What is common may be as important as what is different: Examining the general factor shared by dispositional shame and guilt using bi-factor models

Abstract

Previous research on shame-proneness and guilt-proneness has tended to focus on their unique associations with other variables, eliminating the substantial shared variance with shame-proneness when examining guilt-proneness, and eliminating the shared variance with guilt-proneness when examining shame-proneness. However, this approach does not examine the variance shared by shame- and guilt-proneness. To address this issue, we conducted three studies employing bi-factor models to examine the general factor shared by the proneness to experience shame and guilt, and their relationship to various personality traits and psychopathology (i.e., externalizing and internalizing psychopathology). Our results showed that the general factor was strongly and positively associated with personality traits related to moral emotions (empathy, agreeableness, and conscientiousness), and strongly but inversely associated with both self-reported and informant-reported externalizing psychopathology. The general factor was also associated with self-consciousness, but not with self-criticism, vulnerable narcissism or neuroticism. These findings have important implications for the conceptualization of shame-proneness and guilt-proneness.

Plain Language Summary

Uncovering the Shared Core Between Shame and Guilt: Insights Into Personality Traits and Behaviors: Shame and guilt are closely related traits that play a significant role in shaping how people see themselves and interact with others. Shame typically involves feeling badly about oneself, whereas guilt focuses on specific actions that one regrets. Despite these differences, these traits overlap, sharing a common psychological foundation. This study used advanced statistical methods to explore the shared “general factor” of shame and guilt, examining how it connects to personality traits and behaviors. Our results, based on data from thousands of participants, showed that this general factor is strongly linked to personality traits associated with morality, such as empathy, agreeableness, and conscientiousness. These traits are often associated with prosocial behaviors like cooperation and responsibility. At the same time, the general factor was inversely related to antisocial tendencies, such as aggression and rule-breaking. Importantly, the shared aspect of shame and guilt appears to reflect a personality dimension that helps regulate social behavior and strengthen interpersonal bonds. Unlike some views that associate shame and guilt with negative psychological outcomes, our findings suggest their overlap might contribute positively to emotional resilience and moral behavior. By focusing on the shared characteristics of these traits, this research provides new insights into how shame and guilt influence broader personality dimensions. This perspective helps us better understand the complexities of human behavior, emphasizing the need to consider both unique and shared elements of personality traits. These findings could inform approaches to personal growth and mental health, especially for individuals whose personality traits influence their emotional experiences and social interactions. This research may also help therapists better support people who struggle with harsh self-judgment, anxiety, or withdrawal due to strong feelings of shame or guilt.

Keywords

shame-proneness guilt-proneness moral emotion self-conscious emotion psychopathology

Introduction

The predispositions to experience shame and guilt, known as shame-proneness and guilt-proneness, are relatively stable over time (Lear et al., 2022; Tangney et al., 2009). Recognized as a central force in regulating people’s thoughts, feelings, and behaviors (Fischer & Tangney, 1995), these traits are considered the affective core of important personality traits (Russel, 2003). Indeed, these traits have been linked to agreeableness (Graziano et al., 2007), conscientiousness (Fayard et al., 2012), and neuroticism (Woien et al., 2003), to name a few. Furthermore, they have also been found to predict various important outcomes, including but not limited to interpersonal adaptiveness (Tignor & Colvin, 2017), personality disorders (e.g., Schoenleber & Berenbaum, 2010), narcissism (Montebarocci et al., 2004), depression, and anxiety (Bennet et al., 2010; Cândea & Szentagotai-Tătar, 2018). From a personality science perspective, shame and guilt are fundamental to moral functioning within social contexts (Sheikh & Janoff-Bulman, 2010). A lack of these emotions may facilitate moral disengagement and exploitative behavior, while excessive levels may lead to chronic self-blame or even extreme responses such as vigilante behavior (Mazzone et al., 2019; Tangney et al., 1992). Clinically, elucidating the distinct roles of shame and guilt—particularly the specific contributions of shame—is critical for identifying individuals vulnerable to self-criticism, social withdrawal, and internalizing symptoms (de Hooge et al., 2018; Gilbert & Procter, 2006; Nelissen, 2012). This differentiation can inform more precise clinical assessments and guide targeted intervention strategies. Furthermore, understanding how these traits relate to broader patterns of externalizing and internalizing psychopathology can enhance treatment planning. Thus, achieving a more refined understanding of dispositional shame and guilt holds significant implications for both psychological theory and clinical practice.

Shame is typically considered an unpleasant emotion that arises from the awareness of flaws in one’s self, combined with a desire to hide, whereas guilt is typically considered to arise from the awareness of flaws in one’s behavior and subsequently, a desire to repair (e.g., Lewis, 1971). Although typically viewed as two distinct emotions, substantial positive correlations (around .5) are typically found between shame- and guilt-proneness (e.g., Tangney et al., 1992), suggesting significant overlap between the two. To address the overlap, researchers have routinely computed partial correlations to factor out shame-proneness from guilt-proneness, and vice versa (e.g., Tangney et al., 1992). While the approach of partial correlations has led to important discoveries about the nature of shame- and guilt-proneness, it has also created some confusion in the field. By focusing on what is unique to dispositional shame and guilt, critical information may be missed regarding that which is shared by the two. Additionally, different conclusions have been drawn regarding the propensity to experience shame- and guilt-proneness and their relationships with various outcomes because some researchers draw conclusions based on analyses that partial out their overlap (e.g., Tangney et al., 1992), whereas other researchers do not (e.g., Klasen et al., 2015; Haag et al., 2015). Indeed, Tignor and Colvin (2017) found, through meta-analytic analyses, the use of partial versus zero-order correlations constitutes one of the main methodological moderators when investigating the relationship between dispositional shame/guilt and pro-social orientations.

Modeling the general factor using bi-factor models

Examining the general factor shared by shame-proneness and guilt-proneness using a bi-factor model is particularly advantageous because it allows us to specify the common variance of shame- and guilt-proneness, and examine its relation with personality and psychopathology. This approach has increasingly been used in recent research (e.g., Mu et al., 2016), given its advantages over an individual score approach or second-order models (Chen et al., 2012; Choi et al., 2025). We are particularly interested in the common variance shared by shame- and guilt-proneness. It is important to note that the other approaches that researchers have used, such as partial correlations or hierarchical regressions or summing across shame- and guilt-proneness, are capable of examining: (a) what is unique to shame-proneness; (b) what is unique to guilt-proneness; or (c) the sum of what is unique to shame-proneness, what is unique to guilt-proneness, and what is shared by shame- and guilt-proneness (as seen in Figure 1). However, none of these approaches is capable of specifically examining only what is shared by shame- and guilt-proneness. By contrast, employing a bi-factor model allows us to partition the variance into the shared variance between shame- and guilt-proneness, the remaining variance specific to shame-proneness, and that specific to guilt-proneness. Given that the primary goal of the current research is to model and test associations with common and specific sources of variance of shame- and guilt-proneness, we believe the bi-factor approach is appropriate and advantageous compared to the traditional approaches.

Before interpreting the meaning of general and specific factors derived from bi-factor models, it is critical to evaluate their statistical robustness. Only when a factor is sufficiently well-defined—in terms of both its explanatory power (e.g., proportion of explained variance) and replicability across models and samples—can we confidently draw theoretical conclusions or examine its associations with external variables. This concern is particularly salient for specific factors that may account for only a small amount of residual variance once the general factor is extracted. Indeed, as emphasized by Zhang et al. (2021), factors lacking sufficient reliability or stability may fail to represent coherent psychological constructs, rendering their associations with external criteria potentially misleading or uninterpretable. Therefore, in the current study, we assessed the robustness of both the general and specific factors using multiple indicators, including factor congruence coefficients and omega-based reliability estimates (ω, ωH, and ωHS). These indicators provide an empirical foundation to interpret factor meaning and determine whether each dimension is psychometrically robust enough for predictive or theoretical analyses.

The meaning of the general factor

The first goal of the current research was to explore the meaning and associations of the general factor with important personality traits by testing two hypotheses: (a) the Moral Emotion hypothesis and (b) the Self-Conscious Emotion hypothesis. These are not in competition and both could be supported by the data.

