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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/101549

Title: Improving measurement quality and efficiency with adaptive theory
Authors: Weiss, David J.
Issue Date: 1982
Citation: Weiss, David J. (1982). Improving measurement quality and efficiency with adaptive theory. Applied Psychological Measurement, 6, 473-492. doi:10.1177/014662168200600408
Abstract: Approaches to adaptive (tailored) testing based on item response theory are described and research results summarized. Through appropriate combinations of item pool design and use of different test termination criteria, adaptive tests can be designed (1) to improve both measurement quality and measurement efficiency, resulting in measurements of equal precision at all trait levels; (2) to improve measurement efficiency for test batteries using item pools designed for conventional test administration; and (3) to improve the accuracy and efficiency of testing for classification (e.g., mastery testing). Research results show that tests based on item response theory (IRT) can achieve measurements of equal precision at all trait levels, given an adequately designed item pool; these results contrast with those of conventional tests which require a tradeoff of bandwidth for fidelity/precision of measurements. Data also show reductions in bias, inaccuracy, and root mean square error of ability estimates. Improvements in test fidelity observed in simulation studies are supported by live-testing data, which showed adaptive tests requiring half the number of items as that of conventional tests to achieve equal levels of reliability, and almost one-third the number to achieve equal levels of validity. When used with item pools from conventional tests, both simulation and live-testing results show reductions in test battery length from conventional tests, with no reductions in the quality of measurements. Adaptive tests designed for dichotomous classification also represent improvements over conventional tests designed for the same purpose. Simulation studies show reductions in test length and improvements in classification accuracy for adaptive vs. conventional tests; live-testing studies in which adaptive tests were compared with "optimal" conventional tests support these findings. Thus, the research data show that IRT-based adaptive testing takes advantage of the capabilities of IRT to improve the quality and/or efficiency of measurement for each examinee.
URI: http://purl.umn.edu/101549
Appears in Collections:Volume 06, 1982

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