Modules

A detailed daily schedule is available here.

A more general schematic for the 2012 workshop is shown below. Click on any workshop to get details. The works-in-progress conference on Sunday runs 9am to 5pm. The four main sessions run Monday through Thursday from 8:30am to 4pm. Friday is applications day. We begin with a special session in B&E148 at 8:15 in which we report the results of the network analysis of ourselves. Then the two workshops run 9-5pm.

Works-in-Progress Conference (1 day). Jeff Johnson and Joe Labianca

This is a 1-day developmental conference led by Joe Labianca and Jeff Johnson. In the morning, participants with well-developed papers present their work and obtain help from the audience, which includes our panel of experts. In the afternoon, participants break out into small groups to discuss their work in progress. Participants at all levels of progress are welcome, from those just contemplating a network study to those who already have data collected. More ...

Introduction to Social Networks (4 day module). Dan Brass & Dan Halgin

It is a well-balanced introduction to social network analysis, including theoretical concepts, data collection, research design, network measures, using the software and interpreting results. The first day is theoretical, historical and empirical of the field. The remaining three days introduce the main concepts of SNA, including centrality, cohesion, and social capital, as well research methods such as data collection, data management and testing hypotheses. Participants are also introduced to the UCINET/NetDraw software. Compared to the "Analyzing Social Networks" module, this course is more conceptual, assumes no background, and moves a little slower. Participants are shown how to use the software, but many of the bells and whistles are left out. In the labs, the instructors demonstrate the software and participants follow along on their machines. More ...

Analyzing Social Networks (4 day module). Rich DeJordy

This course covers much of the same material as the Introduction to Social Networks, but is more software intensive. It is also more technically oriented in terms of learning the formulas behind measures. It assumed that the participant has read articles that use network concepts, but wants to learn the nuts and bolts of how to really do network analysis. No prior experience using UCINET is assumed. In the labs, participants work independently to complete a set of exercises, and may also choose to work on their own data. More ...

Advanced Social Network Analysis (4 day module). Steve Borgatti

This course is for people interested in both a deeper and broader look at network theory and methods. It is assumed that participants have used UCINET before and are conversant with network concepts. Topics include: analysis of network change, advanced centrality methods (including KeyPlayer methods), advanced approaches to 2-mode data, analyzing negative ties, working with multiple relations, and testing unusual hypotheses. More ...

Stochastic Modeling of Networks (4 day module). Filip Agneessens

This course begins with a 3-hour mini-module on the general concept of statistical network modeling and an introduction into R. Then there are two days on exponential random graph models (ERGMs). This is followed by a one-and a half-day session on Siena, which is a model /program for analyzing longitudinal network data. More ...

Application Modules

There are two modules in this category. The "networks and management" module is a 1-day workshop taught by David Krackhardt. The "networks and health" module is a 1-day workshop taught by Tom Valente.

Mini-Modules

These are a smorgasbord of 1.5-hour modules taught by a variety of instructors. Participants will be able to sign-up after they get here, on a first-come-first-serve basis. More ...

1-on-1 Help Sessions

In addition to the workshops, Professors Mehra and Labianca will set up appointments to discuss your research one on one.

Data Labs

If you have already collected data, you may wish to avail yourself of the data labs, in which our experienced research assistants help you analyze your data.

If you have comments, suggestions, requests etc. we would love to hear them. Just email steve.borgatti@gmail.com.