A guide to incorporating a racial and ethnic equity perspective throughout the research process
The recommendations in this resource are drawn from How to Embed a Racial and Ethnic Equity Perspective in Research: Practical Guidance for the Research Process, and aim to fill a gap in resources that help researchers approach their work with a racial and ethnic equity lens. This resource and the paper it is drawn from are working documents. If you have suggestions for how to improve them, please reach out to Kristine Andrews, Jenita Parekh, or Shantai Peckoo. All authors contributed equally to this work.
More than 150 years since slavery ended and more than 50 years after the Civil Rights Act became law, racial or ethnic identity still plays a role in defining a person’s life course. In an increasingly diverse society, the persistent issues seen among racial or ethnic minority populations partly reflect the fact that mainstream, majority perspectives shape the research that informs policies, programs, and public opinion around such issues.
Researchers working to address these issues have a responsibility not to perpetuate disparities, inequalities, and stereotypes about populations of color. While disaggregating data is a necessary component of understanding disparities in outcomes by race and ethnicity, it is not sufficient. Researchers must think critically about how they collect, analyze, and present data to avoid masking disproportionalities or disparities that different racial and ethnic groups experience.
We offer five guiding principles to help researchers apply this lens to the stages of the research process detailed in this resource. While there is no “one-size-fits-all” approach to incorporating a racial and ethnic equity perspective into research, these guiding principles can help researchers better identify where inequities exist, their structural cause, and the environments and conditions that perpetuate those inequities.
These guiding principles encourage researchers to examine their own biases, make a commitment to dig deeper into their data, recognize how the research process impacts communities, engage with those communities as research partners, and guard against the implicit or explicit assumption that white is the default experience of the world.
This resource provides concrete ways in which researchers can incorporate a racial and ethnic equity perspective at every stage of the research process: landscape assessment, study design and data collection, data analysis, and dissemination.
Stage 1: Landscape assessment
Know the context
Examining the history and values of the community—and its culture, neighborhood, and people—involved in your research is necessary to determine the preferred method of inquiry. Community members involved in research must be viewed as partners from the earliest stages of research to inform the proper way to address issues and the preferred method of inquiry.
Because they can influence study participants’ behaviors and responses to research questions—and because they control how these data are used and interpreted—researchers, funders, and sponsors inherently hold a higher position of power than community members. Given this power difference, researchers must determine whether community members have agreed to participate in the research. They must also advocate to funders and sponsors for participants’ rights to have agency in how data are used and interpreted.
To obtain a sense of the history and politics of the community—including an understanding of who has been marginalized, how, and by whom—researchers should ask themselves:
- Who is affected—positively or negatively—by the issue you plan to study? Why? How?
- How is power distributed in the community? What power differentials exist?
- Which relationships are prioritized? Which are discouraged?
- How does the community like to be approached and through what gateway?
- How do you refer to individuals in the community?
- What are the historical and cultural antecedents of the community?
Use context to define the research problem
Researchers must be careful to ensure that their perspective (or that of a funder) on research outcomes (e.g., the causes of the problem under investigation) does not bias the study from the beginning stages. For example, if a study focuses on homelessness, researchers and funders must not let their own biases about the causes, outcomes, and other factors of homelessness influence their approach to the topic.
Instead, researchers must look to the community involved in (or affected by) their research to ensure that the issue under examination is defined appropriately for that community.
To avoid biases and clarify the issue or concern in their study, researchers should:
- Collect background data from the neighborhood or community.
- Review publicly available datasets, reports, or media accounts.
- Conduct interviews, focus groups, and community dialogues to gather key stakeholders’ perspectives.
- Hold forums and provide opportunities for community members to share feedback on the issue and whom it affects.
Identify root causes of the issue, along with contributing causal factors
After identifying the relevant issue through discussions with community stakeholders, the researcher should begin to identify contributing causal factors—i.e., conditions that allow the identified issue or concern to occur and persist. Identifying causal factors enables researchers to dig deeper to uncover the systemic and societal root causes of the issue they are researching.
Determining causal factors involves acquiring data from community stakeholders and environmental scans, then mining the data to identify potential root causes of the issue. A root cause is a factor that prevents a negative outcome from occurring when taken away (e.g., structural racism), while causal factors (e.g., lack of transportation access) contribute to the outcome. While removing a causal factor might improve a situation, its absence will not necessarily keep the issue from occurring.
As an illustrative example, a researcher could investigate how to increase mammograms among African American women. This researcher might identify competing needs and limited transportation access as causal factors that prevent women from obtaining mammograms. Although these factors could be addressed, a deeper analysis might lead the researcher to discover that women’s fear of receiving a cancer diagnosis was a root cause for low mammogram uptake among African American women. In this case, resolving the causal factors may help increase mammograms to some extent, but addressing the root cause and counseling the women to move beyond their fears would potentially be more successful.
Stage 2: Study design and data collection
Develop equitable research questions
Researchers can develop research questions that focus on advancing racial and ethnic equity and/or minimizing harmful effects for communities of color. This means that research questions should reflect the community’s values and perspectives. Researchers should also strive to create reciprocal research designs that give back to study participants and the community.
Researchers must also pay close attention to how race, power, language, and privilege affect the community and ensure that their research questions account for these factors. To achieve this, researchers might consider an approach in which the community actively engages in the research process. This community-informed process will lead to research questions that can better assess the impact of social investments and produce more valid findings and better-tailored recommendations.
Researchers should ask themselves:
- Are the community’s values represented in the research questions?
- Have the researchers identified how the answers to the research questions will benefit the community?
- Do the research questions account for the cultural and historical context of the community?
Determine the research design by gathering community input
A community’s values, culture, historical context, and voice should inform research design. Researchers must ensure that the community respects and trusts the design and type(s) of data collected. Additionally, researchers should be keenly aware of differences within communities to ensure that research questions reflect that diversity.
