The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Applications and Extensions of MCMC in IRT: Multiple Item Types, Missing Data, and Rated Responses
Abstract
Patz and Junker (1999) describe a general Markov chain Monte Carlo (MCMC) strategy, based on Metropolis-Hastings sampling, for Bayesian inference in complex item response theory (IRT) settings. They demonstrate the basic methodology using the two-parameter logistic (2PL) model. In this paper we extend their basic MCMC methodology to address issues such as non-response, designed missingness, multiple raters, guessing behavior and partial credit (polytomous) test items. We apply the basic MCMC methodology to two examples from the National Assessment of Educational Progress 1992 Trial State Assessment in Reading: (a) a multiple item format (2PL, 3PL, and generalized partial credit) subtest with missing response data; and (b) a sequence of rated, dichotomous short-response items, using a new IRT model called the generalized linear logistic test model (GLLTM).
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
Kass, R. E., & Steffey, D. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478825 · Cited by 474 · cited 1×
“…most recently sampled values of 6, This is a general feature of hierarchical mixture and Bayesian models (see for example Bryk & Raudenbush, 1992; Kass & Steffey, 1989; Longford, 1987) that has previously been noted (going back at least to Mislevy, 1985, Section 3) in the development of E-M…”
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Cited by
Focus Article: On the Structure of Educational Assessments
Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003) · Measurement: Interdisciplinary Research & Perspective · doi:10.1207/s15366359mea0101_02 · Cited by 802
Bayesian Estimation of a Multilevel IRT Model Using Gibbs Sampling
Fox, J.-P., & Glas, C. A. W. (2001) · Psychometrika · doi:10.1007/bf02294839 · Cited by 398
A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo
Uto, M., & Ueno, M. (2020) · Behaviormetrika · doi:10.1007/s41237-020-00115-7 · Cited by 37
Accuracy of performance-test linking based on a many-facet Rasch model
Uto, M. (2021) · Behavior Research Methods · doi:10.3758/s13428-020-01498-x · Cited by 20
A Bayesian many-facet Rasch model with Markov modeling for rater severity drift
Uto, M. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01997-z · Cited by 19
Metadata
- Authors
- Patz, R. J., & Junker, B. W.
- Venue
- Journal of Educational and Behavioral Statistics
- Published
- 1999
- Volume
- 24
- Issue
- 4
- Pages
- 342
- ISSN
- 1076-9986 (ISSN-L)
- OpenAlex
- W2158518800
- Cited by
- 345
- References
- 4
- FWCI
- 17.59
- Type
- journal article
- Language
- en
- Open access
- closed
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