Analyzing the item-level effects of emotion regulation through a complex systems approach

Emotion regulation significantly influences emotional experiences. While research has extensively explored the overall effects of regulation strategies, little attention has been paid to their item-level impacts. This study addresses this gap by employing a complex systems approach to examine the intricate dynamics and interdependencies among individual regulation items, using network analysis to reveal how these strategies collectively shape emotional outcomes.

This cross-sectional study, conducted in Tehran, Iran in 2023, recruited 528 participants (396 females, 132 males; mean age: 25.9 years) via online platforms. Using R for data analysis, a regularized Gaussian graphical model was estimated utilizing glasso in combination with the extended Bayesian information criterion model. The centrality of nodes in the network was calculated using measures of betweenness, closeness, and strength.

The findings revealed significant connections among various emotion regulation strategies, with the highest edge weights observed between ERQ7 __ ERQ8 (0.371), ERQ2 __ ERQ6 (0.289), ERQ2 __ ERQ4 (0.275), and ERQ6 __ ERQ9 (0.278). Additionally, the analysis demonstrated that ERQ6, ERQ10, and ERQ7 emerged as the most central nodes in the emotion regulation network, indicating their influential role in controlling and transforming emotions.

The findings of this study are expected to advance our theoretical understanding of emotion regulation and offer practical implications for clinical interventions and interventions aimed at enhancing emotional well-being.