Modeling General, Specific, and Method Variance in Personality Measures: Results for ZKA-PQ and NEO-PI-R

Exploratory Bi-factor Analysis with Multiple General Factors

Exploratory bi-factor analysis (EBFA) is a very popular approach to estimate models where specific factors are concomitant to a single, general dimension. However, the models typically encountered in fields like personality, intelligence, and psychopathology involve more than one general factor. To address this circumstance, we developed an algorithm (GSLiD) based on partially specified targets to perform exploratory bi-factor analysis with multiple general factors (EBFA-MGF). In EBFA-MGF, researchers do not need to conduct independent bi-factor analyses anymore because several bi-factor models are estimated simultaneously in an exploratory manner, guarding against biased estimates and model misspecification errors due to unexpected cross-loadings and factor correlations. The results from an exhaustive Monte Carlo simulation manipulating nine variables of interest suggested that GSLiD outperforms the Schmid-Leiman approximation and is robust to challenging conditions involving cross-loadings and pure items of the general factors. Thereby, we supply an R package (bifactor) to make EBFA-MGF readily available for substantive research. Finally, we use GSLiD to assess the hierarchical structure of a reduced version of the Personality Inventory for DSM-5 Short Form (PID-5-SF).

Big five model personality traits and job burnout: a systematic literature review

BACKGROUND

Job burnout negatively contributes to individual well-being, enhancing public health costs due to turnover, absenteeism, and reduced job performance. Personality traits mainly explain why workers differ in experiencing burnout under the same stressful work conditions. The current systematic review was conducted with the PRISMA method and focused on the five-factor model to explain workers' burnout risk.

METHODS

The databases used were Scopus, PubMed, ScienceDirect, and PsycINFO. Keywords used were: "Burnout," "Job burnout," "Work burnout," "Personality," and "Personality traits".

RESULTS

The initial search identified 3320 papers, from which double and non-focused studies were excluded. From the 207 full texts reviewed, the studies included in this review were 83 papers. The findings show that higher levels of neuroticism (r from 0.10** to 0.642***; β from 0.16** to 0.587***) and lower agreeableness (r from - 0.12* to - 0.353***; β from - 0.08*** to - 0.523*), conscientiousness (r from -0.12* to -0.355***; β from - 0.09*** to - 0.300*), extraversion (r from - 0.034** to - 0.33***; β from - 0.06*** to - 0.31***), and openness (r from - 0.18*** to - 0.237**; β from - 0.092* to - 0.45*) are associated with higher levels of burnout.

CONCLUSIONS

The present review highlighted the relationship between personality traits and job burnout. Results showed that personality traits were closely related to workers' burnout risk. There is still much to explore and how future research on job burnout should account for the personality factors.

Cross-Cultural Measurement Invariance in the Personality Inventory for DSM-5✰

The validity of cross-cultural comparisons of test scores requires that scores have the same meaning across cultures, which is usually tested by checking the invariance of the measurement model across groups. In the last decade, a large number of studies were conducted to verify the equivalence across cultures of the dimensional Alternative Model of Personality Disorders (DSM-5 Section III). These studies have provided information on configural invariance (i.e., the facets that compose the domains are the same) and metric invariance (i.e., facet-domain relationships are equal across groups), but not on the stricter scalar invariance (i.e., the baseline levels of the facets are the same), which is a prerequisite for meaningfully comparing group means. The present study aims to address this gap. The Personality Inventory for DSM-5 (PID-5) was administered to five samples differing on country and language (Belgium, Catalonia, France, Spain, and Switzerland), with a total of 4,380 participants. Configural and metric invariance were supported, denoting that the model structure was stable across samples. Partial scalar invariance was supported, being minimal the influence of non-invariant facets. This allowed cross-cultural mean comparisons. Results are discussed in light of the sample composition and a possible impact of culture on development of psychopathology.

A genetic algorithm for optimal assembly of pairwise forced-choice questionnaires

The use of multidimensional forced-choice questionnaires has been proposed as a means of improving validity in the assessment of non-cognitive attributes in high-stakes scenarios. However, the reduced precision of trait estimates in this questionnaire format is an important drawback. Accordingly, this article presents an optimization procedure for assembling pairwise forced-choice questionnaires while maximizing posterior marginal reliabilities. This procedure is performed through the adaptation of a known genetic algorithm (GA) for combinatorial problems. In a simulation study, the efficiency of the proposed procedure was compared with a quasi-brute-force (BF) search. For this purpose, five-dimensional item pools were simulated to emulate the real problem of generating a forced-choice personality questionnaire under the five-factor model. Three factors were manipulated: (1) the length of the questionnaire, (2) the relative item pool size with respect to the questionnaire’s length, and (3) the true correlations between traits. The recovery of the person parameters for each assembled questionnaire was evaluated through the squared correlation between estimated and true parameters, the root mean square error between the estimated and true parameters, the average difference between the estimated and true inter-trait correlations, and the average standard error for each trait level. The proposed GA offered more accurate trait estimates than the BF search within a reasonable computation time in every simulation condition. Such improvements were especially important when measuring correlated traits and when the relative item pool sizes were higher. A user-friendly online implementation of the algorithm was made available to the users.

