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Multidimensional Adaptive Testing with a Minimum Error-Variance Criterion

University of Twente

Adaptive testing under a multidimensional logistic response model is addressed. An algorithm is proposed that minimizes the (asymptotic) variance of the maximum-likelihood estimator of a linear combination of abilities of interest. The criterion results in a closed-form expression that is easy to evaluate. In addition, it is shown how the algorithm can be modified if the interest is in a test with a "simple ability structure". The statistical properties of the adaptive ML estimator are demonstrated for a two-dimensional item pool with several linear combinations of the abilities.

Key Words: Adaptive Testing • Item Response Theory • Maximum-Likelihood Estimation • Multidimensionality

Journal of Educational and Behavioral Statistics, Vol. 24, No. 4, 398-412 (1999)
DOI: 10.3102/10769986024004398


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