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Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model
Author(s) -
De La Torre Jimmy,
Hong Yuan,
Deng Weiling
Publication year - 2010
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2010.00110.x
Subject(s) - bayes' theorem , statistics , item response theory , computer science , estimation theory , bayes classifier , naive bayes classifier , statistical hypothesis testing , sample size determination , mathematics , econometrics , artificial intelligence , bayesian probability , psychometrics , support vector machine
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.