se.tetra

Computes an estimate of a tetrachoric correlation approximation and its
standard error using the frequency counts from a 2 x 2 contingency table
for two artifically dichotomous variables. A tetrachoric approximation
could be compatible with a Pearson correlation in a meta-analysis. The
tetrachoric approximation 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 Pearson correlations
between quantitative variables x and y and other studies have reported
a 2 x 2 contingency table for dichotomous measurements of variables
x and y.

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.tetra function

<dl><dt>f00</dt>
<dd>number of participants with y = 0 and x = 0</dd>
<dt>f01</dt>
<dd>number of participants with y = 0 and x = 1</dd>
<dt>f10</dt>
<dd>number of participants with y = 1 and x = 0</dd>
<dt>f11</dt>
<dd>number of participants with y = 1 and x = 1</dd></dl>

Arguments

Computes the standard error for a tetrachoric correlation approximation — se.tetra

<dl>

<dt>f00</dt>
<dd>number of participants with y = 0 and x = 0</dd>


<dt>f01</dt>
<dd>number of participants with y = 0 and x = 1</dd>


<dt>f10</dt>
<dd>number of participants with y = 1 and x = 0</dd>


<dt>f11</dt>
<dd>number of participants with y = 1 and x = 1</dd>

</dl>

se.tetra: Computes the standard error for a tetrachoric correlation approximation

Description

Usage

Value

Arguments

References

Examples