Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Matthew S. Johnson
Title: Marginal Maximum Likelihood Estimation of Item Response Models in R
Abstract: Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.

Page views:: 20662. Submitted: 2006-10-01. Published: 2007-02-22.
Paper: Marginal Maximum Likelihood Estimation of Item Response Models in R     Download PDF (Downloads: 29638)
gpcm_0.1-3.tar.gz: R source package Download (Downloads: 2109; 54KB) v20i10.R: R example code from the paper Download (Downloads: 2123; 1017B)

DOI: 10.18637/jss.v020.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.