TY - RPRT T1 - Differences in Longitudinal Trajectories between Groups - The Multi-Group Latent Growth Components Approach Y1 - 2020 A1 - Langenberg, Benedikt A1 - Axel Mayer KW - Average effects KW - Latent growth components approach KW - Latent growth models KW - Longitudinal research KW - Multi-group analysis AB - Purpose: In this article, we propose a multi-group approach for analyzing complex nonlinear longitudinal trajectories. Method: The approach is based on the latent growth components approach (LGCA) that offers a flexible framework for defining growth components and extends the same for the use with multiple groups. The approach benefits from known advantages of the LGCA and adds more capabilities from the multi-group framework, that is, (1) it can flexibly include complex nonlinear growth components, (2) incorporate a measurement model for the latent state variables and latent covariates, (3) it can model differences in growth components based on categorical covariates, and (4) treat covariates and group weights as fixed or stochastic. Results and conclusions: We demonstrate the approach using data from the Health and Retirement Study that includes individuals diagnosed with cancer. We analyze trajectories in depressive symptoms before and after the cancer diagnosis with respect to a subset of categorical covariates (i.e., groups). We further present the open-source R package semnova that implements the proposed approach and makes it conveniently accessible for applied researchers. JF - Up in the 'longitudinal research' air symposium PB - 9th European Congress of Methodology UR - https://adeit-estaticos.econgres.es/20_EAM/Symposium/47784_Torre.pdf ER -