ci.slope.prop.bs

Computes a test statistic and an adjusted Wald confidence interval for the
population slope of proportions in a one-factor experimental design with a
quantitative between-subjects factor.

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.slope.prop.bs function

<dl><dt>alpha</dt>
<dd>alpha level for 1-alpha confidence</dd>
<dt>f</dt>
<dd>vector of frequency counts of participants who have the attribute</dd>
<dt>n</dt>
<dd>vector of sample sizes</dd>
<dt>x</dt>
<dd>vector of quantitative factor values</dd></dl>

Arguments

Confidence interval for a slope of a proportion in a single-factor
experimental design with a quantitative between-subjects factor — ci.slope.prop.bs

<dl>

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


<dt>f</dt>
<dd>vector of frequency counts of participants who have the attribute</dd>


<dt>n</dt>
<dd>vector of sample sizes</dd>


<dt>x</dt>
<dd>vector of quantitative factor values</dd>

</dl>

ci.slope.prop.bs: Confidence interval for a slope of a proportion in a single-factor experimental design with a quantitative between-subjects factor

Description

Usage

Value

Arguments

References

Examples