Department of Cognitive Science, University of California-San Diego, La Jolla, CA 92093-0515, USA.
Psychophysiology. 2011 Dec;48(12):1726-37. doi: 10.1111/j.1469-8986.2011.01272.x. Epub 2011 Sep 6.
Mass univariate analysis is a relatively new approach for the study of ERPs/ERFs. It consists of many statistical tests and one of several powerful corrections for multiple comparisons. Multiple comparison corrections differ in their power and permissiveness. Moreover, some methods are not guaranteed to work or may be overly sensitive to uninteresting deviations from the null hypothesis. Here we report the results of simulations assessing the accuracy, permissiveness, and power of six popular multiple comparison corrections (permutation-based control of the familywise error rate [FWER], weak control of FWER via cluster-based permutation tests, permutation-based control of the generalized FWER, and three false discovery rate control procedures) using realistic ERP data. In addition, we look at the sensitivity of permutation tests to differences in population variance. These results will help researchers apply and interpret these procedures.
多元单变量分析是一种用于研究事件相关电位/事件相关磁场的新方法。它由许多统计检验和几种强大的多重比较校正方法组成。多重比较校正方法在功效和宽容度方面有所不同。此外，一些方法不能保证有效，或者可能对偏离零假设的无趣偏差过于敏感。在这里，我们报告了使用真实 ERP 数据评估六种流行的多重比较校正方法（基于置换的总体错误率控制、基于聚类的置换检验的弱总体错误率控制、基于置换的广义总体错误率控制，以及三种错误发现率控制程序）的准确性、宽容度和功效的模拟结果。此外，我们还研究了置换检验对总体方差差异的敏感性。这些结果将帮助研究人员应用和解释这些程序。
