使用确认性潜伏类分析进行复制:学校气候示例
Lisa S Romero1, Meagan O'Malley1, Delwin Carter2
1College of Education, California State University Sacramento, United States.
Journal of school psychology
|June 12, 2025
概括
隐性类分析 (LCA) 在学校心理学中越来越多地使用. 这项研究证实了使用确认LCA (CLCA) 的五年级学生学校气候模型,证明了跨年稳定的响应模式.
科学领域:
- 心理学 心理学 心理学
- 教育心理学教育心理学
- 量化心理学 量化心理学
背景情况:
- 大数据集和计算软件增加了混合模型的使用,包括隐性类分析 (LCA).
- 使用LCA的二次数据分析在学校心理学中很常见,但结果可能是特定于数据集的.
- 对LCA的复制指导是必要的,以确保发现反映稳定的模式.
研究的目的:
- 为从探索性LCA转向确认性LCA (CLCA) 提供指导.
- 通过使用CLCA证实先前对学生学校气候的探索性LCA发现.
- 为了证明在不同数据收集期间识别的响应模式的稳定性.
主要方法:
- 使用确认性潜伏类分析 (CLCA) 审查先前的探索性LCA结果.
- 将CLCA应用于2017-2018学年学生学校气候感知数据 (n=24,051).
- 将CLCA发现与之前对2016-2017年数据的LCA分析进行了比较.
主要成果:
- 与先前的探索性分析相比,确认的LCA结果表明响应模式与之前的探索性分析相比相当相似.
- 该研究证实了学生学校气候体验的五类解决方案.
- 五类解决方案在不同的学年和受访者群体中展示了可复制性.
结论:
- 学生学校气候体验的五类解决方案是稳定的和可复制的.
- 确认性LCA支持识别的模式的有效性超出了数据集特定的文物.
- 为使用新兴CLCA方法的研究人员提供指导,以确保可靠的发现.
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