The primary goal of metapower is to compute statistical power for meta-analyses. Currently, metapower has the following functionality:
Computation of statistical power for:
metapower can currently handle the following designs and effect sizes:
For detailed information about how to use metapower
, see Calculating
statistical power for meta-analysis using
metapower
You can install the released version of metapower from CRAN with:
install.packages("metapower")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("jasonwgriffin/metapower")
Check out the simple and easy to use shiny application
library(metapower)
my_power <- mpower(effect_size = .3, study_size = 20, k = 10, i2 = .50, es_type = "d")
print(my_power)
#>
#> Power Analysis for Meta-analysis
#>
#> Effect Size Metric: d
#> Expected Effect Size: 0.3
#> Expected Study Size: 20
#> Expected Number of Studies: 10
#>
#> Estimated Power: Mean Effect Size
#>
#> Fixed-Effects Model 0.5594533
#> Random-Effects Model (i2 = 50%): 0.3454424
plot_mpower(my_power)
See Vignette “Using metapower” for more information..
All mathematical calculations are derived from Hedges & Pigott (2004), Bornstein, Hedges, Higgins, & Rothstein (2009),Pigott (2012), Jackson & Turner (2017).
Griffin, J. W. (2021). Calculating statistical power for meta-analysis using metapower. The Quantitative Methods for Psychology, 17(1), 24–39. <doi:10.20982/tqmp.17.1.p024>
If you encounter a clear bug, please file a minimal reproducible example on github.