Statistics

To support the development, maintenance, and dissemination of Stan, a probabilistic programming language that simplifies Bayesian modeling and data analysis.

How does the understanding of social networks contribute to social science? In particular, (1) which network features or observable characteristics encode social structure; (2) how do these features contribute to the formation of connections or social ties; and (3) how does network structure impacts diffusion, specifically the spread of influences, opinions, and diseases? A key difficulty in studying these questions is that most contributions to current understanding in this area come from a small number of applications where full network data are readily available.

Some statistical models used in education research are complex. This complexity arises in part because the data structures that underlie these statistical models involve multiple nested (i.e., cross-classified multilevel models) and non-nested groupings (i.e., partially-nested designs). Another source of complexity in these models results from the fact that key variables, such as student achievement, can be measured only indirectly and are represented in the model by latent variables.