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| 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. | ||||
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Page views:: 20643. Submitted: 2006-10-01. Published: 2007-02-22. |
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| Paper: |
Marginal Maximum Likelihood Estimation of Item Response Models in R
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| DOI: |
10.18637/jss.v020.i10
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This work is licensed under the licenses 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. |