Distinguishing Neurotic from Prosocial “Guilt”: Evidence for the Conceptual Distinctiveness of

Checklist and Scenario Measures

by Stefanie M. Tignor

B.A. in Psychology, Binghamton University M.A. in Psychology, Northeastern University

A dissertation submitted to

The Faculty of the College of Science of Northeastern University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

July 28, 2016

Dissertation directed by

C. Randall Colvin Associate Professor of Psychology

ACKNOWLEDGEMENTS

First, thank you to my advisor, Randy Colvin, for your ever-impressive expertise, guidance, and support. From my first day to my last you’ve made the Personality Lab feel like home, and I deeply appreciate all that you have taught me. Thanks for always being willing to chat about research or life, and thanks for always cheering me on. To Judy Hall, thank you for all of your support and mentorship over these past few years. Your immense knowledge of psychology and skills as a researcher are an inspiration to me. And of course, thank you for helping me fall in love with meta-analysis. To Nancy Kim, thank you for your insightful and helpful comments on this dissertation, in meetings and via email. It has been much improved thanks to your guidance, and I have greatly appreciated your help throughout the process.

Thanks to the many research assistants who helped with this project: Emily Burke, David

Kramer, Megan Pinaire, Christina Tebbe, Ronnie Lo, Simone Grant, Alexa Lambros, Fae

Kayarian, Kristen Laws, Catherine Martin, Heather Offermann, and Sangjukta Sen Roy.

Thank you to my research “brother” and “sister” Sun Park and Krista Hill; your welcoming nature and willingness to help a lost first-year will never be forgotten. Thanks to

Adam Brown, Jin Goh, and Paul Condon for all of the very necessary coffee breaks. Thanks to my stellar classmates Tim Shepard, Tom Morrison, Nhi Ngo, and Quan Lei – we made it! And a very special thank you to the amazing Kristin Szuhany. I don’t even want to imagine what graduate school would have looked like without you by my side, hot sauce in hand.

Thank you to my mother, father, nana, and popi for your seemingly limitless love, as well as to my wonderfully supportive parents in-law Sherry, Marko, and JA. A special thank you to

Chrissy Fisher for all of the neighborly visits, emergency cups of tea, and perfectly distracting

YouTube videos; they were always just the thing. And to my husband, Jesse Lyons: thank you

! ii! for listening to me talk about this for five years straight and at least pretending to still be interested. Thank you for your love, patience, encouragement, and serenades. Thank you for making me a better person every day. Here’s to the Future. Finally, thanks to my favorite officemate, Agent Cooper. You definitely made finishing this more difficult than it had to be, but

I love you.

! iii! ABSTRACT OF DISSERTATION

Despite decades of empirical research, conclusions regarding the adaptiveness of dispositional guilt remain mixed. While some researchers proclaim guilt to be maladaptive and indicative of neuroticism or even psychopathology, others declare it to be adaptive, promoting positive interpersonal functioning and prosocial behavior. These discrepancies are likely exacerbated by the diverse collection of measures employed to assess dispositional (i.e., “trait” or “proneness to”) guilt. Such measures vary widely in format, from simple lists of experienced feelings (i.e., “checklist measures”) to detailed hypothetical scenarios (i.e., “scenario measures”).

The current research examines whether these two classes of measures, despite sharing a name, capture two conceptually distinct constructs. More specifically, across four studies I investigate the possibility that checklist measures of dispositional guilt capture a neurotic, dysphoric form of this trait, while scenario measures capture an empathetic, prosocial form.

Study 1 is a meta-analysis in which dispositional guilt and shame’s differential associations to prosocial orientation—one aspect of adaptive interpersonal functioning—are examined. Results revealed an overall positive effect of dispositional guilt on prosocial orientation, and a marginally significant negative effect of shame on prosocial orientation.

Importantly, in the case of guilt (but not in the case of shame) this main effect was significantly moderated by test format. Only scenario measures of dispositional guilt were significantly and positively associated with prosocial orientation; checklist measures exhibited no significant relationship.

Study 2a sought to replicate and extend these meta-analytic findings in the field using a daily diary design. Participants completed self-reports of dispositional guilt and shame via one checklist and one scenario measure, then reported and classified their daily behavior in two-hour increments for one week. A series of random coefficient models revealed scenario-assessed guilt

! iv! to significantly positively predict adaptive interpersonal behavior in everyday life, including: time spent socializing, time spent providing emotional support to others, time spent nurturing relationships, and time spent helping others. In contrast, checklist-assessed guilt was largely unrelated to adaptive interpersonal behavior. Neither checklist nor scenario measures of dispositional shame significantly predicted these behaviors.

Taken together, the results of Study 1 and Study 2a provide strong evidence to suggest that checklist and scenario measures of trait guilt assess two distinct constructs, with the latter representing a prosocial variety. Given such findings, in Study 2b I sought to determine which, if any, of these measures is associated with affective experience in everyday life, and whether checklist-assessed guilt may be classified as “neurotic guilt.” As expected, results revealed checklist-assessed guilt to be predictive of daily feelings of negative affect, guilt, and shame. In contrast, scenario-assessed guilt displayed no significant relationships with affective experience.

Rather, scenario-assessed guilt only exhibited a significant positive relationship with daily well- being. Once again, this division between test formats was not seen in the case of shame. These results go beyond simply suggesting that checklist and scenario measures of trait guilt capture different varieties of guilt; rather, they suggest that scenario measures of guilt capture something other than guilty affect entirely.

Study 2c sought to more appropriately characterize the personality trait assessed via scenario measures of dispositional guilt using self-reports of personality, informant reports of personality, and coded in-lab behavior. Results of these multi-method analyses suggest scenario- assessed guilt may be better characterized as a trait measure of “reparative social concern,” as evidenced by its robust correlations with empathic concern, humanitarianism, agreeableness, and

! v! personal growth, among others. Future research directions are suggested, and an argument for the renaming of scenario-assessed guilt is presented.

! vi! TABLE OF CONTENTS

Acknowledgements ii

Abstract iv

Table of Contents vii

List of Tables x

List of Figures xi

Chapter 1: Introduction 1 I. Research Problem and Objectives 1 II. Theoretical Roots of Contemporary Distinctions Between Guilt and Shame 2 III. Dispositional Guilt and Shame 5 IV. Measurement Issues and Conceptual Concerns in the Study of Dispositional Guilt and Shame 6 V. Differing Perspectives on the Adaptiveness of Guilt and Shame 11

Chapter 2: Study 1 - The Interpersonal Adaptiveness of Checklist Versus Scenario Measures of Guilt and Shame: A Meta-analysis 13 I. Background 13 II. Method 14 III. Results 21 IV. Discussion 26

Chapter 3: Outline of Samples and Data Sources Featured in Studies 2a, 2b, and 2c 30 I. Overview of Hypotheses for Study 2a, 2b, and 2c 30 II. Sample A 35 III. Sample B 43 IV. Summary of Data Sources and Subsequent Studies 50

Chapter 4: Study 2a – Are Checklist- and Scenario-Assessed Guilt Differentially Associated with Daily Diary Reports of Adaptive Interpersonal Behavior? A Replication and Extension of Study 1 52 I. Background 52 II. Method 53 III. Results 55 IV. Discussion 62

Chapter 5: Study 2b – Is Scenario-Assessed “Guilt” Even Guilt? Testing both Measures’ Associations with Daily Affect and Well-being 64 I. Background 64 II. Method 70

! vii! III. Results 72 IV. Discussion 77

Chapter 6: Study 2c – If not Guilt, then What? Further Characterizing and Distinguishing Checklist and Scenario “Guilt” Using Self-reports of Personality, Informant Reports of Personality, and Coded In-lab Behavior 80 I. Background 80 II. Method 82 III. Results 84 IV. Discussion 92

Chapter 7: General Discussion 96 I. Summary of Results 96 II. Implications 97 III. Limitations 98 IV. Suggestions for Future Research 100

References 103

Footnotes 116

Tables 118

Figures 134

Appendix A: Semi-partial Correlation Results from Study 1 Meta-analyses 135

Appendix B: Comprehensive List of Effect Sizes and Attributes for Studies Included in Zero-Order Correlation Guilt Meta-analysis 136

Appendix C: Comprehensive List of Effect Sizes and Attributes for Studies Included in Semi-Partial Correlation Guilt Meta-analysis 139

Appendix D: Comprehensive List of Effect Sizes and Attributes for Studies Included in Zero-Order Correlation Shame Meta-analysis 141

Appendix E: Comprehensive List of Effect Sizes and Attributes for Studies Included in Semi-Partial Correlation Shame Meta-analysis 144

Appendix F: References Included in Study 1 Meta-Analyses 146

Appendix G: Data Sources Collected from Samples A and B not Utilized in the Current Research 154

Appendix H: Guilt Measures Individually Tested as Predictors of Interpersonally Adaptive Behavior in Study 2a 159

! viii!

Appendix I: Shame Measures Individually Tested as Predictors of Interpersonally Adaptive Behavior in Study 2a 161

Appendix J: TOSCA Guilt and Shame Measures Tested as Simultaneous Predictors of Adaptive Interpersonal Behavior in Study 2a 163

Appendix K: Variance Components Models for Dependent Variables in Study 2b 164

Appendix L: Guilt Measures Individually Tested as Predictors of Affect and Well-Being in Study 2b 165

Appendix M: TOSCA Guilt and Shame Measures Tested as Simultaneous Predictors of Affect and Well-being in Study 2b 167

Appendix N: Shame Measures Individually Tested as Predictors of Affect and Well-Being in Study 2b 168

Appendix O: Residualized Guilt and Shame Scores Correlated with Self-Reported Big Five Traits in Study 2c 170

Appendix O: Residualized Guilt and Shame Scores Correlated with Self-Reported Adaptive Traits in Study 2c 171

Appendix Q: Residualized Guilt and Shame Scores Correlated with Self-Reported Maladaptive Traits in Study 2c 172

Appendix R: Residualized Shame and Guilt Scores Correlated with Informant-Reported Personality in Study 2c 173

Appendix S: Residualized Shame and Guilt Scores Correlated with RBQ Behavioral Factors 174

Appendix T: All RBQ Items Correlated with All Measures of Guilt and Shame 175

! ix! LIST OF TABLES

Table 1: Study 1 Results of Guilt and Shame Meta-Analyses: Zero-Order Correlations 118

Table 2: Study 1 Results of Test Format Moderator Analyses 119

Table 3: Study 1 Results of Moderator Analyses: Outcome Measure Class and Outcome Measure Type 120

Table 4: Means and Standard Deviations for all Self-Report Scales of Dispositional Guilt and Shame in Sample A and Sample B 121

Table 5: Inter-Correlations for all Self-Report Scales of Dispositional Guilt and Shame in Sample A and Sample B 122

Table 6: Frequencies for Six Behaviors of Interest in Study 2a 123

Table 7: Predicting Daily Interpersonally Adaptive Behavior From Dispositional Guilt Measures in Study 2a 124

Table 8: Predicting Daily Interpersonally Adaptive Behavior From Dispositional Shame Measures in Study 2a 125

Table 9: Affect and Well-Being Descriptive Statistics by Day of Week in Study 2b 126

Table 10: Predicting Daily Well-being and Affect from Dispositional Guilt Measures in Study 2b 127

Table 11: Predicting Daily Well-Being and Affect from Dispositional Shame Measures in Study 2b 128

Table 12: Measures of Dispositional Guilt and Shame and Self-Reported Big Five Traits in Study 2c 129

Table 13: Measures of Dispositional Guilt and Shame and Self-Reported Adaptive Traits in Study 2c 130

Table 14: Measures of Dispositional Guilt and Shame and Self-Reported Maladaptive Traits in Study 2c 131

Table 15: Measures of Dispositional Guilt and Shame and Informant-Reported Personality Traits in Study 2c 132

Table 16: Measures of Dispositional Guilt and Shame and Coded In-Lab Behavior in Study 2c 133

! x! LIST OF FIGURES

Figure 1: Flow Chart Outlining Study Selection Process for Study 1 Meta-analyses 134!

! xi! !

CHAPTER 1

Introduction

Guilt and shame are two powerful emotions. Once viewed as synonymous, over the past four decades researchers have repeatedly demonstrated theses emotions’ distinctiveness at the state and trait level (Lewis, 1971). Yet despite a wealth of empirical research, their unique implications remain ambiguous (Cohen, Wolf, Panter, & Insko, 2011). Researchers have variously asserted one or both constructs to be maladaptive or adaptive, and as either hindering or promoting positive interpersonal functioning and well-being (Tangney, 1996). These discrepancies are likely exacerbated by the diverse collection of measures employed to assess dispositional (i.e., “trait” or “proneness to”) guilt and shame (Robins, Noftle, & Tracy, 2007).

Such measures vary widely in format, from simple lists of experienced feelings (i.e., “checklist measures”; Harder & Zalma, 1990), to detailed hypothetical scenarios (i.e., “scenario measures”;

Tangney, Wagner, & Gramzow, 1989). This diversity, along with the prevalence of conflicting results, has led some to question whether all measures of dispositional guilt (or shame) assess one unitary construct (Harder, 1995).

Research Problem and Objectives

The current research addresses the above validity issue, arguing for the conceptual distinctness of scenario- and checklist-assessed guilt, and the conceptual sameness of scenario- and checklist-assessed shame. Across four studies, I provide support for this assertion by fulfilling three interrelated research objectives: 1) Meta-analytically summarizing the extant literature on guilt and shame, and the adaptiveness of each, 2) Demonstrating that whereas all measures of dispositional shame assess one unitary construct, measures of dispositional guilt fall into one of two overarching categories: Neurotic guilt and prosocial guilt, and 3) Providing a

! 1! ! portrait of the neurotic versus prosocially guilty individual to fully explicate the construct of guilt.

In addressing Objective 1, I present a meta-analysis of the extant literature linking guilt or shame to prosocial orientation, one aspect of adaptive interpersonal functioning (Study 1). I partially address Objective 2 by testing test format as a moderator in this meta-analysis, then replicate and extend these findings in the field by examining the relationship of each guilt and shame measure to daily adaptive interpersonal behavior (Study 2a). Next, I additionally address

Objective 2 by examining the relationship of each measure type to daily positive affect, negative affect, and well-being (Study 2b). Doing so provides further support for the conceptual distinctiveness (sameness) of checklist- and scenario-assessed guilt (shame), while also suggesting that scenario-assessed guilt should not be named “guilt” at all, due its null relation to actual feelings of guilt in everyday life. Finally, I address Objective 3 by examining the self- reported personality, informant-reported personality, and in-lab behavioral correlates of both measures of guilt and shame (Study 2c).

Below I begin with a brief history of theoretical perspectives distinguishing guilt and shame at the state level, then apply these distinctions to trait-level assessments. Next, I identify the most common test formats employed in assessing dispositional guilt and shame. In doing so,

I underscore key conceptual differences inherent in these tests, and suggest such differences are reflective of researchers’ differing theoretical perspectives on the adaptiveness of guilt but not shame.

Theoretical Roots of Contemporary Distinctions Between Guilt and Shame

Although the current research examines dispositional guilt and shame, to date most definitions and theoretical distinctions have been made at the state level. Accordingly, I present

! 2! ! those here. State guilt is defined as “the dysphoric feeling associated with the recognition that one has violated a personally relevant moral or social standard” (Kugler & Jones, 1992, p. 318).

Typically, guilty individuals feel emotional discomfort due to a belief (accurate or not) that they have physically or emotionally harmed another (Strelan, 2007). State shame is defined as “an affective reaction that follows public exposure (and disapproval) of some impropriety or shortcoming” (Tangney, Wagner, Hill-Barlow, Marschall, & Gramzow, 1996, p. 1256). In contrast to guilty individuals, ashamed individuals feel emotional discomfort due to a belief

(again, accurate or not) that they are personally inferior (Stuewig, Tangney, Heigel, Harty, &

McCloskey, 2010).

Guilt and shame share a number of similarities. Both are negative affective states that occur in response to a transgression or shortcoming, and both are self-conscious emotions, meaning that self-reflection is critical to their occurrence. In addition, although these emotions can be deeply personal, both are crucially linked to interpersonal relationships and experiences.

Neither guilt nor shame can be elicited in the absence of a (real or imagined) social context

(Tangney & Dearing, 2002). Finally, internal attributions are vital to the guilt or shame experience. If an individual does not accept at least some degree of responsibility for a given transgression, a non-self-conscious emotion such as anger will likely be elicited instead (Tracy &

Robins, 2006). Given the above parallels, it is easy to see why early theorists and empiricists conflated the two.

One promising early perspective on how guilt and shame differ is the “public-private distinction,” a framework founded in anthropological theory (Benedict, 1946). This distinction states that guilt and shame may be distinguished via the characteristics of the eliciting transgression. Specifically, private mistakes or shortcomings will result in guilt, whereas public

! 3! ! ones will result in shame (Ausubel, 1955; Smith, Webster, Parrott, & Eyre, 2002). This framework is based on the assumption that state guilt is the result of a personal, private evaluation of one’s own failure to live up to self-imposed standards or norms (Campos, Barrett,

Lamb, Goldsmith, & Stenberg, 1983), whereas state shame is caused by “presumed negative judgments” (Ausubel, 1955, p. 378) of the self by others, or by society at large. This distinction makes intuitive sense, as most concerted shaming efforts tend to be public affairs. Yet in the decades since its inception, the public-private distinction has received little empirical support

(Kim et al., 2011; Tangney & Dearing, 2002; but see Smith et al., 2002; Wolf et al., 2010).

Though individuals may tend to experience shame in more public situations and guilt in more private ones, the distinction is not so clear-cut as to effectively and consistently differentiate the two emotions.

In 1971, Helen Block Lewis posed an alternative theoretical framework. In her seminal work, Shame and Guilt in Neurosis, Lewis argued that these emotions differ not in their antecedents, but rather in individuals’ appraisals of and behavioral or motivational reactions to a given transgression. Specifically, she suggested that guilty individuals are those who respond to a transgression by making unstable and specific attributions about behavior, whereas ashamed individuals are those who attribute an impropriety to stable, global aspects of the self. This framework is commonly referred to as the “self-behavior distinction.” The guilty person thinks “I did a bad thing,” whereas the ashamed person thinks “I am a bad person.” In this way shame may be a more devastating emotion than guilt; in the case of shame it is one’s very identity that is perceived by the individual to be tarnished or flawed, whereas in the case of guilt it is one’s behaviors, which are somewhat removed from the self (Tangney & Dearing, 2002).

! 4! !

Lewis further suggested that these unique attributions beget unique behavioral or motivational responses. As guilty individuals feel regret regarding a specific behavior or action, they often attempt to “make it up to” the offended party via compensation or apology (Tangney et al., 1996; Tangney & Dearing, 2002). Such reparations are relatively feasible, as “bad” behavior can often be remedied. In contrast, as ashamed individuals feel personally inferior, reparations become more difficult. Mending one’s “flawed” self represents a larger hurdle than say, paying to replace a broken vase, or pledging to be on time for lunch dates in the future. The ashamed individual’s feelings of powerlessness and worthlessness are instead associated with a desire to retreat or withdraw from the situation, to hide, or to disappear altogether (Tangney,

1996). If repair (in the case of guilt) or withdrawal (in the case of shame) is not physically possible or likely, the guilty or ashamed transgressor may instead engage in counterfactual thinking, imagining ways to “undo” his or her wrongdoing. Yet whereas the guilty individual mentally reverses his or her behavior, the ashamed individual mentally undoes some aspect of the self (Niedenthal, Tangney, & Gavanski, 1994).

Notably, the above distinctions originally proposed by Lewis have received substantial corroboration over the past few decades. Writers in self psychology (Kohut, 1972), psychoanalysis (Piers & Singer, 1971), emotion development (Barrett, 1995), clinical psychology (Harder, 1995), and social psychology (Tangney, 1995) have all echoed her theoretical framework, and researchers have provided consistent empirical support (e.g.,

Ferguson & Stegge, 1995; Lindsay-Hartz, 1984; Tangney & Dearing, 2002).

Dispositional Guilt and Shame

It is important to re-emphasize that the above definitions and distinctions were originally developed at the state level, as research on state guilt and shame generally preceded research on

! 5! ! these emotions as traits. Yet since the conceptualization of these distinctions, research has confirmed that individuals also differ in their tendency to experience, or propensity for experiencing, guilt (or shame) over time and across situations (Tangney, 1996). In other words, although everyone has likely felt guilty or ashamed at one time or another, individuals differ in the frequency with which they experience, or imagine they would experience, these emotions on a daily basis. The former is sometimes referred to as “trait guilt (or shame),” whereas the latter is often referred to as “guilt (or shame) proneness,” although this terminology is not employed consistently. For the sake of clarity, in the current research I use the phrase “dispositional guilt

(or shame)” as a superordinate label, referring to any trait-like measure of guilt (or shame) proneness or experiential tendencies (see: Tangney, 1996).

Measurement Issues and Conceptual Concerns in the Study of Dispositional Guilt and

Shame

It is interesting to note that soon after the publication of Lewis’s book, a general consensus emerged about shame. By and large, theorists conceptualized shame as generally maladaptive, regardless of whether it is experienced within the context of a single transgression or over sustained periods of time, and empirical research has corroborated this assertion (see: de

Hooge, Breugelmans, & Zeelenberg, 2008; Tangney et al., 2007).1 Yet the conceptualization of guilt remains hazy (Kugler & Jones, 1992). The extant research has yet to provide a clear and consistent indication of what dispositionally guilty individuals look like. Are they neurotic, constantly plagued by negative affect and assumptions of fault, or simply sensitive, empathetic, and careful not to offend? The phrase “dispositional guilt” may connote either, and empirical research has shown both (e.g., Baumeister, Stillwell, & Heatherton, 1994; Harder, Cutler, &

Rockart, 1992). Importantly, whereas the former is indicative of psychopathology, the latter

! 6! ! suggests situational awareness and interpersonal sensitivity. In the current work I suggest this confusion, particularly in the case of guilt, may be partly attributable to the theoretical assumptions that underlie researchers’ methodological choices. Specifically, differing test formats may assess distinct constructs associated with varying degrees of adaptiveness.

Currently there exist over 20 measures purporting to assess dispositional guilt, shame, or both, most of which fall into one of two overarching categories: scenario measures and checklist measures (Tangney, 1996). Scenario measures present participants with a series of common hypothetical situations in which they must imagine themselves, such as breaking a friend’s possession or betraying someone’s trust. Each scenario is followed by up to five affective, cognitive, or behavioral responses, one of which (presumably) reflects guilt or shame.

Participants indicate how likely they would be to enact each response. In this type of test, guilt and shame are distinguished based on Lewis’ (1971) theoretical framework. Responses such as feeling remorse or regret, apologizing, pledging compensation, or wishing to make amends are scored as reflecting guilt. In contrast, decreases in self-worth or fleeing responses, such as exiting the room, quitting one’s job, refusing to speak to the affected individual, or wishing to disappear, are scored as reflecting shame. Some of the most frequently employed scenario measures are The Test of Self-Conscious Affect (TOSCA; Tangney, Wagner, & Gramzow,

1989) and the Guilt and Shame Proneness Scale (GASP; Cohen et al., 2011).

Checklist measures require participants to indicate the extent to which they experience a set of feelings, actions, or cognitions on a regular basis. In this type of test, guilt and shame are distinguished based on lexical representations of emotional experiences. For example, in the

Personal Feelings Questionnaire (PFQ; Harder & Lewis, 1987), frequent feelings of “regret” or

“remorse” reflect dispositional guilt, while frequent feelings of “helplessness” or “humiliation”

! 7! ! reflect dispositional shame. Though the name “checklist” may imply a series of “yes” or “no” questions, in these types of measures participants respond to each affective experience using a

Likert-type scale (i.e., 0 = I never experience this, to 4 = I almost continuously experience this).

Although these two classes of measures are often employed for the same purposes, they possess some key differences. First, while scenario measures are conjectural, checklist measures are retrospective; scenario measures capture anticipated affective, cognitive, or behavioral reactions, while checklist measures capture previously experienced feelings of, or reactions to, guilt and shame. Thus, it is unclear whether individuals who receive high scores on scenario measures of dispositional guilt (or shame) routinely experience unpleasant feelings of guilt (or shame). They may simply believe they would, or should, feel guilty (or ashamed) in the future if they were to experience the situations described in the questionnaire. Accordingly, scenario measures assess the anticipated likelihood of a future response (see: Quiles & Bybee, 1997 for a discussion of “predispositional guilt”), but scores on such measures may be unrelated to an individual’s actual emotional tendencies.2 As scenario measures capture propensities rather than experiences, they are often characterized as tests of “guilt (shame) proneness” (e.g., Cohen et al.,

2011). Yet this labeling is far from consistent, thereby amplifying confusion in the field.

Second, scenario measures are event-contingent; they explicitly outline a series of presumed guilt- or shame-eliciting situations in which the participant clearly (though hypothetically) did something wrong. As such, they are not capable of capturing decontextualized guilty or ashamed feelings, or disproportionately strong emotions elicited by relatively benign situations (see Kim, Thibodeau, & Jorgensen, 2011 for a discussion of

“contextual” versus “generalized” guilt). Checklist measures, in contrast, do not explicitly identify situational attributes. Accordingly, an individual could routinely experience extreme

! 8! ! levels of guilt (shame) in response to appropriately grave situations, such as seriously injuring a friend, or in response to seemingly benign situations, such as stepping on a friend’s toe. As the researcher is not privy to information regarding the eliciting stimuli, both the former and the latter are treated as equivalent. Checklist measures are often (though not consistently) characterized as assessing “trait” guilt or shame, as they capture average levels of affect experienced over time and across situations (Kugler & Jones, 1992).

Each of the above test formats has unique limitations. First, scenario measures are conjectural in nature (Harder & Zalma, 1990). As such, participants may be less accurate in reporting how they “would feel” relative to how they “currently feel” or “have felt” in the past, as is typical of affective forecasts (Wilson & Gilbert, 2005). In contrast, participants are presumed to be at least fairly accurate on checklist measures. The personality assessment literature reveals that many people can recall experiences related to a characteristic of the self, average their responses over time and across situations, and derive a judgment about the characteristic (Funder & Colvin, 1997).

Second, checklist measures are open to the criticism that their specific sets of assessment items or phrases might measure constructs in addition to guilt or shame, such as neuroticism, anxiety, or depression (such a critique has previously been levied by: Gangemi & Mancini, 2011;

Quiles & Bybee, 1997; Tangney, 1996). This issue may be particularly relevant for measures that assess only guilt or shame, as opposed to both; often in such cases the theoretical distinctions between guilt and shame are given less consideration (Tangney & Dearing, 2002). Some researchers have further argued that the decontextualized nature of checklist measures represents a shame-like task (i.e., making global ratings of negative aspects of the self, not behaviors), even when supposedly measuring trait guilt. As such, the resultant guilt scores may lack construct

! 9! ! validity (Tangney & Dearing, 2002). Finally, participants may be confused by checklist measures, as they ask individuals to rate the frequency with which they experience feelings of

“childishness,” versus “regret,” versus “worry” or “guilt” (PFQ; Harder & Zalma, 1990). Though the authors of these measures likely understand the nuanced distinctions between such affective words and phrases, the average participant may not. Because these measures utilize a lexical framework to distinguish guilt from shame, if a participant is not able to effectively discriminate between these terms, his or her resultant guilt and shame scores will be meaningless (Goldberg &

Kilkowski, 1985).

Given these striking differences in test format, and the prevalence of conflicting empirical findings, some have suggested that, in the case of guilt, scenario and checklist measures assess fundamentally distinct traits (e.g., Averill, Diefenbach, Stanley, Breckenridge,

& Lusby, 2002; Einstein & Lanning, 1998; Gangemi & Mancini, 2011; Quiles & Bybee, 1997).

Yet such researchers have not fully explicated how and why the two may differ using a diverse collection of data sources, nor have they examined the possibility that scenario-assessed guilt may not assess guilt at all.

Limited empirical evidence supports the above assertion: scenario-assessed and checklist- assessed guilt share few common correlates (Harder et al., 1992), and the two are only weakly associated with one another. Abe (2004) reported a correlation of just .31 between guilt assessed via the TOSCA and guilt assessed via the Differential Emotions Scale. Similarly, Kulger and

Jones (1992) reported a correlation of .26 between TOSCA-assessed guilt and The Guilt

Inventory (for similar results, see Ferguson & Crowley, 1997). The distinctiveness of scenario and checklist guilt can even be seen in these measures’ respective authors’ construct definitions.

The authors of the TOSCA conceptualize guilt as adaptive and associated with positive

! 10! ! interpersonal functioning (Tangney et al., 1996). In contrast, the authors of the PFQ conceptualize dispositional guilt as dysphoric and maladaptive, a trait that warrants clinical treatment (Harder et al., 1992). Thus, it is likely that the marked differences in item content between the two measures are a manifestation of conflicting theory regarding the nature of guilt.

Although patterns of correlates for checklist-assessed shame and scenario-assessed shame are rarely identical (e.g., Abe, 2004), such differences are less marked than those observed for guilt (e.g., Ferguson & Crowley, 1997). Currently, most researchers agree that checklist and scenario measures of shame assess a common construct (Einstein & Lanning, 1998). Empirical research has shown checklist- and scenario-assessed shame to be more highly correlated than checklist- and scenario-assessed guilt; Abe (2004) reported a .50 correlation between the two measures of shame.

Differing Perspectives on the Adaptiveness of Guilt and Shame

Is dispositional guilt adaptive? The answer is unclear. On one hand, dispositional guilt has empirical links to psychopathology (Harder et al., 1992). Freud (1930/1961) described guilt as the primary source of mental illness, and routinely sought to rid his patients of such feelings.

Personality research has provided some additional support for these clinical observations, as chronically guilty people have been shown to be lonely, resentful, suspicious, and angry (Jones

& Kugler, 1993).

