RLMS


This dataset contains the data about job satisfaction described in:
Bartolucci, F., Bacci, S., and Gnaldi, M. (2015), Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata, Chapman and Hall/CRC press

datasets

education

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version.

Francesco Bartolucci

MLCIRTwithin

Latent Class Item Response Theory (LC-IRT) Models under
Within-Item Multidimensionality

RLMS function

A data frame with 1485 observations about four polytomous items with covariates:
  <dl>
    <dt><code>marital</code></dt><dd>marital status of the respondent</dd> <dt><code>education</code></dt><dd>educational level of the respondent</dd> <dt><code>gender</code></dt><dd>gender of the respondent</dd> <dt><code>age</code></dt><dd>age of the respondent</dd> <dt><code>work</code></dt><dd>work status of the respondent</dd> <dt><code>Y1</code></dt><dd>1st item response</dd> <dt><code>Y2</code></dt><dd>2nd item response</dd> <dt><code>Y3</code></dt><dd>3rd item response</dd> <dt><code>Y4</code></dt><dd>4th item response</dd> </dl> 

RLMS: RLMS dataset

Description

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Examples