title: [Power comparison of new tests to analyze repeated measures data].
abstract: 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.
authors.affiliation: Facultad de Humanidades-Escuela de Psicología, Universidad de Santiago de Chile, Chile. plivacic@usach.cl
authors.forename: Pablo
authors.initials: P
authors.lastname: Livacic-Rojas
authors.forename: Guillermo
authors.initials: G
authors.lastname: Vallejo
authors.forename: Paula
authors.initials: P
authors.lastname: Fernández
mesh_terms: D006801:Humans; D008960:Models, Psychological; D010507:Periodicity; D011581:Psychological Tests; D012680:Sensitivity and Specificity