在以学科为基础的教育研究中呼吁以公平为重点的定量方法论:对潜在类分析的介绍
Tara Slominski1, Oluwatobi O Odeleye2, Jacob W Wainman3
1Department of Biological Sciences, North Dakota State University, Fargo, ND 58108.
CBE life sciences education
|October 21, 2024
概括
隐性类分析 (LCA) 是一种定量方法,用于理解人口中的未观察到的群体. 这篇论文探讨了LCA.
科学领域:
- 量化心理学 量化心理学
- 教育研究方法论的教育研究方法.
- 对于STEM教育来说,这是非常重要的.
背景情况:
- 混合模型提供了一个"以人为中心"的定量方法来分析未被观察到的子群体.
- 隐性类分析 (LCA) 是一个关键的横截面混合物建模技术.
- LCA可以揭示不同人群中的潜在结构.
研究的目的:
- 引入隐性类分析 (LCA) 作为基于学科的教育研究的有价值的统计方法.
- 探索LCA在科学,技术,工程和数学 (STEM) 教育中的应用和好处.
- 检查LCA如何支持STEM教育中以公平为重点的研究议程.
主要方法:
- 这篇论文描述了隐性类分析 (LCA),一种混合模型.
- 它提供了在STEM教育研究中LCA应用的例子.
- 讨论包括LCA的负担和局限性.
主要成果:
- 隐性类分析 (LCA) 提供了一个强大的定量工具,用于识别未被观察到的子群体.
- 可以有效地应用LCA来分析STEM教育中的复杂数据.
- 该方法在推进以股权为重点的研究方面具有重大潜力.
结论:
- 隐性类分析 (LCA) 是一种用于STEM教育的定量研究的多功能方法.
- 鼓励研究人员考虑LCA,因为它有能力支持公平,包容,获取和正义议程.
- LCA有助于利用定量数据与以公平和公平为重点的研究目标保持一致.
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