test.cor2

Computes a z test for a difference of population Pearson or partial
correlations in a 2-group design. Set s = 0 for a Pearson correlation.
The hypothesis testing results should be accompanied with a confidence
interval for the difference in population correlation values
(see ci.cor2).

Implements confidence interval and sample size methods that
are especially useful in psychological research. The methods can be
applied in 1-group, 2-group, paired-samples, and multiple-group designs
and to a variety of parameters including means, medians, proportions,
slopes, standardized mean differences, standardized linear contrasts
of means, plus several measures of correlation and association.
Confidence interval and sample size functions are given for single
parameters as well as differences, ratios, and linear contrasts of
parameters. The sample size functions can be used to approximate the
sample size needed to estimate a parameter or function of parameters
with desired confidence interval precision or to perform a variety of
hypothesis tests (directional two-sided, equivalence, superiority,
noninferiority) with desired power. For details see: Statistical Methods
for Psychologists, Volumes 1 – 4, <https://dgbonett.sites.ucsc.edu/>.

Douglas G. Bonett

statpsych

Statistical Methods for Psychologists

Robert J. Calin-Jageman

test.cor2 function

<dl><dt>cor1</dt>
<dd>estimated correlation for group 1</dd>
<dt>cor2</dt>
<dd>estimated correlation for group 2</dd>
<dt>n1</dt>
<dd>sample size for group 1</dd>
<dt>n2</dt>
<dd>sample size for group 2</dd>
<dt>s</dt>
<dd>number of control variables</dd></dl>

Arguments

Hypothesis test for a 2-group Pearson or partial correlation difference — test.cor2

<dl>

<dt>cor1</dt>
<dd>estimated correlation for group 1</dd>


<dt>cor2</dt>
<dd>estimated correlation for group 2</dd>


<dt>n1</dt>
<dd>sample size for group 1</dd>


<dt>n2</dt>
<dd>sample size for group 2</dd>


<dt>s</dt>
<dd>number of control variables</dd>

</dl>

test.cor2: Hypothesis test for a 2-group Pearson or partial correlation difference

Description

Usage

Value

Arguments

Examples