Race and Ethnicity in Data: Context, Challenges, and Best Practices

This two-part blog series will explore how the Dashboard’s data capture race and ethnicity. The first blog explores the background, importance, and best practices related to the use of race and ethnicity data, while the second will look at how the Dashboard presents these data. This series is intended to highlight the significance of race and ethnicity data, help in understanding who exactly the race and ethnicity data on the Dashboard represent, and provide transparency regarding the Dashboard’s data practices.  

Background of Race and Ethnicity

Race and ethnicity are social-political constructs that have been used to describe perceived shared characteristics of groups of people, including geographic location, religious beliefs, linguistic practices, and customs. One shared characteristic is skin color, though race itself is not a biological characteristic.^1,2 Race and ethnicity are used by individuals to identify with broader communities, but they can also be ascribed to individuals by others and serve as frameworks for discrimination and oppression.

Race and ethnicity are often included as categories in data collection. The way these categories are defined has changed over time, impacting how race and ethnicity data should be used and interpreted.

Race and Ethnicity in Data

Categorizing data on health, social, and economic factors by race can help reveal inequities and illuminate opportunities for improving health outcomes and access to resources. Yet too often, data on race and ethnicity are either missing or imprecisely defined in data collection tools, making it challenging for communities and policymakers to determine how most effectively to improve the health of the populations they represent and are seeking to support.³

The Office of Management and Budget (OMB) publishes guidelines on how race and ethnicity must be reported in federal data. The most recent OMB guidelines were updated in 2024, building upon previous guidelines released in 1997, though the updates have yet to be implemented. These guidelines are important in part because they define which racial and ethnic groups are included in federal data collection. Many non-federal and independent organizations follow the OMB guidelines to align their data collection and ensure compatibility with federal data practices. Despite efforts to improve the specificity of race and ethnicity data collection in recent years, the definitions of racial and ethnic groups in federal guidelines continue to raise concerns in some communities.⁴

Federal guidelines on the collection and reporting of race and ethnicity data continue to evolve. This information is current as of June 2025, and the Dashboard will continue to monitor any updates or changes.

Challenges

There are a number of issues to consider regarding race and ethnicity categorization in data collection and reporting.

1. Understanding Race and Ethnicity Categories

The OMB standards from 1997 identified six required categories when reporting race and ethnicity: White, Black or African American, Hispanic or Latino, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander. A new category – Middle Eastern or North African – was added in the 2024 updated guidelines, though many organizations have not yet implemented this change.⁵ Within these broader categories there are, of course, many more nuanced groupings. For example, though often treated as a single category, the Asian community is diverse, spanning a great range of languages, customs, and shared lived experiences. Although the broader required categories generate standardized data that can be compared, they can also lead to generalizations and mask the variation in subgroups that is so important to health improvement efforts.⁶ At the other extreme, highly granular categories can risk respondent confidentiality or produce unstable or unreliable estimates due to small population sizes.

2. Complex Identities

Race and ethnicity categories on surveys may or may not align with an individual’s identity, as has often been the case with the category of Hispanic ethnicity. While ethnicity and race are typically presented as two separate categories, many Hispanic individuals view their ethnicity as part of their racial background, which can differ from the way in which the OMB guidelines define these concepts.^7,8,9 People may also be reluctant to share their true identities on data collection surveys due to fear of discrimination.¹⁰

3. Bias

The method of collecting race and ethnicity data can influence the results, in some cases leading to inaccuracies. Some groups may be undercounted due to language barriers or to participants not identifying with the race or ethnicity categories provided. This can result in inaccurate counts that misinform inform policymaking and resource allocation.⁷

4. Change over Time

Finally, race and ethnicity constructs themselves change over time, reflecting evolving social, political, and economic factors, and making comparisons over time difficult. For example, in 2020, the Census Bureau made changes to how it asked about race, and how it categorized responses. These changes included adding more options for write-in responses, and providing specific race examples such as German or Irish for the White category, and Nigerian or Jamaican for the Black category. In addition, the number of write-in responses has increased due to changes in the acceptable character limit. Now, up to 200 characters are accepted, and individuals can identify with up to six race categories. These changes improved the census’s ability to capture complex identities, but it also meant that data from 2020 onwards are not directly comparable to data in prior years that used different categorization methods.^11,12,13

Best Practices

Some best practices for collecting and reporting data on race and ethnicity can help ensure the quality of those data. While preferences may differ among individuals and organizations, common themes we have compiled through our research are presented below.

When collecting data:

• Collaboration: When creating surveys and data collection tools, work with involved communities from the beginning – including during the development process – to ensure that questions are appropriate and relatable to survey takers. Such practice also contributes to data accuracy.^6,14

• Cultural Competency: When conducting surveys in multiple languages, ensure language is reflective of the respective community’s voices. If possible, train data collectors in procedures and cultural contexts, and prioritize accuracy, trust, and individuals with lived experience.^6,14

• Inclusivity: Allowing individuals to self-identify their race or ethnicity by including write-in options can allow individuals to add greater specificity to the existing categories and in some cases to identify with more than one race or ethnicity.^6,14

When reporting data:

• Consider multiple identities by presenting specific subsets (i.e. Hispanic women) where available.^14,15,16

• Communicate how race and ethnicity were assessed during data collection (i.e. self-report, assigned by a doctor, nurse or social worker), and how that may affect data accuracy.^14-16

