适应性指数对确认因素分析中完美简单结构的多次轻微违反不敏感
Victoria Savalei1, Muhua Huang2
1Department of Psychology, University of British Columbia.
Psychological methods
|February 13, 2025
概括
确认因素分析 (CFA) 模型经常与探索因素分析 (EFA) 结构相适应,即使有交叉负载. 良好的CFA匹配主要排除具有高度可变负载比率的EFA,而不是大多数其他EFA结构.
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:
- 确认因素分析 (CFA) 模型假设指标衡量一个因素.
- 探索性因素分析 (EFA) 允许指标对多个因素 (交叉负载) 进行加载.
研究的目的:
- 为了研究在哪些条件下,经典的CFA模型可以很好地适应EFA结构.
- 为了确定CFA适合指数的诊断实用性,用于区分CFA和EFA模型.
主要方法:
- 数学推导显示EFA结构可旋转到CFA表示.
- 使用ShinyApp进行模拟研究,以评估CFA模型适合各种EFA结构.
- 分析适合指数行为作为交叉负载特征的函数.
主要成果:
- CFA 模型经常与 EFA 结构非常适合,特别是当交叉负载数量众多且比例时.
- 适应性指数可以与正交叉负载的数量非单调,只有在混合信号交叉负载时才变得单调.
- 良好的CFA适应是对具有高度变化的负载比率的EFA结构的强有力的指标.
结论:
- 经典的CFA模型可能很适合许多EFA结构,在某些情况下挑战它们的理论优势.
- 这些发现表明,当解释CFA合适指数作为单因素结构的最终证据时,特别是在存在潜在的交叉负载时,应该谨慎.
- 良好的CFA匹配并不排除大多数可信的EFA结构,强调理论理由和模型规范的重要性.
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