Making Education Data Visible

Recently, Higher Education Today featured a piece highlighting the lack of disaggregated data and missing narrative on Native students from Tribal Colleges and Universities (TCUs), resulting in “The invisibility of Native American perspectives.”

“Data availability for TCUs would improve the visibility of Native perspectives and drive higher education practice, policy and research toward improving the system of higher education for Native students and communities,” writes Christina A. Nelson in the blog. Instead, Native students are often an “asterisked” group: there is too little data available or not any data available to report, so Native students often receive an ‘ * ’ when education data is reported—making them invisible. When education data on Native students is reported, data on specific tribes often is not.

This scenario is played out similarly across numerous ethnic and racial across the United States. The diversity that exists within racial and ethnic groups in the U.S. is most often not represented in education research nor data. Instead education data is often collected and reported using broad racial categories such as Black, Latino, White, American Indian and/or Alaska Native (AI/AN), and Asian American and Pacific Islander (AAPI). “Invisible Education Equity Gaps,” our latest research brief based on a recent report by the Institute for Immigration, Globalization, and Education at the University of California, Los Angeles, with support from ACT Center for Equity in Learning, looks at the importance of data disaggregation and provides recommendations for education advocates, practitioners, and policymakers.

Broad racial categories fail to recognize the unique background, history, and experiences of racial and ethnic subgroups—leading to the harmful oversight of equity gaps that exist among underserved communities. For example, the single AI/AN category often used in education datasets includes 567 federally recognized tribes in the U.S., along with additional tribes recognized by specific states. There are also 50 ethnic subgroups who speak more than 300 different languages within the single AAPI category. While many of these groups have shared experiences, there are also differences between the different subgroups, including differences in academic outcomes that are not accurately represented under the larger racial/ethnic category so often used.

By disaggregating data and making it easily available, we can ensure data collection practices reflect the differences among racial and ethnic subgroups, helping educators, researchers, policymakers, and advocates better support underrepresented students to close equity gaps and improve academic outcomes. As our country’s population continues to grow in diversity, it’s an imperative that cannot be ignored any longer.