Author:
Ren Hao,Choi Seung W.,van der Linden Wim J.
Abstract
AbstractAn extremely efficient MCMC method for Bayesian adaptive testing with polytomous items is explained both conceptually and in mathematical detail. Results from extensive simulation studies with different item pools, polytomous response models, calibration sample sizes, and test lengths are presented. In addition, the case of adaptive testing from pools with a mixture of dichotomous and polytomous items is addressed.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Clinical Psychology,Experimental and Cognitive Psychology,Analysis
Reference13 articles.
1. Chang HH, Ying Z (2009) Nonlinear sequential design for logistic item response models with applications to computerized adaptive tests. Ann Stat 37:1466–1488
2. Choi SW, Swartz RJ (2009) Comparison of CAT item selection criteria for polytomous items. Appl Psychol Meas 33:419–440
3. Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–511
4. Gelman A, Carlin JB, Stern H, Dunson DB, Vehtari A, Rubin DB (2014) Bayesian data analysis, 3rd edn. Chapman & Hall/CRC, Boca Raton, FL
5. Gilks WR, Richardson S, Spiegelhalter DJ (1996) Introducing Markov chain Monte Carlo. In: Gilks WR, Richardson S, Spiegelhalter DJ (eds) Markov chain Monte Carlo in practice. Chapman & Hall, London, pp 1–19
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