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ASSESSING HOMOGENEITY: A COMPARATIVE STUDY FOR ROBUST STATISTICAL ANALYSIS
Keywords:
homogeneity of variance, Barlett’s test, Welch’s test, Levene’s test, O’Brien’s test, Brown-Forsythe’s test, Cochran’s test, Fligner-Killeen’s test, robustness, power of the testDOI:
https://doi.org/10.17654/0972087125009Abstract
Homogeneity of Variance (HOV) is a fundamental assumption in statistical analysis, particularly in parametric tests such as Analysis of Variance (ANOVA). To make sure our research findings are solid and we avoid any mistakes in our conclusions, it is crucial that the variability among the groups or conditions we are comparing is about the same. While several tests for assessing homogeneity of variance exist in literature, there lacks consensus on their robustness, especially in scenarios where the assumption of normality is not met. The purpose of this simulation study is to evaluate the performance of nine tests for the homogeneity of variance assumption in one-way ANOVA models in terms of Type I error (also robustness) and statistical power. These tests are analyzed through six different shaped distributions: normal, logistic, exponential, beta, Burr and t-distributions. The results from these studies suggest that three O’Brien tests and the Fligner-Killeen test (nonparametric) demonstrate superior Type I error control across all conditions, indicating better performance compared to other tests.
Received: August 13, 2024
Revised: September 16, 2024
Accepted: October 3, 2024
References
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