The Affective Profiles Model: 20 Years of Research and Beyond 303124219X, 9783031242199

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Danilo Garcia   Editor

The Affective Profiles Model 20 Years of Research and Beyond

The Affective Profiles Model

Danilo Garcia Editor

The Affective Profiles Model 20 Years of Research and Beyond

Editor Danilo Garcia Linköping University Linköping, Sweden

ISBN 978-3-031-24219-9    ISBN 978-3-031-24220-5 (eBook) https://doi.org/10.1007/978-3-031-24220-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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, expressed 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

This book is dedicated to Professor Trevor Archer, who supervised, mentored, and took care of many of us using words such as: “Empowerment is facilitated by ideals and models. The animal kingdom offers many:   The Bull offers Strength and Courage,  The Honey badger (or ratel) is Tough and Tenacious,   The Mouse is Meek and Mild,   Sagacious and Silent as an Owl,  Few possess the Endurance and Determination of a Mule.  This may appear a child’s game, but that’s how enterprises begin.” This book is also dedicated to the late Professor Torsten Norlander who, together with Professor Archer, originally worked and created what came to be developed as the Affective Profiles Model.

Foreword

The ancient Greek historian and geographer, Herodotus, hailing from the Asia Minor Greek city of Halicarnassuss, not only covered the lives and works of prominent personages and great battles, such as Marathon, Thermopylae, Artemisium, Salamis, Plataea, and Mycale, but also the resilience and/or downfall of civilizations, motivations of individuals, and psychopathologies of despots in the trajectory of the science of narrative history. Less well-known, generally, is the ancient proponent of the “noble art”, Pierce Egan, propounding the Sweet Science that describes and explains the confrontations associated with pugilism in the boxing ring. Of much later, but no less necessary evolution, is the emerging science of “affective personality”, presently delivered by Danilo Garcia, bringing as it does an operationalization, a spectrum of wholesomeness or distress, predictive validity of disorder propensity, an ethnicity representing the multicultural aspects of human aspirations, and a reliability of convergence of brain regional variability. Despite the youthful 20-year old framework, much basic scientific endeavour has been accumulating describing a plethora of method, construct innovation, individual differences in biological, psychological, and social phenomena, distinctive cross-cultural profiles, and educational-occupational dynamics. The theoretical ramifications of affective personality that materialize from the stringency of the basic instrument are constructed from the concrete and reliable derivation of “selffulfilment”, “high affective”, “low affective”, and “self-destructive” personal profiles, with the universal understanding and agreement that, without exception, each may metamorphosize bidirectionally under the exigences of personal developmental trajectory, the pressures of situational and chronological events, and adverse as opposed to salubrious environments. Thus, the first chapter outlines the model of affective profiles as a person-oriented paradigm of emotional realities consisting of two independent yet inter-connected subsystems (the measurable entities: positive affect and negative affect) that give expression to the four personal profiles. The derivation of these profiles, a subject of experimentation and trial-and-error, bestows a slight advantage to the split-median method, despite gender non-compliance, but yet a sufficiency of predictiveness across populations and measures of well-being and ill-being. The method involving “Latent Profile Analysis” (LPA), used to obtain vii

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a “best-fitting” analysis generated three profiles: self-fulfilling (60% of participants), moderate affective (27% of participants), and high affective (13% of participants) with a further four inferences: (i) the applicability of within- and between-variation of affectivity, (ii) considerations of age and gender, (iii) the cultural context, and (iv) the assumptions ingredient to “best-fit”. A final methodological finesse has been the formulations circulating around quantitative semantics, the study of meaning, through intensive data collection combined with computational statistical methodologies through (a) the expedient of “mapping” individuals’ selfreported affectivity (put simply their PANAS-scores) and (b) through the examination of which words distinguished significantly between the distinct “semantic” affective profiles of different individuals. Variations in personality and identity are too innumerable to even begin guessing at, yet to achieve some extent of limited categorization for an eventual taxonomy, Cloninger’s “Temperament and Character Inventory” (TCI) has shown remarkable versatility and longevity from its biological, social, and psychological perspectives. Accordingly, in over both Swedish and US populations, high positive affect was defined by low harm avoidance, high persistence, and high self-directedness whereas high negative affect by high harm avoidance and low self-directedness. Over the Swedish and US populations, a self-fulfilling profile presented lower harm avoidance, higher reward dependence, higher persistence, higher self-directedness, and higher cooperativeness compared with a self-destructive profile, whereas a high affective profile presented higher novelty seeking, higher persistence, and higher self-transcendence in comparison with a low affective profile. Among Swedish and US responders, high novelty seeking was linked to high positive affect, among Swedish participants, but associated with high negative affect among the US participants; concomitantly high self-transcendence was related to high positive affect among the latter, whereas it related only to high positive affect when negative affect was low among the former. Moral identity, affective status, and subjective wellbeing within an Indonesian population demonstrated a stronger relationship with “harmony-in-life” than “life satisfaction”, whereas high positive effect promoted a high moral self-presentation that were concurrent high levels of negative affect combined with high levels of positive affect modulating individual perceptions of these specific moral traits as adverse. The Bulgarian affective personality study evidenced positive affect as a function of persistence, self-directedness, cooperativeness, self-transcendence, and life satisfaction but with a negative association to harm avoidance; on the other hand, it was related negatively to self-directedness, cooperativeness, and life satisfaction, yet positively to harm avoidance, thereby confirming the close ties of the former with character traits. Among Spanish children, a three-way, distinguished from the four-way (above) affective profile distribution through LPA, including a new group characterized by neutral affect therewith presenting: self-fulfilling, self-destructive, and neutral affective profiles. The ubiquity of affective parameters to all aspects of life, both in health, loss of health, and disorder, may be observed in daily life activities, the reception and girdling of hassles, lesser, and greater stressors, catastrophic events, and sorrows arising from loss of loved ones. Physical exercise and sports participation may induce

Foreword

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dissimilar, even contrasting, expressions of affectivity, whether positive or negative, in individuals. These expressions demand attention and definition. The Indonesian study of affect, incorporating particular relationships to subjective health, life satisfaction, optimism, energy, and stress, serves to emphasize (i) the global intricacies of the affective personality and (ii) the complexities of gender roles in personal attributes. Among Italian university and high-school students that represented young adults and older adolescents, respectively, affect generated attitudes and “suppositions” pertaining to well-being, life-challenges, alienation and higher autonomy, purpose in life, and self-acceptance, in the context of personal resilience and “power-of-resistance”. Regarding academic performance among this population, the connection between procrastination and psychological well-being in relation to students’ own affective profiles was both a strengthening and a limiting factor. Ethnicity, despite its constraining properties, remains not only, but rather a world-embracing, aspect of human hope and endeavour and thereby a major player in the affective profiles of livelihoods. In an Iranian study of different ethnicities, positive affect was correlated positively with life satisfaction and ethnic identity and its subscales whereas negative affect was correlated negatively to those attributes. Among Nigerian teachers, higher levels of negative affect were linked to rumourspreading tendencies about work-colleagues and other malevolent behaviours, only when positive affect was also high, contrastingly, and independent of high or low levels of negative affect, high levels of positive affect resulted in greater capacity for forgiveness towards offenders at work and to less counterproductive behaviors. From a collective viewpoint, specific work climate factors presage an adaptive organizational commitment, with law-office organizations and administrative leaders requiring an awareness of employees’ personality attributes at a general level, and the wherewithal of the promoting of positive affect and diminishing negative affect at work and life must be sought at the practical level. The developmental trajectory of affective profiles and their accompanying personal attributes are necessary to fulfil ambitions for improvement and empowerment. Thus, the expression of a selffulfilling profile at the seventh-level school grade may protect, even enhance, wellbeing through adolescence. Nevertheless, the contention that individuals presenting certain profiles possess greater risk of keeping low levels of positive emotions or high levels of negative emotions poses important implications for the promotion of adaptive and functional affective regulation during adolescence and the continuation of the lifespan. No matter what the “walk-of-life”, ethnic adherence, social status, economic providence, or attitudes-in-general, we confront our own particular affective profile which, as stated earlier, traverses a dynamic, bidirectional ontogenetic reality, changeable according to a bevy or whim of circumstance. Accordingly, we are capable of rendering empowerment and decency to our fellow-journey, men and women, on the basis of our distinctive affective apparatus, or we may disempower them for our own personal desires and/or gains; the encephalic representation formed by the ascendancy of the prefrontal cortex over the limbic system. There are no devils or demons other than those we ourselves create, despite the abundance of bogeymenbullies, whether Putinian, klanian, or white-supremicarian, that our own reinforced

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resilience, whether through education or exercise, or both, cannot withstand. We may choose purposefully our standpoints from the position of Mahatma Gandhi over Genghis Khan, St. Augustine over Donald Trump, Nelson Mandela over Josef Stalin, Abraham Lincoln over the Caesars of Rome, and Mother Theresa over Margaret Thatcher, or we may just end-up like Boris Johnson, a carapace of indecency and procrastination. The distance between positive affect and negative remains strikingly both microscopic and infinite, as with the proximal and transcendental separation of the Pantheon and the Church of St. Ignatius Loyola at the centre of Rome. The acquisition of our positive affective profiles requires attending to the epigenetics of decency, mental and physical effort, and education over a lifelong pursuit of understanding and consideration, no mean quest. We may console ourselves through the awareness that the unavoidable occurrence of predicaments may be mitigated through the determinations lent by the science of historical narrative, the psychological and physical proficiency bequeathed by the sweet science, and the empowerment inherent to an honest and retrospective cogitation of the science of affective profiles and their attributes. Floda, Sweden

