Theoretical and Empirical Integration of "Dark" Traits and Socially Aversive Personality Psychopathology.

Conceptual work integrating constructs from mainstream personality research (especially so-called "dark" traits) and clinical psychopathology research has been limited. Herein, we propose all socially and/or ethically aversive traits as "flavored" manifestations of the D factor of personality (D). We argue that the D framework provides the commonality of all aversive traits, including the aversive traits from the DSM-5 Alternative Model for Personality Disorders (AMPD), a more thorough theoretical foundation. Moreover, D covers aspects that are not captured by any of the aversive AMPD traits directly (e.g., greed), thus offering indications for possible expansions to the AMPD. We tested our predictions in two online studies (N = 1,781 and N = 2,006) using quota-representative samples of the German population regarding age and gender. Twelve aversive traits from mainstream personality research and eight aversive AMPD traits were assessed together with consequential behavior in an economic game. Analyses using structural equation modeling overall confirmed predictions.

How and why aversive personality is expressed in political preferences.

Political orientation reflects beliefs, opinions, and values that are, at least in part, rooted in stable interindividual differences. Whereas evidence has accumulated with regard to the relevance of basic personality dimensions, especially concerning the sociocultural dimension of political ideology, less attention has been paid to the more specific dispositional tendency to assign a higher weight to one's own utility above others' (i.e., socially aversive personality), which is likely to play a pivotal role concerning the economic dimension of political ideology in particular. In three studies with over 66,000 participants from 38 countries, we show that individuals with elevated levels in aversive personality tend to endorse more right-wing political orientations in terms of a single left-right dimension, hold relevant ideological beliefs tied to both sociocultural and economic conservatism, and report corresponding electoral voting behavior. We further provide support for the idea that this overlap between a dispositional tendency toward aversive behavior and a right-wing political orientation can be attributed to shared belief systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Testing the equivalence of the aversive core of personality and a blend of agreeableness(-related) items.

OBJECTIVE: There is an ongoing debate in personality research whether the common core of aversive ("dark") traits can be approximated by or even considered equivalent to one of the constructs that have been labeled "Agreeableness". In particular, it has been suggested that the low pole of (what we term) AG+, a broad blend of Big Five Agreeableness and the HEXACO factors Honesty-Humility, Agreeableness, and Altruism, is essentially equivalent to the Dark Factor of Personality (D). Based on theoretical differences, we herein test empirically whether D and AG+ are isomorphic. METHODS: Self-report data on D, AG+, and eight criterion measures reflecting justifying beliefs, inflicting disutility on others, and affiliative tendencies were collected in a pre-registered study (N = 1156) and analyzed via confirmatory factor modeling. RESULTS: Results speak against unity of D and AG+ (35% shared variance) and support the notion that D subsumes a broader range of aversive content (i.e., justifying beliefs and inflicting disutility on others) than AG+, which, in turn, subsumes a slightly broader range of non-aversive, affiliative tendencies. CONCLUSION: We conclude that AG+ is non-equivalent to the common core of aversive traits, D.

semPower: General power analysis for structural equation models.

Structural equation modeling (SEM) is a widespread and commonly used approach to test substantive hypotheses in the social and behavioral sciences. When performing hypothesis tests, it is vital to rely on a sufficiently large sample size to achieve an adequate degree of statistical power to detect the hypothesized effect. However, applications of SEM rarely consider statistical power in informing sample size considerations or determine the statistical power for the focal hypothesis tests performed. One reason is the difficulty in translating substantive hypotheses into specific effect size values required to perform power analyses, as well as the lack of user-friendly software to automate this process. The present paper presents the second version of the R package semPower which includes comprehensive functionality for various types of power analyses in SEM. Specifically, semPower 2 allows one to perform both analytical and simulated a priori, post hoc, and compromise power analysis for structural equation models with or without latent variables, and also supports multigroup settings and provides user-friendly convenience functions for many common model types (e.g., standard confirmatory factor analysis [CFA] models, regression models, autoregressive moving average [ARMA] models, cross-lagged panel models) to simplify power analyses when a model-based definition of the effect in terms of model parameters is desired.

