A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes
A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes
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DOI：
10.1037/1082-989x.13.2.110
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发表时间：
2008-06-01
影响因子：
7
通讯作者：
Deering, Kathleen N.
中科院分区：
文献类型：
--
作者：
Keselman, H. J.;Algina, James;Deering, Kathleen N.
标准最小二乘方差分析方法在任意小的偏离正态性的情况下功效较差，并且在违反标准假设时无法控制出现 I 类错误的概率。本文描述了一个稳健估计和测试框架，该框架使用具有近似自由度异方差统计量的修剪均值进行独立组和相关组设计，以实现对非正态性和方差异质性的偏差影响的稳健性。作者描述了一种非参数引导方法，可以提供改进的 I 类错误控制。此外，作者还指出了研究人员如何围绕稳健的效应大小参数估计设置稳健的置信区间。在在线增刊中，作者使用了几个示例来说明 SAS 程序的应用来实现这些统计方法。
Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of freedom heteroscedastic statistic for independent and correlated groups designs in order to achieve robustness to the biasing effects of nonnormality and variance heterogeneity. The authors describe a nonparametric bootstrap methodology that can provide improved Type I error control. In addition, the authors indicate how researchers can set robust confidence intervals around a robust effect size parameter estimate. In an online supplement, the authors use several examples to illustrate the application of an SAS program to implement these statistical methods.