Quality & Quantity

, Volume 47, Issue 6, pp 3185–3200 | Cite as

The integration of fuzzy sets and statistics: toward strict falsification in the social sciences



Whilst statistics take up a prominent place in the social science research toolkit, some old problems that have been associated there with have not been fully resolved. These problems include bias through the inclusion of irrelevant variation and the exclusion of relevant variation, which may lead to hidden and spurious correlations in more extreme—however not at all unthinkable—cases. These issues have been addressed by Ragin by building a case for the usage of fuzzy set theory in social science. In this paper, we take a complementary view, insofar as we incorporate fuzzy set theory in current statistical analyses. Apart from shedding new light on the main issues associated with (population based) statistics, this approach also offers interesting prospects for the falsification of theories—rather than single relations between variables—in the social sciences.


Statistical modeling Fuzzy logic Configurations Falsification 


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  1. Arfi B.: Probing the democratic peace argument using linguistic fuzzy logic. Int. Interact. 35(1), 30–57 (2009)CrossRefGoogle Scholar
  2. Atanassov K.T.: Intuitionistic fuzzy sets: theory and applications Studies. in fuzziness and soft computing, vol. 35. Physica-Verlag, Heidelberg, NY (1999)Google Scholar
  3. Cartwright N.: Causal laws and effective strategies. Nous 13(4), 419–437 (1979)CrossRefGoogle Scholar
  4. Cernkovitch S.A., Giordiano P.C., Rudolph J.L.: Race, crime and the American dream. J. Res. Crime Delinq. 37, 131 (2000)CrossRefGoogle Scholar
  5. Dretske F.: Explaining behavior: reasons in a world of causes. MIT Press, Cambridge, MA (1988)Google Scholar
  6. Dretske F.: Psychological vs. biological explanations of behavior. Behav. Philos. 32(1), 167–177 (2004)Google Scholar
  7. Eells E.: Probabilistic causality. Cambridge studies in probability, induction, and decision theory. Cambridge University Press, Cambridge (1991)Google Scholar
  8. Eliason S.R., Stryker R.: Goodness-of-fit tests and descriptive measures in fuzzy-set analysis. Sociol. Methods Res. 38(1), 102–146 (2009)CrossRefGoogle Scholar
  9. Fodor J.C.: A new look at fuzzy connectives. Fuzzy Sets and Systems 57, 141–148 (1993)CrossRefGoogle Scholar
  10. Gottfredson M.R.: Some advantages of a crime-free criminology. In: Bosworth, M., Hoyle, C. (eds) What is criminology?, pp. 35–48. Oxford University Press, Oxford (2011)CrossRefGoogle Scholar
  11. Harloff J.: Extracting cover sets from free fuzzy sorting data. Qual. Quant. 45(6), 1445–1457 (2011)CrossRefGoogle Scholar
  12. Heil J.: Multiple realizability. Am. Philos. Quart. 36(3), 189–208 (1999)Google Scholar
  13. Hempel C.G.: Aspects of scientific explanation, and other essays in the philosophy of science. Free Press, New York (1965)Google Scholar
  14. Humphreys P.: The chances of explanation: causal explanation in the social, medical, and physical sciences. Princeton University Press, Princeton, NJ (1989)Google Scholar
  15. Kron T., Winter L.: Fuzzy thinking in sociology. In: Seising, R. (ed) Views on fuzzy sets and systems from different perspectives, vol. 243. Philosophy and logic, criticisms and applications, pp. 301–320. Springer, Berlin (2009)Google Scholar
  16. Miethe T., Hart T., Regoeczi W.: The conjunctive analysis of case configurations: an exploratory method for discrete multivariate analyses of crime data. J. Quant. Criminol. 24(2), 227–241 (2008)CrossRefGoogle Scholar
  17. Miethe T.D., Drass K.A.: Exploring the social context of instrumental and expressive homicides: an application of qualitative comparative analysis. J. Quant. Criminol. 15(1), 1–21 (1999)CrossRefGoogle Scholar
  18. Pearl J.: Causality: models, reasoning, and inference. Cambridge University Press, Cambridge (2000)Google Scholar
  19. Popper, K.R.: The logic of scientific discovery. Routledge Classics, London (2002 [1959])Google Scholar
  20. Popper K.R., Bartley W.W.: Realism and the aim of science. Postscript to the logic of scientific discovery. Routledge, London (1993)Google Scholar
  21. Ragin C.C.: Fuzzy-set social science. University of Chicago Press, Chicago (2000)Google Scholar
  22. Ragin C.C.: Redesigning social inquiry: fuzzy sets and beyond. University of Chicago Press, Chicago (2008)CrossRefGoogle Scholar
  23. Salmon W.C.: Four decades of scientific explanation. In: Kitcher, P., Salmon, W.C. (eds) Scientific Explanation, pp. 3–219. University of Minneapolis Press, Minneapolis (1989)Google Scholar
  24. Sandis C.: Dretske on the causation of behavior. Behavior and Philosophy 36, 71 (2008)Google Scholar
  25. Seising R.: Views on fuzzy sets and systems from different perspectives : philosophy and logic, criticisms and applications. Studies in fuzziness and soft computing,, vol. 243. Springer, Berlin (2009)CrossRefGoogle Scholar
  26. Zadeh L.: Fuzzy sets. Inf. Control 8, 338 (1965)CrossRefGoogle Scholar
  27. Zadeh L., Kacprzyk J.: Fuzzy logic for the management of uncertainty. Wiley, New York (1992)Google Scholar
  28. Zadeh L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Set. Syst. 100, 9–34 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of Criminal Law and CriminologyGhent UniversityGhentBelgium
  2. 2.Department of Applied Mathematics and Computer ScienceGhent UniversityGhentBelgium

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