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The Random-Effect DINA Model

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Title: The Random-Effect DINA Model
Language: English
Authors: Huang, Hung-Yu, Wang, Wen-Chung
Source: Journal of Educational Measurement. Spr 2014 51(1):75-97.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2014
Document Type: Journal Articles
Reports - Research
Descriptors: Models, Guessing (Tests), Probability, Ability, Markov Processes, Monte Carlo Methods, Accuracy, Bias, Computation, Mathematics Tests, Subtraction, Response Style (Tests)
DOI: 10.1111/jedm.12035
ISSN: 0022-0655
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.
Abstractor: As Provided
Number of References: 48
Entry Date: 2014
Accession Number: EJ1030019
Database: ERIC
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  Data: The Random-Effect DINA Model
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  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Hung-Yu%22">Huang, Hung-Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Wen-Chung%22">Wang, Wen-Chung</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Measurement%22"><i>Journal of Educational Measurement</i></searchLink>. Spr 2014 51(1):75-97.
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA/
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  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.
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      – SubjectFull: Probability
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      – SubjectFull: Ability
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      – SubjectFull: Markov Processes
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      – SubjectFull: Monte Carlo Methods
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      – SubjectFull: Mathematics Tests
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      – SubjectFull: Response Style (Tests)
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