The Effects of Different Item Selection Methods on Test Information and Test Efficiency in Computer Adaptive Testing

The purpose of this study is to examine the effect of different item selection methods on test information and test efficiency in computer adaptive testing (CAT). The study was conducted with simulated data and the constant variables are sample size, ability parameter distribution, item pool size, model of item response theory and distribution of item parameters, ability estimation method, starting rule, item exposure control and stopping rule. The item selection methods, which are the changing conditions of this study, are the interval information criterion, efficiency balanced information, matching -b value, Kullback-Leibler information, maximum Fisher information, likelihood weighted information and random selection. In the comparison of these methods, the best performance in providing test information function is provided by the Fisher information method. In terms of test efficiency, the performances of the methods were similar, except for the random selection method, which had the worst performance in terms of both test information function and test efficiency.