26 August 2016
Mathematical Tools for Social Scientists
Research articles in social science journals and methods textbooks make increasingly use of matrix algebra and calculus. Thus, social scientists who seek to keep up with recent developments in the methodology of data analysis or want to get an in-depth understanding of common methods often find themselves confronted with mathematical notations they cannot read without special training.
The course "Mathematical Tools for Social Scientists" aims to familiarize with the mathematical tools of matrix algebra, calculus, probability, and statistical computation. It covers topics such as matrix products and inverses, formal and numeric solution of equations, taking derivatives and integrating functions in one or more variables, probabilities and random variables, parameter estimation and testing statistical hypotheses, deriving and computing ordinary least squares and maximum likelihood estimates for common statistical models.
You can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule on https://training.gesis.org.
Prof. Dr. Martin Elff is professor of political sociology at Zeppelin University, Friedrichshafen, Germany.
The course is targeted at social scientists who want to deepen their understanding of the foundations of contemporary methods of quantitative data analysis and to acquire an understanding of advanced mathematical notations or who intend to refresh their knowledge in these topics. It thus will help in preparing for the more specialist courses as the GESIS Spring Seminar, the Essex Summer School or the ICPSR Summer Program, but also will benefit researchers who just want to keep pace with recent developments in research methodology.
- Basic understanding of quantitative research methods in the social sciences, such as multiple regression analysis.
- A readiness to deal with mathematical notation and with a command-line oriented statistical software. Participants should bring a laptop computer in order to perform the practical exercises in this course.
- Familiarity with R is not a prerequisite. In case you wish to work on your own computer, downloading and installing R (http://cran.r-project.org) and Rstudio (http://rstudio.org) prior to the course is strongly suggested. Participants are encouraged to play around with the software before the course in order to get a feeling for it. There is now an abundance of tutorials for R in the web. The 'classical' example is the introduction provided by the R project itself: http://cran.r-project.org/doc/manuals/r-release/R-intro.html. The project contains also an extensive list of introductions: http://cran.r-project.org/other-docs.html, of which http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf appears to be the most balanced in terms of size and accessibility. A set of useful tips is http://www.magesblog.com/2013/04/top-10-tips-to-get-started-with-r.html.
The course provides an overview of some mathematical tools to understand the inner workings of common techniques in social science data analysis. At the end of the course, participants should have an understanding how methods in data analysis can be phrased in terms of matrix algebra, calculus, probability, statistical inference and computation.
- Certificate of attendance issued upon completion.
- 2 ECTS points via the University of Mannheim for regular attendance and satisfactory work on daily assignments (EUR 20).
- 4 ECTS points via the University of Mannheim for regular attendance and satisfactory work on daily assignments and for submitting a paper/report of about 5000 words to the lecturer(s) up to 4 weeks after the end of the summer school (EUR 50).
EUR 250: Student/PhD student rate.
EUR 350: Academic/non-profit rate.
Early bird discount: EUR 50 for applicants who book and pay by April 30.
The rates include the tuition fee, course materials, the academic program, access to library and IT facilities, coffee/tea, and a number of social activities.
10 DAAD scholarships are available via the Center for Doctoral Studies in Social and Behavioral Sciences (CDSS) at the University of Mannheim.