Skip Navigation

Ask a REL Response

Effects of disaggregating Asian/Pacific Islander student data by ethnicity — February 2017


What does the research say about the effects of disaggregating Asian American/Pacific Islander student data by ethnicity?


Following an established REL West research protocol, we conducted a search for research reports as well as descriptive study articles on disaggregating Asian American and Pacific Islander student data by ethnicity. The sources included ERIC and other federally funded databases and organizations, research institutions, academic research databases, and general Internet search engines (For details, please see the methods section at the end of this memo.)

We have not evaluated the quality of references and the resources provided in this response. We offer them only for your reference. Also, we searched for references through the most commonly used sources of research, but the list is not comprehensive and other relevant references and resources may exist.

Research References

Ahmad, F. Z., & Weller, C. E. (2014). Reading between the data: The incomplete story of Asian Americans, Native Hawaiians, and Pacific Islanders. Washington, DC: Center for American Progress. Retrieved from

Excerpt: “This report discusses some of the data available on Asian Americans. It then presents and explains the challenges associated with the data and offers policy recommendations to address them. During our research, we discovered that: 1) Asian Americans are a very diverse population group. The term “Asian” in official government statistics is a racial category based on the history of U.S. migration and race relations. It encompasses immigrants from Asia and people of Asian descent born in the United States. Asians come from Chinese, Indian, Pakistani, Bangladeshi, Cambodian, Vietnamese, and Thai backgrounds, among many others. Native Hawaiian and Pacific Islander has been a different racial category in the decennial census since 2000, and the category was added for data collected by all federal agencies no later than January 1, 2003. 2) People of Asian descent are the fastest-growing population in the United States.

The portion of the U.S. population that self-identifies as Asian grew 46 percent from 2000 to 2010. 3) The Asian American population grew by 2.9 percent in 2012, compared to the Hispanic population, which grew 2.2 percent. However, the total population of Hispanics is still markedly bigger at 53 million people; there are still only 18.9 million Asian Americans. 4) Asian Americans have highly varied economic experiences. A substantial share of Asian American subpopulations struggle with high poverty and a lack of health insurance, but these struggles are often masked by the high employment and incomes of other, larger Asian American subpopulations.

To both increase the number of respondents willing to identify their race and ethnicities and better disseminate disaggregated data, we recommend that the federal government do the following:

  • Conduct surveys in the most common languages of relevant subpopulations
  • Encourage the Census Bureau and other federal statistical agencies to continue researching more ways to capture subpopulation data, including national origin
  • Oversample respondents from subpopulations that are likely to underreport
  • Generate disaggregated data in addition to its aggregated data whenever possible
  • Create a central data repository on communities of color, including—but not limited to—Asian Americans.”

Annie E. Casey Foundation (2016). By the numbers: A race for results case study. Baltimire, MD: Author. Retrieved from

Excerpt: “The kind of data collected matters and can help unearth a problem masked by aggregate data — even data already broken down by basic racial categories. For example, consider 2010 U.S. Census Bureau statistics that showed more than half of Asian Americans had a bachelor’s degree or higher by the age of 25, the highest proportion among racial categories. Yet when the data are disaggregated to focus specifically on Southeast Asian Americans, a different picture emerges. Just 15% of Cambodian Americans, 14% of Hmong Americans, 12% of Laotian Americans and 26% of Vietnamese Americans over the age of 25 had a bachelor’s degree, the census reported. The rates for Cambodian Americans, Hmong Americans and Laotian Americans were lower than the 18% rate reported for African Americans, and the rate for Laotian Americans fell below the 13% rate reported for Latinos. As these disaggregated data show, Southeast Asian Americans experience barriers to educational attainment on par with their African-American and Latino peers, a phenomenon that could easily have been overlooked with less specific data.”

Dizon, J. P. M. (2011). Lessons on ethnic data disaggregation from the “count me in “campaign. The Vermon Connection, 32, 21-31. Retrieved from

From the abstract: “This article supports the need to re-evaluate current models of racial/ethnic data collection in order to accurately assess and improve efforts of inclusion for Asian American and Pacific Islander (AAPI) students. Through highlighting the efforts of students in the 2007 “Count Me In” campaign at the University of California, I argue that the campaign serves as an exemplar of AAPIs’ desire to disaggregate. Contrary to the often-referenced depiction of being a monolithic “model minority,” this article discusses the diverse experiences of the various AAPI sub-communities and the ways in which the larger label masks inequalities between AAPI sub-groups and across other communities of color. Additionally, it suggests how more precise data collection may improve recruitment efforts and how universities may be able to enhance and create new student services to address the needs of emergent AAPI ethnic communities.”

