探索需要多少类别来建模顺序密集的纵向数据作为连续的动态结构方程模型.
Daniel McNeish1, Andrea Savord2
1Department of Psychology, Arizona State University.
Psychological methods
|August 7, 2025
概括
在动态结构方程模型 (DSEM) 中将顺序密集的纵向数据 (ILD) 作为连续的建模需要仔细考虑. 结果表明,对于人内影响,至少有七个类别,但对于人间影响,可能更少.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 纵向数据分析 纵向数据分析
背景情况:
- 密集纵向数据 (ILD) 越来越多地通过技术创新收集,包括许多重复测量.
- 动态结构方程模型 (DSEM) 适合ILD,但通常将顺序结果视为连续的,这具有可疑的有效性.
- 现有的指导方针用于将顺序数据建模为连续的,主要来自因子分析,可能不适用于DSEM,因为它们具有独特的特性.
研究的目的:
- 评估探测器DSEM在现实ILD样本大小下对顺序数据的统计特性.
- 确定命令ILD在DSEM中可以被防御性地建模为连续的条件.
主要方法:
- 使用模拟来评估探测器DSEM的性能,使用不同数量的顺序类别.
- 该研究检查了在不同条件下估计人内和人间影响的准确性.
- 与现有的因子分析文献进行了比较,对顺序数据的持续处理进行了比较.
主要成果:
- 在DSEM中准确估计人内效应通常需要至少七个顺序类别.
- 人与人之间的影响可以合理估计,只有五个,有时是三个,普通类别.
- 调查结果表明,对顺序数据的连续建模的因子分析准则并不直接适用于DSEM.
结论:
- 在DSEM中对顺序ILD的连续性假设不是普遍可以辩护的,并且在很大程度上取决于响应类别的数量.
- 需要在DSEM中建模顺序ILD的具体建议,与因子分析惯例分歧.
- 需要进一步的研究来完善使用DSEM分析顺序ILD的最佳实践.
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