我的模型有多可信? 使用贝叶斯后面概率 (BPP) 评估结构方程模型的模型可信性
Ivan Jacob Agaloos Pesigan1, Shu Fai Cheung2, Huiping Wu3
1Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, University Park, PA, USA.
Behavior research methods
|February 23, 2026
概括
研究人员现在可以使用贝叶斯后方概率 (BPP) 和一种新的邻近模型选择方法选择最合理的结构方程模型 (SEM). 一个R包,modelbpp,自动化了这个过程,以便更好地评估模型.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 计算统计学 计算统计学
背景情况:
- 结构方程建模 (SEM) 被广泛用于模型比较.
- 贝叶斯后置概率 (BPP) 为模型选择提供了一种方法,可以从贝叶斯信息标准 (BIC) 得分中计算出来.
- 现有的方法缺乏选择模型进行比较的指导方针,可能会忽视可信的替代方案.
研究的目的:
- 提出一种选择邻近模型的新方法,以促进BPP在SEM中的使用.
- 为研究人员提供一种系统的方法,用于识别一组相关的替代模型进行比较.
- 通过提供更全面的模型评估策略,加强在SEM中的决策.
主要方法:
- 开发了一种用于识别"邻近模型"的新方法,将其纳入基于BPP的模型比较中.
- 将这种方法集成到典型的SEM工作流程中,使研究人员能够对相应模型及其邻居进行评估.
- 创建了一个用户友好的R包",modelbpp",以自动生成邻近模型,模型拟合和BPP计算.
主要成果:
- 拟议的邻近模型选择方法促进了BPP用于比较SEM的使用.
- "modelbpp" R包简化了生成邻近模型和计算BPP的过程.
- 这种方法补充了传统的适合性指数,可以揭示其他指标所遗漏的模型证据.
结论:
- 新的邻近模型选择方法提高了BPP对SEM的实用性.
- "modelbpp" R包为研究人员提供了一种实用工具,以改善模型评估.
- 这有助于在选择最合理的结构方程模型时做出更明智的决策.
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