Estimation of Models in a Rasch Family for Polytomous Items and Multiple Latent Variables

Carolyn J. Anderson, Zhushan Li and Jeroen K. Vermunt

Journal of Statistical Software, 2007, vol. 020, issue i06

Abstract: The Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of log-linear-by-linear association (LLLA) models, which are derived from a Rasch family of models. The LLLA models are special cases of log-linear models with bivariate interactions. Maximum likelihood estimation of LLLA models in this form is limited to relatively small problems; however, pseudo-likelihood estimation overcomes this limitation. Maximizing the pseudo-likelihood function is achieved by maximizing the likelihood of a single conditional multinomial logistic regression model. The parameter estimates are asymptotically normal and consistent. Based on our simulation studies, the pseudo-likelihood and maximum likelihood estimates of the parameters of LLLA models are nearly identical and the loss of efficiency is negligible. Recovery of parameters of Rasch models fit to simulated data is excellent.

Date: 2007-02-02
References: View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v020i06/v20i06.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... 6/plRasch_0.1.tar.gz
https://www.jstatsoft.org/index.php/jss/article/do ... v020i06/v20i06.R.zip

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:020:i06

DOI: 10.18637/jss.v020.i06

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().