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Session prices in 2026:
Early Registration
(until February 6, 2026) $750 per session, or $1,500 for all three sessions
Premium Registration
(all three sessions, includes a year subscription to Scholar) $2,500
Registration
$750 per session, or $1,750 for all three sessions
International Registration
(For attendees not based in the US) $600 per session or $1,500 for all three sessions
Group Registration
$600 per person per session, or $1,500 per person for all three sessions. Minimum of three people, all with the same payment amount, handled in one transaction.
Just the Recording
$1,499 includes one year subscription to Scholar tier of the Intersectionality Collective
Interested in registering for on or two sessions of this training? Email us.
Structural intersectionality, one of three domains of intersectionality that Kimberlé Crenshaw articulated in her groundbreaking 1991 Mapping the Margins article, describes how structures such as laws and policies produce substantially different social, economic and health outcomes for populations historically oppressed at multiple intersections (e.g., Black women) compared with their historically privileged counterparts.
Although attention to structure has always been foundational to intersectionality, to date individual-level, not structural-level, has been the focus of most quantitative intersectionality research. The goal of this new cutting-edge three-series live-facilitated virtual training is to help health equity researchers build competency in structural quantitative intersectionality research.
What is structural quantitative intersectionality research (SQIR)? How do you do it? And how is it different from conventional quantitative intersectionality research? If these are some of your questions, this field-advancing training has answers. This introductory training situates SQIR in the broader history of intersectionality theory and quantitative methods, tracing its origins from Kimberlé Crenshaw’s articulation of structural intersectionality in her 1991 “Mapping the Margins” article to clarification of key concepts relevant to contemporary health equity intersectionality research. We will define structural quantitative intersectionality, contrast structural and individual‑level approaches, and review the current landscape of measures and data sources used to capture structural discrimination, the key exposure of interest within this line of research. Participants will also receive a high‑level tour of analytic strategies commonly used in this work (e.g., multilevel models, MAIHDA), setting the stage for deeper dives into measurement and methods in subsequent trainings. By the end of the series, attendees will have a clearer roadmap for why structural quantitative intersectionality research matters, what it looks like in practice, and where the field is headed.
The training is designed to teach quantitative intersectionality researchers how to conceptualize, measure, and analyze structural forms of discrimination to assess their joint impacts on population health and health inequities.
Structural quantitative intersectionality research is essential because it can identify, and inform interventions to address, the root intersectional causes of health inequities.
Participants may choose to register for:
- Part I only;
- Part I, with Part II or Part III
- All three sessions
By the end of this series of trainings, attendees will be able to:
• Develop strong structural quantitative intersectionality research questions/hypotheses based on domains of interest.
• Assess how to develop, access, or adapt existing measures of structural discrimination.
• Identify publicly or other readily available datasets for structural quantitative data analysis.
• Evaluate the benefits and challenges of diverse quantitative analytic strategies (e.g., conventional quantitative methods, MAIHDA) to examine the joint effects of different types of structural discrimination on population health and health inequities.
• In line with intersectionality’s emphasis on praxis, identify opportunities to translate research results to inform laws, policies, or interventions.
Part I: Introduction to Structural Quantitative Intersectionality Research
- An overview of the history of quantitative intersectionality research from its origins through the present, leading up to an introduction of structural quantitative intersectionality research (SQIR)
- An overview of structural discrimination research (in general), and describe what it means to measure and analyze multiple forms of structural discrimination jointly;
- Define key terms to get everyone on the same page (e.g., structural vs. institutional vs. interpersonal vs. individual-level); and
- Compare and contrast SQIR vs. individual-level quantitative intersectional approaches, noting key differences, benefits and challenges, and the current state of knowledge for both approaches
Part II: Identifying and Developing Structural Discrimination Measures
- Distinctions between “structural” vs. institutional vs. interpersonal vs. individual-level measures;
- Describe the benefits and challenges of using different types of structural measures (e.g., those comprised of laws and policies, cultural attitudes, institutionalized practices; those spanning different levels of geography)
- Discuss the key considerations, from an intersectional perspective, of combining multiple single-axis measures vs. using a single multiple-axis measure for SQIR;
- Provide examples of existing structural measures and information on where to find and how to access them; and
- Discuss common analytic approaches for developing and evaluating novel structural measures (e.g., factor analysis, latent class analysis)
Part III: Methods for Structural Quantitative Intersectionality Data Analysis
- The most important things that people need to know (and avoid) for SQIR analysis (e.g., how to address issues of confounding and selection bias);
- Common analytic approaches for SQIR research (e.g., multilevel modeling approaches, including MAIHDA);
- Applied examples of SQIR research ; and
- Current challenges in the field of SQIR analysis and future areas for growth