- Friday January 9, 12-2PM ET
- Friday January 16, 12-2PM ET
- Friday, January 23, 12-2PM ET
Full Registration
$1,750
Premium Registration:
(Includes 1 year Scholar tier access to the Intersectionality Collective)
$2,250
Early Registration
(until December 9, 2025) $1,250
Group Registration
(3 people minimum, paid together):
$1,250 per person
International Rate
(residing outside the US):
$1,000
Just the Recording
(6 months access, includes 1 year Scholar tier subscription to the Intersectionality Collective):
$1,499
Are you aware that three systematic reviews about quantitative intersectionality research were published in peer-reviewed social and behavioral science journals in 2021 alone?
That’s right, three! Interest in quantitative intersectionality research has flourished in the social and behavioral sciences, but as the three systematic reviews highlighted, many researchers applying intersectionality to their quantitative health equity research projects rarely define intersectionality or mention core tenets of the framework or use statistical methods that either are unaligned with intersectionality, or flat out violate core tenets of intersectionality.
As a course correction, we’ve developed this 3-session virtual training to get you up to speed on quantitative intersectionality research conducted with individual-level data:
(a) Foundations: the foundational (i.e., conceptual) aspects of quantitative intersectionality research
Focused primarily on individual-level data, this introductory session will address foundational issues such as the distinction between quantitative research and qualitative intersectionality research.;
(b) Fundamentals: share our expert recommendations about the most fundamental things you need to know about (and avoid) when doing quantitative intersectionality research aligned with core tenets of intersectionality and best practices outlined in the peer-reviewed literature on the topic
Building on Session 1, this training will focus on the fundamental issues of doing quantitative research.
This session will take a deeper dive with real examples of different types of intersectionality questions matched with an intersectionality quant method. Throughout this component we will tackle three general list of themes through our examples. First, in support of this deeper dive, trainee’s will learn what is a “good” vs. “bad” intersectional question, what is a person-centered vs. process-centered approach, and the strengths and limitations of current measurement strategies in intersectionality.
Second, we will discuss the strength and limitations for both primary and secondary data when utilizing quantitative intersectionality questions. Third, a list of resources to find actionable tools and support for addressing quantitative intersectionality questions will be discussed and provided; and
(c) Applications: share, using real-world examples from facilitator Dr. Stephanie Cook’s quantitative research on the strengths and limitations of different statistical methods in intersectionality, describing how to match different statistical questions with different intersectionality questions (not all methods map quite well to all intersectional statistical methods) and providing resources for accessing code and such for advanced statistical programming for tackling intersectionality questions.
Building on the Fundamentals outlined in Session 2, we’ve designed this training to provide hands-on and practical applications of intersectionality to real-world health equity research. Using examples from the NIH All of Us and R statistical software, this training will highlight:
(a) data transformation: how to code and create new intersectional variables;
(b) using visualizations to explore individual-level quantitative data to better understand patterns and trends of health and social inequity and equity inter and intracategorically;
(c) illustrate how to conduct descriptive and inferential statistical analyses using an intersectional lens; and
(d) demonstrate how to perform different analysis to investigate intersectional hypotheses for descriptive and analytic research; and (e) discuss how to interpret the results of the performed analyses in line with core tenets of intersectionality.
We’ve designed this training for graduate students, postdoctoral fellows, faculty and non-faculty researchers whose familiarity with quantitative research methods and statistics ranges from intermediate-level to advanced.