Applied ML · Statistical Modeling
Can social support change how childhood adversity translates into mental-health risk?
Modeling the Impact of Adverse Childhood Experiences on Mental Health Risk in University Students
- 404 student responses
- Structural equation model
- Logistic regression · random forests
- Mediation
The question
This project started with a dataset of 404 university students and a question that couldn’t be answered well by a single predictive model.
The paper we replicated
The project was an attempt to replicate “People who need people: the relationship between adverse childhood experiences and mental health among college students” by Watt, Kim, Ceballos and Norton, published in the Journal of American College Health in 2020.
The original study surveyed 404 students at a large public university in the US Southwest. It found that students with four or more adverse childhood experiences had significantly higher rates of depression and anxiety, and that perceived social support, rather than health behaviors, helped explain the link.
Replicating it meant asking whether that pattern holds up when you rebuild the analysis yourself, not just whether a model can predict the outcome.
Methods
I used structural equation modeling, logistic regression, and random forests to study the relationship between adverse childhood experiences and mental-health outcomes.
Finding
One result became particularly interesting: social support appeared as a significant mediator between childhood adversity and mental-health outcomes, the same role the original study found for it.
Evidence
- Dataset of 404 university students.
- Structural equation modeling alongside logistic regression and random forests.
- Social support appeared as a significant mediator, replicating the original study’s finding.