MGT 780 Social Network Analysis
Spring 2016, Tuesdays 10am-12:30pm, B&E 323J
Class site: www.mgt780.net
This is a PhD level course on social network analysis. The focus is both theoretical (e.g., what are the key concept of social network analysis) and methodological (e.g., how do we actually carry out research on social networks). What the course is not is a survey of social network research to date (we have a separate course for that taught by Prof. Dan Brass, typically in the Fall). The course begins with a definition of a social network and a review of key concepts from the underlying mathematical field of graph theory. We then move on to dyadic concepts in network analysis, such as the notion of graph-theoretic distance. Next we cover node-level concepts, such as centrality and ego-network structure. Next we cover whole-network level concepts, such as network density. The end of the course is devoted to issues of research design and methodology, including data collection and analysis techniques.
GOALS AND OUTCOMES
This is a hands-on course with the objective of teaching a student how to do a network analysis. At the end of this course, a student should be able design and implement a social network analysis research project, including analyzing the data and writing up the results for a journal.
The deliverables for this course consist of:
- A publishable research paper
- A presentation of your work
- Class participation in general
In addition, I will assign homework exercises which, while not required, are strongly recommended. You should bring a laptop to every class in order to do the in-class exercises.
In past years, I have taught this as a lecture course with questions. This year (with a few exceptions), I will teach it discussion style. This means you need to do the readings and be ready to ask questions about it in class.
Analyzing Social Networks by Borgatti, Everett and Johnson (2013). It is available online at Amazon or Sage. In the schedule, this book is referred to as ASN.
For the serious student, I also recommend purchasing Wasserman and Faust's (1994) Social Network Analysis. Although dated, it is still a very useful reference book. Another book I recommend is the Sage Handbook of Social Network Analysis edited by Scott and Carrington (2012).
In addition, we will be using the UCINET software. You should download and install UCINET as soon as possible in order to iron out any problems. A free registration code will be made available to you in class (but the program will run for 60 days without the registration code).
CONTENT OUTLINE AND SCHEDULE
CONTENT OUTLINE AND SCHEDULE
The topics and assignments for each week are given in the schedule of classes, which can be found at this url:
Please note that while we normally meet in B&E 323J, there is one day (Feb 21) when we have to meet in BE 223J.
ASSIGNMENTS AND GRADING
Grading for this course is based on just three things: (1) a research paper (worth 60% of your grade), (2) a presentation on your research (worth 15%) and (3) class participation (25%). There are no exams in this course.
Research Paper (60%). For the paper, you must design and implement an empirical study of social networks. While you are not required to submit this paper to a journal for publication, it should be of publishable style and written up in a standard format, such as AMJ or APA. Copies of past papers are available on the class website. The paper is due via email two days before grades are due to the University (see the schedule for the exact date). Important: please include all figures and tables in the body of the paper, not at the end.
Presentation (15%). The presentation is an oral version of the research paper. It should be informal in style but otherwise complete, giving the aims, methods and results of the research. The presentation is made on the last day of class.
Class Participation (25%). I expect active participation in the classroom. My hope is that you will want to participate because we will be discussing interesting ideas. The abilities to interact with your colleagues effectively, to contribute to a group discussion, and to advocate an informed position are essential skills that will prepare you for the transition to a professional career. Your participation grade is based on your preparedness for class (e.g., having read the assigned reading), demonstration of a firm grasp of material covered, a willingness to seek clarification as appropriate, and the ability to integrate concepts and multiple perspectives. I will grade your participation according to the following criteria:
- - the frequency and quality of your contributions to classroom activities
- - the frequency and quality of your answers to the case discussion questions
- - the quality of your feedback to presentations of other students
- - the assessment provided by your fellow team members of your contribution to team assignments and discussions
Important: much of the class involves hands-on work on the computer. You need to bring a laptop to every class.
Grading Scales. The correspondence between letter grades and numerical percentages is as follows:
90 – 100%
80 – 89%
70 – 79%
0 – 69%
As a PhD course, attendance is not strictly required, but it is expected (and necessary for a good participation grade). I would appreciate being notified ahead of time if you are not going to be attending any particular class.
You have a responsibility to maintain the highest standards of academic integrity in both individual and group work, and to comply with the University of Kentucky policy on academic integrity. Any instances of cheating or plagiarism will be subject to the disciplinary procedures of the University. Please speak to me if you have any questions about academic integrity or concerns about any classmate’s behavior. Please bring any ethical questions or concerns to me before submitting an assignment or participating in an activity. Two general rules of thumb: When in doubt about using material, make sure you cite it. When in doubt about collaborating, sharing, etc., don’t do it without checking with me.
If you have a documented disability that requires academic accommodations, please see me as soon as possible during scheduled office hours. In order to receive accommodations in this course, you must provide me with a Letter of Accommodation from the Disability Resource Center (Room 2, Alumni Gym, 257‐2754, email address firstname.lastname@example.org) for coordination of campus disability services available to students with disabilities.
The preferred way to contact me is via email (email@example.com). You can also try my office phone (257-2257) or just drop by my office (see above). You can also arrange an appointment to meet -- just send me an email.