Gender-Specific Pathways in Violent Crime: Investigating the Role of Demographic and Mental Health Factors Using Mixed Graphical Models and Bayesian Networks

Abstract

This research aims to uncover gender-specific relationships and pathways that contribute to the perpetration of violent crimes, using sophisticated analytical tools to analyze the complex interactions between various factors. Employing Mixed Graphical Models and Bayesian networks, the study analyzes a sample of 1,254 prisoners (61.64% males and 38.36% females) to investigate the relationships among demographic factors, mental health issues, and violent crime. The study utilizes comprehensive measures, including the Beck Depression Inventory, Beck Anxiety Inventory, and Childhood Trauma Questionnaire, to assess participants’ mental health status. Key findings reveal significant gender differences in the pathways to violent crime. For males, incomplete parental marriages strongly correlate with criminal behavior severity, while marriage status emerges as a significant factor, with married males less likely to commit violent crimes. In contrast, these relationships are not significant for females. Bayesian network analysis indicates that living in urban areas differently influences education and emotional expression across genders, emphasizing the importance of contextual factors. The study highlights the need for gender-specific considerations in criminal justice policies and interventions. It underscores the complex interplay of demographic and mental health factors in influencing violent crime pathways, providing insights for developing more effective prevention strategies. Despite its cross-sectional design and reliance on self-reported data, the research significantly contributes to understanding the gendered dimensions of criminal behavior.