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ERIC Number: ED579801
Record Type: Non-Journal
Publication Date: 2017-Sep-5
Pages: 16
Abstractor: As Provided
A Bayesian Beta-Mixture Model for Nonparametric IRT (BBM-IRT)
Arenson, Ethan A.; Karabatsos, George
Grantee Submission
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.]
Publication Type: Reports - Descriptive
Education Level: Elementary Secondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Trends in International Mathematics and Science Study
Grant or Contract Numbers: NSFSES1156372