Editor’s Note: This week we welcome two guest bloggers Yeejsuab Lee and Bach Mai Dolly Nguyen, PhD. I’ve asked them to write about disaggregating data by race and ethnicity.
A quick note: Race are the broader groups, such as Black/African American, Latinx, Asian, Native American/Indigenous/Alaska Native, Native Hawaiian/Pacific Islander, White. Ethnicity are subgroups such as Lao, Japanese, Mexican, Mestizo, Samoan, Somali, etc.
For those who don’t follow the world of Marvel, here is a quick reference – Thor is good, Thanos is bad. Thanos can make people disappear by snapping his fingers. If you want to understand more watch Marvel’s Infinity Wars, currently on Netflix (hurry and watch it since it will probably leave soon) – it is like Marvel prom since it combines a lot of Marvel characters into one movie.
By Yeejsuab Lee & Bach Mai Dolly Nguyen
Data disaggregation is like the Marvel Universe. Yes, you read that right, and yes, we can imagine how many looks of confusion there are on the other side of this screen. Allow us to elaborate.
For some, Marvel is not just a comic series, it is a cultural phenomenon. It is as real and relevant as if Iron Man was flying through the air, and Stan Lee showed up in the local coffee shop. For those of you who have no clue why we’re talking about metal humans and wondering who Stanley (it’s Stan…Lee…) is, the Marvel Universe is entirely irrelevant and fictitious. Data Disaggregation—breaking down broader categories into smaller sub-categories, such as race into ethnic sub-groups—is very much the same. For those who are familiar, the need for and practice of data disaggregation as an approach to data collection and reporting, wholly understand that it is critical to uncovering the realities of people, groups, communities, school systems, and the inequities they may face. For others, data disaggregation may as well be Thor’s latest adversary. In other words, there is a divide—those who know, already and deeply know. For those who don’t, it is far too distant. We must close that gap, but we can’t close gaps if we can’t see them. One way to see them is through disaggregated data.
And, because disaggregated data is most available for Asian Americans—we start there.
As an aggregate group, Asian Americans perform exceedingly well in academics. For instance, Asian Americans have the highest number of bachelor’s degree attainment of all minority groups (National Center for Education Statistics [NCES], 2016). However, take that population and look at each ethnic sub-group, and what appears is a drastically different story. You will find that among Southeast Asians—14.7% of Hmong , 14.1% of Cambodian, 12.4% of Lao, and 5.8% of Vietnamese—age 25 years or older have far lower rates of degree attainment than East Asians (51.5% of Chinese, 74.1% of Taiwanese, and 52.7% of Koreans) (National Commission on Asian American and Pacific Islander Research in Education [CARE], 2013). The latter of these groups reflect what is more commonly accepted about Asian American success. The same disparate pattern emerges when it comes to median household income (CARE, 2013), school discipline (Nguyen, Noguera, Adkins & Teranishi, 2019), wages (National Partnership for Women and Families, 2019), and a variety of other factors. The difference across ethnic sub-groups is overwhelming, and can only be detected when data disaggregation is used. So why isn’t data disaggregation a more common practice?
In part, because of the model minority stereotype. This decades-old racial stereotype asserts that Asian Americans are the same across the board; do not seek or require educational support or resources; and achieve unparalleled levels of academic and life success (Museus, 2014). The stereotype contributes to the invisibility of Asian Americans in education research, policy, and practice, and also in the public eye. In effect, it leaves the experiences of underrepresented Asian American sub-groups (i.e. Southeast Asians) unaccounted for and unacknowledged. Moreover, Asian Americans are then positioned as the model against which other minorities are unfairly measured. The notion of the model minority allows for the argument that the lower rates of educational attainment and success among racial minorities are due to personal and community explanations, rather than inequitable social systems. School systems, city and state organizations, and the federal government alike have been slow to overcome the model minority stereotype, which drives the resistance to disaggregating data.
But again, that only explains part of the problem. What is the other part? Lack of political will. Take Washington State, for example. Washington State can be considered a leader in disaggregating data, as it has collected disaggregated Asian American and Pacific Islander data since 2010 (CARE, 2015; Hune & Takeuchi, 2008), and are among the first to initiate a statewide effort to disaggregate further within other racial groups (Race & Ethnicity Student Data, 2017). Even so, it is difficult to find reports using these disaggregated categories, and most school districts, as well as state agencies, continue to use aggregate data that obscures the disparities that exist within classrooms, schools and across districts. Why is this a problem? In its simplest form, it is an issue because there is seemingly no issue, and we can’t fix a problem we can’t see. Adding more categories in data collection is only a first step. In order to actually close educational gaps—a goal many educational systems proclaim to prioritize—there must be a genuine commitment to uncovering where gaps exist. Data disaggregation is a necessary practice in that endeavor.