• Provide as detailed data as possible (while protecting confidentiality) by using specific categories (e.g. Filipino, Jamaican) to help illuminate differences between subgroups. If numbers are too small to present subgroups individually, combine them and describe exactly who is included rather than excluding data completely.^14-16

• Avoid certain terms like “Minorities” and “non-White” which are vague and suggest a hierarchy among groups. Use the phrase ‘Race and Ethnicity,’ rather than Race/Ethnicity.^14-16

• Avoid using the term “Other" which does not provide meaningful information about who is part of the group. If the term must be used it should be clearly defined who comprises this group.^14-16

In many organizations, efforts to improve data collection and reporting practices are ongoing. Continually adapting practices is an important part of improving data quality and accurate representation.¹⁷

References

1. Flanagin A, Frey T, Christiansen SL, AMA Manual of Style Committee. Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals. JAMA. 2021;326(7):621–627. doi:10.1001/jama.2021.13304[ii] Magoon, K., Robinson, M., Kissling. A., Ozeua, V. (2022). Best Practice for Demographic Data Collection & Reporting. Public Consulting Group. https://www.publicconsultinggroup.com/media/4124/demographic-data-collection-and-reporting_brief.pdf

2. Magoon, K., Robinson, M., Kissling. A., Ozeua, V. (2022). Best Practice for Demographic Data Collection & Reporting. Public Consulting Group. https://www.publicconsultinggroup.com/media/4124/demographic-data-collection-and-reporting_brief.pdf

3. Chin, M., Doan, L., Russon, R., Roberts, T., Persuad, S., Huang, E., Fu, H., Kui, K., Kwon, S., Yi, S. (2023). Methods for retrospectively improving race/ethnicity data quality: a scoping review. Epidemiologic Reviews, 45(1), 127-139). https://doi.org/10.1093/epirev/mxad002

4. Office of Management and Budget. (1997). Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity. https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf

5. Office of Management and Budget. (2024). Revisions of OMB’s Statistical Policy Directive NO. 15: Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity. https://www.govinfo.gov/content/pkg/FR-2024-03-29/pdf/2024-06469.pdf

6. Kader F, Ðoàn LN, Chin MK, Scherer M, Cárdenas L, Feng L, et al. IDEAL: A Community–Academic–Governmental Collaboration Toward Improving Evidence-Based Data Collection on Race and Ethnicity. Prev Chronic Dis 2023;20:230029. DOI: http://dx.doi.org/10.5888/pcd20.230029

7. Gonzalez-Barrera, A., & Lopez, M. (2015). Is being Hispanic a matter of race, ethnicity or both? Pew Research Center. https://www.pewresearch.org/short-reads/2015/06/15/is-being-hispanic-a-matter-of-race-ethnicity-or-both/

8. Parker, K., Horowitz., Morin, R., Lopez, M. (2015). The Many Dimensions of Hispanic Racial Identity. Pew Research Center. https://www.pewresearch.org/social-trends/2015/06/11/chapter-7-the-many-dimensions-of-hispanic-racial-identity/

9. Noe-Bustamante, L., Gonzalez-Barrera, A., Edwards, K., Mora, L., Lopez, M. (2021). Measuring the racial identity of Latinos. Pew Research Center. https://www.pewresearch.org/race-and-ethnicity/2021/11/04/measuring-the-racial-identity-of-latinos/

10. Demb, G. (2014, June 16). On the Census, Who Checks ’Hispanic,’ Who Checks White,’ and Why. NPR. https://www.npr.org/sections/codeswitch/2014/06/16/321819185/on-the-census-who-checks-hispanic-who-checks-white-and-why

11. United States Census Bureau. (2021, November). Improvement to the Race Question. https://www.census.gov/programs-surveys/acs/technical-documentation/user-notes/2021-03.html

12. Marks, R. & Rios-Vargas, M. (2021, August 3). Improvements to the 2020 Census Race and Hispanic Origin Question Designs, Data Processing, and Coding Procedures. United Census Bureau. https://www.census.gov/newsroom/blogs/random-samplings/2021/08/improvements-to-2020-census-race-hispanic-origin-question-designs.html

13. Starr, Paul, and Christina Pao. 2024. “The Multiracial Complication: The 2020 Census and the Fictitious Multiracial Boom” Sociological Science 11: 1107-1123.

14. Magoon, K., Robinson, M.-J., Kissling, A., & Ozeua, V. (2022). Best practice for demographic data collection & reporting: Evaluator’s guide. Public Consulting Group. https://www.publicconsultinggroup.com/media/4124/demographic-data-collection-and-reporting_brief.pdf

15. Flanagin A, Frey T, Christiansen SL, AMA Manual of Style Committee. Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals. JAMA. 2021;326(7):621–627. doi:10.1001/jama.2021.13304

16. Office of Minority Health. (2018, October 23). Explanation of data standards for race, ethnicity, sex, primary language, and disability. U.S. Department of Health & Human Services. https://web.archive.org/web/20241118062102/https://minorityhealth.hhs.gov/explanation-data-standards-race-ethnicity-sex-primary-language-and-disability

17. United States Census Bureau. (2024). Research to Improve Data on Race and Ethnicity. https://www.census.gov/about/our-research/race-ethnicity.html