**Advanced Social Network Analysis – Statistical Analysis for Cross-sectional and Longitudinal Social Network Analysis (17.5 hours)**

**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 and relevant.

**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**

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

Snijders, T.A.B., G. van de Bunt, G., and Ch. Steglich (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32: 44-60.

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