Bayesian Estimation of the DINA Model with Gibbs Sampling

Autor/in	Culpepper, Steven Andrew
Titel	Bayesian Estimation of the DINA Model with Gibbs Sampling
Quelle	In: Journal of Educational and Behavioral Statistics, 40 (2015) 5, S.454-476 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998615595403
Schlagwörter	Bayesian Statistics; Models; Sampling; Computation; Guessing (Tests); Item Response Theory; Probability; Simulation; Mathematics; Markov Processes; Monte Carlo Methods; Spatial Ability; Cognitive Measurement; Equations (Mathematics); Visualization; Vignettes + Suchen Sie Ihr Suchwort? Analogiemodell; Erraten; Item-Response-Theorie; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Simulation program; Simulationsprogramm; Mathematik; Markowscher Prozess; Monte-Carlo-Methode; Räumliches Vorstellungsvermögen; Equations; Mathematics; Gleichungslehre; Visualisation; Visualisierung
Abstract	A Bayesian model formulation of the deterministic inputs, noisy "and" gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in Béguin and Glas, Culpepper, and Sahu for estimating the guessing and slipping parameters in the three- and four-parameter normal-ogive models. The ability of the model to recover parameters is demonstrated in a simulation study. The technique is applied to a mental rotation test. The algorithm and vignettes are freely available to researchers as the "dina" R package. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01