The Moral Emotion hypothesis posits that shame-proneness and guilt-proneness are moral emotions that promote prosocial behaviors (Haidt, 2003). Guilt-proneness typically occurs when individuals perceive their actions to have violated moral rules or standards, causing harm or distress to others, which drives individuals to undertake reparative actions, thereby reinforcing moral norms. The idea that shame-proneness operates as a moral emotion to promote prosocial behavior is not new (e.g., Benedict, 1946; Tomkins, 1963). Shame-proneness, in general, is considered to be a painful response to perceived failure of living up to certain standards, norms, or ideas and thus triggering imagined or real negative evaluation of the self in the eyes of others (Lewis, 1971). It often drives avoidance and concealment behaviors, aimed at minimizing such negative evaluation and potential social rejection. Given people’s strong need to belong to groups (Baumeister & Leary, 1995), shame-proneness, as it signals a threat to the social bond (Scheff, 2000), can be very painful. However, it is this acute pain that prompts self-reflection, leading individuals to examine the disparity between their behavior and behavioral and moral standards. Consequently, shame-proneness inhibits immoral behaviors and helps people navigate the complexities of fitting into groups without triggering the contempt, anger, and disgust of others (Haidt, 2003)—in this way, shame-proneness fosters moral self-regulation. Therefore, although shame and guilt differ in their emotional experiences and behavioral tendencies, both play crucial roles in moral development and the adjustment of social behavior.

As shame and guilt are considered prototypes of moral emotions, it is expected that shame- and guilt-proneness should be highly associated with empathic responses, given that empathy has long been considered to be a central aspect of morality (Maibom, 2014). Additionally, the general factor is expected to be associated with agreeableness and conscientiousness, as these traits have been found to be relevant to moral processes (Walker, 1999). For example, it was found that prosocial motivation was the affective foundation for agreeableness (Graziano et al., 2007), and guilt-proneness was the affective core of conscientiousness (Fayard et al., 2012).

The Self-Conscious Emotion hypothesis posits that shame-proneness and guilt-proneness are self-conscious emotions that require self-awareness, self-reflection, and self-evaluation. Lewis (1995) maintained that the critical feature of self-conscious emotions is the ability to reflect upon oneself. Indeed, he wrote, “To feel them (self-conscious emotions), individuals must have a sense of self (as well as a set of standards)” (p. 68). Tracy and Robins (2004) proposed that both shame- and guilt-proneness are elicited by a common set of cognitive processes, involving appraising an event as relevant to and incongruent with one’s identity goals. Seen from this light, it is reasonable to expect that the general factor is associated with a predisposition to self-criticism, as well as vulnerable narcissism (a central feature of which is an inadequate view of the self; e.g., Miller et al., 2011). Given the negative valence of shame- and guilt-proneness, it is reasonable to expect the general factor to be positively associated with neuroticism and inversely related to extraversion. Indeed, both shame- and guilt-proneness have been found to be positively correlated with fear, hostility, anxiety, and sadness (Watson & Clark, 1992).

The general factor and psychopathology

Shame- and guilt-proneness are not only central to individuals’ moral functioning but also shape their psychological adjustment and interpersonal behavior in profound ways. These emotional dispositions are associated with a range of disorders, such as depression (Kim et al., 2011), generalized anxiety disorder (GAD; Fergus et al., 2010), social anxiety disorder (SAD; Hedman et al., 2013), and psychopathy (Lyons, 2015). Research suggests that a two-liability structure (externalizing and internalizing disorders) can explain comorbidity between mental disorders (Kotov et al., 2021) and mediate the association between risk factors and multiple disorders (Krueger et al., 2018). Theoretically, the high correlation between shame- and guilt-proneness has long posed a challenge for researchers seeking to understand their distinct roles in moral psychology (Dempsey, 2017). Untangling their shared variance from their unique contributions is critical not only for construct validity but also for clarifying how individuals morally engage with the social world. The present research also aims to clarify the relations between the general factor of shame- and guilt-proneness and higher-order factors of psychopathology (internalizing and externalizing). To our knowledge, the current research is the first to examine the association between shame-proneness, guilt-proneness and higher order psychopathology factors. Our predictions are closely tied to our hypotheses regarding the meaning of the general factor. If the general factor turns out to capture the variance representing the moral aspect of shame- and guilt-proneness, we would then expect the general factor to be negatively associated with externalizing psychopathology, given that empathy and the tendency to behave prosocially have long been considered strong protective factors for externalizing psychopathology (Miller & Eisenberg, 1988). By contrast, if the general factor turns out to represent the negative self-evaluative process underlying shame- and guilt-proneness, we would expect the general factor to be positively associated with internalizing psychopathology, given the long-established link between self-criticism and internalizing psychopathology (Klein et al., 2011). From a clinical perspective, distinguishing individuals with maladaptive shame responses is crucial for tailoring effective interventions, particularly those focused on emotion regulation and self-related cognitions (Krishnamoorthy et al., 2021). Yet, in practice, the conflation of shame and guilt—and the lack of clarity regarding their shared versus unique components—often limits accurate identification and treatment planning (Fine et al., 2023).

Research overview

To our knowledge, this is the first examination of the shared aspect of shame- and guilt-proneness and its personality and clinical correlates. Previous research on this topic has important limitations, including the routine use of partial correlations which misses the common variance shared by the two, treating psychological disorders as distinct entities ignoring the transdiagnostic role of shame- and guilt-proneness, and a lack of replication across independent samples. To address these limitations, we used a bi-factor model to examine the general factor of shame- and guilt-proneness (Study 1, n = 4316). We examined the fit of the bi-factor model, the meanings of the different factors, and the personality and clinical correlates of the factors (Studies 2 and 3, n = 457 & 474). We tested whether the general factor would be associated with: (a) empathy, agreeableness, conscientiousness, and externalizing psychopathology (inversely), as predicted by the Moral Emotion hypothesis; and with (b) neuroticism, extraversion (inversely), private self-consciousness, self-criticism, vulnerable narcissism, and internalizing psychopathology, as predicted by the Self-Conscious Emotion hypothesis¹.

Study 1

Study 1 constructed and compared two bi-factor models to find the best way to specify explicitly the general factor shared by shame-proneness and guilt-proneness.

Method

Data

To test the structure in a large sample, we used all data available in our lab that had used the Test of Self-Conscious Affect-3 (TOSCA-3) and also requested data from Dr Taya Cohen, who had developed the Guilt and Shame Proneness Scale (GASP; Cohen et al., 2011). The overall sample included a total of 4316 participants for GASP, and 1069 participants for TOSCA-3 (58.9% female). A more detailed description of the samples is presented in Table S2 in the supplemental.

Measures

GASP

We used Cohen et al.’s 16-item GASP (Cohen et al., 2011) to measure dispositional shame-proneness and guilt-proneness. The scale has been shown to have good reliability and validity. The GASP consists of four subscales, two measuring aspects of shame-proneness (negative-self-evaluation, withdraw), and two measuring aspects of guilt-proneness (negative-behavior-evaluation, repair). Participants were instructed to imagine themselves in a variety of situations that people could encounter in day-to-day life and indicate the likelihood that they would react in the way described (1 = very unlikely, 7 = very likely). Cronbach’s α for the GASP subscales across the three studies are displayed in Table S1 in the supplemental. Alpha coefficients are lower for scenario-based measures because each item contains both the variance for the psychological construct as well as the variance introduced by the scenarios (Cohen et al., 2011). Sample items for instruments used in all three studies are presented in Table S8 in the supplemental.

TOSCA-3

To increase the reliability and replicability of the present study, we included a second measure of dispositional shame- and guilt-proneness, the TOSCA-3 (Tangney et al., 2000), a measure that has been found to have good reliability and construct validity. The TOSCA-3 presents participants with 16 brief scenarios. After each scenario, the participant is provided with a statement intended to reflect a shame response and a guilt response. Participants were asked to rate the degree to which they would experience the provided response on a scale from 1 to 5. Like the GASP, the TOSCA-3 also measures shame-proneness via negative self-evaluation and withdrawal, and measures guilt-proneness via negative behavior-evaluation and repair. Therefore, although the TOSCA-3 was originally designed to have two subscales (i.e., shame- and guilt-proneness), it can also be divided into four subscales corresponding to those of the GASP: negative self-evaluation (10 items), withdrawal (6 items), negative behavior-evaluation (8 items), and repair (8 items). Internal consistencies for the TOSCA-3 subscales across the three studies are displayed in Table S1 in the supplemental.

Statistical analyses

We first constructed and compared two models to specify the general factor. Model 1 was a bi-factor model with one latent general factor and two specific factors corresponding to shame- and guilt-proneness (Figure 1). Model 2 included a general factor and four specific factors (Figure S3 in the supplemental), corresponding to the appraisals and action tendencies of shame-proneness (i.e., negative-self-evaluation and withdrawal) and guilt-proneness (i.e., negative-behavior-evaluation and repair), respectively. For both Models 1 and 2, all factors were set to be orthogonal to each other, and each item loaded onto both the latent general factor and the specific factors that it belongs to.

