating a meaningful test of significance for comparative rates of crime is not straightforward, and it is the magnitude rather than existence of difference that is arguably most relevant when dealing with this kind of data. This means that it is most useful and consistently reliable to compare crime rates arising from different situations by attention to their ratio (as a measure of effect size) whilst also presenting and keeping in mind the base rate number of data units (e.g. STB hours) and crimes from which the rates are calculated (e.g. Hardie, 2019; Wikström et al., 2010; Wikström, Oberwittler, et al., 2012). Inferential Methods Rigorous and complex statistical methods are not necessary to assess casual situational hypotheses about crime, because the complexity and richness of situation-­ level exposure data means that simple comparison of descriptive rates is capable of accurately and adequately demonstrating situational interaction (or lack thereof). Costello and Laub’s observation that ‘the use of more sophisticated methods of analysis… can lead us away from the big picture’ (2019, p. 33) can be applied as a warning in this context. Simple methods should not be spurned for being unsophisticated if they can make a valuable contribution to building a catalogue of evidence (Cohen, 1990, p. 1305), though simple methods can unfortunately be rejected during the peer review process regardless of the contribution of the findings. However, crucial replication using a range of methodological approaches that have different strengths and weaknesses serves to strengthen a body of empirical evidence (Yong, 2012). For example, Wikström et al. (2018) applied machine learning-inspired analytical techniques to the same situational PADS+ STB data that had been analysed using the descriptive analyses and presentations described above by Wikström et al. (2010, 2012). Using very different analytical methods, these studies confirmed evidence of situational effects consistent with the situational model of SAT. Wikström et al. (2018) represent the only study to date in criminology that has conducted a complex statistical analysis of situational interaction that is compatible with the interactive worldview. This study uses an artificial intelligence technique to learn about the effects of convergence in situation-level exposure data. Wikström et  al. (2018) use artificial neural network models of the likelihood of crime to

 In order to still report the magnitude of the difference between two rates, the convention is to add 0.5 to all four cells in the calculation of the two rates used in the RR calculation (Deeks & Higgins, 2010; Pagano & Gauvreau, 2000); however, in various unpublished versions of published analyses, I have found that this convention is not really suitable for this application. This is primarily because the zeros are theoretically and substantively important. The procedure is not really necessary because it alters and obscures the findings and interpretation in ways that are critical for the study of situational interaction in crime, without adding anything to conclusions that was not already fairly apparent.

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estimate the conditional probability of an act of crime occurring under particular convergent conditions relating to characteristics of people and contexts. Rarely used in criminology generally, this technique had never before been used to study person-­ environment interaction in crime behaviour. This kind of computationally intensive and flexible inferential analysis is able to statistically characterise the convergence. Furthermore, it forecasts in the sense that it quantifies the impact of a change in particular conditions. Wikström et  al. (2018) use a binary measure of the moral context19, but the analytical technique does allow for continuous data and thus could be applied to continuous measures in future. This analytical method is consistent with an interactive rather than an additive worldview because it retains the convergent situational properties of situation-level exposure data. The analysis does not at any point nest or aggregate the situational units of analysis within individuals or environments, which would deconstruct the convergence captured within the data. This method can assess situational data for evidence of situational interaction, but, unlike additive methods, requires no assumption of convergence.

 valuating Approaches to Analysis of Situation-Level Exposure E Data for Appropriate Study of Situational Interaction An emergent theme of Chap. 3 is that most analytical methods solve one problem yet neglect, introduce, exacerbate or mis-address another. This chapter has focused on the analysis of situation-level data. So far it has argued that analysis that retains the convergent nature of the data is the most appropriate for the study of situational interaction; but there are benefits of analytical techniques that are rooted in the additive paradigm for mitigating against the implications of the methodological challenges of situational data collection. The most minimal form of effective situational analysis determines whether the rate or likelihood of acts of crime varies by the convergent (person-environment) conditions of situations. However, this conceptual simplicity is interrupted by a methodological problem. Hierarchy is not theoretically relevant because according to SAT, situations (individuals in environments; a single level in a hierarchy) are causally relevant to action, not individuals and environments (which represent different levels in a hierarchy). Unsurprisingly however, in practice, some elements of situational data are captured at levels other than the situation; for example, generalised features of individuals or environments are captured using traditional techniques (see further above and Hardie & Wikström, in press; Wikström, Treiber, & Hardie, 2012). This discrepancy between conceptualisation and measurement means that the data does, in practice, have a complex hierarchical structure; situational data units

 In order to best characterise the moral context, continuous measures of features of environments were combined with other features that are categorical.

