Syllabus

This Ph.D. level course provides an introduction to the analysis of social networks. It is not a survey of social network research. The focus is on the theoretical concepts and methodology of social network analysis, both from a research and consulting perspective. Although technical in a certain sense, the course will not require any mathematical background.

On finishing this course, you should not only understand the basic concepts of network analysis, but be able to carry out your own analyses of network data. This means mastering the software tools as well as analytical strategies. In short, it is meant to be a very hands-on, practical sort of course.

IMPORTANT NOTE: You will need to bring a laptop to class! (If necessary, you can share with one other person. No 3-person teams.) Until the university gives us wireless (which they promised to do by mid-January), I will set up a wireless network in the classroom.

Schedule of Topics and Readings

The schedule topics and readings is on the web at http://www.analytictech.com/mgt780/schedule.htm. Please note that it is subject to change. Also, there are two weeks when I have to be out of town. I have rescheduled those classes for a couple of Fridays. 

On the last day of the course, students will present the results of their semester-long network research project.

Assignments & Grading

The course has just one formal assignment -- the final paper, worth 75% of your grade. This paper (20-40 pages) should report the results of an empirical study of social network data. Please make sure that the paper answers a clear research question -- it cannot be an aimless application of techniques learned in class. However, the project can be inductive or deductive -- you can test hypotheses derived from grand theory, or you can investigate the relationships among a set of variables, and tell a formal story (i.e., construct a theory) based on the results. Empirical consulting projects, in which you use the network analysis to diagnose an organizational problem and prescribe a solution based on the diagnosis, are also welcome. IMPORTANT: Avoid toy projects that are too trivial to ever be published. The written paper is due May 1st, but a 20-25 minute presentation on the main results will be given on April 23rd and April 25th. Please hand in the paper in electronic form only!

The paper may be done collaboratively if the project is more ambitious than a single person could handle. Also, you should consult the University of Kentucky Office of Research Integrity (beware the Ori!) to see if your project needs IRB approval, and do what ever is necessary on this front. Please share what you learn with others in the class so that everyone learns this key competence.

The remaining 25% of your grade will be based on class participation. This is evaluated on the frequency of relevant, constructive contributions that reflect thoughtful reflection on concepts and active playing with data.

Readings

The schedule gives the key readings for each week. In general, I have tried to provide electronic copies of all key readings (though sometimes these are links through services like JSTOR, so you will need university credentials to reach them). A number of handouts, works in press, slide shows etc are also provided. These should be treated as key readings (the distinction is merely that "key readings" refers to published articles).

The schedule also gives an extensive bibliography for further reading in case you are interested. In putting these together, I have borrowed heavily from Dan Brass and Jim Moody.

Books & Software

There are no required books for this course. However, I think you will find the online book Introduction to Social Network Methods (Hanneman and Riddle, 2005) quite helpful. Also, Martin Everett and Jeff Johnson and I are co-authoring a book called Analyzing Social Networks that we would like to get feedback on. I'm making the drafts available on the class google group. Any feedback you have -- from  general comments to actual editing of text -- would be much appreciated.

The following books (available at the school bookstore) are highly recommended:

• Wasserman and Faust. 1994. Social network Analysis. Cambridge. Paperback. Used as background reading.
• Scott, J. 2001. Social network analysis: A handbook.

In addition, you will need to download the following software (a free registration code will be provided in class):

• Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. UCINET 6 for Windows: Software for Social Network Analysis. Harvard: Analytic Technologies. Downloaded free on the web.

Other useful books:

• Degenne and Forse. 1999. Introducing Social Networks. Sage. paperback. 
• Wasserman, S. & Galaskiewicz, J. 1994. Advances in Social Network Analysis. Sage.
• Harary, F. 1969. Graph Theory. Addison-Wesley.
• Knoke and Kuklinski. Network Analysis. Sage.
• Valente, T.W. 1995. Network Models of the Diffusion of Innovations. Hampton Press. paperback. (Getting hard to find.)
• Nohria and Eccles. Networks and Organizations.
• Burt. The social structure of competition.

Online Groups and Lists

You must join the following online groups/listservs:

• MGT 780 Group. A Google group just for this class.
• UCINET User's Group. A Yahoo tech support group for users of Analytic Technologies network analysis software
• SOCNET. The main listserv for social network researchers.

In addition, if you are serious about social network research, you really should join INSNA, the professional association for social network researchers. It's not expensive.

Miscellaneous

• Interruptions for questions and comments are fine
• I am not bothered by students' multi-tasking (e.g., checking email while paying close attention)
• Feel free to eat lunch in class