Disaggregating Racial-Ethnic Classification Systems to Improve Data Equity (Sponsored by The University of Utah)

The most commonly used classification systems to code race-ethnicity obscure the wide variation in racial-ethnic experiences within these broad categories. This workshop will provide attendees with a more detailed understanding of the impact of data collection, coding, and tabulation on efforts to disaggregate racial-ethnic data into more granular categories in order to provide greater insight into the diversity within these groups. Specifically, it will provide an overview of the ways in which federal data collection guidelines influence the collection and weighting of racial-ethnic data in the United States, methods that have been used to expand upon these guidelines to collect and tabulate more granular data, when such expansions may be warranted, and the effects of data collection methodology on the representativeness of data from small racial-ethnic populations. In addition to discussing how, when, and why one should consider disaggregating racial-ethnic data, it will consider—on a conceptual, rather than statistical level—the ways in which methodological decisions, such as the language(s) in which a survey is administered, the method(s) used to oversample racial-ethnic groups, and weighting decisions can affect the generalizability of statistical estimates derived from this data and influence what we know about more granular racial-ethnic populations.