473 – Tools of Inferential Decision Making in Education and Behavioral Sciences
Conditional and Unconditional ANOVA Tests: An Empirical Comparison of Type I Error Control and Statistical Power Under Variance Heterogeneity and Non-Normality
Yan Wang
University of South Florida
Zhiyao Yi
University of South Florida
Thanh Pham
University of South Florida
Diep Nguyen
University of South Florida
Yi-Hsin Chen
University of South Florida
Eun Sook Kim
University of South Florida
Jeffrey Kromrey
University of South Florida
Yue Yin
University of South Florida
The analysis of variance (ANOVA) F test is a commonly used method to test the mean equality among two or more populations. A critical assumption of ANOVA is homogeneity of variance (HOV), that is, the compared groups have equal population variances. Although it is encouraged to test HOV as part of the regular ANOVA procedure, the efficacy of the initial HOV screening that leads to the choice between the ANOVA F test and robust ANOVA methods (namely, conditional ANOVA) has not been investigated systematically. This simulation study examined the efficacy of conditional ANOVA methods under various research conditions. Results suggested that under a small sample size (e.g., 5 per group) the combination of the Brown-Forsythe test of means with the Levene or O'Brien test of variances is the best choice; with large sample sizes, structured means modeling with maximum likelihood or the Bartlett's correction coupled with Levene or O'Brien are the best combinations; and alpha levels between .20 and .30 for the test of variances are most appropriate.