Item Selection in Adaptive Testing with the Sequential Probability Ratio Test

Abstract

Wald’s (1947) sequential probability ratio test can be implemented as an adaptive test for classifying examinees into categories. However, current implementations use an item selection method that is either random or based on Fisher information (FI), a criterion related to optimized examinee trait estimates. In this study, a method based on Kullback-Leibler information (KLI) was evaluated. Simulation studies were conducted for two- and three-category classifications in which item selection methods based on FI and KLI were compared. Results showed that testing algorithms using KLI-based item selection performed better than or as well as those using FI-based item selection.