se.ave.cor.over

In a study that reports the sample size and three correlations (cor12, cor13,
and cor23 where variable 1 is called the "overlapping" variable), and
variables 2 and 3 are different measurements of the same attribute, this
function can be used to compute the average of cor12 and cor13 and its
standard error. The average correlation and the standard error from this
function can be used as input in the meta.ave.cor.gen function
in a meta-analysis where some studies have reported cor12 and other studies
have reported cor13.

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.ave.cor.over function

<dl><dt>cor12</dt>
<dd>estimated correlation between variables 1 and 2</dd>
<dt>cor13</dt>
<dd>estimated correlation between variables 1 and 3</dd>
<dt>cor23</dt>
<dd>estimated correlation between variables 2 and 3</dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Computes the standard error for the average of two Pearson correlations with
one variable in common that have been estimated from the same sample — se.ave.cor.over

<dl>

<dt>cor12</dt>
<dd>estimated correlation between variables 1 and 2</dd>


<dt>cor13</dt>
<dd>estimated correlation between variables 1 and 3</dd>


<dt>cor23</dt>
<dd>estimated correlation between variables 2 and 3</dd>


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

</dl>

se.ave.cor.over: Computes the standard error for the average of two Pearson correlations with one variable in common that have been estimated from the same sample

Description

Usage

Value

Arguments

Examples