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

Abstract

The purpose of this study is to examine the effect of different item selection methods on test information function (TIF) and test efficiency in computer adaptive testing (CAT). TIF indicates the quantity of information the test has produced. Test efficiency resembles the amount of information from each item, and more efficient tests are produced from the smallest number of good-quality items. The study was conducted with simulated data, and the constants of the study are sample size, ability parameter distribution, item pool size, model of item response theory (IRT) and distribution of item parameters, ability estimation method, starting rule, item exposure control and stopping rule. The item selection methods, which are the independent variables 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 the aspect of TIF is provided by the maximum 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 TIF and test efficiency.