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:
<![if !supportLists]>· <![endif]>individual level information on the social entities
<![if !supportLists]>· <![endif]>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 Course
There will be a two lectures per week. The lecture on Thursday will sometimes be a laboratory session.
[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 Software
The 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 Grades
There 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.