文章基本信息

标题：Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox
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作者：Pernet, Cyril R ; Wilcox, Rand R ; Rousselet, Guillaume A 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2013
卷号：3
页码：1-18
DOI：10.3389/fpsyg.2012.00606
出版社：Frontiers Media
摘要：Pearson’s correlation measures the strength of the association between two variables. The technique is, however, restricted to linear associations and is overly sensitive to outliers. Indeed, a single outlier can result in a highly inaccurate summary of the data. Yet, it remains the most commonly used measure of association in psychology research. Here we describe a free Matlab(R) based toolbox (http://sourceforge.net/projects/robustcorrtool/) that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations. After illustrating how to use the toolbox, we show that robust methods, where outliers are down weighted or removed and accounted for in significance testing, provide better estimates of the true association with accurate false positive control and without loss of power. The different correlation methods were tested with normal data and normal data contaminated with marginal or bivariate outliers. We report estimates of effect size, false positive rate and power, and advise on which technique to use depending on the data at hand.
关键词：robust statistics; Correlation; power; outliers; MATLAB