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Editorial: Why we need to improve research methods for Asian American populations

Building a better understanding of the experiences of Asian American populations will allow us to better respond to them.
Image by Sarah Mai

As a former psychology student, I remember my professors always preaching that research is essential to our field and when performing it our population sample must be large and representative to produce quality and generalizable results.

However, most of the research I’ve come across in my time as a student failed to achieve this when it came to Asian Americans. And when you identify as Asian American yourself, it’s the worst feeling because you feel invisible to the rest of the world. 

Despite Asian Americans making up more than 6.2% of the U.S. population, it is difficult to find research on them. I remember spending hours struggling to find studies with a high Asian American presence for my senior thesis paper.

When I did find one, I often noticed Asian American ethnic groups being combined into labels like “Asian American,” “ethnic minorities,” or even “other,” rather than researched separately because of their small presence in these studies. It was hard to find information on a specific group because of this homogenization of experiences.

Finding these studies always gave me a bittersweet feeling. While it shows that these studies exist, it also reflects the othering of Asian Americans in research because so few are paying attention to their experiences. However, this issue isn’t specific to psychological research.

Top-tier medical research journals like the Journal of the American Medicine Association and New England Journal of Medicine, which have significant influence on medical research, have been found to underrepresent and combine Asian Americans/ethnic subgroups in the studies they publish, reflecting that the root of this issue runs much deeper than faulty research methods in psychology. 

The main culprit — unsurprisingly — is the systemic racism that persists within research as a whole. 

Thanks to the model minority myth, a stereotype that states that Asian Americans are the minority that other ethnic-racial groups should achieve to be (i.e., educated, financially successful, healthy, and supportive of white supremacy), the struggles of Asian Americans are downplayed.

It paints this picture that Asian Americans live the American dream, creating unawareness of issues affecting them and racial bias towards them in settings like healthcare and research, which misleads others to assume social and health issues like racism or even cancer don’t affect them. Because of this, the importance of performing research on this population in academia and other institutions is diminished. In fact, Native Hawaiian, Pacific Islander, and Asian American clinical research projects only made up 0.2% of the entire National Institute of Health budget from 1992 to 2018, exemplifying how insignificant Asian American populations are to research institutions.

Racial and gender bias within research also impacts who gets to do research. Asian American researchers, women especially, are less likely to receive grant funding for clinical research compared to white counterparts, further highlighting the systemic racial and gender bias that exists within research. With fewer researchers able to research Asian American groups, there are fewer opportunities for Asian Americans to participate in research and share their experiences.

Given the little regard for Asian American populations in general already, one can only imagine how little consideration there is for other marginalized Asian populations in research experiencing forms of oppression beyond racism, like LGBTQ+ individuals, women, and individuals with disabilities. 

When combining Asian American ethnic subgroups under one racial category, we overlook the heterogeneity of their experiences. Research fails to capture the nuances between lifestyles, cultures, gender, SES, and even genetics and how they influence people’s experiences. Like underrepresentation, the consequences of aggregating data are that it harms Asian American communities because of the misrepresentation it produces. 

In the case of health outcomes, this misrepresentation results in the overlooking and mistreatment of Asian American populations because healthcare providers do not fully understand how to address the various health problems affecting individual ethnic subgroups. This promotes health inequities and mistrust in healthcare systems because of the negative experiences patients have due to racial bias. Furthermore, aggregation and underrepresentation perpetuate the model minority myth as faulty research findings promote this false notion that Asian Americans do not experience health disparities. Had researchers not disaggregated the impact of COVID-19 on Asian American patients in New York City, we would have been unaware of the significant health burden COVID-19 is for them. They would have found that it generally impacts them similarly to White patients when South Asian and Chinese patients showed poorer health outcomes and higher rates of COVID-19 than any other Asian and White groups.

Ultimately, the reason we need to stop underrepresenting and aggregating data on the Asian race is that we cannot assume that everyone is affected the same way by issues like disease. After all, there are many other factors to consider, like SES, race, culture, gender, etc., influencing outcomes and experiences.

That said, I hope that I can someday look up research on Asian Americans and other marginalized groups without feeling like throwing my laptop across the room. But it starts with being willing to obtain larger, disaggregated, and intersectional samples of Asian Americans in research studies.

Palwasha Khan is a graduate of the University of Minnesota with a Bachelor’s in Psychology.

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  • Maryam
    May 14, 2024 at 3:37 pm

    Really appreciate your research! Well said!

  • Baber Khan
    May 14, 2024 at 3:37 pm

    Well said!
    Thank you for sharing your research on this critical matter.