Using Constrained Factor Mixture Analysis to Validate Mixed-Worded Psychological Scales: The Case of the Rosenberg Self-Esteem Scale in the Dominican Republic

A common method to collect information in the behavioral and health sciences is the self-report. However, the validity of self-reports is frequently threatened by response biases, particularly those associated with inconsistent responses to positively and negatively worded items of the same dimension, known as wording effects. Modeling strategies based on confirmatory factor analysis have traditionally been used to account for this response bias, but they have recently become under scrutiny due to their incorrect assumption of population homogeneity, inability to recover uncontaminated person scores or preserve structural validities, and their inherent ambiguity. Recently, two constrained factor mixture analysis (FMA) models have been proposed by Arias et al. (2020) and Steinmann et al. (2021) that can be used to identify and screen inconsistent response profiles. While these methods have shown promise, tests of their performance have been limited and they have not been directly compared. Thus the objective of the current study was to assess and compare their performance with data from the Dominican Republic of the Rosenberg Self-Esteem Scale (N = 632). Additionally, as this scale had not yet been studied for this population, another objective was to show how using constrained FMAs could help in the validation of mixed-worded scales. The results indicated that removing the inconsistent respondents identified by both FMAs (≈8%) reduced the amount of wording effects in the database. However, whereas the Steinmann et al. method only cleaned the data partially, the Arias et al. (2020) method was able to remove the great majority of the wording effects variance. Based on the screened data with the Arias et al. method, we evaluated the psychometric properties of the RSES for the Dominican population, and the results indicated that the scores had good validity and reliability properties. Given these findings, we recommend that researchers incorporate constrained FMAs into their toolbox and consider using them to screen out inconsistent respondents to mixed-worded scales.

Modeling Wording Effects Does Not Help in Recovering Uncontaminated Person Scores: A Systematic Evaluation With Random Intercept Item Factor Analysis

The item wording (or keying) effect consists of logically inconsistent answers to positively and negatively worded items that tap into similar (but polarly opposite) content. Previous research has shown that this effect can be successfully modeled through the random intercept item factor analysis (RIIFA) model, as evidenced by the improvements in the model fit in comparison to models that only contain substantive factors. However, little is known regarding the capability of this model in recovering the uncontaminated person scores. To address this issue, the study analyzes the performance of the RIIFA approach across three types of wording effects proposed in the literature: carelessness, item verification difficulty, and acquiescence. In the context of unidimensional substantive models, four independent variables were manipulated, using Monte Carlo methods: type of wording effect, amount of wording effect, sample size, and test length. The results corroborated previous findings by showing that the RIIFA models were consistently able to account for the variance in the data, attaining an excellent fit regardless of the amount of bias. Conversely, the models without the RIIFA factor produced increasingly a poorer fit with greater amounts of wording effects. Surprisingly, however, the RIIFA models were not able to better estimate the uncontaminated person scores for any type of wording effect in comparison to the substantive unidimensional models. The simulation results were then corroborated with an empirical dataset, examining the relationship between learning strategies and personality with grade point average in undergraduate studies. The apparently paradoxical findings regarding the model fit and the recovery of the person scores are explained, considering the properties of the factor models examined.

Comparing the Prediction of Dimensional Personality Disorders (PID-5) After Three Personality Trait Models

Abstract. A dimensional approach for Personality Disorders was proposed in the DSM-5. To assess this approach, a new instrument (the Personality Inventory for DSM-5 [PID-5]) was developed in 2012. One research line has analyzed its convergent validity with personality traits, focusing almost exclusively on the Five-Factor Model (FFM). However, previous evidence about the relationships between Categorical Personality Disorders and other personality trait models shows that they can improve our understanding of Personality Disorders beyond the FFM. The aim of the present study is to compare the power of three personality models (FFM, Cloninger’s, and Zuckerman’s) to predict PID-5 domains. Three samples from the Spanish and Catalan general population were collected for this study depending on which personality questionnaire was applied (1,052 for revised NEO Personality Inventory [NEO-PI-R], 465 for Zuckerman-Kuhlman-Aluja Personality Questionnaire [ZKA-PQ], and 332 for Temperament and Character Inventory Revised [TCI-R-140]). The PID-5 was also applied to all subjects. Factor and regression results indicate that the three models were able to predict Dimensional Personality Disorders well, although some differences emerge between them. Specific relationships between dimensional disorders and traits, the role of the facets as well as the utility of the results reported are discussed.

