在时间序列中模拟人与人之间的定性异质性,使用隐性类向量自回归模型
Anja F Ernst1, Jonas M B Haslbeck2,3
1Department Psychometrics & Statistics, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, The Netherlands. a.f.ernst@rug.nl.
Behavior research methods
|December 26, 2025
概括
隐性类向量自回归 (VAR) 模型通过识别不同的个人群体来研究心理动态的新方法. 这种方法为分析复杂的个人数据提供了可访问的工具和方法.
科学领域:
- 心理学研究 心理学研究
- 量化心理学 量化心理学
- 时间序列分析 时间序列分析
背景情况:
- 时间序列数据对于理解心理学中人与人之间的动态至关重要.
- 矢量自回归 (VAR) 模型通常用于近似这些动态.
- 现有的等级模型经常假设个体之间的定量异质性.
研究的目的:
- 引入隐性类向量自回归 (LC-VAR) 模型作为传统方法的替代方案.
- 解决应用心理学研究中LC-VAR模型缺乏可访问性的问题.
- 为估计和解释LC-VAR模型提供实用工具和指导.
主要方法:
- 开发了对隐性类VAR模型的可访问的介绍.
- 在现实场景中进行模拟研究以评估模型估计.
- 引入了R包ClusterVAR,用于用户友好的LC-VAR模型估计.
- 提供了一个可复制的教程,用于模拟使用LC-VAR的情绪动态.
主要成果:
- 隐性类VAR模型有效地捕捉了人内动态中的定性异质性.
- 模拟证明了使用应用数据估计LC-VAR模型的可行性.
- 集群VAR包简化了LC-VAR模型的应用.
- 该教程说明了LC-VAR分析的完整工作流程.
结论:
- 潜在类VAR模型为理解心理过程中的个体差异提供了有价值的框架.
- 开发的R包和教程提高了LC-VAR模型的可访问性和应用性.
- 这种方法在心理学研究中推进了复杂时间序列数据的分析.
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