Would you rather adopt a standard and unchanging set of identity categories (like race or sexual orientation) across all the data projects in your country so that current and future data sets can be easily merged/compared, or would you rather that every data project create its own unique categorizations that are the best fit for the people in that particular project without regard for standardization?
- Standardize the categories!
- Custom categories for each project!
Obviously, these questions are intended to spark discussion by juxtaposing two possible worlds at extreme ends of this dilemma, but what are your thoughts on custom vs standard when it comes to something as complex and evolving as identity?
I voted for custom categories, but with the following caveat: include a plan for monitoring the changes in the way people identify from project to project. I anticipate that clear trends or clusters of categories will emerge, which will give a fascinating picture of how identity evolves over time. You could even include a side project that tries to “map” significant economic, political, or cultural shifts onto the emerging clusters.
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While custom categories (my vote) are important so that programs or research projects can understand the communities of focus, I would love to see a taxonomy-style approach, where there would be a roadmap for rolling up customized categories into standardized categories to allow for aggregation and simplify comparison.
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I believe one could do both. It would involve adding some more (and probably redundant for participants) questions but would make the data more comprehensive. For example, in our survey, we have to ask people to choose between binary gender because we use census data for weighting (and census data only does binary gender). But we’ve been trying to brainstorm how to be more inclusive and capture non-binary genders and cis vs trans, and we’re thinking of adding an additional question that has more options.
