Multiple Comparisons for Mixed Models
George Zhengzhi Xia
Broschiertes Buch

Multiple Comparisons for Mixed Models

Concepts, Theoretical Reasons, and Case Studies on Longitudinal and Repeated Measures Data Analysis

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The assumptions of constant variance and uncorrelated errors that are made in general linear models often do not hold for longitudinal data, repeated measures, and general mixed models. The traditional multiple comparison (MCP) methods based on the studentized range distribution and multivariate t distribution are exact only under the assumptions of linearity, normality, constant variance, and uncorrelated error. The element of MCPs is often to compare the means of two measures. For the two-sample t test, even a moderate correlation to observations will result in serious bias; this problem def...