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Article: Model evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data

TitleModel evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data
Authors
KeywordsCognitive diagnosis
Issue Date2008
Citation
Psychometrika, 2008, v. 73, n. 4, p. 595-624 How to Cite?
AbstractThis paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the DINA model to allow for multiple strategies of problem solving. For each of these models the joint distribution of the indicators of skill mastery is modeled using a single continuous higher-order latent trait, to explain the dependence in the mastery of distinct skills. This approach stems from viewing the skills as the specific states of knowledge required for exam performance, and viewing these skills as arising from a broadly defined latent trait resembling the θ of item response models. We discuss several techniques for comparing models and assessing goodness of fit. We then implement these methods using the fraction subtraction data with the aim of selecting the best of the three models for this application. We employ Markov chain Monte Carlo algorithms to fit the models, and we present simulation results to examine the performance of these algorithms. © 2008 The Psychometric Society.
Persistent Identifierhttp://hdl.handle.net/10722/228070
ISSN
2015 Impact Factor: 1.831
2015 SCImago Journal Rankings: 2.153

 

DC FieldValueLanguage
dc.contributor.authorDe La Torre, Jimmy-
dc.contributor.authorDouglas, Jeffrey A.-
dc.date.accessioned2016-08-01T06:45:07Z-
dc.date.available2016-08-01T06:45:07Z-
dc.date.issued2008-
dc.identifier.citationPsychometrika, 2008, v. 73, n. 4, p. 595-624-
dc.identifier.issn0033-3123-
dc.identifier.urihttp://hdl.handle.net/10722/228070-
dc.description.abstractThis paper studies three models for cognitive diagnosis, each illustrated with an application to fraction subtraction data. The objective of each of these models is to classify examinees according to their mastery of skills assumed to be required for fraction subtraction. We consider the DINA model, the NIDA model, and a new model that extends the DINA model to allow for multiple strategies of problem solving. For each of these models the joint distribution of the indicators of skill mastery is modeled using a single continuous higher-order latent trait, to explain the dependence in the mastery of distinct skills. This approach stems from viewing the skills as the specific states of knowledge required for exam performance, and viewing these skills as arising from a broadly defined latent trait resembling the θ of item response models. We discuss several techniques for comparing models and assessing goodness of fit. We then implement these methods using the fraction subtraction data with the aim of selecting the best of the three models for this application. We employ Markov chain Monte Carlo algorithms to fit the models, and we present simulation results to examine the performance of these algorithms. © 2008 The Psychometric Society.-
dc.languageeng-
dc.relation.ispartofPsychometrika-
dc.subjectCognitive diagnosis-
dc.titleModel evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11336-008-9063-2-
dc.identifier.scopuseid_2-s2.0-62949122638-
dc.identifier.volume73-
dc.identifier.issue4-
dc.identifier.spage595-
dc.identifier.epage624-

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