**Martin Elff **is Professor at Zeppelin University in Friedrichshafen, Germany. He is a political scientist with research interests in political behaviour, comparative politics, and political methodology. His research appeared in various journals including the *British Journal of Political Science* and *Political Analysis.* He has published the R packages ‘memisc’, ‘mclogit’, and ‘munfold’, on the ‘Comprehensive R Archive Network’ (http://cran.r-project.org) as well as a book on *Data Management with R: A Guide for Social Scientists* with SAGE.

**Course content**

The course introduces to quantitative social science data analysis using *R*. It focuses on the practical aspects of data analysis, including the management of social science data. It also shows how patterns within data can be visualised and how statistical models can be illustrated using appropriate diagrams. Consequently, the course cannot introduce basic or advanced statistical concepts, but such concepts are reviewed as appropriate.

As far as possible, the contents of the course will be adapted to the existing statistical knowledge of the participants. However, it covers at least the following topics: (1) basic concepts of data analysis with *R*; (2) elementary programming techniques in *R*, (3) data management – working with variables and data frames; (4) summarising data using tables and graphics; (5) linear regression – model construction and interpretation; (6) generalised linear models for categorical responses, counts, and survival times; (7) advanced statistical graphics. In addition to these, a few more topics are optionally covered as time permits, such as principal components and factor analysis; structural equations models; random numbers and Monte Carlo simulations; linear algebra with *R* and regression in matrix form; multilevel models; advanced programming techniques – depending on participants’ interests.

**Course objectives**

Participants who successfully complete this module will be able to bring their knowledge about statistical concepts and techniques to fruition in practical analyses. They will also know how to prepare data in a way suitable for this purpose, how to explore data with statistical graphics and how to present analysis results by means of tables and diagrams.

**Course prerequisites**

In order to gain the most from the course, participants ideally have an understanding of what kind of analysis they intend to conduct and require guidance on how to apply the relevant techniques using the open source software *R. *While the course may become helpful in acquiring new statistical concepts, it cannot* teach* them. Therefore participants should have a good understanding of linear regression and at least a basic understanding of any other method they want to put into practice with *R*.

A certain level of “computer literacy” will certainly helpful. That is, participants should not be afraid of command-line oriented (as opposed to menu-driven) software and of writing short command scripts. The *ability* to do that is not a prerequisite, but the *motivation* to learn such things is.

**Representative Background Reading
Dalgaard, Peter 2002. Introductory Statistics with R. New York: Springer.
Fox, John 2008. Applied Regression Analysis, and General Linear Models. (2nd ed.) Thousand Oaks: Sage.
Fox, John 2002. An R and S-Plus Companion to Applied Regression. Thousand Oaks: Sage.
Gill, Jeff 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.
Venables, W.N., and Ripley, B.D. 2002. Modern Applied Statistics with S. (4th ed.) New York: Springer.**

**Background knowedge required
Statistics
OLS = moderate
Maximum Likelihood = elementary**