Disaggregating Racial Data: How Studying Ethnic Subgroups Can Improve Research

A graduate student from the University of Wisconsin–Madison is pushing for the disaggregation of data in research to better understand how individuals from different ethnic subgroups are represented as research participants and as researchers. Kao Lee Yang began writing and discussing the topic after the Howard Hughes Medical Institute’s Gilliam Fellowship for Advanced Study rejected her application for not meeting their racial and ethnic underrepresentation criteria, despite often being the only Hmong American scientist in many research spaces. Yang joins the podcast to discuss her opinion piece for STAT News, the problems with using aggregated data, and how the push to study individual ethnic groups could improve Alzheimer’s disease research. Guest: Kao Lee Yang, MPA/PhD candidate in the Neuroscience and Public Policy Program and Bendlin Laboratory, University of Wisconsin–Madison Episode Topics 6:12 Why is combining all Asian people into one category detrimental? What is improved when this population is broken down by specific heritages and ethnicities? 8:40 How did people respond to your initial article in STAT News? 9:30 Why do you think it’s important to look at the individual ethnic groups within research? 11:17 How does the problem of aggregating data on Asian Americans impact the field of Alzheimer’s disease research? Show Notes Read Yang’s opinion piece, “I’m almost always the only Hmong American scientist in the room. Yet I was told I come from a group overrepresented in STEM,” on STAT News’ website. Read Yang’s correspondence, “Disaggregate data on Asian Americans — for science and scientists,” on Nature’s website. To learn about more Hmong researchers and scientists like Kao Lee Yang, follow the Twitter account she recently launched, @HmongInBioSci. Read about Alzheimer’s disease research in the Bendlin Lab.

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