ABSTRACT

There has been tremendous growth in research on measurement invariance over the past two decades. However, given that psychological tests are commonly used for making classification decisions such as personnel selections or diagnoses, surprisingly, there has been little research on how noninvariance impacts classification accuracy. Millsap and Kwok previously proposed a selection accuracy framework for that purpose, which has been recently extended to categorical data. Their framework, however, only deals with classification using a unidimensional test. In contrast, classification in practice usually involves multidimensional tests (e.g., personality) or multiple tests, with different weights assigned to each dimension. In the current paper, we extend Millsap and Kwok’s framework for examining the impact of noninvariance to a multidimensional test on classification. We also provide an R script for the proposed method and illustrate it with a personnel selection example using data from a published report featuring a five-factor personality inventory.

摘要

在过去的二十年中，关于测量不变性的研究取得了巨大的发展。然而，鉴于心理测试通常用于做出分类决策，例如人员选择或诊断，令人惊讶的是，关于非不变性如何影响分类准确性的研究很少。Millsap 和 Kwok 此前为此提出了一个选择准确性框架，该框架最近已扩展到分类数据。然而，他们的框架只处理使用一维测试的分类。相比之下，实践中的分类通常涉及多维测试（例如，人格）或多个测试，每个维度分配不同的权重。在目前的论文中，我们将 Millsap 和 Kwok 用于检查非不变性影响的框架扩展到对分类的多维测试。我们还为所提出的方法提供了一个 R 脚本，并通过一个人员选择示例来说明它，该示例使用了一个已发布的报告中的数据，该报告具有一个五因素人格清单。