Exploring fathers' psychological well-being using supervised machine learning random forest analysis

Abstract

Objective

Guided by a family systems theoretical framework, the study reported herein explores the utility of using supervised machine learning random forest regression for understanding fathers' psychological well-being.

Background

Although fathers' psychological well-being has not received much attention, understanding how familial factors contribute to fathers' mental health will benefit fathers themselves as well as their families, given the interdependence of the family system. Supervised machine learning using a random forest regression can be useful for identifying the hierarchical relationships between factors that shape fathers' psychological well-being.

Method

The study includes 277 U.S. fathers with at least one preschool-aged child as participants. Study variables include fathers' psychological well-being, father involvement, parental competency, parent–child relationships, coparenting relationship quality, work and family conflict, and fathers' demographic information.

Results

The supervised machine learning model was trained using a random forest regression. After tuning, the random forest regression with the training data identified parent–child relationship conflict as the most important predictor, followed by father involvement, coparenting relationships, work and family conflict, and parental competency (R² = .62).

Conclusion

This research shows the benefits of taking a supervised machine learning random forest statistical approach to increasing understanding of the complexity of factors related to fathers' psychological well-being.

Implications

To support fathers' psychological well-being, practitioners and family educators may consider addressing familial factors such as parent–child relationship conflict, father involvement, and coparenting relationship quality within a family.