Demographics and Intersectionality in Astronomy

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Demographics and Intersectionality in Astronomy

Results from the Longitudinal Study of Astronomy Graduate Students
July 2017
Susan White, Rachel Ivie, and Raymond Chu

Using data from the Longitudinal Study of Astronomy Graduate Students (LSAGS) and the 2016 Demographics Survey of US American Astronomical Society Members (AASDS), we examine the effects of ethnicity, gender, and disabilities on salaries, persistence in astronomy, and encountering discrimination at school or work. Using multivariate models, we examine the effects of ethnicity, gender, disability, and being a woman of color upon one’s salary, whether or not one persists in astronomy, and whether or not one has encountered discrimination at school or work. At the same time, we can control for other potential explanatory factors including highest degree, year of degree (as a proxy for work experience), employment sector, and whether or not a respondent did a postdoc. Multivariate statistical techniques allow us to consider the effects of gender, ethnicity, disability, and combinations of these statuses on dependent variables (such as salary, persistence in astronomy, and whether or not one encounters discrimination). Use of this statistical method means that the effects of multiple statuses on people’s experiences can be ascertained and that statuses need not be analyzed as if they have isolated effects. Furthermore, these models allow us to avoid problems of using potentially identifiable data in univariate or bivariate analyses.