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R Statistics: survey and review of packages for the estimation of Rasch models

Abstract - R Statistics is a comprehensive and widely-used suite of packages for statistical operations. From 27 R packages indexed with the word “Rasch”, 11 packages capable of Rasch estimation and analysis are identified and critiqued. A commercial Rasch application is included for comparison. Thr...

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Autor principal: Linacre, John M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IJME 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902209/
https://www.ncbi.nlm.nih.gov/pubmed/35759222
http://dx.doi.org/10.5116/ijme.629d.d88f
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author Linacre, John M.
author_facet Linacre, John M.
author_sort Linacre, John M.
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description Abstract - R Statistics is a comprehensive and widely-used suite of packages for statistical operations. From 27 R packages indexed with the word “Rasch”, 11 packages capable of Rasch estimation and analysis are identified and critiqued. A commercial Rasch application is included for comparison. Three R data frames are used. A larger and a smaller 0/1 data frame are analyzed with the Dichotomous Rasch Model. A polytomous 0/1/2 data frame is analyzed with the Partial Credit Model. The R packages can all use the same data frame. They are easy to use and mostly fast, though their documentation is generally skimpy. Every package has obvious shortcomings, but the unique features of each package could make them all useful. For general Rasch estimation and fit analysis of dichotomous data, three packages stand out: eRm, TAM and autoRasch. Two packages stand out for polytomous data: TAM and autoRasch.
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spelling pubmed-99022092023-02-07 R Statistics: survey and review of packages for the estimation of Rasch models Linacre, John M. Int J Med Educ Editorial Abstract - R Statistics is a comprehensive and widely-used suite of packages for statistical operations. From 27 R packages indexed with the word “Rasch”, 11 packages capable of Rasch estimation and analysis are identified and critiqued. A commercial Rasch application is included for comparison. Three R data frames are used. A larger and a smaller 0/1 data frame are analyzed with the Dichotomous Rasch Model. A polytomous 0/1/2 data frame is analyzed with the Partial Credit Model. The R packages can all use the same data frame. They are easy to use and mostly fast, though their documentation is generally skimpy. Every package has obvious shortcomings, but the unique features of each package could make them all useful. For general Rasch estimation and fit analysis of dichotomous data, three packages stand out: eRm, TAM and autoRasch. Two packages stand out for polytomous data: TAM and autoRasch. IJME 2022-06-24 /pmc/articles/PMC9902209/ /pubmed/35759222 http://dx.doi.org/10.5116/ijme.629d.d88f Text en Copyright: © 2022 John M. Linacre https://creativecommons.org/licenses/by/3.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted use of work provided the original work is properly cited. http://creativecommons.org/licenses/by/3.0/ (https://creativecommons.org/licenses/by/3.0/)
spellingShingle Editorial
Linacre, John M.
R Statistics: survey and review of packages for the estimation of Rasch models
title R Statistics: survey and review of packages for the estimation of Rasch models
title_full R Statistics: survey and review of packages for the estimation of Rasch models
title_fullStr R Statistics: survey and review of packages for the estimation of Rasch models
title_full_unstemmed R Statistics: survey and review of packages for the estimation of Rasch models
title_short R Statistics: survey and review of packages for the estimation of Rasch models
title_sort r statistics: survey and review of packages for the estimation of rasch models
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902209/
https://www.ncbi.nlm.nih.gov/pubmed/35759222
http://dx.doi.org/10.5116/ijme.629d.d88f
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