Power Approximations for Meta-Analysis of Dependent Effect Sizes

Mikkel Helding Vembye, James Pustejovsky and Terri Pigott
Additional contact information
Terri Pigott: Georgia State University

No 6tp9y, MetaArXiv from Center for Open Science

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 the most common models for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of common 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.

Date: 2022-01-06
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/61d70a6072da231306bfa96e/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:metaar:6tp9y

DOI: 10.31219/osf.io/6tp9y

Access Statistics for this paper

More papers in MetaArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().