Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

An 8-Hour Livestream Seminar Taught by Craig Rawlings, Ph.D.

The focus of this seminar is the application of social network methods to the empirical analysis of real-world data sets. Rather than discussing the basics of social network analysis (SNA), we will focus instead on the “now what?” question that comes after having obtained social network data. In short, we will get to work on the various ways of describing and analyzing social structures. The emphasis is on getting lots of results that can eventually be curated and refined for a research report.

We will briefly review the big picture of why social network analysis is so useful for social scientists, especially for understanding core theoretical concerns such as modeling diffusion, social influence, and integrated social structures (i.e., roles and communities). The bulk of the course will then be aimed at providing the techniques needed to “see” social structures using visualizations and various metrics. Following this course, you should be more prepared to take a advanced course on the statistical modeling of social networks, such as the one offered here.

Detailed sets of tutorials in R will be provided. Each tutorial will walk you through the logic of social network tools using real-world social network data, with the goal of developing the tools needed to understand where these social structures come from and how they affect the social world.

The course will draw on my recent coauthored book Network Analysis: Integrating Social Network Theory, Method, and Application with R. The book has a large set of accompanying detailed R tutorials.

Rather than try to cover the entire book, we will focus on the chapters and tutorials that get at the key results that help the researcher understand social structural features in their networks. The course does not emphasize theory or math, but rather how to solve the practical problems that arise when actually analyzing network data. The emphasis will be on generating the visualizations and analyses that are the core endeavor of seeing the structure of a network.

On Day 1 we will introduce the basics of seeing structure. The guiding question of the day will be: “How cohesive is my network?” We will answer this question using basic network visualizations, dyad/triad censuses, and centrality/centralization measures. We will delve deeper into the question by analyzing social structure as more or less cohesive and integrated communities, as well as role positions.

On Day 2 we will introduce the basics of structural prediction. The guiding question of the day will be: “what are some of the causes and consequences of such a structure?” We will focus on the exogenous and endogenous factors that generally combine in forming social structures, and then some ways to think about structures as conduits for diffusion and social influence. We will close with a discussion of two-mode networks and duality in network structures.