Emile Durkheim, one of the founders of modern empirical social science, once stated that the comparative method is the only one that suits the social sciences. But Descartes already had reminded us that “comparaison n’est pas raison”, which means that comparison is not reason (or theory) by itself. This course provides an introduction and overview of systematic comparative analyses in the social sciences and shows how to employ this method for constructive explanation and theory building.
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It begins with comparisons of very few cases and specific “most similar” and “most different” research designs. A major part is then devoted to the often occurring situation of dealing with a small number of highly complex cases, for example when comparing EU member states. Latin American political systems, or particular policy areas. In response to this complexity, new approaches and software have been developed in recent years (“Qualitative Comparative Analysis”, QCA, and related methods). These procedures are able to reduce complexity and to arrive at “configurational” solutions based on set theory and Boolean algebra, which are more meaningful in this context than the usual broad-based statistical methods.
In the last section, these methods are contrasted with more common statistical comparative methods at the macro-level of states or societies and the respective strengths and weaknesses are discussed. 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.
Syllabus
An introduction to Comparative Research
Module 1
This module presents fundamental notions of comparative research designs. To begin with, you will be introduced to multi-dimensional matters. Subsequently, you will delve into John Stuart Mill’s methods and limitations.
Comparative Research Designs
Module 2
This module presents further advances in comparative research designs. To begin with, you will be introduced to case selection and types of research designs. Subsequently, you will delve into most similar and most different designs (MSDO/MDSO) and observe their operationalization.
QCA Analysis
Module 3
This module presents Boolean Algebra and the main steps of QCA. The first lesson will introduce basic features of QCA and provide an example of such analysis. The second lesson will focus on QCA applications, troubleshooting, Multi-Value QCA (mv-QCA), and more specific features of QCA.
Fuzzy set analyses
Module 4
This module presents the basic features of the fuzzy set analyses and application, and analyzes in greater depth QCA. The first lesson will introduce basic features of fuzzy set analyses and provide examples of such analysis. The second lesson will focus on fuzzy set applications, its purposes and advantages, and explores more specific features of QCA.
Macro-quantitative (statistical): Methods and perspectives
Module 5
This module presents the macro-quantitative (statistical) methods by giving examples of recent research employing them. It analyzes the regression analysis and the various ways of analyzing data. Moreover, it concludes the course and opens to further perspectives on comparative research designs and methods.
