Application of Dimension Reduction to CAT Item Selection Under the Bifactor Model.

ABSTRACT: Multidimensional computerized adaptive testing (MCAT) based on the bifactor model is suitable for tests with multidimensional bifactor measurement structures. Several item selection methods that proved to be more advantageous than the maximum Fisher information method are not practical for bifactor MCAT due to time-consuming computations resulting from high dimensionality. To make them applicable in bifactor MCAT, dimension reduction is applied to four item selection methods, which are the posterior-weighted Fisher D-optimality (PDO) and three non-Fisher information-based methods-posterior expected Kullback-Leibler information (PKL), continuous entropy (CE), and mutual information (MI). They were compared with the Bayesian D-optimality (BDO) method in terms of estimation precision. When bo

SUBMITTER: Mao X 

PROVIDER: S-EPMC6696870 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature