Projection-Based Stopping Rules for Computerized Adaptive Testing in Licensure Testing

Abstract

The 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 θ 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 θ 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 θ 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.