一个关于贝叶斯多组比较的教程 潜增长曲线模型与数量分布变量的贝叶斯多组比较
Jasper Bendler1,2, Jost Reinecke3
1Faculty of Law, University of Münster, Bispinghof 24/25, 48143, Münster, Germany. jasper.bendler@uni-muenster.de.
Behavior research methods
|March 11, 2025
概括
多组比较提供了一个更简单的替代方案,用于分析纵向模型中适度的潜在变量产品术语. 这种方法有效地模拟了青少年犯罪轨迹,揭示了性别和学校类型的显著群体差异.
科学领域:
- 量化心理学 量化心理学
- 发展心理学 发展心理学
- 犯罪学 犯罪学
背景情况:
- 潜变量产品术语对于分析纵向结构方程模型中的适度是复杂的,特别是在许多面板波中.
- 对复杂的纵向模型而言,分类调节变量需要更简单,更精确的估计技术.
研究的目的:
- 为了证明多组比较作为一种更简单的替代品隐性变量产品术语用于纵向数据中调节分析.
- 用潜伏增长曲线分析和贝叶斯估计来建模青少年犯罪的发展轨迹.
- 检查群体差异 (性别,学校类型) 和这些变量对犯罪轨迹的缓解效应.
主要方法:
- 潜增长曲线建模应用于计数代表青少年犯罪轨迹的数据.
- 使用Mplus软件实现的贝叶斯估计.
- 使用R编程语言处理数据并分析群体差异.
主要成果:
- 在性别和学校类型的无条件增长轨迹中发现了显著的群体差异.
- 一个有条件的增长模型揭示了学校类型对法律规范接受和增长轨迹之间的关系具有显著的缓和作用.
- 多组比较提供了一种简单的方法,用于在复杂的纵向模型中区分效应.
结论:
- 多组比较是分析复杂纵向模型中类别变量的适度的一个实用和有效的技术.
- 该研究强调了这种方法对于理解发育轨迹的有用性,特别是在发育心理学和犯罪学研究中.
- 调查结果强调了考虑学校类型作为理解影响青少年犯罪因素的调节者的重要性.
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