etasqr.gen.2way

Computes generalized eta-square estimates in a two-factor design where one
or both factors are classification factors. If both factors are treatment
factors, then partial eta-square estimates are typically recommended.
The eta-squared estimates from this function can be used in the
etasqr.adj function to obtain bias adjusted estimates.

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

etasqr.gen.2way function

<dl><dt>SSa</dt>
<dd>sum of squares for factor A</dd>
<dt>SSb</dt>
<dd>sum of squares for factor B</dd>
<dt>SSab</dt>
<dd>sum of squares for A x B interaction</dd>
<dt>SSe</dt>
<dd>error (within) sum of squares</dd></dl>

Arguments

Generalized eta-squared estimates in a two-factor design — etasqr.gen.2way

<dl>

<dt>SSa</dt>
<dd>sum of squares for factor A</dd>


<dt>SSb</dt>
<dd>sum of squares for factor B</dd>


<dt>SSab</dt>
<dd>sum of squares for A x B interaction</dd>


<dt>SSe</dt>
<dd>error (within) sum of squares</dd>

</dl>

etasqr.gen.2way: Generalized eta-squared estimates in a two-factor design

Description

Usage

Value

Arguments

Examples