01240nas a2200145   4500008003900000245009000039210006900129300001200198490000700210520076800217100001900985700001701004700002001021856005301041       2012                          d00aComputerized Adaptive Testing Using a Class of High-Order Item Response Theory Models0 aComputerized Adaptive Testing Using a Class of HighOrder Item Re  a689-7060 v363 a<p>In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of situations was examined using simulations. The results showed that the CAT algorithms were very effective. The progressive method for item selection, the Sympson and Hetter method with online and freeze procedure for item exposure control, and the multinomial model for content balancing can simultaneously maintain good measurement precision, item exposure control, content balance, test security, and pool usage.</p>
1 aHuang, Hung-Yu1 aChen, Po-Hsi1 aWang, Wen-Chung  uhttp://apm.sagepub.com/content/36/8/689.abstract