Network estimation and visualization
The baseline multilevel graphical vector autoregression model (ml GVAR) for the first analysis showed close fit (CFI = 0.8; TLI = 0.81; RMSEA = 0.09, 95% confidence intervals (CI) [0.081–0.1]; BIC = 5416.47). The pruned model showed better fit (CFI = 0.8; TLI = 0.81; RMSEA = 0.089 with 95% CI [0.08–0.098]; BIC = 5356.11), performing slightly better than the baseline model (∆BIC = 60.36). The pruned model using the step-up search did not improve fit indices compared to the pruned model without the step-up search (CFI = 0.8; TLI = 0.81; RMSEA = 0.089 with 95% CI [0.08–0.098]; BIC = 5356.11). Figure Associations Between Affective Elements and GS. The baseline ml GVAR model for the second analysis showed close to acceptable fit (CFI = 0.89; TLI = 0.9; RMSEA = 0.08 with 95% CI [0.08–0.094]; BIC = 4070.6). The pruned model also showed close fit (CFI = 0.88; TLI = 0.89; RMSEA = 0.09 with 95% CI [0.08–0.1]; BIC = 4017.88), performing slightly better than the baseline model (∆BIC = 52.72). The pruned model using the step-up search performed slightly better than the pruned model alone (CFI = 0.89; TLI = 0.9; RMSEA = 0.08 with 95% CI [0.08–0.09]; BIC = 4011.29) with ∆BIC = 6.59. Figure Associations Between Cognitive Elements and GS. The baseline ml GVAR model for the third analysis showed close to acceptable fit (CFI = 0.85; TLI = 0.86; RMSEA = 0.087 with 95% CI [0.078–0.097]; BIC = 4746.27). The pruned model also showed close to acceptable fit (CFI = 0.85; TLI = 0.86; RMSEA = 0.087 with 95% CI [0.078–0.096]; BIC = 4681.85). It performed slightly better than the baseline model (∆BIC = 64.42). The pruned model using the step-up search performed minimally better than the pruned model alone (CFI = 0.85; TLI = 0.86; RMSEA = 0.086 with 95% CI [0.077–0.095]; BIC = 4676.73) with ∆BIC = 5.12. Figure Associations Between Behavioral Elements and GS. To examine the stability of the estimated network parameters, we created 1000 bootstrap samples, where 25% of the original data was dropped each time. We estimated the temporal, contemporaneous, and between-subject networks with the bootstrap samples, using the model search algorithm applied in the main analyses and calculated the frequencies with which each edge was included in the 1000 estimations. We provide the frequencies for each edge in Supplementary Tables 