Statistical Analysis for Cognitive Diagnosis - Theory and Applications

认知诊断的统计分析 - 理论与应用

• 批准号: 1323977
• 负责人: Jingchen Liu
• 金额: $ 29万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1323977&HistoricalAwards=false
• 关键词: Statistical; Analysis; Cognitive; Diagnosis; Theory

Diagnostic classification models are an important statistical tool in cognitive diagnosis and can be employed in a number of disciplines, including educational assessment and clinical psychology. This project focuses on the statistical analysis of cognitive assessment. The research addresses issues concerning fundamental statistical inference and experimental design. It aims at both theoretical development and applications. First, the project will focus on the statistical inference of the item-attribute relationship, which in the current context is formulated as the so-called Q-matrix. The topics include point estimation of the Q-matrix, hypothesis testing, dimension reduction, and model diagnosis. Second, the project will focus on the individualized adaptive design of items so as to measure the attribute profiles more accurately with fewer items. In particular, a criterion is first proposed to measure the efficiency of an item-selection rule based on the large deviations theory. In addition, adaptive item selection schemes are proposed to approach the optimal design. This research is motivated by applications in educational assessments, psychiatric evaluations, and other disciplines using classification models. In educational applications, the research will help to obtain a data-driven calibration of the skill requirements for exam problems and also to validate the subjective beliefs of such skill requirements, so that better and more accurate assessments of students' knowledge status and skill mastery are obtained. In psychiatric assessment, this study will help to improve evidence-based diagnosis by more accurately identifying the symptom-disorder relationship. The adaptive item selection helps by shortening the lengths of exams (in educational testing) and interviews (in psychiatric evaluation) while maintaining the assessment and diagnosis accuracy. Project results have the potential to positively impact these and other areas of study.

诊断分类模型是认知诊断中的重要统计工具，可以用于许多学科，包括教育评估和临床心理学。 本项目主要研究认知评估的统计分析。 该研究涉及基本的统计推断和实验设计的问题。 它旨在理论发展和应用。 首先，该项目将侧重于项目属性关系的统计推断，在目前的情况下，这是制定为所谓的Q矩阵。 主题包括Q矩阵的点估计、假设检验、降维和模型诊断。 第二，项目将侧重于项目的个性化适应性设计，以便用更少的项目更准确地测量属性概况。 特别是，首次提出了一个标准来衡量的项目选择规则的大偏差理论的基础上的效率。 此外，自适应项目选择方案，提出了接近最优设计。本研究的动机是在教育评估，精神评估和其他学科使用分类模型的应用。 在教育应用中，该研究将有助于获得考试问题的技能要求的数据驱动校准，并验证这种技能要求的主观信念，从而获得对学生的知识状况和技能掌握的更好和更准确的评估。 在精神病评估中，本研究将有助于通过更准确地识别精神障碍与疾病的关系来提高循证诊断。 自适应项目选择有助于缩短考试（教育测试）和访谈（精神病评估）的时间，同时保持评估和诊断的准确性。 项目成果有可能对这些和其他研究领域产生积极影响。