Non-normal data: Is ANOVA still a valid option?
Non-normal data: Is ANOVA still a valid option?
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DOI：
10.7334/psicothema2016.383
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发表时间：
2017-11-01
期刊：
影响因子：
3.6
通讯作者：
Bendayan, Rebecca
中科院分区：
文献类型：
--
作者：
Blanca, Maria J.;Alarcon, Rafael;Bendayan, Rebecca
背景：从20世纪30年代至今，人们一直在研究F检验对非正态性的稳健性。然而，这一广泛的研究机构产生了相互矛盾的结果，有证据支持和反对其鲁棒性。本研究提供了一个系统的检查F-检验的鲁棒性违反正态性的第一类错误，考虑到各种各样的分布常见于健康和社会科学。方法：我们进行了Monte Carlo模拟研究，涉及三组和几个已知和未知的分布设计。操纵变量为：相等和不相等的组样本量;组样本量和总样本量;样本量变异系数;分布形状和组分布的相等或不相等形状;以及组大小与分布中污染程度的配对。结果如下：结果表明，在I型错误方面，F检验在100%的研究病例中具有稳健性，与操作条件无关。
Background: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. Method: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. Results: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.