测量后结构 (SAM) 方法用于适应潜在的二次和相互作用效应
Yves Rosseel1, Elissa Burghgraeve2, Wen Wei Loh3
1Department of Data Analysis, Ghent University, Henri Dunantlaan 2, 9000, Ghent, Belgium. yves.rosseel@ugent.be.
Behavior research methods
|February 26, 2025
概括
本研究介绍了测量后结构 (SAM) 方法,作为具有多个非线性效应的复杂结构方程模型的实际解决方案. 当模型复杂性增加时,SAM方法为UPI和LMS等传统的一步方法提供了可行的替代方案.
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:
- 结构方程模型 (SEMs) 中潜伏二次方程和相互作用效应的传统方法,如无约束产品指标 (UPI) 和潜伏调节结构方程 (LMS),对于更简单的模型是有效的.
- 随着众多非线性项的增加,模型的复杂性增加,从而降低了UPI和LMS等一步估计方法的可行性.
研究的目的:
- 提出和评估结构测量后 (SAM) 方法,作为在复杂的SEM中估计非线性效应的替代方案.
- 引入一种新的本地SAM方法,并将其性能与现有的SAM技术和传统的一步方法进行比较.
主要方法:
- 该研究主张采用两阶段的估计过程:第一,估计测量参数,第二,估计结构参数 (SAM方法).
- 讨论了三种现有的SAM方法,以及最近提出的本地SAM方法.
- 进行模拟研究以评估SAM方法的实用性和性能.
主要成果:
- SAM方法为处理SEM中隐藏的二次和相互作用效应提供了实用和可行的策略.
- 模拟研究表明SAM方法的有效性,特别是在与单步方法相比复杂度增加的模型中.
结论:
- 结构后测量 (SAM) 方法为具有多个非线性效应的复杂的SEM提供了强大的替代方案.
- 建议本地SAM方法和其他SAM策略适用于处理复杂结构模型的研究人员.
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