Statistical Analysis for Cognitive Assessment

认知评估的统计分析

• 批准号: 1123698
• 负责人: Jingchen Liu
• 金额: $ 3.7万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1123698&HistoricalAwards=false
• 关键词: Statistical; Analysis; Cognitive; Assessment

This project will address issues concerning fundamental statistical inference for diagnostic classification models. It aims at both theoretical development and applications to educational study, psychiatry, and other disciplines using classification models. The project will concentrate on several aspects that are challenging in the analysis of multivariate latent variable models. In particular, the research will focus on the statistical inference of the item-attribute relationship, which in the current context is formulated as the so-called Q-matrix. Topics for research will include point estimation of the Q-matrix, hypothesis testing, dimension reduction, model diagnosis, and classifying Q-matrices, including tasks such as constructing appropriate equivalence classes that are estimable based on the data.The proposed research is motivated mainly by applications in educational assessment and psychiatric evaluations and has the potential to positively impact these two areas of study. In educational applications, this research will help to obtain a data-driven estimate of the skill requirements for each exam problem and also validate the subjective belief of such skill requirements based on the data. In psychiatric assessments, this study will help to improve diagnosis accuracy by learning the symptom-disorder relationship objectively via the data.

该项目将解决与诊断分类模型的基本统计推断有关的问题。它的目标是利用分类模型在教育研究、精神病学和其他学科的理论发展和应用。该项目将集中在多变量潜变量模型分析中具有挑战性的几个方面。特别是，研究将重点放在项目-属性关系的统计推断上，在当前的背景下，它被表述为所谓的Q-矩阵。研究主题将包括Q矩阵的点估计、假设检验、降维、模型诊断和Q矩阵分类，包括构建基于数据的适当等价类的任务。拟议的研究主要受教育评估和精神病学评估中的应用的推动，并有可能对这两个研究领域产生积极影响。在教育应用中，本研究将有助于获得数据驱动的对每个考试问题的技能要求的估计，并基于数据验证这种技能要求的主观信念。在精神病学评估中，本研究将通过数据客观地了解症状与障碍的关系，有助于提高诊断的准确性。