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The arrival of the computer in educational and psychological testing has led to the current popularity of adaptive testing---a testing format in which the computer uses statistical information about the test items to automatically adapt their selection to a real-time update of the test taker's ability estimate. This book covers such key features of adaptive testing as item selection and ability estimation, adaptive testing with multidimensional abilities, sequencing adaptive test batteries, multistage adaptive testing, item-pool design and maintenance, estimation of item and item-family parame

AbstractConstrained adaptive testing is reviewed as an instance of discrete maximization with the shadow-test approach delivering its solution. The approach may look counterintuitive in that it assumes sequential assembly of full test forms as its basic operation. But it always produces real-time solutions that are optimal and satisfy the set of specifications in effect for the test. Equally importantly, it can be used to run testing programs with different degrees of adaptation for the same set of specifications and/or as a tool to manage programs with simultaneous processes as adaptive item calibration, time management, and/or item-security monitoring.

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.