01626nas a2200145   4500008003900000245008600039210006900125300001200194490000700206520117400213100001701387700001801404700001301422856004501435       2019                          d00aApplication of Dimension Reduction to CAT Item Selection Under the Bifactor Model0 aApplication of Dimension Reduction to CAT Item Selection Under t  a419-4340 v433 aMultidimensional 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 both the general and group factors are the measurement objectives, BDO, PDO, CE, and MI perform equally well and better than PKL. When the group factors represent nuisance dimensions, MI and CE perform the best in estimating the general factor, followed by the BDO, PDO, and PKL. How the bifactor pattern and test length affect estimation accuracy was also discussed.1 aMao, Xiuzhen1 aZhang, Jiahui1 aXin, Tao  uhttps://doi.org/10.1177/0146621618813086