We used Item Response Theory (IRT; Reise et al., 1993) to examine the measurement structure of our focal constructs. Analyses were carried out in the R package MIRT (Chalmers, 2012). When dealing with Likert-type data, IRT models do not impose strict assumptions (e.g., normal distribution, linear relationship between latent and observed variables; Wirth & Edwards, 2007), and therefore are preferred to fit Likert-type categorical data. Specifically, we adopted the Graded Response Model (GRM) designed for ordinal data (Samejima, 1969). Full information maximum likelihood estimation (FIML) was used to deal with missing data (information regarding missing data can be found in Table S9 in the supplemental). M2 Chi-square (M2χ²) was calculated to evaluate the fitness of IRT models due to its superior performance over Pearson Chi-square (Cai & Hansen, 2013). M2χ²-based Root Mean Square Error of Approximation (RMSEA) and Comparative Fit Index (CFI) were also reported. Conventional guidelines (Hooper et al., 2008; Hu & Bentler, 1999) suggest that RMSEA values of .08 or lower is acceptable and .05 or lower indicates excellent fit; CFI of .90 or higher indicate adequate fit and .95 or higher indicate excellent fit.

Results and discussion

Fit indices of the models and factor loadings are presented in Table 1. The means and standard deviations of the measures are displayed in Table S3 in the supplemental.

Table 1.

Study 1: Factor Loadings for Bi-Factor Models of Shame-Specific and Guilt-Specific.

Model 1				Model 2
Item	General factor	Shame-specific	Guilt-specific	General factor	Shame-NSE	Shame-Withdraw	Guilt-NBE	Guilt-Repair
GASP
GASP3-ShameNSE: You rip an article out of a journal in the library and take it with you. Your teacher discovers what you did and tells the librarian and your entire class. What is the likelihood that this would make you would feel like a bad person?	0.71	0.24		0.64	0.38
GASP6-ShameNSE: You give a bad presentation at work. Afterward your boss tells your coworkers it was your fault that your company lost the contract. What is the likelihood that you would feel incompetent?	0.53	0.27		0.41	0.61
GASP10-Shame-NSE: You successfully exaggerate your damages in a lawsuit. Months later, your lies are discovered and you are charged with perjury. What is the likelihood that you would think you are a despicable human being?	0.72	0.21		0.66	0.29
GASP13-ShameNSE: You make a mistake at work and find out a coworker is blamed for the error. Later, your coworker confronts you about your mistake. What is the likelihood that you would feel like a coward?	0.62	0.15		0.56	0.32
GASP4-ShameWithdraw: After making a big mistake on an important project at work in which people were depending on you, your boss criticizes you in front of your coworkers. What is the likelihood that you would feign sickness and leave work?	−0.25	0.73		−0.19		0.72
GASP7-ShameWithdraw: A friend tells you that you boast a great deal. What is the likelihood that you would stop spending time with that friend?	−0.14	0.55		−0.09		0.56
GASP8-ShameWithdraw: Your home is very messy and unexpected guests knock on your door and invite themselves in. What is the likelihood that you would avoid the guests until they leave?	−0.21	0.57		−0.16		0.59
GASP12-ShameWithdraw: You take office supplies home for personal use and are caught by your boss. What is the likelihood that this would lead you to quit your job?	−0.01	0.59		0.06		0.61
GASP1-GuiltNBE: After realizing you have received too much change at a store, you decide to keep it because the salesclerk doesn’t notice. What is the likelihood that you would feel uncomfortable about keeping the money?	0.55		0.24	0.62			0.33
GASP9-GuiltNBE: You secretly commit a felony. What is the likelihood that you would feel remorse about breaking the law?	0.75		0.20	0.79			0.17
GASP14-GuiltNBE: At a coworker’ s housewarming party, you spill red wine on their new cream-colored carpet. You cover the stain with a chair so that nobody notices your mess. What is the likelihood that you would feel that the way you acted Was pathetic?	0.74		0.17	0.77			−0.02
GASP16-GuiltNBE: You lie to people but they never find out about it. What is the likelihood that you would feel terrible about the lies you told?	0.65		0.36	0.75			0.22
GASP2-GuiltRepair: You are privately informed that you are the only one in your group that did not make the honor society because you skipped too many days of school. What is the likelihood that this would lead you to become more responsible about attending school?	0.54		0.32	0.58				0.29
GASP5-GuiltRepair: You reveal a friend’s secret, though your friend never finds out. What is the likelihood that your failure to keep the secret would lead you to exert extra effort to keep secrets in the future?	0.54		0.49	0.64				0.26
GASP11-GuiltRepair: You strongly defend a point of view in a discussion, and though nobody was aware of it, you realize that you were wrong. What is the likelihood that this would make you think more carefully before you speak?	0.55		0.45	0.60				0.49
GASP15-GuiltRepair: While discussing a heated subject with friends, you suddenly realize you are shouting though nobody seems to notice. What is the likelihood that you would try to act more considerately toward your friends?	0.56		0.43	0.61				0.40
Model fit	M²χ² = 24.16, df = 8, RMSEA = 0.02, CFI = 0.99			M²χ² = 43.36, df = 8, RMSEA = 0.03, CFI = 0.99
TOSCA
TOSCA1-ShameNSE: You make plans to meet a friend for lunch. At 5 o’clock‚ you realize you stood him up. You would think: “I’m inconsiderate.”	0.26	0.30		0.30	0.33
TOSCA4-ShameNSE: At work‚ you wait until the last minute to plan a project‚ and it turns out badly. You would feel incompetent.	0.31	0.55		0.36	0.56
TOSCA6-ShameNSE: For several days you put off making a difficult phone call. At the last minute you make the call and are able to manipulate the conversation so that all goes well. You would feel like a coward.	0.34	0.33		0.29	0.32
TOSCA7-ShameNSE: While playing around‚ you throw a ball and it hits your friend in the face. You would feel inadequate that you can’t even throw a ball.	0.15	0.52		0.17	0.53
TOSCA8-ShameNSE: You have recently moved away from your family‚ and everyone has been very helpful. A few times you have needed to borrow money‚ but you paid it back as soon as you could. You would feel immature.	0.11	0.47		0.11	0.50
TOSCA9-ShameNSE: You are driving down the road‚ and hit a small animal. You would think: “I’m terrible.”	0.27	0.45		0.34	0.41
TOSCA10-ShameNSE: You walk out of an exam thinking you did extremely well. Then you find out you did poorly. You would feel stupid.	0.15	0.60		0.22	0.59
TOSCA12-ShameNSE: While out with a group of friends‚ you make fun of a friend who’s not there. You would feel small … like a “rat.”	0.52	0.27		0.51	0.19
TOSCA14-ShameNSE: You volunteer to help with the local Special Olympics for handicapped children. It turns out to be frustrating and time-consuming work. You think seriously about quitting‚ but then you see how happy the kids are. You would feel selfish and you’d think you are basically lazy.	0.24	0.56		0.33	0.50
TOSCA15-ShameNSE: You are taking care of your friend’s dog while they are on vacation and the dog runs away. You would think: “I am irresponsible and incompetent.”	0.15	0.64		0.25	0.63
TOSCA2-ShameWithdraw: You break something at work and then hide it. You would think about quitting.	0.03	0.39		0.05		0.37
TOSCA3-ShameWithdraw: You are out with friends one evening and you’re feeling especially witty and attractive. Your best friend’s spouse seems to particularly enjoy your company. You would probably avoid eye-contact for a long time.	0.19	0.35		0.26		0.32
TOSCA5-ShameWithdraw: You make a mistake at work and find out a co-worker is blamed for the error. You would keep quiet and avoid the co-worker.	−0.58	0.44		−0.39		0.51
TOSCA11-ShameWithdraw: You and a group of co-workers worked very hard on a project. Your boss singles you out for a bonus because the project was such a success. You would feel alone and apart from your colleagues.	0.29	0.31		0.31		0.27
TOSCA13-ShameWithdraw: You make a big mistake on an important project at work. People were depending on you‚ and your boss criticizes you. You would feel like you wanted to hide.	0.24	0.61		0.40		0.63
TOSCA16-ShameWithdraw: You attend your co-worker’s housewarming party‚ and you spill red wine on their new cream-colored carpet‚ but you think no one notices. You would wish you were anywhere but at the party.	0.02	0.53		0.16		0.61
TOSCA3-GuiltNBE: You are out with friends one evening and you’re feeling especially witty and attractive. Your best friend’s spouse seems to particularly enjoy your company. You would think: “I should have been aware of what my best friend is feeling.”	0.38		0.18	0.43			0.08
TOSCA4-GuiltNBE: At work‚ you wait until the last minute to plan a project‚ and it turns out badly. You would feel: “I deserve to be reprimanded for mismanaging the project.”	0.33		0.11	0.36			0.07
TOSCA6-GuiltNBE: For several days you put off making a difficult phone call. At the last minute you make the call and are able to manipulate the conversation so that all goes well. You would regret that you put it off.	0.32		−0.09	0.28			−0.33
TOSCA9-GuiltNBE: You are driving down the road‚ and hit a small animal. You’d feel bad you hadn’t been more alert driving down the road.	0.35		0.31	0.47			0.14
TOSCA10-GuiltNBE: You walk out of an exam thinking you did extremely well. Then you find out you did poorly. You would think: “I should have studied harder.”	0.30		0.34	0.43			0.09
TOSCA11-GuiltNBE: You and a group of co-workers worked very hard on a project. Your boss singles you out for a bonus because the project was such a success. You would feel you should not accept it.	0.45		−0.01	0.43			−0.32
TOSCA13-GuiltNBE: You make a big mistake on an important project at work. People were depending on you‚ and your boss criticizes you. You would think: “I should have recognized the problem and done a better job.”	0.38		0.47	0.53			0.30
TOSCA14-GuiltNBE: You volunteer to help with the local Special Olympics for handicapped children. It turns out to be frustrating and time-consuming work. You think seriously about quitting‚ but then you see how happy the kids are. You would think: “I should be more concerned about people who are less fortunate.”	0.38		0.37	0.52			0.17
TOSCA1-GuiltRepair: You make plans to meet a friend for lunch. At 5 o’clock‚ you realize you stood him up. You think you should make it up to him as soon as possible.	0.32		0.52	0.43				0.51
TOSCA2-GuiltRepair: You break something at work and then hide it. You would think: “This is making me anxious I need to either fix it or get someone else to.”	0.46		0.22	0.53				0.06
TOSCA5-GuiltRepair: You make a mistake at work and find out a co-worker is blamed for the error. You would feel unhappy and eager to correct the situation.	0.68		0.09	0.57				0.05
TOSCA7-GuiltRepair: While playing around‚ you throw a ball and it hits your friend in the face. You would apologize and make sure your friend feels better.	0.36		0.66	0.54				0.56
TOSCA8-GuiltRepair: You have recently moved away from your family‚ and everyone has been very helpful. A few times you have needed to borrow money‚ but you paid it back as soon as you could. You would return the favor as quickly as you could.	0.27		0.55	0.40				0.49
TOSCA12-GuiltRepair: While out with a group of friends‚ you make fun of a friend who’s not there. You would apologize and talk about that person’s good points.	0.57		0.10	0.56				−0.04
TOSCA15-GuiltRepair: You are taking care of your friend’s dog while they are on vacation and the dog runs away. You would vow to be more careful next time.	0.28		0.64	0.48				0.41
TOSCA16-GuiltRepair: You attend your co-worker’s housewarming party‚ and you spill red wine on their new cream-colored carpet‚ but you think no one notices. You would stay late to help clean up the stain after the party.	0.45		0.31	0.47				0.24
Model Fit	M²χ² = 1129.21, df = 334, RMSEA = 0.05, CFI = 0.82			M²χ² = 1247.35, df = 334, RMSEA = 0.06, CFI = 0.79

