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

Autor/inn/en	de la Torre, Jimmy; Hong, Yuan; Deng, Weiling
Titel	Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model
Quelle	In: Journal of Educational Measurement, 47 (2010) 2, S.227-249 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2010.00110.x
Schlagwörter	Classification; Computation; Models; Simulation; Bayesian Statistics; Sample Size; Statistical Analysis; Item Response Theory + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Simulation program; Simulationsprogramm; Statistische Analyse; Item-Response-Theorie
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10