Analyzing repeated measures using resampling methods
Abstract
This article evaluated the robustness of several approaches for analyzing repeated measures designs when the assumptions of normality and multisample sphericity are violated separately and jointly. Specifically, the authors’ work compares the performance of two resampling methods, bootstrapping and permutation tests, with the performance of the usual analysis of variance (ANOVA) model and the mixed linear model procedure ad-justed by the Kenward–Roger solution available in SAS PROC MIXED. The authors found that the permutation test outperformed the other three methods when normality and sphericity assumptions did not hold. In contrast, when normality and multisample sphericity assumptions were violated the results clearly revealed that the Bootstrap-F test provided generally better control of Type I error rates than the permutation test and mixed linear model approach. The execution of ANOVA approach was considerably influenced by the presence of heterogeneity and lack of spheric-ity, but scarcely affected by the absence of normality.Downloads
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