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Motivation
This
course is an introduction to the analysis of social structure, conceived in
terms of social relationships. Social
structure is conceptualized as a system of social relations tieing distinct
social entities to one another. Social network theory is the attempt to
represent the structure in social relations via networks. It is then a theory
pertaining to types of observable social spaces and their relation to
individual and group behavior. Observations on the
social relations are of two forms: ·
individual
level information on the social entities ·
relational data
on pairs of entities While both forms
are important for the study of social relations, social network theory recognizes
fundamental role of the relational information. It is based on the premise
that social context is an important determinant of individual behavior. It
seeks to understand individual and group behavior in terms of relational
information rather than as solely the aggregation of individual
characteristics. The
focus of the course are modern methods for the
statistical analysis of social networks. The course covers the major concepts
of social network theory and the mathematical representation of social
concepts such as “role” and “position”. Visualization
plays a central in social network analysis. With the development of high
speed computing and graphical display tools, visualization has become a
flexible and powerful tool in the exploration of social relations. Graphics
exploit the power of our visual senses to convey information in a direct
way. In this course we will
emphasize the use of graphical representations of network information as much
as possible. The
course will have an applied focus on the development of tools for research in
the social sciences. The course will involve the practical application of the
ideas and their implementation through statistical software, particularly R, to make them accessible to social
scientists. This course is part
of the curriculum of the new Center
for Statistics and the Social Sciences (CSSS), with funding from the
University Initiatives Fund. The CSSS is includes faculty members from the
Department of Statistics and a broad-range of social science disciplines
including Anthropology, Economics, Geography, Political Science, and
Sociology. This curriculum is been developed to complement and strengthen the
quantitative methods course offerings for social science students at both the
undergraduate and graduate levels. Structure of the CourseThere
will be a two lectures per week. The lecture on Thursday will sometimes be a
laboratory session. Textbooks[WF] [H] Robert A. Hanneman
and Mark Riddle “Introduction to
Social Network Methods” (2005). Mailing list and
Discussion Forum
I will be using a mailing list to provide discussion of issues in class and related questions. For questions that might be of interest to other students, please use the mailing list rather than solely emailing me. Example of questions are about interesting articles you have seen in the media, problems with access to resources, homework or computer questions. Enjoy! Please regularly consult this class home page and archive of the mailing list. It will contain lecture notes, homework, solutions and course information. Computer Usage and SoftwareThe computer is the scientific laboratory of the applied researcher in quantitative fields. As such this course requires the student to develop a degree of comfort and competence “in the lab”. If you want more background consult the lecture notes in CSSS 505. Course Requirements and GradesThere will be weekly homeworks
and exercises both the theory and real data analysis. Students will be graded
on a scale of 1 to 10 for each homework. This will
be 50% of the grade. Discussion of homework
problems is encouraged. However, each student is required to prepare
and submit solutions (including computer work) to the assignments and project
on their own; solutions prepared “in committee” are not
acceptable. Duplication of homework solutions and computer output prepared in
whole or in part by someone else is not acceptable and is considered
plagiarism. If you receive assistance from anyone, you must give due
credit in your report. (Example: “Since the data are all
positive, and skewed to the right, a logarithmic transformation is clearly
appropriate as a next step. I thank David Cox for pointing this out to
me.”) There will be a final project
worth 50% of the grade. The project will be to
undertake an analysis of a social network that you find interesting. You can
select any network dataset you like, but preferable related to your graduate
work or thesis area. I do not
want a quick and routine analysis; a good job will show understanding of the
problem and possible solutions and techniques to consider. The technical results
should be stated clearly. The report must contain a clearly written
conclusion section giving a non-technical summary that is concise and
informative. The data set should contain at
least 20 nodes, and at least two variable measured
for each node. Do not merely use
data from a textbook – the world is an interesting place! All data
sources must be cited, and described. I welcome comments or suggestions about the course at any time, either in person, by letter, or by anonymous email. Please feel free to use these ways make comments to me about any aspect of the course. Use the menu on the top-left of this page to find out more about the course. STUDENTS WITH DISABILITIES If you have a disability that requires special testing accommodations or other classroom modifications you need to notify the instructor and the Office of Disabled Student Services as soon as possible. You may contact the DSS office at 543-8925. |
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