Note. GASP = Guilt and Shame Proneness Scale; TOSCA = Test of Self-Conscious Affect-3; NSE = negative-self-evaluation; NBE = negative-behavior-evaluation.

GASP

Both Model 1 and Model 2 showed an excellent fit to the data. For both models, the items of three subscales (i.e., negative-self-evaluation, negative-behavior-evaluation, and repair) loaded well onto both the general factor and the specific factors. Notably, the withdrawal subscale did not load high onto the general factor with loadings, but loaded strongly onto the specific factor that they belong to.

TOSCA-3

As seen in Table 1, Model 1 and Model 2 revealed a similar fit to the data. It should be noted that the fit indices suggest that the model fit was mediocre for CFI but good for RMSEA. Although very few studies have examined the factor structure of the TOSCA-3, that the theoretically driven structure of the TOSCA-3 does not show an ideal fit to the data is consistent with past research (Strömsten et al., 2009). Moreover, we focused on the comparative fit of different models for the TOSCA-3 data, rather than maximizing absolute fit of each model.

As with the GASP, for both models, the items of three subscales (i.e., self-devaluation, behavior-devaluation, repair) loaded reasonably well onto both the general factor and the specific factors. Again, the withdrawal subscale did not load well onto the general factor, but loaded strongly onto the specific factor that they belong to. It should be noted that a few TOSCA negative self-evaluation (NSE) items did not load highly on the general factor. Upon closer scrutiny, these lower-loading items share the common feature that they do not involve a clear, notable social transgression. For example, the lowest loaded NSE item is TOSCA 8 “You have recently moved away from your family, and everyone has been very helpful. A few times you have needed to borrow money, but you paid it back as soon as you could” and the response is “You would feel immature.” By contrast, the highest loaded NSE item is TOSCA 12 “While out with a group of friends, you make fun of a friend who’s not there” and the response is “You would feel small like a rat.”

Congruence coefficients and factor omegas

We further calculated the congruence coefficients across both models (2-factor vs. 4-factor), as well as across samples (Study 1 vs. Study 2 vs. Study 3), for both GASP and TOSCA. The results are presented in Tables S5 and S6 in the supplemental.² Congruence coefficients ranging from .85 to .94 indicate fair similarity, whereas those higher than .95 imply that the two factors or components compared can be considered equal (Lorenzo-Seva & Ten Berge, 2006). The congruence coefficients for the general factor range from .93 to 1.00 across measures, samples and models, suggesting that the general factor is highly stable and very robust. The congruence coefficients for the shame-specific factor are also high across samples, suggesting high stability and replicability. The guilt-specific factor demonstrates variable stability, perhaps due to measurement challenges, such as item phrasing or differential sensitivity to behavioral nuances across samples.

To evaluate the magnitude of the variance explained by the general and the specific factors, we calculated the coefficient omega (ω), coefficient omega hierarchical (ωH), and coefficient omega hierarchical subscale (ωHS) indices for the 2- and 4-factor models. The results are presented in Table S7 in the supplemental. Omega (ω) estimates the proportion of variance in the observed total score attributable to all “modeled” sources of common variance. When Omega exceeds 0.7, the scale score can be considered reliable (Bentler, 2009). Coefficient omega hierarchical (ωH) estimates the proportion of variance in the total score attributable to the general factor, treating variability due to group factors as measurement error. Coefficient omega hierarchical subscale (ωHS) indicates the proportion of variance in the subscale total score attributable to specific factors after removing the influence of the general factor. As this index only reflects the explanatory power of specific factors, it tends to have smaller values. There is no specific cut-off for this value, but higher values suggest that the specific factor is more meaningful (Rodriguez et al., 2016).

As presented in Table S7 in the supplemental, the ω, ωH, and ωHS values were consistent across three studies. For the 2-factor model, across GASP and TOSCA, the ω for the total score ranged from .84 to .94, and the ωH for the general factor ranged from .45 to .72, indicating meaningful variances being explained by the general factor. For the shame-specific factor, the ω values ranged from .70 to .89, with ωHS values ranging from .40 to .69, indicating high reliability and significance of the shame-specific factor. In contrast, the ω for the guilt-specific factor ranged from .79 to .89, with ωHS values ranging from .01 to .21, suggesting very little variance is left for the guilt-specific factor when the general factor is controlled for. The pattern is quite similar for the 4-factor model, including high ωH for the general factor, high ωHS for Shame-NSE, Shame-withdraw, as well as Guilt-repair. The ωHS coefficients for Guilt-NBE is very low (ranging from .00 to .09), suggesting that this factor captures little meaningful variance when the general factor is controlled for.

Compared to the 4-factor model, the 2-factor model demonstrated comparable and excellent model fits, congruence coefficients and omega values, indicating similarly high reliability and structural integrity. While the model fits were similar, the two-factor model (Model 1) was preferred for its parsimony and conceptual clarity in capturing the shared and unique aspects of shame- and guilt-proneness. Although our analyses indicate the general factor is robust and reliable, the results of Study 1 are insufficient to determine the meaning of the different factors. We therefore proceeded in Study 2 to begin testing: (a) the Moral Emotion hypothesis that the general factor would be positively associated with empathy, agreeableness and conscientiousness, and negatively associated with externalizing psychopathology; and (b) the Self-Conscious Emotion hypothesis that the general factor would be positively associated with neuroticism and negatively associated with extraversion, and positively associated with internalizing psychopathology.

Study 2

Participants

Participants were recruited through Amazon Mechanical Turk in 2016. Only USA-based Mechanical Turk user accounts were eligible to respond to the advertisement. To ensure data quality, we included seven attention check questions to ensure that participants were paying attention to the questionnaires, and limited participation to those who had an IP address that did not match that of any other participant. A total of 28 (5.9%) participants were excluded from the analyses because they failed on at least one of the attention check questions. This resulted in a total of 457 participants (72.2% female), aged between 18 to 98, (M = 40.6, SD = 14.1); 78.1% European American, 10.7% African American, and 5.5% Asian/Asian American, 3.7% multiracial, and 1.7% other. This sample size was appropriate for the analysis described below (Jiang et al., 2016).

