meta.ave.semipart

Computes the estimate, standard error, and confidence interval for an
average semipartial correlation from two or more studies.

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

meta.ave.semipart function

<dl><dt>alpha</dt>
<dd>alpha level for 1-alpha confidence</dd>
<dt>n</dt>
<dd>vector of sample sizes</dd>
<dt>cor</dt>
<dd>vector of estimated semipartial correlations</dd>
<dt>r2</dt>
<dd>vector of squared multiple correlations for a model that
includes the IV and all control variables</dd>
<dt>bystudy</dt>
<dd>logical to also return each study estimate (TRUE) or not</dd></dl>

Arguments

Confidence interval for an average semipartial correlation — meta.ave.semipart

<dl>

<dt>alpha</dt>
<dd>alpha level for 1-alpha confidence</dd>


<dt>n</dt>
<dd>vector of sample sizes</dd>


<dt>cor</dt>
<dd>vector of estimated semipartial correlations</dd>


<dt>r2</dt>
<dd>vector of squared multiple correlations for a model that
includes the IV and all control variables</dd>


<dt>bystudy</dt>
<dd>logical to also return each study estimate (TRUE) or not</dd>

</dl>

meta.ave.semipart: Confidence interval for an average semipartial correlation

Description

Usage

Value

Arguments

Examples