从缺少的数据到有信息的GPA预测:通过部分识别方法导航选择过程的信念
Eduardo Alarcón-Bustamante1,2,3,4, Jorge González3,4,5, David Torres Irribarra1,3,4
1Escuela de Psicología, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
The British journal of mathematical and statistical psychology
|December 24, 2024
概括
从招生考试成绩中预测大学GPA是具有挑战性的,因为对于未被选择的申请人缺乏数据. 这项研究使用了部分识别理论和较温和的假设来改进招生数据的回归分析.
科学领域:
- 教育测量教育的测量
- 统计 统计 统计 统计
- 高等教育 高等教育
背景情况:
- 预测有效性研究通常使用回归分析来评估大学入学考试成绩预测大学GPA的能力.
- 一个关键的挑战是缺少数据的问题:所有申请人的考试成绩都可用,但只有被录取的学生才能观察到GPA.
研究的目的:
- 提出一种替代方法来处理大学招生预测有效性研究中缺少的数据.
- 探索结果如何根据对选择过程的假设而有所不同.
主要方法:
- 利用部分可识别性的理论来解决回归分析中缺失的数据.
- 与标准方法相比,应用了较温和的假设,而标准方法需要强有力的假设来识别数据.
主要成果:
- 证明回归分析的结果可以根据所采用的关于入学选择过程的假设有显著差异.
- 展示了使用大学招生数据集的部分识别方法的应用.
结论:
- 部分可识别性理论提供了一个灵活的框架,用于根据各种假设集分析大学招生中的预测有效性.
- 强调在评估招生考试的预测能力时仔细考虑和说明假设的重要性.
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