Power Approximations for Overall Average Effects in Meta-Analysis With Dependent Effect Sizes

Mikkel Helding Vembye, James Eric Pustejovsky and Therese Deocampo Pigott
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Mikkel Helding Vembye: The Danish Center for Social Science Research, VIVE
James Eric Pustejovsky: University of Wisconsin-Madison
Therese Deocampo Pigott: Georgia State University

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 1, 70-102

Abstract: Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach.

Keywords: power; meta-analysis; dependent effect sizes; robust variance estimation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:1:p:70-102

DOI: 10.3102/10769986221127379

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