Still, other streams of research have identified guilt as a powerful motivator, leading people to behave in socially acceptable ways (Ausubel, 1955; Baumeister et al., 1994). Routinely experiencing this emotion may play a crucial role in the maintenance of healthy relationships, as it helps individuals recognize when they have done harm to others (Scheff, 1984). Dispositional guilt has further been identified as a component of trait morality (Cohen, Panter, & Turan, 2012),

! 11! ! leading some to conclude that it fosters altruism (Haidt, 2003) and ethical behavior (Tangney &

Dearing, 2002; Tangney et al., 2007). In contrast, despite lay theories to the contrary (Smith et al., 2002), shame has been consistently identified as detrimental for or unrelated to adaptive traits and behavior (Tangney et al., 2007; but see: de Hooge et al., 2008; Declerck et al., 2014).

In sum, there exist two distinct perspectives on the adaptiveness of dispositional guilt.

Yet, surprisingly, the association between methodological choices and theoretical perspectives often goes unacknowledged (see Tangney & Dearing, 2002 for further elaboration). Researchers claiming guilt to be maladaptive frequently employ checklist measures of guilt (e.g., Harder et al., 1992; Jones & Kugler, 1993), while those characterizing it as adaptive often employ scenario measures (e.g. Cohen et al., 2012; Tangney et al., 2007). In other words, it appears as though scenario measures of guilt assess the adaptive, prosocial, and reparative guilt portrayed by

Baumeister and colleagues (Baumeister et al., 1994), whereas checklist measures assess the maladaptive, neurotic guilt conceptualized by Freud (1930/1961), yet to date this has not been tested fully. Once again, although some have identified limited benefits of shame (e.g., de Hooge et al., 2008; Declerck, Boone, & Kiyonari, 2014), most view shame as universally maladaptive, regardless of the manner in which it is assessed (Tangney, 1996).

! 12! !

CHAPTER 2

Study 1 – The Interpersonal Adaptiveness of Checklist Versus Scenario Measures of Guilt

and Shame: A Meta-analysis

Study 1 is composed of a series of meta-analyses summarizing dispositional guilt and shame’s differential associations with prosocial orientation, one aspect of adaptive interpersonal functioning. Accordingly, Study 1 addresses two key objectives. The first (Objective 1) is to meta-analytically summarize dispositional guilt and shame’s differential associations with prosocial orientation. More importantly, the second (Objective 2) is to examine the role that test format plays in determining the strength and direction of the above relationships, thereby providing initial evidence for the conceptual distinctiveness (sameness) of checklist- and scenario-assessed guilt (shame). Below I provide background on prosocial orientation, the dependent variable of interest in Study 1, then present an outline of my hypotheses.

Background

Prosocial Orientation

Prosocial orientation is defined as the traits, attributes, and behavioral tendencies that reflect an individual’s proclivity toward “caring about others’ welfare, and avoiding behaviors that may damage another’s welfare” (Feinberg, Willer, & Keltner, 2012, p. 81). This term encompasses a wide array of phenomena, including but not limited to: altruism, cooperation, morality, empathy, forgiveness, compassion, and helping behavior. Similar to guilt and shame, people display individual differences in prosocial thoughts, feelings, and behavior. Although most people have acted prosocially at one time or another, certain individuals are consistently more prosocial than others (Batson, Bolen, Cross, & Neuringer-Benefiel, 1986; Carlo, Eisenberg,

Troyer, Switzer, & Speer, 1991). I have chosen prosocial orientation as my correlate of interest

! 13! ! in these meta-analyses for two reasons: 1) there exists a diverse yet conflicting literature regarding the relation between dispositional guilt (but rarely shame) and prosocial orientation, and 2) prosocial orientation is reflective of adaptive interpersonal tendencies, allowing me to test my assertion that scenario measures capture an adaptive form of guilt, while checklist measures capture a maladaptive form of guilt via moderator analysis.

Hypotheses

Because moderators are of chief interest, I do not offer hypotheses for main effects of guilt or shame on prosocial orientation. Instead, I form four independent hypotheses regarding the association between guilt and shame and prosocial orientation: two for scenario measures, and two for checklist measures.

First, I predict that test format will be a significant moderator for guilt only, such that:

H1: Scenario-assessed dispositional guilt will be positively associated with prosocial

orientation.

H2: Checklist-assessed dispositional guilt will be negatively associated with prosocial

orientation.

Second, I do not expect test format to significantly moderate the relationship between shame and prosocial orientation; instead, I expect both test formats to be negatively associated with prosocial orientation.

H3: Scenario-assessed dispositional shame will be negatively associated with prosocial

orientation.

H4: Checklist-assessed dispositional shame will be negatively associated with prosocial

orientation.

Method

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Literature Search

In conducting my literature search, I first queried PsycINFO for articles assessing dispositional guilt or shame, regardless of whether prosocial orientation was measured. Although prosocial orientation is the primary dependent variable in all analyses, searching for specific prosocial terms could lead to the unintentional exclusion of relevant articles (e.g., if the authors measured empathic concern, but did not explicitly use the word “prosocial”). Instead, only the following search terms were used to identify empirical studies: guilt-proneness (with and without the hyphen), shame-proneness (with and without the hyphen), trait guilt, trait shame, proneness to guilt, proneness to shame, dispositional guilt, and dispositional shame. The database also was queried for the most frequently employed measures of dispositional guilt and shame: Test of Self-

Conscious Affect, Guilt and Shame Proneness Scale, Personal Feelings Questionnaire, Mosher

Guilt Inventory, Internalized Shame Scale, Experience of Shame Scale, Buss-Durkee and guilt

(searched as separate keywords), Anger Self-report and guilt (searched as separate keywords),

Perceived Guilt Index, Dimensions of Conscience Questionnaire, Shame Disclosure Scale,

Situational Shame and Guilt, Revised Shame-Guilt Scale, Situational Guilt Inventory, and

Adapted Shame and Guilt Scale. The above searches resulted in a total of 635 potential records.

Unpublished data were additionally solicited via the Society for Personality and Social

Psychology’s website. Active researchers in the field were also emailed directly for unpublished data. This process yielded 11 unpublished records.

Inclusion Criteria

Studies were selected for inclusion if they: (1) were published in an English language book or journal (or, in the case of unpublished data, were written in English), (2) featured participants who were college-aged or older, (3) used a non-clinical population, (4) included at

! 15! ! least one self-report measure of dispositional guilt, shame, or both, assessed prior to any experimental manipulations, (5) included at least one measure of prosocial orientation, (6) presented an effect size indicating the strength of the relationship between guilt, shame, or both, and prosocial orientation, and (7) were published prior to January, 2014 (published data only).

Exclusion Criteria

Studies featuring a population recently experiencing a traumatic event, such as mothers who recently gave birth to a stillborn child, were excluded from the current analyses, as such an event could lead to heightened levels of guilt or shame. Studies that assessed dispositional guilt and shame in a highly specific situation, such as at a sporting event (e.g., Partridge & Wiggins,

2008) or after receiving a free airline upgrade (e.g., Mattila, Hanks, & Zhang, 2013) were additionally excluded.3 Similarly, studies assessing guilt or shame regarding a particular transgression or salient life event, such as survivor guilt or disloyalty guilt, as opposed to global, trait-like levels of these emotions, were excluded (e.g., O’Connor, Berry, Weiss, Bush, &

Sampson, 1997). Studies that employed the Apprehension (O) factor of the 16-PF (Cattell, 1957) as a measure of dispositional guilt were also excluded, as this measure conflates a number of factors with guilt, including shame, insecurity, and neuroticism. Finally, studies employing the

Other as Shamer Scale (Goss, Gilbert, & Allan, 1994) were excluded, as this scale does not measure the dispositional tendency to experience shame, but rather meta-perceptions of adequacy (i.e., “I think other people see me as inadequate”).

Prosocial Orientation

Although prosocial orientation can be defined in a number of ways, I selected studies that included behavioral, attitudinal, or trait measures falling into at least one of the following four categories: hostility, empathy, forgiveness, and morality. Eligible hostility measures included

! 16! ! any hostility, trait anger, agreeableness or affiliation scale, or any assessment of aggression or aggressive behavior. All correlations between dispositional guilt or shame and hostility were reversed in direction prior to being combined with other measures of prosocial orientation (i.e., high scores represent a low level of hostility).

Empathy measures were defined as any scale measuring general empathy, empathic concern, perspective taking, or fantasy. Although many studies featured the “personal distress” subscale of the Interpersonal Reactivity Index (IRI; Davis, 1983), this was not included in the current analyses. Unlike its companion IRI subscales, personal distress is not explicitly prosocial.

Rather, it has been found to motivate “withdrawal from the distress-eliciting situation, attenuating helping behaviors toward the sufferer” (Sheikh & Janoff-Bulman, 2010, p. 392).

Behavioral indicators of perspective taking were also included as measures of empathy.

Forgiveness measures were defined as any behavioral indicator of forgiveness or any trait scale assessing the general tendency to forgive. Measures of self-forgiveness were excluded, as the current meta-analyses are concerned with other-focused traits, attitudes, and behaviors. As few studies measured forgiveness, this class of measures was combined with empathy measures to create one larger “empathy/forgiveness” category. Finally, morality measures were defined as any scale measuring morality, honesty, or ethics, or any behaviors indicative of the presence or absence of the above traits, such donation behavior, helping behavior, stealing, cheating, or lying. The latter four variables were reversed in sign prior to being combined with other effect sizes, as these represent low morality. Prisoner’s dilemma games were eligible for inclusion if behavior was coded in the absence of any experimental manipulations (i.e., if effects were presented for the control group only). Additionally, only prisoner’s dilemma games assessing

! 17! ! interpersonal, not intergroup, prosociality were eligible. Studies in which the only measures of prosocial orientation were biological (i.e., EEG) were excluded.

Coding Procedure

All eligible studies were coded for each of the following, when available: (1) author(s), title, and year of publication, (2) gender composition of the sample (recorded as “percent women”), (3) racial composition of the sample (recorded as “percent White”), (4) mean age of the sample, (5) nationality of the sample (North America, South America, Europe, Asia, Africa, or Australia/New Zealand), (6) the test employed to assess dispositional guilt or shame, (7) test format (scenario vs. checklist), (8) outcome variable type (hostility, empathy/forgiveness, morality, or a composite), and (9) outcome variable class (self-report only vs. multi-method4). If a given record contained multiple studies featuring unique participants, or presented effects sizes separately for subgroups such as men and women, each was coded as an independent study.

Effect sizes are reported as Pearson r correlations between dispositional guilt or dispositional shame and prosocial orientation. Studies reporting statistics other than correlation coefficients, such as F values, were transformed into Pearson r correlations. Although I recognize that this effect size holds no implications for causality, for the sake of clarity from here forward I refer to prosocial orientation as the “dependent” or “outcome” variable.

Dispositional guilt and shame are often positively correlated with one another, particularly when checklist measures are used (Tangney et al., 1996). Some researchers address this issue statistically by partialing out guilt from shame and shame from guilt to create scores for “guilt-free shame” and “shame-free guilt.” This is because dispositional guilt and shame often operate as mutual suppressors (Paulhus, Robins, Trzesniewski, & Tracy, 2004). That is, zero-order correlations between guilt and a third variable that are of small magnitude may

! 18! ! increase after partialing out shame variance from guilt. Although an interesting methodological issue, statistical analysis is not immediately relevant to my research goals. As such, I only present results for zero-order correlations here, as this represents the more comprehensive set of studies for both guilt and shame. Results for semi-partial correlations can be found in Appendix

A. As most studies present both semi-partial and zero-order correlations in tandem, there is considerable overlap between the studies featured in the semi-partial correlation meta-analyses and the studies featured in the zero-order correlation meta-analyses.

Meta-analysis necessitates that analysts include only one effect size per group of participants, as meta-analytic methods rest on the assumption that all effect sizes are derived from independent samples. In my current analyses, I faced two potential threats to non- independence: studies presenting effects for more than one measure of dispositional guilt or shame, and studies presenting effects for more than one indicator of prosocial orientation. In both cases, I maintained assumptions of independence by r to z transforming all relevant effect sizes within a given study, then averaging those to create one effect size for that sample. For example, if a study reported correlations between prosocial orientation and guilt as assessed via the

TOSCA and the PFQ, effects for both measures were averaged, and the guilt measure was coded as “combination.” In samples utilizing the GASP as a measure of dispositional guilt or shame, negative evaluation and action subscales were averaged. Similarly, if a given study reported correlations between guilt or shame and more than one indicator of prosocial orientation, such as both empathy and morality, all relevant effect sizes were averaged, and the study was classified as having “mixed” outcomes.

Positive effect sizes indicate a positive relationship between prosocial orientation and dispositional guilt or shame, while negative effect sizes represent a negative relationship. Again,

! 19! ! all correlations between dispositional guilt or shame and hostility, aggression, dishonesty, or delinquency, or any other measure of aggressive, anti-social, or immoral behavior, were reversed in sign when added to the database. Thus, from here forward, relationships between guilt and shame and hostility should be interpreted as “negative hostility,” or agreeableness. Finally, studies that stated their data were gathered as “part of a larger investigation” were cross-checked with all other studies featuring any authorship overlap to ensure assumptions of independence were maintained. This resulted in the elimination of two studies from Cohen, Panter, Turan,

Morse, and Kim (2014), as these utilized an online database of participants already featured in another study.

I first coded all study attributes and effect sizes, then a research assistant who was trained on the coding procedures over 10 sample studies independently coded all remaining study attributes and effect sizes. This research assistant and I discussed our ratings after all studies had been coded, then resolved any discrepancies. Finally, I scanned the reference sections of all eligible articles to identify additional eligible studies. This process yielded five additional records.

Meta-analytic Strategy

Analyses were performed using SAS version 9.3 and Comprehensive Meta-Analysis

(CMA) version 3 (Borenstein, Hedges, Higgins, & Rothstein, 2014). Here I present results using random effects models; however, both random and fixed effects are presented in all tables. Fixed effect models assume one true effect size underlies all observed effects, and any observed differences are due to sampling error. Accordingly, results from fixed effects models may only be generalized to participants who are subject to identical study designs. In contrast, random effects models assume that true effects vary across studies. Thus, the results from random effects

! 20! ! models can be generalized more broadly; future participants need only be placed in conceptually similar conditions, not in methodologically identical ones (Lipsey & Wilson, 2001).

Although my main moderator of interest is test format (scenario or checklist), I additionally tested the following moderators: outcome variable class (hostility, empathy/forgiveness, morality, or mixed), outcome variable type (self-report only or multi- method), percent female, percent White, and average age. To evaluate sample homogeneity and to test most categorical moderators (test format, outcome variable class, and outcome variable type), the Q statistic (Cochran, 1954) was used. Meta-regression with maximum likelihood estimation was used to evaluate continuous moderators (percent female, percent White, and average age).

Results

Study Characteristics

The application of the above inclusion and exclusion criteria resulted in a final sample of

63 independent zero-order effect sizes for dispositional guilt (N = 12,272), and 47 independent zero-order effect sizes for dispositional shame (N = 9,634). These criteria also resulted in 31 independent semi-partial effect sizes for dispositional guilt (N = 7,574) and 33 semi-partial effect sizes for dispositional shame (N = 7,970) (see Figure 1 for a flow chart illustrating the study selection process). Once again, only results for studies using zero-order correlations are presented here, as this represents the most inclusive and diverse collection of studies; results for studies using semi-partial correlations—most of which are included in the zero-order correlation analyses—are presented in Appendix A. Tables outlining all individual effect sizes included in all meta-analyses, and attribute codings for each of these studies, are presented in Appendices B,

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C, D, and E. A complete reference list of all studies included in all meta-analyses is provided in

Appendix F.

When broken down by outcome variable, 29 effect sizes were recorded indexing the zero- order correlation between guilt and hostility, 19 effect sizes for shame and hostility, 12 effect sizes for guilt and empathy/forgiveness, 11 effect sizes for shame and empathy/forgiveness, 5 effect sizes for guilt and morality, and 4 effect sizes for shame and morality. Seventeen guilt studies were coded as having “mixed outcomes,” as were 13 shame studies. Given that it is common to assess guilt and shame in tandem, a number of studies are featured in both meta- analyses.

In the guilt sample, most studies were conducted in the United States/Canada (k = 52).

Five studies were conducted in Europe, one was conducted in Asia, and five were conducted in

Australia/New Zealand. In the shame sample, most studies were also from the United

States/Canada (k = 36). Eight studies were conducted in Europe, and three were conducted in

Australia/New Zealand. The guilt sample was predominantly White (69%), based on those studies reporting race information (k = 25), and a little over half of participants were female

(56%), based on the 60 studies reporting gender information. The shame sample was also predominantly White (70%, k = 21) and female (56%, k = 47).

Main Analyses

Dispositional guilt: Zero-order correlations. Dispositional guilt was positively correlated with prosocial orientation, k = 63, Mr = .13, CI [.09, .18], median r = .15. This effect was statistically significant, Z = 5.51, p < .001, and robust; the file drawer N for this effect was equal to 4,896, meaning that 4,896 unpublished or inaccessible studies with effect sizes averaging to zero would need to exist for this effect to drop below significance (Lipsey &Wilson,

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2001). The Q-statistic revealed significant heterogeneity within the guilt studies, Q = 395.17, df

= 62, p < .001, providing justification for moderator tests. For full results, see Table 1.

Dispositional shame: Zero-order correlations. Dispositional shame was negatively correlated with prosocial orientation, k = 47, Mr = -.05, CI [-.09, .00], median r = -.01, though this effect was only marginally significant, Z = -1.83, p < .10, file drawer N = 192. The Q- statistic revealed significant heterogeneity within the shame studies, Q = 238.60, df = 46, p <

.001, providing justification for moderator tests. For full results, see Table 1.

Publication Bias

Publication bias represents a threat to the validity of meta-analytic findings. Statistically non-significant findings are less likely to be submitted for publication, and, if submitted, are less likely to be published (Easterbrook, Berlin, Gopalan, & Matthews, 1991). Thus, empirical studies that are published and accessible to meta-analysts may not be representative of all extant research, which could in turn bias meta-analytic findings toward significance (Duval & Tweedie,

2000).

In the current meta-analyses, I addressed publication bias in three ways. First, I solicited unpublished data from fellow researchers, and included these data in all analyses. Second, I calculated the file drawer N statistic for both guilt and shame studies, as indexed above. Third, I utilized Duval and Tweedie’s (2000) trim and fill method to correct for funnel plot asymmetry. A funnel plot is a scatterplot of all effect sizes included in a meta-analysis, plotted against sample size. In the absence of publication bias, the plot should approximate the shape of a symmetric funnel, with the largest studies being located closest to the mean. If the data do not resemble a symmetric funnel, there is likely a relationship between sample size and effect size, and publication bias is present. Due to the subjective nature of identifying funnel plot asymmetry,

! 23! ! many researchers utilize the trim and fill method as a more concrete indicator of publication bias.

This method identifies studies that contribute to asymmetry, and suggests their removal. Once these studies are removed, a symmetric funnel plot with a new mean is constructed, with trimmed studies being replaced around the center (Duval & Tweedie, 2000).

I constructed funnel plots for guilt and shame effect sizes using CMA software, then utilized the trim and fill method to correct for asymmetry. In the case of zero-order guilt two studies, located below the mean, were identified as contributing to asymmetry. Removing these studies did not impact the mean effect size, k = 61, Mr = .13, CI [.08, .17], thus indicating a lack of publication bias among guilt studies. For zero-order shame, the procedure suggested that three studies below the mean be removed. Removing these studies did not drastically impact the observed effect, k = 44, Mr = -.07, CI [-.12, -.02], again indicating a lack of publication bias.

Moderator Analyses

Test format. Test format (scenario vs. checklist) was tested as a moderator in both the guilt and shame meta-analyses using CMA software. Forty guilt studies employed scenario measures, 18 employed checklist measures, and five employed a combination of checklist and scenario measures. Test format significantly moderated the relationship between dispositional guilt and prosocial orientation, Q = 19.47, df = 2, p < .001. Scenario measures of guilt were significantly and positively correlated with prosocial orientation, Mr = .20, CI [.16, .25], Z =

8.33, p < .001, whereas checklist measures of guilt were not significantly correlated, Mr = -.07,

CI [-.18, .04], Z = -1.22, p = .22. Combination measures displayed positive effects overall, Mr =

.16, CI [.01, .30], Z = 2.06, p < .05. These findings provide support for H1, but not for H2, despite a trend in the predicted direction. Still, as expected, test format was a significant moderator in the guilt sample. For full results, see Table 2.

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Thirty-eight shame studies employed scenario measures, six employed checklist measures, and three employed a combination. As expected, test format did not significantly moderate the relationship between shame and prosocial orientation, Q = 3.97, df = 2, p = .14, although this effect reached significance in the fixed effects model. Both checklist and scenario measures of shame were negatively correlated with prosocial orientation; however, only the effect for the scenario measure studies reached marginal significance. These results support H3 but fail to support H4, despite a trend in the predicted direction. Still, both sets of results are in line with my predictions, namely, that test format moderates the relationship between guilt and prosocial orientation, but not the relationship between shame and prosocial orientation.

Outcome measure class. Outcome measure class significantly moderated the relation between guilt and prosocial orientation, Q = 40.74, df = 3, p < .001. Guilt was positively and significantly correlated with empathy/forgiveness, Mr = .29, CI [.24, .35], Z = 9.44, p < .001, morality, Mr = .26, CI [.12, .39], Z = 3.61, p < .001 and mixed outcomes, Mr = .20, CI [.14, .26],

Z = 6.36, p < .001. Interestingly, guilt was not significantly associated with reverse-signed hostility, Mr = -.01, CI [-.08, .07], Z = -.17, p = .87. For full results, see Table 3.

Outcome measure class also significantly moderated the relation between shame and prosocial orientation, Q = 26.68, df = 3, p < .001. Shame exhibited the strongest relationship with reverse-signed hostility, Mr = -.15, CI [-.22, -.08], Z = -4.00, p < .001, indicating people high in dispositional shame were more likely to be hostile. Shame was not significantly correlated with empathy/forgiveness, nor was it correlated with mixed outcomes. Interestingly, dispositional shame and morality were significantly and positively correlated, Mr = .14, CI [.05, .23], Z =

2.91, p < .01. Only four effect sizes were included in this sub-group, however; so, this finding should be interpreted with caution. For full results, see Table 3.

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Outcome measure type. In a related set of analyses, outcome measure type (self-report only vs. multi-method) was tested as a moderator. For guilt, 47 studies assessed prosocial orientation using self-report only, while 16 studies used mixed methodologies. For shame, 37 studies assessed prosocial orientation using self-report only, while 10 used mixed methodologies.

This moderator was not significant in either meta-analysis, Qguilt = .23, df = 1, p = .63, Qshame =

.28, df = 1, p = .60, though for guilt this effect did reach significance in the fixed effect model.

Percent women, percent White, and mean age. Finally, I tested whether percent women, percent White, or mean age significantly moderated the effect of guilt (shame) on prosocial orientation. Percent women was not a significant moderator among guilt studies, bguilt

= .01, CI [-.18, .20], p = .91; however, it did reach marginal significance among shame studies, bshame = .18, CI [-.01, .37], p = .06, meaning studies including more women tended to exhibit slightly stronger positive effects. Percent White was not a significant moderator in either meta- analysis, bguilt = -.06, CI [-.39, .26], p = .71, bshame = .09, CI [-.30 , .49], p = .64, nor was mean age, bguilt = -.00, CI [-.01, .01], p = .55, bshame = .00, CI [-.01 , .01], p = .64. This relative lack of demographic effects was surprising, given that others have shown significant gender and age differences in mean levels of guilt and shame (Else-Quest, Higgins, Allison, & Morton, 2012).

Discussion

The current meta-analyses examined the interpersonal adaptiveness of dispositional guilt and shame by summarizing the distinct relation of each to prosocial orientation, in fulfillment of

Research Objective 1. Additionally, and perhaps more importantly, in doing so I highlighted the implications of researchers’ test format choices. Specifically, guilt assessed via scenario measures was significantly positively correlated with prosocial orientation, whereas guilt assessed via checklist measures was unrelated to prosocial orientation. Such results support

! 26! ! previous speculation that these two approaches may assess separate constructs, and provide partial fulfillment of Research Objective 2. In contrast, test format did not moderate the relation between shame and prosocial orientation; similar effect sizes were observed for both measure types.

Theoretical Implications

Prior to conducting these meta-analyses, I hypothesized that, despite sharing a name, checklist and scenario measures of dispositional guilt capture distinct varieties of guilt. More specifically, I suggested that while checklist measures tap maladaptive, neurotic guilt, scenario measures tap adaptive, prosocial guilt. I further hypothesized that, in contrast to these differing perspectives on guilt, checklist and scenario measures of shame capture a unitary shame construct. Results of moderator analyses confirmed most of these hypotheses, although checklist guilt was not observed to be explicitly maladaptive within the framework of prosocial orientation, the current dependent variable of choice. Checklist measures of guilt have been shown via meta-analysis to be negatively correlated with maladaptive intrapsychic traits, such as depression (Kim et al., 2011). Thus, it is perhaps the case that these measures capture maladaptive tendencies on a personal, but not interpersonal, level; this possibility will be tested in subsequent studies. The empirical relations between shame and prosocial orientation were similar for both scenario and checklist measures, suggesting their conceptual likeness.

Nevertheless, the argument that scenario and checklist measures capture two distinct forms of guilt cannot be made unequivocally from these findings alone. Scenario measures may predict prosocial orientation better than checklist measures, but both types of measures might represent the same underlying construct. Still, this explanation is unlikely, as factor analytic studies have demonstrated the two test formats’ uniqueness (Abe, 2004; Kugler & Jones, 1992;

! 27! ! but see: Ferguson & Crowley, 1997; Wolf et al., 2010). Furthermore, authors’ theoretical perspectives on guilt differ along test format lines; Harder (1995), in comparing his definitions of guilt to those of Tangney (1995), questioned whether the TOSCA assesses the same guilt construct that clinicians routinely treat in therapy. To address this point more fully, I additionally conducted two small meta-analyses synthesizing all correlations between these two test formats, as reported within the guilt and shame studies featured in my meta-analyses. The correlation between checklist- and scenario-assessed guilt, as derived from six independent studies (N =

907), was relatively weak, Mr = .26, CI [.14, .36], p < .001, strengthening my argument for the conceptual distinctiveness of these guilt measures. The correlation between scenario- and checklist-assessed shame, as derived from three independent studies (N = 412) was quite a bit stronger, Mr = .53, CI [.39, .64], p < .001. Still, further research is needed to confirm these traits’ conceptual distinctiveness, and thus fully fulfill Research Objective 2.

Next Research Directions

Meta-analysis, apart from its utility in quantitatively summarizing large and potentially diverse bodies of empirical research, is additionally useful in that it allows the meta-analyst to uncover gaps in the literature. One such gap, immediately apparent in the current meta-analyses, is the preponderance of self-report measures of prosocial orientation. Of the 63 studies featured in my zero-order correlation guilt meta-analysis, just 16 assessed prosocial orientation using a data source other than self-report, and even fewer assessed behavior5. Moreover, of those few studies assessing behavioral dependent variables (e.g., Bracht & Regner, 2013), just one assessed adaptive interpersonal behavior outside of the laboratory in everyday life (Cohen, Panter, Turan,

Morse, & Kim, 2014). Even this one study, though highly informative, examined scenario- assessed guilt only. As such, despite the contributions of the above meta-analyses and the studies

! 28! ! contained therein, the field continues to lack a complete picture of the interpersonally adaptive behavior enacted by individuals who score highly on checklist-assessed versus scenario-assessed dispositional guilt. Study 2a addresses this limitation by examining the connection of each of these types of guilt (and both shame measures) to prosocial behavior in participants’ daily lives over seven days. Prior to presenting such research, I first provide a comprehensive outline of the data sources and samples utilized in Studies 2a through 2c.

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CHAPTER 3

Outline of Samples and Data Sources Featured in Studies 2a, 2b, and 2c

Studies 2a, 2b, and 2c are empirical studies aimed at providing further evidence for the conceptual distinctiveness of scenario- and checklist-assessed guilt, and, in turn, more fully explicating each of these constructs. Although each of these studies presents and fulfills unique research goals, all three utilize participants from one or both of two samples, which I have named

“Sample A” and “Sample B.” Below I provide a brief summary of the hypotheses tested in each of these three studies. I then provide a comprehensive outline of the participants in Sample A and

Sample B, all experimental procedures, and a full summary of the self-report, informant-report, and behavioral data collected from each.

Overview of Hypotheses for Study 2a, 2b, and 2c

Study 2a

Study 2a utilizes participants from Sample B to replicate and extend Study 1’s meta- analytic findings by examining the differential associations of scenario- and checklist-assessed guilt to adaptive interpersonal behavior in everyday life, assessed over the course of one week.

Though “adaptive interpersonal behavior” could be defined in many ways, in Study 2a I define it as any behavior indicative of relationship maintenance or interpersonal helping behavior (thus in line with Study 1’s definition). Behaviors intended to assess interpersonal helping were: time spent volunteering, time spent helping someone else, and time spent providing emotional support to someone else (see Weinstein & Ryan, 2010 for further explanation and definition of daily prosocial behavior). Behaviors intended to assess relationship maintenance were: time spent socializing, time spent engaging in meaningful conversation, and time spent nurturing relationships.

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In replication of Study 1’s meta-analytic findings, I expect scenario-assessed guilt to be positively associated with daily adaptive interpersonal behavior. More specifically, I hypothesize that individuals who score highly in this “prosocial” form of guilt will be more likely to engage in behaviors that foster positive interpersonal relationships, perhaps as a proactive, preventative technique to mitigate guilt experiences in everyday life. In contrast, I expect checklist-assessed guilt to be unrelated to daily adaptive interpersonal behavior, as was the case in Study 1. Finally, as was the case in Study 1, I expect both checklist- and scenario-assessed shame to be unrelated to daily adaptive interpersonal behavior.

