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Comparing the Performance of Approaches for Testing the Homogeneity of Variance Assumption in One-Factor ANOVA Models

机译：比较一个因子Anova模型中的差异假设的均匀性的性能的比较

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摘要

Various tests to check the homogeneity of variance assumption have been proposed in the literature, yet there is no consensus as to their robustness when the assumption of normality does not hold. This simulation study evaluated the performance of 14 tests for the homogeneity of variance assumption in one-way ANOVA models in terms of Type I error control and statistical power. Seven factors were manipulated: 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. Overall, the Ramsey conditional, O'Brien, Brown-Forsythe, Bootstrap Brown-Forsythe, and Levene with squared deviations tests maintained adequate Type I error control, performing better than the others across all the conditions. The power for each of these five tests was acceptable and the power differences were subtle. Guidelines for selecting a valid test for assessing the tenability of this critical assumption are provided based on average cell size.
机译：在文献中提出了各种测试检查方差假设的均匀性，但在常态的假设不持有时，对其鲁棒性没有达成共识。该仿真研究评估了在I型错误控制和统计功率方面以单向ANOVA模型在单向ANOVA模型中对方差假设的均匀性的14个测试的性能。操纵七种因素：组数，每组观察数，样本尺寸模式，种群差异，最大方差比，人口分布形状和标称α水平的差异。总体而言，Ramsey条件，o'brien，棕色锋利，释放棕色叉子和levene与平方偏差测试保持了足够的I型错误控制，比其他条件更好地表现更好。这五种测试中的每一个的力量都是可以接受的，力量差异很微妙。根据平均细胞大小提供用于评估该关键假设的有效测试的有效测试的指南。

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