Trevor Archer

Preface

Affectivity comprises positive affect and negative affect, which reflect two independent but inter-related temperamental dispositions or signal sensitivity dimensions that are influenced by genes and the environment to different extents (Cloninger & Garcia, 2015; Garcia, 2011, 2018). From a person-oriented framework, these two affectivity dimensions within the individual can be seen as interwoven components with whole-system properties (cf. Bergman & Wångby, 2014). This independent inter-relationship between the two affectivity dimensions implies that individuals do not only differ in affectivity between each other but also within themselves, that is, the two dimensions are part of a complex dynamic adaptive meta-system (Garcia et al., 2015; Garcia, 2011). The outlook of the individual as a whole-system unit is then best studied by analyzing patterns of information (Bergman & Wångby, 2014). Although at a theoretical level there is a myriad of probable patterns of combinations of peoples’ levels of positive affect and negative affect, if viewed at a global level, there should be a small number of more frequently observed “common profiles” (cf. Bergman & Wångby, 2014; Bergman & Magnusson, 1997; see also Cloninger et  al., 1997, who explain nonlinear dynamics in complex adaptive systems). In this line of thinking, Archer and colleagues (Norlander et  al., 2002, 2005) coined the notion of the affective profiles by proposing four possible combinations using individuals’ experience of high/low positive affect and negative affect: (1) the self-fulfilling profile (i.e., high positive affect and low negative affect), (2) the low affective profile (i.e., low positive affect and low negative affect), (3) the high affective profile (i.e., high positive affect and high negative affect), and (4) the self-­ destructive profile (i.e., low positive affect and high negative affect). During the last 20 years, research using the affective profiles model has distinguished individual differences in biological, psychological, and social phenomena. Most of the studies have been conducted among Swedes and investigated differences in ill-being and well-being, and some of them also in personality (e.g., Garcia & Siddiqui, 2009a, b; Garcia, 2011, 2012). More recently, however, some studies have used the affective profiles model to discern individual differences in other cultures, such as French (Orri et al., 2017), US residents (Schütz et al., 2013), Dutch xi

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(Kunst, 2011), Italian (De Caroli & Sagone, 2016; Di Fabio & Bucci, 2015), Indonesian (Adrianson et  al., 2013), Iranian (Garcia & Moradi, 2013), and Salvadorian (Garcia et al., 2014). However, from both theoretical and methodological perspectives, there is still the need to test, validate, and debate different conceptualizations, procedures, and measures for our understanding of the affective profiles model. In addition, very few studies have investigated differences in personality between individuals with distinct profiles, even though the affective profiles model is a model of the affectivity meta-system that not only reflects temperamental dispositions but also personality traits that involve mood-related and social traits. The first part of this volume (Chaps. 1, 2, 3, and 4) focuses on concepts and both old and new methods for the operationalization and measurement of the affective profiles model. Part 2 (Chaps. 5, 6, 7, and 8) addresses individual differences in personality and identity between individuals with distinct profiles in different cultures (e.g., Bulgaria, Indonesia, Sweden, Spain, USA). In the third and final part (Chaps. 9, 10, 11, 12, 13, 14, 15, and 16), we investigate individual differences in health and well-being between individuals with distinct affective profiles in different cultural contexts (e.g., Indonesia, Iran, Italy, Nigeria, Portugal, Sweden) and work settings (e.g., police officers, students, sports, teachers).  Linköping, Sweden

Danilo Garcia

References Adrianson L, Ancok, D., Ramdhani, N., & Archer T (2013) Cultural influences upon health, affect, self-esteem and impulsiveness: an Indonesian-Swedish comparison. International Journal of Research Studies of Psychology, 2, 25-44. DOI: 10.5861/ijrsp.2013.228. Bergman, L. R., Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9(2), 291–319. DOI: 10.1017/ S095457949700206X. Bergman, L. R., Wångby, M. (2014). The person-oriented approach: a short theoretical and practical guide. Eesti Haridusteaduste Ajakiri, 2: 29–49. DOI: 10.12697/eha.2014.2.1.02b. Cloninger, C. R., & Garcia, D. (2015). The Heritability and Development of Positive Affect and Emotionality. In M. Pluess (Ed.), Genetics of Psychological Well-Being – The Role of Heritability and Genetics in Positive Psychology (pp. 97-113). New York: Oxford University Press. Cloninger, C. R., Svrakic, N. M., Svrakic, D. M. (1997). Role of personality self-organization in development of mental order and disorder. Development and Psychopathology, 9(04), 881-906. De Caroli, M. E., & Sagone, E. (2016). Resilience and psychological well-being: differences for affective profiles in Italian middle and late adolescents. Revista INFAD de Psicologia, 1:149-160. DOI: 10.17060/ijodaep.2016.n1.v1.237. Di Fabio, A., & Bucci, O. (2015). Affective profiles in Italian high school students: life satisfaction, psychological well-being, self-esteem, and optimism. Frontiers in Psychology, 6:1310. DOI: 10.3389/fpsyg.2015.01310. Garcia, D. (2011). Adolescents’ happiness: the role of the affective temperament model on memory and apprehension of events, subjective well-being and psychological well-being. PhD thesis, University of Gothenburg, Gothenburg, Sweden

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Garcia, D. (2012). The Affective Temperaments: Differences between Adolescents in the Big Five Model and Cloninger’s Psychobiological Model of Personality. Journal of Happiness Studies, 13: 999–1017. DOI: 10.1007/s10902-011-9303-5. Garcia, D. (2018). Affective Profiles Model. In V. Zeigler-Hill & T. Shackelford (Eds.), Encyclopedia of Personality and Individual Differences (pp. 1-7). Cham, Switzerland: Springer. DOI: 10.1007/978-3-319-28099-8_2303-1. Garcia, D., Ghiabi, B., Rosenberg, P., Nima, A. A., & Archer, T. (2015b). Differences between Affective Profiles in Temperament and Character in Salvadorians: The Self-fulfilling Experience as a Function of Agentic (Self-directedness) and Communal (Cooperativeness) Values. International Journal of Happiness and Development, 2, 22-37. DOI: 10.1504/ IJHD.2015.067592. Garcia, D., MacDonald, S., & Archer, T. (2015a). Two Different Approaches to The Affective Profiles Model: Median Splits (Variable-Oriented) and Cluster Analysis (Person-Oriented). PeerJ, 3:e1380. DOI: 10.7717/peerj.1380. Garcia, D., & Moradi, S. (2013). The affective temperaments and well-being: Swedish and Iranian adolescents’ life satisfaction and psychological well-being. Journal of Happiness Studies 14, 689-707. DOI 10.1007/s10902-012-9349-z. Garcia, D., Siddiqui, A. (2009a). Adolescents’ psychological well-being and memory for life events: Influences on life satisfaction with respect to temperamental dispositions. Journal of Happiness Studies, 10(4), 407-419. DOI: 10.1007/s10902-008-9096-3. Garcia, D., & Siddiqui, A. (2009b). Adolescents’ affective temperaments: Life satisfaction, interpretation, and memory of events. The Journal of Positive Psychology, 4(2), 155-167. DOI: 10.1080/17439760802399349. Kunst, M. J. J. (2011). Affective personality type, post-traumatic stress disorder symptom severity and post-traumatic growth in victims of violence. Stress and Health, 27:42–51. Norlander, T., Bood, S-Å., Archer, T. (2002). Performance during stress: affective personality, age and regularity of physical exercise. Social Behavior and Personality, 30, 495-508. Norlander, T., Johansson, Å., & Bood, S. Å. (2005). The affective personality: Its relation to quality of sleep, well-being and stress. Social Behavior and Personality: An International Journal, 33(7), 709–722. https://doi.org/10.2224/sbp.2005.33.7.709 Orri, M., Pingault, J-P., Rouquette, A., Lalanne, C., Falissard, B., Herba, C., Côté S., & Berthoz, S. (2017). Identifying affective personality profiles: A latent profile analysis of the Affective Neuroscience Personality Scales. Scientific Reports, 7, 4548. DOI 10.1038/s41598-017-04738-x. Schütz, E., Sailer, U., Nima, A., Rosenberg, P., Andersson-Arntén, A-C., Archer, T., & Garcia, D. (2013). The affective profiles in the USA: happiness, depression, life satisfaction, and happiness-increasing strategies. PeerJ 1:e156. DOI 10.7717/peerj.156.

Contents

Part I Concepts and Methods  1 The  Story of the Affective Profiles Model: Theory, Concepts, Measurement, and Methodology����������������������������    3 Danilo Garcia  2 The  (Mis)measurement of the Affective Profiles Model: Should I Split or Should I Cluster?��������������������������������������������������������   25 Danilo Garcia and Shane MacDonald  3 Innovative  Methods for Affectivity Profiling: Latent Profile Analysis����������������������������������������������������������������������������   49 Danilo Garcia, Maryam Kazemitabar, Ricardo Sanmartín, and Shane McDonald  4 Innovative  Methods for Affectivity Profiling: Quantitative Semantics����������������������������������������������������������������������������   67 Danilo Garcia and Sverker Sikström Part II Individual Differences in Personality and Identity  5 Differences  in Temperament and Character Among Americans and Swedes with Distinct Affective Profiles����������   91 Danilo Garcia and Erica Schütz  6 A  Mad Max World or What About Morality? Moral Identity and Subjective Well-Being in Indonesia����������������������  111 Danilo Garcia and Lillemor Adrianson  7 Affectivity  in Bulgaria: Differences in Life Satisfaction, Temperament, and Character����������������������������������������������������������������  127 Danilo Garcia, Patricia Rosenberg, Drozdstoj Stoyanov, and C. Robert Cloninger

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 8 Affective  Latent Profiles and Personality Dimensions in Spanish Children������������������������������������������������������������  145 Ricardo Sanmartín, Danilo Garcia, María Vicent, Carolina Gonzálvez, and José Manuel García-Fernández Part III Differences in Health and Well-Being  9 Affective  Profiles in Sport Settings: Antecedents, Outcomes, and Implications for Intervention ��������������������������������������  161 Martinent Guillaume 10 Affective  Profiles, Health, and Well-Being in Indonesia����������������������  175 Lillemor Adrianson 11 Affectivity  and Well-Being in Italian Samples of Adolescents and Young Adults������������������������������������������������������������  191 Elisabetta Sagone and Maria Elvira De Caroli 12 Differences  in Procrastination, Well-being, and Average Grades in Exams Among Italian University Students with Different Affective Profiles��������������������������������������������������������������  207 Elisabetta Sagone, Maria Elvira De Caroli, and Maria Luisa Indiana 13 Affective  Profiles, Ethnic Identity, and Life Satisfaction in Iran��������  225 Mojtaba Habibi Asgarabad, Danilo Garcia, Fatemeh Jafari, Mohammadali Taghizadeh, and Maede Sadat Etesami 14 Nigerian  Teachers’ Affective Profiles and Workplace Behavior����������  245 JohnBosco Chika Chukwuorji, Precious Eze, Chidera Charity Ugwuanyi, Nneoma Gift Onyedire, Ebele Evelyn Nnadozie, and Danilo Garcia 15 The  “Cold Case” of Individual Differences in Organizational Psychology: Learning Climate and Organizational Commitment Among Police Personnel��������������������������������������������������  269 Danilo Garcia, Fredrik Ryberg, Ali Al Nima, Clara Amato, Erica Schütz, Erik Lindskär, and Patricia Rosenberg 16 Stability  and Change in Portuguese Adolescents’ Affective Profiles over a 2-Year and a 6-Year Period���������������������������  287 Susana Pedras, Magda Rocha, Danilo Garcia, Sara Faria, and Paulo A. S. Moreira Index������������������������������������������������������������������������������������������������������������������  313

About the Editor

Danilo  Garcia, Ph.D.  (in psychology in 2012), is an Associate Professor at the University of Gothenburg (2015) and a Senior Associate Professor at Linköping University (2022). He is also one of the founders and the head of research of the International Network for Well-Being, a network of senior and junior researchers and students interested in the Science of Well-Being. The International Network for Well-Being works on innovations in health and practice through interdisciplinary scientific research, person-centered methods, community projects, and the dissemination of knowledge to promote well-being. Dr. Garcia has over 350 publications including scientific articles, chapters, encyclopedia entries, scientific presentations, and books.

