Exploring the Interplay Between Self‐Identity, Affective Style, Emotion Regulation, and Anxiety: Based on Bayesian Network Model
Ruizhi Huang, Huiqing Shen, Ye Yuan, Ke Jiang, Zhilin WangABSTRACT
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.