Advances in Local Independence and Unidimensionality Assessment for Dichotomous and Polytomous Item Exams

二分和多分项目考试的局部独立性和一维性评估进展

• 批准号: 0095987
• 负责人: Brian Habing
• 金额: $ 11.5万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-15 至 2006-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0095987&HistoricalAwards=false
• 关键词: Advances; Local; Independence; Unidimensionality; Assessment

This research is concerned with the statistical analysis of large-scale standardized educational and psychological tests. The data from these tests are commonly analyzed using the item response theory (IRT) paradigm. Many IRT based procedures rely on the assumptions that testing data can be modeled as if it measures a single ability or dimension (unidimensionality), and that the responses to the questions on the exam are independent of one another once the examinee's ability level is known (local independence). The standard procedures for verifying that these two assumptions hold suffer from a variety of weaknesses. In particular, they either require additional assumptions about an underlying parametric statistical model or an ad-hoc correction for statistical bias. This research will refine recently proposed methods for verifying these assumptions by using the parametric bootstrap procedure and nonparametric regression. The research will expand the theory of the parametric bootstrap and nonparametric regression from other areas of application to IRT. This will be done both for tests whose items are scored simply as correct or incorrect as well as for those that allow for partial credit. The end result of this research will be a theoretically justified, computationally feasible method of verifying the assumptions of unidimensionality and local independence without need of a specified parametric model or an ad-hoc bias correction.The role of standardized tests has been ever increasing in education in the United States. They are used for everything from determining college admissions decisions to attempting to judge the effectiveness of schools and school districts. As such, it is vital to continually improve the statistical methods used to insure the accuracy and fairness of these standardized tests. Many of the statistical procedures currently used to insure the reliability of these tests are based on some underlying technical assumptions that can be difficult to verify. The result of this research will be methods that the practitioner can use to insure that the procedures for verifying the accuracy and fairness of the standardized tests are performing as expected. Given the historically close interaction and spirit of collaboration between the large national testing companies, university researchers, and state and local departments of education, these results will be immediately disseminated to agencies which directly affect every student in America. In addition to this immediate application, this research also will provide insights into the future development of reliable methods for insuring accuracy and fairness of standardized tests even when the desired underlying technical assumptions are not met.

这项研究涉及大规模标准化教育和心理测试的统计分析。 这些测试的数据通常使用项目响应理论（IRT）范式分析。 许多基于IRT的程序都依赖于可以对测试数据进行建模的假设，就像它测量单个能力或维度（UNIDISESIDEALIDE）一样，并且一旦已知考生的能力水平（本地独立性），对考试问题的回答彼此独立。 验证这两个假设所具有的标准程序遭受了各种弱点。 特别是，他们要么需要有关基础参数统计模型的其他假设，要么需要对统计偏差进行临时校正。 这项研究将完善最近提出的方法，用于使用参数引导程序和非参数回归来验证这些假设。 该研究将扩大参数引导和非参数回归的理论，从其他应用领域到IRT。 这两个都将用于对项目的评分简单正确或不正确的测试，以及允许部分信用的测试。 这项研究的最终结果将是一种理论上是合理的，在计算上可行的方法，可以验证单位性和局部独立性的假设，而无需指定的参数模型或临时偏见校正。标准化测试的作用在美国的教育中一直在增加。 它们用于从确定大学录取决定到试图判断学校和学区有效性的一切。 因此，至关重要的是，不断改善用于确保这些标准化测试的准确性和公平性的统计方法。 当前用于确保这些测试可靠性的许多统计程序都是基于一些可能难以验证的基本技术假设。 这项研究的结果将是从业人员可以使用的方法来确保验证标准化测试的准确性和公平性的过程，如预期。 鉴于大型国家测试公司，大学研究人员以及州和地方教育部门之间的密切互动和合作精神，这些结果将立即分发到直接影响美国每个学生的机构。 除了这项立即应用外，这项研究还将为未来开发的可靠方法提供见解，以确保标准化测试的准确性和公平性，即使不满足所需的基本技术假设。