Study 2b

Study 2b seeks to examine the differential association of checklist- and scenario-assessed guilt and shame to daily positive affect, daily negative affect, and daily well-being evaluations, using participants from Sample B. In doing so, I aim to find further support for the conceptual distinctiveness of checklist and scenario measures of dispositional guilt, and the conceptual sameness of checklist and scenario measures of dispositional shame (Research Objective 2). In addition, and more importantly, I hope to demonstrate that scenario measures of dispositional guilt exhibit no relation to affect as experienced in daily life, thereby challenging their conceptualization as “guilt” (Research Objective 3).

Based on prior meta-analytic research examining the association of guilt and shame to depressive symptoms (e.g., Kim et al., 2011), I expect scenario-assessed guilt to be positively associated with daily well-being evaluations (defined as meaning in life, satisfaction with life, and self-esteem) and positive affect, but unrelated to negative affective experience. In contrast, I expect checklist-assessed guilt, checklist-assessed shame, and scenario-assessed shame to all

! 31! ! positively predict daily negative affect, negatively predict daily positive affect, and to negatively predict daily well-being evaluations.

Study 2c

Study 2c examines scenario- and checklist-assessed guilt and shame’s associations with a broad spectrum of personality traits, as assessed via self-report and informant-report, as well as coded in-lab behavior. In doing so, I aim to more appropriately define—and suggest an alternate name for—the construct of scenario-assessed guilt (Research Objective 3). Though previous research has reported some of the same correlations I report in Study 2c, such research possesses important limitations. Specifically, to date most studies have correlated dispositional guilt with only a limited range of traits (for exception, see Cohen et al., 2011), and very few of these studies have compared checklist and scenario measures directly. Most importantly, even those studies that have compared checklist and scenario measures directly (e.g., Harder et al., 1992;

O’Connor et al., 1999) have not done so with the goal of proposing an alternate name for scenario-assessed guilt. In other words, though these authors present differing correlates for the two traits, they do not fully acknowledge the construct validity implications of their findings, nor do they argue for the renaming of either trait.

Though I do not offer specific hypotheses for every correlation run, based on prior research (and based on my findings from Studies 1, 2a, and 2b), I expect scenario-assessed guilt to be positively associated with what I have labeled as “adaptive traits” and negatively associated with what I have labeled as “maladaptive traits.” In contrast, I expect checklist-assessed guilt, scenario-assessed shame, and checklist-assessed shame to be positively associated with maladaptive traits, and negatively associated with adaptive traits. Below I provide further information regarding these trait categories, and the basis of such categorization.

! 32! !

Adaptive traits. In Study 2c, I characterize adaptive personality traits as any trait (self- reported or informant-reported) that is indicative of positive interpersonal functioning or intrapsychic adjustment and well-being. At the interpersonal level, I characterize the following as adaptive: empathic concern, perspective taking, positive relations with others, trait forgiveness, and humanitarianism-egalitarianism. Prior research has shown empathic concern and perspective taking to be positively associated with concern for others and high interpersonal functioning, respectively (Davis, 1983). Scoring high on measures of positive relations with others is negatively associated with depression (Ryff, 1989) and positively predicts frequent engagement in satisfying social activities in everyday life (Cooper, Okamura, & McNeil, 1995). Individuals high in trait forgiveness have been shown to have low levels of anger, anxiety, and hostility

(Thompson et al., 2005). Finally, humanitarianism-egalitarianism is characterized by concern for others, particularly those who are less fortunate, and is negatively associated with prejudice

(Katz & Hass, 1988).

At the intrapsychic level, I characterize the following as adaptive: environmental mastery, autonomy, personal growth, purpose in life, self-acceptance, meaning in life, flourishing, satisfaction with life, self-esteem, and trait positive affect. Environmental mastery, autonomy, personal growth, purpose in life, and self-acceptance are all subscales from Ryff’s

(1989) Psychological Well-Being inventory. These subscales have been shown to exhibit a host of adaptive correlates, including emotional stability and high levels of positive affect (Schmutte

& Ryff, 1997). Meaning in life has been shown to be negatively associated with anxiety and rumination (Steger, Kashdan, Sullivan, & Lorentz, 2008). Trait flourishing represents social- psychological prosperity, and thus touches upon both intrapsychic and interpersonal adaptiveness. This trait is positively correlated with psychological adjustment and optimism, and

! 33! ! negatively correlated with loneliness (Diener et al., 2010). Satisfaction with life and self-esteem are two constructs that characterize an individual’s subjective evaluations of his or her own life and the self, respectively. Both measures are widely perceived as adaptive, and as key indicators of well-being (see Diener et al., 2010; Rosenberg, 1965; but see also Baumeister, Campbell,

Krueger, & Vohs, 2003). Finally, trait positive affect is a key component of subjective well- being and important predictor of success across multiple life domains, from career to health to positive social functioning (Lyubomirsky, King, & Diener, 2005).

Maladaptive traits. In Study 2c, I characterize maladaptive personality traits as any trait

(self-reported or informant-reported) that is indicative of low levels of interpersonal functioning or poor intrapsychic adjustment or well-being. Though some of these traits essentially capture the absence of the adaptive traits listed above (e.g., depressivity can be conceived of as the extreme low end of satisfaction with life), others assess the presence of maladaptive tendencies not fully captured by the measures listed above (e.g., frequency of physically or verbally aggressive behaviors). At the interpersonal level, I assess the following traits: physical aggression, verbal aggression, anger, hostility (assessed via two measures), and withdrawal.

Aggression and hostility are detrimental to individuals’ personal relationships, and can even negatively impact one’s physical health (Smith & Frohm, 1985). Withdrawal is characterized as a lack of interest in others and general mistrust and avoidance, and is indicative of poor psychological adjustment (Hopwood, Schade, Krueger, Wright, & Markon, 2013).

At the intrapsychic level, I assess emotional lability, depressivity, and trait negative affect. Emotional lability is a trait characterized by highly changeable or variable affect (i.e., emotional dysregulation), while depressivity and trait negative affect are traits characterized by

! 34! ! negative affective experience over a sustained period of time. Each of these is viewed as maladaptive and indicative of poor psychological adjustment (Hopwood et al., 2013).

The Big Five. The Big Five (agreeableness, conscientiousness, extraversion, openness to experience, and neuroticism) are general personality dimensions. Though this model of five traits was developed with the intention of capturing personality data beyond simple adaptiveness or maladaptiveness, researchers typically conceive of neuroticism as generally maladaptive, and the remaining four as generally adaptive (Costa & McCrae, 1992). Accordingly, I expect scenario- assessed guilt to be positively associated with agreeableness, conscientiousness, extraversion, and openness to experience, and negatively associated with neuroticism. In contrast, I expect checklist-assessed guilt, scenario-assessed shame, and checklist-assessed shame to all be positively correlated with neuroticism, and negatively correlated with extraversion, agreeableness, conscientiousness, and openness to experience (see Abe, 2004; Cohen et al.,

2011; Schaumberg & Flynn, 2012 for studies that have previously correlated dispositional guilt and shame with the Big Five).

In-lab social behavior. No studies to date have examined dispositional guilt or shame’s correlations with social behavior as exhibited in an unstructured social interaction in the laboratory. Rather, most extant empirical research examining the impact of guilt and shame on behavior has done so using prisoner’s dilemmas or other economic games. As such, I offer no specific predictions for those correlations, though it is reasonable to suspect scenario-assessed guilt will positively predict adaptive social behavior, in line with the meta-analytic findings presented in Study 1.

Sample A

Participants

! 35! !

Sample A featured 76 undergraduate student participants (38 female), recruited as part of a larger investigation on personality and behavior. Participants were recruited via posters hung throughout Northeastern University’s campus. The average age of this sample was 20.6 years.

Sample A data were collected in the summer and fall of 2014.

Materials

Participants in Sample A engaged in a multi-part personality assessment, providing self- reports of personality, informant-reports of personality (personality ratings obtained from participants’ friends), and in-lab behavioral data. Data from Sample A are utilized in Study 2c only. Those data sources relevant to Study 2c’s central research objectives and hypotheses are outlined in detail below. For the sake of clarity and transparency, in Appendix G I describe all additional self- and informant-reports collected from participants in Sample A not analyzed in the current research.

Self -reports of dispositional guilt and shame. All participants in Sample A completed

The Test of Self-Conscious Affect (TOSCA, Tangney, Wagner, & Gramzow, 1989) as a scenario-based measure of dispositional guilt and shame. As a checklist measure of dispositional guilt and shame, participants completed the Personal Feelings Questionnaire (PFQ; Harder &

Lewis, 1987). These two self-report measures are explained in greater detail below. Means and standard deviations for the TOSCA and the PFQ can be found in Table 4; inter-correlations between these measures can be found in Table 5.

The TOSCA. The TOSCA contains 16 hypothetical scenarios in which the participant is asked to imagine that he or she has enacted a transgression or wrongdoing. Participants indicate the perceived likelihood of their engaging in four to five responses to each hypothetical transgression using a 1 to 5 scale (1= not at all likely, 5 = extremely likely), one of which reflects

! 36! ! guilt and one of which reflects shame. This scale also includes subscales for alpha pride, beta pride, externalization of blame, and internalization of blame; however, these are not germane to the current research goals, and thus are not discussed. In the TOSCA, guilt responses are characterized by feelings of regret regarding a specific action, wishing to make amends, or reparative tendencies (e.g., “You would feel unhappy and eager to correct the situation”), whereas shame responses are characterized by feelings of inferiority, wishing to disappear, or avoidance behavior (e.g., “You would feel small…like a rat”). In Sample A, the TOSCA guilt subscale exhibited an internal reliability (Cronbach’s alpha) of α = .80; the TOSCA shame subscale exhibited an internal reliability of α = .84.

The PFQ. Participants also completed the Personal Feelings Questionnaire (PFQ; Harder

& Lewis, 1987), a checklist measure of dispositional guilt and shame. This measure presents participants with 22 emotion words and descriptive phrases. Participants indicate the frequency with which they experience each descriptor on a Likert type scale of 0 (I never experience this) to 4 (I experience this continuously or nearly continuously). Six of these items are indicative of guilt (e.g. “regret”), and ten are indicative of shame (e.g. “feeling humiliated”). The remaining six items are distractors. In sample A, the PFQ guilt subscale exhibited an internal reliability of α

= .73; the PFQ shame subscale exhibited an internal reliability of α = .79.

Self-reports of the Big Five. The Big Five (agreeableness, neuroticism, extraversion, conscientiousness, and openness to experience) were assessed in Sample A via the 240-item

NEO-PI-R (NEO-PI-R; Costa & McCrae, 1992). The NEO-PI-R also provides six facet scores for each of the Big Five via 8-item subscales. To reduce the number of correlations run in subsequent studies, only the five superordinate factor scales, and not their underlying facets, were examined (see Appendix G for a list of facet scales and reliabilities). Internal reliabilities

! 37! ! for each of the Big Five scales were as follows: agreeableness (α = .80), neuroticism (α = .93), extraversion (α = .91), conscientiousness (α = .94), and openness to experience (α = .90). All items from the NEO-PI-R were assessed on a 1 (strongly disagree) to 5 (strongly agree) scale.

Self-reports of adaptive traits. Participants in Sample A also completed a battery of self-report questionnaires intended to capture a wide array of adaptive interpersonal and intrapsychic traits, listed below. All scales reported herein were assessed on a 1 (strongly disagree) to 5 (strongly agree) scale, unless otherwise noted.

Trait empathy. Trait empathy was assessed via the Interpersonal Reactivity Index (IRI;

Davis, 1983). Though many other operational definitions exist, here I define empathy as the 7- item perspective-taking (α = .73) and empathic concern (α = .79) subscales of the IRI. Though the IRI also includes a subscale for personal concern (i.e., anxiety or distress in response to the suffering of another individual) and a subscale for fantasy (i.e., the ability to place oneself into the mind of a fictional character), in the interest of preventing participant fatigue these subscales were not included in the questionnaire battery, as they are less commonly employed than the other two (see Leith & Baumeister, 1998 for a more comprehensive discussion of guilt, shame, and empathy).

Sample items from the perspective-taking subscale include: “I try to look at everybody’s side of a disagreement before I make a decision,” and “Before criticizing somebody, I try to imagine how I would feel if I were in their place.” Sample items from the empathic concern subscale include: “I am often quite touched by things that I see happen,” and “I would describe myself as a pretty soft-hearted person.” Due to an error in online survey formatting, the perspective taking subscale of the IRI was assessed using six rather than seven items in Sample

A.

! 38! !

Self-esteem. Self-esteem—or, one’s self-perceived feelings of worth—was assessed via the 10-item Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965, α = .92). Sample items from the RSE include: “I feel that I have a number of good qualities,” and “On the whole, I am satisfied with myself.”

Psychological well-being. Ryff’s Psychological Well-Being scale (PWB; Ryff, 1989) was employed to assess trait well-being. Though the full PWB battery contains 84 items and six subscales, in Sample A I collected scores for select subscales of the PWB, in the interest of preventing participant fatigue: environmental mastery (α = .83) and positive relations with others

(α = .82). These two subscales were selected from the six total subscales of the PWB as they were most relevant to the original research goals for this dataset. In addition, though the full subscales contain 14 items, in the current sample I utilized Ryff’s shortened 9-item versions

(Ryff, 1989). Environmental mastery is defined as one’s self-perceived skill in managing his or her daily responsibilities. Individuals who score highly in environmental mastery believe they are able to effectively choose or create the contexts or environments in which they hope to live.

Sample items from the environmental mastery subscale are: “In general, I feel I am in charge of the situation in which I live,” and “The demands of everyday life often get me down” (reverse- keyed). Individuals who score highly in positive relations with others feel they have many warm, satisfying, and fulfilling personal relationships, and understand the benefits and responsibilities inherent in close friendship or intimacy. Sample items from the positive relations with others subscale are: “Most people see me as loving and affectionate,” and “I enjoy personal and mutual conversations with family members or friends.”

Trait positive affect. Trait positive affect was assessed using the Positive and Negative

Affect Schedule (PANAS, Watson, Clark, & Tellegen, 1988). The PANAS can be used to assess

! 39! ! either trait or state affect. To assess trait affect specifically, the instructions were adapted to read:

“Please indicate the extent to which you have felt this way during the past few weeks.” Positive affect (α = .88) was assessed using the following 10 items: interested, enthusiastic, proud, excited, strong, alert, inspired, determined, attentive, and active. Trait positive affect was assessed using a 1 (very slightly or not at all) to 5 (extremely) scale.

Self-reports of maladaptive traits. Participants in Sample A also completed a battery of self-report questionnaires intended to capture a wide array of maladaptive traits, both interpersonal and intrapsychic. All scales reported herein were assessed on a 1 (strongly disagree) to 5 (strongly agree) scale, unless otherwise noted.

DSM personality scales. A wide range of maladaptive traits was assessed using the

Personality Inventory for DSM-5 (PID-5; Krueger, Derringer, Watson, & Skodol, 2013). This personality inventory contains a large number of subscales, intended to capture various maladaptive traits at sub-clinical levels, in the general population. Participants in Sample A completed the following subscales: hostility (10 items, α = .77; e.g., “I snap at people when they do little things that irritate me”), withdrawal (10 items, α = .91; e.g., “I prefer not to get too close to people”), emotional lability (7 items, α = .91; e.g., “My emotions sometimes change for no good reason”), and depressivity (14 items, α = .92; e.g., “I’m useless as a person”). Other PID-5 subscale collected but not analyzed in the current research are reported in Appendix G.

Self-reported aggressive behavior. Self-reported aggressive behavior was assessed using the 29-item Buss-Perry Aggression Questionnaire (AQ; Buss & Perry, 1992). Sample items from this questionnaire include: “I have threatened people I know” and “If somebody hits me, I hit back.” This scale is comprised of four subscales: Physical aggression (α = .64), verbal aggression

(α = .72), anger (α = .64), and hostility (α = .88).

! 40! !

Trait negative affect. Trait negative affect was assessed using the Positive and Negative

Affect Schedule (PANAS, Watson et al., 1988). The PANAS can be used to assess either trait or state affect. To assess trait affect specifically, the instructions were adapted to read: “Please indicate the extent to which you have felt this way during the past few weeks.” Negative affect

(α = .84) was assessed using the following 10 items: distressed, upset, guilty, scared, hostile, irritable, nervous, jittery, afraid, and ashamed. Trait negative affect was assessed using a 1 (very slightly or not at all) to 5 (extremely) scale.

Informant reports of personality. Each participant in Sample A recruited up to three close friends to provide personality data about him or her. Informants provided ratings of the participant’s Big Five personality traits (agreeableness, extraversion, openness to experience, neuroticism, and conscientiousness) via the NEO-PI-R, using a 1 (strongly disagree) to 5

(strongly agree) scale. Informants returning incomplete surveys—defined as surveys with fewer than 50% of items completed—were discarded. All complete informant ratings for a given participant were averaged to create one informant personality profile for that individual. Of the

135 informants providing complete ratings for participants in Sample A, 73 (54%) were female.

The average age of this informant sample was 21.0 years.

In-lab behavior. Participants each engaged in a 5-minute unstructured videotaped interaction in the laboratory with an opposite sex partner (also a participant in the study).

Participants’ behavior throughout this interaction was later independently coded by up to five trained coders using the Riverside Behavioral Q-Sort, a tool for characterizing verbal and nonverbal social behavior (RBQ; Funder, Furr, & Colvin, 2000). The RBQ is a deck of 64 cards with a single high-level behavior printed on each, such as: “Dominates the interaction,” “Is physically animated,” or “Is talkative.” Coders code participants’ behavior by sorting these cards

! 41! ! into an approximately normal distribution of nine piles, ranging from “extremely uncharacteristic” (given a value of 1) to “extremely characteristic” (given a value of 9), based on the interaction as a whole.

Coders were instructed to watch one participant in each video two full times, then code his or her behavior using the RBQ; no one coder ever coded both participants in a single interaction. In addition, coders were instructed to “avoid playing psychologist,” and to only code behavior that was present in the interaction, rather than attempt to infer what the participant might behave like in other situations. Once all codings were complete, Cronbach’s alphas were used to assess inter-rater reliability for each RBQ item, across all targets. If the reliability for a given item was less than .30, ratings from the single rater exhibiting the lowest level of agreement were discarded if doing so increased reliability (which it did in all cases). All remaining coders’ ratings were averaged to create a behavioral profile for each participant. No one behavior or participant was coded by fewer than four trained coders.

Procedure

Sample A participants reported to the laboratory two at a time (one male and one female).

Upon providing consent, both were seated on a couch in front of two visible video cameras. The experimenter informed the participants they could talk about whatever they liked for five minutes, turned on the cameras, left the room, and returned to turn off the cameras when five minutes had passed. Both participants were then relocated to computers, where they completed the battery of self-report questionnaires described above, presented in a randomized order. Upon their completion of the survey participants provided the contact information for three informants, and were compensated $25 for their time. Informants were later contacted via email, and

! 42! ! compensated $5 for their time. If all three informants completed their surveys, participants received an additional $15 bonus ($5 for each completed informant survey).

Sample B

Participants

Sample B featured 96 undergraduate student participants (56 female) with an average age of 18.8 years. Participants were recruited from psychology courses at Northeastern University, and took part in exchange for partial course credit. Data were collected from Sample B in the fall of 2014.

Materials

As was the case with Sample A, participants in Sample B engaged in a multi-part personality assessment, providing self-reports of personality, recruiting friends to provide informant-reports, and engaging in a five-minute unstructured interaction, later to be behaviorally coded. Participants in Sample B participants additionally had the option to complete a daily diary assignment outside of the laboratory for seven consecutive days. The structure of this diary assignment is described in greater detail below. Participants were given partial course credit if they completed the in-lab session, and earned additional course credit if they completed at least six of the seven daily diaries.

Data from Sample B are utilized in Studies 2a, 2b, and 2c. Data sources utilized in Study

2a are marked with an “a” superscript below; data utilized in Study 2b are marked with a “b,” and data utilized in Study 2c are marked with a “c.” All self-report questionnaires collected from participants in Sample B not analyzed in the current research are outlined in Appendix G.

Self-reports of dispositional guilt and shamea,b,c. As was the case in Sample A, participants in Sample B completed the Test of Self-Conscious Affect (TOSCA, Tangney,

! 43! !

Wagner, & Gramzow, 1989) as their scenario measure of dispositional guilt and shame, and the

Personal Feelings Questionnaire (PFQ; Harder & Lewis, 1987) as their checklist measure of dispositional guilt and shame. All guilt and shame measures were employed using standard scales, as outlined in the description of Sample A. In sample B, the TOSCA guilt subscale exhibited an internal reliability of α = .79; the TOSCA shame subscale exhibited an internal reliability of α = .73. The PFQ guilt subscale exhibited an internal reliability of α = .77; The PFQ shame subscale exhibited an internal reliability of α = .81. Means and standard deviations for all dispositional guilt and shame self-report measures can be found in Table 4; inter-correlations between these measures can be found in Table 5.

Self-reports of the Big Fivec. The Big Five was assessed in Sample B using the 240-item

NEO-PI-R (Costa & McCrae, YEAR), assessed on a 1 (strongly disagree) to 5 (strongly agree) scale. For further information regarding the structure of the NEO-PI-R, see the description provided above for Sample A. Total scale internal reliabilities for Sample B were as follows:

Agreeableness (α = .92), neuroticism (α = .92), extraversion (α = .90), conscientiousness (α =

.93), and openness to experience (α = .89). As was the case in Sample A, only the higher-order

Big Five factor scores are utilized in subsequent analyses. For internal reliabilities for all facet subscales collected with Sample B, see Appendix G.

Self-reports of adaptive traitsc. In replication of Sample A, self-reports of trait empathic concern (α = .80) and perspective taking (α = .80), two common operationalizations of empathy, were obtained using the IRI (IRI; Davis, 1983). Self-esteem was once again assessed using the

Rosenberg Self-esteem scale (RSE; Rosenberg, 1965, α = .90), and trait positive affect (α = .86) was assessed using the PANAS (PANAS, Watson et al., 1988). Empathy and self-esteem were assessed using the standard 1 (strongly disagree) to 5 (strongly agree) scale. As was the case for

! 44! !

Sample A, trait positive affect was assessed using a 1 (very slightly or not at all) to 5 (extremely) scale. The questionnaire battery administered to Sample B also featured a more extensive examination of intrapsychic and interpersonal traits, outlined below. All scales subsequently listed were assessed using a 1 (strongly disagree) to 5 (strongly agree) scale, unless otherwise noted. Trait negative affect (also assessed using the PANAS; α = .84) was the only maladaptive personality trait assessed in Sample B.

Psychological well-beingc. Ryff’s full 84-item Psychological Well-Being scale (PWB;

Ryff, 1989) was employed to assess trait well-being among participants in Sample B.

Participants completed the full PWB, which includes the following six 14-item subscales, each constructed to assess a unique component of well-being: Self-acceptance (e.g., “I like most aspects of my personality”; α = .92), purpose in life (e.g., “I have a sense of direction and purpose in life”; α = .86), positive relations with others (e.g., “I enjoy personal and mutual conversations with family members or friends”; α = .86), personal growth (e.g., “In general, I feel that I continue to learn more about myself as time goes by”; α = .84), environmental mastery

(e.g., “I am quite good at managing the many responsibilities of my daily life”; α = .82), and autonomy (e.g., “My decisions are not usually influenced by what everyone else is doing”; α =

.89). Though Ryff’s Psychological Well-Being scale was also utilized in Sample A, only the positive relations with others and environmental mastery subscales were employed in that sample.

Trait flourishingc. Flourishing, or, self-perceived success across multiple life domains, was assessed using the 8-item Flourishing Scale (FS; Diener et al., 2010; α = .78). Sample items from the FS include: “My social relationships are supportive and rewarding,” and “People respect me.”

! 45! !

Meaning in lifec. Meaning in life was assessed using the Meaning in Life

Questionnaire’s presence of meaning subscale (MLQ; Steger, Frazier, Oishi, & Kaler, 2006).

This 5-item scale (α = .87) includes items such as “I understand my life’s meaning.”

Satisfaction with lifec. Satisfaction with life is defined as an individual’s subjective global appraisal of the quality of his or her life. Satisfaction with life was assessed in Sample B using the 5-item Satisfaction with Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985,

α = .81). Sample items from this scale include “In most ways my life is close to my ideal,” and “I am satisfied with my life.”

Humanitarianism-egalitarianismc. Humanitarianism-egalitarianism is defined as adherence to ideals and values related to equality, social justice, and concern for the welfare of others (Katz & Hass, 1988). This trait was assessed using the 10-item Humanitarianism-

Egalitarianism Scale (HE; Katz & Hass, 1988, α = .84). Sample items from this scale include:

“One should find ways to help others less fortunate that oneself,” and “A good society is one in which people feel responsible for one another.”

Trait forgivenessc. Trait forgiveness, or, the tendency to forgive others following transgressions or wrongdoings, was assessed using the 10-item Heartland Forgiveness Scale

(HFS; Thompson et al., 2005, α = .83). Sample items from this scale include: “I can forgive a friend for almost anything,” and “I have always forgiven those who have hurt me.”

Informant reports of personality and well-beingc. In sample B, informants provided ratings of participants’ Big Five personality traits using the 60-item NEO Five Factor Inventory

(NEO-FFI; Costa & McCrae, 1989). Unlike the NEO-PI-R, the NEO-FFI provides scores for the

Big Five personality traits (agreeableness, extraversion, neuroticism, conscientiousness, and openness to experience), but does not subdivide the Big Five into facets. Informants also

! 46! ! provided ratings of participants’ well-being using both the PWB (full 84-item version) and the

SWLS. All informant scales were completed using a 1 (strongly disagree) to 5 (strongly agree) scale. Ratings from informants returning incomplete surveys—defined as providing responses to fewer than 50% of the questions asked—were discarded. All complete informant ratings for a given participant were averaged to create one informant personality profile for that individual. Of the 161 individuals providing complete informant ratings, 115 (71.4%) were female. The average age of informants for participants in Sample B was 19.3 years.

In-lab behaviorc. As was the case in Sample A, participants in Sample B engaged in a videotaped five-minute unstructured interaction in the laboratory with an opposite sex partner

(also a participant in the study). Participants’ behavior was coded by up to five trained coders using the Riverside Behavioral Q-Sort (RBQ; Funder et al., 2000).

Behavioral coding procedures for Sample B were identical to those for Sample A, and the same group of coders coded both samples. Coders were instructed to watch one participant in each video two full times, then code his or her behavior using the RBQ. Coders independently rated each participant, and no coder ever provided ratings for both participants in any one interaction. Cronbach’s alphas were used to assess inter-rater reliability for each RBQ item, across all targets. If the reliability for a given item was less than .30, ratings from the rater exhibiting the lowest level of agreement were discarded, provided doing so resulted in an increase in reliability. All remaining coders’ ratings were averaged to create a behavioral profile for each participant. No one behavior or participant was coded by fewer than four trained coders.

Daily diaries. Participants additionally completed seven daily diaries: one each day over the course of one week, beginning the Monday following their in-lab participation. These daily

! 47! ! diaries contained two segments: a daily activities log and a full day evaluation, each described in greater detail below. Of the 96 participants in Sample, 91 completed at least one diary entry.

Daily activities loga. The daily activities log was a modified version of Kahneman and colleagues’ (2004) Day Reconstruction Method. In this log, participants’ previous 24 hours were broken down into 2-hour time slots. Participants were asked to identify the single activity they spent the most time doing during each of these slots using an open-ended space. For example, a participant might type “I was at the gym” in the 8:00am to 10:00am time slot if he or she was exercising during that time. Participants were then asked to further characterize each activity along several dimensions using a series of check boxes. Participants were free to check as many boxes as they felt were appropriate to describe each activity, and were told to complete the daily activities log in a way that best reflected their own experiences and perceptions. Specifically, participants were reminded that the current research was not concerned with whether or not a given activity was objectively “meaningful” or whether most people would classify it as something done “for pleasure”; rather, the research was concerned with their own opinions and classifications.

All possible check boxes in the daily activities log were grouped into four categories: whom the participant was with, what the participant was doing, why the participant did the activity, and how the activity made the participant feel. Participants first identified whom they were with by checking one or more of the following boxes: I was alone, I was with acquaintances, I was with close friend(s), I was with a significant other, and I was with parents.

They then indicated what they were doing using the following check boxes: I was attending class, I was at a job, I was shopping, I was studying/doing homework, I was relaxing, I was commuting/traveling somewhere, I was preparing or eating food, I was doing

! 48! ! housework/cleaning, I was socializing, I was having an intimate or personal conversation, I was providing emotional support to someone else, I was praying/worshipping/meditating, I was watching TV, I was sleeping, I was exercising, I was using the computer/internet, I was playing video games, I was talking on the telephone, or I was volunteering. Participants were asked to identify why they did the activity using one or more of the following check boxes: for myself, for someone else, purely for pleasure, to help someone, because I wanted to, because I was forced or obligated to, to improve myself, to learn something new, to pursue a personal goal, to nurture a friendship or relationship, or for my own health/well-being. Finally, participants identified whether the activity was meaningful, important, reflected them as a person, or if it was a mistake, as well as how they felt during the activity or soon after using the following: happy, fulfilled, good about myself, close to/connected to others, isolated/alone, guilty, and ashamed.

Checked boxes were later coded as “1” (i.e., “yes” I did this or “yes” I felt this way), and all non-checked boxes were coded as “0” (i.e., “no”). All box values for each category were summed within participants across each 24-hour time period prior to any analyses, to produce a score on a given dimension for a given day. For example, if a participant checked the box “I was alone” three times over the course of a 24-hour period, that individual was given a “time spent alone” score of “3” for that day, translating to about six hours spent alone. Only the following behaviors are examined in Study 2a, as these were selected a priori as reflecting interpersonal helping and relationship maintenance behavior: Time spent socializing, time spent nurturing relationships, time spent helping others, time spent engaging in meaningful conversation, time spent volunteering, and time spent providing emotional support. Additionally, because check box categories were potentially but not necessarily independent (i.e., an individual could classify a

! 49! ! single activity as both “meaningful conversation” and “socializing”), all are analyzed individually in the studies that follow.

Full day evaluationb. The second segment of the daily diary, the full day evaluation, asked participants to reflect on their previous 24 hours as a whole, in a more global fashion.

