调查注意力和不注意的响应以及它们的上下文相关的动态,使用一种新型混合IRT模型用于密集的纵向数据
Leonie V D E Vogelsmeier1, Irina Uglanova2, Manuel T Rein1
1Tilburg University, Tilburg, The Netherlands.
The British journal of mathematical and statistical psychology
|December 7, 2024
概括
这项研究引入了一种新方法,用于检测生态瞬间评估 (EMA) 数据中的疏忽反应. 该方法有助于识别不可靠的反应,提高心理研究结果的质量.
科学领域:
- 心理测量 心理测量
- 心理测量 心理测量 心理测量
- 行为科学 行为科学
背景情况:
- 生态瞬间评估 (EMA) 提供了关于心理构造的频繁数据,但风险是受访者负担.
- 不小心和不充分的努力响应 (C/IER) 可以污染EMA数据,危及有效性.
- 现有的方法在EMA内难以准确地识别和分析C / IER.
研究的目的:
- 开发和验证一种用于在EMA数据中检测和分析C/IER的新方法.
- 区分注意力和不小心的响应模式.
- 在EMA研究中探索C/IER的动态和相关性.
主要方法:
- 结合确认混合物物响应理论 (IRT) 模型与潜伏马尔科夫因子分析.
- 开发了一种使用R包实现的方法.
- 将方法应用于经验数据,并进行模拟研究.
主要成果:
- 这种新方法有效地识别了EMA数据中的C / IER实例和响应模式.
- 检测直线线需要异质和负面措辞的项目.
- 该方法证明了对某些测量模型变化的稳定性,但不是因子结构异质性.
结论:
- 拟议的IRT和潜在的马尔科夫分析提供了一个强大的框架,用于在EMA中解决C / IER.
- 这种方法提高了数据质量,并为响应行为提供了更深入的见解.
- 需要进一步扩展以适应因子结构的异质性.
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