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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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IJME
2022
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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. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-9902209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IJME |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT linacrejohnm rstatisticssurveyandreviewofpackagesfortheestimationofraschmodels |