General Cognitive Diagnosis Models: Development, Estimation, and Applications

这一研究项目将推进认知诊断建模中现代统计理论和方法的前沿。认知诊断模型(CDM)是一种心理测量工具，旨在从受访者对测试或问卷中一组项目的明显反应中推断出他们未被观察到的心理属性。CDMS已被用于教育评估，并成功应用于心理学和社会科学。然而，现有的CDM的实用性有限，因为它们通常采用二进制属性。该项目将通过开发一套为清洁发展机制分析提供统一框架的通用模型，进一步扩大CDM的适用性，并可作为开发新的CDM的基础。该项目的科学产品将通过研讨会、会议报告和同行评议期刊上的出版物进行传播。该项目将开发开源软件，使更广泛的受众能够接触到先进的疾病预防控制中心。该项目的成果将对教育、心理学和社会科学的应用研究人员有所帮助。本科生和研究生都将参与这项研究，调查人员将尽一切努力将代表性不足的群体的学生纳入他们的研究团队。这项研究项目将开发、评估和应用一系列新的认知诊断模型(CDM)，以同时适应多分类反应数据和多范畴心理属性。特别是，该项目将(1)审查拟议模型的理论性质，包括模型可识别性和模型等价性，以确保CDM在实践中的原则使用；(2)开发计算效率高的参数估计方法，使大数据中高维CDM的参数能够估计；(3)为处理高维模型和大型数据开发有效的统计推断方法；以及(4)开展跨学科合作，将新方法应用于不同科学领域的代表性数据集，以解决感兴趣的实质性研究问题。为了提高拟议工作的影响力，调查人员将创建一个自由软件计划，使应用研究人员能够获得方法创新。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will advance the frontiers of modern statistical theory and methodology in cognitive diagnosis modeling. Cognitive diagnosis models (CDMs) are psychometric tools designed to infer respondents' unobserved psychological attributes from their manifest responses to a set of items in a test or questionnaire. CDMs have been used in educational assessments and successfully applied in psychology and the social sciences. However, existing CDMs have limited utility because they often assume binary attributes. This project will further extend the applicability of CDMs by developing a general family of models that offer a unified framework for CDM analyses, and that also can be used as a basis for the development of new CDMs. The scientific products of this project will be disseminated via workshops, conference presentations, and publications in peer-reviewed journals. The project will develop open-source software to make advanced CDMs accessible to a broader audience. The outcomes of this project will be useful for applied researchers in education, psychology, and the social sciences. Both undergraduate and graduate students will be involved in the conduct of this research, and the investigators will make every effort to include students of underrepresented groups in their research teams. This research project will develop, estimate, and apply a novel family of cognitive diagnosis models (CDMs) to simultaneously accommodate polytomous response data and multi-categorical psychological attributes. In particular, the project will (1) examine the theoretical properties of the proposed models, including model identifiability and model equivalence to ensure the principled use of CDMs in practice, (2) develop computationally efficient parameter estimation methods to make it possible to estimate parameters of CDMs of high dimensions in big data, (3) develop valid statistical inference methods for handling models of high dimensions and data of large sizes, and (4) conduct interdisciplinary collaborations to apply the new methods to representative datasets in various scientific fields to address substantive research questions of interest. To boost the impact of the proposed work, the investigators will create a free software program to make the methodological innovations accessible to applied researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.