Bad Gender Measures & How to Avoid Them
A guide by a trans psychologist.
Researchers: This is your regularly-scheduled reminder that if you are measuring gender in a survey, your options should not be “Male” “Female” and “Transgender”.
As a psychologist, survey designer, and statistical consultant, I encounter a lot of bad gender measures in surveys, and have to work with a lot of the bad data sets that result from them. A lot of scientists are making a lot of poorly-thought-out decisions regarding the role of gender in their studies, leading to data that either excludes transgender people entirely, masks their presence in the study, or forces them to pick a nonsensical option that the research won’t know what to analytically do with.
But all these pitfalls can be avoided. All you have to do is be a bit more intentional about your use of gender in your studies. Consider the following factors:
Why are you measuring gender in your study?
First, ask yourself what you are really trying to measure with a gender item. Are you looking at how gender identity is related to some outcome? Are you interested in gender presentation — how a person dresses or is read by society? Are you interested in a person’s biology? Do you care about how they were raised? Are you not sure…
