The case of adaptive testing under a multidimensional response model with large numbers of constraints on the content of the test is addressed. The items in the test are selected using a shadow test approach. The 0–1 linear programming model that assembles the shadow tests maximizes posterior expected Kullback-Leibler information in the test. The procedure is illustrated for five different cases of multidimensionality. These cases differ in (a) the numbers of ability dimensions that are intentional or should be considered as nuisance dimensions and (b) whether the test should or should not display a simple structure with respect to the intentional ability dimensions.
- Mathematical programming
- Multidimensional adaptive testing
- Multidimensional item response theory
- Posterior expected Kullback-Leibler information