Demographic Labels:
A Worksheet for Researchers

Navigating competing conventions around labeling can be challenging. Ultimately, critical self-reflection can inform data curators and analysts:

What labeling schema was used in the initial dataset(s)? 

Do you have reason to believe this schema may lack accuracy or specificity?

For instance, perhaps patients in your dataset are grouped exclusively as male or female. Consider how this schema affects stigma or usability of health
research by gender expansive people.

Can you update this schema in a way that is more consistent with community-informed approaches without affecting the integrity of your dataset?

Researchers using secondary data may feel constrained by the labeling schema used in the dataset(s), but there may be methodologically sound ways to work with the data in a way that is more consistent with updated recommendations.

What labeling schema is the most relevant to your research question?

For instance, there may be reasons to aggregate or disaggregate cis and trans women, depending on whether factors like social oppression, exogenous hormone use, or menarche are germane to your research question.

Does a sociodemographic labeling schema need to be used to answer your research question? 

For instance, NASEM’s recent guidelines on population descriptors recommend that “​​Researchers conducting human genetics studies should directly evaluate the environmental factors or exposures that are of potential relevance to their studies, rather than rely on population descriptors as proxies” (emphasis added). If a more precise set of descriptors can be used rather than demographic categories, use those descriptors.

How will you acknowledge constraints in your limitations?

If you plan to publish your work, consider how you may thoughtfully articulate the constraints of your labeling schema, especially when working with secondary data.

Finally, remember that demographic labeling is a choice with ethical consequences.

Health research can provide the foundation for clinical care, drug development, public policy, and allocation of resources. Researchers should strive to categorize groups in ways that reflect those groups’ values and enable them to seek relevant care.