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ERIC Number: EJ886512
Record Type: Journal
Publication Date: 2010
Pages: 23
Abstractor: As Provided
Reference Count: 23
ISSN: ISSN-0022-0655
Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model
de la Torre, Jimmy; Hong, Yuan; Deng, Weiling
Journal of Educational Measurement, v47 n2 p227-249 Sum 2010
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A