On the (number of) aversive traits it takes to approximate the common core of aversive personality.

The Dark Triad and Dark Tetrad are the constellation of aversive traits that are most commonly assessed to study their common (aversive) and unique (non-aversive) features. Nonetheless, it is unclear whether these particular traits (in combination) indeed closely approximate the common core of all aversive traits. A close approximation of the aversive core, however, is crucial if one is strictly interested in an aversive trait's unique variance. Therefore, the present study aims to specify how many and which aversive traits jointly approximate the aversive core to a satisfactory extent. To this end, the aversive core was first estimated from 16 aversive traits and then correlated to the aversive cores extracted from all 63,002 possible combinations of two to eleven of these traits. Results showed that most combinations of four, or essentially any combination of at least six aversive traits, approximated the aversive core reasonably well, whereas the Dark Triad and Dark Tetrad lead to systematically poorer approximations compared to other combinations of three of four aversive traits.

The role of impulse and interference control in aversive personality: A comprehensive assessment.

Ethically and socially aversive behaviors have been attributed to several personality traits, including characteristics collectively referred to under the umbrella term of impulsivity. It is an open question, however, whether such characteristics are an integral part of ethically and socially aversive personality. Relying on three large samples (total N = 9854) and implementing longitudinal assessments, the present study provides a comprehensive investigation of the role of impulse and interference control in aversive personality. Based on contemporary conceptualizations of the impulsivity domain, a total of 17 dimensions spanning both self-reports and behavioral tasks are assessed. To represent aversive personality, we consider the D Factor of Personality (D), i.e., the basic disposition shared by all aversive traits. Results indicate that D co-occurs with a deficit in inhibiting the incorrect action when multiple actions are available, a preference for immediate rewards while failing to consider the consequences of one's actions, and maladaptive behavior directed towards regulating strong affect. However, most associations between D and dimensions of impulsivity were small or non-significant, thereby disconfirming the notion that characteristics related to a lack of impulse control are an integral feature of aversive personality in general.

A Three-Armed Randomized Controlled Trial to Evaluate the Effectiveness, Acceptance, and Negative Effects of StudiCare Mindfulness, an Internet- and Mobile-Based Intervention for College Students with No and "On Demand" Guidance.

The college years can be accompanied by mental distress. Internet- and mobile-based interventions (IMIs) have the potential to improve mental health but adherence is problematic. Psychological guidance might promote adherence but is resource intensive. In this three-armed randomized controlled trial, "guidance on demand" (GoD) and unguided (UG) adherence-promoting versions of the seven-module IMI StudiCare Mindfulness were compared with a waitlist control group and each other. The GoD participants could ask for guidance as needed. A total of 387 students with moderate/low mindfulness were recruited. Follow-up assessments took place after 1 (t1), 2 (t2), and 6 (t3) months. Post-intervention (t2), both versions significantly improved the primary outcome of mindfulness (d = 0.91-1.06, 95% CI 0.66-1.32) and most other mental health outcomes (d = 0.25-0.69, 95% CI 0.00-0.94) compared with WL, with effects generally persisting after 6 months. Exploratory comparisons between UG and GoD were mostly non-significant. Adherence was low but significantly higher in GoD (39%) vs. UG (28%) at the 6-month follow-up. Across versions, 15% of participants experienced negative effects, which were mostly mild. Both versions effectively promoted mental health in college students. Overall, GoD was not associated with substantial gains in effectiveness or adherence compared with UG. Future studies should investigate persuasive design to improve adherence.

A tutorial on assessing statistical power and determining sample size for structural equation models.

Structural equation modeling (SEM) is a widespread approach to test substantive hypotheses in psychology and other social sciences. However, most studies involving structural equation models neither report statistical power analysis as a criterion for sample size planning nor evaluate the achieved power of the performed tests. In this tutorial, we provide a step-by-step illustration of how a priori, post hoc, and compromise power analyses can be conducted for a range of different SEM applications. Using illustrative examples and the R package semPower, we demonstrate power analyses for hypotheses regarding overall model fit, global model comparisons, particular individual model parameters, and differences in multigroup contexts (such as in tests of measurement invariance). We encourage researchers to yield reliable-and thus more replicable-results based on thoughtful sample size planning, especially if small or medium-sized effects are expected. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Dispositional Essence of Proactive Social Preferences: The Dark Core of Personality vis-à-vis 58 Traits.

