一个两步估计器用于多层次的隐性类分析与共变量.
Roberto Di Mari1, Zsuzsa Bakk2, Jennifer Oser3
1Department of Economics and Business, University of Catania, Corso Italia 55, 95128, Catania, Italy. roberto.dimari@unict.it.
Psychometrika
|August 6, 2023
概括
我们介绍了一种更快的两步方法,用于与共变量进行多层次隐性类分析 (LCA). 这种方法提供了高效的计算和准确的参数估计,与现有方法相比.
科学领域:
- 统计 统计 统计 统计
- 社会科学 社会科学 社会科学
- 心理测量 心理测量 心理测量
背景情况:
- 多级潜在类分析 (LCA) 对于理解层次数据结构至关重要.
- 现有的阶段性估计器可能是计算密集的.
- 准确估计测量和协变效应是必不可少的.
研究的目的:
- 提出一个新的两步估计器,用于多层次的LCA与共变量.
- 为了提高计算效率,同时保持估计准确性.
- 为分析复杂的社会科学数据提供实用工具.
主要方法:
- 一个两步估计程序:首先,估计测量模型,然后纳入共变量.
- 导出一个预期最大化 (EM) 算法,以实现高效的实现.
- 广泛的模拟研究来评估性能,并与现有方法进行比较.
主要成果:
- 拟议的两步估计器显示了与现有的多层次LCA阶段性方法可比的性能.
- 与传统方法相比,计算时间显著减少.
- 与单步估计器相比,与最小的效率损失相比,大约是公正的参数估计.
结论:
- 两个步骤的估计器提供了一个计算高效和准确的替代方案,用于多层次的LCA与共变量.
- 这种方法适用于分析复杂的数据集,例如关于公民规范的跨国研究.
- 该方法平衡了效率和准确性,使其对各种领域的研究人员有价值.
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