混合多层向量自回归建模
Anja F Ernst1, Marieke E Timmerman1, Feng Ji2
1Department Psychometrics and Statistics, University of Groningen.
Psychological methods
|August 10, 2023
概括
本研究引入了一种新的混合多层向量自回归模型,用于在密集的纵向数据中识别特征和动态过程中的明显个体差异. 该模型成功地在COGITO研究中的情感数据中识别了三个组件.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 纵向数据分析 纵向数据分析
背景情况:
- 密集的纵向研究正在增长,需要捕捉个人差异的模型.
- 现有的多层向量自回归模型可以扩展,以考虑动态过程中的异质性.
研究的目的:
- 引入和验证混合物多层向量自回归模型.
- 在纵向数据中识别具有相似特征和动态过程的独特子组.
- 通过共同建模不同年龄组,分析COGITO研究中的异质情感数据.
主要方法:
- 混合物多层向量自回归建模的开发.
- 模拟研究以验证模型性能并检查预测器集中.
- 适用于来自COGITO研究的情感数据,该研究涉及200多名参与者,测量时间超过100天.
主要成果:
- 拟议的模型成功地确定了三种不同的混合物成分.
- 这些组件代表了在平均值,自行回归和交叉回归中具有相似性的个体.
- 对COGITO研究数据的分析表明,该模型能够联合分析异质样本.
结论:
- 混合多层向量自回归建模对于在密集的纵向数据中识别子组是有效的.
- 该模型提供了对心理特征和动态的个体差异的见解.
- 已识别的组件为不同年龄组的情感过程提供了基于发育心理学的解释.
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