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Article: Category-Level Model Selection for the Sequential G-DINA Model

TitleCategory-Level Model Selection for the Sequential G-DINA Model
Authors
Keywordscognitive diagnosis
G-DINA
sequential CDM
polytomous data
model selection
Issue Date2019
PublisherSage Publications, Inc. The Journal's web site is located at http://jeb.sagepub.com/
Citation
Journal of Educational and Behavioral Statistics, 2019, v. 44 n. 1, p. 45-77 How to Cite?
AbstractSolving a constructed-response item usually requires successfully performing a sequence of tasks. Each task could involve different attributes, and those required attributes may be “condensed” in various ways to produce the responses. The sequential generalized deterministic input noisy “and” gate model is a general cognitive diagnosis model (CDM) for graded response items of this type. Although a host of dichotomous CDMs with different condensation rules can be used to parameterize the success probability of each task, specifying the most appropriate one remains challenging. If the CDM specified for each task is not in accordance with the underlying cognitive processes, the validity of the inference could be questionable. This study aims to evaluate whether several hypothesis tests, namely, the Wald test using various variance–covariance matrices, the likelihood ratio (LR) test, and the LR test using approximated parameters, can be used to select the appropriate CDMs for each task of graded response items. Simulation studies are conducted to examine the Type I error and power of the hypothesis tests under varied conditions. A data set from the Trends in International Mathematics and Science Study 2007 mathematics assessment is analyzed as an illustration.
Persistent Identifierhttp://hdl.handle.net/10722/274091
ISSN
2019 Impact Factor: 2.042
2015 SCImago Journal Rankings: 2.025

 

DC FieldValueLanguage
dc.contributor.authorMa, W-
dc.contributor.authorde la Torre, J-
dc.date.accessioned2019-08-18T14:54:52Z-
dc.date.available2019-08-18T14:54:52Z-
dc.date.issued2019-
dc.identifier.citationJournal of Educational and Behavioral Statistics, 2019, v. 44 n. 1, p. 45-77-
dc.identifier.issn1076-9986-
dc.identifier.urihttp://hdl.handle.net/10722/274091-
dc.description.abstractSolving a constructed-response item usually requires successfully performing a sequence of tasks. Each task could involve different attributes, and those required attributes may be “condensed” in various ways to produce the responses. The sequential generalized deterministic input noisy “and” gate model is a general cognitive diagnosis model (CDM) for graded response items of this type. Although a host of dichotomous CDMs with different condensation rules can be used to parameterize the success probability of each task, specifying the most appropriate one remains challenging. If the CDM specified for each task is not in accordance with the underlying cognitive processes, the validity of the inference could be questionable. This study aims to evaluate whether several hypothesis tests, namely, the Wald test using various variance–covariance matrices, the likelihood ratio (LR) test, and the LR test using approximated parameters, can be used to select the appropriate CDMs for each task of graded response items. Simulation studies are conducted to examine the Type I error and power of the hypothesis tests under varied conditions. A data set from the Trends in International Mathematics and Science Study 2007 mathematics assessment is analyzed as an illustration.-
dc.languageeng-
dc.publisherSage Publications, Inc. The Journal's web site is located at http://jeb.sagepub.com/-
dc.relation.ispartofJournal of Educational and Behavioral Statistics-
dc.rightsJournal of Educational and Behavioral Statistics. Copyright © Sage Publications, Inc.-
dc.subjectcognitive diagnosis-
dc.subjectG-DINA-
dc.subjectsequential CDM-
dc.subjectpolytomous data-
dc.subjectmodel selection-
dc.titleCategory-Level Model Selection for the Sequential G-DINA Model-
dc.typeArticle-
dc.identifier.emailde la Torre, J: jdltorre@hku.hk-
dc.identifier.authorityde la Torre, J=rp02159-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.3102/1076998618792484-
dc.identifier.scopuseid_2-s2.0-85052599507-
dc.identifier.hkuros302288-
dc.identifier.volume44-
dc.identifier.issue1-
dc.identifier.spage45-
dc.identifier.epage77-
dc.publisher.placeUnited States-

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