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首页> 外文期刊>Journal of Modern Applied Statistical Methods >Empirical Comparison of Tests for One-Factor ANOVA Under Heterogeneity and Non-Normality: A Monte Carlo Study
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Empirical Comparison of Tests for One-Factor ANOVA Under Heterogeneity and Non-Normality: A Monte Carlo Study

机译：异质性和非正常性下单因素Anova测试的经验比较：蒙特卡罗研究

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摘要

Although the Analysis of Variance (ANOVA) F test is one of the most popular statistical tools to compare group means, it is sensitive to violations of the homogeneity of variance (HOV) assumption. This simulation study examines the performance of thirteen tests in one-factor ANOVA models in terms of their Type I error rate and statistical power under numerous (82,080) conditions. The results show that when HOV was satisfied, the ANOVA F or the Brown-Forsythe test outperformed the other methods in terms of both Type I error control and statistical power even under non-normality. When HOV was violated, the Structured Means Modeling (SMM) with Bartlett or SMM with Maximum Likelihood was strongly recommended for the omnibus test of group mean equality.
机译：虽然方差分析（ANOVA）F测试是比较组手段最受欢迎的统计工具之一，但它对违反方差的均匀性（HOV）假设敏感。 该仿真研究在众多（82,080）条件下的I型错误率和统计功率方面，在单因素ANOVA模型中检查了十三个测试的性能。 结果表明，当HOV满足时，ANOVA F或棕色涡旋测试在I型错误控制和统计功率方面，即使在非正常性上也表明了其他方法。 当何时违反HOV时，强烈建议将结构化手段建模（SMM）与BARTLETT或SMM具有最大可能性，用于组分组的综合性均等。

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