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Bias in the Analysis of Repeated-Measures Designs: Some Alternative Approaches
Robert B. McCall and Mark I. Appelbaum
Vol. 44, No. 3 (Sep., 1973), pp. 401-415
Stable URL: http://www.jstor.org/stable/1127993
Page Count: 15
You can always find the topics here!Topics: Covariance, Analysis of variance, Degrees of freedom, Polynomials, Statistical variance, Design analysis, Mathematical dependent variables, Child development, Correlations, Statistical discrepancies
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The conventional analysis of variance applied to designs in which each subject is measured repeatedly requires stringent assumptions regarding the variance-covariance (i. e., correlations among repeated measures) structure of the data. Violation of these assumptions results in too many rejections of the null hypothesis for the stated significance level. This paper considers several alternatives when heterogeneity of covariance exists, including nonparametric tests, randomization and matching procedures, Box and Greenhouse-Geisser corrections, and multivariate analysis. The presentation is from an applied rather than theoretical standpoint. Multivariate techniques that make no covariance assumptions and provide exact probability statements represent the most versatile solution.
Child Development © 1973 Society for Research in Child Development