A study featured in BMJ Open Sports and Exercise Medicine sheds light on the significance of incorporating both sex and gender identity within comprehensive data collection methods.
Dr. John Armstrong from King’s College, Dr. Alice Sullivan from University College London, along with independent researcher George M. Perry from the USA, undertook a comprehensive analysis. Their study delved into the performance data of individuals competing in the non-binary category across 21 races in the New York Road Runners database.
Apart from biological outcomes and criminology, limited empirical research has focused on validating the premise that gender identity outweighs biological sex in influencing disparities in outcomes. The researchers meticulously examined a dataset comprising 166 race times achieved by non-binary athletes, sourced from a pool of 85,173 race times. This dataset was selected due to its consistent format and comprehensive coverage of non-binary athletes.
The absence of explicit sex information for non-binary athletes necessitated innovative methodologies. The researchers derived the sex of these athletes from their previous races. If that was not available, they employed a novel technique to model athletes’ sex based on their given names using data from the US Social Security Administration.
Linear models were constructed, employing race times as the outcome variable, with explanatory variables derived from biological sex, gender identity, age, and specific race events.
The analysis revealed a notable discrepancy in race times among individuals identifying as non-binary. Interestingly, the findings didn’t support the idea that the gap between biological males and females diminishes for those identifying as non-binary.
Furthermore, the study indicated that non-binary athletes might exhibit slower race times when factors such as sex and age are accounted for.
Dr. John Armstrong is a respected figure in Financial Mathematics at King’s College. He emphasized the importance of recognizing the role of both gender identity and biological sex.
He highlighted, “Gender identity is clearly important to many people, but nevertheless sex matters. Given the lack of empirical evidence supporting gender-identity theory, one should not assume by default that gender-identity is a more powerful explanatory variable than sex.”
“Being an objectively measurable binary variable, sex has considerable explanatory advantages over gender identity. Our results illustrate that if we want to understand the needs of gender non-conforming individuals, it is vital to control for biological sex as it is likely to play a significant role in any analysis. Both sex and gender identity should therefore both be considered useful explanatory variables in data collection.”