Participants first reported their state positive and negative affect using the Positive and Negative

Affect Schedule (PANAS; Watson et al., 1988). State positive affect was assessed using the following ten items: Interested, enthusiastic, proud, excited, strong, alert, inspired, determined, attentive, and active. State negative affect was assessed using the following ten items: Distressed, upset, guilty, scared, hostile, irritable, nervous, jittery, afraid, and ashamed. All items within the

PANAS positive and negative affect scales were assessed using a 1 (very slightly or not at all) to

5 (extremely) scale.

Next, participants provided ratings of their personal well-being over the previous 24 hours using three related items. Each participant provided a state assessment of satisfaction with life (“Over the past 24 hours, how satisfied did you feel with your life?”), meaning in life (“Over the past 24 hours, how meaningful did your life feel?”), and self-esteem (“Over the past 24 hours, how positively did you feel about yourself?”), all using a 1(very slightly or not at all) to 5

(extremely) scale.

Summary of Data Sources and Subsequent Studies

In sum, all subsequent studies utilize a subset of data obtained from Sample A, Sample B, or both. Study 2a utilizes Sample B’s daily activities log data in examining checklist and scenario measures’ differential associations with adaptive interpersonal behavior in everyday life. Study 2b utilizes Sample B’s full day evaluation in examining if one, both, or neither measure of dispositional guilt predicts affective experience in everyday life. Finally, Study 2c

! 50! ! uses self-reported personality data, informant-reported personality data, and in-lab behavioral data obtained from Sample A and Sample B to more fully characterize the construct captured via scenario measures of dispositional guilt.

! 51! !

CHAPTER 4

Study 2a – Are Checklist- and Scenario-Assessed Guilt Differentially Associated with Daily

Diary Reports of Adaptive Interpersonal Behavior?

A Replication and Extension of Study 1

Researchers and theorists have long argued that dispositional guilt is useful in that it pushes individuals to “do good and avoid doing bad” (Tangney, 2003, p. 386). As reviewed in

Chapter 1, such frameworks argue that individuals who score high on measures of dispositional guilt are more likely behave in ethical (Cohen, Panter, Turan, Morse, & Kim, 2013; Cohen,

Wolf, Panter, & Insko, 2011; Tangney et al., 2007), prosocial (Bracht & Regner, 2013; Roos,

Hodges, & Salmivalli, 2014), and interpersonally adaptive ways (Covert, Tangney, Maddux, &

Heleno, 2003). Researchers who adhere to this framework posit that these individuals, due to their proclivity for experiencing guilt, may feel a greater sense of urgency in their desires to take constructive action in the face of a (perceived, anticipated, or experienced) problem or wrongdoing (Flynn & Schaumberg, 2012).

Yet my meta-analysis (Study 1) suggests that the above statements may only apply to scenario-assessed (i.e., “prosocial”) guilt. In other words, although the prosocial, adaptive form of the guilt captured by scenario measures is likely positively associated with adaptive interpersonal behavior, the neurotic, maladaptive form of guilt captured by checklist measures is likely not. The current research seeks to replicate and extend the meta-analysis by examining the differential associations of scenario- and checklist-assessed guilt to adaptive interpersonal behavior (defined as interpersonal helping and relationship maintenance behaviors) in everyday life, assessed over the course of one week. In addition, I present results for checklist- and

! 52! ! scenario-assessed shame to further highlight their conceptual similarity. Given my previous meta-analytic findings, I expect to observe the following:

H1: Scenario-assessed guilt will positively predict daily adaptive interpersonal behavior

H2: Checklist-assessed guilt will be unrelated to daily adaptive interpersonal behavior

H3: Scenario-assessed shame will be unrelated to daily adaptive interpersonal behavior

H4: Checklist-assessed shame will be unrelated to daily adaptive interpersonal behavior

Method

Participants

Study 2a features participants from Sample B only, a sample of 96 undergraduate student participants (56 female), with an average age of 18.8 years. For further information regarding

Sample B, see Chapter 3.

Materials

Participants in Sample B were engaged in a multi-part, multi-method personality assessment that featured self-reports, informant reports, coded in-lab behavior, and out-of-lab self-reported behavior. Here I present only those measures and procedures relevant to the current hypotheses; for more complete information regarding Sample B’s full assessment procedures, see Chapter 3 and Appendix G.

Self-reports of dispositional guilt and shame. In the current study, I will present results for two self-report measures of dispositional guilt and shame: one scenario measure, and one checklist measure. The Test of Self-Conscious Affect (TOSCA, Tangney, Wagner, & Gramzow,

1989) is the scenario-based measure of choice in the current research; the Personal Feelings

Questionnaire (PFQ; Harder & Lewis, 1987) is the checklist measure of choice.

! 53! !

Daily diary. Participants in Sample B, in addition to providing self-reports of dispositional guilt and shame, completed seven daily diaries: one each day over the course of one week, beginning the Monday following their in-lab participation. In the current study, I utilize only the daily activities log portion of this diary only (see Kahneman et al., 2004). Briefly, the daily activities log broke participants’ days into 12 two-hour time slots, instructing each participant to identify the central behavior he or she engaged in during each slot. Next, each participant was presented with a series of check boxes, allowing him or her to more clearly and fully characterize each activity along several dimensions. Of the 96 participants in Sample B, 91 completed at least one diary entry. For more information regarding the structure of this daily activities log, as well as a comprehensive list of all potential check boxes, see Chapter 3.

Though participants provided a wide range of information regarding their daily activities, the current study is concerned with everyday adaptive interpersonal behavior only. As such, a priori I identified six behavioral categories of interest that capture various aspects of adaptive interpersonal behavior (defined as interpersonal helping and relationship maintenance behaviors): time spent socializing, time spent nurturing relationships, time spent helping others, time spent engaging in meaningful conversation, time spent providing emotional support to others, and time spent volunteering. All check marks made by a participant on any given day in each of these six categories were summed within-person, per day. For example, a participant who checked the “volunteering” box twice within a 24-hour period was given a volunteering behavior score of “2” for that day. A participant who did not check the volunteering box at all within a 24-hour period was given a volunteering behavior score of “0.” As such, six “time spent” dependent variables were calculated per person per day: one for each of these six categories.

! 54! !

Procedure

Participants in Sample B reported to the laboratory two at a time (one male and one female). Upon providing consent, they provided self-report ratings of dispositional guilt and shame using the TOSCA and the PFQ, presented as part of a larger battery of self-report questionnaires. Upon their completion of the survey participants were given instructions for the daily diary, and partial course credit for their participation. Participants were then instructed to complete the daily diary each night at 10:00pm for seven consecutive days, starting the Monday following their in-lab participation. Participants who completed at least six of the seven daily diaries were given additional course credit. For further information regarding the daily diary format and procedures, reference Chapter 3.

Results

Dispositional Guilt and Shame: Descriptives

In Sample B, participants’ TOSCA guilt and TOSCA shame scores were significantly and positively correlated, r = .50, p < .001. Such a finding is consistent with previous research utilizing the TOSCA (Tangney, 1996), though slightly stronger than previously reported. Also consistent with previous research (Tangney et al., 1996), the PFQ guilt and PFQ shame scores were more strongly correlated than guilt and shame assessed via the TOSCA, r = .64, p < .001, though the difference between these correlations was not statistically significant. As expected,

PFQ shame and TOSCA shame were positively correlated, r = .40, p < .001, once again suggesting their conceptual similarity. Also as expected, PFQ guilt and TOSCA guilt were not significantly correlated, r = .15, p = .14, suggesting their conceptual distinctiveness. The difference between these two correlations was statistically significant, according to Zou’s (2007)

! 55! ! confidence interval test. See Table 4 for full sets of descriptive statistics from the TOSCA and the PFQ in Sample B.

Daily Interpersonally Adaptive Behavior: Descriptives

Given the structure of the daily activities log, it was possible for participants’ total time spent engaging in any given behavior type over the course of one day to range from 0 to 12 (a score of 12 meaning that all 24 hours were spent engaging in that activity). However, it is improbable that an individual would in actuality engage in any one type of activity for nearly 24 hours without sleeping. Thus, any behavioral frequencies greater than “8” (meaning, greater than

16 hours total) for a given day were treated as outliers and removed from all analyses. This resulted in the removal of four total data points: one for time spent socializing, one for time spent providing emotional support, one for time spent nurturing friendships, and one for time spent in conversation.

The modal response for all six interpersonally adaptive behaviors of interest was “0,” meaning that any one type of activity was relatively rare. For most activities, the next most common response was “1,” meaning that the participant engaged in that type of activity for one of his or her twelve 2-hour time chunks on that particular day. For full frequencies of each of my six behaviors of interest, pooled across all participants and across all seven days, see Table 6.

Analytic Approach

The repeated measures nature of this study was such that each participant could complete up to seven daily diary entries. As such, multilevel models were most appropriate for analyzing these data, with behavioral frequencies (again, calculated as the sum of all relevant check boxes per day in the daily activities logs) nested within individuals in chronological order, from

Monday to Sunday. Of the 91 total participants who chose to participate in the daily diary

! 56! ! portion of the study, 16 failed to complete all seven diary entries; however, when multilevel modeling is used, missing data are not problematic, and incomplete data do not need to be removed prior to parameter estimation.

The behavioral dependent variables of interest in the current research possess some unique features. Although framed as “time spent” variables for ease of interpretation, these behavioral frequencies, as obtained via check boxes in the daily activities log, are in actuality count data. Participants checked whether a given descriptor applied to their behavior in each two-hour time chunk (either “yes,” this applies to my behavior, or “no,” this does not apply).

When count data are utilized and the dependent variable of interest is not normally distributed, traditional Gaussian approaches are not appropriate. Instead, all models were estimated using a negative binomial distribution. The negative binomial distribution is most appropriate for count data, particularly for cases in which discrete events are meaningful yet relatively rare, such as number of arrests, hospitalizations, or cancer diagnoses (Rabe-Hasketh & Skrondal, 2012). This is because such data are typically highly positively skewed, and, unlike the Poisson distribution

(another method for analyzing count data), the negative binomial distribution allows the mean and standard deviation to differ. This was indeed the case for my data, as highlighted in Table 6.

All multilevel models were estimated using maximum likelihood estimation via R’s lme4 package (Bates, Maechler, Bolker, & Walker, 2015). lme4 is an open-source package for fitting generalized linear mixed-effects models. Models were estimated using the “glmer.nb” function, which specifies the negative binomial distribution in a mixed effects model. Further information regarding the fixed and random effects estimated in my models is provided in the “Fixed and random effects” section below.

Multilevel Models

! 57! !

Independent and dependent variables. It is important to re-emphasize here that my six behaviors of interest in the current study are not independent; a participant could classify any given behavior using as many check boxes as he or she felt descriptive and appropriate. For example, a single participant could classify a given behavior as both “socializing” and “nurturing a personal relationship.” As such, combining these into one broader dependent variable was not appropriate. It would be feasible to combine these dimensions by forbidding each time slot’s total to exceed a score of one, thereby avoiding doubly counting interpersonally adaptive classifications for the same activity; however, this would result in a considerable loss of information. As such, all six dependent variables are examined in isolation, resulting in six multilevel models for guilt, and six multilevel models for shame.

Additionally, as the goal of this research is to compare checklist versus scenario measures of guilt and shame, in the guilt models I simultaneously tested the effects of PFQ guilt and

TOSCA guilt. In the shame models, I simultaneously tested the effects of PFQ shame and

TOSCA shame. This directly compares both assessment methods, while avoiding multicollinearity issues that may be caused by including both the guilt and shame subscales from one measure, give their high correlations. Such a precaution is particularly relevant for the PFQ, which exhibited a correlation of r = .64 between its guilt and shame subscales in Sample B.

This is not the only appropriate way to examine the effects of guilt and shame on dependent variables of interest, however. Many researchers feel it is most appropriate to simultaneously test the effects of guilt and shame from the same measure (i.e., simultaneously testing the effects of TOSCA guilt and TOSCA shame) on one’s dependent variable of interest, thereby obtaining parameter estimates for “guilt free” shame and “shame free” guilt (Tangney,

1996). Still, not all researchers adhere to this practice, for a variety of reasons (see Tignor &

! 58! !

Colvin, 2016 for further discussion). In the main body of the results section below, I do not present results for “guilt free” shame and “shame free guilt.” Instead, as mentioned above, I test scenario- and checklist-assessed guilt simultaneously in one set of models, and scenario- and checklist-assessed shame simultaneously in a second set of models. Appendices H, I, and J contain additional models in which all measures are tested in isolation, as well as models in which guilt and shame are simultaneously tested, for comparison.

Model construction. All models in Study 2a were constructed to include the following two independent variables, unless otherwise noted: checklist guilt (or shame) and scenario guilt

(or shame). All guilt and shame scales were standardized prior to being entered, so that effect sizes could be read as standardized betas, and thus directly compared between measure types, despite their differing scales. Each model also included day of the week as a covariate, to account for the longitudinal nature of the data. Day of the week was not standardized in these models, as its zero-point is inherently meaningful. Instead, Monday night was coded as “1,” meaning one day had passed since the start of the study, Tuesday night was coded as “2,” meaning two days had passed, and so on through Sunday night, which was coded as “7.”

Fixed and random effects. Multilevel modeling is useful in that it allows analysts to estimate both group-level or “fixed” effects, as well as individual-level or “random” effects, simultaneously. Fixed effects can be interpreted as beta coefficients, and characterize patterns observed in the sample as a whole. Random effects characterize individuals’ own unique deviations from these group-level patterns, and are typically displayed as standard deviations or variances for all individuals’ parameter estimates. All models presented here are random coefficient models, meaning participant-level intercepts (i.e., each participant’s behavioral score at the start of sampling) and certain participant-level slopes (i.e., rates of change in behavior over

! 59! ! time) are permitted to vary from person to person. Day of the week’s slope was allowed to vary randomly between participants, as individuals likely differ in the degree to which their behavior changes over the course of one week. For example, some individuals may engage in more interpersonally adaptive behavior as the weekend approaches. Random slopes were not modeled for dispositional guilt and shame measures, as there is no theoretical argument for why the same guilt or shame scale may be a more effective predictor of daily behavior for one individual as compared to another. Rather, I am only interested in how effective these measures are for predicting interpersonally adaptive behavior at the group level.

Fixed effects for guilt and shame measures are listed in Tables 7 and 8, respectively, as beta coefficients. Once again, these beta coefficients are standardized in the case of all guilt and shame scales, but unstandardized in the case of day of the week. Random effects can be found at the base of each model, listed as standard deviations in intercepts and slopes for the total sample.

Though it is customary to report the residual variance for mixed effects models, models constructed using negative binomial distributions do not yield residual values.

Hypothesis tests. Table 7 displays results for all six guilt models. It is important to note that in negative binomial regression (and, consequently, multilevel models constructed using a negative binomial distribution), parameter estimates are impacted by the distribution of the dependent variable. So, if an event is exceedingly rare, parameter estimates can be relatively small, yet still reach significance. Conversely, models constructed to predict more common events may produce larger parameter estimates that do not reach significance. Thus, when examining the parameter estimates for the models presented below, note that the threshold for significance varies from model to model, based on the commonality of the dependent variable.

Effect sizes cannot be compared across models.

! 60! !

Scenario-assessed guilt (as assessed via the TOSCA) significantly and positively predicted daily time spent socializing, β = .29, p < .01, nurturing relationships, β = .36, p < .01, and helping others, β = .59, p < .01, and marginally significantly predicted time spent in meaningful conversation, β = .30, p < .10. Though the effects of scenario-assessed guilt on time spent providing emotional support and volunteering did not reach significance, both were trending in the expected direction. Such a failure to reach significance may have been due in part to the rarity of these behaviors in my sample (for more information, see Table 6). Still, these results overall provide support for H1, demonstrating that individuals who score highly on a scenario measure of guilt tend to engage in more interpersonally adaptive behaviors on a daily basis.

As expected, such positive effects were not observed for checklist-assessed guilt.

Participants’ scores on the PFQ did not significantly predict five of these six interpersonally adaptive behaviors. In the case of providing emotional support to others, checklist-assessed guilt was marginally negatively associated with this behavior, β = -.25, p < .10. These findings provide support for H2, and further exhibit the differing nature of checklist- and scenario- assessed guilt.

Table 8 displays results for all six shame models. Checklist-assessed shame was not significantly associated with any positive interpersonal behaviors, thereby confirming H4.

Interestingly, though scenario-assessed shame was not significantly associated with most positive interpersonal behaviors, it was positively associated with time spent nurturing relationships, β = .28, p < .05, and marginally positively associated with time spent socializing, β

= .17, p < .10. Still, I suspected that such findings may be due to TOSCA-assessed shame’s positive association with TOSCA-assessed guilt, as suppressor effects have been well-

! 61! ! documented in the use of this scale (Tangney, 1996). Indeed, these results dropped below significance when TOSCA-assessed guilt was added as a covariate, in place of PFQ-assessed shame (see Appendix J). These findings generally support H3, with caveats.

Discussion

The results of this study provide further evidence for the conceptual distinctiveness of scenario- and checklist-assessed guilt. Specifically, they replicate Study 1’s meta-analysis by demonstrating that while scenario-assessed guilt is positively associated with interpersonally adaptive behavior, checklist-assessed guilt is unrelated to interpersonally adaptive behavior.

These findings become particularly impactful when one considers they were collected outside of the laboratory over several days; to date, an overwhelming majority of behavioral research involving dispositional guilt and shame has been conducted in laboratory settings at one time point only.

Although both measures of shame were mostly unrelated to interpersonally adaptive behavior, this was not always the case. Scenario-assessed shame was found to be positively associated with time spent nurturing relationships, and marginally positively associated with time spent socializing. These effects dropped below significance in alternate models that included scenario-assessed guilt, however; suggesting that such effects may have been a consequence of

TOSCA shame’s positively correlation with TOSCA guilt, which was found to be adaptive.

Still, such findings, taken in conjunction with my meta-analytic findings presented in

Study 1, may lead one to question not just whether checklist and scenario measures of dispositional guilt capture different types of guilt, but rather whether scenario-assessed guilt is really even “guilt” at all. The TOSCA guilt scale, for example, captures behavior-specific feelings of regret and remorse and repair tendencies as elicited by an objectively “bad” or

! 62! !

“wrong” transgression. This approach is based on a sound theoretical framework (Lewis, 1971), and is useful for distinguishing guilt from shame without relying on fuzzy lexical boundaries.

Yet some researchers have noted that the reliance on such a framework may have unintentionally produced measures that capture an action orientation, as opposed to a dispositional emotional tendency (Cohen et al., 2011; Luyten et al., 2002). That is, it may be the case that scenario measures of guilt capture the tendency to do the “right thing,” or even simply the ability to recognize the “right thing.” To score highly on scenario measures, an individual needn’t experience guilt regularly or even at all; he or she must simply possess knowledge of socially appropriate responding.

Given the above concerns, it is entirely possible that scenario measures of dispositional guilt are unrelated to daily experiences of negative affect or guilt. Yet, surprisingly, to date researchers have not fully examined this possibility. In the next chapter I present results outlining both guilt and shame measures’ relations to daily negative affect, positive affect, and state well- being. In doing so, I aim to confirm that scenario measures of guilt fail to capture actual experiences of guilty or negative affect, but instead capture daily levels of well-being, and thus might be better suited to an alternate name.

! 63! !

CHAPTER 5

Study 2b – Is Scenario-Assessed Guilt Even Guilt? Testing both Measures’ Associations

with Daily Affect and Well-being

Despite its conceptualization as an affective trait, surprisingly little research to date has examined the connection between dispositional guilt (as assessed via either measurement format) and affective experience, particularly among non-clinical populations outside of the laboratory.

This gap is alarming, given that such research would have important construct validity implications. If scenario measures of guilt fail to assess actual experiences of guilt, theoretical orientations that proclaim the experience of guilt to be adaptive while using only scenario measures would be in need of reconsideration, and researchers would be well-advised to rename the construct.

Below I review the limited body of research that has examined the connection between dispositional guilt and state affect. Among this small body of work, even more rare is the examination of dispositional guilt and affective states in everyday life, outside of the laboratory.

Furthermore, few of these studies have examined checklist and scenario measures in tandem, making it difficult to recognize the divergent nature of these two measurement types. Though not an affective state per se, I also review research examining dispositional guilt or shame and well- being, as I expect dispositional guilt to positively predict this state. Finally, although dispositional shame is not my central research interest, I review research on that topic as well.

Dispositional Guilt, Shame, Affect, and State Well-being

Anger. Researchers have conducted a fair amount of research connecting various measures of dispositional guilt and shame to the affective experience of anger. This is perhaps because some of the earliest checklist measures of guilt, the Anger Self-report (ASR; Zelin,

! 64! !

Adler, & Myerson, 1972) and the Buss-Durkee Hostility-Guilt Inventory (BDH; Buss & Durkee,

1957), were originally developed within an anger-hostility framework. Throughout this rich body of research, consistencies emerge. Tangney and colleagues have repeatedly demonstrated that while scenario-assessed dispositional shame is consistently positively associated with trait anger, angry reactivity, and anger arousal, scenario-assessed dispositional guilt is unrelated to or weakly negatively correlated with these constructs (Tangney, Wagner, Fletcher, & Gramzow, 1992;

Tangney, Wagner, Hill-Barlow, Marschall, & Gramzow, 1996). Similarly, Peters and Geiger

(2016) found that while scenario-assessed dispositional shame was positively related to trait hostility and aggression, scenario-assessed dispositional guilt was negatively related to these traits. Not surprisingly, this disparity between guilt and shame’s relation to anger is not seen with checklist measures. Both checklist-assessed guilt and checklist-assessed shame appear to be positively associated with anger and hostility, even when such measures were not explicitly devised as part of an anger or hostility scale (see O’Connor et al., 1999).

Depression and anxiety. A fair amount of work has been conducted relating dispositional guilt and shame to depression. One recent meta-analysis found that both guilt and shame are positively correlated with depressive symptoms. Importantly, moderator analyses revealed critical differences in effect sizes based on measurement type. Specifically, the authors found that “contextual-legitimate guilt” measures (a broad category of measures which included scenario measures of dispositional guilt) were unrelated to depressive symptoms, whereas

“generalized” guilt measures (i.e., checklist measures) were positively associated with depression (Kim et al., 2011). These findings may not just be relevant to short-term laboratory research. Longitudinally, scenario-assessed shame has been shown to maintain its positive correlations with depression across the lifespan, whereas scenario-assessed guilt remains

! 65! ! unrelated to depression, or negatively correlated when shame is partialled (Orth, Robins, & Soto,

2010).

Though single studies that directly compare checklist to scenario measures of dispositional guilt and shame are rare, one particularly relevant study demonstrated the differential associations of checklist- and scenario-assessed guilt to depressive and anxious symptomatology. Importantly, whereas all shame scales exhibited maladaptive correlates, only checklist-assessed guilt was shown to be maladaptive (Harder, Cutler, & Rockart, 1992). These results were later echoed by O’Connor and colleagues, who compared both scenario- and checklist-assessed guilt and shame based on their relations to depression and anxiety (O’Connor et al., 1999). Even in clinical samples, scenario-assessed dispositional shame, but not scenario- assessed guilt, has been found to be related to anxiety disorder symptoms (Fergus et al., 2010).

Overall, such findings suggest that while all measures of shame and checklist measures of guilt capture maladaptive affective patterns to some degree, scenario-assessed guilt may be unrelated to negative affect, despite its conceptualization as an affective trait.

State well-being. Although dispositional guilt and shame likely have important well- being implications, most research examining the connection between these traits and state well- being has done so using positive and negative affect measures only (or depression and anxiety, as reviewed above). Checklist-assessed guilt and shame have both been shown to be negatively correlated with positive affect (Greenwald & Harder, 1996; Harder & Greenwald, 1999), though others have failed to observe this relationship (Hochwarter et al., 2007). Contrastingly, scenario- assessed guilt has been found to be unrelated to negative affect in the workplace, whereas scenario-assessed shame has been found to be positively related (Flynn & Schaumberg, 2012).

! 66! !

Not surprisingly, checklist-assessed guilt has been observed as exhibiting positive relationships with negative affect (Hochwarter et al., 2007). Such effects hold in laboratory experiments with negative affect-inducing primes. Scenario-assessed guilt, in contrast, was found to be unrelated to state affect in a laboratory setting following a manipulation in which participants were told they behaved prejudicially. Scenario-assessed shame was only significantly related to compunction, and negatively related to positive affect in one study

(Giner-Sorolla, Piazza, & Espinosa, 2011).

These findings, though somewhat inconsistent, seem to corroborate the notion that scenario-assessed guilt is unrelated to negative affective experience. Some authors have noted this pattern, and suggested that individuals high in scenario-assessed guilt may simply display more appropriate affective patterns, as opposed to higher levels of negative affect on the whole.

For example, one study found this trait to be associated with valence-matched writing responses and prompts. These individuals reported more negative affect only in response to negative prompts; this was not the case for dispositional shame (Sutin & Robins, 2005).

Examinations of other state well-being indices, such as satisfaction with life or self- esteem, are exceedingly rare. In one particularly relevant study, checklist-assessed guilt in the workplace was found to be unrelated to positive affect, life satisfaction, and job satisfaction

(Hochwarter et al., 2007). Still, this study did not examine scenario-assessed guilt, the most likely trait to exhibit positive relationships with state well-being. In one relevant longitudinal study, scenario-assessed “shame-free” guilt was found to be positively correlated with self- esteem across the lifespan, while scenario-assessed shame remained negatively correlated with self-esteem (Orth et al., 2010). Still, further research is needed to examine these traits’

! 67! ! association with a wider range of well-being variables, particularly studies in which scenario and checklist measures are compared directly.

Guilt and shame. Finally, some studies have examined dispositional guilt and shame’s correlations with the experience of state guilt and shame directly (i.e., not generalized negative affect). In such research, scenario-assessed guilt has been found to be only weakly related to sustained experiences of guilt and shame, whereas scenario-assessed shame has been found to correlate very highly with these feelings (Fontaine, Luyten, De Boeck, & Corveleyn, 2001). For example, using several different scenario measures, Fayard and colleagues reported an average correlation between guilt experience and dispositional guilt of just r = -.02 (Fayard, Roberts,

Robins, & Watson, 2012). These same authors reported a higher correlation between trait guilt as assessed via a checklist measure and state guilt (i.e., momentary or temporary experiences of guilt), r = .54, as well as between checklist-assessed dispositional guilt and state guilt regarding performance on an exam, r = .32. Though based on healthy non-clinical samples, such findings have been replicated among clinical samples as well as well (Ritter et al., 2014; Rüsch et al.,

2007).

Summary and Hypotheses

The aforementioned studies suggest that whereas scenario-assessed shame, checklist- assessed shame, and checklist-assessed guilt all represent negative affective traits, scenario- assessed guilt may be unrelated to negative affective experience. Still, this limited collection of studies presents multiple limitations. First, most of the above studies assessed state affect within the context of one laboratory session. As such, the field continues to lack a picture of how these traits predict affect on a daily basis, in everyday life. Second, few studies examining traits and affect have done so using scenario and checklist measures in tandem. As such, critical

! 68! ! differences in the affective nature of checklist- versus scenario-assessed guilt continue to be obscured. Third, though a handful of studies has examined state well-being, such work has only examined a narrow range of well-being states. Given my findings in Study 1 and Study 2a, it is reasonable to suspect that scenario-assessed guilt holds important daily well-being implications, yet to date this has not been examined comprehensively.

Finally, and perhaps most importantly, few of the authors of the above studies explicitly underscored the construct validity implications of their work (for an exception, see Harder et al.,

1992). This is particularly relevant for studies examining the connections between scenario- assessed guilt and affect. Most of these authors present null correlations between scenario measures of dispositional guilt and guilty affect, then continue to refer to the former as “guilt.”

Such a practice only amplifies confusion in the field regarding the adaptiveness of dispositional guilt. If scenario-assessed guilt fails to capture patterns of daily negative affect, researchers would be well-advised to rename the construct.

In the current research I address the above limitations, and examine the association of checklist- and scenario-assessed guilt and shame to daily positive affect, daily negative affect, and daily state well-being. In doing so, I aim to find further support for the conceptual distinctiveness of checklist and scenario measures of dispositional guilt, and the conceptual sameness of checklist and scenario measures of dispositional shame (Research Objective 2). In addition, I hope to provide evidence for the notion that scenario measures of dispositional guilt exhibit no relation to negative affect as experienced in daily life, despite their conceptualization as “guilt.” Rather, I seek to identify this trait’s well-being implications (Research Objective 3).

Below I outline the hypotheses.

Scenario-assessed guilt will be:

! 69! !

H1a: Positively associated with daily well-being

H1b: Positively associated with positive affect

H1c: Unrelated to negative affect

Checklist-assessed guilt will be:

H2a: Negatively associated with daily well-being

H2b: Negatively associated with positive affect

H2c: Positively associated with negative affect

Scenario-assessed shame will be:

H3a: Negatively associated with daily well-being

H3b: Negatively associated with positive affect

H3c: Positively associated with negative affect

Checklist-assessed shame will be:

H4a: Negatively associated with daily well-being

H4b: Negatively associated with positive affect

H4c: Positively associated with negative affect

Method

Participants

Study 2b utilized participants from Sample B only. Sample B featured 96 undergraduate student participants (56 female), and the average age of this sample was 18.8 years.

Materials

Self-reports of dispositional guilt and shame. Participants provided self-reports of dispositional guilt and shame using the Test of Self-Conscious Affect (TOSCA, Tangney,

Wagner, & Gramzow, 1989) and the Personal Feelings Questionnaire (PFQ; Harder & Lewis,

! 70! !

1987). The former is a scenario measure; the latter is a checklist measure. For further information regarding personality assessment materials and procedures employed with Sample

B, see Chapter 3.

Full day evaluation. Following their in-lab participation, participants in Sample B completed a daily diary for seven consecutive days. Though this diary contained a number of unique features, here I describe only the segments relevant to the current research goals. For a more in-depth and comprehensive description of the structure and format of the daily diary, see

Chapter 3.

