Welcome everyone to my website. My name is Jeffrey Smith and I am an assistant professor at the Department of Sociology at the University of Nebraska-Lincoln. I joined the faculty at UNL in 2013, shortly after receiving my Ph.D. from Duke University. My work falls at the intersection of network analysis, substantive social stratification research and traditional statistical methods. Much of my work is focused on developing new models and frameworks for studying stratification. My work has been published in the American Sociological Review, Sociological Methodology, Social Networks, Social Science & Medicine, as well as other venues. You can find detailed information on my publications (including links to the actual papers) on this site, as well as from my Google Scholars page. This site also includes information about the courses I have taught, including all of the materials from a recent workshop on network analysis. Feel free to contact me at jsmith77(at)unl.edu.
My research explores large-scale social inequalities by addressing three core questions. In what ways can we use social network data to study systems of stratification? What are the limitations and promises of network data? And what unique questions can we address using network data that we would be otherwise unable to answer? For example, I am currently working on a series of projects on social boundaries. My work on social boundaries began with an interest in homophily, or the idea that birds of a feather flock together. It is a pervasive social fact that people who are similar are more likely to know each other than people who are dissimilar. I take data on social relationships, such as marriage or friendship rates, and use it to measure the social boundaries in a population. I am currently using this approach to explore the changing meaning of education and race/ethnicity in contemporary U.S. society. Methodologically, I am working on a number of projects on network sampling and simulation. For example, I am working on a project to make comparative network analysis more feasible. The goal is to make inference about the social cohesion of different schools, organizations or neighborhoods using sampled network data (rather than a full census, which is typically required) and simulation techniques. I am also working on a related project using sampled network data to make inference about different diffusion processes. Other ongoing projects involve looking at the effect of missing data on network measurement.