Bootstrapping an Item Bank
Cees. A. W. Glas
Department of Research Methodology, Measurement and Data Analysis, Faculty of Behavioral Sciences, University of Twente, the Netherlands
Master Management International A/S, Alleroed, Denmark
An accurately calibrated item bank is essential for a valid computerized adaptive test. However, in some settings, such as in occupational testing, there is limited access to test takers for calibration. As a result of the limited access to possible test takers, collecting data to accurately calibrate an item bank is usually difficult. In such a setting, the item bank can be calibrated online while in operation. We explore three possible automatic online calibration strategies, with the intent of calibrating items accurately while estimating ability precisely and fairly. That is, the item bank is calibrated in a situation where actual test takers are processed and the scores they obtain have consequences. A simulation study is used to identify the optimal calibration strategy. Manipulated variables were: the calibration strategy, the size of the calibration sample, the size of the item bank, and the item response model.