ltm-package {ltm} | R Documentation |
Latent Trait Models for Item Response Theory Analyses
Description
This package provides a flexible framework for Item Response Theory analyses for dichotomous and polytomous data under a Marginal Maximum Likelihood approach. The fitting algorithms provide valid inferences under Missing At Random missing data mechanisms.
Details
Package: | ltm |
Type: | Package |
Version: | 1.2-0 |
Date: | 2022-02-18 |
License: | GPL |
The following options are available:
- Descriptives:
samples proportions, missing values information, biserial correlation of items with total score, pairwise associations between items, Cronbach's
\alpha
, unidimensionality check using modified parallel analysis, nonparametric correlation coefficient, plotting of sample proportions versus total score.- Dichotomous data:
Rasch Model, Two Parameter Logistic Model, Birnbaum's Three Parameter Model, and Latent Trait Model up to two latent variables (allowing also for nonlinear terms between the latent traits).
- Polytomous data:
Samejima's Graded Response Model and the Generalized Partial Credit Model.
- Goodness-of-Fit:
Bootstrapped Pearson
\chi^2
for Rasch and Generalized Partial Credit models, fit on the two- and three-way margins for all models, likelihood ratio tests between nested models (including AIC and BIC criteria values), and item- and person-fit statistics.- Factor Scoring - Ability Estimates:
Empirical Bayes (i.e., posterior modes), Expected a posteriori (i.e., posterior means), Multiple Imputed Empirical Bayes, and Component Scores for dichotomous data.
- Test Equating:
Alternate Form Equating (where common and unique items are analyzed simultaneously) and Across Sample Equating (where different sets of unique items are analyzed separately based on previously calibrated anchor items).
- Plotting:
Item Characteristic Curves, Item Information Curves, Test Information Functions, Standard Error of Measurement, Standardized Loadings Scatterplot (for the two-factor latent trait model), Item Operation Characteristic Curves (for ordinal polytomous data), Item Person Maps.
Author(s)
Dimitris Rizopoulos
Maintainer: Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl>
References
Baker, F. and Kim, S-H. (2004) Item Response Theory, 2nd ed. New York: Marcel Dekker.
Rizopoulos, D. (2006) ltm: An R package for latent variable modelling and item response theory analyses. Journal of Statistical Software, 17(5), 1–25. URL doi: 10.18637/jss.v017.i05