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, traditional statistical methods and social stratification. I have done methodological work on network sampling and missing data, as well as more substantive work on network processes like homophily and status. My work has been published in the American Sociological ReviewSociological MethodologySocial Networks, Social Science & Medicine, as well as other venues. You can find detailed information on my publications (including links to the 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.

Current Research

My research explores methodological and substantive problems related to networks, individuals, and the broader social context. Methodologically, my work centers on a core problem in network studies: how can network structure be measured when there is incomplete information, either because there is missing data or because the data itself come from a sample. The larger agenda is to make it easier to specify and test theories based on a network view of the social world—where the complex interdependencies between actors are explicitly mapped, even with limited data. For example, I am currently working on a project that uses sampled network data to make inference about diffusion processes. Other ongoing projects involve looking at the effect of missing data on network measurement. Substantively, my work pushes a contextual approach to studying social networks. The key question is how network features, like hierarchy, cohesion or group structure, vary across contexts, such as schools or neighborhoods. The larger goal is to analyze contexts, networks and individuals holistically: where contextual features, like demographic composition and economic inequality, shape network features, which, in turn, shape outcomes at the individual-level, like deviance or health.