OUTLIER DETECTION AND TREATMENT IN I/O PSYCHOLOGY: A SURVEY OF RESEARCHER BELIEFS AND AN EMPIRICAL ILLUSTRATION

JOHN M. ORR, PAUL R. SACKETT, CATHY L.Z. DUBOIS

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)473-486
Number of pages14
JournalPersonnel Psychology
Volume44
Issue number3
DOIs
StatePublished - Sep 1991

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