Publication Details

Abstract

Higher education leaders have repeatedly called for improved diversity, equity, and inclusion efforts, but many institutions continue to fall short. Data can play an integral role in this work; key among them are data on student demographics, including race/ethnicity. Meeting diversity, equity, and inclusion goals requires a thorough and nuanced understanding of the diversity within student bodies through intentional and systematic data disaggregation from broad racial/ethnic categories (e.g., Asian American, Black or African American [hereafter Black], Latinx) into finer subgroups (e.g., Hmong, Haitian, Salvadoran). Without further data disaggregation, minoritized student populations can remain invisible to institutional leaders who seek to provide focused, targeted equity programming. To offer actionable guidance for race data disaggregation, we present a case study on the Asian Pacific Islander Desi American (APIDA) undergraduate population at a large public university in the Southwest United States as a roadmap for institutions seeking to further disaggregate student race/ethnicity data. APIDA students are often homogenized as a group that has been very successful in higher education; our case study, however, found significant heterogeneity in demographic profiles and academic outcomes, showing that this model minority myth belies tremendous diversity within the group. When disaggregated into regional and national origin groups, the APIDA population demonstrates first-generation college status and Pell Grant (hereafter Pell) eligibility proportions, as well as 1st-year GPA and 2nd-year retention rates, that range from the lowest to the highest at the university level across all racial/ethnic groups. Building on the insights gained, we present a Race Data Disaggregation Readiness framework to contextualize the continuum of readiness of postsecondary institutions to do this work, and we offer suggestions on how they can progress—or level up—in their readiness.

Authors

• Nathan Lieng
• Jason L. Morín
• Que-Lam Huynh
• Janet S. Oh

Copyright © Association for Institutional Research 2024