Goyal, A. (2016). Why Asian students need disaggregated data—now. Minneapolis, MN: MinnPost. Retrieved from

From the website: “Upon a first look at 2011 MCA scores for Asian students, it would appear they’re doing well as a group overall. More than 50 percent of Asian students were proficient in both math and reading — a higher percentage than Hispanic, American Indian and African-American students. However, once CAPM disaggregated MCA score data based on home language, it was instantly clear that not everyone grouped under the Asian umbrella was experiencing the same successes. An incredible 83 percent of students identified as Chinese by CAPM were considered proficient in math, whereas less than 15 percent of students identified as Karen passed the math tests. The disparity between those two groups makes it apparent that we are not serving Karen students in ways that benefit them. But it’s not just the Karen population in Minnesota who’s being neglected. Just 36 percent of Hmong students were proficient on the math MCA in 2011. Hmong people are the largest Asian ethnicity in Minnesota. More than a quarter of all Asians in the state are Hmong, yet somehow only 36 percent of Hmong students were proficient in math. What’s preventing us from reaching these students?”

Hune, S., & 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. Retrieved from

Excerpt: “The study begins with the premise that the academic challenges of Asian American students are hidden by: (1) the “model minority” stereotype that assumes all Asian Americans are academically successful; (2) the practice of lumping disparate Asian American groups into a single category; and (3) a predominant reliance on mainstream sources to explain Asian American educational experiences. To uncover Asian American achievement gaps, the study features disaggregated data to identify differences across and within Asian American ethnic groups in education and other variables. It also incorporates the findings of community-based research that provide Asian American voices and insights of their situation in schools and U.S. society. The researchers reviewed State education reports and incorporated quantitative data from the U.S. Census, Office of the Superintendent and Public Instruction (OSPI), Seattle School District, and other sources. The study makes use of qualitative studies on Asian American student experiences and reports of community-based organizations. We also conducted a survey of Asian/Asian American teachers, consulted with youth and social service agencies, and met monthly with an advisory committee of community representatives. The study began on August 1, 2008 and was submitted to CAPAA at the end of December 2008.”

Jaschik, S. (2013). The deceptive data on Asians. Washington, DC: Inside Higher Ed. Retrieved from

From the website: “The report's authors -- clearly aware of the stereotype of uniform academic success for Asian-Americans -- provide data on the educational attainment (disaggregated of course) of different Asian-American subgroups. Then the report describes how a few colleges and universities have moved toward more nuanced data collection about Asian-Americans, and calls for more colleges to do the same. The report does not set some minimum standard for Asian-American data disaggregation, but suggests that many colleges are far short of where they should be. And the report argues that just as colleges and other institutions use data to identify problems and track the impact of various strategies, they must do so for different Asian-American subgroups, not all of which are doing as well as the "model minority" image would suggest.”

Lee, J., Lee, J., Khachikian, O. (2016). The untold Asian American success story. Data Bits. Retrieved from

From the website: “Disaggregating data by ethnic group alone fails to capture three important, yet untold, stories about the mobility patterns among Asian Americans. But disaggregating data by ethnic group alone fails to capture three important, yet untold, stories about the mobility patterns among Asian Americans. First, generational status matters, and second-generation Asian Americans have made considerable gains in educational attainment. For example, Figure 2 shows that only 15 percent of first-generation Cambodians have attained a college degree, but by the second generation, this figure nearly doubles to 27 percent. Moreover, the high school drop-out rate falls from 38 percent in the first generation to 13 percent in the second.”

Museus, S. D., & Truong, K. A. (2009). Disaggregating quality data from Asian American college students in campus racial climate research and assessment. New Directions for Institutional Research, 142, 17-26. Retrieved from

Excerpt: “This chapter highlights the utility of disaggregating qualitative research and assessment data on Asian American college students. Given the complexity of and diversity within the Asian American population, scholars have begun to underscore the importance of disaggregating data in the empirical examination of Asian Americans, but most of those discussions have been limited to considerations in analyzing quantitative data (see, for example, Museus, 2009; Teranishi, 2007; Teranishi and others, 2004). In this chapter, using data from a qualitative study of campus climate, we explicate how the disaggregation and critical analysis of data can be an important consideration in qualitative examinations of the experiences of Asian American college students.”

National Center Brief (2012). The importance of disaggregating student data. Retrieved from

From the website: “Disaggregating data means breaking down information into smaller subpopulations. For instance, breaking data down into grade level within school aged students, country of origin within racial/ethnic categories, or gender among student populations are all ways of disaggregating data. Disaggregating student data into subpopulations can help schools and communities plan appropriate programs, decide which evidence based interventions to select (i.e. have they been evaluated with the target population), use limited resources where they are needed most, and see important trends in behavior and achievement. Collecting and analyzing data can seem intimidating to someone without a strong statistics background, however, many of the tools you need are readily available.”

