Bayesian Network Modeling of Marital Conflict: Attachment Styles, Communication Patterns, Emotional Reactivity, and Cognitive Distortions - O'Meara , Kieran L.; Giles-Haigh , Stuart; Panebianco , Antonella; Marques, Richard (Journal of Assessment and Research in Applied Counseling (JARAC), 2026)
Objective: The present study aimed to develop and validate a Bayesian network model to examine the probabilistic relationships among attachment styles, communication patterns, emotional reactivity, cognitive distortions, and marital conflict in married adults. Methods and Materials: This cross-sectional correlational study was conducted on 486 married individuals (243 couples) residing in Canada, selected through community-based and online recruitment strategies. Participants met inclusion criteria including a minimum of two years of marital duration and absence of severe psychiatric conditions. Data were collected using validated instruments, including the Revised Conflict Tactics Scales (CTS2) for marital conflict, the Experiences in Close Relationships-Revised (ECR-R) for attachment styles, the Communication Patterns Questionnaire (CPQ), the Emotional Reactivity Scale (ERS), and the Cognitive Distortions Scale (CDS). Data preprocessing included missing value imputation and normalization procedures. Bayesian network modeling was conducted using a hybrid structure-learning approach combining constraint-based and score-based algorithms. Parameter estimation was performed using maximum likelihood and Bayesian methods. Model validation included 10-fold cross-validation, bootstrapping, and evaluation of predictive accuracy indices. Findings: The Bayesian network revealed that demand-withdraw communication (β = 0.73), emotional reactivity (β = 0.71), and cognitive distortions (β = 0.69) were the strongest direct predictors of marital conflict, while constructive communication showed a significant negative effect (β = -0.66). Attachment anxiety exerted indirect effects through emotional reactivity (β = 0.64), whereas attachment avoidance influenced conflict via demand-withdraw patterns (β = 0.58). Emotional reactivity also significantly predicted cognitive distortions (β = 0.62), indicating a mediating pathway. The model demonstrated high predictive performance (AUC = 0.87, accuracy = 0.81), confirming strong discriminative capacity and structural stability. Conclusion: The findings highlight marital conflict as a multifactorial phenomenon emerging from dynamic interactions among emotional, cognitive, and communication processes, with attachment styles functioning as distal vulnerabilities. The Bayesian network approach provides a robust framework for capturing these complex interdependencies and offers valuable implications for targeted and personalized interventions in couple therapy.