OUTLIER DETECTION AND TREATMENT IN I/O PSYCHOLOGY: A SURVEY OF RESEARCHER BELIEFS AND AN EMPIRICAL ILLUSTRATION
调查了研究者对异常值处理的态度，并通过183项效度研究展示了不同检测与排除方法对效应量的影响，发现研究者意见不一且多依赖视觉检查，但异常值对大数据集影响有限。
Extreme data points, or outliers, can have a disproportionate influence on the conclusions drawn from a set of bivariate correlational data. This paper addresses two aspects of outlier detection. The results of a survey regarding how published researchers prefer to deal with outliers are presented, and a set of 183 test validity studies is examined to document the effects of different approaches to the detection and exclusion of outliers on effect size measures. The study indicates that: (a) there is disagreement among researchers as to the appropriateness of deleting data points from a study; (b) researchers report greater use of visual examination of data than of numeric diagnostic techniques for detecting outliers; and (c) while outlier removal influenced effect size measures in individual studies, outlying data points were not found to be a substantial source of variance in a large test validity data set.