Application of an intersectional lens to bias-based bullying among LGBTQ+ youth of color in the United States.

Bias-based bullying influences health, academic success, and social wellbeing. However, little quantitative work takes an intersectional perspective to understand bias-based bullying among youth with marginalized social positions, which is critical to prevention. This paper describes the application of exhaustive chi-square automatic interaction detection (CHAID) to understand how prevalence of race-, gender-, and sexual orientation-based bullying varies for youth with different intersecting social positions.