探索性结构方程建模:使用R Project软件的一步一步的简化方法
Maria Prokofieva1, Daniel Zarate2, Alex Parker1
1Institute for Health and Sport, Victoria University, Melbourne, Australia.
BMC psychiatry
|July 28, 2023
概括
探索性结构方程建模 (ESEM) 为理解复杂的心理尺度提供了一种比确认因素分析 (CFA) 更灵活的方法. 本教程简化了使用R的ESEM实现,增强了其在研究中的应用.
科学领域:
- 心理测量 心理测量 心理测量
- 结构方程建模 结构方程建模
- 统计心理学 统计心理学
背景情况:
- 确认因素分析 (CFA) 在评估多因素尺度方面存在局限性,因为它假设独特的项目负载.
- 探索性结构方程建模 (ESEM) 集成探索性因素分析 (EFA) 和CFA,允许交叉加载以进行更现实的模型评估.
- 由于软件的限制和编码的复杂性,ESEM的采用受到阻碍.
研究的目的:
- 为实施探索性结构方程建模 (ESEM) 提供简化,逐步的教程.
- 为ESEM分析提供R和Mplus语法,解决软件和编码障碍.
- 通过使用现实数据分析强项和困难问卷 (SDQ) 的因素结构来证明ESEM.
主要方法:
- 使用开源统计软件R进行ESEM教程.
- 提供了R和Mplus软件包的比较语法.
- 应用ESEM来分析优势和困难问卷 (SDQ) 的因子结构,使用来自澳大利亚儿童纵向研究 (LSAC) 的数据.
主要成果:
- 该教程成功地展示了ESEM分析的简化序列.
- 开源的R方便了ESEM程序的可访问的实施.
- 使用ESEM对SDQ的分析为其复杂的因素结构提供了洞察力.
结论:
- 与传统的CFA相比,ESEM提供了对多维规模结构的更细致的理解.
- 本教程提高了ESEM在心理学研究中的可访问性和应用性.
- 优化ESEM应用程序可以改善精神疾病的流行病学和临床评估.
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