A Comparison of Data Analysis Strategies for Testing Omnibus Effects in Higher-Order Repeated Measures Designs.

Methods for analyzing repeated measures data, in addition to the conventional and corrected degrees of freedom univariate and multivariate solutions, are presented in this review. These "newer" methods offer researchers either improved control over Type I errors and/or greater power to detect treatment effects when (a) certain assumptions are violated, and/or (b) missing data exists. In particular, Huynh's (1978) Improved General Approximate method, a multivariate Welch (1951)/James (1951)-type test, the mixedmodel approach (Littell, Milliken, Stroup, & Wolfinger, 1996) and Boik's (1997) empirical Bayes method are discussed. We review the literature regarding these procedures with respect to their robustness, ability to handle missing data, and availability of software to obtain numerical results.