19

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are measured within both people and environments.20 Measurement error occurs at the level of measurement and is the difference between the observed measure of a particular feature and its true value. Random error occurs in any measure, and additional systematic error is introduced by an imperfect measurement instrument. Measurement error in situational data is therefore clustered at the level of measurement of each component part. Measurement error may become a problem when situational data units (e.g. STB hours) are selected out for analysis on the basis of individual-level or environment-­ level measures in order to assess and compare situational interaction within this subset of situations. This problem is therefore relevant to the group comparison method described above. Some hierarchical and regression-based analyses are beneficial in this context because they can account for measurement error in data that has been captured at various different levels; Beier (2018) applied multilevel regression in his analysis of STB data for this reason. However, such methods have been developed within the additive paradigm and their treatment of interaction as a dependency effect introduces an assumption of convergence, which is problematic in principle. This is because it is conceptually paramount that analysis retains the convergent structure of situation-level data about individuals in environments in order to provide a true situational analysis that appropriately evidences the interactive situational process (Table  4.1). Solving this methodological issue requires a trade-off at the heart of decisions about the analytical process. Conceptually, the data is situational, but methodological limitations mean that some aspects of the situational data are measured as component parts. Analytical techniques compatible with the interactive paradigm are most conceptually appropriate, whilst additive analytical techniques are most methodologically appropriate for analysing situational data as currently captured. Considerations of such a conceptual-methodological trade-off should take into account the scale of the methodological problem that is being addressed. The appropriateness of generalised individual- or environment-level measures for situation-­ level analyses varies. For example, whilst an individual-level measure of generalised crime propensity does change gradually from year to year (Chrysoulakis, 2020 Wikström et  al., forthcoming), SAT argues that personal propensity might be expected to be relatively stable across situations and will be generally related to the probability that the individual will carry out an act crime in response to a specific criminogenic setting (Wikström, Oberwittler, et al., 2012, p. 132). In contrast, the current author states that her study of situational processes would be much improved by a situation-level rather than generalised measure of parental knowledge about their children’s activities, since it may vary greatly (and have differing impact) across settings and circumstances (Hardie, 2019). These examples show that methodological problems such as measurement error are not uniform and will not present the same difficulty in every study. Methodological problems should not just be addressed in a vacuum or without contemplation. The technical and theoretical

 Longitudinal data from multiple data collection waves are also nested in interviews/waves (Beier, 2018).

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magnitude of the problem should be considered, especially when solutions may introduce conceptual problems. No single methodological approach is yet able to (or ever can?) provide a panacea, but ultimately, the unique and defining feature of appropriate situational methods for analysing situational data is that they evidence the convergent conditions under which an act of crime is more or less likely. The application of additive analytical techniques to situational data can serve to complement situational research by addressing methodological shortcomings in measurement. Or, there may be analytical techniques that are compatible with an interactive worldview which are yet to be applied but are able to address such methodological challenges.

Studying Situational Interaction: Conclusion and Next Steps Approach This volume began by describing the fragmentation of criminology into psychological and environmental traditions. Combined with a regular focus on studying crimes aggregated to people or places, rather than acts of crime, this fragmentation is hindering our ability to explain crime. An inability to explain behaviour is a barrier to understanding how best to change or prevent that behaviour. So, in our efforts to improve our knowledge about the causes of crime, we must integrate features of people and places in our explanation of crime events. Such integration is also relevant to other behavioural sciences. This integration must be interactive in nature, rather than additive (Chap. 1), and an interactive worldview determines a family of interactive theory, concepts, and methodological procedures which are incompatible with those of the additive approach (Chap. 2). Traditional attempts to study person-environment interaction in behaviour struggled because researchers attempted to answer questions about how people and environments interact, which are rooted in an interactive worldview, using concepts and methods developed within the incompatible additive paradigm. Chapter 2 argues that the nature of this struggle was not identified because researchers had no integrative theoretical framework with which to guide their research. In contrast, Situational Action Theory (SAT) provides a well-specified model of person-environment interaction in acts of crime. SAT’s situational model is consistent with (and the epitome of) the interactive paradigm, and it is conceptually precise about the mechanisms it clearly specifies. SAT’s situational model therefore has strong and unambiguous implications for the kind of data collection and analytical methods required to empirically test it (Chap. 2). Adopting a truly integrative situational approach to understanding action means shifting from drawing generalisations about what individuals or environments are like to discovering what people do (cognitively and behaviourally) in response to their perceived conditions. This represents a major shift in the approach to criminological research which has associated implications for research methods.