At 10:00pm each evening, participants were e-mailed a link to their full day evaluation.

In this evaluation, participants self-reported their levels of positive affect, negative affect, and state well-being over the previous 24 hours. Positive and negative affect were assessed using the

Positive and Negative Affect Schedule (PANAS; Watson et al., 1988). Positive affect was assessed using the following ten items: Interested, enthusiastic, proud, excited, strong, alert, inspired, determined, attentive, and active. Negative affect was assessed using the following ten items: Distressed, upset, guilty, scared, hostile, irritable, nervous, jittery, afraid, and ashamed.

All items within the positive and negative affect scales of the PANAS were assessed using a 1

(very slightly or not at all) to 5 (extremely) scale.

State well-being was assessed using three items, adapted from three different well- validated well-being scales. Specifically, each participant provided state assessments of satisfaction with life (“Over the past 24 hours, how satisfied did you feel with your life?”), meaning in life (“Over the past 24 hours, how meaningful did your life feel?”) and self-esteem

(“Over the past 24 hours, how positively did you feel about yourself?”) using a 1 (very slightly or not at all) to 5 (very slightly or not at all). As these items were highly correlated on each of the

! 71! ! seven days, all were summed to create one state well-being variable for each participant for each day.

Procedure

Participants in Sample B reported to the laboratory two at a time (one male and one female). Upon providing consent, they completed a battery of computerized self-report questionnaires, including the TOSCA and PFQ, presented in a randomized order. Upon their completion of the survey participants were given instructions for the daily diary, and partial course credit for their participation. They then completed this daily diary each night for seven consecutive days, starting the Monday following their in-lab participation. Participants who completed at least six of the seven daily diaries were given additional course credit. For further information regarding the daily diary layout and procedures, reference Chapter 3.

Results

Daily Affect and Well-being: Descriptives

Scales for positive and negative affect were created by averaging each individual’s scores for the 10 positive (or negative) affect items assessed via the PANAS. Accordingly, a given individual’s positive and negative affect score for any given day could range from one to five.

Daily state assessments of satisfaction with life, meaning in life, and self-esteem were averaged within individuals as well to provide one well-being score per person per day. Thus, as is the case with positive and negative affect, an individual’s well-being score on any given day could range form one to five. Sample means and standard deviations for positive affect, negative affect, and state well-being, split by day of the week, are displayed in Table 9.

Analytic Approach

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The repeated measures design of this study was such that each participant could provide up to seven completed daily diaries. As such, multilevel models were most appropriate for analyzing these data, with daily states (positive affect, negative affect, or well-being) nested within individuals in chronological order, from Monday to Sunday. Multilevel models were estimated using the “lmer” function from R’s lme4 package (Bates et al., 2015), an open-source package for fitting linear mixed effects models. Of the 91 total participants who participated in the daily diary portion of the study, 16 failed to complete all seven diary entries. When multilevel models are used, missing data are not problematic, and incomplete data do not need to be removed prior to parameter estimation. As my dependent variables of interest in the current study were assessed using Likert type scales, standard Gaussian approaches were utilized, and all models were fit using maximum likelihood estimation.

Multilevel Models

Here I present results from two sets of multilevel models: three examining guilt’s connection to well-being, positive affect, and negative affect, and three examining shame’s connection to each of these dependent variables. Because negative affect scores were positively skewed in this sample, I re-tested all models predicting negative affect using a reciprocal transformation of this variable (i.e., 1/negative affect score). This mitigated the skewness seen in negative affect. All models using this reciprocal transformation are consistent with those calculated using the original score. As such, for ease of interpretation, I only present the non- transformed results here. For results using this reciprocal transformation, see Model 3b in Tables

10 and 11. Finally, the PANAS negative affect scale includes a “guilty” and an “ashamed” item.

As such, results of analyses conducted utilizing the total PANAS negative affect could be assumed to apply to guilt and shame specifically as well; yet these models do not directly test

! 73! ! this assertion. To do so, I created a daily guilt-shame composite score for each individual for each day, by averaging his or her two guilt and shame items from the PANAS negative affect scale from that day. Statistical combination of “Guilty” and “Ashamed” was warranted, as across all participants and across all days, these two items were correlated r = .81.

Interestingly, experiences of guilt and shame were fairly rare in our sample. On each of the seven days, the modal score for this guilt-shame composite was 1, meaning on any given day the majority of participants reported no guilt or shame experiences. To deal with this high level of skewness, I created a dichotomous guilt-shame composite variable. Participants experiencing any guilt or shame (i.e., a guilt-shame composite score greater than 1) were given a score of 1 for that day; participants who did not experience any guilt or shame on a given day (i.e., a guilt- shame composite score equal to 1) were given a score of 0. This resulted in 389 “no guilt-shame” experiences, and 198 “guilt-shame” experiences total.

As the goal of this research is to compare checklist measures of dispositional guilt and shame to scenario measures, in all guilt models I simultaneously test the effects of PFQ (i.e., checklist) guilt and TOSCA (i.e., scenario) guilt, as was done in Study 2a. In all shame models, I simultaneously test the effects of PFQ shame and TOSCA shame. This directly compares both assessment methods, while avoiding multicollinearity issues that may be caused by including both the guilt and shame subscales from one measure, give their high correlations. Such a precaution is particularly relevant for the PFQ, which exhibited a correlation of r = .64 between its guilt and shame subscales in Sample B. As was the case in Study2a, in the body of the results section I only discuss the results of models examining all guilt measures in tandem, and all shame measures in tandem. Appendices L and N contain additional models in which all guilt and

! 74! ! all shame measures are tested in isolation; Appendix M displays models in which guilt and shame are simultaneously tested, for comparison.

Model construction. All models were constructed to include the following two independent variables: checklist guilt (or shame) and scenario guilt (or shame). All guilt and shame scales were standardized prior to being entered into the models, so that effect sizes could be interpreted as standardized beta coefficients, and thus could be directly compared despite these measures’ differing scales. In addition, day of the week was included in all models as a covariate, to model the longitudinal nature of the data. Day of the week was not standardized in these models, as its units are inherently meaningful. Instead, Monday night was coded as “1,” meaning one day had passed since the start of the study, Tuesday night was coded as “2,” meaning two days had passed, and so on through Sunday night, which was coded as “7.”

Fixed and random effects. Multilevel modeling is useful in that it allows analysts to estimate both group-level or “fixed” effects, as well as individual-level or “random” effects, simultaneously. All models presented here—apart from the variance components models displayed in Appendix J—are random coefficient models, with participant-level intercepts varying randomly. Day of the week’s slope was allowed to vary randomly between participants, as individuals likely differ in the degree to which their affect or well-being changes over the course of one week. As was the case in Study 2a, random slopes were not modeled for dispositional guilt and shame measures, as there is no theoretical basis for why a given guilt or shame measure might be a more effective predictor of daily affect for some individuals than others.

Fixed effects are listed in Table 10 and Table 11 as beta coefficients. Once again, these coefficients are standardized in the case of all guilt and shame scales, and unstandardized in the

! 75! ! case of day of the week. Random effects can be found at the base of each model, listed as standard deviations in intercepts and slopes for the total sample, as well as the residual variance in each model. Table 10 provides results for all guilt models, while Table 11 provides results for all shame models.

Well-being. In predicting daily levels of well-being, significant effects were found for checklist-assessed guilt and scenario-assessed guilt. Whereas checklist-assessed guilt was negatively associated with well-being, β = -.27, p < .001, scenario-assessed guilt was positively associated, β = .16, p < .05, providing support for H1a and H2a. In the case of shame, only checklist-assessed shame significantly predicted well-being, β = -.18, p < .05, providing support for H4a. Scenario-assessed shame was unrelated to daily well-being, β = -.04, p > .05. Still, I suspected this lack of an effect may be due to the suppressor effect that scenario-assessed guilt can exert on scenario-assessed shame. Indeed, when TOSCA guilt and TOSCA shame were both examined simultaneously, scenario-assessed shame was found to be negatively associated with daily well-being, β = -.24, p < .05, providing support for H3a (with a caveat). See Appendix M for this model.

Positive affect. In my second set of models, neither checklist-, β = -.08, p > .05, nor scenario-assessed guilt, β = .03, p > .05 were significantly associated with daily positive affect, failing to support H1b and H2b. Similarly, neither checklist-assessed shame, β = -.03, p > .05, nor scenario-assessed shame, β = -.02, p > .05, significantly predicted daily positive affect, again failing to support H3b and H4b.

Negative affect. As expected, checklist-assessed guilt was positively associated with daily levels of negative affect, β = .31, p < .001. In contrast, scenario-assessed guilt was unrelated to daily levels of negative affect, β = -.02, p > .05, thus confirming H1c and H2c.

! 76! !

Similarly, checklist-assessed shame was found to be positively associated with daily levels of negative affect, β = .27, p < .001, confirming H4c. Surprisingly, scenario-assessed shame was found to be unrelated to daily negative affect, β = .07, p > .05. Once again, I suspected this lack of an effect may by due scenario-assessed guilt’s suppressor effect. Accordingly, I tested a model featuring both TOSCA guilt and TOSCA shame (see Appendix M). This model revealed a significant positive relationship between TOSCA shame and daily negative affect, β = .22, p <

.01, thereby confirming H3c, with a caveat.

Guilt-shame composite. The impact of dispositional guilt and shame measures on the guilt-shame dichotomous variable was examined using logistic multilevel modeling via the glmer function in R (part of the lme4 package), with the “family = binominal” option specified.

These findings aligned with those obtained using the total PANAS negative affect scale.

Checklist-assessed guilt was significantly positively associated with daily experiences of guilt and shame, β = .97, p < .001, whereas scenario-assessed guilt was unrelated to daily experiences of guilt and shame, β = -.09, p > .05. Both checklist and scenario measures of dispositional shame were significantly and positively associated with daily experiences of guilt and shame.

Discussion

The current research examined the connection between checklist and scenario measures of dispositional guilt and shame and daily affect and well-being. As predicted, all measures of dispositional shame were generally maladaptive; both checklist- and scenario-assessed shame were negatively associated with daily well-being, and positively associated with negative affect, though often such effects were not revealed in the case of scenario-assessed shame until the effects of scenario-assessed guilt were partialled. In contrast, and as predicted, checklist-assessed guilt and scenario-assessed guilt displayed marked differences. While checklist-assessed guilt

! 77! ! was negatively associated with daily well-being, scenario-assessed guilt was positively associated with it. Additionally, and perhaps more importantly, while checklist-assessed guilt was positively associated with experiencing negative affect (and more specifically, guilt and shame) on a daily basis, scenario-assessed guilt was unrelated to daily experiences of general negative affect, and unrelated to experiences of guilt and shame. Contrary to my predictions, no significant effects were seen for positive affect for any of the four scales.

Despite the lack of effects seen for positive affect, my current findings solidify the notion that whereas checklist measures of guilt capture a maladaptive trait, scenario measures capture an adaptive one. Furthermore, these findings directly challenge the conceptualization of scenario-assessed dispositional guilt as a measure of guilty affect, and call its name into question.

More specifically, if scenario measures of dispositional guilt do not capture experienced feelings of guilt, should the construct they purport to assess continue to be called “guilt?” This is of course not to say that scenario-assessed guilt is not a useful construct; Study 1, Study 2a, and the current study all are testaments to this trait’s utility in predicting interpersonally and personally adaptive traits, states, and behavior. Rather, as others have previously noted (Cohen et al., 2011;

Harder, 1995), this construct may be better suited to a label other than “guilt,” as the adaptive, event-specific evaluations and behaviors captured therein share little in common with the dysphoric affect state of guilt. Additionally, these findings call for a reinterpretation of the large body of research claiming dispositional guilt to be adaptive whilst employing scenario measures of this trait. Based on my current research findings, such researchers are likely forming conclusions regarding the nature of “guilt” while capturing something else entirely with their measures.

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While my findings have provided clarity regarding the construct validity of checklist and scenario measures of dispositional guilt and shame, they have also engendered an important new question: if scenario-assessed dispositional guilt is not really “guilt,” what is it? Study 2c addresses this question using self-reports of personality, informant reports of personality, and in- lab behavioral coding. In doing so, I hope to provide a comprehensive roadmap of scenario- assessed dispositional guilt’s interpersonal and intrapsychic correlates, and utilize such correlates to put forth a suggestion for an alternate name for this trait.

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CHAPTER 6

Study 2c – If not Guilt, then What? Further Characterizing and Distinguishing Checklist

and Scenario Guilt Using Self-reports of Personality, Informant Reports of Personality,

and Coded In-lab Behavior

Studies 1 and 2a confirmed the conceptual distinctiveness of checklist and scenario measures of dispositional guilt via their divergent relationships with prosocial orientation and daily interpersonally adaptive behavior, respectively (Research Objectives 1 and 2). Study 2b extended these findings, demonstrating not just that scenario and checklist measures of dispositional guilt assess different types of guilt, but that the former likely does not assess guilt at all, as it exhibits no significant relationships to daily feelings of general negative affect, guilt, or shame (Research Objectives 2 and 3).

Study 2c follows by examining the connections of scenario and checklist measures of guilt and shame to a broad spectrum of adaptive and maladaptive personality traits, as assessed via self-report and informant-report, and coded in-lab behavior. In doing so, I aim to more appropriately define—and suggest an alternate name for—the construct of scenario-assessed guilt (Research Objective 3).

Various scale development studies have stated similar goals, examining the connection between each measure of guilt or shame and various personality traits (e.g., Cohen et al., 2011;

Harder & Zalma, 1990; Tangney, 1991). However, such studies to date have examined only a limited range of variables, with a particular focus on anger, empathy, and ethics-related traits.

For example, various studies of the Self-Conscious Affect and Attribution Inventory (SCAAI;

Tangney et al., 1988), a precursor to the TOSCA, have examined this trait’s connections to empathy (Tangney, 1991) and anger (Tangney et al., 1992) only, in near isolation. Similarly,

! 80! ! though a few behavioral studies exist (e.g., Bracht & Regner, 2013; Cohen et al., 2011), these tend to focus on in-lab behavior and decisions within the context of economic games, or highly specific ethical behaviors. Finally, as is the case for most research on dispositional guilt and shame, few studies examine checklist and scenario measures in tandem.

Importantly, it is worth noting that the presumed—and sometimes explicitly stated— intention of correlational personality studies featuring scenario measures of guilt and shame is to demonstrate the former’s positive associations to a number of adaptive traits and behaviors, and the latter’s scenario-assessed shame’s negative associations with them. When one considers the fact that the creators of scenario measures founded their measures on a “guilt is good,” “shame is bad” theoretical framework, results confirming such hypotheses are not surprising. The same can be said for checklist measures of guilt and shame; given that these measures were developed based on a framework that proclaims both guilt and shame to be maladaptive, it is not surprising when both display a host of maladaptive correlates. This is of course not to say that correlational scale development and construct validity studies of dispositional guilt and shame do not provide valuable information, as they do. Rather, reviewing the relatively narrow and isolated research that has been conducted to date underscores the need for comprehensive studies that incorporate a wide range of traits and behaviors and a diverse collection of data sources, conducted by researchers who are not beholden to either theoretical viewpoint.

Due to the exploratory nature of this study, I do not offer specific predictions for each correlation performed. Generally, I predict that scenario-assessed guilt will display relationships with self-reported traits, informant-reported traits, and in-lab behaviors that are divergent from those displayed by checklist-assessed shame. Specifically, I suspect scenario-assessed guilt will be positively correlated with adaptive traits and behaviors, particularly those with interpersonal

! 81! ! components, and will be negatively correlated with maladaptive traits and behaviors. Conversely,

I hypothesize that checklist-assessed guilt will exhibit positive relationships with maladaptive traits and behaviors, and be negatively correlated with adaptive ones. For more information regarding measures and hypotheses for Study 2c, see Chapter 3. If scenario-assessed guilt and checklist-assessed guilt are shown to diverge as expected, I will utilize the strongest correlations observed will be used to inform a proposed alternate name for the former. As has been the case in previous studies, I expect to observe similarly maladaptive correlates for checklist- and scenario-assessed shame.

Method

Participants

Study 2c features participants from both Sample A and Sample B. Sample A featured 76 undergraduate student participants (38 female), recruited as part of a larger investigation on personality and behavior. The average age of this sample was 20.6 years. Sample B featured 96 undergraduate student participants (56 female) with an average age of 18.8 years. For further information regarding these two samples, reference Chapter 3.

Materials

The current study examines the correlations of dispositional guilt and shame, as assessed via checklist and scenario measures, to the Big Five, adaptive personality traits, maladaptive personality traits, and in-lab behavior. Below I briefly discuss all variables of interest. For a more comprehensive discussion of these traits and behaviors, including the measures utilized and the internal reliabilities of each scale in each sample, refer to Chapter 3.

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Self-reported dispositional guilt and shame. Participants in both samples provided self- reports of dispositional guilt and shame using a checklist measure (the PFQ; Harder & Zalma,

1990) and a scenario measure (the TOSCA; Tangney et al., 1989).

Self-reported Big Five Scores. Participants in Sample A and Sample B both provided self-reports of the Big Five personality traits (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness) using the NEO-PI-R (Costa & McCrae, 1992). In general, extraversion, openness to experience, agreeableness, and conscientiousness have been characterized as adaptive traits, while neuroticism has been characterized as maladaptive.

Self-reported adaptive personality traits. Participants in Sample A provided self- reports of the following adaptive personality traits: Empathic concern, perspective taking, environmental mastery, positive relations with others, and trait positive affect. Sample B participants provided self-reports of the following adaptive personality traits: Empathic concern, perspective taking, environmental mastery, positive relations with others, autonomy, personal growth, purpose in life, self-acceptance, searching for meaning in life, presence of meaning in life, flourishing, trait forgiveness, satisfaction with life, self-esteem, humanitarianism- egalitarianism, and trait positive affect.

Self-reported maladaptive personality traits. Participants in Sample A provided self- reports of physical aggression, verbal aggression, anger, hostility (as assessed via the Aggression

Questionnaire), trait negative affect, hostility (as assessed via the DSM personality inventory), withdrawal, emotional lability, and depressivity. Participants in Sample B only provided self- reports of one maladaptive trait: trait negative affect.

Informant-reported Big Five traits. Participants in both Sample A and Sample B recruited up to three friends to provide ratings of them on the Big Five (neuroticism,

! 83! ! extraversion, openness to experience, agreeableness, and conscientiousness). Informants for

Sample A did so using the 240-item NEO-PI-R (Costa & McCrae, 1992), while informants for

Sample B did so using the 60-item NEO-FFI (Costa & McCrae, 1989).

Informant-reported adaptive personality traits. Informants for participants in Sample

B were also asked to rate participants on the following adaptive traits: Satisfaction with life, autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. Informants for participants in Sample A did not provide ratings for any of these adaptive personality traits.

Coded in-lab behavior. Finally, participants in both Sample A and Sample B engaged in a videotaped five-minute unstructured mixed-gender interaction in the laboratory. This interaction was coded by up to five trained coders using the Riverside Behavioral Q-Sort (RBQ;

Funder et al., 2000). For in-depth information regarding coding procedures and inter-rater reliabilities, see Chapter 3.

Procedure

Participants in both samples reported to the laboratory, and first engaged in the videotaped five-minute unstructured interaction. They then completed a battery of self-report questionnaires; those relevant to the current study are highlighted above. Finally, after their participation, participants provided the email addresses of up to three friends who knew them well to provide informant ratings. Informants were later emailed a link to the informant survey.

For more detailed information regarding the experimental procedures enacted with each sample, reference Chapter 3.

Results

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Results of all analyses can be found in Tables 12 through 16. Correlations are reported for all samples that provided reports for each trait. For example, since only participants in

Sample B provided self-reports of flourishing, an effect size is not presented in the Sample A column. For any traits that were assessed in both Sample A and Sample B, such as each of the

Big Five, results for each sample are displayed side-by-side, and a combined effect size for both samples was calculated (listed in the “Comb.” column in each table). To do so, I first r-to-z transformed each effect size, then calculated an average of the two, weighted by sample size (i.e., a “fixed effects” approach). I then z-to-r transformed this combined effect size to create one combined effect for both samples. Significance levels were calculated for combined effects using a Z-score table (for further information regarding conducting mini meta-analyses on small sets of studies, see Goh, Hall, & Rosenthal, unpublished manuscript).

Most results below are presented as zero-order correlations between guilt or shame and the trait or behavior of interest, unless otherwise noted. I additionally calculated semi-partial correlations to provide results for “shame-free” guilt and “guilt-free” shame, as some researchers advocate for this approach (e.g., Tangney et al., 1996). Such results are reported in Appendices

O through S. For the sake of consistency and transparency I calculated semi-partial correlations using both the PFQ (i.e., checklist-assessed guilt and shame) and the TOSCA (i.e., scenario- assessed guilt and shame); however, semi-partial correlations should only be considered in the case of the TOSCA. As PFQ guilt and PFQ shame are highly correlated and exhibit similar patterns of correlates with external personality scales and behaviors, calculating semi-partial correlations can remove too much variance to provide meaningful results, and instead produce mostly non-significant effects.

The Big Five

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Shame and self-reports. Correlations between self-reported Big Five traits and all measures of dispositional guilt and shame can be found in Table 12. As expected, PFQ shame and TOSCA shame exhibited similarly maladaptive patterns of correlates. These two traits were positively and strongly correlated with neuroticism and weakly negatively associated with extraversion. Interestingly, TOSCA shame was additionally positively correlated with openness to experience, rcomb = .21, p < .01, and agreeableness, rcomb = .20, p < .01. These relationships were found to be mostly a function of TOSCA shame’s positive association with TOSCA guilt, however. When “guilt-free” TOSCA shame was calculated by partialing the effects of TOSCA guilt, these correlations dropped below significance, and a weak negative correlation with conscientiousness was revealed, rcomb = -.15, p < .05 (see Appendix O). Overall, these results suggest that PFQ shame and TOSCA shame exhibit similar patterns of Big Five correlates, particularly when TOSCA guilt is partialled from TOSCA shame.

Guilt and self-reports. Such similarities were not observed for PFQ and TOSCA guilt.

TOSCA “shame-free” guilt was positively associated with extraversion, rcomb = .19, p < .01, openness to experience, rcomb = .29, p < .001, agreeableness, rcomb = .37, p < .001, and conscientiousness, rcomb = .17, p < .05, and marginally negatively associated with neuroticism, rcomb = -.12, p < .10. In contrast, PFQ-assessed guilt was strongly positively correlated with neuroticism, rcomb = .53, p < .001, negatively correlated with extraversion, rcomb = -.20, p < .01, weakly positively correlated with openness to experience, rcomb = .18, p < .05, and negatively correlated with conscientiousness, rcomb = -.28, p < .001. Though partialing TOSCA shame from

TOSCA guilt results in a more clear differentiation between the two, such differences are nonetheless visible among zero-order correlations, particularly in the case of agreeableness and neuroticism (see Table 12 for more information).

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Guilt and shame and informant reports. The above similarities in the case of shame measures, and differences in the case of guilt measures, were reconfirmed using informant reports of the Big Five. Both TOSCA shame and PFQ shame were positively correlated with neuroticism (see Table 15). Though TOSCA shame was also seen to exhibit positive relationships with openness and agreeableness, these effects appeared to be driven by the

TOSCA guilt scale, as they dropped below significance when guilt was partialled (see Appendix

R).

TOSCA-assessed guilt was positively correlated with openness to experience, rcomb = .27, p < .01, agreeableness, rcomb = .37, p < .001, and conscientiousness, rcomb = .20, p < .05. In contrast, PFQ-assessed guilt was positively correlated with neuroticism, rcomb = .38, p < .001, negatively correlated with extraversion, rcomb = -.24, p < .01, and marginally negatively correlated with conscientiousness, rcomb = -.16, p < .10.

Adaptive Traits

Shame and self-reports. Results of correlations between all measures of dispositional guilt and shame and self-reported adaptive traits can be found in Table 13. Once again, similarities were observed between TOSCA shame and PFQ shame. Both were negatively correlated with autonomy, positive relations with others, self-acceptance, flourishing, trait forgiveness, self-esteem, and trait positive affect. Surprisingly, TOSCA shame and PFQ shame were also positively associated with empathic concern; however, this effect was small in the case of PFQ shame, and dropped below significance for TOSCA shame when guilt was partialled (see

Appendix P for full set of semi-partial correlations for self-reported adaptive traits).

Additionally, though PFQ shame was highly negatively correlated with environmental mastery, rcomb = -.49, p < .001, this effect only reached significance for TOSCA shame when TOSCA guilt

! 87! !

was partialled rcomb = -.32, p < .001. The same pattern was observed for satisfaction with life; its negative relationship with TOSCA shame was only revealed with TOSCA guilt was partialled.

Finally, though TOSCA shame exhibited a weak positive correlation with humanitarianism- egalitarianism, r = .24, p < .05, this effect dropped to near zero when TOSCA guilt was partialled.

Guilt and self-reports. In contrast to the shame results presented above, well-being relevant traits were particularly useful in differentiating PFQ guilt and TOSCA guilt. TOSCA guilt exhibited moderate to strong positive relationships with empathic concern, rcomb = .51, p <

.001, perspective taking, rcomb = .38, p < .001, personal growth, r = .42, p < .001, and humanitarianism-egalitarianism, r = .47, p < .001. TOSCA guilt also exhibited weak positive correlations with positive relations with others, rcomb = .19, p < .01, flourishing, r = .19, p < .10, and trait forgiveness, r = .23, p < .05; these effects became stronger when TOSCA shame was partialled (see Appendix P).

Guilt and shame and informant reports. Informants for participants in Sample B additionally provided ratings for participants on several well-being dimensions; these results are highlighted in Table 15. Some convergence between shame measures was observed with respect to informant-rated well-being correlates. Both TOSCA shame and PFQ shame were negatively correlated with informant-rated autonomy and self-acceptance. Interestingly, PFQ shame was additionally negatively correlated with environmental mastery, r = -.30, p < .01, and purpose in life, r = -.25, p < .05; these correlations failed to reach significance in the case of TOSCA shame.

In the case of guilt, the TOSCA and the PFQ exhibited nearly completely opposing relationships with informant-reported well-being traits, as assessed for Sample B. Whereas the

TOSCA guilt scale was positively correlated with personal growth only, r = .27, p < .05, the PFQ

! 88! ! scale was negatively correlated with environmental mastery, r = -.33, p < .01, personal growth, r

= -.26, p < .05, positive relations with others, r = -.21, p < .10, purpose in life, r = -.28, p < .05, and self-acceptance, r = -.39, p < .001. Many of these same well-being variables (i.e., positive relations, environmental mastery, and purpose in life) were trending in the positive direction in the case of TOSCA guilt, but failed to reach significance. Importantly, these results for PFQ- and

TOSCA-assessed guilt generally converge with those seen using self-reported well-being scores.

Maladaptive Traits

Shame and self-reports. Results of correlations between measures of guilt and shame and self-reported maladaptive traits can be found in Table 14. As expected, PFQ shame was positively correlated with a host of maladaptive traits, including anger, r = .38, p < .001, hostility

(as assessed via the Anger Questionnaire), r = .61, p < .001, trait negative affect, rcomb = .52, p <

.001, DSM Personality Inventory-assessed hostility, r = .35, p < .01, withdrawal, r = .27, p < .05, emotional lability, r = .45, p < .001, and depressivity, r = .59, p < .001. TOSCA shame displayed similar patterns of correlates, correlating positively with anger, r = .19, p < .10, hostility (as assessed via the Anger Questionnaire), r = .33, p < .01, trait negative affect, rcomb = .37, p < .001,

DSM Personality Inventory-assessed hostility, r = .20, p < .10, emotional lability, r = .40, p <

.001, and depressivity, r = .49, p < .001, though these effects were consistently weaker for

TOSCA shame as compared to PFQ shame.

Guilt and self-reports. As was the case in previous analyses, TOSCA guilt and PFQ guilt greatly diverged in their relationships with maladaptive traits. PFQ guilt was moderately to strongly positively correlated with anger, r = .38, p < .001, hostility (as assessed via the Anger

Questionnaire), r = .53, p < .001 trait negative affect, rcomb = .58, p < .001, emotional lability, r =

! 89! !

.45, p < .001, and depressivity, r = .62, p < .001, and weakly positively correlated with the DSM

Personality Inventory subscales of hostility, r = .25, p < .05, and withdrawal, r = .19, p < .10.

In contrast, TOSCA guilt was unrelated to most maladaptive personality traits, and even negatively correlated with physical aggression, r = -.31, p < .01. A weak positive relationship was observed between trait negative affect and TOSCA guilt, r = .18, p < .05; however, this effect dropped below significance when TOSCA shame was partialled (see Appendix Q).

Coded In-lab Behavior

Analyses for coded in-lab behavior were conducted on a combined sample of all participants from Sample A and from Sample B, as these two studied utilized an identical paradigm, study procedures, and were coded by the same group of coders. To increase the interpretability and reliability of my results, I created factor scores for the RBQ by submitting all behavioral codings for all participants (i.e, in one pooled sample) to an exploratory factor analysis. A scree plot indicated that a four- or five-factor solution would fit the data best. Upon further inspection, a five-factor solution was preferable, as it resulted in fewer cross-loadings, lower correlations between factors, and more internally consistent factors. This five-factor solution explained 47% of the variance in in-lab behavior.

Factor scores were created by summing all RBQ items for a given factor that displayed factor loadings greater than or equal to .30. If a single item cross-loaded on multiple factors, the stronger factor loading was used. This resulted in the creation of five behavioral factors:

Neuroticism, timidness, playfulness, self-assuredness, and agreeableness. The neuroticism factor contained 18 items, including “Tries to undermine, sabotage, or obstruct,” “Expresses self-pity,” and “Behaves in a cheerful manner” (reverse-keyed). The timidness factor contained 11 items, including “Is reserved and unexpressive,” “Seems detached from the interaction,” and “Is

! 90! ! physically animated” (reverse-keyed). The playfulness factor contained 13 items, including

“Laughs frequently,” “Acts playful,” and “Shows interest in intellectual matters” (reverse- keyed). The self-assuredness factor consisted of eight items, including “Appears to be relaxed and comfortable,” “Exhibits social skills,” and “Shows physical signs of tension/anxiety”

(reverse-keyed). Finally, the agreeableness factor consisted of eight items, including “Expresses agreement frequently,” “Seems to genuinely like the partner,” and “Tries to control the interaction” (reverse-keyed). Six items did not load onto any of these five factors, and thus were excluded from subsequent analyses. For a table displaying individual correlations between

TOSCA and PFQ guilt and shame and all 64 RBQ items, see Appendix T.

