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Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

University of Oviedo, gvallejo{at}uniovi.es

Jaime Arnau Gras

University of Barcelona

Manuel Ato García

University of Murcia

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown—Forsythe (MBF) procedure and the mixed-model procedure adjusted by the Kenward—Roger solution available in SAS PROC MIXED. The authors found that, overall, the MBF procedure appeared to be the least sensitive to the factors examined in the present study; however, this is not necessarily the case for all data sets. As the results show, for tests of the between-subjects main effect, the MBF approach outperformed the mixed-model method when fitting either a patterned or nonpatterned covariance structure. But for tests of within-subjects effects, its Type I error control advantages decrease.

Key Words: covariance heterogeneity • empirical generalized least squares estimation • modified Brown—Forsythe procedure • multivariate mixed model

Educational and Psychological Measurement, Vol. 67, No. 3, 410-432 (2007)
DOI: 10.1177/0013164406294777


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