Modeling maladaptive personality traits with unipolar item response theory: The case of Callousness.

Most IRT applications in personality assume that the measured trait is a bipolar dimension, normally distributed in the population. These assumptions, however, could be questionable for maladaptive, (quasi) pathological traits that still fall in the normal range. This study focuses on one such trait, Callousness, and uses two different instruments and samples to determine whether there is a basis for modeling it as a unipolar trait instead of a bipolar one. More specifically, the following community samples were used, recruited in several Spanish high schools: a) 719 adolescents (13-19 years old, 55.8% girls), b) 681 adolescents (13-19 years old, 44.9% girls). Callousness was assessed with the Inventory of Callous-unemotional traits and Antisocial behavior in the first sample and with the Inventory of Callous Unemotional traits in the second sample. We compared the outcomes of fitting the Graded-Response model (a bipolar-trait model) and the Log-Logistic model (a unipolar trait model) in these community samples and found that they differed considerably at the scoring level. In terms of accuracy, the conditional reliability functions had opposite patterns: it was maximum at high levels in the Graded-Response model and at low levels in the Log-Logistic model. In terms of validity, the models showed different results regarding the prediction of indirect aggressiveness and non-planning impulsiveness.

Measuring Unipolar Traits With Continuous Response Items: Some Methodological and Substantive Developments.

In recent years, some models for binary and graded format responses have been proposed to assess unipolar variables or "quasi-traits." These studies have mainly focused on clinical variables that have traditionally been treated as bipolar traits. In the present study, we have made a proposal for unipolar traits measured with continuous response items. The proposed log-logistic continuous unipolar model (LL-C) is remarkably simple and is more similar to the original binary formulation than the graded extensions, which is an advantage. Furthermore, considering that irrational, extreme, or polarizing beliefs could be another domain of unipolar variables, we have applied this proposal to an empirical example of superstitious beliefs. The results suggest that, in certain cases, the standard linear model can be a good approximation to the LL-C model in terms of parameter estimation and goodness of fit, but not trait estimates and their accuracy. The results also show the importance of considering the unipolar nature of this kind of trait when predicting criterion variables, since the validity results were clearly different.

Which profile of people tends to ignore preventive measures against COVID-19? The role of intelligence and the big five personality traits.

Although a considerable amount of research has been done on the role of personality traits in the prediction of compliance with COVID-19 preventive measures, the possible role of intelligence has been studied far less. For this reason, the main goal of the current study was to determine what the predictive role of intelligence is when considered together with the Big Five personality traits. A total of 404 participants answered three instruments: the Overall Personality Assessment Scale (OPERAS), which assesses personality traits, the COmpliance with pandemic COmmands Scale (COCOS), which assesses compliance, and the test of intelligence International Cognitive Ability Resource (ICAR Sample Test). The results show that all variables are correlated with compliance, although the weight of emotional stability on compliance was not significant. The only variable negatively related to compliance was extraversion. The results of the structural equation modelling suggest that intelligence has a direct relationship with compliance, and an indirect relationship through openness to experience. According to the results, intelligence is an important variable that should be considered in the prediction of compliance with these preventive measures.

[Decalogue for the Factor Analysis of Test Items].

Decalogue for the Factor Analysis of Test Items. BACKGROUND: In the study of the psychometric properties of the items of a test, a fundamental aspect is the analysis of their dimensional structure. The objective of this work is to provide some guidelines that allow the factor analysis of the items to be carried out in a rigorous and systematic way. METHOD: A review of the recent psychometric literature was carried out to identify the fundamental steps to be followed in order to carry out an adequate factor analysis of the items of a test. RESULTS: Ten main recommendations were identified to carry out the factorial analysis of the items of a test: adequacy of the data and the sample, univariate statistics, justification of the analysis, selection of the analyzable items, type of model, most appropriate factorial solution, estimation of the parameters, adequacy of the factorial solution, substantive coherence of the model, and final version of the test. CONCLUSIONS: If the ten recommendations proposed in the current psychometric literature are systematically followed, it will be possible to optimize the quality of the tests and the decision-making based on the estimates of the scores obtained through them. These recommendations should be useful to both researchers and practitioners.

Is Correcting for Acquiescence Increasing the External Validity of Personality Test Scores?

BACKGROUND: Balanced scales control for acquiescence (ACQ) because the tendency of the respondent to agree with the positive items is cancelled out by the tendency to agree with opposite-pole items. When full balance is achieved, ACQ is not expected to affect external validity. Otherwise, attenuated estimates are expected to appear if no control methods such as Lorenzo-Seva & Ferrando's (2009) are used. METHOD: Expected results were derived analytically. Subsequently, a simulation was carried out to assess (a) how ACQ impacted external validity and (b) how validity estimates behaved when ACQ was corrected. Two illustrative examples are provided. RESULTS: A sizable number of items and/or high content loadings tended to decrease ACQ's impact on validity estimates, making the empirical coefficient closer to its structural value. Furthermore, when scales were well balanced, the controlled and uncorrected scores were close to each other, and led to unbiased validity estimates. When the scales were unbalanced and no corrections were used, attenuated empirical validity coefficients inevitably appeared. CONCLUSIONS: Designing a well-balanced test or correcting for ACQ are the best ways to minimize attenuation in external validity estimation.