By Jacqueline Olvera
When the Center for Health Innovation (CHI) announced its Summer Scholar Program in 2014, I jumped at the opportunity to apply. CHI made it possible for AU faculty to enroll in a variety of summer skill building courses and seminars offered at the Mailman School of Public Health at Columbia University. Since I had been working on a project on Mexican migration, I was particularly interested in the Social Network Analysis (SNA) course. I had conducted in-depth interviews with migrants from Tlaxcala, Mexico with funding from the Russell Sage Foundation and was interested in finding out if network analysis would enrich my study. Based on preliminary analysis of these interviews, the data revealed that relationships between migrants were meaningful structures. That is, I was finding that social ties are how migrants find housing, jobs, and information about community resources. And equally as important, the formalized structures of these relationships seemed to be the basis for inclusion and exclusion when forming community. Given my interests in social relationships, I was delighted when I found out that I would indeed be able to enroll in the SNA course.
The course I participated in provided an excellent introduction to the conceptual and computational principles of SNA. On our first day, we covered what Social Network Analysis is and is not, what counts as network data, and how to collect it. We spent time using R, a language and platform for statistical computing and graphing in order to manipulate network matrices and visualize network data. Thereafter, we quickly moved on to a discussion of ego-networks and the meaning of distance, density, and balance within an individual’s networks. By day three, we covered higher-order network structures: the group and entire networks. All the while, we focused on important network structural features such as equivalence, clustering, centrality, and cohesion. For example, in a migrant network computing centrality indices might tell us which individual in a network is the most central or popular.
The fourth day of the course was by the best part – we put our knowledge to work by focusing on applications of SNA. We explored examples from epidemiological research such as the transmission of AIDS, the structure of adolescent romantic and sexual networks, and the dynamics of smoking in large networks. Each of these empirically motivated problems gave us a glimpse into how network analysis is applied. More specifically, we focused on three network processes: Search, Diffusion, and Influence. And, we replicated analyses of classic studies that examine how network ties facilitate the exchange of employment information as well as the efficiency of job search strategies in networks.
On the final day of the course, we covered statistical models and worked through tutorials in R. The instructor introduced network autocorrelation and stochastic actor oriented (SIENA) models. Since I had experience with spatial analytic techniques, the network autocorrelation model was familiar. In a recently published paper, I used neighborhood-level data to estimate spatial lag models that follow similar autocorrelation properties.
After completing the course, I have spent much time thinking more about how I might use the insights of network analysis to ask, what network practices in the small migrant community I have been studying might tell us something about the raising and blurring ethnic boundaries? My hope is that by integrating social network analysis into my research I will make new inroads into the ways in which migrant communities come to make sense of the places they live and work in.