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 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.
Beal
Daniel J
DJ
Department of Psychology, Tulane University, USA. dbeal@purdue.edu
Corey
David M
DM
Dunlap
William P
WP
D015982:Bias; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical