Comparing computer adaptive testing stopping rules under the generalized partial-credit model

An important consideration of any computer adaptive testing (CAT) program is the criterion used for ending item administration-the stopping rule, which ensures that all examinees are assessed to the same standard. Although various stopping rules exist, none of them have been compared under the generalized partial-credit model (Muraki in Applied Psychological Measurement, 16, 159-176, 1992). In this simulation study we compared the performance of three variable-length stopping rules-standard error (SE), minimum information (MI), and change in theta (CT)-both in isolation and in combination with requirements of minimum and maximum numbers of items, as well as a fixed-length stopping rule. Each stopping rule was examined under two termination criteria-one a more lenient requirement (SE = 0.35, MI = 0.56, CT = 0.05), and one more stringent (SE = 0.30, MI = 0.42, CT = 0.02). The simulation design also included content-balancing and exposure controls, aspects of CAT that have been excluded in previous research comparing variable-length stopping rules. The minimum-information stopping rule produced biased theta estimates and varied greatly in measurement quality across the theta distribution. The absolute-change-in-theta stopping rule had strong performance when paired with a lower criterion and a minimum test length. The standard error stopping rule consistently provided the best balance of measurement precision and operational efficiency and was based on the fewest number of administered items necessary to obtain accurate and precise theta estimates, particularly when it was paired with a maximum-number-of-items stopping rule.