National Commission on Asian American and Pacific Islander Research in Education (2008). Asian Americans and Pacific Islanders: Facts, not fiction: Setting the record straight. New York: New York University. Retrieved from

Excerpt: “In reality, there are significant numbers of Asian American and Pacific Islander students who struggle with poverty, who are English-language learners increasingly likely to leave school with rudimentary language skills, who are at risk of dropping out, joining gangs, and remaining on the margins of society, and who are subjected to violence and discrimination on account of race, class, gender, ethnicity, or language. In other words, the facts tell a dramatically different story. In this report we identify three dominant fictions that permeate higher education, are critical for future research, and that contribute to misperceptions about Asian Americans and Pacific Islanders. Our conclusions call on educators to implement policies and practices that are based on the realities of students’ lives—an approach that will surely serve in the advancement of all.”

National Forum on Education (2016). Forum guide to collecting and using disaggregated data on racial/ethnic subgroups. (NFES 2017-017). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved from

Excerpt: “Disaggregation of student data refers to breaking down data about a student population into smaller groupings, based on qualities or characteristics such as gender, racial/ethnic group, or family income. Educators need both high-level data summaries as well as disaggregated data that accurately describe smaller groups of students they serve. Access to and analysis of more detailed data—that is, disaggregated data—can be a useful tool for improving educational outcomes for small groups of students who otherwise would not be distinguishable in the aggregated data used for federal reporting. Disaggregating student data into subpopulations can help schools and communities plan appropriate programs; decide which interventions to implement; target limited resources; and recognize trends in educational participation, outcomes, and achievement. Definitions of race and ethnicity are complicated because they incorporate aspects of biology, identity, and culture, among other factors. In order to place boundaries on the topic, this guide focuses on the federal government’s seven racial/ethnic categories as the baseline for aggregated, and subsequently disaggregated, data collection, reporting, and use.”

Nguyen, B. M. D., Nguyen, M. H., & Nguyen, T. L. K. (2014). Advancing the Asian American and Pacific Islander data quality campaign: Data disaggregation practice and policy. Asian American Policy Review. Retrieved from

From the abstract: “This study examines the impact of disaggregated data on shaping programs, services, and improving student outcomes for Asian American and Pacific Islander (AAPI) student populations at Coastline Community College (CCC). Using a mixed methods approach, including institutional data analysis and semi-structured staff interviews to examine the Asian American Native American Pacific Islander–Serving Institutions (AANAAPISI) program and its use of disaggregated data to inform programmatic implementation and decision making, our findings indicate that disaggregated data largely shapes and improves the planning, implementation, and delivery of services to specific ethnic groups in order to enhance student experiences and outcomes.”

Nguyen, B. M. D., Nguyen, M. H., Teranishi, R. T., & Hune, S. (2015). The hidden academic opportunity gaps among Asian Americans and Pacific Islanders: What disaggregated data reveals in Washington State. Los Angeles, CA: National Commission on Asian American and Pacific Islander Research in Education. Retrieved from

Excerpt: “Asian Americans and Pacific Islanders (AAPIs) are a remarkably diverse community, comprising 48 different ethnic subgroups that speak over 300 different languages and represent a range of different immigration histories. The AAPI population is also rapidly growing and was the fastest growing racial group in 2012. Among the key civil rights issues AAPI scholars and advocates have pressed for are improvements to data practices in order to represent the heterogeneity in the AAPI community. As AAPI students continue to experience a range of educational outcomes, data practices that aggregate AAPIs into one category continue to be a significant barrier for understanding and responding to their unique and diverse needs.”

Sellers, D. (2015). What’s data disaggregation got to do with educational equity? Minneapolis, MN: MinnCan. Retrieved on December 21, 2018, from

From the website: “What exactly is disaggregated data, and how would it help Hmong students in Minnesota, as well as other Asian American and Pacific Islander (AAPI) learners? According to the Southeast Asia Resource Action Center, the disaggregation of data involves requiring schools, school districts, and states with significant proportions of AAPI and immigrant communities to collect and report academic achievement and growth data that is disaggregated by Southeast Asian ethnicities to better reflect the real experiences and needs of individual AAPI ethnic student subgroups.”

Vagul, K., & Edlagan, C. (2016). How data disaggregation matters for Asian Americans and Pacific Islanders. Washington, DC: Washington Center for Equitable Growth. Retrieved from

From the website: “Aggregated data for all Asian Americans and Pacific Islanders’ socioeconomic indicators, such as median income, employment rate, and educational attainment, helps to preserve ethnic-based inequities among many sub-groups in this catch-all category. Taking a closer look at the data through detailed race and ethnicity categories provided by the U.S. Census Bureau’s American Community Survey, we find a different story: Asian Americans and Pacific Islanders in the United States are richly diverse, and so too are their socioeconomic experiences.”

