A Global Information Approach to Computerized Adaptive Testing

not close to the true &thetas;, especially at early stages of an adaptive test when the test length (number of items) is too short to provide an accurate estimate for true &thetas;. It is argued here that selection procedures based on global information should be used, at least at early stages of a test when &thetas; estimates are not likely to be close to the true &thetas;. For this purpose, an item selection procedure based on average global information is proposed. Results from pilot simulation studies comparing the usual maximum item information item selection with the proposed global information approach are reported, indicating that the new method leads to improvement in terms of bias and mean squared error reduction under many circumstances.