具有多种反应的DINA模型识别的充分和必要条件
1Department of Statistics, University of Michigan, 456 West Hall, 1085 South University, Ann Arbor, 48109, MI, USA.
Psychometrika
|March 22, 2024
概括
本研究为具有多种反应的认知诊断模型 (CDM) 建立了可识别条件. 它解决了理解复杂,多选项数据的这些模型的关键差距.
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
- 隐性变量建模 隐性变量建模
背景情况:
- 认知诊断模型 (CDM) 对于理解受访者属性至关重要.
- CDM越来越多地使用多种 (多选项) 响应数据.
- 可识别性对于CDM中准确的参数估计至关重要,但对于多种类型的数据研究不足.
研究的目的:
- 提供足够和必要的条件,以确定具有多种反应的DINA模型.
- 为了解CDM多选项数据的可识别性弥合差距.
- 为未来关于认知诊断模型的研究提供信息.
主要方法:
- 对多种类型反应的DINA模型的理论分析.
- 可识别性条件的推导.
- 专注于必要和充分的标准.
主要成果:
- 确定了多种类型的DINA模型的特定条件.
- 提供了一个评估CDM可识别性与多选项数据的框架.
- 强调了这些条件对于有效推断的重要性.
结论:
- 这些发现有助于全面了解多种类型数据的CDM可识别性.
- 这项研究对于CDM在教育和心理评估中的准确应用至关重要.
- 进一步的研究可以建立在这些可识别条件上,用于先进的CDM开发.
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