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A Test Can Have Multiple Reliabilities

It is argued that the generalizability theory interpretation of coefficient alpha is important. In this interpretation, alpha is a slightly biased but consistent estimate for the coefficient of generalizability in a subjects x items design where both subjects and items are randomly sampled. This int...

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Autor principal: Ellis, Jules L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636415/
https://www.ncbi.nlm.nih.gov/pubmed/34498211
http://dx.doi.org/10.1007/s11336-021-09800-2
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author Ellis, Jules L.
author_facet Ellis, Jules L.
author_sort Ellis, Jules L.
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description It is argued that the generalizability theory interpretation of coefficient alpha is important. In this interpretation, alpha is a slightly biased but consistent estimate for the coefficient of generalizability in a subjects x items design where both subjects and items are randomly sampled. This interpretation is based on the “domain sampling” true scores. It is argued that these true scores have a more solid empirical basis than the true scores of Lord and Novick (1968), which are based on “stochastic subjects” (Holland, 1990), while only a single observation is available for each within-subject distribution. Therefore, the generalizability interpretation of coefficient alpha is to be preferred, unless the true scores can be defined by a latent variable model that has undisputed empirical validity for the test and that is sufficiently restrictive to entail a consistent estimate of the reliability—as, for example, McDonald’s omega. If this model implies that the items are essentially tau-equivalent, both the generalizability and the reliability interpretation of alpha can be defensible.
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spelling pubmed-86364152021-12-03 A Test Can Have Multiple Reliabilities Ellis, Jules L. Psychometrika Revisiting Cronbach’s Alpha It is argued that the generalizability theory interpretation of coefficient alpha is important. In this interpretation, alpha is a slightly biased but consistent estimate for the coefficient of generalizability in a subjects x items design where both subjects and items are randomly sampled. This interpretation is based on the “domain sampling” true scores. It is argued that these true scores have a more solid empirical basis than the true scores of Lord and Novick (1968), which are based on “stochastic subjects” (Holland, 1990), while only a single observation is available for each within-subject distribution. Therefore, the generalizability interpretation of coefficient alpha is to be preferred, unless the true scores can be defined by a latent variable model that has undisputed empirical validity for the test and that is sufficiently restrictive to entail a consistent estimate of the reliability—as, for example, McDonald’s omega. If this model implies that the items are essentially tau-equivalent, both the generalizability and the reliability interpretation of alpha can be defensible. Springer US 2021-09-08 2021 /pmc/articles/PMC8636415/ /pubmed/34498211 http://dx.doi.org/10.1007/s11336-021-09800-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Revisiting Cronbach’s Alpha
Ellis, Jules L.
A Test Can Have Multiple Reliabilities
title A Test Can Have Multiple Reliabilities
title_full A Test Can Have Multiple Reliabilities
title_fullStr A Test Can Have Multiple Reliabilities
title_full_unstemmed A Test Can Have Multiple Reliabilities
title_short A Test Can Have Multiple Reliabilities
title_sort test can have multiple reliabilities
topic Revisiting Cronbach’s Alpha
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8636415/
https://www.ncbi.nlm.nih.gov/pubmed/34498211
http://dx.doi.org/10.1007/s11336-021-09800-2
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