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Part I

Concepts and Methods

Chapter 1

The Story of the Affective Profiles Model: Theory, Concepts, Measurement, and Methodology Danilo Garcia

Introduction Twenty years ago, Archer and colleagues (Norlander et  al., 2002) coined the notion of the affective profiles model1 by proposing four possible combinations of people’s experience of high/low positive affect and negative affect—(1) high positive affect and low negative affect: self-fulfilling profile2; (2) low positive affect

 In the earlier papers, the affective profiles were referred as affective personalities (e.g., Norlander et al., 2002, 2005), later as affective temperaments (e.g., Garcia & Siddiqui, 2009a, b); and about 10 years after their first appearance in the literature, as a model of affectivity that conveys more than temperamental dispositions but that does not cover the full concept of personality (Garcia, 2011), hence, the term affective profiles model. 2  The self-fulfilling profile was first labeled self-actualizing after the term coined and later revised by Maslow (1943, 1969). However, even though Maslow (1969) criticized and revised self-actualization to include self-transcendent values, self-actualization is often thought as a need at the top of physiological needs, the need for safety, the need for love and belonging, and the need for self-esteem. Therefore, self-actualizing was exchanged for self-fulfilling. The term self-fulfillment, which is more common in philosophy, is of course related to the term selfactualization and even the term self-realization, which are more common in psychology, but self-fulfillment represents the attainment of a satisfying and worthwhile life well lived (Gewirth, 2009) that not necessary is at the top of a hierarchical model of human needs—self-fulfillment 1

D. Garcia (*) Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden Centre for Ethics, Law and Mental Health (CELAM), University of Gothenburg, Sweden Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being, Sweden Department of Psychology, University of Gothenburg, Gothenburg, Sweden Department of Psychology, Lund University, Lund, Sweden © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Garcia (ed.), The Affective Profiles Model, https://doi.org/10.1007/978-3-031-24220-5_1

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D. Garcia

and low negative affect: low affective profile; (3) high positive affect and high negative affect: high affective profile; and (4) low positive affect and high negative affect: self-destructive profile. This line of research has distinguished individual differences in biological, psychological, and social constructs between and within individuals with distinct affective profiles (see Fig. 1.1 for a compilation of some of these results). Most of past studies have been conducted among Swedes and investigate individual differences in ill-being, well-being, and personality (e.g., Garcia & Siddiqui, 2009a, b; Garcia, 2011, 2012; Schütz, 2015). Recently, some researchers have used the affective profiles model to discern individual differences in psychosocial constructs in a variety of populations, such as French (Orri et  al., 2017), US residents (Schütz et  al., 2013a), Dutch (Kunst, 2011), Italian (De Caroli & Sagone, 2016; Di Fabio & Bucci, 2015), Indonesian (Adrianson et  al., 2013), Iranian (Garcia & Moradi, 2013), and Salvadorian (Garcia et al., 2015a). These studies have shown identical results to those among Swedes (Garcia, 2018). Individuals with a self-fulfilling profile are constantly depicted as high in energy, high in locus of control, high in life satisfaction, high in optimism, high in psychological well-being, high in autonomy, self-acceptance, and responsibility, being hard-workers and conscientious, and high in self-regulatory strategies related to agentic (e.g., locomotion, frequently exercising, active leisure, goal-pursuit) and communal behavior (e.g., helpful, empathetic, socially tolerant). In contrast, individuals with a self-destructive profile are depicted as experiencing low levels in all these well-being measures along high levels in shyness, anticipatory worry, neurotic behavior, and self-regulatory strategies that promote inaction and rumination (e.g., assessment, suppression of negative thoughts) and anti-social behavior (e.g., manipulation, deceit, mistrust in others, cynicism). Moreover, individuals with a self-­fulfilling profile do not only report higher well-being but also lower levels in many ill-being constructs, such as depression, anxiety, pessimism, and sleep disorders and problems. As it could be expected, individuals with a self-destructive profile experience higher levels in these ill-being constructs, thus suggesting generalizability across the life span, gender, and cultures. Nevertheless, these cross-cultural studies have also added some new insights. For example, while the self-fulfilling experience (high positive affect, low negative affect) is a function of behavior related to agentic (i.e., self-directed behavior), communal (i.e., cooperative behavior), and spiritual behavior among US residents (Schütz et  al., 2013a) and Salvadorians (Garcia et al., 2015a), spirituality is not usually associated with the self-fulfilling affective experience among Swedes (Schütz et al., 2013a; see also Chap. 5 in this volume)—indeed, according to the World Value Survey (Inglehart, 2018a, b), Sweden is a country at the extreme of both secular-rationalist values (i.e., less emphasis on religion, tradition, and authority) and self-expressive values (i.e., more emphasis on social tolerance, life satisfaction, public expression, has been proposed as superior to other values and goals and involves altruism (Gewirth, 2009), thus, more in line with what Maslow (1969) intended to convey: (a) the role of meaning of life; (b) the motivational roots of altruism, social progress, and wisdom; and (c) the need for spirituality (cf. Koltko-Rivera, 2006).

1  The Story of the Affective Profiles Model: Theory, Concepts, Measurement…

5

High Positive Affect Self-Fulfilling

High Affective

• High levels of psychological well-being: autonomy, positive relations with others, environmental mastery, self-acceptance, personal growth, and purpose in life. • High levels of subjective well-being: life satisfaction, positive affect, low negative affect, and harmony in life. • Low levels of ill-being: depression, stress, and sleeping and psychophysiological problems.

• Other important characteristics: frequently physically active, high on spiritual behavior, high in energy, high in locomotion (‘just do it’ mentality), and low in assessment (pensiveness and rumination).

• Low levels of psychological well-being: autonomy. • High levels of subjective well-being: life satisfaction, positive affect, and harmony in life. • Low levels of subjective well-being: high negative affect. • Low levels of ill-being: depression. • High levels of ill-being: stress and sleeping and psychophysiological problems. • Personality: high in Neuroticism, high in Extraversion, high in Harm Avoidance, high in Reward Dependence, high in Self-directedness, low in Self-transcendence, high in Machiavellianism, high in Psychopathy, and high in Narcissism. • Other important characteristics: frequently physically active, high in energy, high in locomotion (‘just do it’ mentality), and high in assessment (pensiveness and rumination).

Low Affective

Self-Destructive

• High levels of psychological well-being: autonomy, environmental mastery, selfacceptance • High levels of subjective well-being: life satisfaction, low negative affect, and harmony in life. • Low levels of subjective well-being: low positive affect. • Low levels of ill-being: depression and stress. • High levels of ill-being: psychophysiological and sleeping problems. • Personality: low in Extraversion, high in Emotional Stability, low in Persistence, low in Self-directedness, low in Cooperativeness, low in Machiavellianism, low in Psychopathy, and low in Narcissism. • Other important characteristics: not physically active, low in energy, low in locomotion (‘just do it’ mentality), high in assessment (pensiveness and rumination).

• Low levels of psychological well-being: autonomy, positive relations with others, environmental mastery, self-acceptance, personal growth, and purpose in life.

High Negative Affect

Low Negative Affect

• Personality: low in Neuroticism, high in Extraversion, low in Harm Avoidance, high in Persistence, high in Self-directedness, high in Cooperativeness, low in Machiavellianism, low in Psychopathy, and low in Narcissism.

• High levels of psychological well-being: environmental mastery, self-acceptance, personal growth, and purpose in life.

• Low levels of subjective well-being: life satisfaction, low positive affect, high negative affect, and harmony in life. • High levels of ill-being: depression, stress, and psychophysiological and sleeping problems. • Personality: high in Introversion, high in Neuroticism, low in Persistence, high in Harm Avoidance, low in Self-directedness, low in Cooperativeness, high in Machiavellianism, high in Psychopathy, and high in Narcissism. • Other important characteristics: not physically active, low in spiritual behavior, low energy, low in locomotion (‘just do it’ mentality), high in assessment (pensiveness and rumination),

Low Positive Affect

Fig. 1.1  Summary of results using the affective profiles model during the past 20 years. (Note. Adapted from Cloninger and Garcia (2015))

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aspiration to liberty, and even spirituality); but where, in contrast to what selfexpressive values convey, spiritual behavior is somewhat frown upon (Thurfjell, 2015). Some of the most important differences, however, have been discerned between individuals who are similar in one affectivity dimension but dissimilar in the other (i.e., matched differences). Indeed, matched differences show, for example, that life satisfaction is higher among individuals with a low affective profile than among individuals with a self-­ destructive profile. In other words, suggesting that when negative affect is low, an individual with low levels of positive affect can still experience life as satisfying. This person-oriented way of analyzing and discussing results shades some light on how changes in a third variable under study would lead to increases/decreases in one affectivity dimension, while the other affectivity dimension is kept constant (Garcia, 2018; Garcia et al., 2015b). For instance, matched comparisons (see Fig. 1.2) help to discern that increases in psychological well-being constructs, such as positive relations with others, might lead to increases in positive affect when negative affect is high (self-destructive vs. high affective) and when negative affect is low (low affective vs. self-fulfilling) and to decreases in negative affect when positive affect is low (self-destructive vs. low affective) and when positive affect is high (high affective vs. self-fulfilling). Put in another way, the ability to create and maintain warm and close relationships with other people seems to allow us to experience feelings of engagement, interest, inspiration, etcetera both in times of calm (i.e., when we have few negative emotional experiences) or hardship (i.e., when frequently experiencing feelings such as distress, irritability, anxiety, and etcetera). This same pattern was found for increases in environmental mastery (i.e., a person’s ability to organize, control, and personal flexibility to adapt and shape life conditions) and self-acceptance (i.e., the ability to accept all aspects of the self). In contrast, increases in personal growth do not lead to fewer negative feelings and emotions when positive affect is low (self-destructive vs. low affective), but to high positive affect when negative affect is high (self-destructive vs. high affective) and when negative affect is low (low affective vs. self-fulfilling) and to low negative affect when positive affect is high (high affective vs. selffulfilling). In other words, at least for an individual with a self-destructive profile, increases in the sense of life as a learning experience will not lead to low affectivity by decreasing the amount of negative emotions and feelings that she experiences, it will rather lead to either feeling more engaged and proud and at the same time less irritated and scared (i.e., self-fulfillment) or to more positive emotions and feelings but keeping the same level of negative emotions (i.e., high affectivity). Increases in autonomy were associated to low negative affect when positive affect was low (self-destructive vs. low affective) and when positive affect was high (high affective vs. self-fulfilling) and to high positive affect when negative affect was low (low affective vs. self-fulfilling). In contrast, increases in autonomy were not linked to high positive affect when negative affect was high (selfdestructive vs. high affective). That is, increases in our sense of being able to take decisions and resolutions, resist social pressures, and to control and regulate personal behavior when interacting with others, help us to experience fewer negative

