ERIC Number: ED579801
Record Type: Non-Journal
Publication Date: 2017-Sep-5
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
EISSN: N/A
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.]
Descriptors: 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
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