A Continuous a-Stratification Index for Item Exposure Control in Computerized Adaptive Testing

The method of $$ -stratification aims to reduce item overexposure in computerized adaptive testing, as items that are administered at very high rates may threaten the validity of test scores. In existing methods of $$ -stratification, the item bank is partitioned into a fixed number of nonoverlapping strata according to the items' $$ , or discrimination, parameters. This article introduces a continuous $$ -stratification index which incorporates exposure control into the item selection index itself and thus eliminates the need for fixed discrete strata. The new continuous $$ -stratification index is compared with existing stratification methods via simulation studies in terms of ability estimation bias, mean squared error, and control of item exposure rates.