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Analysing social network dynamics & peer effects Christian Steglich, University of Groningen [email protected] Summary This week-­‐long course addresses the statistical analysis of social network data, with a focus on network change processes (selection and de-­‐selection of peers) and processes of social influence (selected peers affecting individual behaviour and attitudes), following the stochastic actor-­‐based approach of Snijders & colleagues (Snijders, van de Bunt & Steglich, 2010; Steglich, Snijders & Pearson, 2010). Classes will mix methodological lectures and hands-­‐on computer work. Students participating in this course should develop problem awareness related to the analysis of non-­‐independent data, in particular complete network data collected in a longitudinal design. They should acquire knowledge about the prevalent statistical approaches and techniques to analyse complete network data, and develop the practical skills to perform these analyses with the pertinent software. Content Social network analysis is the study of interdependencies, between social actors and between dyads (i.e., pairs of actors). As such, the whole discipline is at odds with the independence assumptions underlying the most common statistical methods. In consequence, social network data require non-­‐standard techniques of data analysis. While for personal (a.k.a. ego-­‐centered) network data, some independence can be retained through sampling (e.g., by assessing personal networks of a random sample of focal individuals), this is not the case for complete network data, where the totality of network relations in a well-­‐defined group of social actors is assessed. During the course, we cover prominent statistical approaches and techniques designed for analysing single observations of complete networks, such as: Methods based on permutation tests, various conditional independence models, and exponential random graph models. The larger part of the course will be devoted to the longitudinal case. Here, stochastic actor-­‐based models for network evolution will stand central. The basic model allows analysing global network change as emerging from local decisions taken by the actors in response to their personal network environment. Extensions of the model facilitate the analysis of co-­‐evolving individual actor characteristics (i.e., peer influence processes), multiplex networks, and two-­‐
mode networks. In the accompanying computer labs, students will learn how to practically work with the models and methods, making use of different software packages. Prerequisites Course participants should have a basic understanding of statistical principles (bachelor level statistics). Prior knowledge of social networks is of advantage, but not a strong requirement; a social networks primer will be part of the first course meeting. Students are expected to bring their own laptop to the course, on which they should already pre-­‐installed the R statistical software environment and some R packages, as well as the visone software for network visualization. Instructions for this will be given well before the course starts. The operating system on the laptops can be Windows (32 or 64 bit), Linux, or Mac OS; in principle, all software we will work with is designed for platform-­‐independence. Participants who want to analyse own data should make sure beforehand that their data sets are of suitable size (between 20 and a few hundred actors per network) and in a format that the software can handle. For this to happen smoothly, please contact the instructor at least one week prior to the course. Literature Ripley, Ruth, Tom Snijders, and Paulina Preciado, 2012. “Manual for RSiena.” University of Oxford : Department of Statistics; Nuffield College. http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf Snijders, Tom, Gerhard van de Bunt, and Christian Steglich, 2010. “Introduction to stochastic actor-­‐based models for network dynamics.” Social Networks 32, 44-­‐60. http://dx.doi.org/10.1016/j.socnet.2009.02.004 Steglich, Christian, Tom Snijders, and Michael Pearson, 2010. “Dynamic Networks and Behavior: Separating Selection from Influence.” Sociological Methodology 40, 329-­‐393. http://dx.doi.org/10.1111/j.1467-­‐9531.2010.01225.x Software & documentation The R project for Statistical Computing: http://www.r-­‐project.org Analysis and Visualization of Social Networks: http://visone.info The SIENA homepage: http://www.stats.ox.ac.uk/~snijders/siena