Subjective well-being (SWB) is a complex multidimensional construct with manifold positive implications. However, research on the structure of SWB faces some challenges, as empirical results regarding the popular tripartite model of SWB are inconclusive, and domain satisfaction is often not sufficiently considered when analyzing the structure of SWB. Furthermore, since the different SWB components show diverging
developmental trajectories along the lifespan, structural analysis of SWB should consider the possibility of structural changes over time. To tackle these challenges, we utilized the analytical exploratory power of
psychometric network analysis combined with longitudinal data (years 2015-2019) from the most recent version (V37) of the German Socio-Economic Panel (SOEP) to deepen our understanding of the structure
of SWB. We analyzed the associations between general life satisfaction, domain satisfaction (health, work, personal income, leisure time, family life, and dwelling), and positive and negative affect with a multi-level
graphical vector auto-regression (GVAR) model. The estimations were performed with functions from the psychonetrics R Package. According to node strength, life satisfaction was the most central node in all estimated networks. The various SWB components showed strong between-subject and within-contemporaneous relationships resulting in very dense networks. Temporal relationships tended to be comparably weaker, especially across nodes. Comparisons of the network structures between age groups indicated minimal differences.