This paper presents a review of recent developments in statistical techniques for repeated-measures analysis of variance. Since the literature has emphasized the issue of mixed model assumptions and their violation, we present an updated perspective on the nature of these assumptions and their implications for mixed model, adjusted mixed model, or multivariate significance tests. However, the central theme of the review is that the validity of mixed model assumptions is but one consideration in selection of an appropriate method of repeated-measures ANOVA. In particular, we recommend the avoidance of omnibus significance tests in favor of specific planned comparisons whenever hypotheses more specific than the omnibus null hypothesis may be formulated a priori. The analyst must also consider whether multiple dependent measures are to be analyzed, and the paper discusses alternative approaches to true multivariate repeated-measures designs. It also includes discussion of other relevant issues, including a brief review of the strengths and weaknesses of commonly available statistical software when applied to the analysis of repeated-measures data.