Monday, September 14, 2026 from 12noon-2PM ET,
Wednesday, September 16, 2026, from 12noon-2PM ET,
and Friday, September 18, 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
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.
Tired of using social identity variables as proxies for exposure to structural discrimination? Eager to develop and evaluate a novel measure of structural discrimination but unsure of how to do so? In this three-part series, we will provide an overview of existing measures of structural discrimination, introduce relevant data sources for constructing measures, and discuss analytic methods used to construct and evaluate such measures. We will then focus on applications of what we have learned by walking through how to create structural measures using publicly available data sources and then evaluate such measures using methods such as latent class analysis and exploratory factor analysis. By the end of the series, participants will have the tools necessary to critically evaluate (and identify important gaps in!) existing measures as well as develop and evaluate novel measures of structural discrimination for use in quantitative intersectionality research.
Session 1:
Examples of existing measures and describe how they were created and introduce different data sources for constructing measures.
Session 2:
Applied examples of how to create structural measures using publicly available data sources (e.g., how to create inequity indicators based on administrative data sources, how to compile data from publicly available policy databases).
Session 3:
Applied examples of how to evaluate measures such as latent class analysis/latent profile analysis and exploratory factor analysis.