CDM和 (M) IRT在衡量潜在技能增长中的相对稳定性
Qi Helen Huang1, Daniel M Bolt1
1University of Wisconsin-Madison, USA.
Educational and psychological measurement
|July 3, 2023
概括
认知诊断模型 (CDM) 可能会低估增长,当潜在的技能是连续的,而不是二进制的. 多维项目响应理论 (MIRT) 为测量技能增长提供了更强大的替代方案.
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
- 认知科学 认知科学
背景情况:
- 潜在的技能连续性可以挑战二进制技能测量假设.
- 技能测量的错误规范可能导致参数不变性问题.
- 增长测量是受这些测量假设影响的关键应用.
研究的目的:
- 为了比较认知诊断模型 (CDM) 和多维物件响应理论 (MIRT) 测量增长的稳定性.
- 调查二进制与连续潜伏技能分布对增长测量的影响.
- 评估增长测量模型中错误规范的后果.
主要方法:
- 模拟研究比较CDM和MIRT在不同的潜在技能分布下.
- 在二进制和连续技能假设下对增长测量的分析.
- 将模型应用于真实数据示例以进行增长估计.
主要成果:
- 当潜在技能是连续的时,认知诊断模型 (CDM) 在量化增长方面表现不那么稳健.
- 在CDM中对二进制技能的错误规范导致了低估增长.
- 多维物件响应理论 (MIRT) 在增长测量方面显示出更大的稳定性.
结论:
- 研究人员应该仔细考虑在他们的测量模型中假设潜在的二进制技能.
- 当技能的离散性质不确定时,多维项目响应理论 (MIRT) 是一个潜在的更强大的替代方案.
- 低估增长是使用具有持续潜伏技能的CDM的可能后果.
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