LAUSR

In this article, I describe how data and econometric methods can be used to study the science of broadening participation. I start by showing that theory can be used to structure the approach to using data to investigate gender and race/ethnicity differences in career outcomes. I also illustrate this process by examining whether women of color who apply for National Institutes of Health research funding are confronted with a double bind where race and gender compound their disadvantage relative to Whites. Although high-quality data are needed for understanding the barriers to broadening participation in science careers, it cannot fully explain why women and underrepresented minorities are less likely to be scientists or have less productive science careers. As researchers, it is important to use all forms of data—quantitative, experimental, and qualitative—to deepen our understanding of the barriers to broadening participation.