TY - JOUR T1 - Multidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models JF - Applied Psychological Measurement Y1 - 2019 A1 - Chia-Ling Hsu A1 - Wen-Chung Wang AB - Current 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. VL - 43 UR - https://doi.org/10.1177/0146621618800280 ER - TY - CONF T1 - Using Bayesian Decision Theory in Cognitive Diagnosis Computerized Adaptive Testing T2 - IACAT 2017 Conference Y1 - 2017 A1 - Chia-Ling Hsu A1 - Wen-Chung Wang A1 - ShuYing Chen KW - Bayesian Decision Theory KW - CD-CAT AB -

Cognitive diagnosis computerized adaptive testing (CD-CAT) purports to provide each individual a profile about the strengths and weaknesses of attributes or skills with computerized adaptive testing. In the CD-CAT literature, researchers dedicated to evolving item selection algorithms to improve measurement efficiency, and most algorithms were developed based on information theory. By the discontinuous nature of the latent variables in CD-CAT, this study introduced an alternative for item selection, called the minimum expected cost (MEC) method, which was derived based on Bayesian decision theory. Using simulations, the MEC method was evaluated against the posterior weighted Kullback-Leibler (PWKL) information, the modified PWKL (MPWKL), and the mutual information (MI) methods by manipulating item bank quality, item selection algorithm, and termination rule. Results indicated that, regardless of item quality and termination criterion, the MEC, MPWKL, and MI methods performed very similarly and they all outperformed the PWKL method in classification accuracy and test efficiency, especially in short tests; the MEC method had more efficient item bank usage than the MPWKL and MI methods. Moreover, the MEC method could consider the costs of incorrect decisions and improve classification accuracy and test efficiency when a particular profile was of concern. All the results suggest the practicability of the MEC method in CD-CAT.

Session Video

JF - IACAT 2017 Conference PB - Niigata Seiryo University CY - Niigata Japan ER -