贝叶斯式对逐块增长混合模型的方法:学校心理学中的问题和应用
Ihnwhi Heo1, Sarah Depaoli1, Fan Jia1
1Department of Psychological Sciences, University of California, Merced, United States.
Journal of school psychology
|December 7, 2024
概括
贝叶斯分片增长混合模型 (PGMMs) 提供了一种强大的方法来分析不同群体的发育轨迹. 本研究介绍了学校心理学中的PGMM,详细介绍了数学成绩分析中的实施和应用.
科学领域:
- 统计 统计 统计 统计
- 发展心理学 发展心理学
- 教育心理学教育心理学
背景情况:
- 贝叶斯分片增长混合模型 (PGMMs) 是用于分析子群体复杂发育模式的先进统计工具.
- 贝叶斯式PGMM在学校心理学中的实证应用目前是有限的,尽管它们具有潜在的好处.
研究的目的:
- 将贝叶斯式PGMM引入学校心理学领域.
- 为实施贝叶斯式PGMMs提供实用指导,解决关键的方法考虑.
- 为了说明贝叶斯式PGMMs的应用,使用对数学成就的真实世界数据.
主要方法:
- 利用贝叶斯框架开发逐步增长混合模型.
- 解决了关键的方法学方面:类分离,类列举和先前敏感性.
- 应用贝叶斯式PGMMs来分析早期儿童纵向研究-幼儿园队列中的数学成就轨迹.
主要成果:
- 成功模拟了跨潜阶级的数学成就的非线性,阶段性发展轨迹.
- 证明了贝叶斯式PGMMs在识别具有不同成就模式的不同亚群的实用性.
- 强调在模型解释中将统计标准与实质理论相结合的重要性.
结论:
- 贝叶斯式PGMM是学校心理学研究的一个有价值的工具,能够对发育轨迹进行细微分析.
- 强调需要透明的报告和仔细考虑方法选择,以便更广泛地采用.
- 鼓励研究人员利用贝叶斯式PGMMs来更深入地了解学生的发展和成绩.
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