Bootstrap resampling approaches for repeated measure designs: Relative robustness to sphericity and normality violations

The current article proposes a bootstrap-F method and a bootstrap-T-2 method for use in a one-way repeated measure ANOVA design. Using a Monte Carlo approach in which sample size, nonsphericity, and nonnormality are systematically manipulated, the Type I error rate of the two bootstrap methods are compared to that of the traditional F test, the Geisser-Greenhouse adjusted F test, the Box adjusted F test, the Huynh-Feldt adjusted F test, the beta -trimmed mean method using beta = .1 and beta = .2, and the one-sample multivariate T-2 test. Results show the bootstrap-F method controls Type I error better than all other methods considered when normality and sphericity assumptions are violated simultaneously.