在结构方程建模中形成启动过程的置信区间和检查标准系数的启动过程分布:使用R包 semboottools 的简化工作流程.
1Department of Psychology, Faculty of Social Sciences, University of Macau, Macao SAR, China.
Behavior research methods
|January 16, 2026
概括
该 semboottools R 包通过为标准化系数提供简单的启动置信区间 (CI) 来简化结构方程建模 (SEM). 该工具通过提供可访问的方法来评估估计稳定性和比较CI方法来增强SEM分析.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 计算统计学 计算统计学
背景情况:
- 结构方程建模 (SEM) 中的标准化系数对于解释至关重要,但通常具有偏斜的采样分布.
- 传统的对称置信区间 (CI) 无法捕捉这些倾斜的分布,因此需要像引导CI这样的方法.
- 像lavaan这样的现有R包对启动标准化系数的内置支持有限,需要先进的编码技能.
研究的目的:
- 开发一个用户友好的 R 包, semboottools,简化了生成 SEM 的引导置信区间的过程.
- 为研究人员提供工具,以便在非标准化和标准化估计中轻松形成引导CI.
- 为了能够直接比较不同的CI方法,并可视化启动式分布来评估估计稳定性.
主要方法:
- 开发了semboottools R包,为SEM启动提供了简化的工作流.
- 实现函数以生成百分位或偏差校正的启动链CI,标准错误和不对称的p值.
- 包括工具来比较引导CI与delta方法CI,并可视化引导估计分布.
主要成果:
- semboottools 便于在最小的编码工作中为 SEM 参数形成引导 CI.
- 该包允许轻松生成各种引导统计数据,并可视化它们的分布.
- 研究人员可以轻松地将引导CI与传统方法进行比较,有助于评估引导必要性.
结论:
- 该 semboottools R 包通过提供可访问的引导 CI 功能来解决现有 SEM 软件的局限性.
- 它使研究人员能够更严格地评估SEM估计的稳定性,并比较不同的推理方法.
- 这种工具有助于在心理学研究中更强大,更可靠地解释SEM结果.
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