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Social Networks: Statistical Approaches - Online Course

A 3-Day Livestream Seminar Taught by

John Skvoretz
Course Dates: Ask about upcoming dates
Schedule: All sessions are held live via Zoom. All times are ET (New York time).

10:00am-12:30pm (convert to your local time)

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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 a survey of statistical methods for analyzing social network data. It will focus on testing hypotheses about network structure and building models to account for regularities in observed social networks of research interest to the participants.

Starting February 8, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 

Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

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“My favorite part of this seminar was walking through examples in R in real time."

“My favorite part of this seminar was walking through examples in R in real time. This allowed me to follow along, and write comments in the provided scripts that interpreted the commands and output.” 

Nicole Johnson

Temple University 

"... very knowledgeable and provided relevant materials and interactive sessions..."

“As a grad student working full time, this survey course on social network statistical approaches was an excellent opportunity to learn, and improve my understanding of, some very useful social network statistical tools. The instructor was excellent – very knowledgeable and provided relevant materials and interactive sessions that helped me with questions I had about using social network statistics in my dissertation research. Highly recommended!”

Nelson Jatel

Roads University

"John understands every detail of complex network models..."

“This course was really helpful because I had access to real R scripts that I can use and replicate for my own research. John understands every detail of complex network models and explains them in a very clear way.”

Seungho (Andy) Back

University of Toronto

“I thought this was a fantastic course..."

“I thought this was a fantastic course. This course helped to clarify and expand upon what I had previously learned. Learning how to test if certain network structures are significantly different from chance was useful and getting a detailed explanation of how to interpret ERGM terms and ERGM model fit statistics was much better through this course than what I’ve previously tried to teach myself with textbooks.”

Megan Evans

Pennsylvania State University

“The material is very relevant to audiences outside of its origins..."

“The material is very relevant to audiences outside of its origins in sociology and related fields. It demonstrates the presence of mathematical rigor in an aspect of studying relationships that allows researchers to go beyond descriptive statistics and pretty pictures.”

Steve Vejcik