Estimating parameters of dichotomous and ordinal item response models with gllamm

Xiaohui Zheng () and Sophia Rabe-Hesketh
Additional contact information
Xiaohui Zheng: Graduate School of Education, University of California, Berkeley

Stata Journal, 2007, vol. 7, issue 3, 313-333

Abstract: Item response theory models are measurement models for categorical responses. Traditionally, the models are used in educational testing, where responses to test items can be viewed as indirect measures of latent ability. The test items are scored either dichotomously (correct–incorrect) or by using an ordinal scale (a grade from poor to excellent). Item response models also apply equally for measurement of other latent traits. Here we describe the one- and two-parameter logit models for dichotomous items, the partial-credit and rating scale models for ordinal items, and an extension of these models where the latent variable is regressed on explanatory variables. We show how these models can be expressed as generalized linear latent and mixed models and fitted by using the user-written command gllamm. Copyright 2007 by StataCorp LP.

Keywords: gllamm; gllapred; latent variables; Rasch model; partial-credit model; rating scale model; latent regression; generalized linear latent and mixed model; adaptive quadrature; item response theory (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0129
http://www.stata-journal.com/software/sj7-3/st0129/ (text/html)

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:tsj:stataj:v:7:y:2007:i:3:p:313-333

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().