Our Research

Bias-based bullying influences health, academic success, and social well-being. 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 article describes the application of exhaustive chisquare automatic interaction detection (CHAID) to understand how the prevalence of race-, gender-, and sexual orientation-based bullying varies for youth with different intersecting social positions. We used two data sets—the 2019 Minnesota Student Survey (MSS; N = 80,456) and the 2017–2019 California Healthy Kids Survey (CHKS; N = 512,067). Students self-reported sex assigned at birth, sexual orientation, gender identity, race/ethnicity, and presence of any race-, gender-, and sexual orientation-based bullying (MSS: past 30 days, CHKS: past 12 months). Exhaustive CHAID with a Bonferroni correction, a recommended approach for large, quantitative intersectionality research, was used for analyses. Exhaustive CHAID analyses identified a number of nodes of intersecting social positions with particularly high prevalences of bias-based bullying. Across both data sets, with varying timeframes and question wording, and all three forms of bias-based bullying, youth who identified as transgender, gender diverse, or were questioning their gender and also held other marginalized social positions were frequent targets of all forms of bias-based bullying. More work is needed to understand how systems of oppression work together to influence schoolbased bullying experiences. Effective prevention programs to improve the health of youth with marginalized social positions must acknowledge the complex and overlapping ways bias and stigma interact.