A New Stopping Rule for Computerized Adaptive Testing

The goal of the current study was to introduce a new stopping rule for computerized adaptive testing (CAT). The predicted standard error reduction (PSER) stopping rule uses the predictive posterior variance to determine the reduction in standard error that would result from the administration of additional items. The performance of the PSER was compared with that of the minimum standard error stopping rule and a modified version of the minimum information stopping rule in a series of simulated adaptive tests, drawn from a number of item pools. Results indicate that the PSER makes efficient use of CAT item pools, administering fewer items when predictive gains in information are small and increasing measurement precision when informa-tion is abundant. Keywords computerized adaptive testing (CAT), stopping rule, testing burden According to Wainer (2000), an adaptive test can be considered complete after a pre-determined number of items have been administered, when a predetermined level of measurement precision has been reached, or when a predetermined length of time has elapsed. The two most commonly used methods for determining when a computerized adaptive test is complete are the fixed length and variable length stopping rules. Under a fixed length stopping rule, an adaptive test is terminated when a predeter-mined number of items have been administered. Accordingly, all examinees ar