Multiple Comparison Procedures for Trimmed Means

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Abstract

Stepwise multiple comparison procedures (MCPs) based on least squares and trimmed estimators were compared for their rates of Type I error and their ability to detect true pairwise group differences. The MCPs were compared in unbalanced one-way completely randomized designs when normality and homogeneity of variance assumptions were violated. Results indicated that MCPs based on trimmed means and Winsorized variances controlled rates of Type I error, whereas MCPs based on least squares estimators typically could not, particularly when the data were highly skewed. However, MCPs based on least squares estimators were substantially more powerful than their counterparts based on trimmed means and Winsorized variances when the data were only moderately skewed, a finding which qualifies recommendations on the use of trimmed estimators offered in the literature.

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