Individuals differ in how they weigh their own utility versus others'. This tendency codefines the dark factor of personality (D), which is conceptualized as the underlying disposition from which all socially and ethically aversive (dark) traits arise as specific, flavored manifestations. We scrutinize this unique theoretical notion by testing, for a broad set of 58 different traits and related constructs, whether any predict how individuals weigh their own versus others' utility in proactive allocation decisions (i.e., social value orientations) beyond D. These traits and constructs range from broad dimensions (e.g., agreeableness), to aversive traits (e.g., sadism) and beliefs (e.g., normlessness), to prosocial tendencies (e.g., compassion). In a large-scale longitudinal study involving the assessment of consequential choices (median N = 2,270; a heterogeneous adult community sample from Germany), results from several hundred latent model comparisons revealed that no meaningful incremental variance was explained beyond D. Thus, D alone is sufficient to represent the social preferences inherent in socially and ethically aversive personality traits.

Rethinking aversive personality: Decomposing the Dark Triad traits into their common core and unique flavors.

OBJECTIVE: To explain and predict unethical behavior, much attention has been devoted to the "Dark Triad of Personality", a set of three socially aversive personality traits: Machiavellianism, narcissism, and psychopathy. Despite its popularity, research on the Dark Triad has been beset by recurring concerns surrounding the distinctiveness of its components. Herein, we propose an alternative theoretical view that conceptualizes the Dark Triad traits as specific manifestations of the common core of aversive traits (conceptualized as the Dark Factor of Personality, D) flavored by unique, essentially non-aversive characteristics. METHOD: In two studies (total N > 1000), we test this idea by examining the conceptual and empirical overlap and specificity of the Dark Triad traits vis-à-vis D and each other. RESULTS: Findings support the conceptualization of the Dark Triad traits as flavored manifestations of D and also reveal a marked discrepancy between the current conceptualization of the Dark Triad traits and the empirical structure of its subdimensions. In fact, evidence clearly failed to support the existence of a triad of traits. CONCLUSION: Conceptualizing single aversive traits as a conjunction of core D aspects and essentially non-aversive characteristics (admiration; disinhibition vs. planfulness; vulnerability vs. boldness) holds promise to move the field forward.

Assessing the fitting propensity of factor models.

Model selection is an omnipresent issue in structural equation modeling (SEM). When deciding among competing theories instantiated as formal statistical models, a trade-off is often sought between goodness-of-fit and model parsimony. Whereas traditional fit assessment in SEM quantifies parsimony solely as the number of free parameters, the ability of a model to account for diverse data patterns-known as fitting propensity-also depends on the functional form of a model. The present investigation provides a systematic assessment of the fitting propensity of models typically considered and compared in SEM, namely, exploratory and confirmatory factor analysis models positing a different number of latent factors or a different hierarchical structure (single-factor, correlated factors, higher-order, and bifactor models). Furthermore, the behavior of commonly used fit indices (CFI, SRMR, RMSEA, TLI) and information criteria (AIC, BIC) in accounting for fitting propensity was assessed. Although the results demonstrated varying degrees of fitting propensity for the models under scrutiny, these differences were mostly driven by the number of free parameters. There was little evidence for additional differences in the functional form of the compared models. Fit indices adjusting for the number of free parameters such as the RMSEA and TLI thus adequately accounted for differences in fitting propensity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ACTonDiabetes: study protocol of a pragmatic randomised controlled trial for the evaluation of an acceptance and commitment-based internet-based and mobile-based intervention for adults living with type 1 or type 2 diabetes.

