用密集的纵向数据进行动态建模:一步和两步的DSEM方法
Lijuan Wang1, Yuan Fang1, Cindy S Bergeman1
1University of Notre Dame.
对于密集的纵向数据,建议使用单步动态结构方程建模 (DSEM) 和带辅助变量的双步DSEM. 没有辅助变量的双步DSEM显示了显著的估计偏差和糟糕的性能.
科学领域:
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
- 统计建模 统计建模
背景情况:
- 密集的纵向数据 (ILD) 分析需要复杂的统计方法.
- 动态结构方程建模 (DSEM) 是ILD的一个强大的技术.
- 对比一步式和两步式的DSEM方法对于准确的分析至关重要.
研究的目的:
- 评估和比较用于ILD的一步和两步DSEM的性能.
- 调查辅助变量对两步式DSEM的影响.
- 为在ILD研究中提供DSEM应用的建议.
主要方法:
- 进行了一项模拟研究,以比较DSEM方法.
- 一步式DSEM同时估计人内和人间模型.
- 两步DSEM将人内和人间模型估计分开,有或没有辅助变量.
主要成果:
- 没有辅助变量的双步DSEM证明了估计偏差,覆盖率低,I型错误率降低.
- 一步式DSEM和两步式DSEM与辅助变量在足够的数据 (≥30个时间点,≥100个个体) 中得到了满意的执行.
- 辅助变量提高了双步DSEM的性能.
结论:
- 一步式DSEM是一种可靠的方法来分析ILD.
- 两步式DSEM需要仔细实施,最好使用辅助变量,以获得有效的结果.
- 研究人员在选择用于ILD分析的DSEM方法时应考虑这些发现.
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