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Causal asymmetry is a situation where the causal factors under study are more suitable for explaining the outcome than its absence (or vice versa); they do not explain both equally well. In such a situation, presence of a cause leads to presence of the effect, but absence of the cause may not lead to absence of the effect. A conceptual discussion is followed by the empirical example of gaining a degree (or not), comparing the methods logistic regression and Qualitative Comparative Analysis (QCA). While logistic regression, being based on correlational analysis and thus assuming symmetric relationships between variables, does not lend itself automatically to detecting causal asymmetry, it can be used to bring out asymmetry nevertheless. QCA, on the other hand, uncovers asymmetry, if present, by default. The closing recommendation is for researchers to be more aware of the possibility of asymmetry existing, regardless of the particular method employed. [ABSTRACT FROM AUTHOR]

Ragin's Qualitative Comparative Analysis (QCA) is often used with small to medium samples where the researcher has good case knowledge. Employing it to analyse large survey datasets, without in-depth case knowledge, raises new challenges. We present ways of addressing these challenges. We first report a single QCA result from a configurational analysis of the British National Child Development Study dataset (highest educational qualification as a set theoretic function of social class, sex and ability). We then address the robustness of our analysis by employing Dusa and Thiem's R QCA package to explore the consequences of (i) changing fuzzy set theoretic calibrations of ability, (ii) simulating errors in measuring ability and (iii) changing thresholds for assessing the quasi-sufficiency of causal configurations for educational achievement. We also consider how the analysis behaves under simulated re-sampling, using bootstrapping. The paper offers suggested methods to others wishing to use QCA with large n data.

The use of Charles Ragin’s Qualitative Comparative Analysis (QCA) is increasing in the social sciences. However, some of its characteristics, especially those of its fuzzy set variant, are still not well understood by users. QCA, a set theoretic method, aims to describe, in a Boolean form, the configurations of conditions that are necessary and/or sufficient for some outcome. The calibration of set memberships is a central feature. We discuss how two alternative calibrations of a condition affect the assessment of consistency with sufficiency. Using first an abstract example and then an empirical one from the sociology of education, we explain why “stricter” calibration of conditions results in higher consistency with sufficiency. We demonstrate that conventional truth table analysis is not an ideal way to compare the analytic consequences of alternative calibrations and therefore employ an alternative which allows a more direct comparison of consistency indices while keeping comparative configurational contexts intact. [ABSTRACT FROM AUTHOR]

The paper undertakes a comparison of Ragin's fuzzy set Qualitative Comparative Analysis with cluster analysis. After describing key features of both methods, it uses a simple invented example to illustrate an important algorithmic difference in the way in which these methods classify cases. It then examines the consequences of this difference via analyses of data previously calibrated as fuzzy sets. The data, taken from the National Child Development Study, concern educational achievement, social class, ability and gender. The classifications produced by fsQCA and fuzzy cluster analysis (FCA) are compared and the reasons for the observed differences between them are discussed. The predictive power of both methods is also compared, employing both correlational and set theoretic comparisons, using highest qualification achieved as the outcome. In the main, using the real data, the two methods are found to produce similar results. A final discussion considers the generalisability or otherwise of this finding. [ABSTRACT FROM AUTHOR]