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The agreement chart

BACKGROUND: When assessing the concordance between two methods of measurement of ordinal categorical data, summary measures such as Cohen’s (1960) kappa or Bangdiwala’s (1985) B-statistic are used. However, a picture conveys more information than a single summary measure. METHODS: We describe how to...

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Detalles Bibliográficos
Autores principales: Bangdiwala, Shrikant I, Shankar, Viswanathan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3733724/
https://www.ncbi.nlm.nih.gov/pubmed/23890315
http://dx.doi.org/10.1186/1471-2288-13-97
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author Bangdiwala, Shrikant I
Shankar, Viswanathan
author_facet Bangdiwala, Shrikant I
Shankar, Viswanathan
author_sort Bangdiwala, Shrikant I
collection PubMed
description BACKGROUND: When assessing the concordance between two methods of measurement of ordinal categorical data, summary measures such as Cohen’s (1960) kappa or Bangdiwala’s (1985) B-statistic are used. However, a picture conveys more information than a single summary measure. METHODS: We describe how to construct and interpret Bangdiwala’s (1985) agreement chart and illustrate its use in visually assessing concordance in several example clinical applications. RESULTS: The agreement charts provide a visual impression that no summary statistic can convey, and summary statistics reduce the information to a single characteristic of the data. However, the visual impression is personal and subjective, and not usually reproducible from one reader to another. CONCLUSIONS: The agreement chart should be used to complement the summary kappa or B-statistics, not to replace them. The graphs can be very helpful to researchers as an early step to understand relationships in their data when assessing concordance.
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spelling pubmed-37337242013-08-06 The agreement chart Bangdiwala, Shrikant I Shankar, Viswanathan BMC Med Res Methodol Technical Advance BACKGROUND: When assessing the concordance between two methods of measurement of ordinal categorical data, summary measures such as Cohen’s (1960) kappa or Bangdiwala’s (1985) B-statistic are used. However, a picture conveys more information than a single summary measure. METHODS: We describe how to construct and interpret Bangdiwala’s (1985) agreement chart and illustrate its use in visually assessing concordance in several example clinical applications. RESULTS: The agreement charts provide a visual impression that no summary statistic can convey, and summary statistics reduce the information to a single characteristic of the data. However, the visual impression is personal and subjective, and not usually reproducible from one reader to another. CONCLUSIONS: The agreement chart should be used to complement the summary kappa or B-statistics, not to replace them. The graphs can be very helpful to researchers as an early step to understand relationships in their data when assessing concordance. BioMed Central 2013-07-29 /pmc/articles/PMC3733724/ /pubmed/23890315 http://dx.doi.org/10.1186/1471-2288-13-97 Text en Copyright © 2013 Bangdiwala and Shankar; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Advance
Bangdiwala, Shrikant I
Shankar, Viswanathan
The agreement chart
title The agreement chart
title_full The agreement chart
title_fullStr The agreement chart
title_full_unstemmed The agreement chart
title_short The agreement chart
title_sort agreement chart
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3733724/
https://www.ncbi.nlm.nih.gov/pubmed/23890315
http://dx.doi.org/10.1186/1471-2288-13-97
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