探索性的结构方程建模和维度的诅咒
Tra T Le1, Jeroen K Vermunt2, Nicola Ballhausen3
1Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands. T.T.Le_1@tilburguniversity.edu.
Behavior research methods
|March 12, 2026
概括
本研究引入了一种新的两阶段规范化方法,用于探索性结构方程建模 (ESEM). 它有效地处理复杂的行为科学数据,提高模型稳定性和准确性,特别是在大量变量和小样本大小的情况下.
科学领域:
- 行为科学 行为科学
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 下一代行为科学研究涉及密集的数据收集和复杂的模型,与样本大小相关的许多参数.
- 传统的潜变量方法在较大的变量与样本大小比率上扎,导致解决方案不稳定.
- 在这些条件下,现有的方法在准确估计测量和结构模型方面面临挑战.
研究的目的:
- 为探索性结构方程建模 (ESEM) 提出一种新的两阶段规范化方法.
- 解决传统方法在行为研究中处理具有小样本大小的高维数据时的局限性.
- 在复杂的数据场景中提高潜变量建模的稳定性和准确性.
主要方法:
- 为ESEM开发了一种两阶段的规范化方法.
- 第一阶段采用了一种新的探索性近似因子分析,使用LASSO惩罚和枢密度约束来估计测量模型和因子得分.
- 第二阶段使用估计的因子得分来确定结构模型.
主要成果:
- 提出的方法在恢复测量模型的基本简单结构方面表现出卓越的性能.
- 在低维高样本大小和高维低样本大小设置中都显示出有效性.
- 该方法的实用性通过使用两个实证数据集来验证.
结论:
- 新的两阶段规范化的ESEM方法为复杂的行为科学数据提供了强大的解决方案.
- 这种方法提高了测量和结构模型的估计,特别是在处理大量变量和有限的样本大小时.
- 一个R软件实现是公开可用的,用于更广泛的应用.
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