LAYERS OF IDENTITY - RETHINKING AMERICAN INDIAN AND ALASKA NATIVE DATA COLLECTION IN HIGHER EDUCATION

Oct 15, 2026

The brief examines how American Indian and Alaska Native (AI/AN) identity is represented—or masked—in U.S. higher education postsecondary data collection. It argues that prevailing federal race and ethnicity aggregates fail to capture the political, legal, and cultural complexities of AI/AN identity and the diversity among federally recognized and other Tribes. It reviews how OMB standards have shaped AI/AN categorization and highlights how weak disaggregation, sampling limits, and insufficient Tribal consultation affect reporting, analysis, and education policy. The brief proposes strategies to improve collection and representation through active engagement and better reporting practices.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Layers of Identity: Rethinking AI/AN Data Collection in Higher Education

Why AI/AN identity needs careful data collection

How federal standards define and reshape AI/AN categorization

Gaps in disaggregation and representation in postsecondary data

Proposed strategies to improve data collection and reporting

LAYERS OF IDENTITY: RETHINKING AMERICAN INDIAN AND ALASKA NATIVE DATA COLLECTION IN HIGHER EDUCATION By: Janiel Santos and Amanda R. Tachine MAY 2024 All people deserve the opportunity to earn a better living and build a better life for themselves, their families, and their communities through a postsecondary education. But that opportunity is not available equally to all in the United States, and current postsecondary data sets and collection practices at the federal, state, and institutional levels can both lack information on or inadvertently mask disparities in college access and success for different student populations—particularly those from Indigenous communities.a Identity comprises intricate and complex layers not always captured by the aggregate racial and ethnic categories used in postsecondary data sets. The political, legal, and cultural significance of American Indian and Alaska Native (AI/AN) identity requires careful attention to the collection of AI/AN student data. Federal racial and ethnic categories have not historically captured the nuances of AI/AN identity, though new federal standards announced in March 2024 will improve the collection of data on AI/AN populations. This will have consequences for how states and institutions collect and report AI/AN data to federal postsecondary data collections and on how that data are presented. a. In this brief, the term “American Indian and Alaska Native” or“AI/AN” will be used when referring to individuals who would be categorized as AI/AN in federal data sets. However, we are mindful that this term can limit the prospects of Native identity. While all individuals identifying as AI/AN are Indigenous, not all Indigenous individuals are counted as AI/AN in data sets. To encapsulate the entire community, the terms “Indigenous” and “Native” will be used to refer to the broader populations. Currently, the U.S. has 574 federally recognized Tribes, and many more that are state recognized or are not recognized at all.1 Each Tribe has its own culture, geography, and language. While there is incredible diversity within Indigenous communities, most data sets and collection practices do not capture culturally relevant data points, which can lead to inconsistent or overgeneralized analyses. High-quality data that can be disaggregated beyond just race and ethnicity and by key characteristics that are significant to cultural communities can help identify and remove barriers for students. Policymakers need high-quality data to inform policies geared towards increasing college access, persistence, and completion rates for AI/AN students. Unlike other racial groups that are defined in federal data collections, the AI/AN category is both a political and legal classification.2 Individuals identifying as AI/AN may hold Tribal citizenship in Native Nations that have a nation-tonation relationship with the United States government.3 This relationship has implications for how these students navigate their postsecondary journey. However, distinctions between Tribal members are not consistently reflected or incorporated into postsecondary data sets due to inadequate disaggregation by Tribes, lack of stratified sampling, and/or insufficient consultation with Tribal communities. Moreover, the way federal data sets have historically categorized multiracial and multiethnic AI/AN students has contributed to the erasure of Indigenous students from data sets, complicating the presentation and analysis of information about AI/AN students.4 The new federal race and ethnicity standards offer improvements, particularly for the categorization of AI/AN students with Hispanic ancestry, and federal agencies will have until March 2029 to implement those changes. This brief focuses on individuals categorized as AI/AN in federal data sets and highlights the complexities of collecting data about this community, including how AI/AN students are counted and categorized in data collections and

FAQs

Why do current postsecondary data sets risk masking disparities for AI/AN students?

Many datasets rely on aggregate racial and ethnic categories that do not capture nuanced AI/AN identity. Limited culturally relevant data points and weak disaggregation can produce inconsistent or overgeneralized analyses, leaving barriers harder to identify and address.

How do OMB race and ethnicity standards affect AI/AN data collection?

Federal postsecondary collections use OMB standards to classify race and ethnicity. Revised March 2024 standards redefine AI/AN and remove language tied to maintaining tribal affiliation or community attachment, with implementation timelines affecting how states and institutions report AI/AN data.

What causes incomplete representation of AI/AN subgroups in postsecondary datasets?

The brief highlights insufficient disaggregation by Tribes, lack of stratified sampling, and limited consultation with Tribal communities. It also notes that historical categorization of multiracial and multiethnic AI/AN students can contribute to erasure in data.

Oct 15, 2026

Uploaded by Sophia Brooks