A Generally Robust Approach for Testing Hypotheses and Setting Confidence Intervals for Effect Sizes
Psychological Methods Volume 13, Number 2, ISSN 1082-989X
Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of freedom heteroscedastic statistic for independent and correlated groups designs in order to achieve robustness to the biasing effects of nonnormality and variance heterogeneity. The authors describe a nonparametric bootstrap methodology that can provide improved Type I error control. In addition, the authors indicate how researchers can set robust confidence intervals around a robust effect size parameter estimate. In an online supplement, the authors use several examples to illustrate the application of an SAS program to implement these statistical methods. (Contains 4 tables, 1 figure, and 24 footnotes.)
Keselman, H.J., Algina, J., Lix, L.M., Wilcox, R.R. & Deering, K.N. (2008). A Generally Robust Approach for Testing Hypotheses and Setting Confidence Intervals for Effect Sizes. Psychological Methods, 13(2), 110-129.
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Suggested Methods of Effect Size Estimation for Research in Information Technology and Teacher Education
Li-Ting Chen & Leping Liu, University of Nevada, Reno, United States
Society for Information Technology & Teacher Education International Conference 2019 (Mar 18, 2019) pp. 1143–1152
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