如何在大型结构方程模型中评估局部合适性 (余量)
1Department of Psychology, Concordia University, Montréal, Canada.
概括
在大型结构方程模型 (SEM) 中评估本地适合性至关重要. 本教程提供了有效的方法来评估局部合适,即使有许多变量,以识别潜在的模型错误规范.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 结构方程建模 (SEM) 需要在全球和地方层面评估模型的合适性.
- 全球适应性评估了整体模型对应性,而局部适应性则检查了余数 (观察到的和预测的关联之间的差异).
- 满意的全球匹配可以掩盖有问题的本地匹配,特别是在大型复杂的模型中.
研究的目的:
- 为高效评估和描述局部适应在大型结构方程模型中的实际指导提供实用指导.
- 解决当处理众多变量和残留物时评估局部适应的挑战.
主要方法:
- 该研究侧重于评估局部适应结构方程模型的方法.
- 它强调了研究全球适应指数以外的残留物的重要性.
- 这里提供了一个实证示例,其中包含了自由可用的数据和语法.
主要成果:
- 大型结构方程模型可以表现出良好的全球匹配,尽管存在显著的局部错误规范.
- 余量,当平均时,可以稀释局部模型错误的影响.
- 有效地评估局部适应对于准确的模型解释至关重要.
结论:
- 当地合适性评估对于识别大型SEM中模型错误规范的特定领域至关重要.
- 该教程提供了管理和解释局部适应诊断的实用策略.
- 可访问的例子有助于研究人员应用这些方法.
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