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Fig. 1.2  Theoretical affective profile change based on diametrical differences and matched differences in six psychological well-being constructs between and within individuals with distinct affective profiles (Garcia, 2018). (Note. Black Arrows: Differences in psychological well-being between individuals with affective profiles that are diametrically distinct in both affectivity dimensions (i.e., between-differences): self-destructive vs. self-fulfilling (low-high positive affect vs. high-low negative affect) and low affective vs. high affective (low-high positive affect vs. low-high negative affect). Grey Arrows: Differences in psychological well-being between individuals who are similar in one affectivity dimension and dissimilar in the other (i.e., within-differences): self-­ destructive vs. high affective (matching: high-high negative affect, differing: low-high positive affect), self-destructive vs. low affective (matching: low-low positive affect, differing: high-low negative affect), high affective vs. self-fulfilling (matching: high-high positive affect, differing: high-low negative affect), and low affective vs. self-fulfilling (matching: low-low negative affect, differing: low-high positive affect). Reprinted with permission from Well-Being and Human Performance Sweden AB)

emotions independently of our experience of positive emotions, but it probably does not increase positive affect per se. These and other differences and similarities show that distinct combinations of positive affect and negative affect within the individual yield outcomes that mirror the unique type of self-regulation each individual needs for achieving adaptation and homeostasis but also well-being and self-fulfillment (Garcia, 2009, 2011, 2018; Garcia et al., 2010). Next, I briefly introduce the concepts, the theoretical underpinnings, the ways of measurement, and the methodology behind the affective profiles model to disclose the framework in which the Chapters in this volume were developed.

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Two Concepts: Positive Affect and Negative Affect At a general level, I argue that affect is a conscious experience of emotions and moods. That is, our conscious apprehension of: 1. The feelings that are derived from automatic emotional responses, generated by our own nervous system, to internal (e.g., thoughts) and external (e.g., a negative or positive life event) stimuli. 2. The feelings that we tend to experience day-to-day and that color our outlook. In this context, as described in Chap. 9 in this volume, emotions are psychophysiological reactions that are elicited following an appraisal process of specific stimuli, while moods are predominant feelings on how we see the world as a whole and our place in it. Watson et al. (1988) and Watson and Clark (1994) have suggested that positive affect is an independent affectivity dimension that varies from pleasant engagement (high positive affect, such as enthusiastic, active) to unpleasant disengagement (low positive affect, such as sad, bored), while negative affect, also an independent affectivity dimension, varies from unpleasant engagement (high negative affect, such as anger, fear) to pleasant disengagement (low negative affect, such as calm, and serene). However, there is evidence (Russell, 1980) that rather than being completely independent, affect might be inter-related in a two-dimensional circumplex model3 containing not only arousal (vertical axis: activation vs. deactivation) but also a valence dimension (horizontal axis: pleasant vs. unpleasant). For example, while Watson et al. (1988) see serenity as low levels of negative affect, Russell and Feldman-Barrett (1999) conceptualize serenity as a pleasant feeling with low activation. Moreover, it is plausible to feel satisfied and content, both are pleasant feelings and in-between the activation and deactivation axis; whereas it is difficult to feel calm and engaged at the same time—calm is a pleasant but deactivated emotion and engagement is also a pleasant emotion but highly active. Hence, an important question is if modeling affect in a circumplex has any implications on the conceptualization of each profile in the affective profiles model? For instance, is high pleasant engagement (e.g., feeling proud, active, etc. to a great extent) and low unpleasant engagement (e.g., feeling afraid, irritated, etc. to a less extent) the best way to conceptualize the self-fulfilling experience? Despite the criticism (e.g., Larsen & Diener, 1992), I argue that researchers interested in the affective profiles model need to have good knowledge and hands-on experience of the circumplex model of affect. Independently of the conceptualization, most research see positive affect and negative affect as indicators or markers of people’s well-being (Diener, 1984; see Lyubomirsky et  al., 2005 for an overview of relevant studies) or define them as emotional states rather than a “trait-like” dimensions. This confusion in the  The most recent revised circumplex model of affect is suggested as representative of core affect, that is, the most basic feelings, not necessarily directed towards any specific stimuli (Russell & Feldman-Barrett, 1999). 3

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literature, however, is probably due to the lack of knowledge and consensus about what affect is and what the roots and function of affectivity are.

One Theory: Affectivity as a Complex Adaptive Meta-System Although it is easy to think of positive affect and negative affect as opposite ends of a single continuum, there is extensive evidence that they are best thought as two distinct and dissociable phenomena rather than part of a unidimensional model (cf. MacLeod & Moore, 2000; Ito & Cacciopo, 1998; for a review, see Cloninger & Garcia, 2015). First, positive affect and negative affect are suggested to represent more general biobehavioral systems that lead to different motivational orientations. Positive affect is related to the Behavioral Activation System (BAS) or sensitivity to reward as well as approach motivation, while negative affect is related to the Behavioral Inhibition System (BIS) or sensitivity to signals of punishment as well as avoidance motivation (Gray, 1981; Watson, 2002). Also in this line, the Broaden-­ and-­Build Theory (Fredrickson, 1998, 2005, 2006) posits that the function of positive emotions is related to approach-related behavior, which builds an individual’s resources for survival and well-being, while negative emotions inhibit behavior that might lead to pain, punishment, or some other undesirable consequence. Moreover, dispositional etiologies of affectivity posit that positive affect has its origin in Extraversion, while negative affect arises in Neuroticism (e.g., Costa & McCrae, 1980, 1984). For instance, individuals who score high on Extraversion attend and react more intensely to positive stimuli than individuals with low levels of positive affect, that is, those who are high in Introversion (Larsen & Ketelaar, 1991). In contrast, individuals who score high on Neuroticism attend and react more intensely to negative stimuli than individuals with low levels of negative affect, that is, those who are high in Emotionally Stability (Larsen & Ketelaar, 1991). Conversely, situational etiologies of affectivity posit that both positive affect and negative affect are related to the experience of pleasant and unpleasant life events, respectively (e.g., Warr et al., 1983). One way or the other, most researchers nowadays agree that positive affect and negative affect are, besides markers of well-being, two distinctive affectivity dimensions that reflect signal sensitivity systems and stable emotional-­ temperamental dispositions (e.g., Tellegen, 1993; Watson & Clark, 1994; Watson et al., 1988). Nevertheless, the situational and dispositional explanations of the origin of affect do not explain the evidence that supports that positive affect and negative affect are two independent dimensions (Baker et al., 1992)—positive affect has primarily a situational etiology; negative affect has primarily a dispositional etiology (see also Bradburn, 1969; Diener & Larsen, 1984; Emmons & Diener, 1985). In a study among twins and three-generational families, for example, researchers found significant heritability estimates for negative affect but not for positive affect (Baker et  al., 1992). Moreover, environmental influences that have the

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effect of making siblings more similar to one another (i.e., shared or common environment) explained variances in both negative affect and positive affect. Thus, although the environment appears to play an important role for the development of both positive affect and negative affect, “there may be important (heritable) personality factors that play a critical role in determining levels of negative moods from one person to the next in the family. For positive affect, on the other hand, family resemblance is explained primarily by environmental effects common to family members” (Baker et  al., 1992, p.  162). Moreover, later research (Corr et al., 1995, 1997) suggests that people’s sensitivity to negative stimuli is better predicted by a combination of being neurotic introvert (i.e., high in Neuroticism and low in Extraversion or high Harm Avoidance4) rather than just high levels of Neuroticism as previously thought (cf. Larsen & Ketelaar, 1991), whereas sensitivity to positive stimuli is best predicted by being a stable extravert (i.e., being high in Extraversion and low in Neuroticism or high in Novelty Seeking5) rather than just high levels of Extraversion (cf. Larsen & Ketelaar, 1991). Interestingly, this is in line with recent molecular research showing that the basic unit of personality are personality profiles rather than single traits (Cloninger & Zwir, 2018; Zwir et  al., 2020a, b, 2021; see also Garcia et  al., 2022; Moreira et  al., 2021). More importantly, other personality traits, such as Persistence,6 which was not well measured in early personality models like those by Eysenck or Tellegen, are also critical for distinguishing anxiety and depression and associated to the presence or lack of positive affect (e.g., Cloninger et al., 2012; Garcia et al., 2012). For example, anxiety is associated with high Harm Avoidance (i.e., neurotic introversion) and high Persistence (i.e., conscientious), whereas depression is associated with high Harm Avoidance and low Persistence (Cloninger et  al., 2012)—indeed, regarding affectivity, anxiety is suggested as a state of high negative affect whereas depression is a mixed state of high negative affect and low positive affect (Clark & Watson, 1991). In addition, character development in

 Harm Avoidance, associated with the neurotransmitter serotonin, is the tendency to avoid or cease behaviors due to intense response to aversive stimuli expressed as fear of uncertainty, shyness of strangers, quick fatigability, and pessimistic worry of future problems (see Chaps. 5 and 7 in this volume). 5  Novelty Seeking, associated with the neurotransmitter dopamine, is the tendency of frequent activation or initiation of behaviors in response to novel stimuli, potential rewards, and punishments expressed as frequent exploration of new unfamiliar places or situations, quick loss of temper, impulsive decision-making, and active avoidance of monotony (see Chaps. 5 and 7 in this volume). 6  Persistence, associated with the neurotransmitter noradrenaline, is expressed as individual differences in eagerness, ambition, perfectionism, overachieving, and the tendency to persevere despite fatigue or frustration (see Chaps. 5 and 7 in this volume). 4

