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The recommendations in this web resource are drawn from How to Embed a Racial and Ethnic Equity Perspective in Research: Practical Guidance for the Research Process, and aim to help researchers approach their research with a racial and ethnic equity lens.
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.
Five guiding principles of a racial and ethnic equity perspective
1. Examine your own background and biases.
2. Make a commitment to dig deeper into the data.
3. Recognize the impact the research process itself has on communities, and acknowledge your role in ensuring that research benefits communities.
4. Engage communities as partners in research.
5. Guard against the implied or explicit assumption that white is the default position.
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.
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:
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:
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.
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:
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.
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.
The most equitable questions are those that reflect the community’s values and perspectives. This means the researcher must understand who assigns and determines values within each community.
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.
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.
To function well, a diverse team needs an environment in which staff are encouraged to apply their own life experiences and share their unique perspectives.
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:
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.
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:
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.
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:
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 language researchers use to present findings must be appropriate for their identified audiences. Researchers must also ensure that this language does not victim-blame participants or groups in the study.
Researchers can also use resources like Race Forward’s Race Reporting guide and the Native American Journalists Association’s resources to guide their reporting of information that pertains to race, racism, and racial justice.
Additionally, researchers should acknowledge that others may be better equipped to disseminate data to target audiences. Researchers should allocate resources for community members to share their stories, or to cover the cost of a communications team or consultation with an outside expert.
If researchers communicate findings via presentations, the presentations should explicitly describe how the researchers did or did not apply a racial and ethnic equity perspective. The presentation should explain the limitations of the racial and ethnic equity perspective.
The medium used to disseminate findings should match the needs of the community and audiences for whom the research is intended. These mediums could take the form of in-person presentations at community events, data walkthroughs or discussions, infographics for display in community buildings or centers, forums, and interviews with local media.
Finally, dissemination must not end with the report of findings to key stakeholders. These findings should be accompanied by recommendations or actionable items for community members that can be used for sustainability planning or to find solutions to the issues that have been identified. This final step should be considered ongoing. Researchers must sustain their engagement with policymakers and promote ongoing awareness of their findings so that interest in the issue does not wane when research concludes.
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.
To provide feedback please contact Kristine Andrews, Jenita Parekh, or Shantai Peckoo.
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