One approach to the analysis of repeated measures data allows researchers t
o model the covariance structure of the data rather than presume a certain
structure, as is the case with conventional univariate and multivariate, te
st statistics. This mixed-model approach was evaluated for testing all poss
ible pairwise differences among repeated measures marginal means in a Betwe
en-Subjects x Within-Subjects design. Specifically, the authors investigate
d Type I error and power rates for a number of simultaneous and stepwise mu
ltiple comparison procedures using SAS (1999) PROC MIXED in unbalanced desi
gns when normality and covariance homogeneity assumptions did not hold. J.
P. Shaffer's (1986) sequentially rejective step-down and Y. Hochberg's (198
8) sequentially acceptive step-up Bonferroni procedures, based on an unstru
ctured covariance structure, had superior Type I error control and power to
detect true pairwise differences across the investigated conditions.