Find out more: Learning about evaluation with small cohorts: pilots to test the methodologies

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Overview

Qualitative Comparative Analysis (QCA) is a ‘synthetic strategy’ (Ragin, 1987, p. 84) that allows for multiple conjunctural causation across observed cases. QCA analysis recognises that multiple causal pathways can lead to the same result and that each pathway consists of a combination of conditions (i.e. they are conjunctural). The method draws on the assumption that it is often a combination of multiple causes that has causal power (Befani, 2016). Furthermore, the same cause can have different effects depending on the other causes it is combined with and, thus, lead to different outcomes.

In QCA, each case is changed into a series of features, including several condition variables and one outcome variable. The method generally starts with a Theory of Change identifying the ‘conditions’ (factors) that may contribute to the anticipated outcomes. QCA analysis is an iterative process that requires in-depth knowledge of specific cases.

There are three main techniques: crisp set (csQCA), fuzzy set (fzQCA) and multi-value (mvQCA). They differ in how they code and consider membership of the cases. In csQCA, membership is dichotomous (e.g. 1 = member, 0 = non-member). However, this dichotomous nature is not always adapted to real-life situations. Therefore, fsQCA was developed in response to this limitation to assign gradual values to conditions, such as quality or satisfaction, and allow for variance in observations. In fsQCA and mvQCA, membership is multichotomous and partial (e.g. 1 = full member, 0.8 = strong but not full member, 0.3 = weak member, 0 = non-member). Here, we will focus on csQCA as a good introduction to the methodology. The logic underpinning the technique is then extended to fsQCA and mvQCA.

What is involved?

Rihoux and De Meur (2009) identify six steps for csQCA:

Step 1 Building a dichotomous data table: Drawing on the Theory of Change, data is coded for each condition and outcome dichotomously (e.g. 1 = member, 2 = non-member).

Step 2 Constructing a Truth Table: Using software, a first ‘synthesis’ of the raw data is produced in what is called a truth table. This is a table of configurations (i.e. a number of combinations of conditions associated with a given outcome).

Step 3: Resolving Contradictory Configurations: Contradictory configurations are a normal part of QCA. This is when the reiterative dialogue between data and theory occurs. The evaluators need to resolve these contradictions by using their knowledge of the cases and reconsidering their theoretical perspective in order to obtain more coherent data.

Step 4: Boolean Minimisation: This step is generally completed using software and a synthesis of the truth table. It identifies conditions that are either present or absent in configurations leading to the same outcome.

Step 5: Bringing in the ‘Logical Remainders’ Cases: Logical remainders are a pool of potential cases that can be used to produce a shorter (i.e. more parsimonious) causal explanation.

Step 6: Consistency and coverage: When running a Boolean minimisation, specialist software can calculate consistency and coverage for each configuration and the solution as a whole.
Download a QCA case study here
Download a longer briefing on QCA here

Useful resources

Compass is a website that specialises in QCA, listing events and providing extensive resources including a very comprehensive bibliography.

Ragin’s (2017) User’s Guide to fzQCA is a good starting point for fzQCA.

A good introduction to QCA is:

Befani, B. (2016) Pathways to change: Evaluating development interventions with Qualitative Comparative Analysis (QCA). Sztokholm: Expertgruppen för biståndsanalys (the Expert Group for Development Analysis). Available here.