01396nas a2200133   4500008003900000245007900039210006900118300001200187490000700199520096200206100001401168700001701182856006301199       2018                          d00aA Top-Down Approach to Designing the Computerized Adaptive Multistage Test0 aTopDown Approach to Designing the Computerized Adaptive Multista  a243-2630 v553 aAbstract The top-down approach to designing a multistage test is relatively understudied in the literature and underused in research and practice. This study introduced a route-based top-down design approach that directly sets design parameters at the test level and utilizes the advanced automated test assembly algorithm seeking global optimality. The design process in this approach consists of five sub-processes: (1) route mapping, (2) setting objectives, (3) setting constraints, (4) routing error control, and (5) test assembly. Results from a simulation study confirmed that the assembly, measurement and routing results of the top-down design eclipsed those of the bottom-up design. Additionally, the top-down design approach provided unique insights into design decisions that could be used to refine the test. Regardless of these advantages, it is recommended applying both top-down and bottom-up approaches in a complementary manner in practice.1 aLuo, Xiao1 aKim, Doyoung  uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/jedm.1217401828nas a2200169   4500008003900000020001400039245009100053210006900144260001500213300001400228490000700242520131200249100001401561700001701575700002101592856004501613       2017                          d  a0146-621600aProjection-Based Stopping Rules for Computerized Adaptive Testing in Licensure Testing0 aProjectionBased Stopping Rules for Computerized Adaptive Testing  c2018/06/01  a275 - 2900 v423 aThe confidence interval (CI) stopping rule is commonly used in licensure settings to make classification decisions with fewer items in computerized adaptive testing (CAT). However, it tends to be less efficient in the near-cut regions of the ? scale, as the CI often fails to be narrow enough for an early termination decision prior to reaching the maximum test length. To solve this problem, this study proposed the projection-based stopping rules that base the termination decisions on the algorithmically projected range of the final ? estimate at the hypothetical completion of the CAT. A simulation study and an empirical study were conducted to show the advantages of the projection-based rules over the CI rule, in which the projection-based rules reduced the test length without jeopardizing critical psychometric qualities of the test, such as the ? and classification precision. Operationally, these rules do not require additional regularization parameters, because the projection is simply a hypothetical extension of the current test within the existing CAT environment. Because these new rules are specifically designed to address the decreased efficiency in the near-cut regions as opposed to for the entire scale, the authors recommend using them in conjunction with the CI rule in practice.1 aLuo, Xiao1 aKim, Doyoung1 aDickison, Philip  uhttps://doi.org/10.1177/0146621617726790