Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed- and random-effects methods.
The efficacy of the Hedges and colleagues, Rosenthal-Rubin, and Hunter-Schmidt methods for combining correlation coefficients was tested for cases in which population effect sizes were both fixed and variable. After a brief tutorial on these meta-analytic methods, the author presents two Monte Carlo simulations that compare these methods for cases in which the number of studies in the meta-analysis and the average sample size of studies were varied. In the fixed case the methods produced comparable estimates of the average effect size; however, the Hunter-Schmidt method failed to control the Type I error rate for the associated significance tests. In the variable case, for both the Hedges and colleagues and Hunter-Schmidt methods, Type I error rates were not controlled for meta-analyses including 15 or fewer studies and the probability of detecting small effects was less than .3. Some practical recommendations are made about the use of meta-analysis.
Field
A P
AP
Psychology Group, School of Cognitive and Computing Science, University of Sussex, Falmer, Brighton, East Sussex BN1 9QH, United Kingdom. andyf@cogs.susx.ac.uk
D006801:Humans; D015201:Meta-Analysis as Topic; D009010:Monte Carlo Method; D011594:Psychometrics; D015203:Reproducibility of Results