Comparing means under heteroscedasticity and nonnormality: Further exploring robust means modeling
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Date
2018
Authors
Counsell, Alyssa
Chalmers, Phil
Cribbie, Robert
Journal Title
Journal ISSN
Volume Title
Publisher
Wayne State University Library System
Abstract
Researchers are commonly interested in comparing the means of independent groups when distributions are nonnormal and variances are unequal. Robust means modeling (RMM) has been proposed as an alternative to ANOVA-type procedures when the assumptions of normality and variance homogeneity are violated. This paper extends work comparing the Type I error and power rates of RMM to those for the trimmed Welch procedure. A Monte Carlo study was used to investigate RMM and the trimmed Welch procedure under several conditions of nonnormality and variance heterogeneity. Our results suggest that the trimmed Welch provides a better balance of Type I error control and power than RMM.
Description
Keywords
normality, equal population variances, assumption violation
Citation
Counsell, A., Chalmers, R. P., & Cribbie, R. A. (in press). Comparing means under
heteroscedasticity and nonnormality: Further exploring robust means modeling. Journal of Modern Applied Statistical Methods.