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Home - Conducting research through an anti-racism lens - Research Guides at University of Minnesota Minneapolis
Conducting research through an anti-racism lens
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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): "Conducting Research Through an Anti-racism Lens." (2024). University of Minnesota.
Table of contents
Decenter whiteness in primary research
Decenter whiteness in secondary research
Acknowledge that data is not objective
Acknowledge that scholarly publishing is racist
Acknowledge that search algorithms are racist
Acknowledge that library cataloging systems are racist
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 grab
U.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
):
Ethnic minorities, youth, and the underinsured in cancer research
African Americans in cancer research
Older minority adults in heart disease research
Black individuals in cardiovascular disease research
Racial and ethnic minorities in clinical trials
Underrepresented populations in chronic obstructive pulmonary disease research
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:
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.
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."
The US is a Western, industrialized, rich, democratic country. Can you claim generalizability to a global population?
How can you recruit a sample within the US that is not mainstream?
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:
Stop blaming the U.S. Public Health Service atrocities at Tuskegee, critics say. It's not an 'excuse' for current medical racism
Considerations for employing intersectionality in qualitative health research
Use interdisciplinary-informed research strategies "to improve identification and treatment for patients who have too often been overlooked."
Disparities in clinical trials
(curated list of articles)
Beyond WEIRD
White Centering is a Form of Privilege
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
Read Black women's work
Integrate Black women into the CORE of your syllabus (in life & in the classroom)
Acknowledge Black women's intellectual production
Make space for Black women to speak
​Give Black women the space and time to breathe
These principles from the
Cite Black Women Collective
which 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:
Who are the authors?
What are their backgrounds and identities?
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:
CiteASista
Beyond WEIRD
Cite Black Women podcast
Uncovering hidden gems
White Centering is a Form of Privilege
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:
Thicker than blood: how racial statistics lie
(sociology)
Weapons of math destruction
(mathematics)
White logic, white methods: racism and methodology
(sociology)
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:
How to “QuantCrit:” Practices and Questions for Education Data Researchers and Users
(education)
Can you really measure that? Combining critical race theory and quantitative methods
(education)
A critical policy analysis: the impact of zero tolerance on out-of-school suspensions and expulsions of students of color in the state of Texas by gender and school level
(education)
QuantCrit: education, policy, 'big data' and principles for a critical race theory of statistics
(education)
Racial disparities in breast cancer and genomic uncertainty: a QuantCrit mini-review
(medicine)
Sent out or sent home: understanding racial disparities across suspension types from critical race theory and quantcrit perspectives
(education / social work)
Translating context to causality in cardiovascular disparities research
(biostatistics)
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.
AISP Working Paper: Addressing Racial and Ethnic Inequities in Human Service Provision
Asian Development Bank's Practical Guidebook on Data Disaggregation for the Sustainable Development Goals
Asian and Pacific Islander American Health Forum's Advocating For Data Disaggregation By Race And Ethnicity
Center for Antiracist Research’s Toward Evidence-Based Antiracist Policymaking: Problems and Proposals for Better Racial Data Collection and Reporting
COVID Black
Ethnicity Data and AAPIs: Resources on Data Disaggregation
Minnesota Compass' Race data disaggregation: What does it mean? Why does it matter?
National Congress of American Indians
Policy Research Center
Urban Indian Health Institute's Best Practices for American Indian and Alaska Native Data Collection
We All Count
(search for their forum pieces on small data as a starting point)
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:
Antiracist reading list from Purdue Department of Mathematics
Data Genocide of American Indians and Alaska Natives in COVID-19 Data
Developing and advancing anti-racist scholarly practice
(Dr. Amanda Sullivan at 4:52:00)
Tableau's Racial Equity Data Hub
(repository of "small data" datasets)
Acknowledge that racism exists in scholarly publishing
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:
Archives & Special Collections, University of Minnesota Libraries
Minnesota's Immigrants
South Asian American Digital Archive
Umbra Search African American History
Minnesota Historical Society
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:
Minnesota Spokesman-Recorder
International Index of Black Periodicals
Ethnic NewsWatch
American Indian Newspapers
Chicano Database
Hispanic American Periodicals Index
The Somali American
Hmong Times
The Circle: Native American News and Arts
Sahan Journal
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.
Learn more about the structural inequalities of publishing behind paywalls
See how and why scholars and labs have committed to free, global access
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
Jisc's Open Policy Finder
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.
Search for an anti-racism pledge or statement (
example from Written Communication
Conduct a broader search for reactions/responses to that pledge (
example from Journal of Occupational Science
Look at the editorial board make-up
Browse journal website for anti-racism language
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:
A Case Study for a New Peer-Review Journal on Race and Ethnicity in American Higher Education
Even as medicine becomes more diverse, main authors in elite journals remain mostly white and male
Psychological Research: Racial Biases in the Peer-review and Publishing Enterprise
Upending Racism in Psychological Science: Strategies to Change How Our Science is Conducted, Reported, Reviewed & Disseminated
Who Writes about Archaeology? An Intersectional Study of Authorship in Archaeological Journals
Acknowledge that racism exists in search algorithms
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:
Dr. Safiya Noble
Algorithms of Oppression: How Search Engines Reinforce Racism
Santa Clara University's Guide on Bias in Search Engines and Algorithms
Wichita State University’s Guide on Search Engine Bias
San Jose State University's Guide on AI & Bias: Search Engines
Acknowledge that racism exists in library cataloging systems
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:
Antiracism in the Catalog: an analysis of records
Browsing through Bias: The Library of Congress Classification and Subject Headings for African American Studies and LGBTQIA Studies
Classification along the Color Line: Excavating Racism in the Stacks
The Racist Problem with Library Subject Classifications
Change the Subject
(documentary)
Removal of Offensive ‘Illegal aliens’ Subject Headings
Down With Dewey
Remembering the Howard University Librarian Who Decolonized the Way Books Were Catalogued
Cataloging Lab
Mar 18, 2026 9:06 AM
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