DOI
https://doi.org/10.25772/BWFP-YE95
Defense Date
2015
Document Type
Thesis
Degree Name
Master of Science
Department
Biostatistics
First Advisor
Dr. Wen Wan
Second Advisor
Dr. Donna McClish
Third Advisor
Dr. Qin Wang
Abstract
Analysis of variance (ANOVA) is a robust test against the normality assumption, but it may be inappropriate when the assumption of homogeneity of variance has been violated. Welch ANOVA and the Kruskal-Wallis test (a non-parametric method) can be applicable for this case. In this study we compare the three methods in empirical type I error rate and power, when heterogeneity of variance occurs and find out which method is the most suitable with which cases including balanced/unbalanced, small/large sample size, and/or with normal/non-normal distributions.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-4-2015
Comments
F-test in analysis of variance is unsuitable in three-group heterogeneity cases, even though it is a robust test when data are homogeneous, normal and equal/unequal sample sizes.
Welch’s test performs the best in three-group heterogeneity cases when data are normal and equal and unequal sample sizes. And this result is consistent with the results from Moder (2010) that Welch’s F-test is useful for a small number of group cases.
Kuskal-Wallis test is acceptable in mild heterogeneity cases when data are normal and equal sample sizes.