se.slope

Computes a slope and its standard error for a simple linear regression
model (random-x model) using the estimated Pearson correlation and the
estimated standard deviations of the response variable and predictor
variable. This function is useful in a meta-analysis of slopes of a
simple linear regression model where some studies report the Pearson
correlation but not the slope.

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.slope function

<dl><dt>cor</dt>
<dd>estimated Pearson correlation</dd>
<dt>sdy</dt>
<dd>estimated standard deviation of the response variable</dd>
<dt>sdx</dt>
<dd>estimated standard deviation of the predictor variable</dd>
<dt>n</dt>
<dd>sample size</dd></dl>

Arguments

Computes a slope and standard error — se.slope

<dl>

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


<dt>sdy</dt>
<dd>estimated standard deviation of the response variable</dd>


<dt>sdx</dt>
<dd>estimated standard deviation of the predictor variable</dd>


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

</dl>

se.slope: Computes a slope and standard error

Description

Usage

Value

Arguments

References

Examples