WP Series » WP Series 2014

Emmenegger, Patrick, Dominik Schraff and André Walter. 2014. "QCA, the Truth Table Analysis and Large-N Survey Data: The Benefits of Calibration and the Importance of Robustness Tests". COMPASSS WP Series 2014-79. Published online 16 October 2014.
Available from:

Abstract: This paper argues that QCA is a suitable methodological choice for the analysis of a specific but widely used form of large-N data in the social sciences, namely survey data collected through computer-assisted telephone interviews or internet surveys. The reason is that the linguistic form of survey data often lends itself to a direct translation into fuzzy sets. Likert-scaled survey items let respondents make qualitative statements of agreement, disagreement and indifference. Fuzzy sets can capture these qualitative differences in a way that classical intervalscaled indicators cannot. Moreover, fuzzy algebra allows researchers to combine multiple sets in a simple and transparent manner, thereby giving QCA an important advantage over regression-based approaches. However, the analysis of large-N survey data removes one of the characteristic strengths of QCA: its case orientation. In the case of large-N survey data, the case orientation is typically weak and causal inference thus questionable. To remedy this shortcoming QCA methodologists have suggested robustness tests to enhance confidence in the proposed relationships. This paper shows how these robustness tests can be used in a large-N setting and suggests a new robustness test that is particularly suited for large-N survey data.

Cooper, Barry, Judith Glaesser and Stephanie Thomson. 2014. "Schneider and Wagemann's proposed Enhanced Standard Analysis for Ragin's Qualitative Comparative Analysis: Some unresolved problems and some suggestions for addressing them". COMPASSS WP Series 2014-77. Published online 6 February 2014.
Available from:

Abstract: Ragin's (2008) Qualitative Comparative Analysis (QCA) provides a way of undertaking case-based configurational analysis, focusing on necessary and sufficient conditions. QCA is increasingly used to undertake systematic set-theoretic analyses of small qualitative datasets and, occasionally, to analyse survey datasets. Ragin has discussed the problems caused by the "limited diversity" characteristic of social scientific data, and demonstrated how counterfactual analysis can alleviate these. The Standard Analysis module of his fsQCA software (Ragin 2008) incorporates this counterfactual reasoning approach. Schneider and Wagemann (2012, 2013) argue that there are problems with Ragin's approach and propose an Enhanced Standard Analysis. They focus on the ways in which, during a QCA, necessary conditions can become "hidden" during the analysis of "truth tables" characterised by limited diversity. Our paper, having introduced the necessary background, argues that their proposed solutions introduce new problems, some of a logical kind, and must be treated with care.© - Page last modified 16.10.2014 17:17:07