It doesn’t have to be exceedingly complicated. This is not like learning to undo Thanos’ snap (reverse the disappearance of half of the Marvel Universe). There are now models from which organizations can learn, examples to build on, and experts with whom to engage. Data disaggregation is certainly technical, but it is much more about developing a willingness to counter the model minority stereotype, and start collecting/using better data. It may seem like a vast unknown at first, but just as it is as one emerges in the Marvel universe, the complexities and the spectacle become the norm. Let us take the heroism out of data disaggregation and make it common practice.
Bach Mai Dolly Nguyen is an assistant professor of education at Lewis & Clark College. Her research examines how categorization reveals, maintains, and mitigates inequality in education, with particular attention to racial and organizational classifications.
Yeejsuab Lee is a graduate student in the MA in Student Affairs Administration at Lewis & Clark College.
Hune, S. and Takeuchi, D. (2008). Asian Americans in Washington State: Closing Their Hidden Achievement Gaps. A report submitted to The Washington State Commission on Asian Pacific American Affairs. Seattle, WA: University of Washington.
Museus, S.D. (2014). Asian American students in higher education. New York, NY: Routledge.
National Center for Education Statistics. (2016). Status and trends in the education of racial and ethnic groups 2016. Retrieved from https://nces.ed.gov/pubs2016/2016007.pdf.
National Commission on Asian American and Pacific Islander Research in Education. (2013). iCount: A data quality movement for Asian Americans and Pacific Islanders in higher education. New York, NY: Educational Testing Services.
National Commission on Asian American and Pacific Islander Research in Education. (2015). The hidden academic opportunity gaps among Asian Americans and Pacific Islanders: What disaggregated data reveals in Washington State. Los Angeles, CA: CARE.
National Partnership for Women and Families. (2019). Asian American and Pacific Islander women and the wage gap. Washington, DC: NPWF. Retrieved from http://www.nationalpartnership.org/our-work/resources/workplace/fair-pay/asian-women-and-the-wage-gap.pdf.
Nguyen, B. Noguera, P., Adkins, N. & Teranishi, R. (2019). Ethnic discipline gap: Unseen dimensions of racial disproportionality in school discipline. American Educational Research Journal. [Available online first].
Race & Ethnicity Student Data Task Force. (2017). Race & ethnicity student data: Guidance for Washington’s public education system. Olympia, WA: Office of Superintendent of Public Instruction. Retrieved from http://www.k12.wa.us/Workgroups/RET/pubdocs/RESDTaskForce2017GuidanceWAPublicEducationSystem.pdf.
Thank you to our Patreon subscribers who help to keep the blog going: Adrienne, Aimie, Ali, Aline, Alissa, Amy, Amy R., Andrea, Angie, Annie, Annie G., Ashlie, Ben, Brooke, Brian, C+C, Calandra, Carolyn C., Carolyn M., Carrie, Carrie S., Casey, Chandra, Chelsea, Chicxs Happy Brownies, Claudia, Cierra, Clark, Colleen, Crystal, Dean, Denise, Denyse, Donald, Edith, Elena, emily, Erica (2), Erica R.B., Erin, Evan, eve, Freedom, Greg, Hannah, Heather, Heidi, Heidi and Laura, Heidi S., Jake, Janis, Jean, Jennet, Jennifer M., Jennifer T., Jessica, Jillian, Jody, John, Julia, Julie Anne, K.T., Kari, Karen, Katheryn, Kathi, Katie, Keisha, Kelli, Kristen, Kristen C., Kumar, Laurel, Laurie, Lisa, Lisa C., Liz, Lori, Lynn, Makeba, Marc, Maura, McKenzie, Megan, Melissa, Michael, Michelle, Mikaela, Mike, Milo, Minesh, Miranda, Miriam, Misha, Molly, Nathan, Nicole, Norrie, Paola, Patrick, Priya, Rebecca, Rise Up for Students, Robin, Ruby, Sarah, Sarah S., Sean, Selina, Shannon, Shaun, Shawna, Shelby, Stephanie, Stephanie S., Tana, Tara, Terri, and Vivian. If I missed anyone my apologies and thank you for your support. Support the blog by becoming a Patreon supporter.
If you subscribe to the blog, thank you. Please check fakequity.com for the most up to date version of the post. We often make grammatical and stylistic corrections after the first publishing which shows up in your inbox. Please subscribe, the sign-up box on the right sidebar (desktop version).