Comparing Means under Heteroscedasticity and Nonnormality: Further Exploring Robust Means Modeling Further Exploring Robust Means Modeling

Comparing the means of independent groups is a concern when the assumptions of normal-ity and variance homogeneity are violated. Robust means modeling (RMM) was proposed as an alternative to ANOVA-type procedures when the assumptions of normality and vari-ance homogeneity are violated. The purpose of this study is to compare the Type I error and power rates of RMM to the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnor-mality and variance heterogeneity. The results suggest that the trimmed Welch provides a better balance of Type I error control and power than RMM