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Article: Choosing between CDM and Unidimensional IRT: The Proportional Reasoning Test Case

TitleChoosing between CDM and Unidimensional IRT: The Proportional Reasoning Test Case
Authors
KeywordsCognitive diagnosis
item response model
model comparison
proportional reasoning
Issue Date2020
PublisherLawrence Erlbaum Associates, Inc. The Journal's web site is located at http://www.leaonline.com/loi/mea
Citation
Measurement: interdisciplinary research and perspectives, 2020, v. 18 n. 2, p. 87-96 How to Cite?
AbstractCognitive diagnosis models (CDMs) have gained increasing popularity recently because of their potential to provide diagnostic inferences that can inform learning and teaching. However, the development of cognitive diagnostic assessments (CDAs) is lagging behind the development of the associated psychometric models. At present, it is not clear whether and how much extra information can be obtained by analyzing CDA data with a CDM rather than simply using unidimensional item response theory models. This study fits CDMs and three-parameter logistic (3PL) model to the data from a CDA. Results showed that the CDMs fitted the data well, but the 3PL model did not. Compared with the 3PL model, the CDMs can highlight student differences from a multidimensional perspective, thus offering finer-grained information that unidimensional IRT models are not capable of providing.
Persistent Identifierhttp://hdl.handle.net/10722/289305
ISSN
2023 Impact Factor: 0.6
2023 SCImago Journal Rankings: 0.327
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, W-
dc.contributor.authorMinchen, N-
dc.contributor.authorde la Torre, J-
dc.date.accessioned2020-10-22T08:10:46Z-
dc.date.available2020-10-22T08:10:46Z-
dc.date.issued2020-
dc.identifier.citationMeasurement: interdisciplinary research and perspectives, 2020, v. 18 n. 2, p. 87-96-
dc.identifier.issn1536-6367-
dc.identifier.urihttp://hdl.handle.net/10722/289305-
dc.description.abstractCognitive diagnosis models (CDMs) have gained increasing popularity recently because of their potential to provide diagnostic inferences that can inform learning and teaching. However, the development of cognitive diagnostic assessments (CDAs) is lagging behind the development of the associated psychometric models. At present, it is not clear whether and how much extra information can be obtained by analyzing CDA data with a CDM rather than simply using unidimensional item response theory models. This study fits CDMs and three-parameter logistic (3PL) model to the data from a CDA. Results showed that the CDMs fitted the data well, but the 3PL model did not. Compared with the 3PL model, the CDMs can highlight student differences from a multidimensional perspective, thus offering finer-grained information that unidimensional IRT models are not capable of providing.-
dc.languageeng-
dc.publisherLawrence Erlbaum Associates, Inc. The Journal's web site is located at http://www.leaonline.com/loi/mea-
dc.relation.ispartofMeasurement: interdisciplinary research and perspectives-
dc.rightsPreprint: This is an Author's Original Manuscript of an article published by Taylor & Francis Group in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/doi/abs/[Article DOI]. Postprint: This is an Accepted Manuscript of an article published by Taylor & Francis Group in [JOURNAL TITLE] on [date of publication], available online at: http://www.tandfonline.com/doi/abs/[Article DOI].-
dc.subjectCognitive diagnosis-
dc.subjectitem response model-
dc.subjectmodel comparison-
dc.subjectproportional reasoning-
dc.titleChoosing between CDM and Unidimensional IRT: The Proportional Reasoning Test Case-
dc.typeArticle-
dc.identifier.emailde la Torre, J: j.delatorre@hku.hk-
dc.identifier.authorityde la Torre, J=rp02159-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/15366367.2019.1697122-
dc.identifier.scopuseid_2-s2.0-85086095832-
dc.identifier.hkuros317587-
dc.identifier.volume18-
dc.identifier.issue2-
dc.identifier.spage87-
dc.identifier.epage96-
dc.identifier.isiWOS:000538017900002-
dc.publisher.placeUnited States-
dc.identifier.issnl1536-6359-

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