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对结构方程模型进行贝叶斯估计和结构测量后方法的比较,这些模型具有潜在的相互作用和复杂的数据结构.

Kyle Cox1, Benjamin Kelcey2

  • 1University of North Carolina at Charlotte, Charlotte, NC, USA. kyle.cox@charlotte.edu.

Behavior research methods
|October 22, 2025
PubMed
概括

具有潜伏相互作用的结构方程模型 (SEM) 从贝叶斯式和结构后测量 (SAM) 方法中获益. 与贝叶斯方法相比,SAM方法在各种复杂的SEM中显示出卓越的多功能性和适应性.

关键词:
贝叶斯估计贝叶斯估计潜伏相互作用 潜伏相互作用多层结构方程模型的多层结构方程模型.部分嵌套数据部分嵌套数据结构后测量 (SAM) 方法.

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科学领域:

  • 统计 统计 统计 统计
  • 心理测量 心理测量 心理测量
  • 量化心理学 量化心理学

背景情况:

  • 传统的估计器在结构方程模型 (SEMs) 中面临潜在相互作用的局限性.
  • 贝叶斯式和结构后测量 (SAM) 方法提供了潜在的解决方案,特别是在小样本研究中.
  • 对于这些先进的SEM方法的好处和权衡,存在有限的系统比较.

研究的目的:

  • 为了比较贝叶斯和SAM估计器在多层次SEM中的性能,与不同类型的潜在相互作用 (内部,之间,交叉层面) 相比.
  • 调查SAM方法在部分嵌套的SEM中具有潜伏调节调解的适应性.
  • 根据SEM复杂性和数据结构来确定估计器的适用性.

主要方法:

  • 贝叶斯和SAM估计器的比较性能分析.
  • 用SAM方法应用于具有各种潜在相互作用的多层SEM.
  • 在部分嵌套的SEM中扩展和评估SAM,涉及潜伏中和的中介.

主要成果:

  • 观察到估计器性能的显著差异,取决于潜在相互作用的类型.
  • 在多层次和部分嵌套的SEM中,SAM方法在多种潜在相互作用中表现出强的性能.
  • 贝叶斯式方法遇到了跨层次潜伏相互作用的困难,并且不太适应部分嵌套的SEM.

结论:

  • SAM方法为传统的SEM估计器提供了一个多功能和适应性的替代方案或补充.
  • 在选择估计器时,应考虑特定的SEM类型,潜伏相互作用的性质和数据结构.
  • 对于具有潜在相互作用的复杂多层和部分嵌套的SEM,SAM方法特别有利.