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.

Decálogo para el Análisis Factorial de los Ítems de un Test / Decalogue for the Factor Analysis of Test Items

Antecedentes: en el estudio de las propiedades psicométricas de los ítemsde un test un aspecto fundamental es el análisis de su estructura. El objetivodel presente trabajo es dar unas pautas que permitan llevar a cabo el análisisfactorial de los ítems de una forma rigurosa y sistemática. Método:se llevó a cabo una revisión de la literatura reciente para identifi car lospasos fundamentales que se han de seguir para llevar a cabo un análisisfactorial adecuado de los ítems de un test. Resultados: se identifi carondiez recomendaciones principales para llevar a cabo el análisis factorialde los ítems de un test: adecuación de los datos y la muestra, estadísticosunivariados, justifi cación del análisis, selección de los ítems analizables,tipo de modelo, solución más apropiada, estimación de los parámetros,adecuación de la solución factorial, coherencia sustantiva del modelo yversión fi nal del test. Conclusión: si se siguen de forma sistemática las diezrecomendaciones propuestas, se conseguirá optimizar la calidad de los testy la toma de decisiones basadas en las estimaciones de las puntuacionesobtenidas mediante los mismos. Estas directrices son recomendablestanto en el ámbito de la investigación como en contextos más aplicadosy profesionales.

Background: In the studyof the psychometric properties of the items of a test, a fundamental aspectis the analysis of their dimensional structure. The objective of this workis to provide some guidelines that allow the factor analysis of the items tobe carried out in a rigorous and systematic way. Method: A review of therecent psychometric literature was carried out to identify the fundamentalsteps to be followed in order to carry out an adequate factor analysis ofthe items of a test. Results: Ten main recommendations were identifi ed tocarry out the factorial analysis of the items of a test: adequacy of the dataand the sample, univariate statistics, justifi cation of the analysis, selectionof the analyzable items, type of model, most appropriate factorial solution,estimation of the parameters, adequacy of the factorial solution, substantivecoherence of the model, and fi nal version of the test. Conclusions: If theten recommendations proposed in the current psychometric literature aresystematically followed, it will be possible to optimize the quality of thetests and the decision-making based on the estimates of the scores obtainedthrough them. These recommendations should be useful to both researchersand 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.

Is Correcting for Acquiescence Increasing the External Validity of Personality Test Scores? / ¿La Corrección por Aquiescencia Aumenta la Validez Externa de las Puntuaciones en Personalidad?

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. (AU)

Antecedentes: construir escalas balanceadas permite controlar la aquiescencia (ACQ), haciendo que la tendencia del encuestado a estar de acuerdo con los ítems positivos se cancele con la tendencia a estar de acuerdo con los ítems del polo opuesto. En caso contrario, se esperarán estimaciones atenuadas de los coeficientes de validez externa en caso de no utilizar algún método de control (Lorenzo-Seva & Ferrando, 2009). Método: se llevó a cabo (a) un desarrollo analítico (b) una simulación para evaluar (a) el impacto de ACQ en la validez externa y (b) el comportamiento de las estimaciones de validez cuando se corrige por ACQ. Incluyendo finalmente dos ejemplos ilustrativos. Resultados: número alto de ítems y/o cargas altas en el factor de contenido tienden a disminuir el impacto de ACQ en las estimaciones de validez. Además, con escalas balanceadas por diseño, las diferencias entre las puntuaciones corregidas y no corregidas son menores, llevando a estimaciones de validez insesgadas. En escalas no balanceadas ni corregidas aparece una atenuación en el coeficiente de validez empírico. Conclusiones: diseñar pruebas balanceadas o corregir ACQ son las mejores maneras de minimizar la atenuación en la estimación de la validez externa. (AU)