Calculating statistical power for meta-analysis using metapower
Open accessAbstract
Meta-analysis is an influential evidence synthesis technique that summarizes a body of research.Though impactful, meta-analyses fundamentally depend on the literature being sufficiently large to generate meaningful conclusions.Power analysis plays an important role in determining the number of studies required to conduct a substantive meta-analysis.Despite this, power analysis is rarely conducted or reported in published meta-analyses.A significant barrier to the widespread implementation of power analysis is the lack of available and accessible software for calculating statistical power for meta-analysis.In this paper, I provide an introduction to power analysis and present a practical tutorial for calculating statistical power using the R package metapower.The main functionality includes computing statistical power for summary effect sizes, tests of homogeneity, categorical moderator analysis, and subgroup analysis.This software is free, easy-to-use, and can be integrated into a continuous work flow with other meta-analysis packages in R.
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At least seven studies are required to achieve 80% power at an α of .05 for the estimated small-to-medium effect size (Hedge's g = .35) and average sample size (N = 39).
“The analysis suggests that at least seven studies are required to achieve 80% power at an α of .05.”
From Menstrual cycle effects on cognitive performance: A meta-analysis (2025) · cites this paper as [73]
The Number of Studies section (page unavailable) explains that the number needed for adequate meta-analytic power depends on effect size, study size, error probability, test type, and model assumptions. The Summary effect size section uses 80% as a conventional power target. Neither section reports that seven studies suffice for the stated effect size and sample size; the paper’s worked example uses different inputs. (LLM, conf=0.82)
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Kategaya, A., & Yavuz, Y. (2026) · Business Strategy and the Environment · doi:10.1002/bse.71631
Bryant, S., & Mikheyev, A. S. (2026) · Pest Management Science · doi:10.1002/ps.71341
- Authors
- Griffin, J. W.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Griffin, Jason W.
- ORCID
- Griffin, Jason W.
- Venue
- The Quantitative Methods for Psychology
- Published
- 2021-03-01
- Volume
- 17
- Issue
- 1
- Pages
- 24-39
- ISSN
- 2292-1354 (ISSN-L)
- OpenAlex
- W3138345420
- Cited by
- 84
- References
- 24
- Citations by year
- 2026: 22 · 2025: 17 · 2024: 13 · 2023: 18 · 2022: 9 · 2021: 5
- FWCI
- 6.52
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The Quantitative Methods for Psychology (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- statistical power, power analysis, meta-analysis, R package, sample size determination, heterogeneity testing, subgroup analysis, meta-analysis software
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