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This chapter examines qualitative comparative analysis (QCA), which strives to bridge the methodological rift between case study-based research and quantitative studies. QCA belongs to the broader family of configurational comparative methods (CCMs). From an analytical perspective, QCA can be distinguished from quantitative approaches. The emphasis shifts from covariance to the analysis of set relations. Being strongly tied to a profound theoretical and conceptual reasoning which is typical for comparison in general, the analysis of set relations is based on three steps: first, a score is attributed to a social phenomenon (representing either a dichotomous or a graded set membership), usually in relation to other phenomena. Second, necessary conditions are defined. Third, through the help of a truth table analysis, (combinations of) sufficient conditions are analysed.

This chapter explores the principles of comparative research design as well as the issues and problems associated with different aspects of the approach. In particular, it considers the issue of case selection, the common sources of error that are associated with comparative research, and what can be done to try and avoid or minimize them. The comparative method is one of the most commonly used methods in political research and is often employed to investigate various political phenomena, including democratization, civil war, and public policy. The chapter discusses the three main forms of comparison, namely case study, small-N comparison, and large-N comparison. It also describes two main approaches used to select cases for small-N studies: Most Similar Systems Design and Most Different Systems Design. It also evaluates qualitative comparative analysis and concludes with an analysis of issues arising from case selection and data collection in large-N comparative research.

This chapter addresses Boolean algebra, which is based on Boolean logic. In the social sciences, Boolean algebra comes under different labels. It is often used in set-theoretic and qualitative comparative analysis to assess complex causation that leads to particular outcomes involving different combinations of conditions. The basic features of Boolean algebra are the use of binary data, combinatorial logic, and Boolean minimization to reduce the expressions of causal complexity. By calculating the intersection between the final Boolean equation and the hypotheses formulated in Boolean terms, three subsets of causal combinations emerge: hypothesized and empirically confirmed; hypothesized, but not detected within the empirical evidence; and causal configurations found empirically, but not hypothesized. This approach is both holistic and analytic because it examines cases as a whole and in parts.