A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT)

Autor/inn/en	Arenson, Ethan A.; Karabatsos, George
Titel	A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT)
Quelle	(2017), (16 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Bayesian Statistics; Item Response Theory; Nonparametric Statistics; Models; Accuracy; Data Analysis; Achievement Tests; Elementary Secondary Education; International Assessment; Mathematics Achievement; Foreign Countries; Mathematics Tests; Science Achievement; Science Tests; Test Items; Computation; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Auswertung; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Ausland; Test content; Testaufgabe
Abstract	Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model for dichotomous items, which constructs monotone item characteristic (step) curves by a finite mixture of beta distributions, which can support the entire space of monotone curves to any desired degree of accuracy. A simple adaptive random-walk Metropolis-Hastings algorithm is proposed to estimate the posterior distribution of the model parameters. The Bayesian IRT model is illustrated through the analysis of item response data from a 2015 TIMSS test of math performance. [At time of submission to ERIC this article was in press with "Journal of Modern Applied Statistical Methods" v17 n2 2018.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01