您的确认因素分析的最佳实践:一个JASP和lavaan教程.
1Federal University of Uberlândia, João Naves de Ávila 2121, 38.408-100, Uberlândia, Minas Gerais, Brazil. pablorogers@ufu.br.
Behavior research methods
|March 14, 2024
概括
对顺序数据提出了确认因素分析 (CFA) 准则,解决了SEM教科书和软件中的局限性. 这项研究为社会科学中准确的测量模型评估提供了实际解决方案.
科学领域:
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
- 结构方程建模 结构方程建模
背景情况:
- 确认因素分析 (CFA) 对于测量有效性至关重要.
- 普通数据在社会科学中很常见,但在CFA方法中经常被忽视.
- 现有的SEM软件可能缺乏适合典型的CFA与顺序数据的解决方案.
研究的目的:
- 为使用顺序数据进行确认因素分析 (CFA) 提供指导方针.
- 为了弥补SEM文献和软件的差距,用于典型的CFA应用程序.
- 为使用社会科学测量仪器的研究人员提供实用解决方案.
主要方法:
- 基于最近的经验研究的概念贡献.
- 使用JASP和lavaan软件开发一个教程示例.
- 为实际应用提供补充材料 (视频,文件,脚本).
主要成果:
- 介绍了使用顺序数据进行典型CFA的指导方针.
- 一个实用的教程展示了CFA在JASP和lavaan中的实现.
- 该研究强调了当前用于顺序数据分析的SEM软件的局限性.
结论:
- 这本书为典型的CFA使用顺序数据提供了概念和实践指导.
- 研究人员可以通过使用提供的指导方针和教程来改进测量模型评估.
- 解决软件和教科书的局限性提高了社会科学中CFA的准确性.
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