INTRODUCTION: Living with diabetes can be burdensome and lead to serious emotional distress and impaired mental health. Acceptance and commitment therapy (ACT) can support people facing the challenges of living with diabetes. This trial aims to evaluate the effectiveness and cost-effectiveness of the internet-based and mobile-based intervention (IMI) 'ACTonDiabetes' in reducing diabetes distress against enhanced treatment as usual (TAU+) following specialised diabetes care. METHODS AND ANALYSIS: A two-armed pragmatic randomised controlled trial will be conducted to evaluate the guided IMI ACTonDiabetes against TAU+. A total of 210 adults with type 1 or type 2 diabetes and elevated diabetes distress (Problem Areas in Diabetes ≥40) will be recruited at a specialised diabetes centre. The intervention begins 2-4 weeks after hospital discharge and takes about 7-10 weeks to complete. Assessments are performed at baseline and 5 and 10 weeks as well as 6 and 12 months after randomisation. The primary outcome is diabetes distress at a 10-week follow-up (T2). Secondary outcomes are depression (Patient Health Questionnaire-8), psychological well-being (WHO-5), quality of life (Assessment of Quality of Life-8 Dimension), Diabetes-related Self-Management Questionnaire, diabetes acceptance (Acceptance and Action Diabetes Questionnaire) and negative treatment effects (Inventory for the Assessment of Negative Effects of Psychotherapy). All statistical analyses will be performed based on the intention-to-treat principle with additional per-protocol analyses. Changes in outcomes will be evaluated using the general linear model. A health-economic evaluation will be conducted from a societal perspective. Reasons for drop-out will be systematically investigated. ETHICS AND DISSEMINATION: This clinical trial has been approved by the State Medical Chamber of Baden-Württemberg (file no. B-F-2019-010). Trial results will be submitted for publication in a peer-reviewed journal and presented at conferences. TRIAL REGISTRATION NUMBER: DRKS00016738.

The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling.

Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of non-normality is considered. We conducted a Monte Carlo simulation manipulating the underlying multivariate distribution to assess the effect of the source of non-normality (latent, error, and marginal conditions with either multivariate normal or non-normal marginal distributions) on different measures of fit (empirical rejection rates for the likelihood-ratio model test statistic, the root mean square error of approximation, the standardized root mean square residual, and the comparative fit index). We considered different estimation methods (maximum likelihood, generalized least squares, and (un)modified asymptotically distribution-free), sample sizes, and the extent of non-normality in correctly specified and misspecified models to investigate their performance. The results show that all measures of fit were affected by the source of non-normality but with varying patterns for the analyzed estimation methods.

Comparative effectiveness of three versions of a stepped care model for insomnia differing in the amount of therapist support in internet-delivered treatment: study protocol for a pragmatic cluster randomised controlled trial (GET Sleep).

INTRODUCTION: It is unclear how internet-delivered cognitive-behavioural therapy for insomnia (CBT-I) can be integrated into healthcare systems, and little is known about the optimal level of therapist guidance. The aim of this study is to investigate three different versions of a stepped care model for insomnia (IG1, IG2, IG3) versus treatment as usual (TAU). IG1, IG2 and IG3 rely on treatment by general practitioners (GPs) in the entry level and differ in the amount of guidance by e-coaches in internet-delivered CBT-I. METHODS AND ANALYSIS: In this randomised controlled trial, 4268 patients meeting International Classification of Diseases, Tenth Revision (ICD-10) criteria for insomnia will be recruited. The study will use cluster randomisation of GPs with an allocation ratio of 3:3:3:1 (IG1, IG2, IG3, TAU). In step 1 of the stepped care model, GPs will deliver psychoeducational treatment; in step 2, an internet-delivered CBT-I programme will be used; in step 3, GPs will refer patients to specialised treatment. Outcomes will be collected at baseline, and 4 weeks, 12 weeks and 6 months after baseline assessment. The primary outcome is insomnia severity at 6 months. An economic evaluation will be conducted and qualitative interviews will be used to explore barriers and facilitators of the stepped care model. ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of the Medical Centre-University of Freiburg. The results of the study will be published irrespective of the outcome. TRIAL REGISTRATION NUMBER: DRKS00021503.

The role of base-rate neglect in cyberchondria and health anxiety.

