“Overlooked and Undercounted,” New IHEP research exposes challenges in data collections on American Indian and Alaska Native College Students

Washington, DC (May 15, 2024) – A new research brief, Layers of Identity: Rethinking American Indian and Alaska Native (AI/AN) Data Collection in Higher Education, released today by the Institute for Higher Education Policy, reveals significant shortcomings in the way higher education collects, reports, and analyzes data on Indigenous students. These gaps in our postsecondary data system undermine efforts to promote equitable student outcomes by limiting institutions’ and policymakers’ understanding of the experiences of distinct student groups.  

“For too long, AI/AN students have been overlooked and undercounted in higher education. Current data collection methods fail to capture the full tapestry of who AI/AN students are,” says Amanda R. Tachine, Navajo from Ganado, Arizona, and Náneesht’ézhí Táchii’nii (Zuni Red Running into Water clan) born for Tł’izilani (Many Goats clan), IHEP board member, and contributing author of the research brief. “This lack of high-quality data is perpetuating historical harms and enabling present-day erasure. Our research highlights the need for more nuanced data collection methods that accurately reflect and respect individual identities, tribal affiliations, and lived experiences, ultimately informing more equitable policies and support systems for AI/AN students.” 

The two primary methods used to identify and count AI/AN individuals in higher education, self-identification and tribal enrollment verification, each have limitations that can lead to exclusion or misrepresentation. Federal definitions of race and ethnicity have not historically captured the nuances of AI/AN identity, which has led to the undercounting of Hispanic and multiracial AI/AN students, diminishing the ability of researchers, policymakers, and Tribal nations to fully understand trends among Indigenous students. New standards from the Office of Management and Budget (OMB) announced in March 2024 will improve the collection of data on AI/AN populations, particularly for the categorization of AI/AN students with Hispanic ancestry.  

In current federal data collections, using the previous set of standards, an AI/AN student with Hispanic ancestry is automatically categorized as Hispanic or Latino, regardless of Tribal affiliation and involvement. When the new standards are fully implemented, agencies will use a single combined question for race and ethnicity, so AI/AN students with Hispanic ancestry will no longer be subsumed into the Hispanic or Latino category. Additionally, multiracial students who mark more than one race in federal postsecondary data collections, even if they are Tribal citizens, are often counted under the “Two or More Races” category, not the AI/AN category. Current data collections on AI/AN students are also limited by small counts, inconsistent classification, and overgeneralized representations that mask differences in educational outcomes between hundreds of Tribes, diminishing the ability of researchers and policymakers to fully understand trends among Indigenous students. 

To better understand AI/AN student identities and experiences, Layers of Identity urges these shifts in data collection practices:  

• Engage AI/AN communities: Building strong partnerships with Tribal leaders and researchers is crucial for designing inclusive, culturally-responsive data collection methods. 
• Improve research approaches for data collection, reporting, and analysis: Collecting Tribal affiliation data, oversampling AI/AN student populations, and incorporating Indigenous data collection methods can enhance data quality and accuracy. 
• Collaborate with Indigenous leaders and Tribes when implementing OMB’s revised race and ethnicity standards. In developing an Action Plan to bring its data collections and publications into compliance with OMB’s revised standards, the Department of Education (ED) should collaborate with Indigenous leaders and researchers to establish clear guidance on how to report AI/AN data to federal postsecondary data collections, including how to best present data on multiracial AI/AN students.  

High-quality data are essential for understanding the needs of all student populations and combatting systemic inequalities, particularly for historically underserved groups including AI/AN students. In partnership with Indigenous communities, researchers, institutions, and policymakers can strengthen data collection and reporting practices to better understand students’ progress and design effective interventions to meet their needs.  

This brief is the second in a series about ensuring all students are seen and supported in higher education. The previous report, Everyone Deserves to Be Seen: Recommendations for Improved Federal Data on Asian Americans and Pacific Islanders, explored similar challenges in data collection for the Asian American and Pacific Islander community.  

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