The purpose of this session is to (a) provide an overview of both CritQuant and QuantCrit frameworks and (b) offer approaches for music education researchers to consider when engaging in future research. Critical quantitative inquiry, or “CritQuant,” questions “the models, measures, and analytic practices of quantitative research in order to offer competing models, measures, and analytic practices that better describe the experiences of those who have not been adequately represented” (Stage, 2007, p. 10). Within the broader field of CritQuant is quantitative critical race theory, or “QuantCrit,” which “makes more explicit connections to race and its intersections in their quantitative inquiries” (Tabron & Thomas, 2023; p. 771). Music education researchers are encouraged to begin by interrogating conclusions and assumptions of prior quantitative work. Although samples appear representative of a broader population, ask: whose voices may have been excluded? How was data collected, reported, and disaggregated across social categories such as race/ethnicity, socioeconomic status, and gender? Revisiting an existing study offers an opportunity to reimagine sampling strategies and research questions to center perspectives that may have been overlooked. Next, we will explore strategies for data collection and analysis. Countries report race and ethnicity differently, reflecting varied national histories, politics, and social considerations. Categories are continually evolving, and while sorting individuals into categories will never capture the complexities of identity, this practice is an important part of quantitative research to identify trends and patterns. Castillo & Strunk (2025) recommend researchers collect more granular information within broader categories. For example, the University of California disaggregates “African-American or Black” into African American/Black, African, Caribbean, and Other African American/Black. Although disaggregating data into small subgroups may present challenges, researchers may explore non-parametric statistical tests, which require lower sample sizes and do not assume normality. One other compelling method to explore intersectionality within a QuantCrit framework is MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy). MAIHDA nests individuals within their social identities in a multilevel model, where level 1 individuals are nested within Level 2 social identity groups (e.g. race, class, gender, socioeconomic status). MAIHDA has been observed to improve predictions for small subgroups, allowing researchers to report findings for marginalized groups who are often excluded in quantitative research (VanDeusen et al., 2024). By offering both a theoretical overview and strategies for collecting and analyzing data, we hope to empower session attendees to consider CritQuant and QuantCrit approaches in their own research.