Propiedades psicométricas de un cuestionario para medir la calidad en educación física

El objetivo fue analizar los parámetros psicométricos y las estructuras factoriales del Cuestionario de EFC en el contexto ecuatoriano. La muestra estuvo conformada por 374 profesionales de Educación Física. Se utilizó el cuestionario Índice Global de la Educación Física de Calidad. Se realizó un Análisis Factorial Exploratorio (AFE) para evaluar la estructura factorial y el análisis se implementó utilizando una matriz policórica y el método de extracción Robust Diagonally Weighted Least Squares (RDWLS). Los tests de esfericidad de Bartlett (4092,8, gl = 1225, p < .001) e KMO (.97) sugieren la interpretabilidad muy buena de la matriz de correlación de los ítems.

Structural validity of the Chronic Pain Coping Inventory—Brazilian version

Background The Chronic Pain Coping Inventory (CPCI) has been widely used to measure coping with pain, however, the psychometric properties of the Brazilian CPCI are unknown. Aim To verify the validity and reliability of the CPCI-Brazilian version. Materials and methods A sample of 705 outpatients with chronic pain participated in the study. Cronbach’s alpha, corrected item-total correlations, and confirmatory factor analysis were performed, using the method of Diagonally Weighted Least Squares. Results Construct validity was supported with a factor loading range of 0.36–0.90 (9 factors) corroborating original loads. The final model had adequate fit with items 42 and 54 excluded, D.F = 2174, TLI = 0.96; CFI = 0.96 and RMSEA = 0.051(p = 0.067). Eight of the nine CPCI scales showed satisfactory reliability (Cronbach’s alpha ranged from 0.70 to 0.92). The Relaxation scale obtained a low alpha value (0.53). Conclusion The CPCI-Brazilian version, after exclusion of items 42 and 54, is valid to measure chronic pain coping in Brazilian adults.

Categorical Omega With Small Sample Sizes via Bayesian Estimation: An Alternative to Frequentist Estimators

When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a substantially biased estimate of categorical omega. In this study, we applied Bayesian estimation methods for computing categorical omega. The simulation study investigated the performance of categorical omega under a variety of conditions through manipulating the scale length, number of response categories, distributions of the categorical variable, heterogeneities of thresholds across items, and prior distributions for model parameters. The Bayes estimator appears to be a promising method for estimating categorical omega. M plus and SAS codes for computing categorical omega were provided.

Comparison of Alternative Estimation Methods in Confirmatory Factor Analyses of the General Health Questionnaire

This paper examines the implications of violating assumptions concerning the continuity and distributional properties of data in establishing measurement models in social science research. The General Health Questionnaire-12 uses an ordinal response scale. Responses to the GHQ-12 from 201 Hong Kong immigrants on arrival in Australia showed that the data were not normally distributed. A series of confirmatory factor analyses using either a Pearson product-moment or a polychoric correlation input matrix and employing either maximum likelihood, weighted least squares or diagonally weighted least squares estimation methods were conducted on the data. The parameter estimates and goodness-of-fit statistics provided support for using polychoric correlations and diagonally weighted least squares estimation when analyzing ordinal, nonnormal data.