一种模型隐含的仪器变量方法用于探索性因子分析 (MIIV-EFA)
Kenneth A Bollen1,2, Kathleen M Gates3, Lan Luo3
1Thurstone Psychometric Laboratory, Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Chapel Hill, NC, 27599-3270, USA. bollen@unc.edu.
Psychometrika
|March 27, 2024
概括
探索性因素分析 (EFA) 的新模型暗示工具变量 (MIIV) 方法准确地识别了因素和负载的数量. 这种方法即使在复杂的模型和较小的样本大小中也表现良好,提高了因子分析的可靠性.
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:
- 由斯皮尔曼开创的因子分析已经显著发展.
- 确定了确认因素分析 (CFA) 和探索因素分析 (EFA) 之间的区别.
- 现有的EFA方法在处理复杂的因子结构和确定因子数量方面存在局限性.
研究的目的:
- 为了引入一种新的模型,暗示工具变量 (MIIV) 方法用于探索性因子分析 (EFA).
- 通过结合诸如测量方程拦截,相关因子和错误以及强大的标准错误估计等功能来增强EFA.
- 开发一种方法来确定因素的数量,并通过去除不重要的负载来简化结构.
主要方法:
- 拟议的方法是探索性因子分析 (EFA) 的模型隐含工具变量 (MIIV) 方法.
- 它允许在测量方程中拦截,相关的共同因子和相关的错误.
- 包括过度识别测试和确定因子数量的程序,并选择更简单的结构.
主要成果:
- 模拟证明了MIIV-EFA程序在恢复正确数量的因子方面的有效性.
- 该方法成功地恢复了初级和二级负载,即使在复杂的模型中.
- 精确的因子数识别可在样本大小为100或以上时实现;负载可在N=500时恢复.
结论:
- MIIV-EFA方法为探索性因素分析提供了一种强大而可靠的方法.
- 它解决了传统EFA的局限性,特别是在确定因素数量和估计负载方面.
- 该程序在各种模型复杂度和样本大小中表现出强的性能,表明其广泛适用性.
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