Qualitative Comparative Analysis: Addressing Methodological Challenges, with particular reference to survey data

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Ragin's Qualitative Comparative Analysis (QCA) is an innovative research method which aims to establish the configurations of necessary and/or sufficient conditions for an outcome. It is used increasingly within the social sciences, often by researchers who make policy recommendations based on their analyses. It is therefore important that its strengths and weaknesses are understood by its users and policymakers. We will evaluate outstanding problems with QCA, and develop responses to them. QCA reflects Ragin's assumption that much theorising in the social sciences concerns sets of cases, especially the causal properties of their characteristics, considered singly and in combination. Cases are treated as combinations of characteristics, with the latter described using sets. Consider, e.g., theoretical claims about why white working class boys have low educational achievement. These are claims about cases in the intersection of three sets: white, working class and male. QCA would assess the degree to which being a "white working class boy" is a sufficient or necessary condition for some outcome. It also assesses this for all other combinations of the presence or absence of these three factors.QCA has a form employing crisp sets where a case is either simply in or out of some set and a form employing fuzzy sets where a case can have partial membership in a set, scaled from 0 to 1. Fuzzy set QCA was developed to address the concern that some factors are not well described by crisp sets. Consider "adulthood". An 18 year old is not simply adult or non-adult. A fuzzy membership score of, say, 0.8 in the set of adults might be appropriate here. The degree to which conditions are sufficient or necessary for some outcome can be assessed for fuzzy sets, as for crisp sets, but additional complications arise.QCA was developed in political science for small numbers of cases. It has since fruitfully been used with survey data by researchers including ourselves. The most commonly used methods for such datasets are forms of regression analysis. With regression, it is much harder to assess relations of sufficiency and necessity. Regression, in determining the net effects of supposedly independent variables, also carries the danger of ignoring, as well as the configurational aspects of causation, the context of particular cases. QCA addresses these problems. However, given that, compared to more established methods, it is still in its early stages of development, it has unresolved problems of its own which we will address.We will work in three areas: sampling, technical problems and the interpretation of QCA results. Sampling concerns include how biased samples and missing data affect QCA results. Technical concerns include, e.g., logical paradoxes arising from the use of fuzzy logic, such as situations where conditions can be sufficient for both an outcome and its absence. Another important technical issue is calibration, i.e. the allocation of fuzzy values to cases (e.g., 0.8 for an 18 year old for "adulthood"). Calibration affects which configurations of conditions are identified as sufficient and/or necessary for an outcome. Interpretation concerns issues such as whether, and if so, how, causal claims can be made on the basis of QCA results, and whether multiple causal pathways uncovered by QCA might reflect inadequate conceptualisation of outcomes.We will undertake mathematical-logical work, but also work with existing datasets. The latter work will serve as a test bed for the more abstract arguments developed, providing illustrative case studies of the use of QCA. It will also enable us to evaluate the relative merits of QCA and conventional techniques as ways of analysing data of various types and for various purposes, and it will allow us to discuss the relative merits of QCA and regression techniques as means of informing policymakers about the complex causal pathways that mediate the production of socially desirable outcomes.