Coding Qualitative Data for Social Network Analysis

Support networks are important for anyone’s career development, and they are especially important for professionals with marginalized identities. The support networks and social journeys of women and LGBTQ+ physicists are worth understanding because they can give us valuable insight into the experiences of marginalized people in physics, which will put us in a better position to make physics spaces more equitable and safe for everyone. This talk will review methods for coding egocentric social network data collected during interviews with physicists in academia, industry, and government career sectors. Specifically, we will discuss coding qualitative data for alter, group, institutional, and collegial affiliations and identifications; types of support (or gaps in support); and prominence in a network based on both frequency of appearance in discussion and placement on sociograms. We will illustrate our technique with qualitative excerpts and present some preliminary results. This work represents novel techniques in coding qualitative data for social network analysis and is informed by critical theory on research methodologies.