A step towards gender inclusivity in research

Written by: Michele Marenus

Editors: Christina Del Greco, Madeline Barron, and Emily Glass

The lack of guidance on how to be a gender inclusive researcher is frustrating and exclusionary. I’m not a gender researcher—but I do study gender. Meaning, my primary research aims are not to examine gender or gender identities, but it is an important construct in my work. I study the intersection of physical activity and mental health, which has been on the forefront of research for some time now, especially during the ever-challenging coronavirus pandemic. Depression symptoms have increased three-fold since the start of the pandemic and there has been a worldwide decrease in physical activity levels. The relationship between physical activity and mental health has been found to differ by gender but is typically only examined on a gender binary. The American Psychological Association (APA) specifically encourages researchers to protect the dignity of all persons by removing biased language and avoiding misrepresentation of participants, yet most studies still refer to gender in a binary manner. This practice contradicts the empirical evidence that undermines the gender binary and finds that gender exists on a spectrum, and ignoring this evidence therefore violates the ethical principles that guide researchers.

Last fall, my team was conducting a large-scale study on college students to measure physical activity levels and mental health status during COVID-19. College students have been uniquely affected by COVID-19, and we wanted to get a sense of the current profile of college students today. When creating this survey to disseminate to students, I wanted to make sure we were using equitable and inclusive language throughout, particularly while collecting demographic data. So, I set out to find some best practices in survey research. There are many blog articles on survey inclusivity that pop up on a quick Google search, but there is little consistency or consensus across articles. Still, we tried our best to follow these best practices. Some of these practices included putting demographic questions at the end of the survey to prevent stereotype threat (although this is still up for debate), allowing participants to select more than one option for race and gender as well as providing a ‘write in’ option, and using 7 choices for gender and gender identity. These choices included female, male, genderqueer, agender, transgender, cisgender, a gender not listed, and prefer not to say. This was by no means perfect, but I was content with the conscious attempt for inclusivity.

We received hundreds of responses to our survey from a wide variety of people of different ages, races, and gender. Most analyses on data like these are typically done looking at gender on a binary, especially in my field of Kinesiology. When researchers do provide more inclusive options, there typically aren’t enough respondents who identify outside of the gender binary to have a large enough sample size to be analyzed. Because our survey had inclusive options and had such a high response rate, a significant portion of my sample did not identify as only male or only female. However, there was almost no guidance on how to analyze these data. There were tips on writing without gender bias, but not on performing an analysis on more than two gender groups. We didn’t have enough respondents in each of the 7 categories we listed, but if we used three groups (male, female, and a combined group of the 5 other categories), then we would have a large enough sample size for analysis. I wasn’t sure if this was the right thing to do— I had never come across this issue, and I hadn’t seen any other papers address it in this way. I mentioned my frustration of my own lack of knowledge around this subject to some friends and colleagues, and although none of them had ever encountered this issue in their own work, they all encouraged me to pursue this further.

I tasked my small research team with finding the best bias-free language we could use to accurately represent the participants in our analysis, and we found the term TGNC, which stands for ‘transgender and gender non-conforming people’. TGNC is often used in the health care setting and is an umbrella term to include individuals “who have a gender identity that is not fully aligned with their sex assigned at birth”. With this knowledge, we proceeded to look at our gender variables as a trinary: cisgender females, cisgender males, and TGNC peoples. Luckily, we had enough statistical power to see significant differences within these gender categories, which has allowed us to gain insight into the mental and physical health experiences of all people.  I think many people want to be inclusive in their work and life but struggle with how best to do it. I spent many hours, frustrated, trying to find guidance on how to address gender inclusivity in data collection, analysis, and reporting. I expected there to be some sort of inclusivity guidebook for researchers, but resources proved to be difficult to find. Even though many researchers acknowledge that gender doesn’t exist on a binary, many articles still only use two categories because they either don’t include non-binary options or lack the sample size to analyze non-binary categories. I’m hoping this piece can provide a bit of guidance for anyone else coming across this issue. I have the privilege of existing as a cisgender female, and I know that my gender identity is important to me in terms of how I think about my physical and mental health. I want to be able to talk about gender in my work, and I want to be able to do it in a way that represents each person. This solution is by no means a perfect one: gender can be fluid and is on a spectrum—a trinary is not much better than a binary. But I’m hoping it’s a step in the right direction.

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