Results from zero-order correlations between all guilt and shame scales and the five behavioral factors can be found in Table 16. Across the table, no consistent patterns emerged.

TOSCA shame was negatively correlated with confidence, r = -.24, p <.01, and marginally positively correlated with playfulness, r = .15, p < .10. PFQ shame was only negatively correlated with timidness, r = -.19, p < .05. TOSCA guilt exhibited a marginally significant negative correlation with timidness, r = -.15, p < .10, and a marginally significant positive correlation with playfulness, r = .14, p < .10. In contrast, PFQ guilt displayed no significant correlates. This lack of consistency and pattern of weak effects was also seen when semi-partial correlations were conducted (see Appendix S).

Though according to the above results TOSCA guilt appears to be more adaptive than

PFQ guilt in that it is marginally significantly associated with adaptive patterns of behavior, such results should be interpreted with caution. The strongest effect size in Table 16 is r = -.24, indicating that overall, in-lab behavior as obtained via the current research paradigm and as

! 91! ! coded via the RBQ is not an effective differentiator of measures of dispositional guilt (or shame), as relationships between such traits and behaviors are weak at best.

Discussion

The current research sought to better explicate the construct of scenario-assessed guilt by examining its connections to self-reported personality, informant-reported personality, and coded in-lab behavior. Results generally aligned with findings from Studies 1 through 2b, in that they showed both measures of shame to exhibit maladaptive patterns of correlates. In contrast (yet also in accordance with Studies 1 through 2b), checklist and scenario measures of dispositional guilt exhibited divergent relationships with adaptive and maladaptive traits. Results are discussed in greater detail below.

Personality Traits

While checklist-assessed guilt consistently displayed maladaptive patterns of correlates, scenario-assessed guilt consistently displayed adaptive ones. Specifically, scenario-assessed guilt was highly and positively correlated with adaptive interpersonal traits, including humanitarianism-egalitarianism, empathic concern, and agreeableness. Interestingly, scenario- assessed guilt for the most part was not negatively correlated with maladaptive traits, apart from physical aggression. In other words, it appears as though scenario-assessed guilt promotes well- being and adaptive interpersonal behavior, but may not necessarily be indicative of low levels of maladaptive behavior and low levels of maladjustment. This is consistent with results found for affect and well-being in Study 2b; scenario-assessed guilt positively predicted well-being, but was not a negative predictor of negative affect. In contrast, checklist-assessed guilt exhibited significant positive correlations with most maladaptive traits, including depression, neuroticism,

! 92! ! and hostility, and significant negative correlations with adaptive traits such as self-esteem, satisfaction with life, self-acceptance, and environmental mastery.

Importantly, informant-reports of personality generally corroborated these findings.

Informants rated participants who were high in checklist-assessed guilt as neurotic, introverted, and low on nearly every indicator of well-being. In contrast, informants rated participants who were high in scenario-assessed guilt as agreeable, open, conscientious, and high in personal growth. Both checklist and scenario measures of dispositional shame exhibited similar relationships to self-reported and informant-reported personality, though the effect sizes observed were consistently slightly higher for checklist-assessed, as compared to scenario- assessed shame.

In-lab Behavior

Interestingly, though some correlations did reach significance, coded in-lab behavior was not strongly or consistently related to dispositional guilt or shame, regardless of measure type, and regardless of whether zero-order or semi-partial correlations were calculated. This relative lack of effects may be attributable to the nature of the behaviors included in the RBQ. It is perhaps the case that dispositional guilt and shame measures more effectively and more strongly predict ethics-related behaviors such as dishonorable business decisions and resource allocation in an economic game (e.g., Bracht & Regner, 2013; Cohen et al, 2011), but are not useful in predicting the specific social behaviors captured by the RBQ.

Alternatively, or perhaps additionally, the structure of the five-minute interaction task may have played a role. In the current research, participants engaged in an open-ended conversation with an opposite-sex stranger. It may be the case that dispositional guilt and shame measures exhibit stronger correlates in same-sex interactions, or with friends instead of strangers,

! 93! ! or when a specific goal or instructions are given to guide the interaction, such as engagement in an explicitly cooperative game or task, or a structured debate. Or, perhaps guilt and shame exhibit meaningful correlates for only certain short portions an in-lab interaction, but not for the entire five minutes. Future research should investigate these possibilities, and examine the boundaries of when dispositional guilt and shame predict—and fail to predict—behavior; specific suggestions are outlined in the subsequent chapter.

Renaming Scenario-assessed Guilt

A central goal of Study 2c was to more fully explicate the construct of scenario-assessed guilt. Importantly, if my empirical findings provided justification for doing so, I further aimed to suggest an alternate name for this construct. Among the personality traits examined in the current research, scenario-assessed guilt exhibited the strongest correlations with the following: agreeableness, empathic concern, humanitarianism-egalitarianism, and personal growth. Such correlates paint a picture of altruism, interpersonal concern, and adjustment. More importantly, each of these scales requires a basic understanding of social conventions and norms, and a willingness to comply with such norms, particularly when another is in need. Given all of the above, taken in conjunction with results found in Studies 1, 2a, and 2b, I propose that a more appropriate name for scenario-assessed guilt might be “reparative social concern.”

Reparative social concern suggests an individual with a keen awareness of relationship functioning. Individuals who score highly in this trait understand how to maintain relationships by appropriately remedying transgressions, or by recognizing the potential for a transgression, then taking proactive steps to avoid it altogether. This trait’s high correlation with agreeableness and humanitarianism-egalitarianism further demonstrates that these individuals know how to identify “good,” “generous,” or “socially responsible” patterns of action outside of a personal

! 94! ! transgression context. Though the above may imply self-presentation motives, more complex processes are likely at work. The friends of participants who scored highly in reparative social concern also viewed them as high in agreeableness and personal growth, suggesting that at least these individuals are projecting a proactive and altruistic image to those closest to them.

Furthermore, the behavioral studies included in the meta-analyses presented in Study 1, and daily diary behavioral frequency results shown in Study 2a, further suggest that these individuals are not just “talking the talk”; they are actually behaving in adaptive ways on a daily basis. Finally, as shown in Studies 2b and 2c, individuals high in reparative social concern exhibit high levels of well-being, perhaps due to their heightened interpersonal attunement.

Importantly, the label “reparative social concern” intentionally suggests a cognitive, not affective, tendency. As individuals need not experience guilt to score highly on scenario measures of dispositional guilt (and Study 2b confirmed this notion), this trait should not be labeled as affective. Furthermore, this trait’s strongest correlates in the current study could be best described as interpersonal orientations, values, and beliefs, not affects. Moving forward, researchers should further examine this trait’s cognitive and behavioral implications to better understand the mechanism by which it fosters interpersonal functioning and well-being, if not via affect. Some specific suggestions are provided in the following chapter.

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CHAPTER 7

General Discussion

The current research presented three research objectives: 1) to meta-analytically summarize the extant literature on dispositional guilt and shame, and the adaptiveness of each, 2) to demonstrate that whereas all measures of dispositional shame assess one unitary construct, measures of dispositional guilt fall into one of two overarching categories: Neurotic guilt and prosocial guilt, and 3) to provide a portrait of the neurotic versus prosocially guilty (i.e., high in

“reparative social concern”) individual to fully explicate the construct of guilt. Across one set of meta-analyses and three empirical studies, each of these objectives was fulfilled.

Summary of Results

Results of Study 1 revealed dispositional guilt and shame to be differentially associated with prosocial orientation (Research Objective 1). Moreover, this finding was significantly moderated by test format in the case of guilt only; whereas scenario measures of guilt were positively correlated with prosocial orientation, checklist measures did not exhibit a significant relationship (Research Objective 2). Study 2a replicated and extended these meta-analytic findings using a daily diary design. As was the case in Study 1, participants’ scenario-assessed guilt was positively associated with their adaptive interpersonal behavior in everyday life; checklist-assessed guilt was unrelated to such behaviors (Research Objective 2). Study 2b, in an attempt to further differentiate checklist- and scenario-assessed guilt, examined the utility of each of these traits in predicting daily affect and well-being. Results demonstrated that while checklist-assessed guilt positively predicted feelings of negative affect—and, more specifically, guilt and shame—in everyday life, scenario-assessed guilt was unrelated to daily affect

(Research Objective 2). This trait was instead positively associated with daily levels of well-

! 96! ! being. In sum, Studies 1 through 2b suggest while checklist-assessed guilt is indicative of chronic guilt experience, and thus maladjustment, scenario-assessed guilt is indicative of interpersonally adaptive behavior and concern, and thus heightened well-being.

Finally, Study 2c examined the self-reported, informant-reported, and behavioral correlates of checklist and scenario measures of guilt and shame, with the goal of more fully explicating, and renaming, scenario-assessed guilt. Results revealed scenario-assessed guilt to be strongly associated with adjustment, altruism, and interpersonal concern, as indexed by its relationships with humanitarianism-egalitarianism, empathic concern, personal growth, and agreeableness, obtained via self-report and informant-report. In contrast, checklist-assessed guilt was not significantly associated with these traits (Research Objective 3). Taken together, these four studies provide strong evidence for the conceptual distinctiveness of checklist and scenario measures of dispositional guilt, as well as the conceptual sameness of measures of dispositional shame, and provide a roadmap for the renaming of scenario-assessed guilt (i.e., “reparative social concern).

Implications

The current research underscores the important role of measurement choice in shaping one’s results, both in the assessment of dispositional guilt specifically, and in the field of personality more broadly. Specifically, these studies provide an argument for examining not just structure or format prior to utilizing a measure, but underlying theory as well. When two measures of a single trait are founded on divergent theoretical perspectives, it is not surprising when the empirical results obtained using each are in conflict.

It is important to re-emphasize here that my findings go beyond suggesting that checklist and scenario measures of dispositional guilt assess different types of guilt; rather, my findings

! 97! ! suggest that scenario-assessed guilt may not be “guilt” at all. My current research demonstrates that this trait is unrelated to negative affect in everyday life, and instead positively correlated with a host of well-being-relevant and interpersonally adaptive outcomes. In the presence of such information, continuing to refer to this construct “guilt” is flawed, and only serves to propagate confusion within the field. Instead, as Giner-Sorolla and colleagues have proposed (Giner-

Sorolla et al., 2011), scenario-assessed guilt appears to be more of an action orientation than an affective trait. Given my empirical findings, I suggest scenario-assessed guilt may be best characterized as a measure of “reparative social concern.”

Limitations

The current work is not without limitations. First, though Study 2a examined behavioral frequencies, it did so using self-report. As such, these results ultimately were influenced by participants’ flawed memories, attention gaps, effort, and perhaps self-presentation biases. Future research would benefit from research that incorporates more unbiased behavioral measures, such as automatically activated audio recordings (e.g., the Electronically Activated Recorder or

“EAR”; Mehl et al., 2001) and smartphone behavioral tracking (Wang et al., 2013), among others. As individuals who score highly on scenario measures of “guilt” (i.e., “reparative social concern”) are highly skilled at recognizing socially appropriate and adaptive responses, they may simply be more likely to classify their own behavior as interpersonally adaptive. Additionally, in

Study 2a I examined only check boxes relevant to adaptive interpersonal behavior, as this study was intended to replicate and extend my meta-analytic findings. Accordingly, though we now know about the daily adaptive interpersonal behavioral implications of scenario and checklist measures, we still know little about the maladaptive ones, as these were not assessed in my study. Still, the results of personality analyses in Study 2c suggest that scenario-assessed “guilt”

! 98! !

(i.e., “reparative social concern”) should be unrelated to such patterns of behavior, while checklist-assessed guilt should positively predict it.

Study 2b possessed limitations as well. First, in this study affect was assessed just once per day. Although these data still yielded fruitful results, perhaps more information could be gleaned from more intensive surveying methodologies, such as experience sampling questionnaires distributed every few hours. Additionally, guilt and shame were assessed using one item each, as is customary with the PANAS general positive and negative affect scales.

Though this approach revealed important differences between checklist- and scenario-assessed guilt and shame, perhaps more information could be gleaned using a more comprehensive guilt- shame scale, such as the State Guilt and Shame Scale (SSGS; Marschall, Sanftner, & Tangney,

1994).

Though the personality analyses conducted in Study 2c proved fruitful, the in-lab behavioral findings were underwhelming. Few significant correlates were revealed, and even those effects that reached significance were still relatively weak. As mentioned in the previous chapter, there are numerous potential explanations for these observed effect sizes, including the length of the interaction, the lack of a formal activity or task, and the fact that participants were interacting with a stranger. It may be the case that scenario-assessed dispositional “guilt” is only related to the high-level behaviors captured by the RBQ when the target individual is interacting with a close other. Future research should further examine the non-self-reported social behaviors associated with reparative social concern, as behavioral coding methodologies are underrepresented in the field.

Finally, all empirical studies in the current body of work utilized student participants.

Though meaningful effects can still be gleaned from student samples, these effects may not

! 99! ! necessarily hold when extended to middle-aged or older adult populations. The triggers for experiencing guilt or shame are likely very different among these populations, particularly among adults with children or those in other caretaker roles. Similarly, what is considered

“adaptive interpersonal behavior” may change as individuals age and accrue new responsibilities.

As such, future research should examine these phenomena in more demographically diverse samples.

Suggestions for Future Research

The current research presents many avenues for future work. First, though Study 2b failed to find a connection between scenario-assessed “guilt” (i.e., “reparative social concern”) and actual feelings of guilt, this does not mean that this trait is necessarily fully unrelated to guilt-relevant processes, such as transgression perception, transgression avoidance, and transgression frequency. For example, perhaps individuals high in reparative social concern are more likely to perceive relatively benign situations or behaviors as transgressions. Furthermore, perhaps they are particularly averse to transgressing, even to such a point that it may be pathological. In other words, do these individuals not experience high levels of guilt due to a fear of guilty experience? It may be the case that individuals high in this trait are simply so averse to transgressions that they proactively engage in compensatory behavior, to avoid the transgression and the guilt experience. The current research suggests this is the case, but wa s not designed to test this assertion directly. Such further research would help explain the mechanisms behind the lack of observed relationships between this trait, one founded on the notion of transgressions, and the experience of guilt.

Second, further research on the guilt and shame subscales of the PFQ, as well as other checklist-based measures of dispositional guilt and shame, is warranted. In the current research

! 100! ! these guilt and shame subscales were highly positively correlated, and exhibited nearly identical correlates. This observed relationship was not surprisingly, and aligns with previous research utilizing this measure (see Tangney et al., 1996). Some have claimed this severe overlap may be due to ambiguity in word meaning, as checklist measures rely on linguistic features to distinguish guilt from shame, which can often be ambiguous or difficult for participants to interpret (Goldberg & Kilkowski, 1985). In other words, it may be the case that participants lack the vocabulary to effectively discriminate words like “guilt” and “regret” from “embarrassment” and “shame.” On the other hand, perhaps this particularly strong correlation between guilt and shame as obtained using this measure is not necessarily the result of poor test construction or participant confusion; perhaps the individuals who experience high levels of guilt on a daily basis also experience high levels of shame. These individuals may be effectively distinguishing guilt from shame, yet still report near perfect correlations between the two because they experience both in equal measure, possibly simultaneously. Future researchers should examine this question more closely.

Following the above point, research examining how guilt and shame change over time is lacking. To date, many researchers have treated guilt and shame (both traits and states) as though they are entirely separable experiences. But it may be the case that these two emotions commonly interact and meld over time. For example, it is easy to imagine that a man may first feel guilty regarding a personal transgression, such as being unfaithful to his spouse. He regrets what he has done, and pledges to make it up to his wife in whatever way he can. Now imagine, just a few days later, his work colleagues find out about his transgression, and begin to behave rather coldly. He then may additionally feel shame regarding his actions, and choose to avoid his

! 101! ! coworkers, or withdraw from the workplace entirely. Examples such as this are likely fairly common, yet to date empirical research has not examined them or their underlying mechanisms.

Fourth, future research should seek to identify situations in which shame might be interpersonally adaptive for individuals. With a small number of exceptions, research to date has overwhelmingly declared shame as maladaptive among healthy adults, or as unrelated to adaptive phenomena. Future studies aimed at uncovering shame’s silver lining (apart from its broader societal function of maintaining social order), could aid in better understanding why a guilt versus shame experience may occur.

Finally, it is interesting to note that across four studies, dispositional shame measures exhibited similar patterns of correlates, regardless of their structure. Though shame was not the chief focus of the current research, future research should examine why this might be the case, despite these measures’ substantial differences in format. Some have noted that whereas the

TOSCA shame scale includes mostly negative self-esteem items, such as “You would feel stupid,” or “You would feel incompetent,” the TOSCA “guilt” scale encompasses a substantial number of behavioral repair items, such as “You would apologize and make sure your friend feels better” (Luyten et al., 2002). Accordingly, the item structure of the TOSCA shame subscale appears to share more in common with the item structure of the PFQ shame subscale than is true for the TOSCA guilt scale and PFQ guilt scale. Future research using the GASP could provide additional clarity, as this measure separates the evaluative components of guilt and shame from their behavioral or motivational components.

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Footnotes

1It is important to note that the portrait of shame as maladaptive has largely been based on relatively benign transgressions, for which the experience of shame is not necessarily warranted or appropriate (e.g., spilling wine on a friend’s new carpet does not a “bad person” make). Some research with special populations such as prisoners who have committed felonies has shown shame to be adaptive (Tangney, Stuewig, & Martinez, 2014). In such cases, not feeling a sense of shame is maladaptive, and can hinder one’s attempts at personal growth and change.

2It is also quite likely that participants, when rating their anticipated behavioral or affective reactions to various scenarios, may extrapolate from past experience. Accordingly, it is possible that participants’ responses contain both retrospective and conjectural elements. Regardless, scenario measures contain an element of speculation not seen in checklist measures.

3The Self-conscious Emotions at Work Scale was deemed eligible for inclusion in both meta- analyses, as the one study that utilized it (Groenvynck, Dillen, & Fontaine, 2011) did so with employed adults. When used with such a population, this scale captures a large percentage of individuals’ daily experience.

4Here “multi-method” refers to any study that did not use self-reported prosocial orientation as its sole outcome variable. This includes studies featuring any combination of self-report, informant, and behavioral data. Many studies included self-reports of behavior, such as frequency of physical aggression or volunteerism; these were coded as self-report.

5Some examined self-reported behavioral frequencies, i.e., “What is the approximate total dollar value of all items that you have stolen?” (Ashton & Lee, 2008). Though behavioral in nature, individuals likely have difficulty accurately reporting such information, as it must be summed

! 116! ! over a substantial timeframe. Accordingly, surveying participants on a daily basis is likely more accurate, and thus more informative.

! 117! !

Table 1

Study 1 Results of Guilt and Shame Meta-Analyses: Zero-Order Correlations

Random effects Fixed effects

!! ! N k Mr 95% CI Z k Mr 95% CI Z ! FD ! Homog. Guilt Zero! ! -order correlations ! ! ! ! ! ! ! ! ! ! ! ! ! Main analysis 12,272 63 .13 .09, .18 5.51*** 63 .16 .14, .18 17.74*** 4,896 395.17*** ! Trim and fill 61 .13 .08, .17 58 .17 .15, .19 Shame! Zero!-order correlations ! ! ! ! ! ! ! ! ! ! ! ! ! Main analysis 9,634 47 -.05 -.09, .00 -1.83+ 47 -.03 -.05, -.01 -3.19*** 19 2 238.60*** ! Trim and fill 44 -.07 -.12, -.02 46 -.04 -.06, -.02 Note.! k = number of independent studies; Mr = average r; CI = confidence interval; Z = combined Z; FD = file drawer N; Homog. = homogeneity test (Q);! ! a *** p ≤ .001, + p < .10 ! ! ! ! ! ! ! ! ! ! ! !

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Table 2

Study 1 Results of Test Format Moderator Analyses

Random effects Fixed effects !! k Mr 95% CI Z Mod. Mr 95% CI Z Mod. Guilt: zero-order correlations Test format 19.47*** 85.37*** ! Scenario 40 .20 .16, .25 8.33*** .20 .18, .22 19.46*** ! ! Checklist 18 -.07 -.18, .04 -1.22 -.02 -.06, .02 -.88 ! ! Combination 5 .16 .01, .30 2.06* .16 .09, .23 4.52*** !Shame: zero-order! correlations Test format ! ! ! ! ! 3.97 ! ! ! ! 12.96**! ! Scenario 38 -.0 5 -.09, .00 -1.85+ -.04 -.06, -.02 -3.37*** ! ! Checklist 6 -.13 -.39, .15 -.90 -.10 -.18, -.01 -2.25* !! !! Combination 3 .11 -.05, .25 1.37 .13 .03, .22 2.59** Note. k = number of independent studies; Mr = average r; CI = confidence interval; Z = combined Z; Mod. = test of moderator (Q) a *** p ≤ .001, **p < .01, * p < .05, + p < .10 ! ! ! ! ! ! ! ! ! ! !

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Table 3

Study 1 Results of Moderator Analyses: Outcome Measure Class and Outcome Measure Type

Random effects Fixed effects k Mr 95% CI Z Mod. !!! Mr 95% CI Z Mod. Guilt: zero-order correlations Outcome measure class 40.74*** ! 136.27*** Neg. hostility 29 -.01 -.08, .07 -.17 ! .0 2 -.02, .05 .92 ! Empathy/forgiveness 12 .29 .24, .35 9.44*** ! .29 .25, .34 11.80*** ! Morality 5 .26 .12, .39 3.61*** ! .27 .19, .36 6.05*** ! Mixed 17 .20 .14, .26 6.36*** ! .21 .18, .23 16.57*** Outcome! measure type .23 ! 5.79* Self-report only 47 .14 .09, .19 4.95*** ! .17 .15, .19 17.09*** ! Multi-method 16 .11 .02, .20 2.41* ! .11 .07, .16 5.33*** Shame: zero! -order correlations ! Outcome measure class 26.68*** ! 83.76*** Neg. hostility 19 -.15 -.22, -.08 -4.00*** ! -.17 -.20, -.13 -9.10*** ! Empathy/forgiveness 11 -.04 -.17, .10 -.53 ! .02 -.04, .07 .57 ! Morality 4 .14 .05, .23 2.91** ! .14 .05, .23 2.91** ! Mixed 13 .03 -.01, .06 1.44 ! .02 -.01, .05 1.49 !Outcome measure type .28 ! .07 Self-report only 37 -.04 -.09, .02 -1.42 ! -.03 -.05, -.01 -2.82** !! Multi-method 10 -.07 -.19, .05 -1.22 !!! -.04 -.09, .01 1.50 Note. k = number of independent studies; Mr = average r; CI = confidence interval; Z = combined Z; Mod. = test of moderator (Q) a *** p ≤ .001, **p < .01, * p < .05 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

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Table 4

Means and Standard Deviations for all Self-Report Scales of Dispositional Guilt and Shame in Sample A and Sample B

Sample A Sample B Mean SD Mean SD Scenario measures TOSCA guilt 63.07 8.03 63.05 7.94 ! TOSCA shame 46.87 10.85 47.41 8.51 Checklist! measures PFQ guilt 9.25 3.62 8.29 4.16 !! PFQ shame 16.83 5.65 15.31 6.01 Note. Sample A N = 76; Sample B N = 96

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Table 5

Inter-Correlations for all Self-Report Scales of Dispositional Guilt and Shame in Sample A and Sample B ! !! TOSCA guilt TOSCA shame PFQ guilt PFQ shame TOSCA guilt --- .53*** .30** .25* ! TOSCA shame .50*** --- .51*** .51*** ! PFQ guilt .15 .34*** --- .73*** ! PFQ shame .16 .40*** .64*** --- ! ! Note. TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire a *** p < .001; ** p < .01; * p ≤ .05; + p ≤ .10; Sample A N = 76; Sample B N = 96 + Correlations for Sample A are listed above the diagonal; correlations for Sample B are listed below the diagonal.

! !

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Table 6

Frequencies for Six Behaviors of Interest in Study 2a

Behavioral frequencies pooled across all participants and across all seven days Outliers 0 1 2 3 4 5 6 7 8 n Missing removed Helping others 499 63 22 7 1 1 0 0 0 44 0 Socializing 193 112 99 64 56 30 25 9 4 44 1 Providing emotional support 515 51 18 4 1 2 1 0 0 44 1 Nurturing a friendship 325 114 73 34 22 11 11 1 1 44 1 Volunteering 552 31 9 1 0 0 0 0 0 44 0 Meaningful conversation 410 106 46 13 11 2 4 0 0 44 1 Note. Each row in this table sums to 637 with missing data and outliers included; all multilevel models feature 592 or 593 complete observations.

! 123! !

Table 7

Predicting Daily Interpersonally Adaptive Behavior From Dispositional Guilt Measures in Study 2a

Model 1: DV = Model 2: DV = Time Model 4: DV = Time Model 5: DV = Time Time spent spent nurturing Model 3: DV = Time spent in meaningful spent providing Model 6: DV = Time socializing relationships spent helping others conversation emotional support spent volunteering Fixed effects Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.08 .13 -1.14*** .20 -2.13*** .46 -1.36*** .25 -2.37*** .48 -3.34*** .78 PFQ guilt -.07 .09 -.01 .12 -.01 .15 -.25+ .15 .05 .22 -.14 .25 TOSCA guilt .29** .09 .36* .14 .59** .19 .30+ .16 .31 .24 .23 .26 Day of week .11*** .02 .15*** .04 .02 .10 .02 .06 -.19 .14 .00 .17

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std . dev. Std. dev. Intercept .85 1.05 1.31 .93 .64 1.36 Day of week .12 .14 .15 .18 .29 .01 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a All models were run in R using the glmer.nb function + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Table 8

Predicting Daily Interpersonally Adaptive Behavior from Dispositional Shame Measures in Study 2a

Model 2: DV = Model 4: DV = Model 5: DV = Model 1: DV = Time spent Model 3: DV = Time spent Time spent in Model 6: DV = Time spent nurturing Time spent helping providing meaningful Time spent socializing relationships others emotional support conversation volunteering Fixed effects Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.08 .13 -1.09*** .20 -2.07*** .42 -2.30*** .47 -1.30*** .25 -3.23*** .66 PFQ shame .26** .10 .03 .13 -.04 .17 .18 .23 -.06 .16 -.06 .25 TOSCA shame .02 .09 .28* .14 .16 .18 -.06 .23 .05 .16 .39 .25 Day of week .11*** .02 .14*** .04 -.02 .09 -.21 .14 .00 .06 -.04 .14 Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .85 1.00 1.52 .60 .92 1.22 Day of week .12 .15 .25 .31 .18 .04 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91

Note. *** p < .001; ** p ≤ .01; * p < .05; + p < .10 a All models were run in R using the glmer function with Poisson distribution specified + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Table 9

Affect and Well-Being Descriptive Statistics by Day of Week in Study 2b

State well-being Positive affect Negative affect Guilt-shame composite Mean SD Mean SD Mean SD Mean SD Monday (N = 87) 3.08 1.01 2.77 .69 1.90 .84 1.64 .98 Tuesday (N = 88) 3.13 .94 2.67 .76 1.67 .73 1.58 .95 Wednesday (N = 86) 3.07 .99 2.74 .76 1.62 .69 1.58 1.00 Thursday (N = 85) 3.26 .92 2.80 .78 1.56 .64 1.44 .88 Friday (N = 82) 3.25 1.06 2.87 .88 1.50 .63 1.28 .72 Saturday (N = 80) 3.27 1.09 2.77 .89 1.51 .68 1.38 .84 Sunday (N = 79) 3.19 1.08 2.71 .88 1.58 .74 1.49 .89 Note. All variables were assessed on a 1 to 5 scale

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Table 10

Predicting Daily Well-Being and Affect from Dispositional Guilt Measures in Study 2b ! ! ! Model 4: DV! = guilt-shame composite Model 3b: DV = (dichotomous Model 1: DV = state well- Model 2: DV = positive Model 3a: DV = negative reciprocal transformed dependent being affect affect negative affect variable) Fixed effects ! !Std. Estimate Std. error Estimate Std. error Estimate Std. error Estimate Std. error Estimate error Intercept 3.08*** .10 2.74*** .07 1.82*** .07 .63*** .02 -.50 .38 PFQ guilt -.27* .08 -.08 .06 .31*** .05 -.08*** .02 .97*** .23 TOSCA guilt .16* .08 .03 .06 -.02 .05 -.00 .02 -.09 .23 Day of Week .03 .02 .01 .02 -.05*** .01 .02*** .00 -.14+ .08 Random effects Std. Dev. Std. Dev. Std. Dev. Std. Dev. ! Std. Dev.! Intercept .78 .49 .56 .16 2.40 Day of week .11 .11 .08 .03 .33 Residual .60 .57 .44 .15 n/a N observations 587 587 587 587 587 N participants 90 90 90 ! 90 ! 90 ! ! Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Model 3b is a replication of Model 3a utilizing a reciprocal transformation of negative affect to account for skewness. Model! ! 4 utilizes !logistic MLM! !via the ! glmer function in R: days on which a participant experiences guilt or shame are coded as 1; days on which no guilt or shame is experienced are coded as 0. Due it its binary outcome, no residual value is provided for this model + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire !

