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.

A 2-Day Seminar Taught by John Skvoretz, Ph.D.

The study of social networks focuses on relationships among the units of some population, and on how the structure of these ties affects outcomes experienced by both the units and the population. Often the units are persons or individuals, but they may be families, households, corporations, or nation states.

Social network analysis is a set of methods for describing, quantifying and analyzing the properties of social networks. This seminar is survey of statistical methods for analyzing social network data. It will focus on testing hypotheses about:

• network structure (e.g. reciprocity, transitivity, centralization),
• the formation/dissolution of ties based on attributes (e.g. homophily),
• local structural effects.

The course begins with models for the local structure of dyads and triads, and next moves to models based on the assumption of dyadic independence. We will then consider models that permit structured forms of dependence between dyads.

Topics include statistical models for local structure (dyads and triads) and graph-level indices, biased net models for complete networks and for aggregated tie count data, exponential random graph models, and stochastic actor-oriented models. Each morning and afternoon will be divided into a presentation of methods and a lab using those methods. This workshop assumes that participants have already taken a first course in network analysis, or have acquired equivalent knowledge through self study.