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three dimensions, Self-directedness,7 Cooperativeness,8 and Self-Transcendence,9 is a strong determinant for the promotion of well-being along with positive affect and negative affect and, if poorly developed, a strong determinant for vulnerability to anxiety, depression, and other types of mental illness (Cloninger et al., 2012; Schütz et al., 2013b). Thus, positive affect and negative affect are dimensions that go beyond being independent markers of well-being, or just two separate signal sensitivity systems, or single temperamental dispositions (for a review see Gunderson et  al., 1999). Put in another way, the affectivity dimensions are not only temperamental dispositions but may also depend on personality traits that involve more mood-­related and social features than Extraversion and Neuroticism. In short, I argue that positive affect and negative affect are independent but inter-­ related dimensions rather than two ends of one dimension or two separate dimensions that are totally independent, that is, affectivity is a complex dynamic adaptive meta-system with two independent inter-related subsystems (Garcia et al., 2015b; Garcia, 2011, 2018). The interaction of these two independent but inter-related affectivity dimensions implies that individuals do not only differ in affectivity between each other but also within themselves. From a person-oriented framework, these two affectivity dimensions within the individual can be seen as interwoven components with whole-system properties (Bergman & Wångby, 2014). The outlook of the individual as a whole-system unit is then best studied by analyzing patterns of information (Bergman & Wångby, 2014). Although at a theoretical level there is a myriad of probable patterns of combinations of people’s levels of positive and negative affect, if viewed at a global level, there should be a small number of more frequently observed patterns or “common profiles” (Bergman & Wångby, 2014; Bergman & Magnusson, 1997; see also Cloninger et al., 1997, who explain nonlinear dynamics in complex adaptive systems). From this perspective, the affective profiles model coined by Archer and colleagues (e.g., Norlander et al., 2002, 2005) go beyond the view of affect as two separate subsystems and consider their interaction within a meta-system that is best represented as combinations of high-­ to-­low positive affect and high-to-low negative affect (see Keren & Schul, 2009 for a point of view on two-system theories).

 Self-directedness refers to a person’s concept of the self as an autonomous individual and is characterized by individual differences in responsibility, purposefulness, resourcefulness, self-acceptance, and self-actualization. That is, a person’s ability to control, regulate, and adapt her own behavior and emotional reactions in accordance with being self-sufficient, self-acceptant, responsible, reliable, and effective (see Chaps. 5 and 7 in this volume). 8  Cooperativeness refers to a person’s concept of the self as an integrated part of society and is characterized by individual differences in the capacity for identification with and acceptance of others. In other words, a person’s ability to show social tolerance, empathy, helpfulness, compassion, and conscience (see Chaps. 5 and 7 in this volume). 9  Self-transcendence refers to a person’s concept of the self as part of something beyond the self (e.g., nature, humanity, the universe). In other words, individual differences in self-forgetfulness, transpersonal identification, spiritual acceptance, contemplation, and idealism (see Chaps. 5 and 7 in this volume). 7

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Measurement: Operationalizing Affective Experience The most commonly used measure, if not the only one,10 to operationalize affectivity when the affective profiles model is implemented in research is the Positive Affect Negative Affect Schedule (PANAS), which is a self-report instrument developed by Watson et al. (1988). Initially, Watson et al. (1988) conducted several analyses on 60 adjectives that were earlier identified as weakly correlated to each other but that loaded in one single affectivity dimension (Zevon & Tellegen, 1982). These analyses yielded 10 adjectives to measure positive affect11 and 10 adjectives to measure negative affect12 that are best represented as orthogonal factors due to the opposing pleasant–unpleasant relationship in the factor loadings (Watson et  al., 1988). The PANAS has a five-point response Likert scale ranging from 1 (“not at all”) to 5 (“very much”); it has been used in several studies and has displayed high internal consistency (e.g., Cronbach’s alphas ranging between 0.83 to 0.90 for positive affect and between 0.85 to 0.93 for negative affect). The two-factor solution of the PANAS has been replicated several times, but sometimes as correlated factors and at other times with subfactors within each dimension as uncorrelated or correlated first-order factors (see Crawford & Henry, 2004; Nima, 2022). Importantly, the PANAS’ adjectives reflect high activation affect (Watson et al., 1988) and therefore do not include low-activation emotions such as “Sad,” “Depressed,” and “Bored” (cf. Russell & Feldman-Barrett, 1999). Other scales, such as the Emotional Well-Being Scales by Diener et  al. (2009) include both high- and low-activation emotions. As stated earlier, it is plausible that using scales that differ in which aspect of activation (i.e., low or high) they measure might yield different results regarding the affective profiles. Thus, using the PANAS as the only self-report to operationalize the affective profiles model needs to be critically and systematically challenged. In addition, emotions might also have a third aspect that is often neglected in the literature of the affective profiles model, namely, frequency vs. intensity. Indeed, most of the times researchers discuss valence (positive vs. negative) or activation (high vs. low) but forget that emotions can be experienced frequently and with low intensity, frequently and with high intensity, infrequently and with low intensity, and infrequently with high intensity—how often do we feel sadness and how intensively we experience it; are two different aspects of sadness, which is a  To the best of my knowledge, however, only one study has used other measure than the PANAS to operationalize the affective profiles. Orri et  al. (2017) used the Affective Neuroscience Personality Scales (Davis et al., 2003), which were developed to measure six affective neurobiological systems: play, seek, care, fear, anger, and sadness. Nevertheless, this scale has been criticized for having several problems, including poor factor structure and questionable content validity (Barrett et  al., 2013). Moreover, this scale seems to rather measure personality in the form of temperamental dispositions, rather than affectivity per se. 11   Interested, Enthusiastic, Proud, Alert, Inspired, Determined, Attentive, Active, Excited, and Strong. 12  Distressed, Upset, Guilty, Afraid, Hostile, Irritable, Ashamed, Nervous, Jittery, and Scared. 10

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low-­activation and unpleasant emotion. This aspect of our emotions is probably what gives the affectivity meta-system its’ power or strength, in turn, influencing to a different extent our intentional and unconscious decisions and behavior and even our health. For example, research on subjective well-being shows that how often people experience specific emotions, rather than how intensive, is a better measure of people’s emotional well-being and that the tendency to experience intensive emotions is detrimental for our health (Diener et al., 1991a, b; Schimmack & Diener, 1997). Interestingly, people who experience emotions with high intensity do so in both affectivity dimensions (i.e., positive affect and negative affect), while we either experience positive emotions frequently and negative emotions infrequently or vice versa (Diener & Iran-Nejad, 1986; Diener et al., 1991a, b; Myers & Diener, 1995; Garcia & Erlandsson, 2011)—that is the intensity of positive affect and negative affect is positively correlated but their frequency is inversely related. Moreover, personality psychology research has shown that individuals who experience emotions intensively are less physiologically aroused, tend to be more sociable, more impulsive, and more extraverted (Gilboa & Revelle, 1994). Neuroticism, on the other hand, might be related to the experience of intensive and frequent negative emotions, in turn, making the experience of frequent positive affect less probable (Garcia & Erlandsson, 2011). Thus, depending on which aspect we are measuring, frequency vs. intensity, we might find different results regarding individual differences in personality (Garcia & Erlandsson, 2011). Also in this line, when people are instructed to report to what extent, as is done in the PANAS, rather than how often they have experienced specific emotions, their responses vary depending on the time frame they are expected to recall. If they are asked to recall a long time period (e.g., last year), people report rare but intense emotions, but if asked to report a short time period (e.g., last week) they report frequent but less intense emotions (Winkielman et al., 1998). In sum, affect is not only a matter of valence (i.e., positive vs. negative) and activation (i.e., high activation vs. low activation), it is also a matter of power (frequency vs. intensity)—affect is tridimensional in nature (see Chap. 1 in Volume 2). Hence, the psychometric properties, framing of the instructions (e.g., how often, how intensive, to what extent), and time period that participants are asked to recall (e.g., last weeks, last year) need still to be critically researched in the context of the affective profiles model.

 ethodology: Median Split, Percentiles Split, Cluster M Analysis, and Latent Profile Analysis The most common approach to the categorization of individuals in different affective profiles is by means of median splits. Basically, individuals’ self-reported scores on positive affect and negative affect are divided into high and low in reference to the median (Norlander et al., 2002, 2005). The individuals’ high and low

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scores in each affectivity dimension are then combined into the four profiles—the percentiles method is very similar with few practical differences.13 Since median splits distort the meaning of high and low, it is plausible to criticize the validity of this approach to create the affective profiles (Garcia et  al., 2015b)—scores just-­ above and just-below the median become high and low by arbitrariness, not by reality; which means that the median splits method is variable-oriented because it categorizes individuals in different affective profiles based on each variable’s cut-­ off scores rather than on actual similarities in both dimensions between people (see also Chap. 2 in this volume). Thus, there is a risk that dichotomizing positive affect and negative affect using median splits might lead to spurious main effects (cf. MacCallum et al., 2002; Maxwell & Delaney, 1993; Fitzsimons, 2008). Nevertheless, there is recent evidence of the statistical robustness and validity of the use of median splits under certain conditions, such as when multicollinearity is low between the dichotomized variables (Iacobucci et al., 2015a, b)—which is the case for positive affect and negative affect. In short, although there is a risk for misleading results when using median splits, stating that median splits produce inferior analytic conclusions is a simplification and misconception of the real issue (Iacobucci et  al., 2015a, b). Other researchers have suggested the use of K-means cluster analysis to ensure that individuals are assigned to a profile with people that are most similar to them (see Kormi-Nouri et al., 2015; MacDonald & Kormi-Nouri, 2013). In this respect, cluster analytic methods are data-driven and create profiles that are relative to each other, thus, regarded as person-oriented (Garcia et al., 2015b). Data-driven methods, compared to median splits, come closer to modeling the dynamic nature of within- and between-group variability of individual patterns of affectivity, while the median splits method yields profiles that are static in nature—in the median split, equally sized groups are pre-determined because each one of the two variables is divided in high and low using the median; which by the way applies to the use of percentiles as well. Thus, it is plausible that K-means cluster analysis is a better method for affectivity profiling than both the median split and percentiles methods. Nevertheless, there is also evidence of median splits being as reliable as cluster methods (Garcia et  al., 2015b; see also Chap. 2 in this volume), so the debate goes on. Indeed, the choice of method (i.e., median splits, percentiles, or K-means cluster analysis) for affectivity profiling might depend on the distribution of the data at  Percentile scores are calculated by mapping scores in positive affect and negative affect to rank the order of each participant in relation to a normal population or sometimes just a larger cohort. Thus, instead of dividing participants’ scores in each affectivity dimension into two equal halves, as in the median splits method; the percentile method divides each affectivity score in 100 equal parts. This allows, for example, to use scores ≥ the 60th percentile as the cutoff for high affectivity and scores ≤59th percentile for low affectivity—if a participant is in the 60th percentile in one affectivity dimension, we then know that she/he scored higher than 60% of the population in that specific affectivity dimension and about 40% lower. 13