Measures

Table S4 in the supplemental presents the reliability coefficients for all measurement tools employed in Studies 2 and 3, except GASP and TOSCA (information for which is presented in Table S1).

Guilt and shame proneness

Like Study 1, guilt and shame proneness was measured using the GASP and TOSCA.

Empathy

Empathy was measured using the seven-item (α = .89) Empathic Concern Subscale from the Interpersonal Reactivity Index (Davis, 1983), which has been shown to have good reliability and construct validity. The empathy subscale assesses “other-oriented” feelings of sympathy and concern for unfortunate others. Participants rated each item on a five-point scale (1 = does not describe me well; 5 = describes me very well).

Big-five personality traits

The Big-Five personality traits were measured using the BFI-44 self-report personality inventory (John & Srivastava, 1999). It has been shown to have good reliability and construct validity (John & Srivastava, 1999). Participants rated each item on a five-point scale (1 = strongly disagree, 5 = strongly agree). The internal consistencies were .84, .85, .82, .82, and .90 for the openness, conscientiousness, extraversion, agreeableness, and neuroticism scales, respectively.

Externalizing psychopathology

Externalizing psychopathology was measured using 57 items from the Externalizing Spectrum Inventory—Brief Form (ESI-bf; Patrick et al., 2013). These items form the Item-based Factor Scales, developed as an efficient measurement of the ESI’s three higher-order factors (Patrick et al., 2013): the general disinhibition subfactor (ESI_GDIS), 20 items, α = .87; the callous-aggression subfactor (ESI_AGG), 18 items, α = .88; the substance abuse subfactor (ESI_SUB), 17 items, α = .92. Items were rated on a four-point scale (T = True, t = somewhat true, f = somewhat false, F = False).

Depression

Depression was measured with seven items (α = .92) from the Anhedonic Depression subscale from Mood and Anxiety Symptoms Questionnaire (MASQ-AD; Watson & Clark, 1991; Watson et al., 1995). The MASQ-AD subscale assesses aspects unique to depression (i.e., anhedonia) rather than those shared with anxiety (e.g., high negative affect; Clark & Watson, 1991). The selection of items was informed by previous work in which the eight-item version of Anhedonic Depression subscale was shown to better predict current depressive episodes than the full version (Bredemeier et al., 2010). Due to IRB-related issues, the suicidality item was not included. Items were rated using a five-point scale (1 = not at all; 5 = extremely).

Social Anxiety

Social anxiety was measured with the eight-item (α = .96) version of the Brief Fear of Negative Evaluation Scale (BFNE-S; Weeks et al., 2005). It has been shown to measure social anxiety as validly as the full version of the BFNE. Items were rated using a five-point scale (1 = Not at all characteristic of me; 5 = Extremely characteristic of me).

Worry

Worry was measured using the eight-item (α = .97) Penn State Worry Questionnaire—Abbreviated (PSWQ-A; Hopko et al., 2003), which has been found to have good reliability and construct validity in both older adults and younger adults (Crittendon & Hopko, 2006). Participants rated each item on a five-point scale (1 = Not at all typical of me; 5 = Very typical of me).

Panic

Panic was measured using the eight-item (α = .88) panic subscale from the Inventory of Depression and Anxiety Symptoms (IDAS; Watson et al., 2007). The IDAS has demonstrated strong reliability, construct validity as well as substantial criterion validity in relation to DSM-IV anxiety disorder diagnoses (Watson et al., 2008). Participants rated each item on a five-point scale (1 = Not at all; 5 = Extremely).

Statistical analyses

Three structural equation models were constructed to examine the relationships between the general factor and empathy (Model 3, Figure S2 in the supplemental), the Big-Five personality traits (Model 4, Figure 2), and internalizing and externalizing psychopathology (Model 5, Figure 2). As seen in Figures 2 and 3, in each of these models, the shame-proneness and guilt-proneness part of the model was constructed following the basic format of Model 1 from Study 1, that is, one general factor and two specific factors (shame- and guilt-proneness). In Model 4 (big-five personality), each of big-five personality factor was set to correlate with the others. In Model 5 (Psychopathology), the internalizing liability factor was indexed by depression, worry, social phobia and panic disorder (Krueger & Markon, 2006), and the externalizing liability factor was indexed by general disinhibition, callous aggression, and substance abuse (Krueger et al., 2007). We used parcels as indicators for outcome variables, as parcels have been shown to produce more reliable latent variables than individual items (Little et al., 2002). It is important to note that for each of these models, the general factor and specific shame- and guilt-proneness factors were set to predict outcomes simultaneously. These analyses were conducted using R with the lavaan package (Rosseel, 2012).

We also analyzed the data using both the traditional partial correlation approach and the raw bivariate zero-order correlation approach for the purpose of comparison with the bi-factor model approach. The same approach was also utilized in Study 3.

Results and discussion

The fit indices and standardized regression coefficients are presented in Table 2 for Model 3 (Empathy), Model 4 (Big-Five Personality), and Model 5 (Psychopathology).

Table 2.

Study 2: Coefficients Between GASP and TOSCA and Psychopathology and Personality Factors Using Bi-Factor Model Versus Traditional Methods.

Outcomes	Bi-factor Standardized Regression Coefficients				Partial Correlations		Bi-variate Correlations
Outcomes	General factor	Shame-specific	Guilt-specific	Model Fit	Shame-proneness	Guilt-proneness	Shame-proneness	Guilt-proneness	Shame and Guilt Total
GASP
Model 3: Empathy
Empathy	0.58**	−0.01	0.05	χ2 = 297.28, df = 133, RMSEA = 0.05, CFI = 0.93	0.08	0.19**	0.14**	0.20**	0.21**
Model 4: Big Five Personality
Extraversion	−0.01	−0.22**	−0.11	χ2 = 940.68, df = 393, RMSEA = 0.06, CFI = 0.90	−0.18**	0.08	−0.17**	0.03	−0.01
Agreeableness	0.43**	−0.13*	−0.15		−0.09	0.40**	0.04	0.37**	0.27**
Conscientiousness	0.36**	−0.32**	−0.16		−0.20**	0.34**	−0.10*	0.30**	0.15**
Neuroticism	−0.03	0.39**	0.07		0.28**	−0.12*	0.25**	−0.04	0.10*
Openness	0.18*	−0.18*	0.12		−0.14**	0.19**	−0.08	0.17**	0.07
Model 5: Psychopathology
Internalizing	−0.09	0.47**	0.05	χ2 = 1277.71, df = 599, RMSEA = 0.05, CFI = 0.94	0.33**	−0.14**	0.29**	−0.07	0.11*
Externalizing	−0.63**	0.13*	0.12	χ2 = 1277.71, df = 599, RMSEA = 0.05, CFI = 0.94	0.17**	−0.24**	0.09*	−0.23**	−0.11*
TOSCA
Model 3: Empathy
Empathy	0.64**	−0.11	−0.01	χ2 = 1528.17, df = 525, RMSEA = 0.07, CFI = 0.81	0.10*	0.17**	0.17**	0.20**	0.22**
Model 4: Big Five Personality
Extraversion	0.08	−0.45**	−0.13*	χ2 = 2415.73, df = 977, RMSEA = 0.06, CFI = 0.83	−0.32**	0.19**	−0.27**	0.06	−0.15**
Agreeableness	0.54**	−0.36**	−0.12		−0.19**	0.39**	−0.01	0.34**	0.17**
Conscientiousness	0.28**	−0.36**	0.11		−0.28**	0.34**	−0.15**	0.25**	0.04
Neuroticism	−0.05	0.57**	0.15*		0.41**	−0.19**	0.36**	−0.01	0.23**
Openness	0.24**	−0.23**	0.17*		−0.23**	0.30**	−0.11*	0.23**	0.06
Model 5: Psychopathology
Internalizing	−0.07	0.69**	0.11	χ2 = 2932.60, df = 1279, RMSEA = 0.05, CFI = 0.88	0.50**	−0.24**	0.44**	−0.02	0.28**
Externalizing	−0.45**	0.30**	−0.09	χ2 = 2932.60, df = 1279, RMSEA = 0.05, CFI = 0.88	0.34**	−0.25**	0.26**	−0.13**	0.10*

Note. GASP = Guilt and Shame Proneness Scale; TOSCA = Test of Self-Conscious Affect-3. *p < .05. **p < .01.

Personality

As in Study 1, fit indices suggested that the models fit was acceptable when the GASP was used (CFI = .90, RMSEA = .06). For the TOSCA-3, the model fit was mediocre for CFI (CFI = .83) but good for RMSEA (RMSEA = .06).

