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Graduate Course Proposal Form Submission Detail - SYA7357
Tracking Number - 4840

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Current Status: Approved by SCNS - 2016-08-01
Campus: Tampa
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

Detail Information

  1. Date & Time Submitted: 2013-10-30
  2. Department: Sociology
  3. College: AS
  4. Budget Account Number: 126300
  5. Contact Person: John Skvoretz
  6. Phone: 47288
  7. Email: jskvoretz@usf.edu
  8. Prefix: SYA
  9. Number: 7357
  10. Full Title: Introduction to Social Network Analysis
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?: N
  17. If repeatable, how many times?: 0
  18. Abbreviated Title (30 characters maximum): Social Network Analysis
  19. Course Online?: C - Face-to-face (0% online)
  20. Percentage Online: 0
  21. Grading Option: R - Regular
  22. Prerequisites:
  23. Corequisites:
  24. Course Description: 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).

  25. Please briefly explain why it is necessary and/or desirable to add this course: Replacing Selected Topics with Permanent number; already listed in program
  26. 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
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, 3 or more times
  28. 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.
  29. Objectives: • 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.

  30. 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.

  31. Major Topics: • A Quick Survey of Social Network Analysis

    • Introduction to Network Analysis Packages and Collection, Representation, and Visualization of Network Data

    • Centrality

    • 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

  32. Textbooks: 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.

  33. 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

  34. 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.

  35. Assignments, Exams and Tests: See AF
  36. 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,

    http://usfweb2.usf.edu/usfgc/ogc%20web/currentreg.htm)

    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. It’s 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.

  37. Policy on Make-up Work: Same as General University Policy.
  38. Program This Course Supports: MA, PhD Sociology
  39. 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.


- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.