Data Disaggregation: Taking CARE of the Model Minority Myth
Mark Anthony Florido
NYU Center for Multicultural Education & Programs Graduate Assistant
Andy C. Ng
CARE Project Research Assistant
Asian American and Pacific Islanders (AAPI) students exist in an interesting place, especially in the context of higher education. On one hand, they are often grouped together with White students because of their perceived success as a group, however, one cannot deny that they encounter the same struggles that their Black and Latino/a counterparts have to deal with as well. AAPI students often have to battle this Model Minority Myth.
Take for example, the Pew Report that stated that “Asian Americans are the highest-income, best-educated and fastest-growing racial group in the United States” (Pew Report, 2012). However, data can be misleading. With so many different countries, languages, and cultures that fall under the term API, the common practice of lumping so many different groups into one label actually only highlights the experiences of a few. While some subgroups have a moderately high average income, others like many of the Southeast Asian populations have high rates of poverty. In fact, the average percentage of people the poverty line is higher in the AAPI population than the general U.S. Population (CARE, 2008). Within the Cambodian population, 65.8% of adults 25 years or older have not attended college. Comparatively, only 20.4% of Asian Indians 25 or older have not attended college (CARE, 2011). This discrepancy in numbers highlights the importance in data disaggregation.
Thankfully, research is being conducted to set the record straight and the National Commission on Asian American and Pacific Islander Research in Education (CARE) is leading the movement to disaggregate the data on AAPI students. Led by Principal Investigator, Robert Teranishi, CARE identifies and examines key issues affecting Asian American and Pacific Islander (AAPI) access and success in U.S. higher education.
The AAPI population is categorically unique, with a high degree of heterogeneity that is difficult to capture comparatively relative to other racial groups. Over the last 10 years or so, the U.S. Census Bureau has changed its inclusion of ethnic sub-groups represented by AAPIs. Defining these sub-groups has helped lay the groundwork for a deeper analysis into the factors contributing to varying rates of educational attainment between AAPI sub-groups. For example, Southeast Asians (Hmong, Cambodian, Laotian, and Vietnamese) are more likely to drop out of high school than others. Not only does this dispute the model minority myth, but it exemplifies how the AAPI community has distinct needs that must be addressed.
As high education professionals, we must be aware of how we gather and interpret data. Some questions to ask ourselves:
- Do we lump all Asian American and Pacific Islanders into one category?
- Do we differentiate between Asians born outside of the US and Asian Americans born in the US?
- When we speak of students of color are we including the AAPI population?
- Are there scholarships and academic support services that target AAPI students?
- Are there additional ethnic groups we can add to admissions forms or assessment materials that will better capture the needs of AAPI students?
With data being so crucial to decision-making in education, CARE partnered with the White House Initiative on Asian Americans and Pacific Islanders (WHIAAPI) to launch the data quality campaign, iCount, earlier this year. The campaign offers a forward looking perspective for a more effective and responsive system of education, by focusing the benefits of collecting and reporting disaggregated data in the AAPI population. In June 2013, the symposium, iCount: Equity Through Representation was held at the U.S. Department of Education. Members of Congress, policy-makers and current educators, among others, came together for productive discussions about enhancing data systems and solutions that address the diverse needs of AAPI students.
To read the actual iCount Report, please visit: http://www.nyu.edu/projects/care/docs/2013_iCount_Report.pdf