Utilizing Response Time Distributions for Item Selection in CAT

Traditional methods for item selection in computerized adaptive testing only focus on item information without taking into consideration the time required to answer an item. As a result, some examinees may receive a set of items that take a very long time to finish, and information is not accrued as efficiently as possible. The authors propose two item-selection criteria that utilize information from a lognormal model for response times. The first modifies the maximum information criterion to maximize information per time unit. The second is an inverse time-weighted version of a-stratification that takes advantage of the response time model, but achieves more balanced item exposure than the information-based techniques. Simulations are conducted to compare these procedures against their counterparts that ignore response times, and efficiency of estimation, time-required, and item exposure rates are assessed.