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Article: Factors affecting the item parameter estimation and classification accuracy of the DINA model

TitleFactors affecting the item parameter estimation and classification accuracy of the DINA model
Authors
Issue Date2010
Citation
Journal of Educational Measurement, 2010, v. 47, n. 2, p. 227-249 How to Cite?
AbstractTo better understand the statistical properties of the deterministic inputs, noisy " and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the prior distribution matches the latent class structure. However, when the latent classes are of indefinite structure, the empirical Bayes method in conjunction with an unstructured prior distribution provides much better estimates and classification accuracy. Moreover, using empirical Bayes with an unstructured prior does not lead to extremely poor results as other prior-estimation method combinations do. The simulation results also show that increasing the sample size reduces the variability, and to some extent the bias, of item parameter estimates, whereas lower level of guessing and slip parameter is associated with higher quality item parameter estimation and classification accuracy. © 2010 by the National Council on Measurement in Education.
Persistent Identifierhttp://hdl.handle.net/10722/228103
ISSN
2015 Impact Factor: 1.528
2015 SCImago Journal Rankings: 2.067

 

DC FieldValueLanguage
dc.contributor.authorde la Torre, Jimmy-
dc.contributor.authorHong, Yuan-
dc.contributor.authorDeng, Weiling-
dc.date.accessioned2016-08-01T06:45:11Z-
dc.date.available2016-08-01T06:45:11Z-
dc.date.issued2010-
dc.identifier.citationJournal of Educational Measurement, 2010, v. 47, n. 2, p. 227-249-
dc.identifier.issn0022-0655-
dc.identifier.urihttp://hdl.handle.net/10722/228103-
dc.description.abstractTo better understand the statistical properties of the deterministic inputs, noisy " and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the prior distribution matches the latent class structure. However, when the latent classes are of indefinite structure, the empirical Bayes method in conjunction with an unstructured prior distribution provides much better estimates and classification accuracy. Moreover, using empirical Bayes with an unstructured prior does not lead to extremely poor results as other prior-estimation method combinations do. The simulation results also show that increasing the sample size reduces the variability, and to some extent the bias, of item parameter estimates, whereas lower level of guessing and slip parameter is associated with higher quality item parameter estimation and classification accuracy. © 2010 by the National Council on Measurement in Education.-
dc.languageeng-
dc.relation.ispartofJournal of Educational Measurement-
dc.titleFactors affecting the item parameter estimation and classification accuracy of the DINA model-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1745-3984.2010.00110.x-
dc.identifier.scopuseid_2-s2.0-77954736976-
dc.identifier.volume47-
dc.identifier.issue2-
dc.identifier.spage227-
dc.identifier.epage249-
dc.identifier.eissn1745-3984-

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