Combining Correlation Matrices: Simulation Analysis of Improved Fixed-Effects Methods

Adam R. Hafdahl
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Adam R. Hafdahl: University of Missouri–Columbia

Journal of Educational and Behavioral Statistics, 2007, vol. 32, issue 2, 180-205

Abstract: The originally proposed multivariate meta-analysis approach for correlation matrices—analyze Pearson correlations, with each study’s observed correlations replacing their population counterparts in its conditional-covariance matrix—performs poorly. Two refinements are considered: Analyze Fisher Z -transformed correlations, and substitute better estimates of correlations in the conditional covariances. Fixed-effects methods with and without each refinement were examined in a Monte Carlo study; number of studies and the distribution of within-study sample sizes were varied. Both refinements improved element-wise point and interval estimates, as well as Type I error control for homogeneity tests, especially with many small studies. Practical recommendations and suggestions for future methodological work are offered. An appendix describes how to transform Fisher- Z (co)variances to the Pearson- r metric.

Keywords: meta-analysis; correlation; generalized least squares; Monte Carlo study (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:32:y:2007:i:2:p:180-205

DOI: 10.3102/1076998606298041

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