Literaturnachweis - Detailanzeige
Autor/inn/en | Arenson, Ethan A.; Karabatsos, George |
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Titel | A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT) |
Quelle | (2017), (16 Seiten)
PDF als Volltext |
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 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 |