Does anyone have any best practices they can share about disaggregating data that contains small populations where an individual may be identified? I’ll be working with data about our employees and I’m excited that people are on board with using a more intersectional lens to see how our staff does or does not look like the population we serve. Because of the history of the field I’m in, we have a long way to go to make sure we represent the people we serve. That means some of our staff will be identifiable in that intersectional/disaggregated lens. I’ve read through tons of info about disaggregation but can’t find any best practices to make sure we have an intersectional lens while keeping in mind that we don’t want anyone to feel like they are a token or in the spotlight. In the past, my org has just chosen not to show any group that has less than 10 people. That will miss a lot of our staff if we use an intersectional lens. I’d appreciate any resources people have to share!
Kind regards,
Rebecca
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Hello I agree with data suppression under 10. It aligns with how data are often suppressed in research and reporting. I’ve also seen suppression under 6 in some research articles and data reporting . What I do in my role is combine like groups in an effort to have a sample large enough to share. However even then some groups aren’t large enough to report out. Privacy of identifiable data is upmost important in my opinion. So I show suppressed data with a * . Even when your Ns are too low to show indicators such a * can still be useful as they say the counts of this group are low.