Comparison of Two Procedures for Analyzing Small Sets of Repeated Measures Data

Abstract

This article compares two methods for analyzing small sets of repeated measures data under normal and non-normal heteroscedastic conditions: a mixed model approach with the Kenward-Roger correction and a multivariate extension of the modified Brown-Forsythe (BF) test. These procedures differ in their assumptions about the covariance structure of the data and in the method of estimation of the parameters defining the mean structure. Simulation results show that the BF test outperformed its competitor, in terms of Type I errors, particularly when the total sample size was small, and the data were normally distributed. Under non-normal distributions the BF test tended to err on the side of conservatism. Results also indicate that neither method was uniformly more powerful. With very few exceptions, the power differences between these two methods depended on the population covariance structure, the nature of the pairing of covariance matrices and group sizes, and the relationship between mean vectors and dispersion matrices.