se.semipart

Computes the standard error of a semipartial correlation using the
estimated correlation, sample size, and squared multiple correlation
for the full model. The full model includes the independent variable
of interest and all control variables. The effect size estimate and
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.semipart function

<dl><dt>cor</dt>
<dd>estimated semipartial correlation</dd>
<dt>r2</dt>
<dd>estimated squared multiple correlation for a model that
includes the IV and all control variables</dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Computes the standard error for a semipartial correlation — se.semipart

<dl>

<dt>cor</dt>
<dd>estimated semipartial correlation</dd>


<dt>r2</dt>
<dd>estimated squared multiple correlation for a model that
includes the IV and all control variables</dd>


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

</dl>

se.semipart: Computes the standard error for a semipartial correlation

Description

Usage

Value

Arguments

Examples