300 – Models: Assumptions and Novel Applications
Comparing the Performance of Approaches for Testing the Homogeneity of Variance Assumption in One-Factor ANOVA Models
Yan Wang
University of California at Los Angeles Fielding School of Public Health
Aarti P. Bellara
University of South Florida
Thanh v. Pham
University of South Florida
Diep Thi Nguyen
University of South Florida
Patricia RodrÃguez de Gil
University of South Florida
Yi-Hsin Chen
University of South Florida
Harold Holmes
University of South Florida
Tyler Hicks
University of South Florida
Isaac Li
University of South Florida
Eun Sook Kim
University of South Florida
Jeanine Romano
University of South Florida
Jeffrey D. Kromrey
University of South Florida
The validity of the results of an ANOVA test is largely dependent on satisfying the homogeneity of variance, normality, and independence assumptions. Violations of these assumptions lead to distorted Type I error rates. Various tests to check the homogeneity of variance assumption for non-normal data have been proposed in the literature, yet there is no consensus as to which test is most appropriate. A simulation study was conducted to explore the Type I error rates and statistical power of fourteen approaches for testing the homogeneity of variance assumption in one-way ANOVA models. Seven factors were manipulated in the study: number of groups, average number of observations per group, pattern of sample sizes in groups, pattern of population variances, maximum variance ratio, population distribution shape, and nominal alpha level for the test of variances. Results from this study delineate the performance of the tests under a wide variety of conditions, providing researchers with information to guide the selection of a valid test for assessing the tenability of this critical assumption.