建模结构随着时间的推移而变化,而结构测量中的潜在变化:一种纵向调节因素分析方法
Siyuan Marco Chen1, Daniel J Bauer1
1Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.
Psychological methods
|August 29, 2024
概括
这项研究引入了一种新的贝叶斯方法来分析纵向数据,通过准确地区分构造变化和随时间推移的测量变化来改进传统的增长曲线模型. 该方法提高了研究结果在发展和心理学研究的有效性.
科学领域:
- 心理测量 心理测量 心理测量
- 纵向数据分析 纵向数据分析
- 发展心理学 发展心理学
背景情况:
- 传统的增长曲线模型假定随着时间的推移测量不变性,这往往是违反.
- 使用总和或平均规模项目可以将构造变化与测量的变化混为一谈 (差异性项目功能[DIF]).
- 二级增长曲线 (SGC) 模型提供了改进,但在处理时间,DIF评估和协变量整合方面存在局限性.
研究的目的:
- 提出一种新的,节的框架来分析纵向数据,以解释差异性项目功能 (DIF).
- 解决现有的二级增长曲线 (SGC) 模型的局限性,特别是关于时间,DIF评估和共变量.
- 为了实施这种新方法,使用贝叶斯估计来进行高效的DIF评估.
主要方法:
- 开发一种基于调节的非线性因子分析的替代方法.
- 贝叶斯估计与规范化先验,以进行高效的DIF评估.
- 一个两步的工作流程,包括测量评估,然后是增长建模.
主要成果:
- 拟议的模型提供了一个节的框架,用于结合多个时间点和来自各种协同变量的DIF.
- 贝叶斯估计方便对DIF进行高效的评估.
- 一个实证示例证明了该模型在检查青少年犯罪率随时间变化的实用性.
结论:
- 提出的贝叶斯温和非线性因子分析方法为传统的增长曲线和SGC模型提供了有效和灵活的替代方案.
- 这种方法通过明确建模和计算测量变化 (DIF) 来提高纵向数据分析的准确性.
- 这种方法对于复杂的纵向研究特别有用,涉及多个时间点,组和共变量.
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