Controlling for Response Biases in Self-Report Scales: Forced-Choice vs. Psychometric Modeling of Likert Items

One important problem in the measurement of non-cognitive characteristics such as personality traits and attitudes is that it has traditionally been made through Likert scales, which are susceptible to response biases such as social desirability (SDR) and acquiescent (ACQ) responding. Given the variability of these response styles in the population, ignoring their possible effects on the scores may compromise the fairness and the validity of the assessments. Also, response-style-induced errors of measurement can affect the reliability estimates and overestimate convergent validity by correlating higher with other Likert-scale-based measures. Conversely, it can attenuate the predictive power over non-Likert-based indicators, given that the scores contain more errors. This study compares the validity of the Big Five personality scores obtained: (1) ignoring the SDR and ACQ in graded-scale items (GSQ), (2) accounting for SDR and ACQ with a compensatory IRT model, and (3) using forced-choice blocks with a multi-unidimensional pairwise preference model (MUPP) variant for dominance items. The overall results suggest that ignoring SDR and ACQ offered the worst validity evidence, with a higher correlation between personality and SDR scores. The two remaining strategies have their own advantages and disadvantages. The results from the empirical reliability and the convergent validity analysis indicate that when modeling social desirability with graded-scale items, the SDR factor apparently captures part of the variance of the Agreeableness factor. On the other hand, the correlation between the corrected GSQ-based Openness to Experience scores, and the University Access Examination grades was higher than the one with the uncorrected GSQ-based scores, and considerably higher than that using the estimates from the forced-choice data. Conversely, the criterion-related validity of the Forced Choice Questionnaire (FCQ) scores was similar to the results found in meta-analytic studies, correlating higher with Conscientiousness. Nonetheless, the FCQ-scores had considerably lower reliabilities and would demand administering more blocks. Finally, the results are discussed, and some notes are provided for the treatment of SDR and ACQ in future studies.

Searching for G: A New Evaluation of SPM-LS Dimensionality

There has been increased interest in assessing the quality and usefulness of short versions of the Raven's Progressive Matrices. A recent proposal, composed of the last twelve matrices of the Standard Progressive Matrices (SPM-LS), has been depicted as a valid measure of g. Nonetheless, the results provided in the initial validation questioned the assumption of essential unidimensionality for SPM-LS scores. We tested this hypothesis through two different statistical techniques. Firstly, we applied exploratory graph analysis to assess SPM-LS dimensionality. Secondly, exploratory bi-factor modelling was employed to understand the extent that potential specific factors represent significant sources of variance after a general factor has been considered. Results evidenced that if modelled appropriately, SPM-LS scores are essentially unidimensional, and that constitute a reliable measure of g. However, an additional specific factor was systematically identified for the last six items of the test. The implications of such findings for future work on the SPM-LS are discussed.

Multicultural Validation of the Zuckerman-Kuhlman-Aluja Personality Questionnaire Shortened Form (ZKA-PQ/SF) Across 18 Countries

The aim of this study was to assess the psychometric properties of the Zuckerman-Kuhlman-Aluja Personality Questionnaire shortened form (ZKA-PQ/SF) in 18 cultures and 13 languages of different African, American, Asian, and European cultures and languages. The results showed that the five-factor structure with 20 facets replicated well across cultures with a total congruence coefficient of .97. A confirmatory factor analysis (CFA) resulted in adequate fit indices for the five factors based on the comparative fit index (CFI), Tucker-Lewis index (TLI; >.90), and RMSEA (.031-.081). A series of CFA to assess measurement invariance across cultures resulted in adequate CFIs and TLIs for configural and metric invariance. However, factors did not show scalar invariance. Alpha internal consistencies of five factors ranged between .77 (Sensation Seeking) and .86 (Neuroticism). The average alpha of the 20 facets was .64 with a range from .43 (SS4) to .75 (AG1). Nevertheless, alpha reliabilities were lower in some facets and cultures, especially for Senegal and Togo. The average percentage of the variance explained based on the adjusted R² was 2.9%, 1.7%, and 5.1% for age, sex, and, cultures, respectively. Finally, multidimensional scaling suggested that geographically or culturally close cultures share mean profile similarities.