se.prop.ps

This function computes a standard error of a paired-samples proportion
difference using the frequency counts from a 2 x 2 contingency table and
the number of studies in the planned meta-analysis. The effect size
estimate and standard error output from this function can be used as
input in the meta.ave.gen, meta.lc.gen,
and meta.lm.gen functions in applications where compatible
proportion differences from a combination of 2-group and paired-samples
studies 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.prop.ps 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>
<dt>m</dt>
<dd>number of studies in planned meta-analysis</dd></dl>

Arguments

Computes the estimate and standard error for a paired-samples
proportion difference — se.prop.ps

<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>


<dt>m</dt>
<dd>number of studies in planned meta-analysis</dd>

</dl>

se.prop.ps: Computes the estimate and standard error for a paired-samples proportion difference

Description

Usage

Value

Arguments

References

Examples