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Guessing and cognitive diagnostics: A general multicomponent latent trait model for diagnosis

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dc.contributor Embretson, Susan E.
dc.creator Lutz, Megan Elyse
dc.date 2015-06-08T18:14:33Z
dc.date 2015-06-09T05:30:07Z
dc.date 2014-05
dc.date 2014-04-02
dc.date May 2014
dc.date 2015-06-08T18:14:33Z
dc.date.accessioned 2015-08-04T07:06:18Z
dc.date.available 2015-08-04T07:06:18Z
dc.identifier http://hdl.handle.net/1853/53446
dc.identifier.uri http://evidence.thinkportal.org/handle/1853/53446
dc.description A common issue noted by detractors of the traditional scoring of Multiple Choice (MC) tests is the confounding of guessing or other false positives with partial knowledge and full knowledge. The current study provides a review of classical test theory (CTT) approaches to handling guessing and partial knowledge. When those methods are rejected, the item response theory (IRT) and cognitive diagnostic modeling (CDM) approaches, and their relative strengths and weaknesses, are considered. Finally, a generalization of the Multicomponent Latent Trait Model for Diagnosis (MLTM-D; Embretson & Yang, 2013) is proposed. The results of a simulation study are presented, which indicate that, in the presence of guessing, the proposed model has more reliable and accurate item parameter estimates than the MLTM-D, generally yielding better recovery of person parameters. Discussion of the methods and findings, as well as some suggested directions for further study, is included.
dc.format application/pdf
dc.language en_US
dc.publisher Georgia Institute of Technology
dc.subject Cognitive diagnostic models
dc.subject Achievement testing
dc.title Guessing and cognitive diagnostics: A general multicomponent latent trait model for diagnosis
dc.type Dissertation


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