When Penn doctoral student Gwynne Evans-Lomayesva was a researcher for the National Congress of American Indians in Washington, D.C., between 2018 and 2022, she continuously encountered datasets and analyses that either asterisked the American Indian/Alaska Native (AI/AN) population or based conclusions on questionably small sample sizes. In fact, some of the most-cited findings used information from fewer than 200 AI/AN respondents in total, with some questions receiving fewer than five responses.
“There are 574 Federally Recognized Tribal Nations in the United States, and each of these sovereign nations holds a unique relationship with the federal government,” Evans-Lomayesva says. “Such small survey responses don’t even capture one individual per tribal nation.”
Evans-Lomayesva is a member of Hopi Tribal Nation, so for her the issue is personal. “Being Hopi is just part of who I am. It is like anyone else with dual citizenship. You are both at once, growing up in multiple cultures and with many different experiences,” she says.
She also grew up surrounded by tribal lawyers and educators, who drove home the importance of doing work that benefits tribal nations. So in the years leading to the 2020 Census, Evans-Lomayesva turned her attention to ensuring usable, quality data for tribal populations.
In particular, she was concerned with how new census data privacy measures were affecting tribal geographies, geographic areas of tribal jurisdiction including federally recognized American Indian Reservations and Off-Reservation Trust Lands, which follow tribal jurisdictional boundaries. The Census changes, which had been made to ensure anonymity and prevent reidentification, had also been unintentionally obscuring data from small geographies and demographic groups, with AI/AN populations particularly affected, according to Evans-Lomayesva.
Now, as a member of the Census Scientific Advisory Committee and a demography Ph.D. student in the Population Studies Center in the School of Arts & Sciences, she’s made it her top priority to continue improving equitable representation of AI/AN populations in data analyses.
Demographers study population measures such as fertility, mortality, migration, population projections, and more. Communities, governments, and researchers use demographic data and data analyses for many reasons, such as planning services, allocating federal funding, determining policy, and redistricting. “Basic population data are so essential for everything we do,” says Evans-Lomayesva.
When AI/AN populations aren’t adequately represented, she says, communities may struggle to secure appropriate resources or have their policy needs met. And, because such data necessitate on-the-ground sampling and statistical analyses, improving them will require multifaceted solutions, as Evans-Lomayesva explains in research published in a recent Georgetown Law report.
Though her doctoral research is still in an early phase—this is her first year in the program—she plans to incorporate AI/AN populations into her analyses and promote why it’s imperative to do so in all racial analyses. Her aim, she says is to conduct research that will benefit tribal nations while maintaining the privacy of these populations. That will mean developing more thorough sampling practices, better defining demographic categories within the AI/AN population, and consulting with tribal nation leaders to identify and meet their needs.
Evans-Lomayesva says she is responding to a problem that’s ingrained in the current system, partially due to lack of trust in the government. Beyond that, she says, some tribal nations choose not to participate in data-collection processes like the Census, and others may want to participate but aren’t able to due to their remote locations, limited internet access, or both. She says that a history of bad research has also left trust in researchers lacking. Tribal nations have jurisdiction over research conducted on their lands and determine when—and when not—to allow research to move forward.
For all these reasons, Evans-Lomayesva says she is determined to do work that makes a difference for these populations. “Tribal nations are diverse,” she says. “Even though some are very small, they still deserve equitable representation in analyses and data quality while also maintaining their privacy. My goal is to make sure they are included.”