Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model.
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| Title: | Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model. |
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| Authors: | de la Torre, Jimmy, Yuan Hong, Weiling deng |
| Source: | Journal of Educational Measurement. Jun2010, Vol. 47 Issue 2, p227-249. 23p. 1 Diagram, 5 Charts, 3 Graphs. |
| Subjects: | BAYESIAN analysis, ESTIMATION theory, SIMULATION methods & models, EDUCATIONAL tests & measurements, CLASSES (Groups of students), PARAMETER estimation |
| Abstract: | To 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Teacher Reference Center |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: trh DbLabel: Teacher Reference Center An: 51305518 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22de+la+Torre%2C+Jimmy%22">de la Torre, Jimmy</searchLink><br /><searchLink fieldCode="AR" term="%22Yuan+Hong%22">Yuan Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Weiling+deng%22">Weiling deng</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Jun2010, Vol. 47 Issue 2, p227-249. 23p. 1 Diagram, 5 Charts, 3 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22BAYESIAN+analysis%22">BAYESIAN analysis</searchLink><br /><searchLink fieldCode="DE" term="%22ESTIMATION+theory%22">ESTIMATION theory</searchLink><br /><searchLink fieldCode="DE" term="%22SIMULATION+methods+%26+models%22">SIMULATION methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22EDUCATIONAL+tests+%26+measurements%22">EDUCATIONAL tests & measurements</searchLink><br /><searchLink fieldCode="DE" term="%22CLASSES+%28Groups+of+students%29%22">CLASSES (Groups of students)</searchLink><br /><searchLink fieldCode="DE" term="%22PARAMETER+estimation%22">PARAMETER estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/j.1745-3984.2010.00110.x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 227 Subjects: – SubjectFull: BAYESIAN analysis Type: general – SubjectFull: ESTIMATION theory Type: general – SubjectFull: SIMULATION methods & models Type: general – SubjectFull: EDUCATIONAL tests & measurements Type: general – SubjectFull: CLASSES (Groups of students) Type: general – SubjectFull: PARAMETER estimation Type: general Titles: – TitleFull: Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: de la Torre, Jimmy – PersonEntity: Name: NameFull: Yuan Hong – PersonEntity: Name: NameFull: Weiling deng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 00220655 Numbering: – Type: volume Value: 47 – Type: issue Value: 2 Titles: – TitleFull: Journal of Educational Measurement Type: main |
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