Report: Improving Data Collection on Indigenous Students

Data collection and analysis can help paint a more intricate picture of the student experience in higher education, but a lack of data can limit stakeholders’ abilities to reach, support and share the stories of some student groups.

Indigenous students have been historically underrepresented in data, due to federal standards and data collection practices by states and institutions. A May 2024 research brief by the Institute for Higher Education Policy (IHEP) investigates current practices and opportunities for improved analysis of American Indian and Alaska Native (AI/AN) learners.

“If they’re invisible in data collection, it becomes a reality that they’re invisible in policy making,” says Janiel Santos, senior research analyst at IHEP.

New federal standards, announced March 2024, will improve the collection of AI/AN populations and impact how states and institutions collect and report AI/AN data. In the brief, IHEP offers three suggestions to researchers and higher education practitioners looking to better understand Indigenous learners.

“To create more opportunities for Indigenous students to access and succeed in postsecondary education, researchers and federal, state and institutional leaders need to make data collections about AI/AN students more robust and reflective of the nuanced identity and history of the Native community,” according to the report.

The background: In racial and ethnic categories, students from Indigenous backgrounds are considered American Indian and Alaska Native, which historically has not captured the nuances of identity groups.

Unlike other racial groups, AI/AN is a political and legal classification, identifying students who hold a tribal citizenship in Native Nations, but there are little to no distinctions between different tribes in postsecondary datasets due to inadequate disaggregation, lack of stratified sampling or insufficient consultation with tribal communities.

Other historic data collection methods also have contributed to the erasure of Indigenous students. Students who are Hispanic or Latino, for example, may be from an Indigenous nation but were not categorized as AI/AN because of how federal datasets organized Hispanic students.

The Office of Management and Budget (OMB) sets standards for racial classification, which up until March defined AI/AN as a “person having origins in any of the original peoples of North and South America (including Central America) and who maintains Tribal affiliation or community attachment.”

The new standards recognize Indigenous people beyond those who hold an affiliation and include a collection of different nations. Additionally, the standards now provide a single question for race and ethnicity, eliminating the misclassification of Indigenous individuals as Hispanic or Latino or multiple races, and allows a person to identify their tribal affiliations, as well.

Counting students: In higher education, AI/AN individuals often need to self-identify or provide Tribal enrollment verification, which both have their strengths and weaknesses providing accurate and useful data.

Self-identification makes it easy for any student to claim AI/AN identity without affiliation to a tribe but it also creates an opportunity for non-Indigenous students to misrepresent themselves.

Tribal citizens have special documentation that verifies their affiliation with their Nation, which can provide more detailed information for the state or institution in collecting data on AI/AN students. Some states and institutions also provide specialized supports for students who can prove tribal citizenship.

However, this can be a barrier for Indigenous students who are culturally and communally affiliated with a tribe but not enrolled citizens, and even those with citizenship may not have the means to provide documentation upon enrollment or during the financial aid process.

Different states and institutions use different methods to categorize their Indigenous students, further limiting accurate data depictions.

Challenges in data collection: IHEP researchers identified three primary obstacles in accurately depicting AI/AN students through data:

• Small sample size. At colleges and universities, AI/AN students are generally in the minority and nationally, AI/AN students made up 1 percent of the total undergraduate population in 2021–22 and this limited sample size makes it difficult to analyze their data against other groups. As a result, researchers often omit findings about AI/AN students, collapse them into another category with other groups, or mark trends with an asterisk, which can suggest the data is insufficient, lacking or nonexistent.
• Changing definitions can also complicate looking at historic numbers of AI/AN students because the same students are being counted differently over years. Those who self-identify may also not consistently identify as AI/AN over different data collections, depending on the questions, adding another layer of confusion.
• Overgeneralizations of communities. Within AI/AN, there are hundreds of federally recognized nations and distilling them into one group can ignore cultural, geographic and political differences between them. Simplifying tribes also negates their differences between states. Santos gives the example of two students in the same tribe who are in different states—their experiences in higher ed and their financial aid or admissions processes may be entirely different despite having the same background.

In addition, surveys often don’t incorporate indigenous ways of knowledge or ways of knowing into their methods, failing to capture values from that community accurately and perhaps hurting response rates, Santos says.

So what? To improve data, the brief suggests researchers, institutional leaders and government officials engage in three actions:

• Collaborate with tribes and Indigenous researchers. Having relationships with AI/AN groups in the local area can create more culturally relevant and nuanced research or survey questions, as well as help identify what data is missing in conversations about tribal representation, Santos says.
• Improve collection, reporting and analysis. In addition to enhancing future data collection, stakeholders should oversample AI/AN students to create a larger sample size including stratifying across tribes. When faced with limitations, researchers should clearly document those challenges to not minimize the experiences of AI/AN learners.
• Create deeper data sets. Datasets should gather information on tribal affiliation to help with disaggregation of student experiences, highlighting gaps in interventions, policies and programs that support all AI/AN students. This can help create interventions that serve the unique needs of a specific college or university and its surrounding community, Santos says.

The new OMB definitions around race and ethnicity provides a big step forward in collecting more detailed data for AI/AN students as well as multiracial students, which is another group about which IHEP plans to evaluate the data collection and reporting, Santos says.

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