| var_boot_fis {puniform} | R Documentation |
var_boot_fis
Description
Function for parametric bootstrapping procedure to estimate the variability in outcomes' effect size in case of Fisher-z transformed correlations as effect size measure.
Usage
var_boot_fis(ri, n, r, dv = 10, reps = 1000)
Arguments
ri |
A vector with Pearson correlation coefficients in a primary study (see Details) |
n |
A numerical value specifying the total sample size of a primary study |
r |
A numerical value specifying the Pearson correlation coefficient between variables h and m (see Details) |
dv |
An integer specifying the total number of dependent measures (default is 10, see Details) |
reps |
An integer specifying the number of bootstrap replications (default is 1,000) |
Details
In case of three variables (l, h, and m), overlapping Fisher-z transformed correlation coefficients can be computed between variables l and h and variables l and m. The function computes the variance of the two overlapping Fisher-z transformed correlation coefficients using a parametric bootstrap procedure. For more information see van Aert & Wicherts (2023).
The vector ri can contain a single Pearson correlation coefficient or
multiple coefficients if information on more than one outcome is
available. The integer dv is an optional argument to specify the expected
number of outcomes used in a primary study. This argument can be any
value between 2 and infinity. Larger values yield more accurate estimates of
the variance but slow down the bootstrap procedure.
The variance that is computed with this function can be used to correct for outcome reporting bias by including the variance as a moderator in a (multivariate) meta-analysis. Please see van Aert & Wicherts (2023) for more information.
Value
The var_boot_fis function returns a numerical value that is the
variance of multiple correlated Fisher-z transformed correlations.
Author(s)
Robbie C.M. van Aert R.C.M.vanAert@tilburguniversity.edu
References
van Aert, R.C.M. & Wicherts, J.M. (2023). Correcting for outcome reporting bias in a meta-analysis: A meta-regression approach. Behavior Research Methods.
Examples
### Compute variance for an artificial example
var_boot_fis(ri = 0, n = 100, r = 0.3)