se.cor

Computes the standard error of a Pearson or partial correlation
using the estimated correlation, sample size, and number of
control variables. The correlation, along with the standard error
output from this function, can be used as input in the
meta.ave.cor.gen function in applications where a
combination of different types of compatible correlations are
used in the meta-analysis.

Implements functions for varying coefficient meta-analysis methods.
These methods do not assume effect size homogeneity. Subgroup effect size
comparisons, general linear effect size contrasts, and linear models of
effect sizes based on varying coefficient methods can be used to describe
effect size heterogeneity. Varying coefficient meta-analysis methods do not
require the unrealistic assumptions of the traditional fixed-effect and
random-effects meta-analysis methods. For details see:
Statistical Methods for Psychologists, Volume 5, <https://dgbonett.sites.ucsc.edu/>.

Douglas G. Bonett

vcmeta

Varying Coefficient Meta-Analysis

Robert J. Calin-Jageman

se.cor function

<dl><dt>cor</dt>
<dd>estimated Pearson or partial correlation</dd>
<dt>s</dt>
<dd>number of control variables (set to 0 for Pearson)</dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Computes the standard error for a Pearson or partial correlation — se.cor

<dl>

<dt>cor</dt>
<dd>estimated Pearson or partial correlation</dd>


<dt>s</dt>
<dd>number of control variables (set to 0 for Pearson)</dd>


<dt>n</dt>
<dd>sample size</dd>

</dl>

se.cor: Computes the standard error for a Pearson or partial correlation

Description

Usage

Value

Arguments

References

Examples