For example, some community organizations and schools are wary of participating in randomized control trials, which often entail one group receiving a program intervention while another does not. If the community suffers disproportionately from social, health, and/or psychological issues, randomized control trials may exacerbate these inequalities. Compared with randomized control trials, rigorous implementation, process, and impact studies with both quantitative and qualitative methods better serve populations and settings that are challenging to study. Examples of alternatives to randomized control trials include single case research designs, interrupted time series designs, and regression discontinuity. Propensity score matching would still require a comparison group but may be used as an alternative to randomly assigning participants within a school or community.
Establishing this connection with the community will also enable researchers to mitigate the challenges often encountered when trying to recruit study participants in communities that have experienced abuse, misrepresentation, and/or discrimination.
Decide who will collect the data
A racially diverse team of researchers can contribute multiple perspectives to the study design, process, and findings. In contrast, members of a homogeneous team tend to drift toward similar beliefs and styles of thinking. This groupthink can lead to less rational courses of action and narrower ranges of options and opinions.
When researchers’ life experiences or other characteristics (including race, ethnicity, language, dialect, gender, culture, and class) differ from those of the population being studied, the team should discuss how these differences may influence the research process or reinforce a power differential. Such differences may also cause researchers to interpret data and research findings incorrectly, miss verbal or nonverbal cues, misinterpret nuances of a culture, or be influenced by their personal assumptions or biases.
Identify data collection instruments
When deciding what data collection tools to use, researchers must consider how information is shared in the community and whose information is prioritized. Researchers should select a methodology that is best suited to answering the research question but also eliminates method or measurement biases.
For all measures, it is important to perform cognitive testing with study participants to examine how they will interpret questions, items, and instructions on research instruments. Many measurement tools and scales were developed by non-minority researchers and tested in non-minority samples. This means that a validated survey instrument may be efficient for addressing the research question in one community or population, but not in a different one.
Stage 3: Data analysis
Confront implicit biases in data analysis
Before researchers collect data from study participants, they need to confront the assumptions and implicit biases that influence how they conduct research, interpret data, and present and message findings. Researchers should:
- Engage in self-reflection.
- Ask themselves questions (Who or what makes them uncomfortable and why? To whom do they give second chances and why?).
- Understand what situations trigger these biases.
The Implicit Association Test, a tool for identifying implicit biases, and Public Policy Associates’ self-reflection tool are useful starting points for pivotal conversations that research teams must have before collecting data.
Quantitative data analysis
Researchers can apply a racial and ethnic equity perspective to quantitative analysis by disaggregating data and exploring other facets of identity.
Data disaggregation allows researchers to examine important variables by different racial and ethnic subgroups, and to carefully examine the distribution of important variables for the population. Whenever possible, researchers should disaggregate by subgroups (nativity, country of origin, citizenship status, etc.) to uncover the heterogeneity of experiences both between and within racial and ethnic groups.
However, data disaggregation can also obscure racial and ethnic differences seen in populations that have great ethnic diversity. For small populations, oversampling may mitigate this. For example, oversampling is critical for disaggregating data by race and producing meaningful results for American Indian/Alaska Native and Asian American/Pacific Islander populations, which are often grouped into one single “other” category. Researchers must anticipate needing extra resources to oversample when necessary.
Disaggregation must also go beyond racial and/or ethnic group classification to look at structural and social determinants that might explain observed findings. For example, when showing graduation rates by school and by racial groups, it may be equally important to show the financial resources provided to each school, the local history of school segregation, or the financial hardship faced by students’ families.
Researchers should analyze quantitative data more deeply by:
- Exploring the intersectionality of race with other dimensions of identity.
- Asking why the trends revealed in this intersectional analysis may occur.
Qualitative data analysis
Through the collection and analysis of narrative and storytelling, qualitative research offers important perspectives and information not captured by quantitative research methods.
By asking explicit questions about communities’ concerns—and, if relevant, what they think contributes to those issues—researchers can apply an interpretation of racial and ethnic equity from the perspective of community members themselves. If the interview sample is large enough, responses should be filtered by themes to notice differences by race, gender, power level, and other characteristics.
Community involvement in data interpretation
To involve communities in data interpretation, researchers can facilitate workshops in which community members code, categorize, and develop themes for the data with researchers as supporting partners. Community involvement in data interpretation is helpful for three reasons:
- Communities and populations are not simply research subjects. Research that involves populations and communities must be respectful of, transparent to, and reciprocal with those communities.
- Researchers are not omniscient. A community’s perspective can complement and supplement the researchers’ knowledge with contextual factors that may influence the interpretation of the data.
- A community’s reactions to and interpretation of findings are valuable and could illuminate root causes. While researchers can acknowledge potential biases in the data itself, the community’s interpretation can bring additional insight.
Stage 4: Dissemination
Before research begins, researchers should collaborate with the community or study population to identify the audiences they hope their research will reach, and to consider what information will be most useful for each audience. These discussions will inform which platforms and methods researchers should use to reach those audiences.
Researchers must first and foremost consider the study population or community as one of their multiple primary audiences. Research participants often share intimate details of their lives, but are then abandoned after the research project concludes without knowing or understanding the findings to which they contributed. This experience only fosters distrust between communities and researchers.
Researchers must also consider how their findings can reach decision makers, community leaders, and other changemakers who can support programs and policies related to the findings.
The guidance offered here is a work in progress. However, we hope it will begin to help researchers develop concrete steps to embed a racial and ethnic equity perspective within their work. We welcome any feedback or recommendations for how to expand this important work and empower researchers to better identify where inequities exist, their structural cause, and the environments and conditions that perpetuate those inequities.