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

Ben Heylen UGent and Mike Nachtegael UGent (2013) QUALITY & QUANTITY. 47(6). p.3185-3200
abstract
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Statistical modeling, Configurations, Fuzzy logic, Falsification, CRIME
journal title
QUALITY & QUANTITY
Qual. Quant.
volume
47
issue
6
pages
3185 - 3200
Web of Science type
Article
Web of Science id
000323671200012
JCR category
SOCIAL SCIENCES, INTERDISCIPLINARY
JCR impact factor
0.761 (2013)
JCR rank
38/93 (2013)
JCR quartile
2 (2013)
ISSN
0033-5177
DOI
10.1007/s11135-012-9711-6
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2096930
handle
http://hdl.handle.net/1854/LU-2096930
date created
2012-05-02 11:15:11
date last changed
2015-06-17 10:01:54
@article{2096930,
  abstract     = {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.},
  author       = {Heylen, Ben and Nachtegael, Mike},
  issn         = {0033-5177},
  journal      = {QUALITY \& QUANTITY},
  keyword      = {Statistical modeling,Configurations,Fuzzy logic,Falsification,CRIME},
  language     = {eng},
  number       = {6},
  pages        = {3185--3200},
  title        = {The integration of fuzzy sets and statistics: toward strict falsification in the social sciences},
  url          = {http://dx.doi.org/10.1007/s11135-012-9711-6},
  volume       = {47},
  year         = {2013},
}

Chicago
Heylen, Ben, and Mike Nachtegael. 2013. “The Integration of Fuzzy Sets and Statistics: Toward Strict Falsification in the Social Sciences.” Quality & Quantity 47 (6): 3185–3200.
APA
Heylen, B., & Nachtegael, M. (2013). The integration of fuzzy sets and statistics: toward strict falsification in the social sciences. QUALITY & QUANTITY, 47(6), 3185–3200.
Vancouver
1.
Heylen B, Nachtegael M. The integration of fuzzy sets and statistics: toward strict falsification in the social sciences. QUALITY & QUANTITY. 2013;47(6):3185–200.
MLA
Heylen, Ben, and Mike Nachtegael. “The Integration of Fuzzy Sets and Statistics: Toward Strict Falsification in the Social Sciences.” QUALITY & QUANTITY 47.6 (2013): 3185–3200. Print.