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Table 11

Predicting Daily Well-Being and Affect from Dispositional Shame Measures in Study 2b

Model 4: DV = guilt-shame Model 1: DV = State well- Model 2: DV = Positive Model 3a: DV = Negative Model 3b: DV = reciprocal composite (binary being affect affect transformed negative affect dependent variable) Fixed effects Std. Estimate Std. error Estimate Std. error Estimate Std. error Estimate Std. error Estimate error Intercept 3.08*** .10 2.74*** .07 1.82*** .07 .63*** .02 -.35 .33 PFQ shame -.18* .09 -.03 .06 .27*** .06 -.06*** .02 .88*** .23 TOSCA shame -.04 .09 -.02 .06 .07 .05 -.03+ .02 .45* .22 Day of week .02 .02 .01 .02 -.05*** .01 .02*** .00 -.18* .08 Random effects Std. dev. Std. dev. Std. dev. Std. d ev. Std. dev. Intercept .80 .50 .57 .16 1.76 Day of week .11 .11 .08 .03 .32 Residual .60 .57 .44 .15 n/a N observations 587 587 587 587 587 N participants 90 90 90 90 90

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Model 3b is a replication of Model 3a utilizing a reciprocal transformation of negative affect to account for skewness. Model 4 utilizes logistic MLM via the glmer function in R: days on which a participant experiences guilt or shame are coded as 1; days on which no guilt or shame is experienced are coded as 0. Due to its binary outcome, no residual is provided for this model + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Table 12

Measures of Dispositional Guilt and Shame and Self-Reported Big Five Traits in Study 2c

PFQ guilt PFQ shame TOSCA guilt TOSCA shame

Sample A Sample B Comb. Sample A Sample B Comb. Sample A Sample B Comb. Sample A Sample B Comb. Neuroticism .56*** .50*** .53*** .63*** .52*** .58*** .23* .15 .19** .61*** .50*** .55*** Extraversion -.18 -.22* -.20** -.21+ -.15 -.18* .05 .16 .11 -.19+ -.11 -.15* Openness .28* .09 .18* .15 -.01 .06 .29** .46*** .39*** .30** .14 .21** Agreeableness .18 .00 .08 .09 .04 .06 .45*** .48*** .47*** .21+ .19+ .20** Conscientiousness -.26* -.30** -.28*** -.24* -.31** -.28*** .16 .09 .12+ -.12 -.06 -.09 Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 76; Sample B N = 96; Combined N = 172 + “Comb.” column presents the results of a mini meta-analysis of Sample A and Sample B results.

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Table 13

Measures of Dispositional Guilt and Shame and Self-Reported Adaptive Traits in Study 2c

PFQ guilt PFQ shame TOSCA guilt TOSCA shame

Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Empathy Empathic concern .24* .11 .17* .22* .15 .18* .46*** .55*** .51*** .36*** .28** .32*** Perspective taking .21+ -.01 .09 .10 .01 .05 .36*** .40*** .38*** .13 .02 .07 Psychological well-being Environmental mastery -.48*** -.44*** -.46*** -.58*** -.41*** -.49*** -.08 .0 0 -.04 .13 -.26*** -.09 Positive relations with others -.24* -.18+ -.21** -.26* -.13 -.19** .05 .30** .19** -.24* -.02 -.12+ Autonomy --- -.23* --- -.40*** --- .11 --- - .37*** Personal growth --- -.12 --- -.13 --- .42*** --- .10 Purpose in life --- -.14 --- .08 --- .08 --- -.06 Self-acceptance --- -.58*** --- -.53*** --- .02 --- -.42*** Meaning in Life Presence of meaning --- -.12 --- -.05 --- .11 --- -.15 Flourishing --- -.42*** --- -.34*** --- .19+ --- -.20* Trait forgiveness --- -.20* --- -.17+ --- .23* --- -.17+ Satisfaction with life --- -.46*** --- -.33*** --- .11 --- -.16 Self-esteem --- -.62*** --- -.57*** --- -.08 --- -.47*** Humanitarianism- egalitarianism --- .07 --- .05 --- .47*** --- .24*

Trait positive affect -.22* -.25** -.24** -.19+ -.21* -.20** -.08 .17+ .06 -.30*** -.15 -.22** Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 76; Sample B N = 96; Combined N = 172 + “Comb.” column presents the results of a mini meta-analysis of Sample A and Sample B results.

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Table 14

Measures of Dispositional Guilt and Shame and Self-Reported Maladaptive Traits in Study 2c

PFQ guilt PFQ shame TOSCA guilt TOSCA shame

Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Aggression Physical aggression .12 --- .1 0 --- -.31** --- -.07 --- Verbal aggression .18 --- .05 --- -.09 --- -.12 --- Anger .38*** --- .38*** --- -.03 --- .19+ --- Hostility .53*** --- .61*** --- -.01 --- .33** --- Trait negative affect .58*** .58*** .58*** .50*** .54*** .52*** .24* 0.14 .18* .43*** .32*** .37*** DSM Personality Inventories Hostility .25* --- .35** --- -.03 --- .20+ --- Withdrawal .19+ --- .27* --- -.07 --- .11 --- Emotional lability .45*** --- .45*** --- .17 --- .40*** --- Depressivity .62*** --- .59*** --- .13 --- .49*** --- Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 76; Sample B N = 96; Combined N = 172 + “Comb.” column presents the results of a mini meta-analysis of Sample A and Sample B results.

! 131! !

Table 15

Measures of Dispositional Guilt and Shame and Informant-Reported Personality Traits in Study 2c

PFQ guilt PFQ shame TOSCA guilt TOSCA shame

Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Neuroticism .40** .36*** .38*** .33** .27* .30*** .28* -.08 .08 .45*** .18 .31*** Extraversion -.18 -.28* -.24** -.21 -.05 -.12 .02 .19 .12 -.17 -.03 -.09 Openness .11 .06 .08 .00 .04 .02 .32* .23+ .27** .33** .09 .20* Agreeableness .02 -.06 -.02 .15 .01 .07 .21 .49*** .37*** .22+ .29* .26** Conscientiousness -.13 -.18 -.16+ .08 -.27* -.12 .18 .22+ .20* .00 .09 .05 Satisfaction with Life --- -.25* --- -.18 --- .02 --- -.11 Psychological well-being Autonomy --- -.15 --- -.30** --- .01 --- -.28* Environmental mastery --- -.33** --- -.30** --- .12 --- -.05 Personal growth --- -.26* --- -.09 --- .27* --- .09 Positive relations --- -.21+ --- -.08 --- .19 --- .02 Purpose in life --- -.28* --- -.25* --- .18 --- -.07 Self-acceptance --- -.39*** --- -.31** --- .09 --- -.22+ Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 55; Sample B N = 68 to 70; Combined N = 123 + “Comb.” column presents the results of a mini meta-analysis of Sample A and Sample B results.

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Table 16

Measures of Dispositional Guilt and Shame and Coded In-Lab Behavior in Study 2c

PFQ guilt PFQ shame TOSCA guilt TOSCA shame Neuroticism -.03 -.08 .00 .04 Timidness -.05 -.19* -.15+ .03 Playfulness -.04 .05 .14+ .15+ Confidence -.10 -.03 -.07 -.24** Agreeableness .03 .02 .00 .04 Note. N = 144; ** p < .01, * p < .05, + p < .10 a TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Figure 1

Flow Chart Outlining Study Selection Process for Study 1 Meta-Analyses

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Appendix A

Semi-partial Correlation Results from Study 1 Meta-Analyses

Random effects Fixed effects

!! ! N k Mr 95% CI Z k Mr 95% CI Z ! FD Homog.! Guilt !Semi! -partial correlations! ! ! ! ! ! ! ! ! ! ! ! ! Main Analysis 7,574 !31 ! .20 ! .16, .25 !8.46*** ! !31 ! .19 ! .17, .21 ! 16.36*** !2,882 !113.43*** ! ! Trim and Fill 22 .14 .09, .19 22 .14 .12, .16 Shame! ! Semi! -partial Correlations! ! ! ! ! ! ! ! ! ! ! ! ! Main Analysis 7,970 33 -.13 -.17, -.09 -5.95*** 33 -.11 -.14, -.09 -10.10*** 1,284 105.28*** !! Trim and Fill 33 -.13 -.17, -.09 27 -.09 -.11, -.06 !! !! Note. k = number of independent studies; Mr = average r; CI = confidence interval; Z = combined Z; FD = file drawer N; Homog. = homogeneity test (Q); a *** p ≤ .001, + p < .10 ! ! ! ! ! ! ! ! ! ! ! ! !

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Appendix B

Comprehensive List of Effect Sizes and Attributes for Studies Included in Zero-Order Correlation Guilt Meta-Analysis

Outcome Outcome Effect Study or Effect Percent Nationality Mean Percent Test measure measure size ID Author(s) Year sample size N female of sample age White Measure format type class ( r ) G1 Abe 2004 97 1 .90 COMB 3 2 1 .11

G2 Abramson et al. 1977 Males 108 .00 1 MGI 2 1 1 .21

G3 Abramson et al. 1977 Females 41 1.00 1 MGI 2 1 1 .15

G4 Biaggio 1980 150 .52 1 COMB 2 1 1 -.08

G5 Biaggio et al. 1981 52 .53 1 COMB 2 1 1 .05

G6 Bracht & Regner 2011 96 .63 2 COMB 1 2 3 .10

G7 Cohen 2010 Study 2 172 .23 1 TOSCA 1 1 4 .21

G8 Cohen et al. 2011 Study 1 450 .53 1 GASP 1 1 4 .27

G9 Cohen et al. 2011 Study 2 148 .79 1 37.0 .75 GASP 1 1 4 .33 GASP - NBE G10 Cohen et al. 2011 Study 3 28 1 subscale only 1 2 3 .48

G11 Cohen et al. 2013a 411 .56 1 31.2 .76 GASP 1 1 1 .21

G12 Cohen et al. 2013b Study 1 208 .48 1 29.7 .67 GASP 1 2 4 .29 G13 Cohen et al. 2013b Study 2 341 .50 1 .75 GASP 1 2 4 .27

G14 Cohen et al. 2014 Study 3 461 .54 1 55.6 .72 COMB 1 1 4 .27 G15 Cohen et al. Unpub. Study 1 155 .23 1 32.7 GP-5 1 2 4 -.01

G16 Cohen et al. Unpub. Study 2 699 .63 1 31.0 .64 GP-5 1 1 4 .34 G17 Covert et al. 2003 233 .66 1 20.0 .47 TOSCA 1 2 2 .25 Dennison & G18 Stewart 2006 125 .76 4 25.5 TOSCA 1 1 1 -.07 Einstein & G19 Lanning 1998 Study 1 91 .72 4 COMB 1 1 1 .19

G20 Fedewa et al. 2005 230 .76 1 19.8 .87 TOSCA 1 1 1 .00

G21 Fontaine et al. 2001 1001 .60 2 30.7 TOSCA 1 1 4 .16

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G24 Groenvynck et al. 2011 467 .50 2 39.0 SCEW 1 1 1 -.04

G25 Harder et al. 1992 Study 2 71 .59 1 PFQ 2 1 1 -.24 Harder & G26 Greenwald 1999 41 .46 1 19.0 .68 PFQ 2 1 1 -.14 Ishikawa & G27 Uchiyama 2000 690 .51 3 TGI 2 1 4 .11

G29 Jones & Kugler 1993 54 1 .89 TGI 2 2 1 -.31

G30 Jordan et al. 2015 Study 1 99 .78 1 41.7 COMB 1 1 2 .33

G31 Judge et al. 2006 74 .78 1 37.0 OTHER 2 1 1 -.65

G32 Konstam et al. 2001 137 .81 1 34.0 TOSCA 1 1 2 .22

G33 Kugler & Jones 1992 110 .62 1 26.0 .89 COMB 3 1 3 .21 Leith & G36 Baumeister 1998 Study 2 69 .41 1 TOSCA 1 2 2 .30 Leith & G37 Baumeister 1998 Study 3 36 .38 1 19.4 TOSCA 1 2 2 .35

G40 Lutwak et al. 2003 141 .53 1 20.0 .20 TOSCA 1 1 1 .13

G41 Luyten et al. 2002 619 .53 2 33.9 TOSCA 1 1 4 .15

G42 Mills et al. 2007 Mothers 198 1.00 1 TOSCA 1 2 1 -.05

G43 Mills et al. 2007 Fathers 198 .00 1 TOSCA 1 2 1 .06 Mullins-Nelson et G44 al. 2006 Males 44 .75 1 19.3 .86 TOSCA 1 1 2 .51 Mullins-Nelson et G45 al. 2006 Females 130 .75 1 19.3 .86 TOSCA 1 1 2 .29 G46 Musante et al. 1989 132 .00 1 27.6 COMB 2 2 1 .09

G47 O'Connor et al. 1999 284 .59 1 20.0 .22 COMB 3 1 4 .02

G48 Pinter et al. 2007 Study 2 20 1.00 1 DCQ 1 2 3 .09

G49 Quiles & Bybee 1997 101 .48 1 .79 COMB 3 2 4 .06 Rangganadhan & G50 Todorov 2010 150 .72 4 21.1 .37 TOSCA 1 1 2 .38

G51 Reynolds et al. 1994 246 .48 4 ASRG 2 2 1 -.14

G53 Sarason 1961 Females 68 1.00 1 BDG 2 1 1 -.08

G54 Sarason 1961 Males 80 .00 1 BDG 2 1 1 -.17 Schaumberg & G55 Flynn 2012 Study 2 144 .54 1 21.3 COMB 1 1 1 .39

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Schaumberg & G56 Flynn 2012 Study 3 139 .36 1 26.9 TOSCA 1 1 1 -.11

G57 Schill & Schneider 1970 Males 58 .00 1 MGI 2 1 1 .29

G58 Schill & Schneider 1970 Females 72 1.00 1 MGI 2 1 1 .35

G59 Schill et al. 1990 Males 32 .00 1 ASRG 2 1 1 -.35

G60 Schill et al. 1990 Females 33 1.00 1 ASRG 2 1 1 -.19

G63 Silfver et al. 2008 255 .01 2 19.6 TOSCA 1 1 4 .29

G64 Strelan 2007 176 .72 4 20.0 PFQ 2 1 4 -.15

G65 Stuewig et al. 2010 Sample 1 250 .74 1 20.0 .51 TOSCA 1 1 4 .28 G66 Tangney 1991 Study 1 100 .64 1 19.3 .65 SCAAI 1 1 2 .24 G67 Tangney 1991 Study 2 97 .74 1 19.2 .75 SCAAI 1 1 2 .01 G68 Tangney 1991 Study 3 212 .72 1 19.5 .83 SCAAI 1 1 2 .37 G69 Tangney 1991 Study 4 241 .70 1 21.1 .76 SCAAI 1 1 2 .32 G70 Tangney et al. 1992 Study 1 228 .71 1 21.1 .77 SCAAI 1 1 1 .01 G71 Tangney et al. 1992 Study 2 222 .71 1 19.4 .81 COMB 1 1 1 -.02 G74 Tibbetts 2003 224 .48 1 21.4 COMB 3 1 3 .36

G75 Wolf et al. 2010 Study 1 233 .48 1 COMB 1 1 4 .43 Note. Nationality of sample: 1 = USA/Canada; 2 = Europe; 3 = Asia; 4 = Australia. Measure: TOSCA = Test of Self-Conscious Affect; MGI = Mosher Guilt Inventory; PFQ = Personal Feelings Questionnaire; TGI = The Guilt Inventory; SCAAI = Self-Conscious Affect and Attribution Inventory; GASP = Guilt and Shame Proneness Scale; GP-5 = Five- Item Guilt Proneness Scale; BDG = Buss-Durkee Guilt Scale; ASRG = Anger Self-report Guilt Scale; DCQ = Dimensions of Conscience Questionnaire; SCEW = Self-conscious Emotions at Work Scale; OTHER = any non-standard questionnaire; COMB = combination of two or more scales. Test format: 1 = scenario; 2 = checklist; 3= mixture. Outcome measure type: 1 = self-report only; 2 = mixed methods. Outcome measure class: 1 = reverse-signed hostility; 2 = empathy/forgiveness; 3 = morality; 4 = mixture.

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Appendix C

Comprehensive List of Effect Sizes and Attributes for Studies Included in Semi-Partial Correlation Guilt Meta-Analysis

Outcome Outcome Study or Percent Nationality Percent Test measure measure Effect ID Author(s) Year sample Effect size N female of sample Mean age White Measure format type class size ( r ) G8 Cohen et al. 2011 Study 1 450 .53 1 GASP 1 1 4 .24 ! G9 Cohen et al. 2011 Study 2 148 .79 1 37.0 .75 GASP 1 1 4 .29 G17 Covert et al. 2003 233 .66 1 20.0 .47 TOSCA 1 2 2 .27 ! G20 Fedewa et al. 2005 230 .76 1 19.8 .87 TOSCA 1 1 1 .20 ! G21 Fontaine et al. 2001 1001 .60 2 30.7 TOSCA 1 1 4 .16 Giner-Sorolla et ! ! G22 al. 2011 Study 1 32 .84 2 20.3 TOSCA 1 2 1 .18 Giner-Sorolla et ! G23 al. 2011 Study 2 32 .83 2 20.2 1.00 TOSCA 1 2 1 .16

G24 Groenvynck et al. 2011 467 .50 2 39.0 SCEW 1 1 1 -.01 ! ! G25 Harder et al. 1992 Study 2 71 .59 1 PFQ 2 1 1 -.28 ! G28 Joireman 2004 164 .77 1 .94 TOSCA 1 1 2 .30 Leith & ! G34 Baumeister 1998 Study 1a 154 .41 1 TOSCA 1 1 2 .28 Leith & ! G35 Baumeister 1998 Study 1b 199 .51 1 TOSCA 1 1 2 .23 Leith & ! G36 Baumeister 1998 Study 2 69 .41 1 TOSCA 1 1 2 .22 ! G38 Lutwak et al. 2001 Males 91 .00 1 20.1 .29 TOSCA 1 1 1 .16 G39 Lutwak et al. 2001 Females 174 1.00 1 20.1 .29 TOSCA 1 1 1 .16 G40 Lutwak et al. 2003 141 .53 1 20.0 .20 TOSCA 1 1 1 .22 ! G41 Luyten et al. 2002 619 .53 2 33.9 TOSCA 1 1 4 .18 ! ! G47 O'Connor et al. 1999 284 .59 1 20.0 .22 COMB 3 1 1 -.03 Sandage & ! G52 Worthington 2010 97 .75 1 20.8 .42 TOSCA 1 1 2 .21 ! G61 Shorey et al. 2011 Males 367 .00 1 19.7 .84 TOSCA 1 1 1 .14 G62 Shorey et al. 2011 Females 600 1.00 1 19.7 .84 TOSCA 1 1 1 .11

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G65 Stuewig et al. 2010 Sample 1 250 .74 1 20.0 .51 TOSCA 1 1 4 .31 G66 Tangney 1991 Study 1 100 .64 1 19.3 .65 SCAAI 1 1 2 .37 G67 Tangney 1991 Study 2 97 .74 1 19.2 .75 SCAAI 1 1 2 .15 G68 Tangney 1991 Study 3 212 .72 1 19.5 .83 SCAAI 1 1 2 .39 G69 Tangney 1991 Study 4 241 .70 1 21.1 .76 SCAAI 1 1 2 .31 G70 Tangney et al. 1992 Study 1 228 .71 1 21.1 .77 SCAAI 1 1 1 .09 G71 Tangney et al. 1992 Study 2 222 .71 1 19.4 .81 COMB 1 1 1 .10 G72 Tangney et al. 1996 Travelers 192 .46 0 39.2 .87 TOSCA 1 1 1 .23 G73 Tangney et al. 1996 Students 176 .76 1 22.5 .70 TOSCA 1 1 1 .23 G75 Wolf et al. 2010 Study 1 233 .48 1 COMB 1 1 4 .47

Note. Nationality of sample: 1 = USA/Canada; 2 = Europe; 0 = Airport. Measure: TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire; SCAAI = Self-Conscious Affect and Attribution Inventory; GASP = Guilt and Shame Proneness Scale; SCEW = Self-conscious Emotions at Work Scale; COMB = combination of two or more scales. Test format: 1 = scenario; 2 = checklist; 3= mixture. Outcome measure type: 1 = self-report only; 2 = mixed methods. Outcome measure class: 1 = reverse-signed hostility; 2 = empathy/forgiveness; 3 = morality; 4 = mixture. !

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Appendix D

Comprehensive List of Effect Sizes and Attributes for Studies Included in Zero-Order Correlation Shame Meta-Analysis

Outcome Outcome Study or Effect Percent Nationality Mean Percent Test measure measure Effect ID Author(s) Year sample size N female of sample age White Measure format type class size ( r ) S1 Abe 2004 97 .76 1 .90 COMB 3 2 1 -.06

S2 Bracht & Regner 2011 ! 96 .63 2 COMB 1 2 3 .02 S3 Cohen 2010 ! Study 2 172 .23 1 ! TOSCA 1 1 4 -.03 S4 Cohen et al. 2011 Study 1 450 .53 1 ! GASP 1 1 4 .04 S5 Cohen et al. 2011 Study 2 148 .79 1 37.0 ! .75 GASP 1 1 4 -.03 S6 Cohen et al. 2013b Study 1 208 .48 1 29.7 .67 GASP 1 2 4 .12 S7 Cohen et al. 2013b Study 2 341 .50 1 .75 GASP 1 2 4 -.01 S8 Covert et al. 2003 233 .66 1 20.0 .47 TOSCA 1 2 2 .02 S9 Dennison & Stewart 2006 ! 125 .76 4 TOSCA 1 1 1 .01 25.5 S10 Einstein & Lanning 1998 ! Study 1 91 .72 4 ! COMB 1 1 1 .35

S11 Farmer & Andrews 2009 60 .00 2 ! ESS 2 1 1 -.47 19.3 S12 Fedewa et al. 2005 ! 230 .76 1 19.8 ! .87 TOSCA 1 1 1 -.37 S13 Fontaine et al. 2001 ! 1001 .60 2 30.7 TOSCA 1 1 4 .00 S16 Groenvynck et al. 2011 ! 467 .50 2 ! SCEW 1 1 1 -.17 39.0 S17 Harder et al. 1992 ! Study 2 71 .59 1 ! ASGS 2 1 1 -.02 Harder & S18 1999 41 .46 1 ! .68 PFQ 2 1 1 -.37 Greenwald 19.0 S19 Harper et al. 2005 ! 150 .00 1 19.4 .85 TOSCA 1 1 1 -.31 Hejdenberg & S20 2011 ! 188 .59 2 ESS 2 1 1 -.30 Andrews 21.0 S21 Jakupcak et al. 2005 ! 204 .00 1 25.5 ! .57 TOSCA 1 1 1 -.17 S23 Jordan et al. 2015 ! Study 1 99 .78 1 41.7 COMB 1 1 2 -.16 S24 Konstam et al. 2001 137 .81 1 34.0 ! TOSCA 1 1 2 .02 S25 Kugler & Jones 1992 ! 110 .62 1 26.0 ! .89 COMB 3 1 3 .13 ! ! 141! !

S28 Leith & Baumeister 1998 Study 2 99 .41 1 TOSCA 1 2 2 .13

S29 Leith & Baumeister 1998 Study 3 36 .38 1 ! TOSCA 1 2 2 -.75 19.4 S32 Lutwak et al. 2003 141 .53 1 20.0 ! .20 TOSCA 1 1 1 -.22 S33 Luyten et al. 2002 619 .53 2 33.9 TOSCA 1 1 4 -.03 S34 Mills et al. 2007 Mothers 198 1.00 1 TOSCA 1 2 1 -.15 S35 Mills et al. 2007 Fathers 198 .00 1 TOSCA 1 2 1 -.16 Mullins-Nelson et S36 2006 Males 44 .00 1 .86 TOSCA 1 1 2 .27 al. 19.3 Mullins-Nelson et S37 2006 Females 130 1.00 1 .86 TOSCA 1 1 2 .29 al. 19.3 S38 O'Connor et al. 1999 284 .59 1 20.0 .22 TOSCA 1 1 4 -.03 S39 Pinter et al. 2007 20 1.00 1 DCQ 1 2 3 .02 Study 2 Schaumberg & S41 2012 Study 2 144 .54 1 COMB 1 1 1 .02 Flynn 21.3 Schaumberg & S42 2012 Study 3 139 .36 1 ! TOSCA 1 1 1 -.04 Flynn 26.9 S45 Silfver et al. 2008 255 .01 2 19.6 ! TOSCA 1 1 4 .02 S46 Strelan 2007 ! 176 .72 4 20.0 ! PFQ 2 1 4 .26 S47 Stuewig et al. 2010 !Sample 1 250 .74 1 20.0 ! .51 TOSCA 1 1 4 .02 S48 Tangney 1991 Study 1 100 .64 1 19.3 .65 SCAAI 1 1 2 -.21 S49 Tangney 1991 Study 2 97 .74 1 19.2 .75 SCAAI 1 1 2 -.25 S50 Tangney 1991 Study 3 212 .72 1 19.5 .83 SCAAI 1 1 2 .11 S51 Tangney 1991 Study 4 241 .70 1 21.1 .76 SCAAI 1 1 2 .09 S52 Tangney et al. 1992 Study 1 228 .71 1 21.1 .77 SCAAI 1 1 1 -.15 S53 Tangney et al. 1992 Study 2 222 .71 1 19.4 .81 COMB 1 1 1 -.21 S56 Tibbetts 1997 604 .46 1 19.4 TSPS 1 1 4 .03 S57 Tibbetts 2003 ! 224 .48 1 21.4 ! COMB 3 1 3 .20 S58 Tops et al. 2006 ! 21 1.00 2 20.0 ! ESS 2 1 1 .23 S59 Wolf et al. 2010 ! Study 1 233 .48 1 ! COMB 1 1 4 .11

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Note. Nationality of sample: 1 = USA/Canada; 2 = Europe; 3 = Asia; 4 = Australia. Measure: TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire; SCAAI = Self-Conscious Affect and Attribution Inventory; ASGS = Adapted Shame/Guilt Scale; GASP = Guilt and Shame Proneness Scale; ESS = Experience of Shame Scale; SCEW = Self- conscious Emotions at Work Scale; TSPS = The Shame Proneness Scale; DCQ = Dimensions of Conscience Questionnaire; COMB = Combination of two or more scales. Test format: 1 = scenario; 2 = checklist; 3= mixture. Outcome measure type: 1 = self-report only; 2 = mixed methods. Outcome measure class: 1 = reverse-signed hostility; 2 = empathy/forgiveness; 3 = morality; 4 = mixture.

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Appendix E

Comprehensive List of Effect Sizes and Attributes for Studies Included in Semi-Partial Correlation Shame Meta-Analysis

!! !! !! !! !! !! !! Outcome Outcome Study or Effect Percent Nationality Mean Percent Test measure measure Effect ID Author(s) Year sample size N female of sample age White Measure format type class size ( r ) S4 Cohen et al. 2011 Study 1 450 .53 1 GASP 1 1 4 -.02 ! S5 Cohen et al. 2011 Study 2 148 .79 1 37.0 .75 GASP 1 1 4 -.12 S8 Covert et al. 2003 233 .66 1 20.0 .47 TOSCA 1 2 2 -.11 ! S12 Fedewa et al. 2005 230 .76 1 19.8 .87 TOSCA 1 1 1 -.42 ! S13 Fontaine et al. 2001 1001 .60 2 30.7 TOSCA 1 1 4 -.06 ! ! S14 Giner-Sorolla et al. 2011 Study 1 32 .84 2 20.3 TOSCA 1 2 1 -.36 ! S15 Giner-Sorolla et al. 2011 Study 2 32 .83 2 20.2 1.00 TOSCA 1 2 1 -.14

S16 Groenvynck et al. 2011 467 .50 2 39.0 SCEW 1 1 1 .00 ! ! S17 Harder et al. 1992 Study 2 71 .59 1 ASGS 2 1 1 .12 ! S22 Joireman 2004 164 .77 1 .94 TOSCA 1 1 2 -.03 ! S26 Leith & Baumeister 1998 Study 1a 154 .41 1 TOSCA 1 1 2 .12 ! S27 Leith & Baumeister 1998 Study 1b 199 .51 1 TOSCA 1 1 2 .12 ! S28 Leith & Baumeister 1998 Study 2 69 .41 1 TOSCA 1 2 2 .02 ! S30 Lutwak et al. 2001 Males 91 .00 1 20.1 .29 TOSCA 1 1 1 -.06 S31 Lutwak et al. 2001 Females 174 1.00 1 20.1 .29 TOSCA 1 1 1 -.06 S32 Lutwak et al. 2003 141 .53 1 20.0 .20 TOSCA 1 1 1 -.28 ! S33 Luyten et al. 2002 619 .53 2 33.9 TOSCA 1 1 4 -.09 ! ! S34 Mills et al. 2007 Mothers 198 1.00 1 TOSCA 1 2 1 -.14 ! S35 Mills et al. 2007 Fathers 198 .00 1 TOSCA 1 2 1 -.18 ! S38 O'Connor et al. 1999 284 .59 1 20.0 .22 TOSCA 1 1 1 -.18 Sandage & ! S40 Worthington 2010 97 .75 1 20.8 .42 TOSCA 1 1 2 -.08 !

! 144! !

