Introduction to Social Network Analysis
A 2-Day Seminar Taught by Stephen Borgatti, Ph.D.
To see a sample of the course materials, click here.
Interest in social networks has been climbing exponentially since the 1970s. For social scientists, networks can be seen as a fundamental adaptation that facilitates both the coordination and distribution of resources, while simultaneously maintaining the flexibility of independent agents. For physical scientists, networks can be seen as universal structures underlying everything from molecules to galaxies. And for mathematicians and computer scientists, networks provide an abstract and propitious way of representing problems.
The field of social network analysis consists of a vocabulary of theoretical and mathematical concepts for investigating network phenomena, along with a distinctive data model and set of methodologies for collecting and analyzing network data. This course provides an introduction to doing research on social networks. The course is very hands-on, emphasizing mastering the software and using the concepts and methods to answer research questions. It also covers use of network analysis in applied settings such as management consulting and criminology.
We will use the UCINET software package, which can be downloaded from http://tinyurl.com/ucinet. The software is not free, but can be used on a trial basis for 60 days. Please download the 32-bit version only. The course is heavily hands-on, so you must bring a laptop with UCINET installed. Power outlets will be provided at each seat.
Note that UCINET is a Windows program and will not run on a Mac except through Boot Camp or a Windows emulator like Parallels. Knowledgeable Mac and Linux users have also been known to successfully run UCINET on WINE, but the instructor is a Windows user and will not be able to help you with this.
WHO SHOULD ATTEND
This course assumes no prior knowledge of network analysis, but does assume some background in social science research methods, including survey design and simple regression analysis. It is also helpful but not necessary to be familiar with the basics of matrix algebra, particularly matrix multiplication.
To prepare for the seminar, the instructor recommends reading his book on network analysis, which is available on Amazon. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. SAGE Publications Limited.
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 $995.00 includes all seminar materials. The early registration fee of $895.00 is available until April 3.
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 $165 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 SH0502 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, April 2, 2019.
If you make reservations after the cut-off date ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.
- The network perspective
- History of the field
- Key tenets
- Canonical research designs
- Network data
- Importing survey data into UCINET
- Common data manipulations
- Visualizing networks
- Node-level analysis
- Ego-network composition
- Structural holes and brokerage
- Network-level analysis
- Network cohesion
- Network shape
- Detecting subgroups
- Testing network hypotheses
- Introduction to permutation tests
- QAP correlation and regression
“Dr. Borgatti is knowledgeable and very responsive to participants’ questions. This course has prepared me to start using SNA in my own research and also has provided a great theoretical frame for this kind of work. Excellent course.”
Erika Feinauer, Brigham Young University
“Stephen Borgatti’s course is unique in that you’re being taught by one of the leading academics in the SNA field. His breadth of experience in the method and literature is extremely helpful to understanding the context of the method. He is also very open to questions related to your own specific research areas and will apply these questions and ideas to what he covers in the course.”
Justin Vinton, Rutgers University
“Treating myself to two days of immersion in SNA was a worthwhile investment! Having come in with network research questions, I was able to refine my analytic approach over the two days. Having some previous background in SNA allowed me to do this. I treated it as an intermediate class!”
Melissa McDaniels, Michigan State University
“This course is a great introductory course for social network analysis. It covers a great deal of socio-metric networks and provides an informed stepping stone even for persons naïve to the field.”
Noah Kiwanuka, Makerere University
“I have been working on SNA research for almost a year and this course was really helpful to “order” and get a better understanding of SNA concepts and methods. Great applied examples, and great disposition from the instructor to discuss different topics, examples, and questions.”
Matias Placencio Castro, Boston College
“This is a great course. Amazing, very knowledgeable, extremely approachable and helpful instructor. I had taken two previous network courses and this was hands down the most educational, instructional, and helpful.”
“This workshop provides an excellent foundational understanding of how to conduct network analysis from both a methodological and conceptual standpoint. It is great for quantitative or qualitative researchers in any field.”
Kari Roberts, National High Magnetic Field Laboratory
“I highly recommend Dr. Borgatti’s course! It’s very rare to have the chance to interact with such an expert! And he’s super friendly! Statistical Horizons staff are very helpful and friendly too! Things are very well organized!”
Zhi Tang, Rochester Institute of Technology
“I did not know what SNA was until I was asked by my boss to help write a grant and do SNA. We got the funding. So I decided to teach myself SNA. I used Steve Borgatti’s videos on YouTube and also read his book and materials. Alas, I produced the visuals but still wasn’t sure I knew what I was doing. Came to this class and it was it was one of the best experiences. Steve was very easy to understand, was patient in explaining concepts, and readily answered questions. Helped me with personal data questions I had for my projects. Very hands-on class. Thank you, Steve, I learned a lot from this 2-day workshop. I will be applying for more grants in this area for sure.”