A model for optimal constrained adaptive testing

Wim J. van der Linden, Lynda M. Reese

Research output: Contribution to journalArticleAcademicpeer-review

79 Citations (Scopus)


A model for constrained computerized adaptive testing is proposed in which the information in the test at the trait level (0) estimate is maximized subject to a number of possible constraints on the content of the test. At each item-selection step, a full test is assembled to have maximum information at the current 0 estimate, fixing the items already administered. Then the item with maximum in-formation is selected. All test assembly is optimal because a linear programming (LP) model is used that automatically updates to allow for the attributes of the items already administered and the new value of the 0 estimator. The LP model also guarantees that each adaptive test always meets the entire set of constraints. A simulation study using a bank of 753 items from the Law School Admission Test showed that the 0 estimator for adaptive tests of realistic lengths did not suffer any loss of efficiency from the presence of 433 constraints on the item selection process.
Original languageEnglish
Pages (from-to)259-270
Number of pages11
JournalApplied psychological measurement
Issue number3
Publication statusPublished - 1998


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