新的全贝叶斯式和混合贝叶斯式方法用于建模内部个体变量的模型
1Department of Psychology, University of Notre Dame.
Multivariate behavioral research
|December 2, 2025
概括
在心理学研究中,对个体内变异性 (IIV) 的建模至关重要. 新贝叶斯方法有效地模拟IIV,优于传统方法,特别是有足够的数据.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 量化方法 量化方法
背景情况:
- 个体内变化 (IIV) 描述了心理变量的短期波动.
- 建模IIV,特别是个体内标准偏差,在像DSEM这样的潜在变量框架中具有挑战性.
- 准确的IIV建模对于预测心理学研究结果至关重要.
研究的目的:
- 介绍和评估新的贝叶斯方法来建模IIV作为预测器.
- 将新方法的性能与传统回归方法进行比较.
- 评估样本大小和时间点对参数恢复的影响.
主要方法:
- 使用动态结构方程建模 (DSEM) 开发了两种两步混合贝叶斯方法.
- 开发了一种单步完全贝叶斯方法来建模IIV.
- 进行模拟研究,以在各种数据条件下比较方法性能.
主要成果:
- 混合贝叶斯式多次抽取 (HBM) 和完全贝叶斯式 (FB) 方法显示了具有足够样本大小和时间点的良好参数恢复.
- 与HBM相比,FB需要的数据较少.
- 传统的回归和混合贝叶斯式单一抽取未能恢复参数,即使采用大样本大小.
结论:
- HBM 和 FB 是可行且有效的方法来建模个体内变异性.
- 这些贝叶斯式方法在IIV分析中比传统方法提供了显著的优势.
- 研究人员可以利用这些方法来更准确地预测心理结果.
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