Predicting the Vulnerability of Women to Intimate Partner Violence in South Africa: Evidence from Tree-based Machine Learning Techniques
Predicting the Vulnerability of Women to Intimate Partner Violence in South Africa: Evidence from Tree-based Machine Learning Techniques
Journal of Interpersonal Violence, Ahead of Print.
Intimate partner violence (IPV) is a pervasive social challenge with severe health and demographic consequences. Global statistics indicate that more than a third of women have experienced IPV at some point in their lives. In South Africa, IPV is considered a significant contributor to the country’s broader problem with violence and a leading cause of femicide. Consequently, IPV has been the major focus of legislation and research across different disciplines. The present article aims to contribute to the growing scholarly literature by predicting factors that are associated with the risk of experiencing IPV. We used the 2016 South African Demographic and Health Survey dataset and restricted our analysis to 1,816 ever-married women who had complete information on the variables that were used to generate IPV. Prior research has mainly used regression analysis to identify correlates of IPV; however, while regression analysis can test a priori specified effects, it cannot capture unspecified inter-relationship across factors. To address this limitation, we opted for machine learning methods, which identify hidden and complex patterns and relationships in the data. Our results indicate that the fear of the husband is the most critical factor in determining the experience of IPV. In other words, the risk of IPV in South Africa is associated more with the husband or partner’s characteristics than the woman’s. The models developed in this study can be used to develop interventions by different stakeholders such as social workers, policymakers, and or other interested partners.
Intimate partner violence (IPV) is a pervasive social challenge with severe health and demographic consequences. Global statistics indicate that more than a third of women have experienced IPV at some point in their lives. In South Africa, IPV is considered a significant contributor to the country’s broader problem with violence and a leading cause of femicide. Consequently, IPV has been the major focus of legislation and research across different disciplines. The present article aims to contribute to the growing scholarly literature by predicting factors that are associated with the risk of experiencing IPV. We used the 2016 South African Demographic and Health Survey dataset and restricted our analysis to 1,816 ever-married women who had complete information on the variables that were used to generate IPV. Prior research has mainly used regression analysis to identify correlates of IPV; however, while regression analysis can test a priori specified effects, it cannot capture unspecified inter-relationship across factors. To address this limitation, we opted for machine learning methods, which identify hidden and complex patterns and relationships in the data. Our results indicate that the fear of the husband is the most critical factor in determining the experience of IPV. In other words, the risk of IPV in South Africa is associated more with the husband or partner’s characteristics than the woman’s. The models developed in this study can be used to develop interventions by different stakeholders such as social workers, policymakers, and or other interested partners.
Lateef B. Amusa