Computerized classification testing (CCT) aims to classify persons into one of two or more possible categories to make decisions such as mastery/non‐mastery or meet most/meet all/exceed. A defining feature of CCT is its stopping criterion: the test terminates when there is enough confidence to make a decision. There is abundant research on CCT with a single cut‐off, and two common stopping criteria are the sequential probability ratio test (SPRT) statistic and the generalized likelihood ratio statistic (GLR). However, there is a relative scarcity of research extending the SPRT to the multi‐hypothesis case for when there is more than one cut‐off. In this paper, we propose a new multi‐category GLR (mGLR) statistic as well as a stochastically curtailed version of the CCT with three or more categories. A simulation study was conducted to show that the mGLR statistic outperformed the existing stopping rules by generating shorter average test length without sacrificing classification accuracy. Results also revealed that the stochastically curtailed mGLR successfully increased test efficiency in certain testing conditions.

中文翻译：

---

多类计算机分类测试停止规则

计算机分类测试 (CCT) 旨在将人分为两个或多个可能的类别之一，以做出诸如精通/不精通或满足大多数/满足所有/超越等决定。CCT 的一个定义特征是它的停止标准：当有足够的信心做出决定时，测试终止。有大量关于具有单一截止值的 CCT 的研究，两个常见的停止标准是顺序概率比检验 (SPRT) 统计量和广义似然比统计量 (GLR)。然而，当有多个截止点时，将 SPRT 扩展到多假设案例的研究相对较少。在本文中，我们提出了一种新的多类别 GLR (mGLR) 统计量以及具有三个或更多类别的 CCT 的随机缩减版本。进行了一项模拟研究，通过生成更短的平均测试长度而不牺牲分类精度，mGLR 统计量优于现有的停止规则。结果还表明，随机缩减的 mGLR 在某些测试条件下成功地提高了测试效率。