Stochastic Processes for Assessing Measurement Invariance in Psychometric Models

评估心理测量模型中测量不变性的随机过程

• 批准号: 1061334
• 负责人: Edgar Merkle
• 金额: $ 17万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1061334&HistoricalAwards=false
• 关键词: Stochastic; Processes; Assessing; Measurement; Invariance

This project involves the application of parameter instability tests from theoretical statistics to the study of measurement invariance in psychometrics. The tests, based on Gaussian processes, have the ability to identify subgroups of both individuals and model parameters that violate measurement invariance. In contrast, existing tests of measurement invariance require the (potentially) violating subgroups and model parameters to be specified in advance. The project includes theory development, study of test performance in psychometric applications, and R software development. The work will yield a more comprehensive set of tools than currently is available for studying measurement invariance in a variety of psychometric models.Informally, measurement invariance involves the notion that a scale (or set of scales) measures everyone in the same manner. That is, if two individuals have the same "amount" of an ability of interest, a scale should assign the same score to the two individuals (save for random error). A scale violates measurement invariance if it systematically assigns different scores to the two individuals. For example, this issue is exceptionally problematic when the scales come from a college entrance exam: individuals of equal ability may systematically receive different scores. The project will apply novel statistical methods to the issue of measurement invariance, advancing theoretical knowledge and addressing standing problems on the topic. Open source software and realistic applications of the methods also will be developed, ensuring that the methods are broadly disseminated and accessible to a wide range of researchers. The project will lead to advances in applied knowledge on social science issues. Because the issue of measurement invariance involves the study of group differences, the proposed research will help ensure that psychometric scales treat diverse groups of individuals equally.

本计画将理论统计中的参数不稳定性检定应用于心理测量学中测量不变性的研究。 基于高斯过程的测试，有能力识别违反测量不变性的个体和模型参数的子组。 相比之下，现有的测量不变性的测试需要（潜在的）违反子群和模型参数被预先指定。 该项目包括理论开发，心理测量应用中的测试性能研究和R软件开发。 这项工作将产生一个比目前更全面的工具集，用于研究各种心理测量模型中的测量不变性。非正式地说，测量不变性涉及一个尺度（或一组尺度）以相同的方式测量每个人的概念。 也就是说，如果两个人具有相同的“数量”感兴趣的能力，那么量表应该为这两个人分配相同的分数（除了随机误差）。 如果一个量表系统地给两个人分配不同的分数，那么它就违反了测量不变性。 例如，当量表来自大学入学考试时，这个问题就特别成问题：能力相同的个人可能会系统地获得不同的分数。 该项目将应用新的统计方法来解决测量不变性问题，推进理论知识和解决该主题的长期问题。 还将开发开放源码软件和这些方法的实际应用，确保这些方法得到广泛传播，并为广大研究人员所利用。 该项目将导致社会科学问题应用知识的进步。 由于测量不变性的问题涉及到群体差异的研究，拟议的研究将有助于确保心理测量量表平等对待不同群体的个人。