Qualitative Comparative Analysis

Conceptual Overview and Discussion

QCA was initially developed by Charles Ragin as a comparative method to overcome the methodological divide between case-oriented, qualitative, small-N studies that focus on within-case analysis on the one hand and variable-oriented, quantitative, large-N studies that focus on cross-case analysis on the other. As a comparative method it offers a systematic approach to comparing configurations of causes across a larger number of cases than can usually be accommodated within the classic case study approach, where qualitative analysis quickly exhausts the levels of complexity in patterns it can process. QCA thus aims to provide a middle path between qualitative and quantitative methods that overcomes some of the limitations of conventional qualitative and quantitative research by redirecting the analysis toward set-theoretic relationships. As such, QCA differs from conventional, variable-based approaches that focus on correlational analysis because QCA does not disaggregate cases into independent, analytically separate aspects but instead treats configurations as different types of cases and conceptualizes [Page 758]the relationships between causal conditions and outcomes as set–subset relationships.

To examine how different configurations of attributes contribute to an outcome of interest, QCA uses Boolean algebra, a notational system that permits the algebraic manipulation of logical statements and treats cases as having membership in sets. This allows for a sophisticated assessment of how different causes contribute to an outcome of interest without having to rely on correlational methods. Furthermore, by conceptualizing the relationship between causal conditions and an outcome in terms of set–subset relationships, QCA allows for the analysis of causal necessity and sufficiency in achieving an outcome. Because QCA uses a language that is half verbal–conceptual and half mathematical–logical, it offers the researcher a rigorous way for combining verbal statements with logical relationships to express the relationship between causes and outcomes. In doing so, QCA differs from conventional correlational analysis by explicitly shifting the focus away from the unique contribution of a single cause while holding all other factors constant. Instead of this focus on net effects, QCA emphasizes the importance of understanding how several causes combine rather than compete to achieve an outcome, with the assumption that complex, conjunctive causality will usually be the norm rather than the exception and that causes have to be understood in context for the researcher to understand their contribution to the outcome. In this regard, QCA allows for the expression of complex causal relations in ways that can provide new insights into the relationships in question. In fact, a common finding of a configurational analysis using QCA may be that individual causes are neither necessary nor sufficient to bring about an outcome, but combinations of causes may be jointly sufficient to produce an outcome of interest.

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