My research focuses on questions about ego network data, network inference and missing data. Much of this work deals with methodological issues, developing new algorithms to facilitate making inference from different kinds of sampled network data. I have put together R code to help run the developed methods and have made them publicly available on my github page.

Ego Network Inference: In this repository, you can find example code and all necessary functions to run the Ego Network Configuration (ENC) inferential approach introduced in: Smith, Jeffrey A. 2012. “Macrostructure from Microstructure: Generating Whole Systems from Ego Networks.” Sociological Methodology 42:155-205. The inferential approach offers practical solution for researchers interested in global network structure where only sampled data can be collected. See also: Smith, Jeffrey A. and G. Robin Gauthier 2020. “Estimating Contextual Effects from Ego Network Data”. Sociological Methodology. 50:215-275 doi: 10.1177/0081175020922879.

Case Control Logistic Regression for Ego Network Data: In this repository, you can find the R functions and examples to run the case control logistic regression model introduced in: Smith Jeffrey A., McPherson, Miller, and Lynn Smith-Lovin. 2014.“Social Distance in the United States: Sex, Race, Religion, Age and Education Homophily among Confidants, 1985-2004.” American Sociological Review 79:432-456. The model takes basic ego network data, including number of partners, ego attributes and alter attributes and estimates the strength of homophily (relative to chance) along the demographic dimensions of interest.