A shadow-test approach to adaptive item calibration

DOI10.1007/s11336-020-09703-8zbMath1458.62280OpenAlexW3036319801WikidataQ96581509 ScholiaQ96581509MaRDI QIDQ2220367

Bing-Nan Jiang, Wim J. van der Linden

Publication date: 22 January 2021

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11336-020-09703-8

zbMATH Keywords

Gibbs samplingadaptive testingMCMC algorithmitem calibrationitem response modelsBayesian \(D_{\mathrm{s}} \)-optimalityshadow-test approach

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Applications of statistics to psychology (62P15)

Uses Software

• CPLEX
• Cbc
• XPRESS
• BayesDA

Cites Work

• Optimal Bayesian adaptive design for test-item calibration
• Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests
• Continuous online item calibration: parameter recovery and item utilization
• Inference from iterative simulation using multiple sequences
• Handbook of Item Response Theory, Volume Three
• Linear Models for Optimal Test Design
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