动态合适指数切割值,用于将likert项目视为连续的
1Department of Psychology, Arizona State University.
Psychological methods
|September 25, 2025
概括
传统的因子分析指南可能对利克特类数据不准确. 这项研究扩展了动态合适指数 (DFI),以改进对利克特类型反应的模型合适性评估,确保在心理学研究中得出可靠的结论.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 量化心理学 量化心理学
背景情况:
- 经验因素分析经常使用利克特类型的响应,通常被视为连续数据.
- 传统的模型匹配指数切断值是为连续数据开发的,从而造成了方法学上的断开.
- 当前的指导方针可能不准确地评估模型合适性,当应用到利克特类型的响应.
研究的目的:
- 解决传统的因素分析指导方针与常见的利克特类型响应之间的断开.
- 扩展动态合适指数 (DFI) 框架,以有效地适应利克特类型数据特征.
- 提高模型适合性评估对因子分析中的利克特类型响应的灵敏度.
主要方法:
- 进行了一项示范模拟研究,以评估将利克特类型反应视为连续的影响.
- 动态适合指数 (DFI) 框架被扩展,以纳入数据特征,如利克特比例点和响应分布.
- 进行了两项模拟研究,使用5点利克特类型的反应来评估扩展的DFI方法.
主要成果:
- 将5点的利克特反应视为连续的,可以显著降低传统的适合指数切断值对错误规范的敏感性.
- 扩展的DFI方法与传统的切断值和基于多变量正常性的DFI相比,显示出更好的性能.
- 拟议的DFI扩展始终保持了超过90%的对错误规范的敏感性,具有5分利克特类型的响应.
结论:
- 将传统的因子分析切断值应用于利克特类数据,可能会导致关于模型适应性充分性的不准确结论.
- 扩展的动态合适指数 (DFI) 框架提供了一种更可靠的方法来评估模型与利克特类型响应的合适性.
- 这项研究为研究人员在因子分析中使用利克特类数据提供了关键的方法进步.
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