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This volume aims to bridge the gap between strong integrative theory and appropriate methodology by explaining in detail the implications of SAT for studying person-environment interaction in behavioural outcomes. The volume is dedicated to the data collection and data analysis methods that are most appropriate for the study of situational interaction in action.21 Methods are tools and procedures for achieving an outcome and should not dictate research. At the heart of this volume is the principle that research should be driven by theory and served by method. Therefore, to begin the process of describing the appropriate methods for the study of situational interaction, Chap. 2 clarifies relevant theoretical concepts, particularly ‘interaction’ and ‘situation’, which have been inconsistently applied. Implications of these concepts for the development and application of appropriate methods are crucial.22 The situational approach dictates that data that captures the convergence of people in environments is the critical feature of studies of person-­ environment interaction (Chap. 2). This situation-level exposure data requires specialist research methods that can be costly, and it is therefore rare (this chapter).

Data Collection There are currently only two methods in use that are capable of collecting appropriate situational data for studying situational interaction: Space-Time Budgets (STB; real-world data) and less resource-intensive scenarios (experimental, hypothetical data). Since situational data is vital for studying situational interaction, the development of new and more efficient methods which still collect appropriate situational data would benefit the field. With appropriate caution, technology can play a role in facilitating cheaper and more nuanced and continuous measures of social contexts (real world or experimentally manipulated) and individuals’ responses to them (this chapter). The few appropriate STB datasets in existence were collected in Europe and North America. Of those, the three complete STB studies of representative samples are all Western European. Scenario data is much cheaper to collect and is therefore more common; however, many scenario studies do not collect data that is capable of testing SAT’s situational model. Of those that do, few are located outside of Western Europe.23 Of the nine publications that use situational data to test SAT’s situational

 Interaction is inherent in SAT’s concept of situation (Chap. 2); therefore ‘situational action’ is a concise term for situational interaction in action. Due to confusion surrounding the concept of situation, the longer term is often used throughout this volume for clarity. See further Chap. 2. 22  See Chap. 2 regarding the importance of conceptual clarity for the review and evaluation of this kind of research. 23  Studies by Alruwaili in Saudi Arabia (2019) and Xun in China (forthcoming) have replicated PADS+ scenarios, whilst Craig (2019) conducted a novel but suitable scenario study in the USA.  Most other suitable scenario datasets are Western European (e.g. Eifler, 2016; Pauwels, 2018a; Schulz, 2016; Wikström, Oberwittler, et al., 2012). 21

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hypothesis, six use STB or scenario data from one study (PADS+). Clearly, we need more, and more varied, situational data; but we also need to maximise situational analysis of existing situational data. For example, only PADS+ STB data has so far been used in studies of situational interaction, so data collected by other studies that replicate the PADS+ STB (this chapter) could be efficiently utilised in this way. Future improvements to data collection methods could focus on capturing more nuanced and specific measures of context, including continuous measures. This could include developing situation-level measures of features that are currently generalised but theorised to vary widely across situations in order to reduce systematic measurement error at the level of situations. Theoretical considerations determine that some generalised measures are more methodologically appropriate than others. For generalised measures that are less appropriate because the feature is likely to vary situationally, the most fundamental way to address measurement error is to reduce it rather than account for it statistically.

Data Analysis The complexity of studying situational interaction in the process leading to acts of crime lies in the collection of data that captures the convergent conditions of behaviour. In contrast, appropriate analyses of situational data can be quite straightforward and must minimally demonstrate that the likelihood of an act of crime varies by particular convergent conditions. Crucially, analytical procedures must retain the convergent qualities of the situational data and be compatible with the interactive approach (this chapter); very few studies of situational interaction manage this (this chapter). Some of the analytical methods discussed in Chap. 3 can be suitably applied to situation-level data. These methods are founded in regression methods that are inherently additive. However, when they are applied to situation-level data in which convergence is inherent, methods that evolved within the additive paradigm are able to conduct analysis and produce findings that fall within the interactive paradigm (see above). Some of these methods have the benefit of being able to address methodological problems (such as measurement error). However, analytical methods should not be chosen purely on the basis of methodological considerations, but also theoretical ones. Analysis that is fully appropriate for the interactive approach retains the convergent properties of situational data (Table 4.1). As stated above, the best way to reduce the effect of systematic measurement error is to improve the data collection methods rather than account for it in the analysis. More situational analyses of situational data are needed, including those that use straightforward descriptive comparative presentations and risk ratios. More applications of specialist complex inferential techniques that retain the convergent properties of situation-level data are also required. This is particularly the case if future methodological developments afford the collection of meaningful continuous measures of moral contexts, because analytical techniques will need to be capable of

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handling such continuous data. As initially explored by Wikström et  al. (2018), computationally intensive machine learning techniques such as artificial neural networks, random forests, or coupling functions may inspire suitably flexible and robust statistical procedures that retain the convergent properties of situational data and are analytically and methodologically appropriate (Mann, Spaiser, Hedman, & Sumpter, 2018; Stankovski, Pereira, McClintock, & Stefanovska, 2019; Strobl, Malley, & Tutz, 2009).

