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Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R

Mair, Patrick and Hatzinger, Reinhold (2007) Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R. Journal of Statistical Software, 20 (9). pp. 1-20. ISSN 1548-7660

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Abstract

Item response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch modeling) for computing Rasch models and several extensions. A main characteristic of some IRT models, the Rasch model being the most prominent, concerns the separation of two kinds of parameters, one that describes qualities of the subject under investigation, and the other relates to qualities of the situation under which the response of a subject is observed. Using conditional maximum likelihood (CML) estimation both types of parameters may be estimated independently from each other. IRT models are well suited to cope with dichotomous and polytomous responses, where the response categories may be unordered as well as ordered. The incorporation of linear structures allows for modeling the effects of covariates and enables the analysis of repeated categorical measurements. The eRm package fits the following models: the Rasch model, the rating scale model (RSM), and the partial credit model (PCM) as well as linear reparameterizations through covariate structures like the linear logistic test model (LLTM), the linear rating scale model (LRSM), and the linear partial credit model (LPCM). We use an unitary, efficient CML approach to estimate the item parameters and their standard errors. Graphical and numeric tools for assessing goodness-of-fit are provided. (authors' abstract)

Item Type: Article
Additional Information: Article contains supplementary files. See http://dx.doi.org/10.18637/jss.v020.i09
Keywords: Rasch model / LLTM / RSM / LRSM / PCM / LPCM / CML estimation
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Variance from Published Version: None
Depositing User: Elena Simukovic
Date Deposited: 25 Apr 2016 14:39
Last Modified: 26 Apr 2016 02:38
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/38165/
URI: http://epub.wu.ac.at/id/eprint/5013

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