Graduate Course Proposal Form Submission Detail - SYA7357
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Approved by SCNS
Submission Type: New
Course Change Information (for course changes only):
Comments: Elective for Sociology. Number updated 9/29/14. GC reviewed 10/12/15; pending credit hours; variable?; program fit; emailed 10/12/15. Updted 1/27/16 To GC. 4/11/16. Approved. To USF Sys 5/18/16; to SCNS after 5/25/16. SCNS apprd 7940 as 7357 eff 8/1/16
- Department and Contact Information
Tracking Number Date & Time Submitted 4840 2013-10-30 Department College Budget Account Number Sociology AS 126300 Contact Person Phone John Skvoretz 47288 email@example.com
- Course Information
Prefix Number Full Title SYA 7357 Introduction to Social Network Analysis Is the course title variable? N Is a permit required for registration? N Are the credit hours variable? N Is this course repeatable? N If repeatable, how many times? 0 Credit Hours Section Type Grading Option 3 C - Class Lecture (Primarily) R - Regular Abbreviated Title (30 characters maximum) Social Network Analysis Course Online? Percentage Online C - Face-to-face (0% online) 0
Introduction to the methods by which properties of networks are described, quantified, and analyzed with attention to networks of interest to social scientists (such as, social, knowledge, and semantic networks).
A. Please briefly explain why it is necessary and/or desirable to add this course.
Replacing Selected Topics with Permanent number; already listed in program
B. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service?
6-12 students every other year
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
Yes, 3 or more times
D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
In addition to a doctorate/terminal degree, instructors should have an active research agenda in social network analysis.
- Other Course Information
To understand what social networks are and how to study them.
To gain knowledge about important concepts and variables in the study of social networks.
To become familiar with the methods for analyzing the properties of social networks.
B. Learning Outcomes
Be able to describe what social networks are and how to study them.
Know important concepts and variables in the study of social networks, including the ideas of density, centrality, path length, homophily, clustering coefficient, strong ties vs weak ties, ego networks vs. complete networks, blockmodelling, clique structure, reciprocity, transitivity, dyad census, triad census, structural holes
Understand methods for the analysis of social network data and be able to apply them to new circumstances and data sets.
C. Major Topics
A Quick Survey of Social Network Analysis
Introduction to Network Analysis Packages and Collection, Representation, and Visualization of Network Data
Structural Holes and Brokerage
Subgroups and Positions
Density and Connectivity
Strong Ties and Weak Ties
Statistical Analyses Using Node Level Indices
Statistical Analyses Using Graph Level Indices
Statistical Analyses of Dyads and Triads: Reciprocity, Multiplexity, Exchange, Transitivity, Closure, Similarity
Statistical Analysis of Complete Networks
Hennig, M., U. Brandes, J. Pfeffer, and I. Mergel. 2012. Studying Social Networks: A Guide to Empirical Research. New York : Campus Verlag.
Prell, C. 2012. Social Network Analysis: History, Theory, and Methodology. Los Angeles: Sage.
Valente, T.W. 2010. Social Networks and Health: Models, Methods, and Applications. New York: Oxford University Press.
E. Course Readings, Online Resources, and Other Purchases
Borgatti, S.P., M.G. Everett and L.C. Freeman. 2002. Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies. Version 6.450 (as of 13 December 2012). From http://www.analytictech.com/ucinet/ download free then after 60 days purchase student copy for $40 or download another free copy.
Butts, C.T. 2010. sna: Tools for Social Network Analysis. R package version 2.2-0. http://CRAN.R-project.org/package=sna.
Handcock, M.S., D.R. Hunter, C.T. Butts, S.M. Goodreau, and M. Morris. 2003. statnet: Software tools for the Statistical Modeling of Network Data. http://statnetproject.org.
R Development Core Team. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
Various. 2012. RSiena: Siena - Simulation Investigation for Empirical Network Analysis. R package version 1.1-212. http://CRAN.R-project.org/package=RSiena
F. Student Expectations/Requirements and Grading Policy
Laboratory Exercises and Class Participation (20%). Participation in class is essential. Laboratory exercises involving computation and use of social network analytical methods will be assigned most weeks. You will get full credit as long as you attempt to solve the problems posed.
Social Network Analysis and Your Research Interests (10%). A five page, 1000 word description of how you would apply the social network approach to your own research interests. You should give a description of your research problem and how social network analysis could illuminate some aspect of the problem. You should also propose some network questions that you might use to gather data for the problem and discuss the network constructs that you would explore to examine these data and the problem.
Major Paper (40%). With respect to an area of your own interest, review the social network literature relevant to your problem. This paper can be an extended version of the short assignment above or it can focus on another topic entirely. You may also conduct a network study as part or all of the assignment.
Take-home Final Exam (30%). The take home final exam is intended to assess the skills you have developed in manipulating and analyzing social network data.
G. Assignments, Exams and Tests
H. Attendance Policy
Course Attendance at First Class Meeting Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation Registration - 4.0101,
Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. Its the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.
I. Policy on Make-up Work
Same as General University Policy.
J. Program This Course Supports
MA, PhD Sociology
- Course Concurrence Information
The study of social networks is an interdisciplinary field and so students from a variety of backgrounds are welcome. The course has attracted students from Sociology, Anthropology, Criminology, Public Health, Industrial Engineering, and Computer Science.