Bootstrap Resampling Approaches for Repeated Measure Designs: Relative Robustness to Sphericity and Normality Violations

Abstract

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 β-trimmed mean method using β =.1and β = .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.