**Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously.**

Filip Agneessens is an Associate Professor at the Department of Sociology and Social Research, University of Trento. He has published on a diversity of topics related to social networks, including measures of centrality, statistical models, ego- networks and social support, two-mode networks, negative ties, multilevel networks and issues related to data collection. He has also applied social network analysis to understand the antecedents and consequences of interactions among employees, and in particular within teams. Together with Martin Everett, he was a guest editor for a special issue on “Advances in Two-mode Social Network Analysis” in the journal Social Networks, and together with Nick Harrigan and Joe Labianca he guest-edited a special issue on “‘Negative and Signed Tie Networks”’. He has taught numerous introductory and advanced social network courses and workshops over the last 15 years. Together with Steve Borgatti, Martin Everett and Jeff Johnson he co-authored the book “Analyzing Social Networks with R” (Sage, 2022).

**Course Content**

This module covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), and the formation of ties based on attributes (e.g. homophily). The first three days provide an in depth discussion of exponential random graph models (also known as ERGM or p* models). We then introduce longitudinal models such as RSiena models (SAOMs) and relational event models.

Software used includes: MPNet and R packages RSiena.

**Course Objectives**

The module aims to familiarize participants with the formal statistical analysis of network data for selection mechanisms, as well as selection and influence in longitudinal models. Participants will become familiar with specific programmes designed for these analyses and with the mathematical basis for the modelling approaches, and they will learn how to conduct statistical analyses of their own network data. Participants are encouraged to bring with them their own network data to be analyzed using the techniques covered. The course provides a thorough, yet intuitive introduction to these models and does not require advanced statistical knowledge. It does require basic knowledge of logistic regression and the principle of significance tests in classic (survey) research.

In general the module focuses on how do the characteristics of a network of interest differ from chance? At the end of the module, participants should be able to answer questions, such as:

– Is there more reciprocity in an advice network than could be expected by chance?

– Is there a tendency towards homophily? (do smokers tend to be friends with other smokers? and do non- smokers tend to be friends with other non-smokers)?

– Is there more transitivity (are friends of friends also friends) or closure or cyclicality in a network than expected by chance, controlling for the degree distribution?

– Do advice and friendship ties tend to overlap (multiplexity)?

– Considering the friendship network for different classes at the same time, is there an overall tendency towards clustering? Are there differences in tendency between classes?

**Course Prerequisites**

Participants should have taken an introductory course in social network analysis, so be familiar with such terms as reciprocity, density, indegree and outdegree. Participants should also have taken a basic module in (logistic) regression analysis

**Remedial Reading**

Scott, J. 1992. Social Network Analysis. Sage.

**Representative Background Reading**

Wasserman, S., and Faust, K. 1996. Social Network Analysis. Cambridge University Press.

**Required Reading – (this book will be provided by ESS):**

Lusher, D., Koskinen, J., and G. Robins. Editors. 2013. Exponential Random Graph Models for Social Networks. Cambridge University Press

**Background knowledge required**

*Statistics*

OLS – elementary

Maximum Likelihood – elementary

*Computer Background*

R – elementary

**Maths**

Calculus – moderate

Linear Regression – elementary

**POTENTIAL ESS APPLICANTS ARE TO BE ADVISED THAT RECORDINGS WILL NOT BE MADE AVAILABLE FOR THIS COURSE.**

**For in person study, please note: For participation in this course, students are required to bring with them their own laptops.**