Resumen En Psicología, para obtener evidencias sobre validez de constructo mediante Análisis Factorial Confirmatorio es habitual trabajar con variables ordinales que presentan asimetría. En este estudio de simulación se analiza el comportamiento del método de Mínimos Cuadrados no Ponderados (ULS) en escalas tipo Likert con base en los índices χ2 de razón de verosimilitud (C2) y RMSEA. Para ello, se han manipulado cuatro factores experimentales: el número de factores o dimensiones (2, 3, 4, 5, 6), número de puntos de respuesta (3, 4, 5, 6), grado de asimetría de la distribución de respuestas (simétrica, asimétrica moderada y severa) y tamaño muestral (100, 150, 250, 450, 650, 850) de los modelos simulados. Según los principales resultados, el índice C2 muestra siempre un error Tipo I mayor que RMSEA, con independencia de los factores experimentales analizados. Finalmente, se discuten diferentes alternativas de acción y se presentan futuras líneas de investigación. AbstractIn order to obtain evidences about construct validity through Confirmatory Factor Analysis in Social Sciences, working with skewed ordinal variables has been usual. In this simulation study the performance of Unweighted Least Squares (ULS) method in Likert scales according to Likelihood Ratio Test (C2) and RMSEA indices is analysed through Type I error. For this purpose, four experimental factors have been manipulated: the number of factors or dimensions (2, 3, 4, 5, 6), the number of response points (3, 4, 5, 6), the degree of skewness of the responses distribution (symmetric, moderately and severely asymmetric) and the sample size (100, 150, 250, 450, 650, 850) of the simulated models. According to the main results, C2 index always shows a bigger Type I error than RMSEA, regardless of the experimental factors analysed. Finally, different action alternatives are discussed and future research lines are presented.

We describe the process of testing a measurement model of a satisfaction rating scale for use with training programs. The scale was developed by the Evaluation Unit of the University of Seville's Training Center. Following an initial content validity study in which we reviewed how training evaluation is modeled, a 12-item rating scale was developed and administered to a sample of 2746 subjects. The measurement model was examined through an exploratory factor analysis using polychoric correlations, the results of which were consistent with the previous dimensions obtained in the content validity study. In order to refine the model under study, a confirmatory factor analysis (also based on polychoric correlations) was then performed. The results present empirical evidence of the measurement model's validity. The scale offers simple, direct and reliable training satisfaction scores. Future development of the scale is discussed.

The combination of depression and anxiety is among the most prevalent comorbidities of disorders leading to substantial functional impairment in children and adolescents. The network perspective offers a new paradigm for understanding and measuring psychological constructs and their comorbidity. The present study aims to apply network analysis to explore the comorbidity between depression and anxiety symptoms. Specifically, the study examines bridge symptoms, comorbidity, and shortest pathway networks and estimates the impact of the symptoms in the network’s connectivity and structure. The findings show that “feeling lonely” and “feeling unloved” are identified as the most central bridge symptoms. The shortest path network suggests that the role of a mixed anxiety-depressive symptomatology, and specific and non-specific symptoms of clinical criteria, such as “worries,” “feels depressed,” “fears school,” and “talks about suicide” could serve as a warning for comorbidity.

In order to obtain evidences about construct validity through Confirmatory Factor Analysis, it has been usual treating Likert-type scales as if they were continuous variables measured on an interval scale. Therefore, Maximum Likelihood estimation method has been broadly applied, but in turn it implies problems concerning both Pearson correlations and skewness in the distribution of responses to items. In this simulation study we analyse —through χ2, Type I error, and power— correctly specified and misspecified models comparing five estimation methods (Maximum Likelihood —ml—, Robust Maximum Likelihood —rml—, Weighted Least Squares —wls—, nweighted Least Squares —uls— and Robust Unweighted Least Squares —ruls—) in relation to the models features: number of factors, number of response categories, items’ skewness, and sample size. We advise using ruls estimation method, in which polychoric correlations are implied.Keywords: Confirmatory Factor Analysis, Likert-type scales, estimation methods, Type I error, power.