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Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression

Author

Listed:
  • Elizabeth Tipton

    (Columbia University)

  • James E. Pustejovsky

    (University of Texas at Austin)

Abstract

Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces dependence among the effect size estimates. When the number of studies is large, robust variance estimation (RVE) provides a method for pooling dependent effects, even when information on the exact dependence structure is not available. When the number of studies is small or moderate, however, test statistics and confidence intervals based on RVE can have inflated Type I error. This article describes and investigates several small-sample adjustments to F -statistics based on RVE. Simulation results demonstrate that one such test, which approximates the test statistic using Hotelling’s T 2 distribution, is level-α and uniformly more powerful than the others. An empirical application demonstrates how results based on this test compare to the large-sample F -test.

Suggested Citation

  • Elizabeth Tipton & James E. Pustejovsky, 2015. "Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression," Journal of Educational and Behavioral Statistics, , vol. 40(6), pages 604-634, December.
    • Handle: RePEc:sae:jedbes:v:40:y:2015:i:6:p:604-634
    DOI: 10.3102/1076998615606099
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    References listed on IDEAS

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    1. Xiaohong Chen & Norman R. Swanson (ed.), 2013. "Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis," Springer Books, Springer, edition 127, number 978-1-4614-1653-1, March.
    2. Eric C. Hedberg, 2011. "ROBUMETA: Stata module to perform robust variance estimation in meta-regression with dependent effect size estimates," Statistical Software Components S457219, Boston College Department of Economics, revised 23 Apr 2014.
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