Data Equity for AANHPI Communities

In this project, a large probability panel from Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations is expanded and leveraged to establish a major benchmarking survey of AANHPI adults. AANHP communities have been substantially underrepresented in U.S. research and from a data perspective have largely been invisible. There have been many calls for greater disaggregated data of AANHPI from the White House, the National Institutes of Health (NIH), the Departments of Justice and Commerce, national think tanks and foundations, and a host of varied AANHPI community organizations. Now more than ever before, the voices of AANHPI need to be heard in government, academia, the public sector, and private enterprise. While the diversity of AANHPI communities creates complexities for research, it is crucial to include these voices to ensure policies, programs, resources, and commercial products and services are inclusive. Survey practices therefore must evolve to accurately reflect the needs, opinions, and experiences of AANHPI communities. Through support of multiple foundations, NORC at the University of Chicago has been developing a significant data resource for AANHPI, a large probability panel of AANHPI (“Amplify AAPISM”) to solve this data inequity issue. This data infrastructure allows all researchers to be able to conduct research of AANHPI, the fastest growing racial/ethnic group in the U.S. Further, it enables the important feature of subgroup disaggregation: the ability to not just study AANHPI overall but within its seven largest subgroups, Chinese, Asian Indian, Filipino, Vietnamese, Korean, Japanese, and Native Hawaiians and Pacific Islanders (NHPI). The panel will be an affordable, scientifically valid, and accessible resource to conduct data of AANHPI for years. The large-scale survey benchmarks key metrics of the AANHPI population and focuses on questions of culture, cultural background, health, economics, and other topics. The sample plan uses an expected 16 strata design (a method of selecting a sample population divided into similar subgroups) that leverages Census and consumer data to cluster households into likely Asian subgroups, whereby smaller subgroups can be oversampled to attain parity of interviewing across subgroups. This stratification also, in general, oversamples households more likely to be AANHPI and should attain a survey incidence of about 35 percent, thereby maintaining survey reliability and validity. The survey will attain at least 2,000 interviews. Completed interviews will be weighted and a public use file generated. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.