The Center for Integrated Latent Variable Research (CILVR)
presents the ONLINE short course:
INTRODUCTION TO SOCIAL NETWORK ANALYSIS
December 10-11, 2021 (Friday-Saturday)
Tracy Sweet, University of Maryland
ONLINE SHORT COURSE: INTRODUCTION TO SOCIAL NETWORK ANALYSIS
Social networks are defined by a set of relationships among a group of individuals and are common in any discipline in which individuals interact. Examples include friendship among a group of people, collaborations among employees in an organization and co-authorship among researchers in a field. Analyzing how and why individuals interact can help researchers better understand both the system as a whole as well as how these interactions impact systemic change. The purpose of this two-day course is to introduce methods for analyzing social network data, focusing on the types of network data common in the social sciences. Although some exploratory and descriptive methods in social network analysis will be covered, the focus of this course is to teach participates how to fit and interpret social network models. This course is targeted for participants interested in learning about social network models and is appropriate for researchers at any stage in their career, students included. We recommend that participants be familiar with R and fitting statistical models although previous experience with social network analysis is not necessary. The course will begin with a brief introduction to R for participants who are new to that software. We will then cover descriptive methods and practice visualizing/exploring networks in R. Much of the course will focus on social network models, social selection models in particular. We will also cover multilevel social network models and discuss goodness of fit. Throughout the course, we will incorporate hands-on practice, analyzing real world data and fitting models in R. After completing the course, participants will have an understanding of quantitative methods available for analyzing social networks as well as the current state of model capabilities.
Full-time student*: $195
*Full-time students must submit student status proof at https://go.umd.edu/CILVR-STUDENT for prompt processing of the registration.
Free for registered HDQM Department faculty and degree-seeking students, although you must register through the internal link.
REFUND POLICY: Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.
More details and registration instructions available on the Complex-2021 page.
For any questions, please contact Ms. Yishan Ding: email@example.com