Quotation 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): S. 1-20.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Statistical Software
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
Volume 20
Number 9
Year 2007
Page from 1
Page to 20
Reviewed? Y
URL http://www.jstatsoft.org/v20/i09/paper
DOI http://dx.doi.org/10.18637/jss.v020.i09

Associations

People
Mair, Patrick (Former researcher)
Hatzinger, Reinhold (Former researcher)
Organization
Institute for Statistics and Mathematics IN (Details)
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