Introduction to Social Network Analysis
A 2-Day Seminar Taught by John Skvoretz, Ph.D.
Our lives play out through the relationships we maintain with others. Much social research assumes that these relationships can be ignored, and focuses instead on how individual attributes influence such outcomes as success, health, and sense of well-being. Social network research takes a contrary view, placing explanatory power in the connections we have to others and how the overall patterning of those connections contributes to the important outcomes in our lives.
A social network perspective can provide novel explanatory variables (betweenness, centrality, structural holes, etc.) to account for why individuals and groups experience differential outcomes in wide variety of settings. Here are some examples from the instructor’s current research projects:
- The differential adoption of evidence-based instructional practices as a function of networks of teaching and research discussion among STEM faculty.
- The diffusion of misinformation and competing narratives within and across online platforms.
- The extent of intergroup associations between school children in five European countries.
- Friendship and sexual contact networks among Latino men who have sex with men and their usage of medication to prevent HIV.
More specifically, the study of social networks focuses on relationships among the units of a population. It also investigates how the structure of these ties affects outcomes experienced by both the units and the population. Often the units are persons, but they may be families, households, corporations, or nation states. Social network analysis refers to the methods by which properties of social networks are described, quantified, and analyzed. This workshop is an introduction to these methods.
The course uses network analysis packages for the R environment (network, sna, statnet, igraph, Intergraph, ndtv) and used through the RStudio interface. Some familiarity with R and RStudio is helpful. Both the environment and the interface are free to download and use. Exercises that illustrate the concepts, measures, and types of analysis are plentiful and so students must have a device with the R environment and RStudio interface preinstalled. There are versions of these utilities for all major operating systems.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
WHO SHOULD ATTEND?
The study of social networks is an interdisciplinary field and so students from a variety of backgrounds are welcome: students from Sociology, Anthropology, Criminology, Political Science, Management, Public Health, Industrial Engineering, and Computer Science can all benefit from the workshop, although examples to illustrate concepts and for practice exercises are drawn primarily from social and political science.
The two paperback texts below are recommended. In addition, each session will have readings from the research literature made available as pdfs to participants.
Prell, C. 2012. Social Network Analysis: History, Theory, and Methodology. Los Angeles: Sage.
Robins, G. 2015. Doing Social Network Research: Network-based Research Design for Social Scientists. Thousand Oaks, CA: Sage.
Location, FORMAT, AND MATERIALS
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
REGISTRATION AND LODGING
The fee of $995 includes all course materials. The early registration fee of $895 is available until March 17.
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).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $164 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STH416 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 16, 2020.
If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.
- Introductory Overview of Social Network Analysis: Motivating Examples, Basic Vocabulary, Research Hypotheses Investigated in the Literature
- Data Collection and Network Analysis Packages for the Representation and Visualization of Network Data
- Centrality, Structural Holes and Brokerage
- Subgroups, Blockmodels and Positions
- Density and Connectivity, Weak Ties and Strong Ties
- Network Hypotheses and Methods for Evaluating Them
“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 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, TransUnion
“Very helpful for understanding advanced SNA. Nice instructor.”
Huiwen Xu, University of Rochester
“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