On the bias of Huffcutt and Arthur's (1995) procedure for identifying outliers in the meta-analysis of correlations

This study documents how the use of A. I. Huffcutt & W. A. Arthur's (1995) sample adjusted meta-analytic deviancy (SAMD) statistic for identifying outliers in correlational meta-analyses results in inaccuracies in mean r. Monte Carlo simulations found that use of the SAMD resulted in the ov...

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Bibliographic Details
Published inJournal of applied psychology Vol. 87; no. 3; p. 583
Main Authors Beal, Daniel J, Corey, David M, Dunlap, William P
Format Journal Article
LanguageEnglish
Published United States 01.06.2002
Subjects
Bias
Humans
Meta-Analysis as Topic
Models, Statistical
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Summary:This study documents how the use of A. I. Huffcutt & W. A. Arthur's (1995) sample adjusted meta-analytic deviancy (SAMD) statistic for identifying outliers in correlational meta-analyses results in inaccuracies in mean r. Monte Carlo simulations found that use of the SAMD resulted in the overidentification of small relative to large correlations as outliers. Furthermore, this tendency to overidentify small correlations was found to increase as the magnitude of the population correlation increased and resulted in mean rs that overestimated the population correlation. The implications for meta-analysts are discussed, and 2 possible solutions are offered.
ISSN:0021-9010
DOI:10.1037/0021-9010.87.3.583