Using Network Analysis to Explore Longitudinal Predictors of Goal Progress and Adjustment

OBJECTIVE: Successful goal regulation requires balancing persistence with adjustment when goals become unattainable. Theoretical and empirical work has identified various antecedents of goal adjustment, yet how these factors function together as an interconnected system remains unclear. METHODS: We used Bayesian network analysis of longitudinal data to map conditional associations among personality traits, motivation, affect, goal processes, and goal adjustment capacities over 1 month (N = 492). RESULTS: The network revealed two clusters. The first centered on optimism, self-efficacy, and neuroticism, reflecting stable individual characteristics. The second captured dynamic goal processes, centered on progress, action crisis change, and disengagement. Controlled motivation bridged person-level traits and goal processes. Within the goal cluster, progress and disengagement occupied opposing positions with contrasting patterns of association with affect and action crisis change. Reengagement was connected to the wider network primarily through inter-goal conflict, controlled motivation, and importance change. CONCLUSIONS: Our findings map key associations between person- and goal-related variables, providing an empirical foundation for integrative models of goal adjustment.