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.
