RESUMO
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. We used two datasets - 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 datasets, 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 school-based 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.
