The purpose of this study was to compare a proposed revised a-stratified, or alpha-stratified, USTR method of test item selection with the original alpha-stratified multistage computerized adaptive testing approach (STR) and the use of maximum Fisher information (FSH) with respect to test efficiency and item pool usage using simulated computerized adaptive tests (CATs). Two simulation studies were conducted to compare the proposed USTR with its original STR version and FSH with respect to efficiency of trait estimation and item usage. In the first study, a set of item parameters were generated to form a bank of 400 items and the gains of using the USTR design were explored. In the second study, the comparison was carried out under a more complex design using a real item pool of 300 mathematical items. Results suggest that USTR holds considerable potential for improving test efficiency for STR. It effectively reduced overall and conditional root mean standard error for all the study cases, with only slightly increased chi-square measure and test overlap. Compared to FSH, USTR greatly increased the use of low alpha items, decreased overexposure of high alpha items, and reduced test overlap while achieving comparable test efficiency. When the pool size was large enough relative to test length, FSH was found to result in higher efficiency, but its dramatically skewed item exposure distribution and high test overlap remained a problem. Preliminary results suggest that USTR might be a promising alternative. (Contains 3 tables, 6 figures, and 12 references.) (SLD)