6.1. Introduction
The multivariate and the univariate approaches to analyzing the repeated measures data presented in Chapter 5 represent the extremes of the assumptions made on covariance structures. The former has absolutely no requirements (except that the variance covariance matrix of repeated measures be positive definite) and hence requires one to estimate the maximum possible number of variance and covariance parameters. By contrast, the latter imposes stringent requirements (except when sphericity is required) when the entire covariance structure is governed only by two parameters (in the case of compound symmetry and a few more in the of Huynh-Feldt structure).
Nonetheless, each of these two approaches permits us to use the least squares ...
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