Abstract
Traditional analysis-of-variance (ANOVA) is based on ‘normality’ and ‘homogeneity’ assumptions. If either or both of these assumptions are violated, then the one-way ANOVA may not be as powerful as robust analysis-of-variance (RANOVA) alternatives. We report the results of a simulation study of alternatives to ANOVA: Welch (W*), the first and second methods of James (J1*, 3J11*), Brown- Forsythe (BF*), a Box (B*) procedure, and the Kruskal-Wallis (KW*) procedure. Random samples from 14 distributions—uniform (0, 1), normal (0, 1), contaminated normal, SLATE, SLACU, SLASH, double exponential, Cauchy, half-normal, chi-squared (two degrees of freedom), chi-squared (four degrees of freedom) log normal, gamma (1, 2) and beta (2, 5)—were generated using a composite linear congruential generator. Corresponding test satis tics were computed and the empirical size for each test is given for three nominal a values (0.10, 0.05, 0.01). For k, we choose 3, 4 and 6. The sample sizes and combinations of sample sizes were...
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