对于在视觉模拟尺度数据中检测疏忽受访者的β混合模型
Lijin Zhang1, Benjamin W Domingue1, Leonie V D E Vogelsmeier2
1Graduate School of Education, Stanford Universityhttps://ror.org/00f54p054, Stanford, CA, USA.
Psychometrika
|September 23, 2025
概括
这项研究引入了一种新模型,用于检测视觉模拟量表 (VAS) 中的不小心反应. 该模型通过识别和计算未完全参与的受访者来提高数据质量.
科学领域:
- 心理测量 心理测量 心理测量
- 心理学和医学研究 心理学和医学研究
背景情况:
- 视觉模拟尺度 (VAS) 广泛使用,但可能会增加不小心的响应.
- 现有的检测疏忽反应的方法不适合VAS数据.
研究的目的:
- 开发和评估一种基于模型的方法,用于在VAS数据中检测不小心的受访者.
- 将VAS测量模型与混合物响应理论集成为不小心响应.
主要方法:
- 开发了一种结合VAS测量模型与混合物反应理论的新型模型.
- 通过模拟研究和来自VAS和利克特尺度的真实世界数据来评估模型的有效性.
主要成果:
- 拟议的模型有效地检测不小心响应并恢复关键参数.
- 与利克特尺度数据相比,VAS数据显示,不小心的受访者比例更高.
- 根据新模型估计的项目参数显示了更好的心理测量特性.
结论:
- 新模型提高了使用视觉模拟尺度的研究数据质量.
- 这种方法提供了一种强大的方法来识别和解决VAS和利克特尺度数据中的不小心响应.
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