什么时候以及如何使用集探索性结构方程建模来测试结构模型:使用R包的教程
Herb Marsh1,2, Abdullah Alamer3,4
1Institute of Positive Psychology and Education, Australian Catholic University, Sydney, New South Wales, Australia.
The British journal of mathematical and statistical psychology
|February 16, 2024
概括
集探索性结构方程建模 (set-ESEM) 为结构模型提供了确认因素分析 (CFA) 的实用替代方案. 设置ESEM显示出优越的模型合适性和比CFA更准确的参数估计,可能减少II型错误.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 确认因素分析 (CFA) 是一种测量模型的标准.
- 探索性结构方程建模 (ESEM) 扩展了CFA,但在复杂的结构模型中可能面临估计挑战.
- Set-ESEM是为了平衡ESEM的灵活性与CFA的实用性而开发的.
研究的目的:
- 为了证明set-ESEM对结构模型的完整ESEM和CFA的应用和优势.
- 提供实用指导和代码,用于使用R包实现set-ESEM.
- 使用现实数据,将set-ESEM的性能与基于CFA的结构模型进行比较.
主要方法:
- 使用了两个应用示例与真实数据,避免模拟研究.
- 采用了lavanan R包,提供了set-ESEM实现的实用代码.
- 在set-ESEM和CFA模型之间比较合适度,因子相关性和路径系数.
主要成果:
- 与CFA相比,Set-ESEM结构模型表现出更好的适合性和更现实的因子相关性.
- 设置ESEM模型中的路径系数更准确,一些以前不显著的效应变得显著.
- 设置ESEM模型表明,由于更精确的参数估计,II型错误率较低.
结论:
- Set-ESEM为分析结构模型提供了一种有价值和实用的方法,其表现优于传统的CFA.
- 该方法提高了模型适合性和参数准确性,从而导致更可靠的统计推理.
- 鼓励研究人员考虑set-ESEM用于复杂的结构建模任务,以改善分析结果.
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