Quality & Quantity

, Volume 50, Issue 1, pp 327–346

Analysing necessity and sufficiency with Qualitative Comparative Analysis: how do results vary as case weights change?

Article
  • 396 Downloads

Abstract

Ragin’s Qualitative Comparative Analysis (QCA) and related set theoretic methods are increasingly popular. This is a welcome development, since it encourages systematic configurational analyses of social phenomena. One downside of this growth in popularity is a tendency for more researchers to use the approach in a formulaic manner—something made possible, and more likely, by the availability of free software. We wish to see QCA employed, as Ragin intended, in a self-critical manner. For this to happen, researchers need to understand more of what is going on behind the results generated by the available software packages. One important aspect of set theoretic analyses of sufficiency and necessity is the effect that the distribution of cases in a dataset can have on results. We explore this issue in a number of ways. We begin by exploring how both deterministic and nondeterministic data-generating processes are reflected in the analyses of populations differing in only the weights of types of cases. We show how and why weights matter in causal analyses that focus on necessity and also, where models are not fully specified, sufficiency. We then draw on this discussion to show that a recent textbook discussion of hidden necessary conditions is weakened as a result of its neglect of weighting issues. Finally, having shown that case weights raise a number of difficulties for set theoretic analyses, we offer suggestions, drawing on two imagined population datasets concerning health outcomes, for mitigating their effect.

Keywords

Qualitative Comparative Analysis (QCA) Set theoretic methods  Case weights Necessary conditions Sufficient conditions Simulation 

References

  1. Baumgartner, M.: Regularity theories reassessed. Philosophia 36, 327–354 (2008)Google Scholar
  2. Baumgartner, M.: Detecting causal chains in small-n data. Field Methods 25, 3–24 (2013)CrossRefGoogle Scholar
  3. Baumgartner, M.: Parsimony and causality. Qual. Quant. (2014). doi:10.1007/s11135-014-0026-7
  4. Baumgartner, M., Epple, R.: A coincidence analysis of a causal chain: the Swiss minaret vote. Sociol. Methods Res. 43, 280–312 (2014)Google Scholar
  5. Bhaskar, R.: A Realist Theory of Science. Harvester, Brighton (1975)Google Scholar
  6. Bhaskar, R.: The Possibility of Naturalism. Harvester, Brighton (1979)Google Scholar
  7. Cooper, B.: Applying Ragin’s crisp and fuzzy set QCA to large datasets: social class and educational achievement in the National Child Development Study. Sociol. Res. Online 10(2) (2005). http://www.socresonline.org.uk/10/2/cooper1.html
  8. Cooper, B., Glaesser, J.: Paradoxes and pitfalls in using fuzzy set QCA: illustrations from a critical review of a study of educational inequality. Sociol. Res. Online 16(3) (2011). http://www.socresonline.org.uk/16/3/8.html
  9. Cooper, B., Glaesser, J.: Qualitative work and the testing and development of theory: lessons from a study combining cross-case and within-case analysis via Ragin’s QCA. Forum: Qualitative Social Research/Qualitative Sozialforschung. 13(2), Art. 4 (2012). http://www.qualitative-research.net/index.php/fqs/article/view/1776
  10. Cooper, B., Glaesser, J., Thomson, S.: 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 (2014). http://www.compasss.org/wpseries/CooperGlaesserThomson2014.pdf
  11. Gerring, J.: Social Science Methodology: A Unified Framework, 2nd edn. Cambridge University Press, Cambridge (2012)Google Scholar
  12. Glaesser, J., Cooper, B.: Gender, parental education, and ability: their interacting roles in predicting GCSE success. Camb. J. Educ. 42(4):463–480 (2012)Google Scholar
  13. Goertz, G.: Assessing the trivialness, relevance, and relative importance of necessary or sufficient conditions in social science. Stud. Comp. Int. Dev. 41, 88–109 (2006)CrossRefGoogle Scholar
  14. Pawson, R.: A Measure for Measures: A Manifesto for Empirical Sociology. Routledge and Kegan Paul, London (1989)CrossRefGoogle Scholar
  15. Ragin, C.C.: The Comparative Method. Moving Beyond Qualitative and Quantitative Strategies. University of California Press, Berkeley (1987)Google Scholar
  16. Ragin, C.C.: Fuzzy-Set Social Science. University of Chicago Press, Chicago (2000)Google Scholar
  17. Ragin, C.C.: Set relations in social research: evaluating their consistency and coverage. Polit. Anal. 14(3), 291–310 (2006)CrossRefGoogle Scholar
  18. Ragin, C.C.: Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press, Chicago (2008)CrossRefGoogle Scholar
  19. Schneider, C.Q., Wagemann, C.: Set-Theoretic Methods for the Social Sciences. A Guide to Qualitative Comparative Analysis. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.School of EducationDurham UniversityDurhamUK
  2. 2.School of EducationDurham UniversityDurhamUK

Personalised recommendations