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Fig. 1.3  Distribution of positive affect and negative affect in a sample of 2225 US residents (Garcia et al., 2015b). (Note. The vertical yellow line marks the median for positive affect (3.10) and the horizontal blue line marks the median for negative affect (1.70))

hand (Garcia et al., 2015b). For example, in a sample of 2225 US residents (Garcia et al., 2015b) who self-reported affect using the PANAS, we found that the distribution of the positive affect scores was almost normally distributed (skewness = −0.18, kurtosis  =  −0.30), while the negative affect scores were heavily skewed (skewness = 1.12, kurtosis = 0.98). This was probably caused by the fact that, within the value range of the PANAS (1–5), the median for the negative affect scores (1.70) was very close to the minimum (see Fig. 1.3). One plausible solution to this kind of data distribution problems is to combine the methods. If the data have a symmetric and unimodal distribution in one affectivity dimension, it is reasonable to use median splits or percentiles for the categorization of that specific dimension. If the data have a bimodal distribution that can be well separated into two clusters in the other affectivity dimension, it is reasonable to use K-means cluster analysis in that specific dimension instead. In other words, the choice between median splits, percentiles, and K-means cluster analysis for profiling is better considered as dimension-­ wise data dependent (Garcia et al., 2015b).

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A fourth method, albeit less used, is latent profile analysis (LPA). LPA, lays another dimension to the person-oriented approach needed when the affective profiles model is used as the framework for between- and within-individual differences (see Chaps. 3 and 8 in this volume). LPA, in contrast to other clustering algorithms, including K-means cluster analysis, allows for “model-based clustering” in which clusters use a probabilistic model that describes data distribution (Hagenaars & McCutcheon, 2002). In other words, instead of finding clusters with some arbitrary chosen distance measure, with LPA we first find the best number of profiles that describes the data distribution and based on this model we can assess the membership probability for each participant in the latent profiles found in the data. In yet other words, LPA enables us to model the latent structure behind the data, rather than just looking for similarities between cases. In addition, in contrast to other cluster algorithms, LPA allows the use of a statistical model for data selection and assessment of goodness of fit (Hagenaars & McCutcheon, 2002). Hence, in contrast to the bottom-up approach of cluster analyses in which we cluster by finding similarities between participants, LPA is a top-down approach that might overcome the problems caused by unimodal or bimodal data distribution in the different affectivity dimensions, problems that are difficult to handle with cluster methods. In the context of the affective profiles model, LPA seems like a reasonable method since the main assumption of the affective profiles model is that there is a “latent structure” that underlies people’s affective experience that is best represented as a complex adaptive meta-system (Garcia, 2011, 2018). Nevertheless, at this point in time, there are very few studies using LPA and none comparing it to the other methods mentioned here.

Concluding Remarks The main issues regarding the affective profiles model are the lack of consensus of the concepts of positive affect and negative affect14 and that, although appealing to the senses, dichotomizing (e.g., dividing affect in high and low) has many limitations (see Keren & Schul, 2009). Therefore, the affective profiles model needs to keep being critically investigated to fully assess its usefulness in research and practice. The studies that are perhaps more urgent are those with a longitudinal design and those addressing methodological issues. By increasing our knowledge about peoples’ affective profile stability and change, for example, may lay the ground for the innovation of activities or interventions that target the affectivity meta-system in a person-centered way (cf. Chap. 16 in this volume).  For a critical review on the problems regarding the conceptualization of affect in psychological research, please see https://replicationindex.com/2022/05/28/hedonimus/ 14

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As any other scientific model, the affective profiles model represents complex phenomena in a logical but simplified way (cf. Apostel, 1960; Atkinson, 1960; Chakravartty, 2010; Toon, 2010). In short, it represents affectivity as a complex adaptive meta-system in which positive affect and negative affect are independent subsystems but that interact with each other in a nonlinear manner to self-regulate emotions, thoughts, behavior, and general functioning in different but predictable ways (Garcia, 2011, 2018).

The Present Volume: Our 2 Cents Worth The first part of this volume focus on concepts and methods for the operationalization and measurement of the affective profiles model (Chaps. 1, 2, 3, and 4). In Chap. 2, we discuss and present results regarding the median split method, while in Chaps. 3 and 4 we present two innovative methods for affectivity profiling: latent profile analysis, which is a top-down approach in which profiling starts with describing the distribution of the data; and latent semantic analysis, which is a computational method that uses Artificial Intelligence to quantify people’s own written descriptions of different phenomena, such as how they perceive their own well-­ being. In the second part of this volume (Chaps. 5, 6, 7, and 8), we address individual differences in personality and identity in different cultures (e.g., Bulgaria, Indonesia, Sweden, Spain, USA). In the third and final part (Chap. 9, 10, 11, 12, 13, 14, 15, and 16), we investigate individual differences in health and well-being in different cultural contexts (e.g., Indonesia, Iran, Italy, Nigeria, Portugal, Sweden) and settings (e.g., police officers, students, sports, teachers). By doing this, we hope that the reader gets the tools needed to go beyond the present state of affairs regarding the affective profiles model (see Fig. 1.4 and Table 1.1 for the means and standard deviations across some of the different populations represented in this volume). Acknowledgments  I want to direct my deepest gratitude to Dr. Erica Schütz for her comments to this Chapter. A wise old owl lived in an oak The more he saw the less he spoke The less he spoke the more he heard. Why can’t we all be like that wise old bird? Opie and P.  Opie, The Oxford Dictionary of Nursery Rhymes (Oxford: Oxford University Press, 1951, 2nd edn., 1997), p. 403. Dedicated to Professor Trevor Archer.

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Fig. 1.4  Map diagram displaying the means in positive affect and negative affect across some of the populations studied in this volume. (Note. See Table  1.1 for the descriptive statistics. Minimum = 10; Maximum = 50)

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Table 1.1  Means and standard deviations (±) in positive affect and negative affect across some of the populations in this volume displayed in Fig. 1.3 Country Bulgaria Indonesia Iran Italy Nigeria Sweden USA

Chapter 7 6, 10 13 11, 12 14 5 2, 5

N 443 1094 426 535 527 524 4651

Positive affect 34.37 ± 6.28 39.21 ± 5.06 36.42 ± 6.36 39.21 ± 5.06 35.43 ± 9.77 37.16 ± 6.76 33.50 ± 8.28

Negative affect 19.64 ± 6.87 28.01 ± 8.46 24.38 ± 9.12 28.01 ± 8.46 18.23 ± 7.56 19.22 ± 6.18 21.78 ± 10.15

Note. Minimum = 10; Maximum = 50

References Adrianson, L., Ancok, D., Ramdhani, N., & Archer, T. (2013). Cultural influences upon health, affect, self-esteem and impulsiveness: An Indonesian-Swedish comparison. International Journal of Research Studies of Psychology, 2, 25–44. https://doi.org/10.5861/ijrsp.2013.228 Apostel, L. (1960). Towards the formal study of models in the non-formal sciences. Synthese, 12, 125–161. Atkinson, R. C. (1960). The use of models in experimental psychology. Synthese, 12, 162–171. Baker, L. A., Cesa, I. L., Gatz, M., & Mellins, C. (1992). Genetic and environmental influences on positive and negative affect: Support for a two-factor theory. Psychology of Aging, 7, 158–163. Barrett, F.  S., Robins, R.  W., & Janata, P. (2013). A brief form of the Affective Neuroscience Personality Scales. Psychological Assessment, 25(3), 826–843. https://doi.org/10.1037/ a0032576 Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9(2), 291–319. https://doi. org/10.1017/S095457949700206X Bergman, L. R., & Wångby, M. (2014). The person-oriented approach: A short theoretical and practical guide. Eesti Haridusteaduste Ajakiri, 2, 29–49. https://doi.org/10.12697/eha.2014.2.1.02b Bradburn, N. M. (1969). The structure of psychological well-being. Aldine. Chakravartty, A. (2010). Informational versus functional theories of scientific representation. Synthese, 172, 197–213. Clark, L. A., & Watson, D. (1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100(3), 316–336. https:// doi.org/10.1037/0021-­843X.100.3.316 Cloninger, C.  R., & Garcia, D. (2015). The heritability and development of positive affect and emotionality. In M. Pluess (Ed.), Genetics of psychological well-being – The role of heritability and genetics in positive psychology (pp. 97–113). Oxford University Press. Cloninger, C. R., & Zwir, I. (2018). What is the natural measurement unit of temperament: Single traits or profiles? Philosophical Transactions of the Royal Society B, 373(1744), 20170163. https://doi.org/10.1098/rstb.2017.0163 Cloninger, C. R., Svrakic, N. M., & Svrakic, D. M. (1997). Role of personality self-organization in development of mental order and disorder. Development and Psychopathology, 9(04), 881–906. Cloninger, C. R., Zohar, A. H., Hirschmann, S., & Dahan, D. (2012). The psychological costs and benefits of being highly persistent: Personality profiles distinguish mood disorders from anxiety disorders. Journal of Affective Disorders, 136, 758–766. Corr, P. J., Wilson, G. D., Fotiadou, M., Gray, N. S., Checkley, S., & Gray, J. A. (1995). Personality and affective modulation of the startle reflex. Personality and Individual Differences, 19, 543–553.

