A New Strategy for Testing Structural Equation Models

Many negatively connoted personality traits (often termed "dark traits") have been introduced to account for ethically, morally, and socially questionable behavior. Herein, we provide a unifying, comprehensive theoretical framework for understanding dark personality in terms of a general dispositional tendency of which dark traits arise as specific manifestations. That is, we theoretically specify the common core of dark traits, which we call the (). The fluid concept of D captures individual differences in the tendency to maximize one's individual utility-disregarding, accepting, or malevolently provoking disutility for others-accompanied by beliefs that serve as justifications. To critically test D, we unify and extend prior work methodologically and empirically by considering a large number of dark traits simultaneously, using statistical approaches tailored to capture both the common core and the unique content of dark traits, and testing the predictive validity of both D and the unique content of dark traits with respect to diverse criteria including fully consequential and incentive-compatible behavior. In a series of four studies ( > 2,500), we provide evidence in support of the theoretical conceptualization of D, show that dark traits can be understood as specific manifestations of D, demonstrate that D predicts a multitude of criteria in the realm of ethically, morally, and socially questionable behavior, and illustrate that D does not depend on any particular indicator variable included. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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“…Second, we adopted the recently proposed approach of Neyman-Pearson model testing with balanced error probabilities (Moshagen & Erdfelder, 2016…”

Section: Analysesmentioning

confidence: 99%

The dark core of personality.

Moshagen¹,

Hilbig

Zettler

2018

Psychological Review

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314

353

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“…We applied the following standard criteria for the evaluation of well-established model fits [ 39 , 40 ]: the standardized root mean square residual (SRMR; values below 0.08 indicate good fit with the data), comparative fit indices (CFI/TLI; values above 0.90 indicate a good fit, values above 0.95 an excellent fit), and root mean square error of approximation (RMSEA; “test of close fit”; a value below 0.08 with a significance value below 0.05 indicates acceptable fit). Although some additional model fit criteria have been suggested [ 41 ], we started with using the standard criteria and checked whether certain corrections for large samples were necessary (which was not the case). Cronbach’s α and discriminatory power of the items were calculated with SPSS version 22.0 for Windows (IBM SPSS Statistics).…”

Section: Studymentioning

confidence: 99%

The Pathological Buying Screener: Development and Psychometric Properties of a New Screening Instrument for the Assessment of Pathological Buying Symptoms

et al. 2015

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The study was designed to develop a new screening instrument for pathological buying (PB), and to examine its psychometric properties in a large-scale sample. By using a facet theoretical approach and based on literature as well as on clinical experience, a 20-item Pathological Buying Screener (PBS) was developed and administered to a representative German sample (n = 2,539). Valid data were available from 2,403 participants who were subjects for three subsequent empirical studies. The first study explored the factor structure using exploratory factor analyses in a subsample of 498 participants. Based on factor loadings, a 13-item version with the two factors loss of control / consequences and excessive buying behavior was revealed. This two-factor model was confirmed in study 2 by confirmatory factor analysis performed on another subsample (n = 1,905). Study 3 investigated age and gender effects and convergent validity of the PBS using the Compulsive Buying Scale (CBS) in the full sample (N = 2,403). The total PBS score was adequately correlated with the CBS score. Hierarchical regression analyses with the CBS score as the dependent variable and the two PBS factors as the predictors indicated an own incremental validity of the two factors in participants ≤ 65 years. The reliability of the total score as well as of the two subscales was good to excellent. Overall, the PBS represents a useful measure for PB. Future studies are needed to replicate the two-factor structure in clinical samples and to define a valid cutoff for PB.

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“…Following other authors (e.g., Bayen, Erdfelder, Bearden, & Lozito, 2006;Erdfelder, 1984;Moshagen & Erdfelder, 2016), we deliberately chose a lower Type-1 error probability for the model fit to the aggregate data than usual, namely α = .01, because otherwise-given the large data set of 36,000 data points in Experiment 1 and 9,000 in Experiment 2-even minute deviations from the model assumptions would result in model misfit. Even with α = .01, small deviations from the model (i.e., w = .10 as defined by Cohen, 1988) are detected by a G 2 (df = 2) goodness-of-fit test with a power exceeding (1-β) = .99 (computed with G*Power; see Faul, Erdfelder, Buchner, & Lang, 2009).…”

Section: Overview Of Experimentsmentioning

confidence: 99%

Use of the recognition heuristic depends on the domain’s recognition validity, not on the recognition validity of selected sets of objects

et al. 2017

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According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

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A New Strategy for Testing Structural Equation Models

Cited by 222 publications

References 25 publications

The dark core of personality.

The dark core of personality.

The Pathological Buying Screener: Development and Psychometric Properties of a New Screening Instrument for the Assessment of Pathological Buying Symptoms

Use of the recognition heuristic depends on the domain’s recognition validity, not on the recognition validity of selected sets of objects

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