ci.etasqr

Computes a confidence interval for a population eta-squared, partial
eta-squared, or generalized eta-squared in a fixed-factor between-subjects
design. An approximate bias adjusted estimate is computed, and an
approximate standard error is recovered from the confidence interval.

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

ci.etasqr function

<dl><dt>alpha</dt>
<dd>alpha value for 1-alpha confidence</dd>
<dt>etasqr</dt>
<dd>estimated eta-squared</dd>
<dt>df1</dt>
<dd>degrees of freedom for effect</dd>
<dt>df2</dt>
<dd>error degrees of freedom</dd></dl>

Arguments

Confidence interval for eta-squared — ci.etasqr

<dl>

<dt>alpha</dt>
<dd>alpha value for 1-alpha confidence</dd>


<dt>etasqr</dt>
<dd>estimated eta-squared</dd>


<dt>df1</dt>
<dd>degrees of freedom for effect</dd>


<dt>df2</dt>
<dd>error degrees of freedom</dd>

</dl>

ci.etasqr: Confidence interval for eta-squared

Description

Usage

Value

Arguments

Examples