Xiong, S., & Joubert, C. (2012). Demystifying the model minority: The importance of disaggregating subgroup data to promote success for southeast Asian youth. Fresno, CA: California State University, Fresno. Retrieved from

Excerpt: “Data disaggregated by Asian subgroups is important to obtain an accurate picture of the needs and disparities that are typically hidden in aggregated data (Asian Pacific American Legal Center & Asian American Justice Center, 2011; Chang et al., 2010). This is especially true for SE Asian Americans. For the purpose of this report, individuals identifying as Cambodian, Laotian, Hmong, and Vietnamese are considered SE Asian Americans (Phetchareun, 2012). For years, SE Asian Americans were labeled under the umbrella classification “Asian Americans,” commonly portrayed as the model minority, which has led to an underestimation of their challenges and needs (Hune, 2002; Suzuki, 2002; Yang, 2004). In analyzing available national disaggregated data, Asian Pacific American Legal Center & Asian American Justice Center (2011) found that barriers for SE Asian Americans are higher than other Asian subgroups, for example: 1) over 80% of SE Asian Americans speak a language other than English at home, whereas this is true for only 65% of Malaysian, 57% of Filipino, and 36% of Japanese; 2) over 40% of SE Asian Americans are limited-English proficient, whereas this is so for only 22% of Indian, 19% of Filipino, and 18% of Japanese; 3) SE Asian Americans have the lowest attainment of Bachelor’s degrees among all Asian American ethnic groups, i.e., Laotian (12%), Hmong (14%), and Cambodian (14%), compared to 46% of Japanese, 47% of Indonesian, and 73% of Taiwanese. Although the need and importance of disaggregated data for Asian Americans has been recognized (Hune & Takeuchi, 2009), access to and dissemination of such data has been limited at the local, state, and federal levels (Asian Pacific American Legal Center & Asian American Justice Center, 2011). More disaggregated data are needed to highlight the differences and disparities among these ethnic groups (Chang et al., 2010).”

Yang, L., Frisco, M., &a Pong, S. L. (2013). Educational achievement among Asian children: Ethnic differences in first grade math and reading scores. Student Presentation, Boise State University (ID). Retrieved from

From the abstract: “The burgeoning Asian population in the U.S. makes it imperative to understand the factors influencing their educational attainment. The pan-ethnic category of “Asian American” overgeneralizes about diverse populations and has led to a monolithic view of Asians as high achieving students with little need for educational services. The model minority myth may be masking the drastic variation in educational attainment among ethnic Asian groups. This study uses data from the Early Childhood Longitudinal Study—Kindergarten Class (ECLS-K) to: (1) examine whether there are significant achievement gaps between different Asian ethnic groups in first grade and (2) analyze factors that account for the differences in achievement. To determine if ethnicity is a major factor in student achievement, a linear regression model controlling for school and familial factors is conducted. The findings of this study suggest that the model minority myth may not exist, and the results add to the growing body of literature underscoring both the diversity in academic achievement and the needs of Asian students.”


Keywords and Search Strings

The following keywords and search strings were used to search the reference databases and other sources:

  • Disaggregating Asian by ethnicity
  • Disaggregating Asian and ethnicity
  • Disaggregating Asian and Pacific Islander by ethnicity
  • Disaggregating data and ethnicity

Databases and Resources

We searched ERIC for relevant resources. ERIC is a free online library of over 1.6 million citations of education research sponsored by the Institute of Education Sciences. Additionally, we searched Google Scholar and PsychInfo.

Reference Search and Selection Criteria

When we were searching and reviewing resources, we considered the following criteria:

  • Date of the Publication: References and resources published for last 15 years, from 2001 to present, were included in the search and review.
  • Search Priorities of Reference Sources: Search priority is given to study reports, briefs, and other documents that are published and/or reviewed by IES and other federal or federally funded organizations and academic databases, including ERIC, EBSCO databases, JSTOR database, PsychInfo, PsychArticle, and Google Scholar.
  • Methodology: Following methodological priorities/considerations were given in the review and selection of the references: (a) study types – randomized control trials, quasi experiments, surveys, descriptive data analyses, literature reviews, policy briefs, etc., generally in this order; (b) target population, samples (representativeness of the target population, sample size, volunteered or randomly selected, etc.), study duration, etc.; and (c) limitations, generalizability of the findings and conclusions, etc.

This memorandum is one in a series of quick-turnaround responses to specific questions posed by educational stakeholders in the West Region (Arizona, California, Nevada, Utah), which is served by the Regional Educational Laboratory West at WestEd. This memorandum was prepared by REL West under a contract with the U.S. Department of Education’s Institute of Education Sciences (IES), Contract ED-IES-17-C-00014524, administered by WestEd. Its content does not necessarily reflect the views or policies of IES or the U.S. Department of Education nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.