General Cognitive Diagnosis Models: Development, Estimation, and Applications

一般认知诊断模型：开发、估计和应用

• 批准号: 2150601
• 负责人: Wenchao Ma
• 金额: $ 36万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150601&HistoricalAwards=false
• 关键词: General; Cognitive; Diagnosis; Models; Development

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.

该研究项目将推进现代统计理论和方法在认知诊断建模方面的前沿。认知诊断模型（CDMs）是一种心理测量工具，旨在从被调查者对测试或问卷中一组项目的明显反应中推断出被调查者未观察到的心理属性。CDM已被用于教育评估，并成功地应用于心理学和社会科学。然而，现有的CDM具有有限的效用，因为它们通常假设二进制属性。该项目将进一步扩大清洁发展机制的适用性，开发一系列通用模型，为清洁发展机制分析提供统一框架，并可用作开发新的清洁发展机制的基础。该项目的科学产品将通过讲习班、会议介绍和在同行评审的期刊上发表文章来传播。该项目将开发开放源码软件，使更广泛的受众能够获得先进的清洁发展机制。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用。本科生和研究生都将参与这项研究的进行，研究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。该研究项目将开发，估计和应用一种新型的认知诊断模型（CDM）家族，以同时适应多分类响应数据和多类别心理属性。特别是，该项目将（1）检查拟议模型的理论特性，包括模型可识别性和模型等效性，以确保CDM在实践中的原则性使用，（2）开发计算效率高的参数估计方法，以便能够估计大数据中高维CDM的参数，（3）开发有效的统计推断方法来处理高维模型和大规模数据，以及（4）开展跨学科合作，将新方法应用于各科学领域的代表性数据集，以解决实质性研究问题感兴趣为了提高拟议工作的影响，研究人员将创建一个免费的软件程序，使应用研究人员可以使用方法创新。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

A Testlet Diagnostic Classification Model with Attribute Hierarchies

具有属性层次结构的 Testlet 诊断分类模型

• DOI: 10.1177/01466216231165315
• 发表时间: 2023
• 期刊: Applied Psychological Measurement
• 影响因子: 1.2
• 作者: Ma, Wenchao;Wang, Chun;Xiao, Jiaying
• 通讯作者: Xiao, Jiaying

A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses

• DOI: 10.1007/s11336-022-09887-1
• 发表时间: 2021-03
• 期刊: Psychometrika
• 影响因子: 3
• 作者: Zhenghao Zeng;Yuqi Gu;Gongjun Xu

Discussion: “Vintage Factor Analysis with Varimax Performs Statistical Inference” by Rohe and Zeng

讨论：Rohe 和 Zeng 的“使用 Varimax 进行统计推断的老式因子分析”

• DOI: 10.1093/jrsssb/qkad040
• 发表时间: 2023
• 期刊: Journal of the Royal Statistical Society Series B: Statistical Methodology
• 作者: Chen, Yunxiao;Xu, Gongjun
• 通讯作者: Xu, Gongjun

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data

• 发表时间: 2021-09
• 期刊: J. Mach. Learn. Res.
• 作者: Yuqi Gu;E. Erosheva;Gongjun Xu;D. Dunson

Statistical Inference for Noisy Incomplete Binary Matrix

带噪不完全二元矩阵的统计推断

• 发表时间: 2023
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Chen Y.;Li, C.;Ouyang, J.;Xu, G.
• 通讯作者: Xu, G.