The discriminatory outcomes are not related to people who share protected characteristics

Please refer to the positive action guidance. After that, come back to the tool and start with Question 1. 

• There might be other things you can do to address the issues like gaps in participation if positive action is not permissible .
• There might be secondary factors present that relate to personal characteristics, responsible for the issue to occur. Data settings can be hiding reality. 

E.g. Are people of colour more likely to be overrepresented in other groupings (e.g.) care leavers or people who live in social housing? Could the issue be about care leavers or is it really about race? If the real issue related to race, this is a protected characteristic and therefore we can and should take positive action. Can we look at the data in a way to allow us to consider intersections of characteristics?

E.g. Are trans students less likely to apply to an engineering course than cis gendered students? Is it just trans students who are less likely to apply or, are non-binary or LGBTQ+ students also less likely to apply for an engineering course? Are we looking at the dataset too narrowly?