oswald2013.ex1

<p>Data from a meta-analysis on IAT conducted by Oswald et al., (2013)
examining the predictive validity of the Implicit Association Test (IAT) and
various explicit measures of bias for a variety of criterion measures of
discrimination. Included in the dataset are the study-level correlations
between IAT scores and criterion measures of neurological activity or
response latency from the original <code>oswald2013</code> dataset.</p>

datasets

Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.

Zachary Fisher

robumeta

Robust Variance Meta-Regression

Elizabeth Tipton

Hou Zhipeng

oswald2013.ex1 function

<p>A dataframe containing 32 effect sizes from 9 studies.</p>
<table class="table">
<tr><td>[,1]</td><td>Study</td><td>Factor</td><td>[,2]</td></tr>
<tr><td>Crit.Domain</td><td>Factor</td><td>[,3]</td><td>IAT.ID</td></tr>
<tr><td>Integer</td><td>[,4]</td><td>IAT.Focus</td><td>Factor</td></tr>
<tr><td>[,5]</td><td>Crit.ID</td><td>Integer</td><td>[,6]</td></tr>
<tr><td>Crit.Cat</td><td>Factor</td><td>[,7]</td><td>Scoring</td></tr>
<tr><td>Factor</td><td>[,8]</td><td>Target</td><td>Factor</td></tr>
<tr><td>[,9]</td><td>Type</td><td>Factor</td><td>[,10]</td></tr>
<tr><td>Sample.ID</td><td>Integer</td><td>[,11]</td><td>N</td></tr>
<tr><td>Integer</td><td>[,12]</td><td>R</td><td>Numeric</td></tr>
<tr><td>[,13]</td><td>effect.size</td><td>Numeric</td><td>[,14]</td></tr>
</table><p></p><p></p>

Format

IAT Criterion-Related Correlations — oswald2013.ex1

<p>A dataframe containing 32 effect sizes from 9 studies.</p>
<table class='table'>
<tr><td>[,1]</td><td>Study</td><td>Factor</td><td>[,2]</td></tr>
<tr><td>Crit.Domain</td><td>Factor</td><td>[,3]</td><td>IAT.ID</td></tr>
<tr><td>Integer</td><td>[,4]</td><td>IAT.Focus</td><td>Factor</td></tr>
<tr><td>[,5]</td><td>Crit.ID</td><td>Integer</td><td>[,6]</td></tr>
<tr><td>Crit.Cat</td><td>Factor</td><td>[,7]</td><td>Scoring</td></tr>
<tr><td>Factor</td><td>[,8]</td><td>Target</td><td>Factor</td></tr>
<tr><td>[,9]</td><td>Type</td><td>Factor</td><td>[,10]</td></tr>
<tr><td>Sample.ID</td><td>Integer</td><td>[,11]</td><td>N</td></tr>
<tr><td>Integer</td><td>[,12]</td><td>R</td><td>Numeric</td></tr>
<tr><td>[,13]</td><td>effect.size</td><td>Numeric</td><td>[,14]</td></tr>
</table>



oswald2013.ex1: IAT Criterion-Related Correlations

Description

Arguments

Format

References

[,1]	Study	Factor	[,2]
Crit.Domain	Factor	[,3]	IAT.ID
Integer	[,4]	IAT.Focus	Factor
[,5]	Crit.ID	Integer	[,6]
Crit.Cat	Factor	[,7]	Scoring
Factor	[,8]	Target	Factor
[,9]	Type	Factor	[,10]
Sample.ID	Integer	[,11]	N
Integer	[,12]	R	Numeric
[,13]	effect.size	Numeric	[,14]