估计地方结构方程模型
1IPN-Leibniz Institute for Science and Mathematics Education, Olshausenstraße 62, 24118 Kiel, Germany.
Journal of Intelligence
|September 27, 2023
概括
本研究引入了局部结构方程模型 (LSEM) 的新联合估计方法,以分析调节器如何影响模型参数. 通过R套件的sirt实现,可以在所有调节值的同时进行改进的估计.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 局部结构方程模型 (LSEM) 分析调节器变量如何影响模型参数.
- 之前的LSEM估计涉及每个调节值的单独模型,这是低效的.
研究的目的:
- 审查和扩展现有的LSEM估计方法.
- 为LSEM提出并详细介绍一种新的联合估计方法.
- 提供使用R包软件实施LSEM的指导.
主要方法:
- 建议采用联合估计方法,同时对所有调节值进行估计.
- 该方法允许模型参数在管理员之间不变.
- 提供了R包的实施细节,包括说明性数据集和实证示例.
- 为了评估统计属性,进行了两项模拟研究.
主要成果:
- 联合估计方法为LSEM提供了同时估计方法.
- 在R套件中,它有助于实际实施LSEM.
- 模拟研究研究了LSEM中的参数估计和显著性测试的统计特性.
结论:
- 拟议的联合估计方法在分析温和结构方程模型方面取得了进展.
- 该R包是实施这些先进的LSEM技术的宝贵工具.
- 进一步的研究应该探索新的估计方法的统计特性.
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