@article{TQMP17-1-24,
author = {Griffin, Jason W. },
journal = {The Quantitative Methods for Psychology},
publisher = {TQMP},
title = {Calculating statistical power for meta-analysis using metapower},
year = {2021},
volume = {17},
number = {1},
url = {http://www.tqmp.org/RegularArticles/vol17-1/p024/p024.pdf },
pages = {24-39},
abstract = {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.},
doi = {10.20982/tqmp.17.1.p024}
}