01510nas a2200145   4500008003900000245008500039210006900124300001200193490000700205520103200212100001901244700002301263700002501286856005301311       2015                          d00aNew Item Selection Methods for Cognitive Diagnosis Computerized Adaptive Testing0 aNew Item Selection Methods for Cognitive Diagnosis Computerized   a167-1880 v393 aThis article introduces two new item selection methods, the modified posterior-weighted Kullback–Leibler index (MPWKL) and the generalized deterministic inputs, noisy “and” gate (G-DINA) model discrimination index (GDI), that can be used in cognitive diagnosis computerized adaptive testing. The efficiency of the new methods is compared with the posterior-weighted Kullback–Leibler (PWKL) item selection index using a simulation study in the context of the G-DINA model. The impact of item quality, generating models, and test termination rules on attribute classification accuracy or test length is also investigated. The results of the study show that the MPWKL and GDI perform very similarly, and have higher correct attribute classification rates or shorter mean test lengths compared with the PWKL. In addition, the GDI has the shortest implementation time among the three indices. The proportion of item usage with respect to the required attributes across the different conditions is also tracked and discussed.1 aKaplan, Mehmet1 ade la Torre, Jimmy1 aBarrada, Juan Ramón  uhttp://apm.sagepub.com/content/39/3/167.abstract