Classes will be held full-time during the two-week period, with the exception of the weekend of February 6-7. Given the extreme rigor of the coursework, which will be taught entirely in English, each accepted applicant will be permitted to enroll in only one course. Students who successfully complete the coursework will receive a course certificate and an evaluation of their performance will be sent to their home institution. The six courses on offer are:

1. Mathematical concepts and formal modeling
2. Comparative research design and configurational comparative methods
3. Comparative survey design
4. Multiple regression analysis
5. Methodologies of case studies
6. Mixed methods design

Course description

1. Quantitative 1: Mathematical concepts and formal modeling (Prof. Rebecca Morton, New York University [please note instructor change])

   In this class participants will be introduced to the use of mathematics and formal models to answer research questions in the social sciences. We will begin with understanding how formal models work and how they can be used to derive predictions. We will focus primarily on decision and game theoretic models under complete information and the mathematics used in such models (e.g. constrained optimisation and reaction functions). We will also cover decision making under uncertainty and Bayesian updating. For each class students will be expected to complete two exercises. Class time will be divided into three equal parts: First, participants will present and discuss the exercises they have completed. Then new material will be presented and discussed in preparation for the next class.

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2. Qualitative 1: Comparative research design and configurational comparative methods (Prof. Dirk Berg-Schlosser, Philipps University Marburg)

   This course examines the family of 'configurational comparative methods' (CCM). First, the course spells out the fundamental concepts that underlie the configurational comparative approach. In the framework of the general literature on comparative empirical social research, participants are made familiar with issues such as concept formation, truth tables, basic Boolean algebra, ideal types, and property spaces. Then participants are trained to use the most widely used of the CCM so far: dichotomous Qualitative Comparative Analysis (csQCA). The practical steps and best practices of csQCA (including software use: TOSMANA and fs/QCA) are taught: first the basic procedures, then various refinements. The course is concluded with an overview of linked developments such as fuzzy set QCA (fsQCA) and multi-value QCA (mvQCA) and the combination of QCA with other methods. Real-life, published applications are used throughout the course; participants are also encouraged to bring their own data, if available. Some basic quantitative or qualitative methodological training is probably useful to get more out of the course, but participants with little methodological training should find no major obstacles to follow the course. Above all, participants should be motivated to engage in rigorous comparative analysis.

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3. Research Design 1: Comparative survey design (Prof. Bruno Cautrès, Sciences Po, Paris)

   The course aims to provide an introduction to the main issues raised by the design and use of cross-national surveys. The course will have three main parts:

   1. comparative survey traditions and main goals, problems and issues;
   2. designing a cross-national survey and aiming for optimal comparability;
   3. using cross-national surveys and databases.

   The course will tackle both the theoretical and practical aspects of cross-national (and crosscultural) analysis. Comparative surveys such as the European Social Survey (ESS), the Eurobarometers, the ISSP, and the European Values Study will be compared, but the focus will be on the ESS. The main course objectives are to teach key issues facing the developments of large scale cross-national surveys from the early stages of their conception, to the final stage of data analysis and explanations about the so-called national differences (e.g. the Comparative Manifesto Project). It is also designed to cover many fundamental issues such as inter-coder agreement, reliability, validation, accuracy, and precision. Lessons will consist of a mixture of theoretical grounding in content analysis approaches and techniques, with hands-on analysis of real texts using content analytic and statistical software.

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4. Quantitative 2: Multiple regression analysis (Prof. Guy Whitten, Texas A&M University)

   The course starts with a discussion of the logic of the multivariate regression model and the central assumptions underlying the ordinary least squares approach. Then it proceeds with testing for adequacy of the assumptions and suitable corrections and extensions to the estimation techniques in the context of cross-sectional data. Particular emphasis will be placed on multicollinearity and heteroskedasticity. The second part of the course focuses on functional form. Models that are nonlinear in variables but linear in parameters, dummy variables, and interaction terms will be covered. In the third part, various topics arising with special data are covered. Firstly, the analysis of binary dependent variables is introduced. Secondly, problems involved with the analysis of longitudinal data, i.e. time series and panel data, are discussed, with special emphasis on autocorrelation. The course assumes proficiency with descriptive and inferential statistics at the level of test theory and bivariate regression analysis.

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5. Qualitative 2: Methodologies of case studies (Prof. Delia Schindler, Universität Hamburg [please note instructor change])

   Case study research has a long tradition in social science. There is also an almost equally long tradition of criticising case studies. On the basis of the extensive debates on the case study method, this course will approach this critique from an ontological and methodological perspective. The goal is to understand the unique advantages and disadvantages in comparison with other popular social science methods. Topics to be covered include the small-n problem, variants of case study designs, case selection, methods of causal inference, and generalisation. At the end of the course, participants will be equipped with the necessary skills to conduct their own case study research. In addition, they will be able to understand and to participate in the still on-going discussion on the pros and cons of this method. Basic methodological knowledge in qualitative and quantitative analysis is useful, but not obligatory.

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6. Research Design 2: Mixed methods design (Prof. Max Bergman, University of Basel)

   This course deals with methodological triangulation. We will study when, why, and how to combine and integrate qualitative and quantitative research methods. Our focus is primarily practical, which means that our main emphasis will be on exploring the possibilities and limits of mixing qualitative and quantitative methods. The course includes lectures, practical exercises, computer lab work, and assignments. In the first part of the course, more conventional mixed method designs will be covered, including sequential, convergent, and concurrent designs. The second part of the course will be dedicated to the exploration of a more advanced approach to integrating methods, particularly holistic designs and the immediate context during data collection and analysis. Intermediate knowledge in either qualitative or quantitative methods and basic skills in SPSS and word processing are assumed.

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