应用研究的潜在变化得分模型:使用Mplus的实用指南
Michele Vecchione1, Antonio Zuffianò2
1Department of Social and Developmental Psychology, Sapienza University of Rome, Rome, Italy.
概括
本指南介绍了潜在变化得分 (LCS) 建模,用于分析随时间的变化. 它涵盖了基本和高级应用,包括变量之间的动态关系,为研究人员提供实用Mplus示例.
科学领域:
- 心理学 心理学 心理学
- 量化心理学 量化心理学
- 统计建模 统计建模
背景情况:
- 在许多科学学科中,分析随着时间的推移而发生的变化至关重要.
- 结构方程建模 (SEM) 为此类分析提供了一个强大的框架.
- 隐藏变化得分 (LCS) 在SEM中提供了一种灵活的方法,用于建模发展和动态过程.
研究的目的:
- 为建模和解释隐性变化得分 (LCS) 模型提供实用指南.
- 引入基本的LCS概念和扩展,包括双变化得分 (DCS) 模型.
- 用Mplus软件展示使用施瓦茨基本个人价值观理论的应用程序.
主要方法:
- 介绍基本的单变量潜变得分 (LCS) 模型.
- 扩展到更复杂的模型:双变化得分 (DCS),比例变化和常量变化模型.
- 应用双变量LCS模型来分析变量之间的动态关系.
主要成果:
- 这篇文章说明了LCS模型如何有效地确定跨多个评估波的增长轨迹.
- 两变量LCS模型被证明适用于模拟两个变量之间的动态相互作用.
- 使用Mplus语法和输出的实用示例有助于实现和解释LCS模型.
结论:
- 潜在变化得分 (LCS) 为分析纵向数据变化提供了一个强大而可适应的工具.
- 本指南为学生,研究人员和从业人员提供了有效应用LCS模型的知识.
- 施瓦茨的基本个人价值观理论的使用证明了LCS在心理学研究中的广泛适用性.
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