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Introduces Qualitative Comparative Analysis as a method for identifying causal configurations across cases. Covers crisp-set QCA with necessary/sufficient conditions and truth tables, fuzzy-set QCA for assessing consistency and coverage, and applications to understanding how different combinations of conditions produce outcomes.
Create a data matrix for QCA, identify necessary and sufficient conditions, analyze truth table solutions, interpret contradictions, conduct robustness checks.
Your prerequisite in case study design introduced you to the logic of learning from small numbers of cases by examining them in depth and comparing across them systematically. Qualitative Comparative Analysis (QCA) sits at the boundary between case-based and variable-based reasoning: it retains the idea that cases are configurations — bundles of conditions that must be understood as wholes — while introducing a formal, systematic procedure for comparing them across a medium-N set (typically 10–50 cases). Think of it as a way to bring the rigor of comparative logic to the kind of question that motivates case study work: why did some countries democratize and others not? Why did some social movements succeed while others failed?
The core logical framework is Boolean algebra applied to social causation. In crisp-set QCA (csQCA), each condition is coded as present (1) or absent (0) and the outcome is similarly coded. The method then asks three questions about causation. A condition is necessary if it is always present when the outcome is present — no outcome without this condition. A condition is sufficient if the outcome always follows when the condition is present. Most real-world causation involves neither pure necessity nor pure sufficiency, but INUS conditions: insufficient but necessary parts of an unnecessary but sufficient combination. The idea is that no single factor causes the outcome alone, but certain combinations do. Economic development may cause democratic consolidation only when combined with a strong civil society and the absence of a veto-playing military. This combinatorial, configurational logic is what distinguishes QCA from regression, which estimates the average effect of one variable holding others constant — a very different causal question.
The central analytical tool is the truth table. You enumerate every logically possible combination of your conditions (2k rows for k binary conditions), populate each row with the cases that match that configuration, and assess what outcome is observed. When multiple cases share a configuration, their outcomes should be consistent — contradictions (same configuration, different outcomes) flag measurement problems or omitted conditions that need to be resolved before proceeding. After resolving contradictions, you apply Boolean minimization to simplify the truth table into its most parsimonious solution: the minimal combination of conditions sufficient to produce the outcome. Fuzzy-set QCA (fsQCA) extends this by assigning continuous membership scores between 0 and 1 (a country might be 0.7 "in" the set of consolidated democracies rather than simply in or out), which allows the logic of necessity and sufficiency to be assessed as set-theoretic correlations rather than strict Boolean operations.
The crucial interpretive distinction in QCA is between consistency and coverage. Consistency measures how reliably a solution pathway predicts the outcome — it should be close to 1.0 for a genuine sufficient condition. Coverage measures how much of the total outcome the pathway explains — a path with coverage of 0.2 is real but accounts for only 20% of cases where the outcome occurs. High consistency with low coverage means you found a genuine but narrow pathway; high coverage with low consistency means the condition frequently accompanies the outcome but is not reliably sufficient. Both measures matter, and reporting only one gives a misleading picture. The method is not a replacement for in-depth case analysis — it is a tool for disciplining comparisons and identifying which cases deserve closer examination. The truth table may show that a particular configuration is contradictory; the appropriate response is to return to those cases and ask what differentiates them, using the formal results to guide substantive interpretation.
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