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How I Became a Person of Colour

Our latest story by guest author Larissa Veloso is a fantastic lived experience of how the collection and definition of social identity data - things like race, ethnicity, sexual orientation, marital status and more - have important implications and impacts. These data collection choices affect the human beings providing the data as well as the human beings that are on the receiving end of decisions made based on that data. 

There are important discussions to be had and decisions to be made in terms of how much social identity data to collect, how to store it, how to use it in analysis, and how to communicate about it. None of these questions have simple answers that would apply correctly across situations. Taking the time to understand that we're making these decisions and documenting them is one of the biggest next steps towards embedding equity in data.

 
Getting Past Binary Gender Data
In the collection of social identity data related to gender, there has been some progress. Increasingly, surveys offer more than a simple male/female binary option. However, figuring out how to analyze non-binary gender data is another matter. According to Rena Bivens, a leading thinker on the topic, even though Facebook, which expanded its gender options from 2 or 3 to several dozen, still mainly analyzes the data as binary
How to analyze multiple response data such as multiple choice ethnic heritage variables. For example, a survey question that asks about your ethnic heritage and allows you to "select as many as apply". Data like this is challenge to analyze without simply collapsing multi-ethnic individuals into a category called "other". While it's fairly technical, there is a very nice R package designed to deal effectively with this exact situation. Analyzing multiple response ethnicity variables is often much more nuanced than simply collapsing everyone into one ethnicity or "other".
New Book Alert!

Released last month, this book has an entire section devoted to "Models of Local Practice" that include tips and practices on how to center the steps in the data lifecycle, such as your data collection, in the culture and context of their native locations.

We're looking for equity problems and successes.

If you have a story or idea you want to share, send me a note by replying to this email.

Project for Equity in Data Science
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