The Random-Effect DINA Model.
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| Title: | The Random-Effect DINA Model. |
|---|---|
| Authors: | Huang, Hung‐Yu1 hyhuang@go.utaipei.edu.tw, Wang, Wen‐Chung2 wcwang@ied.edu.hk |
| Source: | Journal of Educational Measurement. Mar2014, Vol. 51 Issue 1, p75-97. 23p. |
| Subject Terms: | Probability theory, Random effects model, Monte Carlo method, Stochastic processes, Markov processes, Estimation theory |
| Abstract: | The DINA (deterministic input, noisy, and gate) model has been widely used in cognitive diagnosis tests and in the process of test development. The outcomes known as slip and guess are included in the DINA model function representing the responses to the items. This study aimed to extend the DINA model by using the random-effect approach to allow examinees to have different probabilities of slipping and guessing. Two extensions of the DINA model were developed and tested to represent the random components of slipping and guessing. The first model assumed that a random variable can be incorporated in the slipping parameters to allow examinees to have different levels of caution. The second model assumed that the examinees' ability may increase the probability of a correct response if they have not mastered all of the required attributes of an item. The results of a series of simulations based on Markov chain Monte Carlo methods showed that the model parameters and attribute-mastery profiles can be recovered relatively accurately from the generating models and that neglect of the random effects produces biases in parameter estimation. Finally, a fraction subtraction test was used as an empirical example to demonstrate the application of the new models. [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: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 95124965 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The Random-Effect DINA Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Hung‐Yu%22">Huang, Hung‐Yu</searchLink><relatesTo>1</relatesTo><i> hyhuang@go.utaipei.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Wen‐Chung%22">Wang, Wen‐Chung</searchLink><relatesTo>2</relatesTo><i> wcwang@ied.edu.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Mar2014, Vol. 51 Issue 1, p75-97. 23p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Random+effects+model%22">Random effects model</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The DINA (deterministic input, noisy, and gate) model has been widely used in cognitive diagnosis tests and in the process of test development. The outcomes known as slip and guess are included in the DINA model function representing the responses to the items. This study aimed to extend the DINA model by using the random-effect approach to allow examinees to have different probabilities of slipping and guessing. Two extensions of the DINA model were developed and tested to represent the random components of slipping and guessing. The first model assumed that a random variable can be incorporated in the slipping parameters to allow examinees to have different levels of caution. The second model assumed that the examinees' ability may increase the probability of a correct response if they have not mastered all of the required attributes of an item. The results of a series of simulations based on Markov chain Monte Carlo methods showed that the model parameters and attribute-mastery profiles can be recovered relatively accurately from the generating models and that neglect of the random effects produces biases in parameter estimation. Finally, a fraction subtraction test was used as an empirical example to demonstrate the application of the new models. [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/jedm.12035 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 75 Subjects: – SubjectFull: Probability theory Type: general – SubjectFull: Random effects model Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Stochastic processes Type: general – SubjectFull: Markov processes Type: general – SubjectFull: Estimation theory Type: general Titles: – TitleFull: The Random-Effect DINA Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Hung‐Yu – PersonEntity: Name: NameFull: Wang, Wen‐Chung IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 00220655 Numbering: – Type: volume Value: 51 – Type: issue Value: 1 Titles: – TitleFull: Journal of Educational Measurement Type: main |
| ResultId | 1 |
