Friday, March 6, 2026 from 12noon-2PM ET,
Friday, March 13, 2026, from 12noon-2PM ET,
and Friday, March 20, 2026, from 12noon-2PM ET
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
Full Registration
$750 per session, or $1,500 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.
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 introduction to structural quantitative intersectionality research (SQIR): from the history of quantitative intersectionality research from its origins through the present.
▪ An overview of structural discrimination research (in general), and what it means to measure and analyze multiple forms of structural discrimination jointly
▪ Key terms (e.g., structural vs. systems or institutional, individual-level),
▪ Compare and contrast SQIR vs. individual-level approaches: challenges, advantages, and why select one over the other.
Part II: Identifying and Developing Structural Discrimination Measures
▪ Define key terms: a measure vs. data source, administrative-level data, policies vs. legislation vs. laws
▪ Key considerations, from an intersectional perspective, of combining single-axis measures for SQIR
▪ How to identify existing structural measures, including the most important considerations ( and things to do or avoid) when seeking these measures for their research.
▪ Data sources/measures still exists for SQIR in light of the current assault on data sources/measures
▪ Applied examples of existing measures/data sources.
▪ Key considerations for developing structural measures
▪ Applied examples of developed measures.
Part III: Methods for Structural Quantitative Intersectionality Data Analysis
▪ The most important things that people need to know (and avoid) for SQIR analysis
▪ The most commonly used analytic strategies/approaches
▪ Resources, applications, and software