Bullying and aggression in schools can have a traumatic and lasting effect on the well-being of children and youths. Using data from the 2013 National Crime Victimization Survey's School Crime Supplement, this study uses a chi-square automatic interaction detection (CHAID) decision tree and logistic regression models to identify factors that increase bullying in schools. Being distracted and fear of being attacked were among the top statistically significant variables, when using both methodologies. Avoiding online activities and knowing someone who brought a gun to school were top predictors using logistic regression. Being involved in a fight and seeing hate-related words or symbols were additional influential predictors in a CHAID decision tree model. Identifying factors that increase likelihood for bullying in schools assists practitioners in implementing programs and policies to improve school climate and reduce youth bullying.
All Science Journal Classification (ASJC) codes
- Health(social science)