连续时间结构方程模型森林
Pablo F Cáncer1, Manuel Arnold2, Eduardo Estrada3
1UNINPSI Clinical Psychology Center, Department of Psychology, Universidad Pontificia Comillas.
Psychological methods
|June 5, 2025
概括
连续时间结构方程模型 (CT-SEM) 森林提供了一种新的方法来分析具有不规则间隔的纵向数据. 这种方法改进了离散时间模型,为变化和动态的个体差异提供了更准确的见解.
科学领域:
- 纵向数据分析的数据分析.
- 结构方程建模 结构方程建模
- 心理测量 心理测量 心理测量
背景情况:
- 传统的纵向SEM树木/森林使用了离散时间模型.
- 离散时间模型产生偏差估计与不均间隔的纵向数据.
- 以前的连续时间SEM (CT-SEM) 实现是计算密集型的,产生了偏差的结果.
研究的目的:
- 介绍一种新的,计算上可行的CT-SEM森林实现.
- 解决纵向研究中离散时间模型的局限性.
- 使用CT-SEM森林研究变化和动态的异质性.
主要方法:
- 用于CT建模的ctsemOMX组合包.
- 使用semtree包用于递归分区.
- 综合得分导向的协变量测试来自结构更改包.
- 进行了蒙特卡洛模拟研究.
- 将该方法应用于来自欧洲健康,衰老和退休调查的经验数据.
主要成果:
- 在计算上,CT-SEM森林的新实施是可行的.
- 该方法有效地处理纵向数据中的不规则采样方案.
- 在现实数据上展示了CT-SEM森林的实用性.
结论:
- CT-SEM森林提供了一种可靠的方法来分析具有不规则时间间隔的纵向数据.
- 这种方法增强了对发展轨迹中的个体差异的调查.
- 为研究人员研究复杂数据集的变化和动态提供了有价值的工具.
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