Department of Psychology, University of Otago.
J Exp Psychol Gen. 2023 Nov;152(11):3189-3217. doi: 10.1037/xge0001450. Epub 2023 Jul 27.
A methodological problem in most reaction time (RT) tasks is that some measured RTs may be outliers, being either too fast or too slow to reflect the task-related processing of interest. Numerous ad hoc procedures have been used to identify these outliers for exclusion from further analyses, but the accuracies of these methods have not been systematically compared. The present study compared the performance of 58 different outlier exclusion procedures (OEPs) using four huge datasets of real RTs. The results suggest that these OEPs are likely to do more harm than good, because they incorrectly identify outliers, increase noise, introduce bias, and generally reduce statistical power. The results suggest that RT researchers should not automatically apply any of these OEPs to clean their RT data prior to the main analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
在大多数反应时间 (RT) 任务中,存在一个方法学问题,即一些测量的 RT 可能是异常值,它们要么过快,要么过慢,无法反映出感兴趣的与任务相关的处理过程。已经使用了许多特定的程序来识别这些异常值并将其排除在进一步的分析之外,但这些方法的准确性尚未得到系统比较。本研究使用四个真实 RT 的大型数据集,比较了 58 种不同的异常值排除程序 (OEP) 的性能。结果表明,这些 OEP 弊大于利,因为它们错误地识别了异常值,增加了噪声,引入了偏差,并且通常降低了统计能力。结果表明,RT 研究人员在进行主要分析之前,不应该自动将这些 OEP 中的任何一个应用于清理他们的 RT 数据。(PsycInfo 数据库记录(c)2024 APA,保留所有权利)。