Available as pdf.
[From IDS website] “A heightened focus on demonstrating development results has increased the stakes for evaluating impact (Stern 2015), while the more complex objectives and designs of international aid programmes make it ever more challenging to attribute effects to a particular intervention (Befani, Barnett and Stern 2014).
Qualitative Comparative Analysis (QCA) is part of a new generation of approaches that go beyond the standard counterfactual logic in assessing causality and impact. Based on the lessons from three diverse applications of QCA, this CDI Practice Paper by Florian Schatz and Katharina Welle reflects on the potential of this approach for the impact evaluation toolbox.”
Rick Davies comment: QCA is one part of a wider family of methods that can be labelled as “configurational” See my video on “Evaluating ‘loose’ Theories of Change” for an outline of the other methods of analysis that fall into the same category. I think they are an important set of alternative methods for three reasons:
(a) they can be applied “after the fact”, if the relevant data is available. They do not require the careful setting up and monitoring that is characteristics of methods such as randomised control trials,
(b) they can use categorical (i.e. nominal) data, not just variable data.
(c) configurational methods are especially suitable for dealing with “complexity” because of the view of causality that is the basis of these configurational methods…it is one that has some correspondence with the complexity of the world we see around us. Configurational methods:
- see causes as involving both single and multiple (i.e. conjunctural) causal conditions
- see outcomes as potentially the result of more than one type of conjuncture (/configuration) of conditions at work. This feature is also known as equifinality
- see causes being of different types: Sufficient, Necessary, both and neither
- see causes as being asymmetric: causes of an outcome not occurring may be different from simply the absence of the causes the outcome
Thanks Rick for commenting on our paper. I agree with your three reasons why configurational approaches are an important set of alternative methods.
Your three reasons are particularly pertinent when comparing configurational approaches to experimental or quasi-experimental approaches. When comparing configurational approaches to other qualitative approaches, there may be additional (practical) reasons to consider:
• Configurational approaches allow to compare a medium or large number of cases in a systematic manner, which is difficult to do with other qualitative approaches (especially if there are more than 7 ± 2 cases – see Miller’s law: https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus_or_Minus_Two).
• By making all assumptions and choices explicit, QCA enforces a very systematic and transparent approach. This is not to say that a systematic and transparent approach is not needed for other qualitative methods, however it is easier to get away without it than in QCA.
I have just come across this website, which provides what might be a useful means of visualising the structure of multiple conjunctural causation models. See http://www.logicflows.org
The basis of it is a diagram like this
http://www.logicflows.org/ltrs.jpg
The logic chains flow from the top to the bottom, through any horizontal line that separates two adjacent cells. Each cell represents a model condition (in QCA terms) and a chain of them represent a causal configuration
So in this diagram there are multiple causal chains:
A>D
A>E>G
A>F>G
A>F>H
A>F>I
B>F>G
B>F>H
B>F>I
C>F>G
C>F>H
C>F>I