Statistical Models for the Inference of Within-person Relations: A Random Intercept Cross-Lagged Panel Model and Its Interpretation
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个体内关系推断的统计模型：随机截距交叉滞后面板模型及其解释
- 研究问题
- 纵向数据中如何区分稳定的个体间差异与个体内变化，并理解随机截距交叉滞后面板模型和其他模型的联系？
- 方法与思路
- 作者梳理 RI-CLPM 的数学与概念结构、实践问题及模型选择，重点讨论稳定特质因子与个体内变异不相关的假设。
- 材料陈述的贡献
- 摘要称明确该模型的解释边界及潜在应用问题，并以稳定特质因子的相关性限制连接 RI-CLPM 与动态面板模型。
- 阅读关注点
- 应核对个体内关系的具体定义、特质因子的相关性限制、与动态面板模型的数学对应，以及因果解释所需额外条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未经核实；摘要未给出因果识别保证，分离个体间异质性本身不足以确立因果关系。
原文摘要
The cross-lagged panel model (CLPM) has been widely used, particularly in psychology, to infer longitudinal relations among variables. At the same time, controlling for between-person heterogeneity and capturing within-person relations as processes of within-person change are regarded as key components to causal inference based on longitudinal data. Since Hamaker, Kuiper, and Grasman (2015) criticized the CLPM for its limitations in inferring within-person relations, the random intercept cross-lagged panel model (RI-CLPM), which incorporates stable trait factors representing stable individual differences, has rapidly spread, especially in psychology. At the same time, although many statistical models are available for inferring within-person relations, the distinctions among them have not been clearly delineated, and discussions over the interpretation and selection of statistical models remain active. In this paper, I position the RI-CLPM as one useful method for inferring within-person relations, explain its practical issues, and organize its mathematical and conceptual relationships with other statistical models, as well as potential problems that may arise in their application. In particular, I point out that a distinctive feature of the stable trait factors in the RI-CLPM, in representing between-person heterogeneity, is the assumption that they are uncorrelated with within-person variability, and that this point serves as an important link to the mathematical relationship with the dynamic panel model, another promising alternative.