Abstract
<p>Researchers have increasingly adopted complex methodological approaches to investigate the co-development of symptoms over longer time frames, such as months and years. Panel studies assess a large group of individuals at multiple time points over an extended period. Various analytical approaches exist for examining the co-development of variables in panel data, yet a comprehensive overview is lacking. This study provides a detailed review and application of four key analytical approaches for examining cross-lagged associations in panel data. The four approaches include two Structural Equation Models (SEM): the Cross- Lagged Panel Model (CLPM) and the Random-Intercept Cross-Lagged Panel Model (RI- CLPM), along with two network model adaptations: Cross-Lagged Panel Network analysis (CLPN) and Panel Graphical Vector Autoregression Model (GVAR). We describe each method’s distinct characteristics, advantages, and limitations. To illustrate these, we applied these models to a panel dataset of adolescents and young adults (NSPN 2400 cohort study), examining the relationships between impulsivity and symptoms of depression and anxiety. Results showed varying temporal associations, highlighting the importance of model selection based on research objectives and data characteristics. A simulation study demonstrated that models separating within- and between-person effects (panel GVAR, RI- CLPM) reproduced true within-person temporal effects more accurately. Our review highlights the value of using multiple approaches in multiverse analyses to assess the sensitivity of findings to different analytical methods. Ultimately, the choice of analytical method greatly influences how dynamic cross-lagged processes in developmental psychopathology are interpreted, affecting the development and refinement of relevant clinical theories.</p>