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

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.

Automated summary

Meta-analytic models for dependent effect sizes have become increasingly complex, posing challenges for a priori power calculations. This study introduces power approximations for tests of average effect sizes based on common approaches for handling dependent effect sizes. Through Monte Carlo simulation, the study demonstrates that the new power formulas can accurately estimate the true power of meta-analytic models for dependent effect sizes. Lastly, the study compares the Type I error rate and power of several common models, finding that tests using robust variance estimation offer better Type I error calibration than tests with model-based variance estimation.