主要组件分析的部分最小方程结构方程建模方案与重要性性能分析的组合
Bambang Widjanarko Otok1, Zulfani Alfasanah1, Diaz Fitra Aksioma1
1Departement of Statistic, Faculty of Science and Data Analytics (Scientics), Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
MethodsX
|January 22, 2026
概括
这项研究通过基于PCA的加权方案来增强部分最小平方结构方程建模 (PLS-SEM),以获得更稳定的估计. 它确定了儿童营养不良的主要驱动因素,并优先考虑诸如纯母乳养等干预措施.
科学领域:
- 社会科学 社会科学 社会科学
- 公共卫生 公共卫生
- 统计 统计 统计 统计
背景情况:
- 结构方程建模 (SEM) 有严格的假设限制其使用.
- 部分最小平方 SEM (PLS-SEM) 提供了灵活性,但面临着权重方案的挑战.
- 改进PLS-SEM估计方法对于强大的因果推断至关重要.
研究的目的:
- 为PLS-SEM引入一种基于PCA的新型权重方案.
- 整合重要性-绩效分析 (IPA) 以确定干预优先级.
- 提高PLS估计的准确性和稳定性.
主要方法:
- 为PLS-SEM开发了一个基于主要组件分析 (PCA) 的权重方案.
- 应用重要性-绩效分析 (IPA) 以确定高影响,低绩效的指标.
- 利用PLS-SEM分析东爪儿童营养不良问题.
主要成果:
- 基于PCA的权重方案提高了PLS估计的稳定性和准确性.
- 社会经济条件被确定为粮食安全,育儿和健康环境服务的主要驱动因素.
- 纯母乳养成为基于IPA的干预的关键领域.
结论:
- 提出的方法方法加强了PLS-SEM.
- 该研究为东爪儿童营养不良政策提供了可行的见解.
- 结合基于PCA的权重和IPA,为应用研究提供了一个强大的工具.
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