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Configurational comparative methods (CCM)

Aim of the tool
To allow systematic cross-case comparisons while at the same time giving justice to within-case complexity, particularly in small- and intermediate-N research designs (i.e. N-research designs here refer to number of cases looked at)

When to use it?
This tool is useful for evaluation.

How difficult is it to use it?
Easy – moderate – for experienced users/facilitators

Tool for thought or tool for action?
Tool for thought and for action


  • Interplay between theoretical and case-oriented knowledge
  • A specific understanding of causality and complexity: “multiple conjunctural causation”, this is a nonlinear, non-additive, non-probabilistic conception that rejects any form of permanent causality and that stresses equifinality (different paths can lead to the same outcome), complex combinations of conditions and diversity. 
  • These methods do not obscure complexity, this enables the researcher to go back to cases to clarify further aspects or to check and improve the relevant data. 
  • Possibility to produce “modest” generalisations.

Issues to be aware of

  • The process of selecting cases and conditions can be challenging.
  • To implement a useful and meaningful dichotomization can be challenging.
  • Software needed:

Description of the tool
Configurational Comparative Methods (CCM) include all methods and techniques that allow systematic cross-case comparisons while at the same time giving justice to within-case complexity, particularly in small- and intermediate-N research designs. In a nutshell this heading indicates that in order to enable systematic comparative analysis of complex cases, those cases must be transformed into configurations. Simply said, a configuration is a specific combination of factors (or stimuli, causal variable, ingredients, determinants etc. – we call these conditions in CCM methodology) that produces a given outcome of interest. The conditions will be envisaged in a combinatorial way – hence enabling one to model quite a high level of complexity even with only a few conditions.

It addresses the key question: Which conditions (or combinations thereof) are “necessary” and/or “sufficient” to produce the outcome? A condition is necessary for an outcome if it is always present when the outcome occurs. In other words, the outcome cannot occur in the absence of the condition. A condition is sufficient for an outcome if the outcome always occurs when the condition is present. However, the outcome could also result from other conditions.

Under the heading of CCM, we place four specific techniques: Qualitative Comparative Analysis using conventional, crisp sets (csQCA, often simply labelled QCA in the literature), multi-value QCA (mvQCA), fuzzy set QCA (fsQCA) and MSDO/MDSO (most similar, different outcome/ most different, same outcome).

Holding competitive elections is a necessary condition for a state to be considered democratic. However, it is not a sufficient condition because comprehensive civil liberties must also be present for a state to be considered democratic. Nonetheless, the absence of competitive elections is a sufficient condition to qualify a stat as non-democratic, as a democracy cannot exist without competitive elections.

Steps involved in using the tool

Steps for crisp-set Qualitative Comparative Analysis (csQCA):
Step 1: Building a dichotomous data table
Step 2: Constructing a “truth table”
Step 3: Resolving contradictory configurations
Step 4: Boolean minimization (with software e.g. Tosmana
Step 5: Bringing in the “logical remainders” cases
Step 6: Interpretation: link between key combinations of conditions and the outcome.

Sources and further readings

  • Complementary readings: Caramani (2008), Lijphart (1971), Ragin (1987, 2000, 2008), Schneider & Wagemann (2007). 
  • Rihoux, B. and Ragin, C.C. (2009). Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques. Sage: