Conducting research through an anti-racism lens

This guide is for students, staff, and faculty who are incorporating an anti-racist lens at all stages of the research life cycle.

How to use this guide

This guide was developed in response to librarians fielding multiple requests from UMN researchers looking to incorporate anti-racism into their research practices. Conducting research through an anti-racism lens is a long-term and ongoing process and must be considered as part of a complex system which oppresses people and groups in multifaceted ways (i.e., classism, ethnocentrism, capitalism, ableism, etc.). Many departments across the university historically and currently practice anti-racist research by centering non-white identities and perspectives, studying critical theories (e.g., race and feminist), and exploring diverse methodologies - and yet some disciplines and researchers are just beginning. If you're new to this work, consult the Related Guides and Resources call-out box in the left pane for reading lists to help form a baseline understanding. You can also reach out to your subject librarian for individualized help locating anti-racism learning experiences. This guide shares racist research systems and practices, followed by resources for mitigating those problematic systems and practices, but we wholeheartedly acknowledge that this guide is not a "solution" to the issues of racism embedded in research. (Note: the links in this guide lead to openly accessible resources unless there isn't one available, in which case the link leads to the UMN library source.)

Suggestion citation (APA 7th edition): Hunt, S., Riegelman, A., & Myers-Kelley, S. (2024, February 28). Conducting Research Through an Anti-racism Lens. University of Minnesota.

Decenter whiteness in primary research

There is a long history in the U.S. of research atrocities committed against racialized people (e.g., UMN land grabU.S. Public Health Service Study of Untreated Syphilis in the Negro Male: 1931-1972, forced sterilization, intellectual property claims). Medical Apartheid is a book detailing how Black people have been non-consensually used at a disproportionate rate compared to their white peers for painful and dangerous medical experiments, but the Black population benefits minimally from the medical gains. Equally important is to acknowledge current medical racism (e.g., pharmacy deserts, Dr. Susan Moore’s death due to COVID, prison experimentation). This has led to an understandable and justified distrust between racialized people and researchers/institutions. Additionally, research studies tend to emphasize the perceptions, thoughts, and interests of white people, widening disparities in research outcomes and impacts. The strategies below are designed to decenter whiteness, think inclusively, and build trust between researcher and communities of color when conducting primary research.

Recruit racialized people and communities for inclusion in studies

Funding agencies are implementing policies that require diversity in study recruitment (e.g., National Institutes of Health Inclusion of Women and Minorities as Participants in Research Involving Human Subjects), particularly for clinical trials. Demand Diversity is an organization dedicated to moving these efforts forward through their blog, podcast, and resources such as a national examination of why racialized people refuse participation in studies (US and UK reports available).

The following studies highlight strategies for decentering whiteness in clinical trial recruitment (note that the use of the term "minorities" is still white centering):

Utilize research methods and practices that decenter whiteness

A Toolkit for Centering Racial Equity Throughout Data Integration, by Actionable Intelligence for Social Policy, helps researchers embed questions of racial equity throughout the data life cycle: planning, data collection, data access, algorithms/use of statistical tools, data analysis, and reporting and dissemination. It includes exercises and examples, and encourages a community engaged framework. See the excerpt below:

Racial Equity in Data Analysis: Positive & Problematic Practices POSITIVE PRACTICE PROBLEMATIC PRACTICE Using participatory research to bring multiple perspectives to the interpretation of the data Describing outcomes without examining larger systems, policies, and social conditions that contribute to disparities in outcomes (e.g., poverty, housing segregation, access to education) Engaging domain experts (e.g., agency staff, caseworkers) and methods experts (e.g., data scientists, statisticians) to ensure that the data model used is appropriate to examine the research questions in local context Applying a “one size fits all” approach to analysis (i.e., what works in one place may not be appropriate elsewhere) Correlating place to outcomes (e.g., overlaying redlining data to outcomes) Leaving out the role of historical policies in the interpretation of findings Using appropriate comparison groups to contextualize findings Making default comparisons to White outcomes (e.g., assuming White outcomes are normative) Employing mixed methods approaches when developing the analytic plan, including purposefully seeking out qualitative data (interviews, focus groups, narrative, long- form surveys) in conjunction with quantitative administrative data to better understand the lived experience of clients Using one-dimensional data to propel an agenda (e.g., use of student test scores in isolation from contextual factors such as teacher turnover, school-level demographics) Disaggregating data and analyzing intersectional experiences (e.g., looking at race by gender) Disregarding the individual or community context in the method of analysis and interpretation of results Empowering professionals and community members to use data to improve their work and their communities Analyzing data with no intent to drive action or change that benefits those being served

Another comprehensive examination of the research lifecycle - similar to the toolkit above - is published in Upending Racism in Psychological Science: Strategies to Change How Our Science is Conducted, Reported, Reviewed & Disseminated. This paper makes recommendations for researchers, such as conducting research from a power perspective and including positionality statements in manuscripts, as well as journal teams, such as creating systems to detect and fix inequities in peer reviews.

Additionally, consider if your research is community-based or community-driven and which methodology benefits the community most. The Index of Community Engagement Techniques can help determine past, current, and future engagement levels, and Lead Local offers resources for a community-driven approach to partnering with communities.

Utilize reporting guidelines for manuscript writing

Reporting guidelines enhance the transparency and value of published research. The EQUATOR Network is a well known curator of health research guidelines, one of which is the Consolidated criteria for strengthening reporting of health research involving indigenous peoples: the CONSIDER statement. The American Medical Association has also published guidelines on the reporting and collection of data on race and ethnicity. For example, they advise collecting specific racial and ethnic terms rather than collective terms - which may be in contrast to funding agency requirements - and reporting an explanation of who identified participant race and ethnicity and the source of the classifications used in the methods section of any publications.

Evaluate whether your human participant research is representative

Most published research is not representative of the majority of populations because the human participant samples lack diversity. For example, behavioral genetic research heavily focuses on white people of European ancestry. Harden refers to these samples as WWEIRD in adding a W (White) to an acronym coined by Henrich, et al., where WEIRD stands for Western, Educated, Industrialized, Rich, and Democratic. Heinrich and others have written about how the majority of research is sampled from WEIRD societies. WEIRD can be applied to behavioral research based on cultural, environmental, and socioeconomic factors but has also been critiqued for not acknowledging values and research practices informed by whiteness, not including race and ethnicity, and not addressing diversification of contexts as well as samples. Additionally, the research community can take steps to welcome non-WEIRD researchers into mainstream literature.  

When developing a research design, ask yourself how you can decenter the status quo characteristics described by WEIRD. 

  1. Are you accounting for lived experience for BIPOC participants such as racial harassment (e.g., "microaggressions in the form of exclusion and outgrouping"). Clancy & Davis (2019) write that that research shows the "negative effects of these factors on mental health, cognition, and school performance."
  2. The US is a Western, industrialized, rich, democratic country. Can you claim generalizability to a global population?
  3. How can you recruit a sample within the US that is not mainstream?
  4. Highly educated individuals are more likely to participate in surveys. How will you recruit a sample that has a range of educational backgrounds?

For more information on decentering whiteness in primary research, see:

Decenter whiteness in secondary research

Secondary research involves the summary, collation, and/or synthesis of existing data or research, and often involves deep dives into existing literature. The strategies below are designed to decenter whiteness and encourage inclusivity when conducting secondary research.

Follow the praxis put forth by the Cite Black Women Collective

  1. Read Black women's work
  2. Integrate Black women into the CORE of your syllabus (in life & in the classroom)
  3. Acknowledge Black women's intellectual production 
  4. Make space for Black women to speak 
  5. ​Give Black women the space and time to breathe

These principles from the Cite Black Women Collectivewhich aim to amplify the frequently marginalized voices of Black women, can be applied to all secondary research that aims to amplify the work of racialized people.

Use inclusive citation practices

Citation bias is the tendency for researchers to cite investigations that show a positive effect and/or cite articles published in preferred journals due to familiarity. There has recently been significant discourse on how citation bias impacts racialized people and female-identifying scholars. Not only does citation bias harm racialized scholars, but it also hinders scientific progress. One method to mitigate the problem is to issue a Citation Diversity Statement, a short paragraph included before the references section of an article where the authors consider their own bias and quantify the equitability of their reference lists. Additionally, an anthropologist who studied the Cite Black Women movement wrote about antiracist citational politics and praxis and makes the following (and more) suggestions:

  • Look online to see how authors self-identify
  • Make a spreadsheet to identify your own citation trends
  • Be explicit that citing racialized scholars is valuable and central
  • Read promiscuously, critically, and counter the observed inequities
  • Look outside of academic books and articles (scholarship of racialized people has been historically excluded from those venues)
Cite oral histories

Knowledge may be passed on through oral teachings and histories, but those are difficult to cite since "personal communication" does not show up in reference lists. If citing oral teachings, from Indigenous elders, for example, utilize the template developed by Lorisia MacLeod and adopted by the American Psychological Association and Modern Language Association.

Consult research found in non-Western journals

The Journals Online Project - aimed at providing increased visibility, accessibility, and quality of peer-reviewed journals published in developing countries so that the research outputs produced in these countries can be found, shared, and used more effectively - was launched by the International Network for Advancing Science and Policy (INASP) in response to voices not heard, wasted talent, and unused research.

Search for existing collections

Collections of resources created by racialized scholars may already exist in your discipline, and a thorough Google search should lead you to them. For example, the Diverse BookFinder is a database of picture books published since 2002 featuring racialized characters that is useful for education disciplines. CiteHER is a database designed to amplify the published scholarly and creative work of Black women in computing. And The Syllabus Project aims to diversify environmental history syllabi by "bringing more women and people of color into our courses" via an open sourced Zotero library.

Evaluate whether the human participant research you are citing is representative

Most published research is not representative of the majority of populations because the human participant samples lack diversity. For example, behavioral genetic research heavily focuses on white people of European ancestry. Harden refers to these samples as WWEIRD in adding a W (White) to an acronym coined by Henrich, et al., where WEIRD stands for Western, Educated, Industrialized, Rich, and Democratic. Heinrich and others have written about how the majority of research is sampled from WEIRD societies. WEIRD can be applied to behavioral research based on cultural, environmental, and socioeconomic factors but has also been critiqued for not acknowledging values and research practices informed by whiteness, not including race and ethnicity, and not addressing diversification of contexts as well as samples. Additionally, the research community can take steps to welcome non-WEIRD researchers into mainstream literature.  

When diving into existing literature, ask yourself:

  1. Who are the authors?
  2. What are their backgrounds and identities?
  3. How many parts of the WEIRD acronym can you check off when you read about the populations that were studied?

For more information on decentering whiteness in secondary research, see:

Acknowledge that data is not objective

Data (even quantitative data) is not neutral, objective, or free of bias. Humans are involved in all aspects of data creation - we decide what data gets collected and from whom, how that data is combined and analyzed, and where and how that data is presented or shared. This guide refers to "data" as a collection of individual measurements or points. While the individual measurement of something (e.g., DNA assays, using a ruler to measure length) may return an objective data point from a given sample, the process of collecting, combining, analyzing, reporting, and using of data imbues a seemingly objective dataset with biases. The strategies below are designed to challenge statistical assumptions and traditional methods of data governance.

Learn the racist history of statistics

Francis Galton, Karl Pearson, and Ronald Fisher are three of the statisticians who established the fundamental basis and techniques of modern statistics, and they were all eugenicists. There is a modern discourse on the racist history of statistics and data collection methodology, and a number of books published:

Put the "human" back in human participant research

While we cannot scrap statistics as a data analysis method, there may be other ways to center the data we collect on the humans who provided that data, rather than just the numbers themselves. See examples of how researchers are doing this below, and Google #QuantCrit for additional resources:

Elevate "communities being researched" to research partners

A data equity framework is essential for community research. A well-cited human participant violation in the Native American community is that of the Havasupai Tribe who learned that researchers at Arizona State University had used blood samples collected for diabetes research to look for other diseases and genetic markers. Many marginalized communities are over-studied and under-consulted and often lumped together into an "other" data category. Researchers can engage with communities as full research partners and follow guidance from Indigenous and First Nations for resources such as the Global Indigenous Data Alliance (GIDA), United States Indigenous Data Sovereignty Network, Urban Indian Health Institute, and First Nations Information Governance Centre. For example, GIDA created the CARE principles for Indigenous data governance - CARE stands for collective benefit, authority to control, responsibility, ethics - that ensure Indigenous self-determination in sharing data.

Disaggregate race and ethnicity data

Justice-focused researchers and advocates call for the disaggregation of data, or breaking data down into sub-populations. Disaggregating data poses challenges to ensuring privacy and protection for human participants. The following resources are disaggregation guides that make recommendations such as identify subgroups that might be falling behind, allow for multiple race self-identification, and utilize storytelling as a data collection method.

Make data publicly accessible

Data 4 Black Lives has advocated for the collection and public accessibility of race data, particularly during the COVID-19 pandemic. Publicly available data stratified by race can inform public policies and improve inequities in funding. Sharing human participant data comes with specific challenges related to consent and de-identification. Consult your institutional repository (Data Repository for the University of Minnesota), a disciplinary repository (ICPSR), methodological repository (QDR), or funding agency repository (e.g., NIH has many) for consultations on best practices for sharing human participant data. University of Michigan's Open Data DEIA Toolkit 1.0 offers curated resources for each stage of the research lifecycle that are particularly useful for researchers planning to share their data.

Present data visualizations to make data accessible

Data visualizations summarize the important and interesting research findings - identifying patterns, enhancing comprehension, and broadening communication for big takeaways. They can be presented via infographics, executive summaries, pamphlets, etc. and are easily made accessible to not only policymakers and the public, but also the people and communities being studied. How not to visualize like a racist is an interactive website that demonstrates how to improve problematic visualizations through strategies such as "comparing apples to apples" and broadening the temporal view. Applying racial equity awareness in data visualization provides several steps to create inclusive visualizations. And this YouTube video titled Equity in data and data visualization practices (47:05) offers thought provoking examples.

For more data equity resources, see:

Acknowledge that scholarly publishing is racist

Peer review in the publication process is meant to ensure rigid methodology and low bias in what gets published, but that system is flawed. Most editors are white and/or from Western nations, and 66-80% of peer reviewers are also white. These gatekeepers control the authorship and content of scholarly journals and books, which ultimately favor white, Western authors - even in works focused on race. The strategies below are designed to diversify bibliographies, publication venues, and the peer review process.

Look outside peer reviewed literature for perspectives from racialized voices and groups

Researchers should look at the gray literature for perspectives from racialized communities. Gray literature refers to works published outside traditional methods, and the easiest way to access it is through Google. There is no perfect solution, and many strategies will be attempted before useful information is found. Google search tips:

  • Keep in mind that Google itself uses biased algorithms, so searching must be specific and incorporate various terms to represent one concept.
  • The following example shows one possible search strategy for housing discrimination: "marginalized voices in housing."
  • You can utilize Boolean operators and other search functionality as you do in literature databases. For example: (marginalized OR underrepresented) AND (hous* OR rent*). See more Google search refinement tips.
  • You might also try using keywords of interest plus "site:org" which tells a search engine that you are looking for an organization rather than a company or government site. Keep in mind that prior to 2003, ".org" was reserved for non-profit organizations, but since then this top-level domain has been obtainable for other purposes, including private companies.

Archives also feature marginalized voices through collections of oral histories, documents, and interviews. Where to start:

Search for racialized scholars through professional organizations

It is not yet possible to search for an author's race in literature databases - the following is a flawed, yet workable, solution to that problem: 

  • Look to university departments that have always centered the identities and perspectives of racialized people and communities. For example, at the University of Minnesota, there is a Race, Indigeneity, Disability, Gender & Sexuality Studies (RIDGS) collective, and the affiliated faculty experts are listed.
  • Search Google for organizations that represent marginalized groups and search their websites for special interest groups, experts, publications, data, etc. For example, perusing the digital magazine available on the National Society of Black Engineer's website to find leaders in the field.
  • Seek out a networking organization, such as ColorComm, which is an essential organization for women of color in all areas of communications including public relations, advertising print media, broadcast, and more. To look for such groups, use a variety of terms - one might be "research organizations run by people of color."
  • It can be helpful to look for underrepresented speakers at conferences. One example is this interactive tool by Diversify STEM Conferences which has compiled a list of prominent underrepresented researchers across every field of STEM and medicine.
  • Search for biographies that are led by non-profits run by racialized people, such as SACNAS, an online archive of first-person stories by and about Chicano/Hispanic and Native American scientists with advanced degrees in science.

Utilize smaller, lesser known databases 

The list below is not exhaustive, but it features a handful of indexes focused on racialized people as well as a selection of local news outlets that feature racialized scholars:

Incorporate community-engagement principles into research dissemination

The Community-Centered Dissemination Toolkit recommends that researchers proactively and mindfully engage with communities to develop effective dissemination plans. Their Resource Directory includes individuals and groups that could help with creating and sharing community-focused outputs. Another resource from the Urban Institute guides researchers through how to design Data Walks for research dissemination to participants and communities.

Publish openly

Some scholarly research resides behind paywalls. Alternatively, publishing open access means removing a barrier for the people and populations being studied.

Find open access journals and their policies through the Directory of Open Access Journals. Learn if and how you can more broadly share your published work by searching the journal on Sherpa/Romeo.

Publish in journals dedicated to anti-racism

Some journals have made anti-racism pledges promising to diversify their editorial teams and authorship. Publishing in these journals, when applicable to your work, shows your support for their efforts toward anti-oppressive practices.

  1. Search for an anti-racism pledge or statement (example from Written Communication)
  2. Conduct a broader search for reactions/responses to that pledge (example from Journal of Occupational Science)
  3. Look at the editorial board make-up
  4. Browse journal website for anti-racism language
  5. Contact editor-in-chief to ask about their commitment to anti-racism (specific actions they're taking)

Engage in anti-racist peer review

Consult Anti-racist scholarly reviewing practices: a heuristic for editors, reviewers, and authors which recommends prioritizing humanity over production, transparency, and valuing labor.

For more information on bias in scholarly publication, see: 

Acknowledge that search algorithms are racist

Algorithms existed pre-internet, but in today's world, they serve as a sequence of instructions to perform computation. There is evidence that algorithms are sometimes racist. Proprietary algorithms (e.g., Google) are customized to users and lack transparency, making it unclear why search results vary from person to person. For this reason alone, you may be missing important information unless you know to search specifically for it. The strategies below are designed to encourage literature search strategies that are comprehensive and inclusive.

Use inclusive search terminology on topics of racism

Terminology used to describe race and ethnicity have evolved over time. This variability in language can make searching comprehensively for literature on race/ethnicity difficult. If you are performing a comprehensive literature search (e.g., systematic review, scoping review, historical perspective), you must include outdated terminology in your search strategy or check to see if old terminology has been included in the databases’ subject headings (e.g., illegal aliens). And other times, you will need to use terms that were common from the preferred date range of the research.

This health sciences example from Ovid MEDLINE shows that older terminology from 1963 forward has been added to the new subject heading "African Americans."

Searching for literature about racism requires a sophisticated search strategy to not only include "racism," but also biases and discrimination against specific races and ethnicities. The example below is a search strategy for racism in Ovid MEDLINE. It uses both MeSH terms and keywords for racism, and also MeSH terms and keywords for bias, discrimination, stigma, stereotyping, and prejudice grouped with MeSH terms and keywords for specific races and ethnicities. Remember that terms evolve. For example, terms like racialized, minoritized, melanated, BIPOC (Black, Indigenous, and People of Color), and anti-oppression should be added to the racism search example below, which was only constructed two years ago.

Utilize existing search filters

Search filters (AKA hedges) are literature search strategies that have been developed and tested by librarians and then made openly available to other researchers. The InterTASC Information Specialists' Sub-group Search Filter Resource offers filters on health equity and population groups.

For more information on bias in algorithms, see: 

Acknowledge that library cataloging systems are racist

When incorporating anti-racism into research, it’s important to acknowledge the context in which information has been shared through library systems. Dewey Decimal, the Library of Congress, and smaller discipline-specific cataloging approaches were designed in a racist and white-centered system. In the case of the Library of Congress, the classification is built based on existing holdings and United States publishing output. This "literary warrant," as it is termed, is a reflection of the white male dominance of American culture and publishing since its founding. Library cataloging systems have always evolved as terminology and attitudes change, but the process can be slow and is often inadequate to meet the needs of all researchers. As new disciplines emerge, classifications can be expanded to accommodate them.

Know that librarians are fighting for change

As a researcher, it is important to be aware of this information. Know that librarians are advocating for anti-racist cataloging, a long-term process. Some libraries have opportunities for users to report racist terminology in catalogs. UMN Archives and Special Collections has guidance for notifying library staff about racist language found in descriptions of materials.

For more information on bias in cataloging, see: 

Positionality statement

This guide was developed by two librarians, Shanda Hunt and Amy Riegelman, and Soph Myers-Kelley, a library intern, from the University of Minnesota. Hunt and Riegelman identify as cis white women. Myers-Kelley identifies as a white transmasculine person. All are immersed in academic research institutions. The first iteration of this guide was developed quickly in response to high demand for the content, an action that we now realize is characteristic of white supremacy culture. We are deeply grateful for the thoughtful additions and feedback we have received from our peers and patrons, both privately and as open calls to do better. As we shift, learn, and reflect, so will the content of this living guide. Akin to what adrienne maree brown writes in Emergent Strategy: Shaping Change, we are conduits for this information, not proprietors or owners. We encourage others to build on, lovingly critique, and evolve the ideas in this guide (Email addresses in the left pane).

Last Updated: Feb 28, 2024 1:01 PM