Kowalchuk R K, Keselman H J
Department of Educational Psychology, University of Wisconsin, P.O. Box 413, Milwaukee, Wisconsin 53201, USA.
Psychol Methods. 2001 Sep;6(3):282-96. doi: 10.1037/1082-989x.6.3.282.
One approach to the analysis of repeated measures data allows researchers to model the covariance structure of the data rather than presume a certain structure, as is the case with conventional univariate and multivariate test statistics. This mixed-model approach was evaluated for testing all possible pairwise differences among repeated measures marginal means in a Between-Subjects x Within-Subjects design. Specifically, the authors investigated Type I error and power rates for a number of simultaneous and stepwise multiple comparison procedures using SAS (1999) PROC MIXED in unbalanced designs when normality and covariance homogeneity assumptions did not hold. J. P. Shaffer's (1986) sequentially rejective step-down and Y. Hochberg's (1988) sequentially acceptive step-up Bonferroni procedures, based on an unstructured covariance structure, had superior Type I error control and power to detect true pairwise differences across the investigated conditions.
一种分析重复测量数据的方法使研究人员能够对数据的协方差结构进行建模，而不是像传统单变量和多变量检验统计那样假定某种结构。在被试间×被试内设计中，对这种混合模型方法进行了评估，以检验重复测量边际均值之间所有可能的成对差异。具体而言，作者在正态性和协方差同质性假设不成立的非平衡设计中，使用SAS（1999）的PROC MIXED程序，研究了多种同时性和逐步多重比较程序的I型错误率和检验功效。基于非结构化协方差结构的J. P. 谢弗（1986）的顺序拒绝逐步下调法和Y. 霍赫贝格（1988）的顺序接受逐步上调邦费罗尼程序，在I型错误控制和检测所研究条件下真正成对差异的功效方面表现更优。
