The Random-Threshold Generalized Unfolding Model and Its Application of Computerized Adaptive Testing

Autor/inn/en	Wang, Wen-Chung; Liu, Chen-Wei; Wu, Shiu-Lien
Titel	The Random-Threshold Generalized Unfolding Model and Its Application of Computerized Adaptive Testing
Quelle	In: Applied Psychological Measurement, 37 (2013) 3, S.179-200 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621612469720
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Models; Bayesian Statistics; Computation; Computer Software; Simulation; Classification; Likert Scales; Test Length; Item Response Theory + Suchen Sie Ihr Suchwort? Analogiemodell; Simulation program; Simulationsprogramm; Classification system; Klassifikation; Klassifikationssystem; Likert-Skala; Item-Response-Theorie
Abstract	The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs Sampler) freeware, which adopts a Bayesian approach for estimation. A series of simulations was conducted to evaluate the parameter recovery of the new model and the consequences of ignoring the randomness in thresholds. The results showed that the parameters of RTGUM were recovered fairly well and that ignoring the randomness in thresholds led to biased estimates. Computerized adaptive testing was also implemented on RTGUM, where the Fisher information criterion was used for item selection and the maximum a posteriori method was used for ability estimation. The simulation study showed that the longer the test length, the smaller the randomness in thresholds, and the more categories in an item, the more precise the ability estimates would be. (Contains 2 tables and 8 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10