01735nas a2200133   4500008003900000245009100039210006900130300001200199490000700211520129100218100001801509700002101527856005301548       2015                          d00aBest Design for Multidimensional Computerized Adaptive Testing With the Bifactor Model0 aBest Design for Multidimensional Computerized Adaptive Testing W  a954-9780 v753 aMost computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm (MCAT) with a bifactor model using simulated data. Four item selection methods in MCAT were examined for three bifactor pattern designs using two multidimensional item response theory models. To compare MCAT item selection and estimation methods, a fixed test length was used. The Ds-optimality item selection improved θ estimates with respect to a general factor, and either D- or A-optimality improved estimates of the group factors in three bifactor pattern designs under two multidimensional item response theory models. The MCAT model without a guessing parameter functioned better than the MCAT model with a guessing parameter. The MAP (maximum a posteriori) estimation method provided more accurate θ estimates than the EAP (expected a posteriori) method under most conditions, and MAP showed lower observed standard errors than EAP under most conditions, except for a general factor condition using Ds-optimality item selection.1 aSeo, Dong, Gi1 aWeiss, David, J.  uhttp://epm.sagepub.com/content/75/6/954.abstract