在估计过渡概率时,使用和不使用剩余关联的LTA模型的比较
Na Yeon Lee1, Sojin Yoon1, Sehee Hong1
1Korea University, Seoul, Korea.
Educational and psychological measurement
|August 19, 2025
概括
隐性过渡分析 (LTA) 模型在计算重复测量中的剩余相关性时表现更好. 这种改进的LTA估计为纵向数据分析提供了更准确的过渡概率估计.
科学领域:
- 心理测量 心理测量 心理测量
- 统计 统计 统计 统计
- 纵向数据分析 纵向数据分析
背景情况:
- 隐性过渡分析 (LTA) 用于纵向数据来识别隐性类别和响应模式.
- 在LTA中重复测量的项目可能会导致剩余的相关性,可能会影响模型的准确性.
- 传统的LTA模型可能无法完全解释这些依赖关系.
研究的目的:
- 调查LTA模型是否包含剩余相关性,比传统LTA模型提供更准确的过渡概率估计.
- 评估剩余相关性对LTA参数估计的影响.
- 为纵向研究提出一个增强的LTA估计方法.
主要方法:
- 进行了一项蒙特卡洛模拟研究.
- 数据是在多次测量的指标变量之间具有或没有指定的剩余相关性时生成的.
- 两种LTA模型进行了比较:一种计算剩余相关性,另一种传统模型.
主要成果:
- 结合剩余相关性的LTA模型在估计过渡概率方面表现出卓越的表现.
- 当残余相关性为0.3或更高时,准确度的提高尤其显著.
- 该研究比较了基于参数偏差,平均平方误差和覆盖范围的估计性能.
结论:
- 考虑剩余相关性的LTA模型在纵向研究中提供了更准确的过渡概率估计.
- 包含剩余相关性对于精确的LTA估计至关重要,特别是在具有显著相关性水平的情况下.
- 这项研究通过整合纵向数据特征,提供了改进的LTA估计方法.
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