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Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items

Asymmetric IRT models have been shown useful for capturing heterogeneity in the number of latent subprocesses underlying educational test items (Lee and Bolt, 2018a). One potentially useful practical application of such models is toward the scoring of discrete-option multiple-choice (DOMC) items. Un...

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Autores principales: Bolt, Daniel M., Lee, Sora, Wollack, James, Eckerly, Carol, Sowles, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240662/
https://www.ncbi.nlm.nih.gov/pubmed/30483187
http://dx.doi.org/10.3389/fpsyg.2018.02175
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author Bolt, Daniel M.
Lee, Sora
Wollack, James
Eckerly, Carol
Sowles, John
author_facet Bolt, Daniel M.
Lee, Sora
Wollack, James
Eckerly, Carol
Sowles, John
author_sort Bolt, Daniel M.
collection PubMed
description Asymmetric IRT models have been shown useful for capturing heterogeneity in the number of latent subprocesses underlying educational test items (Lee and Bolt, 2018a). One potentially useful practical application of such models is toward the scoring of discrete-option multiple-choice (DOMC) items. Under the DOMC format, response options are independently and randomly administered up to the (last) keyed response, and thus the scheduled number of distractor response options to which an examinee may be exposed (and consequently the overall difficulty of the item) can vary. In this paper we demonstrate the applicability of Samejima's logistic positive exponent (LPE) model to response data from an information technology certification test administered using the DOMC format, and discuss its advantages relative to a two-parameter logistic (2PL) model in addressing such effects. Application of the LPE in the context of DOMC items is shown to (1) provide reduced complexity and a superior comparative fit relative to the 2PL, and (2) yield a latent metric with reduced shrinkage at high proficiency levels. The results support the potential use of the LPE as a basis for scoring DOMC items so as to account for effects related to key location.
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spelling pubmed-62406622018-11-27 Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items Bolt, Daniel M. Lee, Sora Wollack, James Eckerly, Carol Sowles, John Front Psychol Psychology Asymmetric IRT models have been shown useful for capturing heterogeneity in the number of latent subprocesses underlying educational test items (Lee and Bolt, 2018a). One potentially useful practical application of such models is toward the scoring of discrete-option multiple-choice (DOMC) items. Under the DOMC format, response options are independently and randomly administered up to the (last) keyed response, and thus the scheduled number of distractor response options to which an examinee may be exposed (and consequently the overall difficulty of the item) can vary. In this paper we demonstrate the applicability of Samejima's logistic positive exponent (LPE) model to response data from an information technology certification test administered using the DOMC format, and discuss its advantages relative to a two-parameter logistic (2PL) model in addressing such effects. Application of the LPE in the context of DOMC items is shown to (1) provide reduced complexity and a superior comparative fit relative to the 2PL, and (2) yield a latent metric with reduced shrinkage at high proficiency levels. The results support the potential use of the LPE as a basis for scoring DOMC items so as to account for effects related to key location. Frontiers Media S.A. 2018-11-12 /pmc/articles/PMC6240662/ /pubmed/30483187 http://dx.doi.org/10.3389/fpsyg.2018.02175 Text en Copyright © 2018 Bolt, Lee, Wollack, Eckerly and Sowles. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Bolt, Daniel M.
Lee, Sora
Wollack, James
Eckerly, Carol
Sowles, John
Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items
title Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items
title_full Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items
title_fullStr Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items
title_full_unstemmed Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items
title_short Application of Asymmetric IRT Modeling to Discrete-Option Multiple-Choice Test Items
title_sort application of asymmetric irt modeling to discrete-option multiple-choice test items
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240662/
https://www.ncbi.nlm.nih.gov/pubmed/30483187
http://dx.doi.org/10.3389/fpsyg.2018.02175
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