I nteractionist Fundamentalism: Any Room for Compromise on the Interactive Approach? This volume summary highlights that more and improved situation-level data is the fundamental requirement of the study of situational interaction and the key to the growth and development of this kind of research. Pauwels suggests that this kind of statement ‘leaves us with the question to what extent one should keep investing in testing situational theories (in SAT’s meaning) using methods which are not spatio-­ temporally linked?’ (2018b, p.  1452). This question is understandable given the unequivocal theoretically-driven arguments for the interactive approach to research methods that have been presented in this volume. However, whilst theory must drive and method must serve, as researchers, we operate within practical limits. These limits may be financial, practical, or even physical. Situation-level data, or some situation-level measures, cannot always feasibly be collected. Chapter 2 describes how analysis of statistical interaction in non-situational data is still very relevant to the study of situational interaction because opportunities to collect or analyse situation-­level data are currently limited. Thus, methodological improvements and innovation in the study of dependence (statistical interaction) are valid contributions to the broad project of studying situational interaction (Chap. 3). There is a limitation of situational analysis of situational data. Although small, this limitation is relevant for real-world applications of this research, such as crime prevention policy and practice. Counter to the ingrained status quo in criminology and classical statistics, situational analysis cannot fully appropriately afford conclusions about either individuals or environments (Table 4.1). When the unit of analysis is the situation, analysis or description of situational data is not concerned with the development, emergence, or resultant nature of individuals or environments, nor how they came to intersect. Theoretically at least, this means that situational data does not have to be nested within individuals or environments (or simultaneously both) for appropriate analysis. Crucially, it also means, in principle, that findings apply only to situations. Therefore, situational analysis of situational data alone cannot determine, for example, that particular kinds of individuals are vulnerable or resistant to particular kinds of environmental exposure. In reality, this distinction is of little consequence to substantive conclusions and is even of questionable

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relevance when some measures are not captured at the situational level in practice.24 However, this distinction is not mere pedantry: acknowledging appropriate levels of conclusion in reports of situational analysis boosts much-needed clarity about the situational approach and emphasises the differences between situational and more traditional analyses (summarised in Table 4.1). This caveat of conclusions from situational research may seem unnecessary, but it actually provides an appropriate role opportunity for analyses of non-situational data in the study of situational interaction. Demonstration of statistical interaction (dependency) in individual- or environment-level exposure data (Chap. 3) provides a level of evidence that may complement the fundamental situation-level evidence. When they are consistent with situation-level findings, individual- or environment-­ level findings may provide policy-relevant clarification about the effect of the evidenced situational interaction on relationships at the individual or environment level. For example, Hardie (2019); Wikström et  al. (2010); and Wikström, Oberwittler, et al. (2012) demonstrate convergence in situation-level data and complement this with consistent evidence of dependence at the individual- or environment-­level, in order to build a picture of evidence that both tests SAT’s model of situational action and allows practical policy recommendations.

Coda Data that captures the convergence of people in environments is fundamental to the appropriate study of person-environment interaction. The future of our understanding of situational interaction in acts of crime rests on appropriate replication. Different forms of replication can use situational data from varied sources, samples, and collection methods and apply various appropriate techniques. Appropriate replication is important to build a comprehensive portfolio of robust evidence that puts SAT’s proposed situational mechanism to rigorous test. Examples of existing studies are cited in this volume, and their methods are described and evaluated in order to aid methodological understanding and guide future study. Suggestions for methodological developments are discussed in relation to their potential contribution.  This distinction is so subtle that, in fact, situation-level findings are usually used to draw conclusions about individuals or environments, for example, that a person’s generalised level of crime propensity is approximately related to how they respond to settings (Hardie, 2017, pp. 253–255; Wikström et al., 2010, 2018; Wikström, Oberwittler, et al., 2012, p. 132). The assumptions that are necessary to draw individual- or environment-level conclusions from situation-level findings relate to the individual- or environment-level of measurement of some constructs. This is because the measurement of data about these concepts is nested in individuals and environments, even though the concepts are ultimately situational (see above). For an example of appropriate language for situation-level conclusions, Hardie (2019) does maintain the level of appropriate conclusion by using findings from situational analysis to only describe the particular convergent conditions under which crime is more or less likely and not discuss independently the features of individuals or environments which contribute to those convergent conditions.

24

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This volume details the specific implications of an interactive worldview for the ideal research methodology that can serve the purpose of testing the situational model of SAT. This should help researchers to design appropriate studies of situational interaction and to recognise and acknowledge distinctions, limitations, tradeoffs, and compromises in their studies. Together, we can improve our knowledge about the situational causes of action.

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