MULTIPLE COMPARISON PROCEDURES FOR TRIMMED MEANS

Stepwise multiple comparison procedures (MCPs) based on least squares and trimmed estimators were compared for their rates of Type I error a nd their ability to detect true pairwise group differences. The MCPs w ere compared in unbalanced one-way completely randomized designs when normality and homogeneity of variance assumptions were violated. Resul ts 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 highl y skewed. However, MCPs based on least squares estimators were substan tially more powerful than their counterparts based on trimmed means an d Winsorized variances when the data were only moderately skewed, a fi nding which qualifies recommendations on the use of trimmed estimators offered in the literature.