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Schütz, E. (2015). The affective profiles model: Ill-being and well-being (PhD thesis). University of Gothenburg, Gothenburg. Schütz, E., Sailer, U., Nima, A., Rosenberg, P., Andersson-Arntén, A.-C., Archer, T., & Garcia, D. (2013a). The affective profiles in the USA: Happiness, depression, life satisfaction, and happiness-increasing strategies. PeerJ, 1, e156. https://doi.org/10.7717/peerj.156 Schütz, E., Archer, T., & Garcia, D. (2013b). Character profiles and adolescents’ self-reported affect. Personality and Individual Differences, 54, 841–844. https://doi.org/10.1016/j. paid.2012.12.020 Tellegen, A. (1993). Folk concepts and psychological concepts of personality and personality disorder. Psychological Inquiry, 4(2), 122–130. Thurfjell, D. (2015). Det gudlösa folket - De postkristna svenskarna och religionen [The People without God - The Post Christ Swedes and Religion]. Stockholm. Toon, A. (2010). The ontology of theoretical modelling: Models as make-believe. Synthese, 172, 301–315. Warr, P., Barter, J., & Brownbridge, G. (1983). On the independence of negative and positive affect. Journal of Personality and Social Psychology, 44, 644–651. Watson, D. (2002). Positive affectivity  – The disposition to experience pleasurable emotional states. In C.  R. Snyder & S.  J. Lopez (Eds.), Handbook of positive psychology. Oxford University Press. Watson, D., & Clark, L. A. (1994). The PANAS-X: Manual for the Positive Affect and 7 Negative Affect Schedule—expanded form. University of Iowa. Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. Winkielman, P., Schwarz, N., & Knäuper, B. (1998). Looking back at anger: Reference periods change the interpretation of emotion frequency questions. Journal of Personality and Social Psychology, 75, 719–728. Zevon, M.  A., & Tellegen, A. (1982). The structure of mood change: An idiographic/nomothetic analysis. Journal of Personality and Social Psychology, 43(1), 111–122. https://doi. org/10.1037/0022-­3514.43.1.111 Zwir, I., Arnedo, J., Del-Val, C., Pulkki-Råback, L., Konte, B., Yang, S.  S., Romero-Zaliz, R., Hintsanen, M., Cloninger, K.  M., Garcia, D., Svrakic, D.  M., Rozsa, S., Martinez, M., Lyytikäinen, L.-P., Giegling, I., Kähönen, M., Hernandez-Cuervo, H., Seppälä, I., Raitoharju, E., de Erausquin, G. A., Raitakari, O., Rujescu, D., Postolache, T. T., Sung, J., Keltikangas-­ Järvinen, L., Lehtimäki, T., & Cloninger, C.  R. (2020a). Uncovering the complex genetics of human temperament. Molecular Psychiatry, 25, 2275–2294. https://doi.org/10.1038/ s41380-­018-­0264-­5 Zwir, I., Arnedo, J., Del-Val, C., Pulkki-Råback, L., Konte, B., Yang, S.  S., Romero-Zaliz, R., Hintsanen, M., Cloninger, K.  M., Garcia, D., Svrakic, D.  M., Rozsa, S., Martinez, M., Lyytikäinen, L.-P., Giegling, I., Kähönen, M., Hernandez-Cuervo, H., Seppälä, I., Raitoharju, E., de Erausquin, G. A., Raitakari, O., Rujescu, D., Postolache, T. T., Sung, J., Keltikangas-­ Järvinen, L., Lehtimäki, T., & Cloninger, C.  R. (2020b). Uncovering the complex genetics of human character. Molecular Psychiatry, 25, 2295–2312. https://doi.org/10.1038/ s41380-­018-­0263-­6 Zwir, I., Del-Val, C., Arnedo, J., Pulkki-Råback, L., Konte, B., Yang, S.  S., Romero-Zaliz, R., Hintsanen, M., Cloninger, K.  M., Garcia, D., Svrakic, D.  M., Lester, N., Rozsa, S., Mesa, A., Lyytikäinen, L.-P., Giegling, I., Kähönen, M., Martinez, M., Seppälä, I., Raitoharju, E., de Erausquin, G., Mamah, D., Raitakari, O., Rujescu, D., Postolache, T.  T., Gu, C.  C., Sung, J., Lehtimäki, T., Keltikangas-Järvinen, L., & Cloninger, C. R. (2021). Three genetic-­ environmental networks for human personality. Molecular Psychiatry, 26, 3858–3875. https:// doi.org/10.1038/s41380-­019-­0579-­x

Chapter 2

The (Mis)measurement of the Affective Profiles Model: Should I Split or Should I Cluster? Danilo Garcia and Shane MacDonald

The affective profiles model is a representation of a person’s affective experience and tendencies (Garcia, 2011). The model is composed of two independent but inter-related subsystems, namely, positive affect (PA) and negative affect (NA) (see Garcia, 2018; Chap. 1 in this volume). Hence, the model is person-oriented because it considers the complex nonlinear interaction of PA and NA as a whole-system unit or a ­meta-system rather than each independent affectivity dimension separately (Garcia et al., 2015; cf. Bergman & Wångby, 2014; Bergman & Magnusson, 1997; see also Cloninger et al., 1997, who explain nonlinear dynamics in complex adaptive systems). The interaction of the subsystems can be viewed as the combination of high/low PA and high/low NA, which yields, at least in theory (cf. Keren & Schul, 2009), four affective profiles: selffulfilling (high PA/low NA), low affective (low PA/low NA), high affective (high PA/ high NA), and self-destructive (low PA/high NA). The question is then, what is the best method for affectivity profiling? That is, a method that can model the structures within the affectivity meta-system, which are organized and function as patterns of two operating subsystems and where each subsystem derives its meaning from its relation to the other (cf. Bergman & El-Khouri, 2003; Bergman et al., 2003). Most research on the affective profiles model use self-report measures to operationalize PA and NA, such as the Positive Affect Negative Affect Schedule D. Garcia (*) Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden Centre for Ethics, Law and Mental Health (CELAM), University of Gothenburg, Sweden Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being, Sweden Department of Psychology, University of Gothenburg, Gothenburg, Sweden Department of Psychology, Lund University, Lund, Sweden S. MacDonald (*) Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being, Linköping, Sweden © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Garcia (ed.), The Affective Profiles Model, https://doi.org/10.1007/978-3-031-24220-5_2

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(PANAS) by Watson et al. (1988). To date, most of these studies have predominately originated in Sweden and used median splits as the method for affectivity profiling—individuals’ self-reported scores on PA and NA are divided into high and low in reference to the median and then combined into the four profiles (see Chap. 1 in this volume). From a methodological point of view, the median splits or any other similar method can be considered as variable-oriented rather than person-oriented— a variable-oriented approach is characterized for the focus on differences between individuals without considering the existence of sub-populations (Lundh, 2015). Indeed, the median splits method focuses on variables and their cut-off values in a population; thus, it is a top-down procedure. Moreover, the median splits impose arbitrary cut-offs for group membership, which can lead to distorted estimates about high and low values in each affectivity dimension (Garcia et al., 2015).1 In this context, other researchers have suggested the use of hierarchical followed by K-means cluster analysis to ensure that individuals are assigned to a profile with people that are like them in both affectivity dimensions (see Kormi-Nouri et  al., 2015; MacDonald & Kormi-Nouri, 2013). This method is data-driven and creates profiles that are relative to each other, thus regarded as person-oriented (Garcia et  al., 2015). Data-driven methods, compared to median splits, are expected to model the dynamic nature of within- and between-group variability of individual patterns of affectivity, while the median splits method yields profiles that are static in nature—in the median split, equally sized groups are pre-determined because each one of the two variables is divided in high and low using the median. In other words, when using the median splits method for affectivity profiling, the proportion of individuals in each profile will most of the time be relatively the same, independently of other characteristics that are important for people’s affective experience and tendencies, such as gender, personality, and cultural values (cf. Myers & Diener, 1995). See for example Table  2.1, where we show the prevalence of affective profiles derived by the median splits method or the percentile method in some of the samples in this volume. For instance, a study using K-means cluster analysis for affectivity profiling (Kormi-Nouri et al., 2015; see also Kormi-Nouri et al., 2013) to investigating cultural differences in perceived academic stress between university students in Iran (i.e., Table 2.1  Prevalence of affective profiles derived by the median splits method or the percentile method in some of the samples in this volume Country Bulgaria Iran Nigeria Sweden

Chapter 7 13 14 5

N 443 426 527 524

Self-fulfilling 26% 26% 34% 32%

High affective 24% 25% 16% 19%

Low affective 23% 23% 17% 20%

Self-destructive 26% 26% 33% 29%

 Another similar, yet slightly different, method is using percentile scores, which are calculated by mapping scores in PA and NA to rank the order of each participant in relation to a normal population or sometimes just a larger cohort. Thus, instead of dividing participants’ scores in each affectivity dimension into two equal halves, as in the median splits method; the percentile method divides each affectivity score in 100 equal parts (see Chap. 1 in this volume). 1

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collectivistic culture) and Sweden (i.e., individualistic culture), found that the selffulfilling profile was much more prevalent in Iran than in Sweden. However, since the sample was relatively small (NIRAN = 100, NSWEDEN = 97) and the fact that we did not calculate the prevalence of the different profiles derived by the median splits method, we do not know whether this difference depended on cultural aspects or the profiling method. In a later study (Garcia et al., 2015), however, we tackled the question of median splits vs. cluster analysis using a large US population (NUSA  =  2225). We found that the profiles generated using the cluster method were more homogeneous (weighted average of cluster homogeneity coefficients = 0.62) and were more distinct from each other (Silhouette coefficients2 = 0.68), compared to those generated by the median splits method (weighted average of cluster homogeneity coefficients = 0.75, Silhouette coefficients = 0.59). In addition, while most of the participants (n = 1736, 78.02%) were allocated to the same profile, independently of which profiling method we used; 489 participants (21.98%) were allocated to different profiles depending on the profiling method. More specifically, 309 individuals who were allocated to the self-destructive profile using the median splits method were allocated to either the low affective (n  =  199) or the high affective (n  =  110) profiles when the cluster method was used; and 180 individuals who were allocated to the high affective profile using the median splits method were allocated to either the self-fulfilling (n = 140) or the self-destructive (n  =  40) profiles when the cluster method was used. Furthermore, even though the prevalence of females and males was similar in three of the four profiles for both methods, only the cluster analysis approach classified men significantly more often than chance and females less often than chance as having a self-fulfilling profile. That is, the cluster analysis was slightly closer to what can be expected regarding gender differences based on past studies among Swedes (e.g., Garcia, 2011)—the prevalence of males is higher in the self-fulfilling profile and the prevalence of females is higher in the high affective profile. Nevertheless, the differences were small and did not rule out the median splits as less reliable than the cluster method (Garcia et al., 2015; see also Chap. 1 in this volume). For example, we did not investigate if the profiles generated with each method were better for the prediction of other variables of interest, such as health (e.g., well-being, ill-being) or expected individual and cultural differences. Moreover, due to problems with data distribution that cannot be handled using the cluster method, it is plausible to recommend a combination of methods—for example, the median splits method is perhaps better for skewed data in one affectivity dimension, while the cluster method might be better for normal distributed data in b  i   a  i  , in which: maxa  i  , b  i  S = silhouette i = each single data point a(i) = the average dissimilarity of i with all other data within the same cluster. That is, a(i) can be interpreted as how well i is assigned to its cluster (the smaller the value, the better the assignment). This allows researchers to define the average dissimilarity of point i to a cluster c as the average of the distance from i to points in c. b(i) = the lowest average dissimilarity of i to any other cluster, of which i is not a member. The cluster with this lowest average dissimilarity is said to be the “neighboring cluster” of i because it is the next best fit cluster for point i (Rousseeuw, 1987).

