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Although it is well known that Black women are underrepresented in computing, less is known about their pre-college experiences. We hypothesize that inequities at the K-12 level result in Black women's underrepresentation in computing, because Black women have accumulated less social capital and are less embedded in courses and organizations related to computing prior to college. This paper reports the initial findings from the first round of a survey designed to gather the pre-college computing experiences of Black women and their peers. Black women in our sample were less likely to report participating in formal computer science (CS) education in school, slightly more likely to report participation in outside-of-school computing programs, about equally as likely to pursue computing experiences independently, and more likely to have had no pre-college computing experiences at all. We found that Black women were less likely to report that they were told they would be a good computer scientist, especially by friends, teachers, and guidance counselors, thus reflecting weaker social connections and lower levels of social capital. These findings suggest that organizational embeddedness or social ties from pre-college computing experiences may indeed be a factor in Black women's underrepresentation in computing and that access to these experiences outside of the formal classroom may be particularly important. The survey is one part of a study that will feature a second round of data collection in another state, analysis of state-level longitudinal data, and interviews with Black women. 

This experience report shares lessons learned when expanding demographic options on an undergraduate survey. The study is designed to better understand the relationship between pre-college computing experiences and the choice to major in computing, particularly focusing on Black women’s experiences. Expansive options for gender (5 plus an openended), race (18 non-mutually-exclusive options), and disability (8) gave respondents more opportunity for specificity. Yet we faced unexpected challenges in analysis and interpretation as we hadn’t considered the implications of being so expansive ahead of time. This paper presents our lessons learned, analysis choices and plans for future iterations of the survey.