Exploring the Interplay Between Self-Identity, Affective Style, Emotion Regulation, and Anxiety: Based on Bayesian Network Model

Abstract

Objective

This study aimed to explore the complex relationships between self-identity, affective style, emotion regulation, and intolerance of uncertainty (IU) in predicting anxiety. A model was proposed to integrate these factors, investigating their combined influence on anxiety.

Method

Involving 608 university students who completed self-report measures of self-identity, affective style, emotion regulation, IU, and anxiety. Network analysis and Bayesian network modeling were used to identify direct and mediating effects among these variables.

Results

Network analysis revealed that self-identity, affective style, and IU directly predicted trait anxiety, with adjusting affective style emerging as a central factor. Bayesian network modeling further showed that IU and affective style mediated the impact of self-identity on anxiety. Notably, emotion regulation did not mediate the relationship between affective style and anxiety, suggesting a possible spurious correlation. The model achieved a predictive accuracy of 90.13% for trait anxiety and 88.49% for state anxiety.

Conclusion

The findings highlight the central role of self-identity in anxiety interventions, while also emphasizing the importance of addressing affective styles and IU. The results suggest that emotion regulation strategies alone may not directly reduce anxiety, indicating a need for more comprehensive clinical approaches.