Estimating classification consistency of screening measures and quantifying the impact of measurement bias.
Estimating classification consistency of screening measures and quantifying the impact of measurement bias.
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DOI：
10.1037/pas0000938
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发表时间：
2021-07
影响因子：
3.6
通讯作者：
Fouladi RT
中科院分区：
文献类型：
--
作者：
Gonzalez O;Georgeson AR;Pelham WE;Fouladi RT
筛选措施用于心理学和医学，以确定受访者谁是高或低的结构。在筛选的基础上，评估人员将受访者分为不同的类别，对应于不同的后续步骤：做出诊断与拒绝诊断;提供服务与拒绝服务;进行进一步评估与结束评估过程。当使用措施对个人进行分类时，重要的是，决策在各个群体之间是一致和公平的。理想情况下，如果受访者快速连续地重复完成筛选措施，他们每次都会被分配到同一个班级。此外，分类的一致性应与受访者的背景特征无关，如性别、种族或民族（即，测量没有测量偏差）。报告分类一致性的估计值是教育测试中的常见做法，但这些估计值在心理学和医学筛查中的应用有限。在本文中，我们提出了两个程序的项目反应理论的基础上，用于（a）估计的分类一致性的筛选措施和（B），以评估分类一致性的影响，测量偏差在受访者群体。我们提供了R函数进行的程序，说明程序与真实的数据，并使用蒙特卡罗模拟，以指导其适当的使用。最后，我们讨论了分类一致性的估计如何帮助评估专家在使用受保护群体的筛选措施时做出更明智的决定（例如，按性别、种族或民族定义的群体）。
Screening measures are used in psychology and medicine to identify respondents who are high or low on a construct. Based on the screening, the evaluator assigns respondents into classes corresponding to different next steps: make a diagnosis vs. reject a diagnosis; provide services vs. withhold services; conduct further assessment vs. conclude the assessment process. When measures are used to classify individuals, it is important that the decisions be consistent and equitable across groups. Ideally, if respondents completed the screening measure repeatedly in quick succession, they would be consistently assigned into the same class each time. In addition, the consistency of the classification should be unrelated to the respondents’ background characteristics, such as sex, race, or ethnicity (i.e., the measure is free of measurement bias). Reporting estimates of classification consistency is a common practice in educational testing, but there has been limited application of these estimates to screening in psychology and medicine. In this paper, we present two procedures based on item response theory that are used (a) to estimate the classification consistency of a screening measure and (b) to evaluate how classification consistency is impacted by measurement bias across respondent groups. We provide R functions to conduct the procedures, illustrate the procedures with real data, and use Monte Carlo simulations to guide their appropriate use. Finally, we discuss how estimates of classification consistency can help assessment specialists make more informed decisions on the use of a screening measure with protected groups (e.g., groups defined by gender, race, or ethnicity).
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影响因子：
1.3
作者：
Lathrop, Quinn N.;Cheng, Ying
通讯作者：
Cheng, Ying
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
7
作者：
Emons, Wilco H. M.;Sijtsma, Klaas;Meijer, Rob R.
通讯作者：
Meijer, Rob R.
影响因子：
4.7
作者：
Kruyen, Peter M.;Emons, Wilco H. M.;Sijtsma, Klaas
通讯作者：
Sijtsma, Klaas
DOI：
10.1207/s15328007sem1103_1
发表时间：
2004-01-01
影响因子：
6
作者：
Dudgeon, P
通讯作者：
Dudgeon, P