We know that women students and staff remain underrepresented in Higher Education STEM disciplines. Even in subjects where equivalent numbers of men and women participate, however, many women are still disadvantaged by everyday sexism. Our recent research found that women who study STEM subjects at undergraduate level in England were up to twice as likely as non-STEM students to have experienced sexism. The main perpetrators of this sexism were not university staff, however, but were men STEM degree students.
They do include the effect size of including non-binary students when they write “(nb. Non-binary students account for 0.3% of this total)” etc. so the impact on the actual data is shown, if you’re concerned about the statistical analysis. It also does make sense to group them together in this context as they are both minorities in STEM. However the way the article is written makes it clear that including non-binary students was an afterthought; if it was clear in all the data and headings that the data is for both non-binary and female students with the interpretation that they are looking at just “students who aren’t men” then it would have been a lot better.
We cannot do effective corollary research if groups are not independently researched with their own data, a ‘minimum impact’ is still an impact, one which can be used to portray a larger or smaller effect than there is between the actual groups being compared against, especially when there’s a distinct call of ‘white males’ being a problem with no determination of class, culture or variance of religious vs non religious.
People are not blocks, they don’t vote as blocks they don’t work as blocks and they most assuredly do not behave as blocks. It’s important to specify, separate, and effectively research each group and sub group in order to determine the veracity rather than just applying a claim to a useful and popular current enemy, e.g. ‘white male’.