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Article: Impact of diagnosticity on the adequacy of models for cognitive diagnosis under a linear attribute structure: A simulation study

TitleImpact of diagnosticity on the adequacy of models for cognitive diagnosis under a linear attribute structure: A simulation study
Authors
Issue Date2009
Citation
Journal of Educational Measurement, 2009, v. 46, n. 4, p. 450-469 How to Cite?
AbstractCompared to unidimensional item response models (IRMs), cognitive diagnostic models (CDMs) based on latent classes represent examinees' knowledge and item requirements using discrete structures. This study systematically examines the viability of retrofitting CDMs to IRM-based data with a linear attribute structure. The study utilizes a procedure to make the IRM and CDM frameworks comparable and investigates how estimation accuracy is affected by test diagnosticity and the match between the true and fitted models. The study shows that comparable results can be obtained when highly diagnostic IRM data are retrofitted with CDM, and vice versa, retrofitting CDMs to IRM-based data in some conditions can result in considerable examinee misclassification, and model fit indices provide limited indication of the accuracy of item parameter estimation and attribute classification. © 2009 by the National Council on Measurement in Education.
Persistent Identifierhttp://hdl.handle.net/10722/228091
ISSN
2015 Impact Factor: 1.528
2015 SCImago Journal Rankings: 2.067

 

DC FieldValueLanguage
dc.contributor.authorDe La Torre, Jimmy-
dc.contributor.authorKarelitz, Tzur M.-
dc.date.accessioned2016-08-01T06:45:10Z-
dc.date.available2016-08-01T06:45:10Z-
dc.date.issued2009-
dc.identifier.citationJournal of Educational Measurement, 2009, v. 46, n. 4, p. 450-469-
dc.identifier.issn0022-0655-
dc.identifier.urihttp://hdl.handle.net/10722/228091-
dc.description.abstractCompared to unidimensional item response models (IRMs), cognitive diagnostic models (CDMs) based on latent classes represent examinees' knowledge and item requirements using discrete structures. This study systematically examines the viability of retrofitting CDMs to IRM-based data with a linear attribute structure. The study utilizes a procedure to make the IRM and CDM frameworks comparable and investigates how estimation accuracy is affected by test diagnosticity and the match between the true and fitted models. The study shows that comparable results can be obtained when highly diagnostic IRM data are retrofitted with CDM, and vice versa, retrofitting CDMs to IRM-based data in some conditions can result in considerable examinee misclassification, and model fit indices provide limited indication of the accuracy of item parameter estimation and attribute classification. © 2009 by the National Council on Measurement in Education.-
dc.languageeng-
dc.relation.ispartofJournal of Educational Measurement-
dc.titleImpact of diagnosticity on the adequacy of models for cognitive diagnosis under a linear attribute structure: A simulation study-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1745-3984.2009.00092.x-
dc.identifier.scopuseid_2-s2.0-71549122894-
dc.identifier.volume46-
dc.identifier.issue4-
dc.identifier.spage450-
dc.identifier.epage469-
dc.identifier.eissn1745-3984-

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