Consistent with our hypothesis, for both the GASP and the TOSCA-3, the general factor strongly and positively predicted empathy, (p’s < .001). For big five personality factors, a consistent pattern of results emerged as well across the GASP and the TOSCA-3, in that the general factor significantly and positively predicted agreeableness (p’s < .001) and conscientiousness (p’s < .001), providing support for the Moral Emotion hypothesis. However, contrary to the Self-Conscious Emotion hypothesis, the general factor did not significantly predict either extraversion or neuroticism.

The shame-specific factor predicted all big five traits in a manner suggesting it is negatively associated with social effectiveness (Rushton & Irwing, 2011). Specifically, the shame-specific factor significantly but negatively predicted extraversion (p’s < .001), agreeableness (p’s = .04 and < .001), conscientiousness (p’s < .001), openness (p’s = .01 and < .001), whereas it significantly positively predicted neuroticism (p’s < .001). There was a weak negative association between shame-specific factor and empathy for the TOSCA-3 (p = .03), but not for the GASP. For the guilt-specific factor, across the GASP and the TOSCA-3, no consistently strong prediction was observed for any of the big five factors, when the general factor was taken into account.

Psychopathology

Fit indices suggested that the models fit was acceptable when the GASP was used (CFI = .94, RMSEA = .05). For the TOSCA-3, the model fit was mediocre for CFI (CFI = .88) but good for RMSEA (RMSEA = .05). The general factor strongly and negatively predicted externalizing psychopathology (p’s < .001). Contrary to the Self-Conscious Emotion hypothesis, the general factor did not predict internalizing psychopathology. Across both the GASP and the TOSCA-3, the shame-specific factor strongly and positively predicted both externalizing psychopathology (p’s = .05 and <.001), and internalizing psychopathology (p’s < .001). The guilt-specific factor did not predict externalizing psychopathology or internalizing psychopathology, for either the GASP or the TOSCA-3.

Comparison of bi-factor models with partial correlation and bivariate zero-order correlation

Table 2 also presents the results of both the partial correlation approach and the raw bivariate zero-order correlation approach. Although the partial correlations and zero-order correlations revealed associations with the same variables with which the bi-factor-derived factors were associated, the magnitudes of the correlations were generally smaller. For example, while the general factor, measured using GASP, is significantly associated with empathy (β = .58, p < .001), the correlation coefficients using partial correlations are .08 for shame-proneness, and .19 for guilt-proneness, and those derived from the bi-variate correlations are .14 and .20, for shame- and guilt-proneness, respectively. The discrepancy in the magnitude of coefficients is even more pronounced when using TOSCA-3. This pattern holds true for variables tested in both the Moral Emotion hypothesis (i.e., agreeableness, conscientiousness, externalizing psychopathology), as well as the Self-conscious Emotion hypothesis (i.e., neuroticism, extraversion, internalizing psychopathology).

Taken together, Study 2 revealed the Moral Emotion hypothesis received strong support and the support for the Self-Conscious Emotion hypothesis was scarce and mixed. In Study 3, we further tested the Self-Conscious Emotion hypothesis by examining variables that are more directly linked to the negative self-conscious emotion process, namely, self-consciousness, self-criticism, and vulnerable narcissism. Additionally, we tested whether the findings of Study 2 regarding psychopathology would replicate in a different sample when employing informant-reported measures.

Study 3

Participants

A total of 474 undergraduate students from a large Midwestern university completed the study in exchange for course credit. To ensure data quality, we included eight attention checks to ensure that participants were paying attention. A total of 28 (5.9%) participants were excluded from the analyses because they failed at least one attention check question. Thus, the final sample consisted of 446 participants (61% female), aged between 18 and 24 years (M = 19.2, SD = 1.3); 45.7% European American, 8.4 % African American, and 34.6% Asian/Asian American, 4.1% multiracial, and 7.3% other). This sample size was appropriate for the analysis described below (Jiang et al., 2016).

Informants

Each participant was asked to identify and invite four close others to provide confidential and independent reports on the participants’ symptoms indicative of externalizing and internalizing psychopathology. These close others could be family, friends, or romantic partners who are “the most important people in your life.” They must have been at least 18 years old, have known the participant for at least six months, and have internet access in order to be eligible. Specifically, participants emailed their close others a brief description of our study and a link to an online survey. A total of 559 close others completed the surveys, providing data for 226 participants (50.7% of the entire sample). Given that no direct incentives were provided, we tried to simplify the survey in order to encourage informants to participate, and we did not ask them for demographic information.

Measures

Study 3 employed the same set of measures used in Study 2 for shame- and guilt-proneness, depression, worry, and social anxiety, with similar internal consistency estimates.

Meanness

Meanness was measured using the 19-item (α = .87) Meanness subscale from the Triarchic Psychopathy Measure (TriPM Meanness; Patrick, 2010). The scale has demonstrated good reliability and construct validity in terms of both normal range and dysfunctional personality traits (Stanley et al., 2013). Items were rated using a four-point scale (1 = “true”; 4 = “false”).

Self-criticism

Self-criticism was measured using the six-item (α = .76) version of the self-criticism subscale from the Depression Experience Questionnaire (DEQ-SC6; Rudich et al., 2008), which has been shown to have reliability and construct validity as high as the full self-criticism subscale (Rudich et al., 2008). The DEQ-SC6 measures the trait of self-criticism (i.e., setting high standards for oneself and self-punitive responding to failures), without referring to depressed mood. Participants rated each item on a seven-point scale (1 = strongly disagree; 7 = strongly agree).

Self-consciousness

Self-consciousness was measured using the private and public self-consciousness subscales from the Self-consciousness Scale (Fenigstein et al., 1975). The scale was designed to assess individual differences in self-consciousness, and has three components: public, private, and social anxiety. We did not include the social anxiety subscale to avoid content overlap with the items in one of our outcome measures (i.e., social anxiety). The scale has been found to have good reliability and construct validity (Fenigstein et al., 1975). The private (10 items, α = .68) and public (7 items, α = .76) self-consciousness scales were used in the current study. Participants rated each item on a five-point scale (0 = Extremely Uncharacteristic; 4 = Extremely Characteristic).

Vulnerable narcissism

Vulnerable Narcissism was measured with the 10-item (α = .72) Hypersensitivity Narcissism Scale (HSNS; Hendin & Cheek, 1997). It has been found to have good psychometric properties and reasonable evidence of convergent and discriminant validity (Hendin & Cheek, 1997). Items were rated using a five-point scale (1 = very uncharacteristic of me; 5 = very characteristic of me).

Dysphoria, informant-report

The informant report of internalizing psychopathology was measured using the 10-item Dysphoria subscale of the Inventory of Depression and Anxiety Symptoms (IDAS; Watson et al., 2007). We revised the instructions, asking informants to determine “how well each item describes your friend/family member’s recent feelings and experiences during the past two weeks.” We also revised the wording of the items so that each item was written in third-person perspective (e.g., “blamed himself/herself for things” rather than “blamed myself for things”). Items were rated using a five-point scale (1 = not at all; 5 = extremely). The internal consistency was α = .89.

Meanness, informant report

The informant report of externalizing psychopathology was measured using the 19-item (α = .90) Meanness subscale from the Triarchic Psychopathy Measure (TriPM Meanness; Patrick, 2010). Again, to reflect the third-person perspective, we revised the instructions (“choose the option that describes your friend/family member best”) and wording of the items (e.g., “How other people feel is important to him/her” rather than “how other people feel is important to me”). Items were rated using a four-point scale (1 = “true”; 4 = “false”).

Statistical analyses

Three structural equation models were constructed to examine the relationships between the general factor and self-related variables (Model 6), internalizing and externalizing and psychopathology measured by self-report (Model 7) and informant-report measures (Model 8). The basic format of Model 6 (Self) is similar to that of Model 4 (big-five personality, Figure 2) in Study 2, except that we replaced the big-five personality traits with self-related variables. All self-related variables were set to be correlated with each other because they are all considered aspects of the self-conscious emotion process. The basic formats of Model 7 (self-reported psychopathology) and Model 8 (informant-reported psychopathology) are similar to that of Model 5 (psychopathology, Figure 3) in Study 2. In Model 7 (self-reported psychopathology), internalizing psychopathology was indexed by depression, worry, and social anxiety; externalizing psychopathology was indexed by meanness, one sub-factor of psychopathy (Patrick, 2010). In Model 8 (informant-reported psychopathology), internalizing psychopathology was indexed by informant-reported dysphoria, and externalizing psychopathology was indexed by informant-reported meanness. We included only one measure to index each construct to minimize the time it took informants to complete the survey.

Results and discussion

Model fit indices and standardized regression coefficients are all presented in Table 3 for Model 6 (Self-related variables), Model 7 (self-reported psychopathology), and Model 8 (informant-reported psychopathology).

Table 3.

Study 3: Coefficients Between GASP and TOSCA and Psychopathology and Personality Factors Using Bi-Factor Model Versus Traditional Methods.

