Disaggregating Data On AAPI College Students

Since it is Asian American and Pacific Islander History Month, now is a good time to look at how (un)disaggregated data in higher ed obscures Asian American, Native Hawaiians and Pacific Islanders experiences in higher education.

Background

In 2022 a client asked me to develop a report on how higher ed data continues to talk about all “underrepresented” students as if they were in one group and about all the ways that is counterproductive. That eventually became Toward Ending the Monolithic View of “Underrepresented Students”: Why Higher Education Must Account for Racial, Ethnic, and Economic Variations in Barriers to Equity.

(This was for Every Learner Everywhere and was originally commissioned by the former Director Jessica Williams, Ph.D .)

One of my guiding lights for this report was Estela Bensimon’s, “The Misbegotten URM as a Data Point.” She and others had argued that, for example, Black was unhelpfully conflated with other races and ethnicities, which was conflated with first-generation, which was further bundled with low-income.

The obvious point is that there are vast differences in the experiences of racially minoritized students, that not all of them are low-income or first gen, that not all low-income or first gen students are racially minoritized, etc. etc. If you start to disaggregate, you spot opportunities to remove barriers to access and equity in #highered.

However, educators and institutions were still talking as if work with students with one so-called "underrepresented" identity was work to help all of them.

Unbundling the students in this discourse to stop obscuring them depends on disaggregated data but, unfortunately, several years after Bensimon’s powerful essay, it was still very difficult to find sources that broke out data by race and ethnicity.?

Even more rare was data that further “cross-tabbed” by income, first-generation status or sex.

One excellent exception at the time was a set of comprehensive reports, supplements and updates from American Council on Education titled Race and Ethnicity in Higher Education.

And, thankfully, disaggregation has become more common since the report I worked on.

AAPI in the data

Of course, however much you divide the categories, the students in them are never a monolith.

But bundling Asian American students together is particularly misleading. The category has very high “intergroup heterogeneity.”

For example, Asian American includes over 22 million people with heritage based in more than 20 countries including China, the Philippines, Singapore, Laos, Japan, India, and Hawai‘i.

That means AAPI students, for example, may be recent war refugees or they may be the great-grandchildren of immigrants to the U.S. They also encompass an enormous range of cultural and language differences.?

These points are explored more fully in the excellent white paper Everyone Deserves to Be Seen: Recommendations for Improved Federal Data on Asian Americans and Pacific Islanders from Southeast Asia Resource Action Center (SEARAC) and Institute for Higher Education Policy .

One source of some disaggregation is the U.S. Census, which has begun to report on subgroups within the AAPI population. This is an important corrective to a long fraught history. For example, during a lifetime from 1920 to 1980 someone with heritage from the South Asian subcontinent would have been categorized by the Census as Hindu, other, white and Asian Indian.

AAPI students in U.S. higher ed

The U.S. Department of Education, however, was not reporting disaggregated data on AAPI students in 2022.

And some institutional research offices at colleges and universities still count AAPI students as “non-underrepresented students” if this group is a larger percentage of their student body than in the population at large.

One, that overlooks how they are underrepresented in certain fields. The model minority myth of the high achieving Asian is insidious in many ways. One form it takes is the stereotype that Asians are careerist and only interest in STEM topics. That effectively excuses non-STEM programs from doing more to create equitable access and learning opportunities for AAPI students.

Categorizing AAPI students as "non-underrepresented " also obscures how subpopulations vary in their access and in degree progress.

In fact, the disaggregated data that is available shows that AAPI students have a greater range of outcomes in college readiness, degree attainment, and career outcomes than is usually recognized.

For example, while the monolithic "Asian" population ranks highest of all groups in the U.S. for degree achievement, the it ranges between 34 percent for people with Korean heritage and 11 percent for people with Laotian heritage.

Similarly, AAPI students arrive on campus with wide variations in family income, poverty rates, parental education and high school experiences.

Perceived as Asian?

I benefitted from several original interviews for my report, including one with a Chemistry professor, Elaine Villanueva Bernal, Ed.D. who illustrated how the monolithic view of Asians obscured her student experience:

“I’m originally from the Philippines, and my background is pretty different from someone who comes from China or Korea or Japan. I went to UCLA, and I saw Asian people, but they didn’t have two parents working graveyard shifts. They were second- and third-generation UCLA students. That was my first exposure to what it means to aggregate communities and to the perception that it matters where you’re coming from.”

Another interview was with a Georgia Institute of Technology student, Eeman Uddin , who talked about the experience of not being perceived as Asian American:

"Most of the time people see a headscarf and assume I’m Arab. Everyone thinks I speak fluent Arabic or don’t have English as my first language. It’s different having to talk about your identity and make sure people know Asian looks like a lot of things . . . . There’s a stereotype for Asian Americans that they’re pretty smart, they know what they’re doing, and you can burden them with most of the work in group projects or assume that they don’t need much help. It has definitely got me in some awkward situations where I’m trying to create boundaries for myself and finding the strength to seek help. That’s about cultural awareness and not projecting cultural stereotypes. There’s more than what you see. There’s a lot more."

As the educators and other experts I talked to explained, disaggregated data is necessary for good program planning, for updating teaching practices and for making institutional decisions. For example, a "first-generation" support program that operates alongside an unspoken model minority myth about AAPI students is going to exclude students who also need support.

Disaggregating will allow:

• Illuminating intergroup heterogeneity
• Illuminating overlooked barriers to equity
• Countering patronizing stereotypes
• Enabling intersectional analysis
• More data-informed teaching practices
• Mitigating systemic inequities

I'm hopeful that the trend to disaggregate data is gaining momentum. If you know of more current reports, please do let me know in the comments.