隐性变量森林用于隐性变量得分估计
Franz Classe1, Christoph Kern2
1Deutsches Jugendinstitut e.V., Munchen, Germany.
Educational and psychological measurement
|November 4, 2024
概括
我们介绍了隐性变量森林 (LV森林),这是一个用于客观隐性变量得分估计的新算法. 在确认因素分析模型中,LV Forest 即使具有参数异质性,也准确地估计了得分.
科学领域:
- 心理测量 心理测量 心理测量
- 机器学习 机器学习
- 统计建模 统计建模
背景情况:
- 隐性变量模型对于理解复杂结构至关重要.
- 确认因素分析 (CFA) 被广泛使用,但可能对参数异质性敏感.
- 现有的方法可能会在存在子组差异的情况下产生偏差的分数.
研究的目的:
- 开发一种新的算法 - - 隐性变量森林 (LV森林),用于不偏见的隐性变量得分估计.
- 在CFA模型中解决混合响应变量中的参数异质性.
- 为了提高隐性变量得分的解释性和准确性.
主要方法:
- LV Forest将参数CFA与非参数树型机器学习相结合.
- 它采用参数模型限制和树组合方法.
- 处理顺序和/或数值响应变量.
主要成果:
- LV Forest提供了无偏见的潜在变量得分估计.
- 该算法考虑了人口子组之间的参数异质性.
- 在模拟和真实调查数据上证明了提高得分估计的准确性.
结论:
- 在异质性存在的情况下,LV Forest提供了一种可靠的方法来估计隐性变量得分.
- 它提高了来自CFA模型的得分的可靠性和可解释性.
- 这种方法有助于更好地理解共变量的影响,而不会引入偏差.
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