01810nas a2200133   4500008003900000245010200039210006900141300001200210490000700222520136300229100001901592700002001611856004501631       2019                          d00aMultidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models0 aMultidimensional Computerized Adaptive Testing Using NonCompensa  a464-4800 v433 aCurrent use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees’ abilities, which shed light on item bank construction for non-compensatory items.1 aHsu, Chia-Ling1 aWang, Wen-Chung  uhttps://doi.org/10.1177/0146621618800280