Outcomes	Bi-factor Standardized Regression Coefficients				Partial Correlations		Bi-variate Correlations
Outcomes	General factor	Shame-specific	Guilt-specific	Model Fit	Shame-proneness	Guilt-proneness	Shame-proneness	Guilt-proneness	Shame and Guilt Total
GASP
Model 6: Self-Related Variables
Self-criticism	0.00	0.19**	−0.03	χ2 = 582.78, df = 316, RMSEA = 0.04, CFI = 0.91	0.21**	−0.08	0.16**	0.04	0.11*
Public self-consciousness	0.16*	0.35**	−0.04		0.26**	−0.05	0.28**	0.12*	0.23**
Private self-consciousness	0.20**	0.06	−0.19*		0.06	0.21**	0.12*	0.15**	0.16**
Vulnerable narcissism	−0.08	0.41**	−0.15*		0.24**	−0.20**	0.22**	−0.07	0.07
Model 7: Self-Reported Psychopathology
Internalizing-self	0.20**	0.38**	0.06	χ2 = 508.81 df = 274, RMSEA = 0.04, CFI = 0.95	0.31**	−0.07	0.30**	0.11*	0.24**
Externalizing-self	−0.62**	0.04	0.03	χ2 = 508.81 df = 274, RMSEA = 0.04, CFI = 0.95	0.02	−0.45**	−0.50**	−0.26**	−0.55**
Model 8: Informant-Reported Psychopathology
Internalizing-informant	−0.01	0.12	−0.10	χ2 = 196.95, df = 114, RMSEA = 0.06, CFI = 0.87	0.10	−0.04	0.08	0.01	0.05
Externalizing-informant	−0.29**	0.18	−0.05	χ2 = 196.95, df = 114, RMSEA = 0.06, CFI = 0.87	0.07	0.10	0.09*	0.08	0.07
TOSCA
Model 6: Self-Related Variables
Self-criticism	0.00	0.55**	−0.08	χ2 = 1918.70, df = 852, RMSEA = 0.05, CFI = 0.79	0.38**	−0.12	0.33**	0.03	0.29**
Public Self-consciousness	0.11	0.57**	0.08		0.34**	−0.03	0.38**	0.15**	0.32**
Private Self-consciousness	0.18**	0.21**	0.22**		0.15*	0.23**	0.19**	0.23**	0.24**
Vulnerable narcissism	−0.01	0.55**	−0.22**		0.36**	−0.27**	0.32**	−0.07	0.16**
Model 7: Self-Reported Psychopathology
Internalizing-self	0.20**	0.77**	−0.03	χ2 = 1865.68, df = 860, RMSEA = 0.05, CFI = 0.87	0.53**	−0.18**	0.50**	0.11*	0.38**
Externalizing-self	−0.48**	0.05	−0.35**	χ2 = 1865.68, df = 860, RMSEA = 0.05, CFI = 0.87	0.01	−0.50**	−0.48**	−0.24**	−0.53**
Model 8: Informant-Reported Psychopathology
Internalizing-informant	0.03	0.20*	0.16*	χ2 = 911.18, df = 490, RMSEA = 0.06, CFI = 0.76	0.08	0.07	0.12	0.11	0.14*
Externalizing-informant	−0.27**	−0.06	−0.01	χ2 = 911.18, df = 490, RMSEA = 0.06, CFI = 0.76	0.03	0.14**	0.10	0.05	0.09

Note. GASP = Guilt and Shame Proneness Scale; TOSCA = Test of Self-Conscious Affect-3. *p < .05. **p < .01.

Personality

The general factor was positively correlated with public self-consciousness for GASP but not for TOSCA-3 (p < .05 and p = .09, respectively), and was positively associated with private self-consciousness for both GASP and TOSCA-3 (p’s < .001). However, contrary to the Self-Conscious Emotion hypothesis, the general factor did not significantly predict either self-criticism or vulnerable narcissism, across both GASP and TOSCA-3.

A strong and consistent pattern emerged between the shame-specific factor and self-related variables, but not for the guilt-specific factor. Particularly, across the GASP and the TOSCA-3, the shame-specific factor significantly and positively predicted most of the self-related variables, including public self-consciousness (p’s < .001), self-criticism (p’s < .001), and vulnerable narcissism (p’s < .001). There was a significant positive link between the shame-specific factor and private self-consciousness for the TOSCA-3 (p’s < .001), but not for the GASP. For the guilt-specific factor, the findings are less consistent and even contradictory across the two instruments. The guilt-specific factor was inversely associated with vulnerable narcissism (p’s = .05 and < .001, respectively). Specifically, the guilt-specific factor negatively predicted private self-consciousness for the GASP (p = .03), but the link was positive for the TOSCA-3 (p < .001).

Psychopathology: Self-report

As with Study 2, across the GASP or the TOSCA-3, the general factor strongly and negatively predicted externalizing psychopathology (p’s <.001). Unlike what we found in Study 2, the general factor positively predicted internalizing psychopathology (p’s < .001).

As with Study 2, the shame-specific factor strongly and positively predicted internalizing psychopathology (p’s < .001) for both the GASP and the TOSCA-3. As in Study 2, the guilt-specific factor did not predict internalizing psychopathology using either GASP or TSOCA, and predicted externalizing psychopathology when using GASP (p < .001) but not TOSCA-3.

Psychopathology: Informant-report

Consistent with what we obtained with the self-reported findings, we found the general factor negatively predicted externalizing psychopathology for both the GASP and the TOSCA-3 (p’s < .001), providing further support for the Moral Emotion hypothesis. Furthermore, consistent with the self-report data, the shame-specific factor was positively associated with internalizing psychopathology for TOSCA-3 (p = .01), but not GASP. Unlike self-report results, the general factor did not significantly predict internalizing psychopathology. And again, the guilt-specific factor was not predictive for externalizing psychopathology when using either GASP or TOSCA-3, and there was a weak association with internalizing psychopathology when using TOSCA-3 (p = .04) but not GASP.

Comparison of bi-factor models with partial correlation and bivariate zero-order correlation

Table 3 also presents the results of both the partial correlation approach and the raw bivariate zero-order correlation approach. Consistent with the past literature, distinct patterns of correlation coefficients for some of the variables were observed for the partial correlation versus the bi-variate correlation approaches. For example, shame-proneness is not associated with externalizing psychopathology when using partial correlations, but is strongly and inversely related to externalizing psychopathology when using the raw bi-variate correlation approach. This is consistent with past studies that reported conflicting results, accounting for the inconsistency and confusion to the literature.

Furthermore, as with Study 2, although the partial correlations and zero-order correlations revealed associations with the same variables with which the bi-factor-derived factors were associated, the magnitudes of the correlations were generally smaller. For example, while the shame-specific factor is significantly associated with vulnerable narcissism (β = .41, p < .001, GASP), the correlation coefficients using partial correlations are .24 for shame-proneness, and −.20 for guilt-proneness, and those derived from the bi-variate correlations are .22 for shame-proneness and −.07 for guilt proneness. The discrepancy in the magnitude of coefficients is even more pronounced when using TOSCA-3. Again, the general factor is most predictive for variables tested in the Moral Emotion hypothesis, and the shame-specific factor exhibits the largest magnitude of coefficients when it comes to the Self-conscious Emotion hypothesis.

General discussion

Using the bi-factor model, we constructed a general factor to capture the significant overlap between shame- and guilt-proneness, that demonstrated high stability and replicability across multiple samples and methods. Our findings regarding the general factor are summarized in Figure 4 (see also Table S10 in the supplemental on page 29). Supporting the Moral Emotion hypothesis, our study is the first to reveal that the general factor of shame- and guilt-proneness exhibited a strong and positive association with empathy, and medium and positive links with agreeableness and conscientiousness—personality traits closely tied to morality, and serves as a strong protective factor for externalizing psychopathology, indicating that the shared aspects of shame- and guilt-proneness represent a very adaptive response. By contrast, support for the Self-conscious Emotion hypothesis is scarce and mixed. As seen in Figure 4, the general factor was not associated with neuroticism, extraversion, self-criticism, or vulnerable narcissism, and was only weakly and inconsistently associated with private and public self-consciousness. Moreover, although a weak positive association emerged between the general factor and self-reported internalizing psychopathology in Study 3, this finding did not replicate in Study 2 using self-reported measures or in Study 3 using informant-reported measures. Hence the link between the general factor and internalizing psychopathology is, at best, mixed, and needs further examination. These findings imply that the commonality between shame- and guilt-proneness is not simply internalizing psychopathology that is associated with negative emotions (Fayard et al., 2012).