Cyberchondria is characterized by excessive health-related online search behavior associated with an unfounded escalation of concerns about common symptomatology. It often co-occurs with health anxiety. We investigated whether base-rate neglect-the cognitive bias to ignore a priori probabilities (e.g., of serious diseases)-plays a significant role in cyberchondria and health anxiety. 368 participants were randomly assigned to eight experimental conditions, manipulating the base-rate (30 % vs. 70 %), the judgment domain (health-neutral versus health-related), and the salience of base-rate information (low vs. high) in a 2×2×2 between-subjects design when asking them for probability judgments with versus without disease relevance. We found that high salience decreased base-rate neglect in participants with low, but not in those with elevated levels of either cyberchondria or health anxiety. Under low salience conditions, however, both cyberchondria and health anxiety severity were uncorrelated with base-rate neglect. These effects were independent of whether health-related or health-neutral problems were evaluated. Our findings suggest a domain-general probabilistic reasoning style that may play a causal role in the pathogenesis of cyberchondria and health anxiety.

No probifactor model fit index bias, but a propensity toward selecting the best model.

Based on an extensive Monte Carlo simulation study, Greene et al. (2019) investigated the behavior of various measures of model fit for competing types of confirmatory factor analysis models of psychopathology, the correlated factors model and the bifactor model. Greene et al. (2019) found that fit indices mostly favored a bifactor model over a correlated factors model, which led to the conclusion of a "probifactor fit index bias." Here we show that this conclusion is misleading as far as conditions without complexities in the data-generating model are concerned and in fact incorrect in conditions with complexities (cross-loadings or correlated residuals) in the data-generating model. Specifically, we demonstrate that the very same data Greene et al. (2019) generated from a correlated three-factor model can be likewise obtained from a higher-order or a bifactor model, so that there is no basis for maintaining that the "true" to-be recovered model conformed to a correlated factors structure. Moreover, we show that a standard bifactor model was factually closer aligned with the data generated in conditions with added complexities. As such, fit indices necessarily and correctly favored the bifactor model in most conditions. We explain the observed behavior of several fit indices, thereby showing that the results were not characterized by bias, but were in fact in line with the expected and desired behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Making rights from wrongs: The crucial role of beliefs and justifications for the expression of aversive personality.

Whereas research focusing on stable dispositions has long attributed ethically and socially aversive behavior to an array of aversive (or "dark") traits, other approaches from social-cognitive psychology and behavioral economics have emphasized the crucial role of social norms and situational justifications that allow individuals to uphold a positive self-image despite their harmful actions. We bridge these research traditions by focusing on the common core of aversive traits (the dark factor of personality [D]) and its defining aspect of involving diverse beliefs that serve to construct justifications. In particular, we theoretically specify the processes by which D is expressed in aversive behavior-namely, through diverse beliefs and the justifications they serve. In six studies (total N > 25,000) we demonstrate (a) that D involves higher subjective justifiability of those aversive behaviors that individuals high in D are more likely to engage in, (b) that D uniquely relates to diverse descriptive and injunctive beliefs-related to distrust (e.g., cynicism), hierarchy (e.g., authoritarianism), and relativism (e.g., normlessness)-that serve to justify aversive behavior, and (c) a theoretically derived pattern of moderations and mediations supporting the view that D accounts for aversive behavior because it fosters subjective justifiability thereof-at least in part owing to certain beliefs and the justifications they afford. More generally, our findings highlight the role of (social) cognitions within the conceptual definitions of personality traits and processes through which they are expressed in behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Effectiveness and cost-effectiveness of a web-based routine assessment with integrated recommendations for action for depression and anxiety (RehaCAT+): protocol for a cluster randomised controlled trial for patients with elevated depressive symptoms in rehabilitation facilities.