S43 Shorey et al. 2011 Males 367 .00 1 19.7 .84 TOSCA 1 1 1 -.09 S44 Shorey et al. 2011 Females 600 1.00 1 19.7 .84 TOSCA 1 1 1 -.09 S47 Stuewig et al. 2010 Sample 1 250 .74 1 20.0 .51 TOSCA 1 1 4 -.12 S48 Tangney 1991 Study 1 100 .64 1 19.3 .65 SCAAI 1 1 2 -.35 S49 Tangney 1991 Study 2 97 .74 1 19.2 .75 SCAAI 1 1 2 -.29 S50 Tangney 1991 Study 3 212 .72 1 19.5 .83 SCAAI 1 1 2 -.15 S51 Tangney 1991 Study 4 241 .70 1 21.1 .76 SCAAI 1 1 2 -.06 S52 Tangney et al. 1992 Study 1 228 .71 1 21.1 .77 SCAAI 1 1 1 -.17 S53 Tangney et al. 1992 Study 2 222 .71 1 19.4 .81 COMB 1 1 1 -.23 S54 Tangney et al. 1996 Travelers 192 .46 0 39.2 .87 TOSCA 1 1 1 -.31 S55 Tangney et al. 1996 Students 176 .76 1 22.5 .70 TOSCA 1 1 1 -.30 S59 Wolf et al. 2010 Study 1 233 .48 1 COMB 1 1 4 -.22 Note. Nationality of sample: 1 = USA/Canada; 2 = Europe; 0 = Airport. Measure: TOSCA = Test of Self-Conscious Affect; SCAAI = Self-Conscious Affect and Attribution Inventory; ASGS = Adapted Shame/Guilt Scale; GASP = Guilt and Shame Proneness Scale; SCEW = Self-conscious Emotions at Work Scale; COMB = combination of two or more scales. Test

format: 1 = scenario; 2 = checklist; 3= mixture. Outcome measure type: 1 = self-report only; 2 = mixed methods. Outcome measure class: 1 = reverse-signed hostility; 2 = empathy/forgiveness; 3 = morality; 4 = mixture.

! 145! !

Appendix F

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Note. [G] denotes inclusion in guilt meta-analysis, [S] denotes inclusion in shame meta-analysis a * denotes record was unpublished at the time of solicitation

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[G] Biaggio, M. K., Supplee, K., & Curtis, N. (1981). Reliability and validity of four anger

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[G] Cohen, T. R., Kim, Y., Jordan, K. P., & Panter, A. T. (unpublished). Measuring guilt

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Appendix G

Data Sources Collected from Samples A and B not Utilized in the Current Research

Sample A

The GASP

The GASP, a measure inspired in part by the TOSCA, also includes 16 hypothetical scenarios in which participants are asked to imagine themselves. Unlike the TOSCA, this scale includes just one response for each scenario, indicative of either guilt or shame. Accordingly, it includes eight distinct transgressions for guilt and eight distinct transgressions for shame. In this measure participants are asked to rate their perceived likelihood of enacting each response on a scale of 1 (not at all likely) to 7 (extremely likely). This measure is typically interpreted using its four 4-item subscales (two for guilt and two for shame), which distinguish forecasted affective experiences and evaluations from forecasted motivations or behaviors. As such, the GASP guilt scale is divided into Negative Behavior Evaluation (NBE; α = .69) and Repair (α = .58) subscales; the GASP shame scale is divided into Negative Self Evaluation (NSE; α = .72) and

Withdrawal (α = .59) subscales. I utilized the TOSCA as my scenario measure of choice in the studies that follow, as this measure is more frequently employed than the GASP (likely due to the GASP’s more recent development). In addition, the two are highly correlated, as shown in

Table 5.

Self-reports of the Big Five: Facet Scores

The Big Five (agreeableness, neuroticism, extraversion, conscientiousness, and openness to experience) were assessed in Sample A via the 240-item NEO-PI-R (NEO-PI-R; Costa &

McCrae, 1992). The NEO-PI-R also provides six facet scores for each of the Big Five via 8-item subscales. The six facets of agreeableness (total scale α = .80) are: trust (α = .85),

! 154! ! straightforwardness (α = .69), altruism (α = .50), compliance (α = .61), modesty (α = .81), and tender-mindedness (α = .54). The six facets of neuroticism (total scale α = .93) are: anxiety (α =

.85), hostility (α = .76), depression (α = .83), self-consciousness (α = .79), impulsiveness (α =

.69), and vulnerability (α = .78). Extraversion’s (total scale α = .91) six facets are: warmth (α =

.74), gregariousness (α = .76), assertiveness (α = .83), activity level (α = .68), excitement (α =

.70), and positive emotions (α = .85). The six facets of conscientiousness (total scale α = .94) are: competence (α = .75), order (α = .80), dutifulness (α = .64), achievement striving (α = .83), self- discipline (α = .83), and deliberation (α = .85). Finally, openness to experience’s (total scale α =

.90) six facets are: fantasy (α = .81), aesthetics (α = .84), feelings (α = .80), actions (α = .62), ideas (α = .79), and values (α = .64). All items from the NEO-PI-R were assessed on a 1

(strongly disagree) to 5 (strongly agree) scale. To reduce the number of correlations run in subsequent studies, only the five superordinate factor scales, and not their underlying facets, are examined.

Need for Cognition

Need for cognition, or, the trait-like affinity for cognitive pursuits, was assessed via the

13-item Need for Cognition Scale (NCS; Cacioppo, Petty, & Kao, 1984, α = .77). Sample items from this scale are: “I find satisfaction in deliberating hard and for long hours,” and “I prefer my life to be filled with puzzles that I must solve.”

Attributional Complexity

Attributional complexity is the trait-like tendency to prefer complex over simple explanations for behavior. This trait was assessed via the 28-item Attributional Complexity Scale

(ACS; Fletcher et al., 1986). Sample items from this scale include: “I really enjoy analyzing the reasons or causes for people’s behavior,” and “I have found that the causes for people’s behavior

! 155! ! are usually complex rather than simple.” This scale is comprised of seven subscales: a motivational component (α = .51), preference for complex explanations (α = .70), metacognition

(α = .69), behavior as a function of interaction (α = .51), complex internal explanations (α = .33), complex contemporary external explanations (α = .63), and the use of temporal dimension (α =

.76). Attributional complexity was not examined in the current research, as I did not have reason to suspect it would be significantly correlated with dispositional guilt or shame.

DSM Personality Scales

A wide range of maladaptive traits was assessed in Sample A using the Personality

Inventory for DSM-5 (PID-5; Krueger, Derringer, Watson, & Skodol, 2013). This personality inventory contains a large number of subscales, intended to capture various maladaptive traits at sub-clinical levels, in the general population. In addition to PID-5 subscales examined in Study

2c, participants also provided self-reports of the following: impulsivity (6 items, α = .87; e.g.,

“Even though I know better, I can’t stop making rash decisions”), risk-taking (14 items, α = .91; e.g. “People would describe me as reckless”), rigid perfectionism (10 items, α = .89; e.g.,

“People tell me that I focus too much on minor details”), manipulativeness (5 items, α = .82; e.g.,

“I’m good at conning people”), and distractibility (9 items, α = .88; e.g., “I lose track of conversations because other things catch my attention”).

Narcissism

Narcissism, or, excessive self-admiration and feelings of superiority, was assessed via the

16-item Narcissistic Personality Index (NPI-16; Raskin & Terry, 1988, α = .84). Though the original version of this scale utilizes a forced-choice format, I assessed narcissism using a Likert type scale (1 to 5; strongly disagree to strongly agree) as others have done in previous research

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(e.g., Park & Colvin, 2014). Sample items from the NPI-16 are: “I really like to be the center of attention,” and “I like having authority over people.”

Sample B

The GASP

Participants in Sample B also completed self-reports of dispositional guilt and shame using the GASP scale. Reliabilities for the four GASP subscales in Sample B were as follows:

Negative Behavior Evaluation (NBE; α = .73), Repair (α = .59), Negative Self Evaluation (NSE;

α = .75), and Withdrawal (α = .49).

Self-reports of the Big Five: Facet Scores

The Big Five (agreeableness, neuroticism, extraversion, conscientiousness, and openness to experience) were assessed in Sample A via the 240-item NEO-PI-R (NEO-PI-R; Costa &

McCrae, 1992). The NEO-PI-R also provides six facet scores for each of the Big Five via 8-item subscales. Facet internal reliabilities were as follows: Trust (α = .89), straightforwardness (α =

.69), altruism (α = .74), compliance (α = .71), modesty (α = .77), tender-mindedness (α = .67), anxiety (α = .72), hostility (α = .68), depression (α = .84), self-consciousness (α = .81), impulsiveness (α = .71), vulnerability (α = .77), warmth (α = .79), gregariousness (α = .80), assertiveness (α = .80), activity level (α = .55), excitement (α = .55), and positive emotions (α =

.81), competence (α = .70), order (α = .74), dutifulness (α = .71), achievement striving (α = .79), self-discipline (α = .86), deliberation (α = .69), fantasy (α = .78), aesthetics (α = .80), feelings (α

= .71), actions (α = .66), ideas (α = .82), and values (α = .69).

Searching for Meaning in Life

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Searching for meaning in life was assessed in Sample B using the Meaning in Life

Questionnaire (MLQ; Steger, Frazier, Oishi, & Kaler, 2006). The searching for meaning subscale

(α = .87) of the MLQ includes items such as “I am looking for something that makes my life feel meaningful.”

Trait Self-Control

Trait self-control, or, the trait-like ability to delay gratification, was assessed using the

36-item Self-control Scale (SCS; Tangney, Baumeister, & Boone, 2004, α = .88). Sample items from this scale include: “I am able to work effectively toward long-term goals,” and “People would say that I have iron self-discipline.” Scores from this scale were not analyzed in the current research, as no relationships between self-control and guilt or shame were hypothesized.

Daily Stress

Participants in Sample B provided daily reports of their stress levels for seven consecutive days as part of the daily diary assignment, using the Daily Inventory of Stressful

Events (DISE; Almeida et al., 2002). This measure presents participants with descriptions of seven stressful situations, such as “Did you have an argument or disagreement with anyone in the last 24 hours?” Participants then indicate “yes” (given a score of 1) if this event happened to them within the past 24 hours, or “no” (given a score of 0) if this event did not happen to them. If a participant indicates “yes” for any of the seven items, he or she then rates the severity of that experience using a 1 (not at all stressful) to 4 (very stressful) scale. If he or she indicates “no” for any of the seven items, he or she does not provide a severity rating. Thus, the DISE provides information regarding both the number of stressful events an individual experiences each day, as well as the degree of stress experienced, calculated by summing all severity ratings.

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Appendix H

Guilt Measures Individually Tested as Predictors of Interpersonally Adaptive Behavior in Study 2a

Model 5: DV = Model 2: DV = Model 4: DV = time spent Model 1: DV = time spent Model 3: DV = time spent in providing Model 6: DV = time spent nurturing time spent meaningful emotional time spent socializing relationships helping others conversation support volunteering Fixed effects

Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.07 .14 -1.05*** .20 -1.95*** .46 -1.29*** .24 -2.30*** .48 -3.28*** .76 PFQ guilt -.03 .09 .05 .12 .07 .16 -.20 .15 .13 .21 -.10 .24 Day of week .10*** .03 .13*** .04 -.02 .10 .00 .06 -.21 .15 -.02 .17

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Std. dev.

Intercept .87 .96 1.24 .87 .63 1.31 Day of week .12 .15 .17 .18 .31 .02 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a All models were run in R using the glmer.nb function ! !

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Model 2: DV = Model 4: DV = Model 5: DV = Model 1: DV = time spent Model 3: DV = time spent in time spent Model 6: DV = time spent nurturing time spent meaningful providing time spent socializing relationships helping others conversation emotional support volunteering Fixed effects

Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.08 .13 -1.14*** .20 -2.13*** .45 -1.37*** .25 -2.38*** .48 -3.35*** .78 TOSCA guilt .27** .09 .35** .14 .58** .19 .26 .16 .33 .23 .21 .26 Day of week .11*** .02 .15*** .04 .02 .10 .02 .06 -.19 .14 .00 .17

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .85 1.05 1.31 .96 .65 1.37 Day of week .12 .14 .15 .18 .29 .01 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a All models were run in R using the glmer.nb function

! !

! 160! !

Appendix I

Shame Measures Individually Tested as Predictors of Interpersonally Adaptive Behavior in Study 2a ! Model 2: DV = Model 4: DV = Model 5: DV = Model 1: DV = time spent Model 3: DV = time spent time spent in Model 6: DV = time spent nurturing time spent helping providing meaningful time spent socializing relationships others emotional support conversation volunteering Fixed effects

Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.07 .14 -1.03*** .19 -1.98*** .46 -2.31*** .48 -1.29*** .24 -3.28*** .75 PFQ shame .00 .09 .15 .12 .02 .16 .15 .20 -.04 .14 .10 .24 Day of week .10*** .03 .12** .04 -.02 .10 -.20 .15 .00 .06 -.02 .16 Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .87 .93 1.27 .61 .89 1.29 Day of week .12 .15 .17 .30 .18 .04 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a All models were run in R using the glmer.nb function ! !

! 161! !

Model 2: DV = Model 1: DV = time spent Model 3: DV = Model 4: DV = time Model 5: DV = time Model 6: DV = time spent nurturing time spent spent providing spent in meaningful time spent socializing relationships helping others emotional support conversation volunteering Fixed effects

Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.08 .14 -1.10*** .20 -1.98*** .46 -2.33*** .48 -1.30*** .25 -3.26*** .72 TOSCA shame .14+ .09 .29* .12 .14 .16 .04 .20 .02 .14 .36 .23 Day of week .11*** .02 .14*** .04 -.02 .10 -.20 .15 .00 .06 -.02 .16 Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .87 1.00 1.27 .65 .90 1.24 Day of week .12 .14 .17 .30 .18 .02 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91 Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a All models were run in R using the glmer.nb function

! 162! !

Appendix J

TOSCA Guilt and Shame Measures Tested as Simultaneous Predictors of Adaptive Interpersonal Behavior in Study 2a

Model 2: DV = Model 4: DV = Model 5: DV = Model 1: DV = time spent Model 3: DV = time spent in time spent in time spent nurturing time spent helping meaningful meaningful Model 6: DV = time socializing relationships others conversation conversation spent volunteering Fixed effects

Std. Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Estimate error Intercept -.08 .13 -1.14*** .20 -2.14*** .45 -2.37*** .48 -1.36*** .25 -3.26*** .73 TOSCA guilt .26** .10 .26+ .15 .64** .20 .43 .28 .32+ .18 -.01 .30 TOSCA shame .02 .09 .18 .14 -.11 .17 -.17 .24 -.11 .16 .37 .27 Day of week .11*** .02 .15*** .04 .02 .10 -.19 .14 .02 .06 -.02 .16 Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .85 1.05 1.32 .65 .94 1.24 Day of week .12 .14 .15 .30 .18 .02 N observations 592 592 593 592 592 593 N participants 91 91 91 91 91 91

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a All models were run in R using the glmer.nb function

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Appendix K

Variance Components Models for Dependent Variables in Study 2b

! ! Model 3b:! DV = Model 1: DV = state Model 2: DV = positive Model 3a: DV = negative reciprocal transformed well-being affect affect negative affect Fixed effects ! Estimate Std. error Estimate Std. error Estimate Std. error ! Estimate Std. error ! Intercept 3.17*** .09 2.77*** .06 1.63*** .06 .70*** .02 Random effects ! Std. dev. Std. dev. Std. dev. ! Std. dev. Intercept .78 .52 .54 ! .15 Residual .65 .62 .48 ! .16 ICC .55 .46 .53 !! .48 N observations 587 587 587 587 N participants 90 90 90 !! 90 Note. *** p < .001; ** p < .01; * p < .05; + p < .10; ICC = Intra-class correlation a A variance components model was not run for the dichotomous guilt-shame variable, as VCMs are only appropriate for continuous! ! dependent variables!

! ! ! ! !

! 164! !

Appendix L

Guilt Measures Individually Tested as Predictors of Affect and Well-Being in Study 2b

Model 4: DV = guilt-shame Model 3b: DV = composite reciprocal (dichotomous Model 1: DV = Model 2: DV = Model 3a: DV = transformed dependent state well-being positive affect negative affect negative affect variable) Fixed effects

Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Intercept 3.08*** .10 2.74*** .07 1.82*** .07 .63*** .02 -.50 .38 PFQ guilt -.23* .08 -.08 .06 .31*** .05 -.08*** .01 .95*** .23 Day of week .02 .02 .01 .02 -.05*** .01 .02 .00 -.14+ .08

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .78 .49 .55 .16 2.40 Day of week .11 .11 .08 .03 .33 Residual .60 .57 .44 .15 n/a N observations 587 587 587 587 587 N participants 90 90 90 90 90

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Residual is not provided for Model 4, as it is a logistic model featuring a dichotomous dependent variable!! !

! 165! !

Model 4: DV = guilt-shame Model 3b: DV = composite reciprocal (dichotomous Model 1: DV = Model 2: DV = Model 3a: DV = transformed dependent state well-being positive affect negative affect negative affect variable) Fixed effects

Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Intercept 3.07*** .10 2.74*** .07 1.83*** .08 .62*** .02 -.45 .39 TOSCA guilt .10 .e9 .02 .06 .05 .06 -.02 .02 .11 .24 Day of week .03 .02 .01 .02 -.05*** .01 .02*** .00 -.16* .08

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .79 .48 .65 .18 2.41 Day of week .11 .11 .08 .03 .31 Residual .60 .57 .44 .15 n/a N observations 597 587 587 587 587 N participants 90 90 90 90 90

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Residual is not provided for Model 4, as it is a logistic model featuring a dichotomous dependent variable

! 166! !

Appendix M

TOSCA Guilt and Shame Measures Tested as Simultaneous Predictors of Affect and Well-Being in Study 2b

Model 4: DV = guilt-shame Model 3b: DV = composite reciprocal (dichotomous Model 1: DV = Model 2: DV = Model 3a: DV = transformed dependent state well-being positive affect negative affect negative affect variable) Fixed effects

Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Intercept 3.07*** .10 2.74*** .07 1.83*** .08 .62*** .02 -.37 .34 TOSCA guilt .24* .10 .05 .07 -.08 .07 .02 .02 -.45+ .26 TOSCA shame -.24* .10 -.06 .07 .22** .07 -.06** .02 1.02*** .26 Day of week .03 .02 .01 .02 -.05*** .01 .02*** .00 -.19* .08

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .79 .50 .61 .17 1.93 Day of week .11 .11 .08 .03 .32 Residual .60 .57 .44 .15 n/a N observations 587 587 587 587 587 N participants 90 90 90 90 90

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Residual is not provided for Model 4, as it is a logistic model featuring a dichotomous dependent variable

! !

! 167! !

Appendix N

Shame Measures Individually Tested as Predictors of Affect and Well-Being in Study 2b

Model 4: DV = Model 3b: DV = guilt-shame reciprocal composite (binary Model 1: DV = Model 2: DV = Model 3a: DV = transformed dependent state well-being positive affect negative affect negative affect variable) Fixed effects

Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Intercept 3.08*** .10 2.74*** .07 1.82*** .07 .63*** .02 -.37 .34 PFQ shame -.20* .08 -.03 .06 .30*** .05 -.07*** .01 1.05*** .22 Day of week .02 .02 .01 .02 -.05*** .01 .02*** .00 -.17* .08

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .79 .49 .58 .16 1.89 Day of week .11 .11 .08 .03 .32 Residual .60 .57 .44 .15 n/a N observations 587 587 587 587 587 N participants 90 90 90 90 90

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Residual is not provided for Model 4, as it is a logistic model featuring a dichotomous dependent variable ! !

! 168! !

Model 4: DV = Model 3b: DV = guilt-shame reciprocal composite (binary Model 1: DV = Model 2: DV = Model 3a: DV = transformed dependent state well-being positive affect negative affect negative affect variable) Fixed effects

Std. Std. Std. Std. Std. Estimate error Estimate error Estimate error Estimate error Estimate error Intercept 3.07*** .10 2.74*** .07 1.83*** .08 .62*** .02 -.38 .35 TOSCA shame -.12 .08 -.03 .06 .18** .06 -.05*** .02 .79*** .23 Day of week .03 .02 .01 .02 -.05*** .01 .02*** .00 -.18* .08

Random effects Std. dev. Std. dev. Std. dev. Std. dev. Std. dev. Intercept .78 .49 .61 .17 2.01 Day of week .11 .11 .08 .03 .31 Residual .60 .57 .44 .15 n/a N observations 587 587 587 587 587 N participants 90 90 90 90 90

Note. *** p < .001; ** p < .01; * p < .05; + p < .10 a Residual is not provided for Model 4, as it is a logistic model featuring a dichotomous dependent variable

! 169! !

Appendix O

Residualized Guilt and Shame Scores Correlated with Self-Reported Big Five Traits in Study 2c

PFQ "shame-free" guilt PFQ "guilt-free" shame TOSCA "shame-free" guilt TOSCA "guilt-free" shame

Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Neuroticism .13 .19+ .16* .26* .24* .25*** -.12 -.12 -.12+ .50*** .43*** .46*** Extraversion -.02 -.13 -.08 -.08 -.01 -.04 .16 .21* .19** -.22+ -.19+ -.20** Openness .17 .10 .13+ -.05 -.07 -.06 .14 .40*** .29*** .15 -.10 .01 Agreeableness .11 -.02 .04 -.04 .04 .00 .34** .39*** .37*** -.03 -.06 -.05 Conscientiousness -.09 -.11 -.10 -.05 -.13 -.10 .23* .12 .17* -.21+ -.10 -.15* Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 76; Sample B N = 96; Combined N = 172 + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Appendix P

Residualized Guilt and Shame Scores Correlated with Self-Reported Adaptive Traits in Study 2c

PFQ "shame-free" guilt PFQ "guilt-free" shame TOSCA "shame-free" guilt TOSCA "guilt -free" shame Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Empathy Empathic concern .09 .02 .05 .04 .08 .06 .29** .43*** .37*** .13 .0 0 .06 Perspective taking .14 -.01 .06 -.06 .01 -.02 .29** .39*** .35*** -.07 -.20* -.14+ Psychological well-being Environmental mastery -.07 -.19+ -.14+ -.26* -.15 -.20** .17 .13 .15* -.39*** -.26** -.32*** Positive relations with others -.05 -.10 -.08 -.09 -.01 -.05 .18 .31** .25** -.27* -.17+ -.21** Autonomy --- .00 --- -.23* --- .32** --- -.41*** Personal growth --- -.04 --- -.05 --- .37*** --- -.12 Purpose in life --- -.17+ --- -.11 --- .24* --- -.31** Self-acceptance --- -.28** --- -.20+ --- .25* --- -.43*** Meaning in Life Presence of meaning --- -.09 --- .03 --- .18+ --- -.20* Flourishing --- -.21* --- -.08 --- .30** --- -.30** Trait forgiveness --- -.09 --- -.04 --- .32** --- -.29** Satisfaction with life --- -.27** --- -.04 --- .19+ --- -.21* Self-esteem --- -.30** --- -.24* --- .17+ --- -.45*** Humanitarianism- egalitarianism --- .03 --- .01 --- .36*** --- .01 Trait positive affect -.09 -.12 -.11 -.03 -.06 -.05 .09 .25** .18* -.26* -.24* -.25** Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 76; Sample B N = 96; Combined N = 172 + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Appendix Q

Residualized Guilt and Shame Scores Correlated with Self-Reported Maladaptive Traits in Study 2c

PFQ "shame-free" guilt PFQ "guilt-free" shame TOSCA "shame-free" guilt TOSCA "guilt-free" shame Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Aggression Physical aggression .05 --- .01 --- -.27* --- .09 --- Verbal aggression .14 --- -.08 --- -.02 --- -.08 --- Anger .12 --- .11 --- -.14 --- .21+ --- Hostility .11 --- .26* --- -.19+ --- .33** --- Trait negative affect .24* .28** .26*** .10 .21* .16* .01 -.02 -.01 .31** .25** .28*** DSM Personality Inventories Hostility -.01 --- .17 --- -.14 --- .21+ --- Withdrawal .00 --- .13 --- -.13 --- .15 --- Emotional lability .13 --- .14 --- -.05 --- .31** --- Depressivity .24* --- .17 --- -.14 --- .42*** --- Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 76; Sample B N = 96; Combined N = 172 + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Appendix R

Residualized Shame and Guilt Scores Correlated with Informant-Reported Personality in Study 2c

PFQ "shame-free" guilt PFQ "guilt-free" shame TOSCA "shame-free" guilt TOSCA "guilt-free" shame Sample Sample Sample Sample Sample Sample Sample Sample A B Comb. A B Comb. A B Comb. A B Comb. Neuroticism .17 .19 .18* .04 .04 .04 .01 -.16 -.09 .29* .22+ .25** Extraversion -.03 -.25* -.15+ -.07 .14 .05 .12 .20+ .16+ -.18 -.11 -.14 Openness .11 .04 .07 -.08 .00 -.04 .12 .19 .16+ .15 -.01 .06 Agreeableness -.09 -.07 -.08 .14 .05 .09 .08 .37** .25** .10 .08 .09 Conscientiousness -.18 -.01 -.09 .17 -.15 -.01 .18 .18 .18* -.11 -.01 -.05 Satisfaction with life --- -.14 --- -.01 --- .07 --- -.12 Psychological well-being Autonomy --- .05 --- -.20+ --- .14 --- -.28* Environmental mastery --- -.14 --- -.09 --- .14 --- -.10 Personal growth --- -.21+ --- .08 --- .23+ --- -.03 Positive relations --- -.16 --- .06 --- .18 --- -.06 Purpose in life --- -.12 --- -.07 --- .21+ --- -.15 Self-acceptance --- -.20+ --- -.06 --- .19 --- -.26* Note. *** p < .001, ** p < .01, * p < .05, + p <.10 a Sample A N = 55; Sample B N = 68 to 70 + TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire

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Appendix S

Residualized Shame and Guilt Scores Correlated with RBQ Behavioral Factors

PFQ "shame free" PFQ "guilt free" TOSCA "shame free" TOSCA "guilt free" guilt shame guilt shame Neuroticism .02 -.05 -.02 .04 Timidness .09 -.15+ -.16* .11 Playfulness -.07 .07 .05 .08 Confidence -.08 .04 .07 -.21** Agreeableness .01 .01 -.02 .04 Note. ** p < .01, * p < .05, + p < .10; N = 144 a TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire; RBQ = Riverside Behavioral Q-Sort

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Appendix T

All RBQ Items Correlated with All Measures of Guilt and Shame PFQ guilt PFQ shame TOSCA guilt TOSCA shame 1. Expresses awareness of being on camera/in experiment -.03 -.02 .03 .10 2. Interviews his or her partner .05 .06 -.10 -.08 3. Volunteers information about self .01 .06 .00 -.13 4. Seems interested in what partner has to say .00 .01 -.08 -.07 5. Tries to control the interaction .05 .02 .02 -.05 6. Dominates the interaction -.01 .01 .04 -.05 7. Appears to be relaxed and comfortable -.14+ -.11 -.11 -.26** 8. Exhibits social skills -.04 -.01 -.04 -.15+ 9. Is reserved and unexpressive .01 -.14+ -.16* .00 10. Laughs frequently -.08 .01 .15+ .11 11. Smiles frequently -.03 .07 .15+ .09 12. Is physically animated .09 .15+ .15+ .02 13. Seems to genuinely like the partner -.04 .06 -.02 -.14+ 14. Exhibits an awkward interpersonal style .09 -.01 .02 .18* 15. Compares self to others -.03 .04 -.04 -.01 16. Shows high enthusiasm/energy level .10 .21** .17* .05 17. Shows a wide range of interests .04 .04 .00 -.04 18. Talks at rather than with partner -.02 -.06 .07 -.05 19. Expresses agreement frequently .06 .04 .03 .09 20. Expreses criticism -.12 -.10 -.01 .04 21. Is talkative .01 .07 .08 -.09 22. Expresses insecurity .05 .04 .06 .24** 23. Shows physical signs of tension/anxiety .09 .02 .02 .22** 24. Exhibits high degree of intelligence .02 -.05 -.11 -.06 25. Expresses sympthay toward partner .03 -.15+ -.09 .03 26. Initiates humor -.08 -.02 .04 .06

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27. Seeks reassurance from partner .00 .04 .03 .05 28. Exhibits condescending behavior -.05 -.09 -.11 -.07 29. Seems likable -.09 -.09 -.13 -.10 30. Seeks advice from partner -.02 -.01 .09 -.09 31. Regards self as physically attractive -.09 .01 -.15+ -.14+ 32. Acts irritated -.14+ -.18* .02 -.02 33. Expresses warmth .07 .07 .05 -.01 34. Tries to undermine, sabotage or obstruct .12 .08 .02 .03 35. Expresses hostility -.12 -.09 -.02 .03 36. Is unusual or unconventional in appearance .09 .05 -.01 .01 37. Behaves in a fearful or timid manner .12 .06 .02 .21** 38. Is expressive in face, voice or gestures .08 .16* .19* .07 39. Expresses interest in fantasy and daydreams -.11 -.08 -.04 .04 40. Expresses guilt -.15+ -.20* -.13 -.01 41. Keeps partner at a distance .01 -.08 -.07 .05 42. Shows interest in intellectual matters -.07 -.10 -.03 -.05 43. Seems to enjoy the interaction -.01 .06 .01 -.07 44. Says or does interesting things .05 .14+ .06 -.04 45. Says negative things about self .08 .06 .01 .20** 46. Displays ambition .03 .03 .01 -.12 47. Blames others -.18* -.11 -.04 -.08 48. Expresses self-pity -.04 -.07 -.02 .03 49. Expresses sexual interest .00 .04 -.04 -.03 50. Behaves in a cheerful manner .09 .18* .15+ .13 51. Gives up when faced with obstacles .01 .02 -.09 .00 52. Behaves in gender stereotyped manner .01 -.12 -.12 .01 53. Offers advice .00 -.14+ -.10 -.10 54. Speaks fluently and expresses ideas well .10 -.04 .09 .16* 55. Emphasizes accomplishments of self -.07 -.11 -.15+ -.28*** 56. Competes with partner -.07 -.10 .01 -.08 57. Speaks in a loud voice -.02 .16* .02 -.08

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58. Speaks sarcastically .00 -.01 .11 .09 59. Makes physical contact with partner -.07 -.01 .02 -.03 60. Engages in constant eye contact with partner -.08 .03 -.10 .01 61. Seems detached from the interaction .02 -.11 -.10 .02 62. Speaks quickly -.03 .00 .03 -.04 63. Acts playful -.04 .02 .09 .04 64. Partner seeks advice from subject -.08 -.09 -.12 -.17* Note. *** p < .001, ** p < .01, * p < .05, + p < .10; N = 144 a TOSCA = Test of Self-Conscious Affect; PFQ = Personal Feelings Questionnaire; RBQ = Riverside Behavioral Q-Sort

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