关于MC-DINA模型的修复参数化
1Teachers College, Columbia University, New York, NY, USA.
Applied psychological measurement
|March 14, 2025
概括
认知诊断模型 (CDM) 的MC-DINA模型被重新表达为一个多项混合模型. 这为其结构和假设提供了更清晰的见解,有助于统计估计和实际应用.
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
- 认知心理学 认知心理学
背景情况:
- 认知诊断模型 (CDM) 对于理解学生掌握特定技能至关重要.
- 传统的CDM通常使用二分法响应,不考虑分心选择.
- 多选诊断模型 (MC-DINA) 通过允许名义响应和建模干扰效应来扩展CDM.
研究的目的:
- 将MC-DINA模型重新表达为具有潜伏离散预测器的多项逻辑模型.
- 为了澄清模型的结构,假设和参数限制.
- 探讨对心理解释和统计估计的影响,特别是对小样本大小的样本.
主要方法:
- 重新对MC-DINA模型进行参数化.
- 使用一个多项混合模型框架.
- 应用一个类似信号检测的参数化.
主要成果:
- 证明了MC-DINA可以作为一个多项混合模型来表示.
- 确定了模型结构内在的参数限制.
- 通过使用TIMSS 2007年四年级考试数据展示了修复参数化的适用性.
结论:
- 重制参数化提供了对MC-DINA的更清晰的理解,特别是在分心效应方面.
- 识别的参数限制对心理解释和统计估计有重大影响.
- 拟议的方法促进了适合实际应用的节模型,包括具有有限样本大小的模型.
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