Setting cutoff scores is one of the most common practices when using scales to aid in classification purposes. This process is usually done univariately where each optimal cutoff value is decided sequentially, subscale by subscale. While it is widely known that this process necessarily reduces the probability of “passing” such a test, what is not properly recognized is that such a test loses power to meaningfully discriminate between target groups with each new subscale that is introduced. We quantify and describe this property via an analytical exposition highlighting the counterintuitive geometry implied by marginal threshold-setting in multiple dimensions. Recommendations are presented that encourage applied researchers to think jointly, rather than marginally, when setting cutoff scores to ensure an informative test.

设置截止分数是使用量表帮助分类时最常见的做法之一。这个过程通常是单变量完成的，其中每个最佳截止值是按顺序确定的，子尺度逐个子尺度。虽然众所周知，这一过程必然会降低“通过”此类测试的概率，但未被正确认识的是，随着引入的每个新子量表，此类测试失去了有意义地区分目标群体的能力。我们通过一个分析说明来量化和描述这一特性，该分析说明突出了多维边际阈值设置所暗示的违反直觉的几何形状。提出的建议鼓励应用研究人员在设置截止分数以确保信息丰富的测试时共同思考，而不是边缘思考。