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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2007-02-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v20/i09/paper |
| _version_ | 1818151850396876800 |
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| author | Patrick Mair Reinhold Hatzinger |
| author_facet | Patrick Mair Reinhold Hatzinger |
| author_sort | Patrick Mair |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T13:45:23Z |
| format | Article |
| id | doaj.art-7ddf7106faec413783c126ccea04e886 |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-11T13:45:23Z |
| publishDate | 2007-02-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-7ddf7106faec413783c126ccea04e8862022-12-22T01:04:31ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602007-02-01209Extended Rasch Modeling: The eRm Package for the Application of IRT Models in RPatrick MairReinhold HatzingerItem 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.http://www.jstatsoft.org/v20/i09/paperRasch modelLLTMRSMLRSMPCMLPCMCML estimation |
| spellingShingle | Patrick Mair Reinhold Hatzinger Extended Rasch Modeling: The eRm Package for the Application of IRT Models in R Journal of Statistical Software Rasch model LLTM RSM LRSM PCM LPCM CML estimation |
| title | 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_short | 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 |
| topic | Rasch model LLTM RSM LRSM PCM LPCM CML estimation |
| url | http://www.jstatsoft.org/v20/i09/paper |
| work_keys_str_mv | AT patrickmair extendedraschmodelingtheermpackagefortheapplicationofirtmodelsinr AT reinholdhatzinger extendedraschmodelingtheermpackagefortheapplicationofirtmodelsinr |