2 

s i  

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the other dimension (Garcia et  al., 2015). Last but not the least, methodological studies show that the median splits method is statistically robust and valid under certain conditions, such as when multicollinearity is low between the dichotomized variables (Iacobucci et al., 2015a, b)—which is the case for PA and NA (see Chap. 1 in this volume). Hence, the debate of if we should split or if we should cluster goes on (cf. Bergman & Trost, 2006).

The Present Chapter Against this background, we conduct a head-to-head comparison of the median split method and the cluster analysis method for affectivity profiling in two different populations, one from the USA and the other from Sweden. More specifically, after conducting the profiling in both populations using both methods, we examined the results side by side to (1) compare the prevalence of individuals allocated to the profiles with both methods in each population, (2) compare the prevalence of gender in relation to the profiles generated with both methods in each population, and (3) compare differences in well-being (i.e., life satisfaction) among US residents and ill-being (i.e., sleep problems) among Swedes with both methods. By conducting our study among US residents and Swedes, we can investigate if the two affectivity profiling methods allocate people in similar or dissimilar ways in two geographically and culturally distinct populations that are expected to show specific differences in affectivity and in its relation to gender, well-being (life satisfaction), and ill-being (sleep problems). First, as shown by previous research (Allik & McCrae, 2004), US residents are higher in extroversion and slightly higher in Neuroticism compared to Swedes, two temperament traits that are associated with high levels of PA and high levels of NA, respectively (Larsen & Ketelaar, 1991)—that is, making the high affective profile more prevalent or at least more culturally accepted in the USA.  Second, according to the World Value Survey (Inglehart, 2018a, b), Sweden is at the top of both secular-­rational values (i.e., emotionally stable, pragmatic, liberal) and self-expressive values (i.e., pro-social, open, idealistic, and spiritual) whereas the USA is more in-between traditional (i.e., emphasize the importance of religion, authority, and traditional family values) and secularrational values and lower in self-expressive values (Inglehart, 2018a, b). Importantly, some of the personal characteristics behind traditional and secularrational values are typical of individuals who are low in PA and low in NA (i.e., low affective), while those behind strong self-expressive values are typical of individuals who are high in PA and low in NA (i.e., self-­fulfilling). As other Scandinavian countries, however, Sweden is a bit of an exception regarding spirituality, which in other parts of the world it increases with age and is included in a self-expressive society—instead, Swedes report decreases in spirituality (i.e., becoming more skeptical, pragmatic, and conventional) with increasing

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age (cf. Josefsson et  al., 2013), which is usually associated with high NA (Cloninger, 2004). In addition, high spirituality is somewhat frown upon in Sweden (Thurfjell, 2015) and perhaps therefore also associated with high NA among Swedes (Schütz et al., 2013a; Schütz, 2015). Ergo, we expect to find more individuals with a self-fulfilling profile among Swedes (due to stronger self-expressive values compared to the USA) and more individuals with a low affective profile among US residents (due to more traditional values compared to Sweden). Regarding high NA profiles (i.e., the self-destructive and high affective profiles), it is plausible to expect that the cultural issues regarding spirituality in Sweden make high negative affectivity more prevalent. Nevertheless, since the high affective profile is expected to have high prevalence among US residents, due to higher levels of Extraversion and Neuroticism compared to Swedes, we expected higher prevalence of individuals with a self-destructive profile among Swedes and no differences in the prevalence of individuals with a high affective profile between the two populations. About gender differences, based on past studies (e.g., Garcia, 2011; Garcia et al., 2015, 2016; Schütz et al., 2013b), we expected to find higher prevalence of males in the self-fulfilling profile and higher prevalence of females in the high affective profile. In this context, recent studies suggest that women and men in less egalitarian societies do not show gender differences in many psychological constructs, such as personality, self-esteem, depression, or even affect (e.g., Schmitt et al., 2017). Put in other words, the more egalitarian values a country have, the more accentuated gender differences become. Therefore, since Sweden is a country with more egalitarian values than the USA, we expected that gender differences would be more clearly found in the Swedish population. Lastly, regarding well-being and ill-being, we expected that individuals with a self-destructive profile will report lower levels of life satisfaction and higher levels of sleep problems compared to individuals with any of the other profiles. Among US residents, we expected that individuals with a self-fulfilling profile and those with a high affective profile would report higher life satisfaction than low PA profiles (i.e., the low affective and the self-destructive profiles), but not necessarily different levels compared to each other—after all high affectivity (due to high Extraversion and high Neuroticism) is expected to be highly prevalent in the USA or at least more culturally accepted therefore having a positive relation to well-being (cf. Myers & Diener, 1995). Among Swedes, we expected that individuals with a self-fulfilling profile and those with a low affective profile would report less sleep problems than high NA profiles (i.e., the high affective and the self-destructive profiles), but not necessarily less compared to each other—after all, low affectivity is expected to be highly prevalent in Sweden (due to extreme secular-rational values) or at least more culturally accepted therefore having a negative relation to ill-being (cf. Myers & Diener, 1995).

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Method Participants The US sample (NUSA = 4651) and the Swedish sample (NSWEDEN = 703) consisted of participants from different published and unpublished studies (i.e., Amato et  al., 2017; Garcia et al., 2015; Nima et al., 2020a, b; Chap. 3 in this volume). Both populations reported affect (Positive Affect Negative Affect Schedule) and demographics (e.g., age and gender). A subsample (nUSA = 2927) of the US residents reported life satisfaction (Satisfaction with Life Scale) and all Swedes reported sleep problems (one single question). The US Sample The US sample (NUSA = 4651) was comprised of 52.3% males (n = 2433) and 47.7% females (n = 2218). Age was reported by 88.6% (n = 4122) of the participants who had an age mean  =  34.66, sd  =  12.12 (Skewness  =  −0.94, Kurtosis  =  0.38). The means and standard deviations (sd) for affect and life satisfaction were as follows: PA = 3.35, sd = 0.83 (Skewness = −0.38, Kurtosis = −0.22), NA = 2.18, sd = 1.02 (Skewness  =  −0.71, Kurtosis  =  −0.55), and life satisfaction  =  4.91, sd  =  1.45 (Skewness = −0.95, Kurtosis = 0.25; n = 2927). The Swedish Sample This sample (NSWEDEN  =  703) was comprised of 64.4% males (n  =  453), 34.9% females (n = 245), and 0.7% missing (n = 5). Age was reported by 35.7% (n = 251) of the participants who had an age mean = 18.12, sd = 1.24 (Skewness = −5.11, Kurtosis 79.56). The means and standard deviations (sd) for affect and sleep problems were as follows: PA = 3.56, sd = 0.65 (Skewness = −0.57, Kurtosis = 0.45), NA = 2.18, sd = 0.69 (Skewness = 0.69, Kurtosis = 0.07), and sleep problems = 2.40, sd = 1.05 (Skewness = −0.71, Kurtosis = 0.03).

Measures Affectivity The PANAS (Watson et al., 1988) was used to operationalize affectivity. The PANAS consist of 20 different adjectives that represent feelings or emotions and are organized in 10 adjectives in the PA-subscale (e.g., strong, proud, interested) and 10 adjectives in the NA-subscale (e.g., afraid, ashamed, nervous). Participants are

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asked to rate to what extent, using a 5-point Likert scale (1  =  very slightly, 5 = extremely), they have experienced these emotions for the past weeks. For each subscale, we summarized the responses to each item and then divided the sum by the number of items on that subscale. The scores in each subscale were then used to calculate the affective profiles with each profiling method. Internal consistency (Cronbach’s α) was high in both samples PAUSA  =  0.89, NAUSA  =  0.94 and PASWEDEN = 0.86, NASWEDEN = 0.85. Well-Being: Life Satisfaction (Only US Subsample) The Satisfaction with Life Scale (Diener et al., 1985) was used to operationalize life satisfaction. It consists of five items (e.g., “In most ways my life is close to my ideal”) using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). We summarized the responses to each item and then divided the sum by the number of items on the scale. Internal consistency (Cronbach’s α) for this scale was high = 0.90. Ill-Being: Sleep Problems (Only Swedish Sample) People’s sleep problems were assessed by a single question asking to rate how frequently they had such problems using a 5-point scale (1 = never, 2 = some time a year, 3 = once a week, 4 = two to three times a week, 5 = five times a week or more).

Statistical Procedure The cluster analysis was conducted using the ROPstat software (Vargha et al., 2015; http://www.ropstat.com). ROPstat’s Cluster Validation Module, however, accepts at the most 3800 cases. Therefore, we decided to combine and average the descriptive statistics from two random halves when describing the median split results in terms of each profiles homogeneity and the explained error sums of squares percent (EESS%). Figure 2.1 shows how peoples standardized scores on PA and NA form the basis for inferring, using ROPstat, whether people have a low or high score in relation to the mean of the sample. SPSS was used for calculating all other statistics for the median splits on each sample, so they were comparable to Ropstat’s cluster analysis results. Likewise, we used SPSS for sub-group comparisons, analyses of variances (ANOVA), and to calculate Cohen’s d effect sizes3 using independent t-tests and the z-score of the dependent variables to estimate the size of observed statistically significant differences between individuals with distinct profiles.

 We use Cohen’s guidelines to interpret observed effect sizes (d): small = 0.20, medium = 0.50, and large = 0.80. 3

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Fig. 2.1  Explanation of the test of high and low affectivity in RopStat. (Note. When the z-score, in an affectivity dimension for a profile, deviates from the overall sample mean and falls in the upper or lower 16–25% tail-probability region of a z-score distribution, it is symbolized with a simple appearance, that is, H high or L low (L in the probability region depicted in this figure). For example: Simple appearance: 0.675