Comparación de la potencia de nuevos enfoques para analizar datos de medidas repetidas

Abstract

Power comparison of new tests to analyze repeated measures data. This work compares the sensitivity of five modern analytical techniques for detecting the effects of a design with measures which are partially repeated when the assumptions of the traditional ANOVA approach are not met, namely: the approach of the mixed model adjusted by means of the SAS Proc Mixed module, the Bootstrap-F approach, the Brown-Forsythe multivariate approach, the Welch-James multivariate approach and Welch-James multivariate approach with robust estimators. Previously, Livacic-Rojas, Vallejo and Fernández found out that these methods are comparable in terms of their Type I error rates. The results obtained suggest that the mixed model approach, as well as the Brown-Forsythe and Welch-James approaches, satisfactorily controlled the Type II error rates corresponding to the main effects of the measurement occasions under most of the conditions assessed.