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An exploratory factor model for ordinal paired comparison indicators()

Suppose the same contestants play in tournaments of chess, shogi, and Go. Per-tournament rankings can be estimated. We may also try to recover a latent board game skill that accounts for some proportion of the variance in per-board game rankings. To accomplish this, a factor model is introduced. Ide...

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Autor principal: Pritikin, Joshua N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492820/
https://www.ncbi.nlm.nih.gov/pubmed/32984579
http://dx.doi.org/10.1016/j.heliyon.2020.e04821
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author Pritikin, Joshua N.
author_facet Pritikin, Joshua N.
author_sort Pritikin, Joshua N.
collection PubMed
description Suppose the same contestants play in tournaments of chess, shogi, and Go. Per-tournament rankings can be estimated. We may also try to recover a latent board game skill that accounts for some proportion of the variance in per-board game rankings. To accomplish this, a factor model is introduced. Identification issues with the ordinal paired item model are discussed. Simulation studies are presented to provide some guidance about sample size requirements. Both single item and multivariate correlation and factor model are validated using simulation-based calibration. We recommend leave-one-out cross-validation to assess model fit. To ease application of the methods described, an open-source companion R extension, pcFactorStan, is published on the Comprehensive R Archive Network. Application of pcFactorStan is demonstrated by analysis of a real-world dataset.
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spelling pubmed-74928202020-09-24 An exploratory factor model for ordinal paired comparison indicators() Pritikin, Joshua N. Heliyon Research Article Suppose the same contestants play in tournaments of chess, shogi, and Go. Per-tournament rankings can be estimated. We may also try to recover a latent board game skill that accounts for some proportion of the variance in per-board game rankings. To accomplish this, a factor model is introduced. Identification issues with the ordinal paired item model are discussed. Simulation studies are presented to provide some guidance about sample size requirements. Both single item and multivariate correlation and factor model are validated using simulation-based calibration. We recommend leave-one-out cross-validation to assess model fit. To ease application of the methods described, an open-source companion R extension, pcFactorStan, is published on the Comprehensive R Archive Network. Application of pcFactorStan is demonstrated by analysis of a real-world dataset. Elsevier 2020-09-14 /pmc/articles/PMC7492820/ /pubmed/32984579 http://dx.doi.org/10.1016/j.heliyon.2020.e04821 Text en © 2020 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Pritikin, Joshua N.
An exploratory factor model for ordinal paired comparison indicators()
title An exploratory factor model for ordinal paired comparison indicators()
title_full An exploratory factor model for ordinal paired comparison indicators()
title_fullStr An exploratory factor model for ordinal paired comparison indicators()
title_full_unstemmed An exploratory factor model for ordinal paired comparison indicators()
title_short An exploratory factor model for ordinal paired comparison indicators()
title_sort exploratory factor model for ordinal paired comparison indicators()
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7492820/
https://www.ncbi.nlm.nih.gov/pubmed/32984579
http://dx.doi.org/10.1016/j.heliyon.2020.e04821
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