Fixed-precision between-item multidimensional computerized adaptive tests (MCATs) are becoming increasingly popular. The current generation of item-selection rules used in these types of MCATs typically optimize a single-valued objective criterion for multivariate precision (e.g., Fisher information volume). In contrast, when all dimensions are of interest, the stopping rule is typically defined in terms of a required fixed marginal precision per dimension. This asymmetry between multivariate precision for selection and marginal precision for stopping, which is not present in unidimensional computerized adaptive tests, has received little attention thus far. In this article, we will discuss this selection-stopping asymmetry and its consequences, and introduce and evaluate three alternative item-selection approaches. These alternatives are computationally inexpensive, easy to communicate and implement, and result in effective fixed-marginal-precision MCATs that are shorter in test length than with the current generation of item-selection approaches.

固定精度的项目间多维计算机自适应测试 (MCAT) 正变得越来越流行。在这些类型的 MCAT 中使用的当前一代项目选择规则通常优化多变量精度的单值客观标准（例如，Fisher 信息量）。相比之下，当所有维度都感兴趣时，停止规则通常根据每个维度所需的固定边际精度来定义。多变量选择精度和停止边缘精度之间的这种不对称性在单维计算机自适应测试中不存在，但迄今为止很少受到关注。在本文中，我们将讨论这种停止选择的不对称性及其后果，并介绍和评估三种备选的项目选择方法。