INTRODUCTION: The integration of a web-based computer-adaptive patient-reported outcome test (CAT) platform with persuasive design optimised features including recommendations for action into routine healthcare could provide a promising way to translate reliable diagnostic results into action. This study aims to evaluate the effectiveness and cost-effectiveness of such a platform for depression and anxiety (RehaCAT+) compared with the standard diagnostic system (RehaCAT) in cardiological and orthopaedic health clinics in routine care. METHODS AND ANALYSIS: A two-arm, pragmatic, cluster-randomised controlled trial will be conducted. Twelve participating rehabilitation clinics in Germany will be randomly assigned to a control (RehaCAT) or experimental group (RehaCAT+) in a 1:1 design. A total sample of 1848 participants will be recruited across all clinics. The primary outcome, depression severity at 12 months follow-up (T3), will be assessed using the CAT Patient-Reported Outcome Measurement Information System Emotional Distress-Depression Item set. Secondary outcomes are depression at discharge (T1) and 6 months follow-up (T2) as well as anxiety, satisfaction with participation in social roles and activities, pain impairment, fatigue, sleep, health-related quality of life, self-efficacy, physical functioning, alcohol, personality and health economic-specific general quality of life and socioeconomic cost and benefits at T1-3. User behaviour, acceptance, facilitating and hindering factors will be assessed with semistructured qualitative interviews. Additionally, a smart sensing substudy will be conducted, with daily ecological momentary assessments and passive collection of smartphone usage variables. Data analysis will follow the intention-to-treat principle with additional per-protocol analyses. Cost-effectiveness analyses will be conducted from a societal perspective and the perspective of the statutory pension insurance. ETHICS AND DISSEMINATION: The study will be conducted according to the Declaration of Helsinki. The Ethics Committee of Ulm University, has approved the study (on 24 February 2021 ref. 509/20). Written informed consent will be obtained for all participants. Results will be published via peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00027447.

Beyond (low) Agreeableness: Toward a more comprehensive understanding of antagonistic psychopathology.

In clinical psychopathology research, up to seven traits have been suggested as instances of antagonistic psychopathology. Those antagonistic traits, in turn, are commonly viewed as reflections of low Agreeableness as per the Big Five (BF-AG). However, specific theoretical differences between antagonistic traits suggest that other broad, basic dimensions beyond BF-AG ought to provide further points of correspondence. Specifically, whereas primarily affective antagonistic traits are closely aligned with BF-AG, primarily behavioral antagonistic traits are better aligned with Honesty-Humility (HH) from the HEXACO model and primarily cognitive antagonistic traits are better aligned with the common core of aversive traits (the Dark Factor of Personality, D). Indeed, from a theoretical perspective, D seems to be the only candidate sufficiently covering all aspects of antagonistic traits (affect, behavior, and cognition) to a comparable extent and thus affording a balanced representation of antagonistic psychopathology. We critically test these conjectures in a large and heterogeneous online sample (N = 3,396), investigating the overlap between antagonistic traits and basic personality via structural equation modeling. Results show that BF-AG, HH, and D each yield particularly strong ties to one group of antagonistic traits (affective, behavioral, and cognitive, respectively), while D offers the most balanced representation of all (groups of) antagonistic traits.

The effect of latent and error non-normality on corrections to the test statistic in structural equation modeling.

In structural equation modeling, several corrections to the likelihood-ratio model test statistic have been developed to counter the effects of non-normal data. Previous robustness studies investigating the performance of these corrections typically induced non-normality in the indicator variables. However, non-normality in the indicators can originate from non-normal errors or non-normal latent factors. We conducted a Monte Carlo simulation to analyze the effect of non-normality in factors and errors on six different test statistics based on maximum likelihood estimation by evaluating the effect on empirical rejection rates and derived indices (RMSEA and CFI) for different degrees of non-normality and sample sizes. We considered the uncorrected likelihood-ratio model test statistic and the Satorra-Bentler scaled test statistic with Bartlett correction, as well as the mean and variance adjusted test statistic, a scale-shifted approach, a third moment-adjusted test statistic, and an approach drawing inferences from the relevant asymptotic chi-square mixture distribution. The results indicate that the values of the uncorrected test statistic-compared to values under normality-are associated with a severely inflated type I error rate when latent variables are non-normal, but virtually no differences occur when errors are non-normal. Although no general pattern regarding the source of non-normality for all analyzed measures of fit can be derived, the Satorra-Bentler scaled test statistic with Bartlett correction performed satisfactorily across conditions.