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

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 m...

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Main Authors: Mair, Patrick, Hatzinger, Reinhold
Format: Others
Language:en
Published: Foundation for Open Access Statistics 2007
Subjects:
Online Access:http://epub.wu.ac.at/5013/1/Mair_Hatzinger_2007_JSS_Extended%2DRasch%2DModeling.pdf
http://dx.doi.org/10.18637/jss.v020.i09
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spelling ndltd-VIENNA-oai-epub.wu-wien.ac.at-50132016-04-27T05:22:35Z Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R Mair, Patrick Hatzinger, Reinhold Rasch model / LLTM / RSM / LRSM / PCM / LPCM / CML estimation 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) Foundation for Open Access Statistics 2007-02-22 Article PeerReviewed en application/pdf http://epub.wu.ac.at/5013/1/Mair_Hatzinger_2007_JSS_Extended%2DRasch%2DModeling.pdf http://www.jstatsoft.org/v20/i09/paper http://www.foastat.org/ https://www.jstatsoft.org/about/editorialPolicies#openAccessPolicy http://dx.doi.org/10.18637/jss.v020.i09 http://epub.wu.ac.at/5013/
collection NDLTD
language en
format Others
sources NDLTD
topic Rasch model / LLTM / RSM / LRSM / PCM / LPCM / CML estimation
spellingShingle Rasch model / LLTM / RSM / LRSM / PCM / LPCM / CML estimation
Mair, Patrick
Hatzinger, Reinhold
Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
description 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)
author Mair, Patrick
Hatzinger, Reinhold
author_facet Mair, Patrick
Hatzinger, Reinhold
author_sort Mair, Patrick
title Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
title_short Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
title_full Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
title_fullStr Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
title_full_unstemmed Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R
title_sort extended rasch modeling: the erm package for the application of irt models in r
publisher Foundation for Open Access Statistics
publishDate 2007
url http://epub.wu.ac.at/5013/1/Mair_Hatzinger_2007_JSS_Extended%2DRasch%2DModeling.pdf
http://dx.doi.org/10.18637/jss.v020.i09
work_keys_str_mv AT mairpatrick extendedraschmodelingtheermpackagefortheapplicationofirtmodelsinr
AT hatzingerreinhold extendedraschmodelingtheermpackagefortheapplicationofirtmodelsinr
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