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The Analysis of Repeated Measurements with Mixed-Model Adjusted F Tests

University of Wisconsin-Milwaukee, rkowal{at}uwm.edu

H. J. Keselman

University of Manitoba

James Algina

University of Florida

Russell D. Wolfinger

SAS Institute

One approach to the analysis of repeated measures data allows researchers to model the covariance structure of their data rather than presume a certain structure, as is the case with conventional univariate and multivariate test statistics. This mixed-model approach, available through SAS PROC MIXED, was compared to a Welch-James type statistic. The Welch-James approach is known to provide generally robust tests of treatment effects in a repeated measures between-by within-subjects design under assumption violations given certain sample size requirements. The mixed-model F tests were based on Kenward-Roger’s adjusted degrees of freedom solution, an approach specifically proposed for small sample settings. The authors investigated Type I error control for repeated measures main and interaction effects in unbalanced designs when normality and covariance homogeneity assumptions did not hold. The mixed-model Kenward-Roger’s adjusted F tests showed superior Type I error control in small sample size conditions in which the Welch-James type statistic was nonrobust; power rates, however, did not favor one approach over the other.

Key Words: repeated measurements • mixed-model analyses • Welch-James adjusted-df test • Kenward-Roger’s adjusted-df test • small sample settings

Educational and Psychological Measurement, Vol. 64, No. 2, 224-242 (2004)
DOI: 10.1177/0013164403260196


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