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.

翻译：交叉滞后面板模型（CLPM）已被广泛用于推断变量间的纵向关系，尤其在心理学领域。同时，控制个体间异质性并将个体内关系视为个体内变化过程，被视为基于纵向数据进行因果推断的关键要素。自Hamaker、Kuiper和Grasman（2015）批评CLPM在推断个体内关系方面的局限性以来，通过纳入代表稳定个体差异的稳定特质因子而构建的随机截距交叉滞后面板模型（RI-CLPM）迅速普及，尤其在心理学领域。然而，尽管已有多种统计模型可用于推断个体内关系，但各类模型间的区别尚未得到清晰界定，且关于统计模型解读与选择的讨论仍在持续。本文中，我将RI-CLPM定位为推断个体内关系的一种有效方法，阐释其实践应用中的关键问题，系统梳理其与其他统计模型在数学与概念层面的联系，并指出实际应用中可能出现的潜在问题。特别要强调的是，RI-CLPM中稳定特质因子在表征个体间异质性时的一个显著特征，在于其假定与个体内变异不相关，这一特征成为其与另一种有前景的替代模型——动态面板模型——建立数学关联的重要纽带。