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

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  • Adam R. Hafdahl

Abstract

Monte Carlo studies of several fixed-effects methods for combining and comparing correlation matrices have shown that two refinements improve estimation and inference substantially. With rare exception, however, these simulations have involved homogeneous data analyzed using conditional meta-analytic procedures. The present study builds on previous evidence about these methods’ relative performance by examining their behavior under heterogeneity, which is more realistic in practice. Results based on both conditional and unconditional estimands indicate that of the two refinements, using estimated correlations in conditional (co)variances improves point and interval estimates of mean correlations more than analyzing Fisher Z correlations, despite the latter’s superiority for testing homogeneity. Recommended choices among methods are offered.

Suggested Citation

  • Adam R. Hafdahl, 2008. "Combining Heterogeneous Correlation Matrices: Simulation Analysis of Fixed-Effects Methods," Journal of Educational and Behavioral Statistics, , vol. 33(4), pages 507-533, December.
    • Handle: RePEc:sae:jedbes:v:33:y:2008:i:4:p:507-533
    DOI: 10.3102/1076998607309472
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    Cited by:

    1. Bornmann, Lutz & Mutz, Rüdiger & Hug, Sven E. & Daniel, Hans-Dieter, 2011. "A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants," Journal of Informetrics, Elsevier, vol. 5(3), pages 346-359.
    2. Sacha Epskamp & Adela-Maria Isvoranu & Mike W.-L. Cheung, 2022. "Meta-analytic Gaussian Network Aggregation," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 12-46, March.

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