This hands-on workshop focuses on advanced applications of Structural Equation Modeling (SEM) in developmental and intervention research. Participants will learn how to analyze longitudinal and experimental data using various SEM techniques, progressing from basic to more complex models. The workshop covers essential longitudinal approaches, including latent change models, latent growth curves, and cross-lagged panel models. Special attention will be given to conditional models incorporating experimental conditions, with emphasis on testing intervention effectiveness and analyzing factorial experimental designs. Participants will learn how to model intervention effects on growth trajectories and handle multiple treatment conditions. The workshop will also address practical challenges such as multiple group analysis and missing data handling. This workshop is designed for researchers and doctoral students who have basic knowledge of SEM and are interested in applying these methods to analyze developmental processes and intervention effects. Participants will gain hands-on experience in model specification, estimation, and interpretation of results in the context of developmental research.

Requirements:
Participants should bring their laptops with their regularly used SEM software (e.g., Mplus, AMOS, R, or similar). Participants are encouraged to bring their own longitudinal or experimental datasets to discuss potential analytical approaches during the workshop.