The authors compare the operating characteristics of the bootstrap-F approach, a direct extension of the work of Berkovits, Hancock, and Nevitt, with Huynh's improved general approximation (IGA) and the Brown-Forsythe (BF) multivariate approach in a mixed repeated measures design when normality and multisample sphericity assumptions do not hold. The results of the simulation show that the three approaches adequately control Type I error when data are generated from normal or slightly nonnormal distributions. However, when data are generated from distributions with moderate or severe skewness, the approaches tend to produce conservative Type I error rates, except the IGA test of the main effect, which has liberal Type I error rates in some conditions. With regard to power, it was found that the bootstrap-F approach can compete with the IGA approach but not with the BF approach: The power differences favoring the bootstrap-F approach are generally small, whereas those favoring the BF approach are substantial.