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Abstract Research shows that skills for improving Psychological Well‐Being (PWB) may belearnedthrough PWB interventions; however, the dynamic mechanisms underlying this learning process are not well understood. Using an Ecological Momentary Intervention (EMI) design, we conducted an 8‐week Randomized Controlled Trial (N = 160; aged 18–22 years), implemented in a mobile Health (mHealth) platform to characterize these dynamical mechanisms. College‐attending early adults were randomized to three groups: an active control group (N = 55); an intervention group (N = 51) with positive practices intervention; and a second intervention group (N = 54) with positive practices and meditation intervention. The mHealth implementation allowed us to introduce the interventions in participants' daily lives while also assessing their PWB (in terms of positive emotions and relationship quality) several times a day. We used a Bayesian process model to analyze changes in PWB in terms of the underlying dynamical characteristics of change. Findings suggested that the mobile assessment tool itself may have longitudinally improved college‐attending early adults' PWB, as evidenced by instances of directional changes in dynamic characteristics (increased within‐person mean levels, decreased intra‐individual variability, and increased regulation) of PWB measures. Moderation analysis also revealed that people who were low on negative affect improved the most in terms of their mean levels of positive emotions and relationship quality. 

Abstract. The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches – GIMME, uSEM, and LASSO gVAR – in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three dynamic network approaches provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches’ respective strengths and limitations.