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| Vol. 1 | |||
| * = Special Volume | |||
| Authors: | Matthew S. Johnson |
| Title: | [download] (1266)Marginal Maximum Likelihood Estimation of Item Response Models in R |
| Reference: | Vol. 20, Issue 10, Feb 2007 Submitted 2006-10-01, Accepted 2007-02-22 |
| Type: | Article |
| 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. |
| Paper: | [download] (1266)Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R (application/pdf, 1.5 MB) |
| Supplements: | [download] (199)gpcm_0.1-3.tar.gz: R source package (application/x-gzip, 54.1 KB) |
| [download] (198)v20i10.R: R example code from the paper (application/zip, 1017 Bytes) |
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| Resources: | BibTeX | OAI |