Bayes Modal Estimation in Item Response Models
- Author(s):
- Mislevy, Robert J.
- Publication Year:
- 1985
- Report Number:
- RR-85-33
- Source:
- ETS Research Report
- Document Type:
- Report
- Page Count:
- 54
- Subject/Key Words:
- Bayesian Statistics, Maximum Likelihood Statistics, Models, Statistical Analysis
Abstract
This article describes a Bayesian framework for estimation in item response models, with two-stage prior distributions on both item and examinee populations. Strategies for point and interval estimation are discussed, and a general procedure based on the EM algorithm is presented. Details are given for implementation under one-, two-, and three-parameter logistic IRT models. Novel features include minimally restrictive assumptions about examinee distributions and the exploitation of dependence among item parameters in a population of interest. Improved estimation in a moderately small sample is demonstrated with simulated data. (55pp.)
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- http://dx.doi.org/10.1002/j.2330-8516.1985.tb00118.x