Consistent with previous research that showed a strong correlation between the two (effect size in the .5 range), we found considerable overlap between shame-proneness and guilt-proneness, highlighting the potential value of investigating the general factor shared between shame- and guilt-proneness. Analysis of congruence coefficients and factor omegas reveal the general factor is robust and replicable across multiple samples and methods, suggesting that it is a very reliable and meaningful psychological dimension. When contrasting the coefficients with personality and psychopathology correlates using the bi-factor approach, partial analysis approach, and the zero-order correlation approach, the general factor consistently demonstrates strong associations with positive moral traits (e.g., empathy, agreeableness, and conscientiousness), serves as a strong protective factor for externalizing psychopathology, while the shame-specific factor aligns with the darker, more pathological traits. The effect sizes of the correlates far surpass those obtained using the traditional partial correlation approach or the zero-order bivariate approach, partially because these traditional methods did not adequately account for measurement error. These findings illustrate that the bi-factor model not only effectively isolates shared variance but also captures patterns that traditional methods might obscure. Taken together, we recommend that this approach be adopted in future research if the focal research question is about the general factor.

The current findings advance our understanding of the nature and significance of the individual difference in the affective propensity for shame and guilt. Our results indicate that the shared aspects of shame-proneness and guilt-proneness represent an adaptive response to social transgressions—a tendency to be concerned with others’ feelings, adhere to rules and standards, and adjust one’s behavior to repair. Fostering prosocial and moral behaviors (Haidt, 2003), such cognitive and behavioral pattern serves as a strong protective factor against externalizing psychopathology. This is consistent with prior research that identified guilt-proneness as the affective core of conscientiousness (Fayard et al., 2012) and agreeableness (Abe, 2004), and those that found guilt-proneness inversely related to various forms of externalizing psychopathology, such as aggression (Tangney et al., 1996) and antisocial personality (Tangney et al., 2011). However, our findings contribute to this body of literature by suggesting that such traits are not solely associated with guilt-proneness, but rather with the general factor shared by shame-proneness and guilt-proneness.

It should be noted that whereas items denoting negative self and behavior evaluation, as well as the action tendency of repair, loaded highly positively on the general factor, items denoting withdrawal tendency had weak negative loadings on the general factor, but loaded strongly onto the shame-specific factor. This pattern is especially pronounced for GASP, and consistent for TOSCA, although a few TOSCA negative self-evaluation items did not load highly on the general factor, potentially due to that these items do not involve notable social transgressions, a speculation that requires further examination. These findings suggest that the shared aspects of shame- and guilt-proneness center on the inclination to self-reflect and self-correct in response to specific wrongdoings, and is distinguishable from the action tendency of withdrawal. Though consistent with past research that found the withdrawal facet of shame-proneness is only weakly correlated with the other three aspects of shame-proneness and guilt-proneness (Cohen et al., 2011), these findings nevertheless post challenges for the popular self versus behavior distinction view of shame- and guilt-proneness (e.g., Tangney, 1995), and calls for future research to examine how to best conceptualize shame- and guilt-proneness, what they share and what distinguish them from each other.

Analyses of the congruence coefficients and the factor omegas reveal that the shame-specific factor was also highly stable and replicable across multiple samples and methods. As seen in Figure 4, after taking account for what is shared with guilt-proneness, the shame-specific factor was strongly and positively associated with self-criticism, public self-consciousness, vulnerable narcissism, neuroticism, and inversely related to extraversion, consistent with past literature. These findings suggest that what is unique to shame-proneness seems to delineate a rather maladaptive tendency to be self-absorbed and self-critical, which further paves the way for elevated risk for both internalizing and externalizing psychopathology, as seen in the association between the shame-specific factor and both internalizing and externalizing psychopathology. By contrast, the congruence coefficients and factor omegas revealed there is not much variance left for the guilt-specific factor when the general factor is modeled, and the guilt factor is not very replicable across samples. This suggests that many traits commonly attributed to guilt-proneness may actually stem from the shared aspects of shame and guilt rather than being unique to guilt-proneness itself. Given the relatively low reliability and strength of the guilt-specific factor, findings related to this factor should be interpreted with caution. Future research may need to refine measurement or model structure to better capture this dimension and further explore the unique aspects of guilt-proneness that are not shared with shame-proneness.

Notably, our findings that suggest that shame- and guilt-proneness share a prosocial foundation and that the shame specific factor represents a rather maladaptive tendency to be self-absorbed and self-critical and withdraw, helps make sense of crucial inconsistencies in the literature. Whereas most researchers would agree that guilt-proneness promotes prosocial and moral behaviors, the view on shame-proneness is mixed and controversial. While some researchers considered shame a maladaptive and pathogenic emotion that thwarts prosocial action and facilitates withdrawal (e.g., Lewis, 1971) and linked shame-proneness to maladaptive outcomes, such as increased risk for psychopathology (Gilbert, 2000), others have contended shame to be a powerful prosocial emotion promoting conforming to the social norms (e.g., Benedict, 1946; Tomkins, 1963). Indeed, we also found the shame-specific factor, after accounting for what is shared with guilt-proneness, to be positively associated with self-criticism, vulnerable narcissism and neuroticism, indicating elevated risk for internalizing psychopathology, echoing this darker view of shame-proneness. Our findings reconcile these inconsistencies and suggest that the individual proneness to experience shame has two very distinct sides. On the one hand, the part specific to shame-proneness represents a rather dark response that should be discouraged and prevented if possible, whereas on the other hand, the part of shame-proneness that is shared with guilt-proneness represents a very prosocial response. When and why shame-proneness will be adaptive and maladaptive likely depends on a variety of factors, such as the perceived availability of options to restore the person’s damaged social image (Leach & Cidam, 2015). We believe the reason why some previous research has failed to find evidence of shame-proneness being a moral emotion is that they routinely removed covariance between shame- and guilt-proneness through partial correlations (Tignor & Colvin, 2017). By clarifying the shared and unique aspects of these emotions, our study provides a more nuanced understanding of their contributions to moral functioning, human behavior, and societal engagement. This deeper understanding could enrich theorizing in the field of moral psychology and offer a more holistic view of how individuals navigate moral decision-making in society. Additionally, these insights could guide clinicians in better tailoring interventions by distinguishing between the adaptive and maladaptive components of shame-proneness. As this is the first empirical study to examine the shared aspect of shame- and guilt-proneness, further research is needed to confirm and expand these findings.

The results of this study must be interpreted in light of its limitations. First, the measures employed in the present research are based on the conceptualization of shame- and guilt-proneness focusing on the self versus behavior distinction. While this approach is widely accepted and forms the basis for most empirical literature on shame- and guilt-proneness, other appraisals and action tendencies have been proposed as central to these two emotions (e.g., Baumeister et al., 1994; Gausel & Leach, 2011; Gilbert, 1998; Mu & Berenbaum, 2019). Future research should employ broader conceptualizations and measurements of these two emotions to investigate their shared aspects and also to clarify their unique features. Second, like previous studies, our study is cross-sectional in nature. Longitudinal research would be valuable to clarify the causal and developmental pathways between the general factor and key correlates. Third, the generalizability of our results, based on adults from the United States, may be limited in other age groups and other cultural contexts. Future studies should explore the generalizability of our findings to diverse samples across different age groups and cultures.

Despite the limitations of the current research, our findings begin to make sense of crucial inconsistencies in the literature, providing a clearer understanding of the nature and significance of negative self-conscious emotions like shame- and guilt-proneness. Our results emphasize the importance of examining the general factor shared by shame- and guilt-proneness, while distinguishing it from the variance specific to shame- and guilt-proneness. We believe this approach is essential for future research examining shame- and guilt-proneness and holds the potential to substantially advance the field.

Supplemental Material

Supplemental Material - What is common May Be as important as what is different: Examining the general factor shared by dispositional shame and guilt using Bi-factor models

Supplemental Material for What is common May Be as important as what is different: Examining the general factor shared by dispositional shame and guilt using Bi-factor models by Wenting Mu, Bo Zhang, Kishimoto Tomoko, Kaiwen Fan, Ziyi He, and Howard Berenbaum in European Journal of Personality.

Footnotes

Acknowledgments

Thanks are due to Taya R. Cohen and her team for providing GASP related data and to Michelle Schoenleber for providing us with data support related to the TOSCA.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Science Foundation of China (Project Number 32200907).

Open science statement

We report how we determined our sample size, all data exclusions and manipulations, missing data, and all relevant measures in the study following the Journal Article Reporting Standards (JARS; Kazak, 2018). Data, analysis code, and research materials are publicly accessible through the OSF repository. All three studies’ design, hypotheses, and analyses were not preregistered. All of the data, analysis codes, and research materials are publicly accessible through OSF: https://osf.io/cwuyn/ and .

ORCID iDs

Wenting Mu

Bo Zhang

Tomoko Kishimoto

Kaiwen Fan

Ziyi He

Supplemental Material

Supplemental material for this article is available online.

Notes

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