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TDCM:用于估计纵向诊断分类模型的R包.
Matthew J Madison1, Minjeong Jeon2, Michael Cotterell3
1Assistant Professor, Quantitative Methodology, Department of Educational Psychology, University of Georgia, Athens, GA, USA.
Multivariate behavioral research
|February 12, 2025
概括
本研究引入了用于估计纵向诊断分类模型 (DCM) 的新R包,解决了追踪受试者属性掌握随时间变化的现有软件的局限性.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 统计建模 统计建模
背景情况:
- 诊断分类模型 (DCM) 根据潜在的属性对个体进行分类.
- 纵向DCM将此扩展到随着时间的推移模型属性变化.
- 纵向DCM的现有软件通常是有限的,昂贵的或难以使用的.
研究的目的:
- 引入和演示一个新的R包,用于估计一般纵向DCMs.
- 为应用研究人员提供一个可访问和多功能工具.
- 为了方便分析随着时间的推移,考生熟练程度的变化.
主要方法:
- 开发一个新的R包,实现过渡诊断分类模型.
- 展示包装用于估计纵向DCM的功能.
- 专注于建模属性掌握动态的一般框架.
主要成果:
- 开发的R包为纵向DCM估计提供了一个功能性和可通用的工具.
- 与现有的软件相比,该软件包简化了应用研究人员的过程.
- 成功证明过渡诊断分类模型的估计能力.
结论:
- 新的R包为研究人员提供了宝贵的资源,研究随着时间的推移潜伏属性的变化.
- 它提高了纵向DCM分析的可访问性和实用性.
- 该工具支持对考生能力发展的更强大的建模.
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