评估在存在噪音的情况下对强度纵向数据的上下文模型
Anja F Ernst1, Eva Ceulemans2, Laura F Bringmann1
1Department Psychometrics and Statistics, University of Groningen, Groningen, The Netherlands.
在温和的自回归模型中,忽视环境事件测量的噪音会导致情绪动态研究的偏见. 该模拟显示,噪声增加导致参数估计不准确,影响日常情绪体验分析.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
背景情况:
- 影响研究越来越多地使用密集的纵向数据来研究日常情绪体验.
- 适度自回归模型是分析情绪动态受上下文事件影响的常见模型.
- 在上下文事件数据中的测量错误在这些分析中经常被忽视.
研究的目的:
- 评价噪声对调节自回归模型准确性的共变量影响.
- 评估估计偏差和方差是如何受到协变噪声的影响.
- 为在有噪音数据的情况下应用这些模型提供建议.
主要方法:
- 进行了一项模拟研究,以评估适度自回归模型.
- 该研究操纵了共变量措施中的噪音水平.
- 估计准确性是根据偏差和方差进行评估的.
主要成果:
- 估计的准确性随着共变量中的噪声量增加而显著下降.
- 偏差因较大的共同变量效应,较慢的共同变量切换频率,离散的共同变量和恒定噪声而加剧.
- 增加观察次数并不能减轻由噪音共变量引起的偏差.
结论:
- 在上下文事件测量中的噪音对调节自回归模型的有效性构成重大威胁.
- 研究人员必须考虑共变量的潜在测量误差,以避免偏差的参数估计.
- 仔细考虑数据特征和模型假设对于准确的情绪动态研究至关重要.
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