🤖 AI Summary
This study addresses the limitations of the traditional cross-lagged panel model (CLPM) in disentangling between-person differences from within-person dynamics, which hampers causal inference in longitudinal data. Focusing on the random intercept cross-lagged panel model (RI-CLPM), the work demonstrates how incorporating random intercepts—representing stable trait-like individual differences—effectively separates between-person heterogeneity from within-person temporal processes. The paper explicitly articulates the core assumption of the RI-CLPM that stable traits are uncorrelated with within-person fluctuations, systematically clarifies its mathematical and conceptual relationships to alternative approaches such as dynamic panel models, and delineates its appropriate scope and limitations. These contributions provide a rigorous theoretical and methodological foundation for model selection, interpretation, and causal inference in longitudinal psychological research.
📝 Abstract
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.