Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model.

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Bibliographic Details
Title:	Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model.
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]
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Database:	Teacher Reference Center

Description
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]
ISSN:	00220655
DOI:	10.1111/j.1745-3984.2010.00110.x