data.gpcm

Dataset with ordered values of 3 indicators

Includes marginal maximum likelihood estimation and joint maximum
likelihood estimation for unidimensional and multidimensional
item response models. The package functionality covers the
Rasch model, 2PL model, 3PL model, generalized partial credit model,
multi-faceted Rasch model, nominal item response model,
structured latent class model, mixture distribution IRT models,
and located latent class models. Latent regression models and
plausible value imputation are also supported. For details see
Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
Formann, 1982 <doi:10.1002/bimj.4710240209>,
Formann, 1992 <doi:10.1080/01621459.1992.10475229>.

Alexander Robitzsch

Test Analysis Modules

data.gpcm function

A data frame with 392 observations on the following 3 items.<dl>
 <dt><code>Comfort</code></dt>
<dd>a numeric vector</dd> <dt><code>Work</code></dt>
<dd>a numeric vector</dd> <dt><code>Benefit</code></dt>
<dd>a numeric vector</dd> 
</dl>

Format

Dataset with Ordered Indicators — data.gpcm

A data frame with 392 observations on the following 3 items.<dl>
 <dt><code>Comfort</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Work</code></dt>
<dd>a numeric vector</dd>

 <dt><code>Benefit</code></dt>
<dd>a numeric vector</dd>

 
</dl>

data.gpcm: Dataset with Ordered Indicators

Description

Usage

Arguments

Format

Examples