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Article: Higher-order latent trait models for cognitive diagnosis

TitleHigher-order latent trait models for cognitive diagnosis
Authors
KeywordsCognitive diagnosis
Issue Date2004
Citation
Psychometrika, 2004, v. 69, n. 3, p. 333-353 How to Cite?
AbstractHigher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling the θ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example.
Persistent Identifierhttp://hdl.handle.net/10722/228026
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.accessioned2016-08-01T06:45:00Z-
dc.date.available2016-08-01T06:45:00Z-
dc.date.issued2004-
dc.identifier.citationPsychometrika, 2004, v. 69, n. 3, p. 333-353-
dc.identifier.issn0033-3123-
dc.identifier.urihttp://hdl.handle.net/10722/228026-
dc.description.abstractHigher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling the θ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example.-
dc.languageeng-
dc.relation.ispartofPsychometrika-
dc.subjectCognitive diagnosis-
dc.titleHigher-order latent trait models for cognitive diagnosis-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-12344281031-
dc.identifier.volume69-
dc.identifier.issue3-
dc.identifier.spage333-
dc.identifier.epage353-

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