01310nas a2200121   4500008003900000245007300039210006900112300001000181490000700191520091400198100002401112856005201136       2007                          d00aMutual Information Item Selection in Adaptive Classification Testing0 aMutual Information Item Selection in Adaptive Classification Tes  a41-580 v673 a<p>A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local- and global-information measures in the multiple-category classification setting. Results from simulation studies using three item selection methods, Fisher information (FI), posterior-weighted FI (FIP), and MI, are provided for an adaptive four-category classification test. Both across and within the four classification categories, it is shown that in general, MI item selection classifies the highest proportion of examinees correctly and yields the shortest test lengths. The next best performance is observed for FIP item selection, followed by FI.</p>
1 aWeissman, Alexander  uhttp://epm.sagepub.com/content/67/1/41.abstract