You are here:

A Generally Robust Approach for Testing Hypotheses and Setting Confidence Intervals for Effect Sizes
ARTICLE

, , , ,

Psychological Methods Volume 13, Number 2, ISSN 1082-989X

Abstract

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.)

Citation

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. Retrieved October 16, 2019 from .

This record was imported from ERIC on April 18, 2013. [Original Record]

ERIC is sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education.

Copyright for this record is held by the content creator. For more details see ERIC's copyright policy.

Keywords

Cited By

View References & Citations Map

These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact info@learntechlib.org.