# Network Science & Statistics

Networks are complex data objects that routinely violate the assumptions of more traditional statistical methods. New methods for analyzing network data are being developed all the time. My focus is on methods for analyzing networks whose individual elements, nodes and edges, do not follow the same data generating process: i.e., heterogeneous networks. By improving our understanding of network properties and how best to analyze them, I hope to support the use of network methods in psychology, neuroscience, and beyond.

**Representative Work**

**Henry, T. R.**, Ye, A., (In Prep) The Issue of Endogeneity in Network Psychometric Modelling.

**Henry, T. R.**, Fisher, Z., & Bollen, K. (In Review). Bayesian model selection for model implied instrumental variable models.

**Henry, T. R., **Gates, K., Prinstein, M., & Steinley, D. (2019). Modeling heterogeneous peer assortment effects using finite mixture exponential random graph models. *Psychometrika. *http://arxiv.org/abs/1610.05747

**Henry, T. R.**,** **Gesell, S., & Ip, E. (2016). Social position predicting physical activity level in youth: An application of hidden Markov modeling on network statistics. *Social Computing, Behavioral-Cultural Modeling and Prediction (Conference Proceedings 2016 Volume).*

**Henry, T. R.**,** **Gesell, S., & Ip, E. (2016). Analyzing heterogeneity in the effects of physical activity in children on social network structure and peer selection dynamics. *Network Science, 4,* 336–363. doi: 10.1017/nws.2016.2