Score-Based Tests of Measurement Bias in Explanatory Item Response Models

解释性项目响应模型中基于分数的测量偏差测试

• 批准号: 1460719
• 负责人: Edgar Merkle
• 金额: $ 28万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1460719&HistoricalAwards=false
• 关键词: Score; Based; Tests; Measurement; Bias

Large-scale, standardized ability tests have a prominent role in society, impacting college admissions, occupational decisions, and resource distribution. This research project will involve the development of novel statistical methods that make it easier to determine whether or not the items making up a test are fair. If a test (or specific items on a test) were to unfairly advantage one group of students over others, then some students from the disadvantaged groups may miss out on the opportunities that they deserve based on their abilities. Thus, it is important to ensure that standardized tests are fair to the diverse groups of students taking the tests. The new methods will be implemented in free software, and all data resulting from the project will be openly disseminated via the internet. The project also will support a doctoral student in psychometrics, which is a STEM discipline with a documented shortage of students.This project will develop statistical methods that are designed to be used with item response models. Item response models are considered to be state-of-the-art methodology for educational test development and analysis. The new methods involve generalizations of the score test (also known as the Lagrange multiplier test) that is well known to statisticians. These generalizations have been relatively unexplored in psychometrics, so that the methods developed in this project will address unresolved problems related to the study of test fairness. In particular, these new methods will allow one to study novel hypotheses of fairness using simpler statistical models than are required for traditional methods. The new methods will further allow for the study of fairness across many groups of students and within a large class of item response models, many of which are more complex than traditional models. Along with theoretical development and software implementation, the project will illustrate the methods' abilities via simulation. The simulations will directly compare the new methods to traditional methods and compare multiple novel statistics to one another. The project will provide researchers with novel methods to study test fairness, free software to carry out the methods, and simulation results that guide researchers in the methods' optimal uses.

大规模的标准化能力测试在社会中发挥着重要作用，影响着大学招生、职业决策和资源分配。 本研究项目将涉及新的统计方法的发展，使其更容易确定是否组成测试的项目是公平的。 如果一个测试（或测试中的特定项目）不公平地使一组学生优于其他学生，那么来自弱势群体的一些学生可能会错过他们根据自己的能力应得的机会。 因此，重要的是要确保标准化考试对参加考试的不同学生群体是公平的。 新方法将在自由软件中实现，项目产生的所有数据将通过互联网公开传播。 该项目还将支持一名心理测量学博士生，这是一个STEM学科，有记录的学生短缺。该项目将开发旨在与项目反应模型一起使用的统计方法。 项目反应模型被认为是教育测试开发和分析的最先进的方法。 新方法涉及统计学家所熟知的评分检验（也称为拉格朗日乘数检验）的概括。 这些概括在心理测量学中相对来说还没有被探索过，因此本项目中开发的方法将解决与测试公平性研究相关的未解决的问题。 特别是，这些新方法将允许人们使用比传统方法所需的更简单的统计模型来研究新的公平性假设。 新方法将进一步允许在许多学生群体中以及在一大类项目反应模型中研究公平性，其中许多模型比传统模型更复杂。 沿着理论的发展和软件的实现，该项目将通过模拟来说明这些方法的能力。 模拟将直接将新方法与传统方法进行比较，并将多个新的统计数据相互比较。 该项目将为研究人员提供研究考试公平性的新方法，执行这些方法的免费软件，以及指导研究人员优化使用这些方法的模拟结果。