Fuzzy Set Qualitative Comparative Analysis (fsQCA)

Date:  25 October 2019
Time: 9am-5pm
Instructor: Wendy Olsen
Level: Intermediate
Fee: £195 (£140 for those from educational, government and charitable institutions). 

CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application. 

Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor. Applications for bursaries must be submitted at least two weeks in advance of the course date; applications submitted after this time will not be accepted. Retrospective applications cannot be made if courses have already taken place or payment has already been made.

Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact methods@manchester.ac.uk (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact cmi-shortcourses@manchester.ac.uk.

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.

Outline

Qualitative Comparative Analysis is a systematic method of studying data on multiple comparable cases from about N=8 through to large datasets of N=10,000 etc. The QCA methods firstly involve casing, i.e. delineating cases; secondly organising a systematic data matrix (we will show these in NVIVO and in Excel); thirdly examining sets of cases known as configurations; fourth interpreting these in terms of ‘necessary cause’ and ‘sufficient cause’ of each major outcome of interest.  We demonstrate the fsQCA software for QCA. A fuzzy set is a record of the membership score of a case in a characteristic or set.  A crisp set is a membership value of 0 (not in the set) or 1 (fully in the set), and thus is a simplified measure compared with a fuzzy set. Fuzzy sets or crisp sets, and combinations can be used in QCA.  All the permutations of the causal factors, known as X variates, are considered one by one.  We test whether X is necessary, or sufficient, or both, for an outcome Y.  We then augment the standard measures of ‘consistency’.  We show that one can generate both within-group and sample-wide consistency levels for testing sufficient cause.

This one-day training course will attract those doing case-study research, those doing comparative research, and those who want to extend their skills in fuzzy set analysis from beginner to intermediate levels. It will suit qualitative as well as quantitative and mixed-methods researchers; all are welcome.

Objectives

  • Learn to compare nested cases, such as households+individuals, or comparable cases of diverse types, such as countries, using multiple methods.

  • Learn to measure fuzzy sets and crisp sets, which measure the characteristics of each case.
  • Learn to test a pair of X and Y variates for whether X is sufficient for Y; or whether it is a necessary condition for Y.
  • Learn some basics of Boolean algebra (such as not, or, and, intersection, and superset).
  • Examine how we measure whether a data pattern is consistent with sufficient causality.
  • Examine and run the fsQCA software (freeware available from fsqca.com).
  • Consider matters of sampling and population-wide descriptive statistics for the data.
  • Be ready to discuss seriously whether to statistically test fsQCA results.

Prerequisites

  1. As an intermediate course, it presumes that you have either had prior experience of statistical regression; or had some experience with verbatim transcripts or document analysis as forms of qualitative analysis.
  2. To ensure all participants reach an intermediate level, about 2-3 hours should be spent on the preliminary readings. You may bring your own data in Excel or another format.

Recommended reading 

  • Marx, A. and G. van Hootegem (2007). "Comparative configurational case analysis of ergonomic injuries." Journal of Business Research 60(5): 522-530.
  • Kent, R. (2005) ‘Cases as configurations: using combinatorial and fuzzy logic to analyze marketing data’, International Journal of Market Research, 4, 2, pp. 205-228
  • Olsen, W.K. (2012) Data Collection:  Key Trends and Methods in Social Research, London:  Sage, the sections on case-study research.
  • Olsen, W.K. (2009), Non-Nested and Nested Cases in a Socio-Economic Village Study, chapter in D. Byrne and C. Ragin, eds. (2009), Handbook of Case-Centred Research Methods, London:  Sage.
  • Ragin, C. (2008). Redesigning social inquiry: Set relations in social research. Chicago: Chicago University Press.
  • Snow, D. and D. Cress (2000). "The Outcome of Homeless Mobilization: the Influence of Organization, Disruption, Political Mediation, and Framing." American Journal of Sociology 105(4): 1063-1104.

About the instructor

Wendy Olsen works as Professor of Socio-Economics and head of the Department of Social Statistics. This is part of Social Sciences. She worked till 2014 for the Institute for Development Policy and Management (IDPM). She has taught sociology, development economics, and research methodology. She teaches statistics and PhD research methodology as well as computerised qualitative data analysis, the comparative method, the case-study method, and topics in political economy (e.g. child labour in India). She has released from some of her teaching duties due to research projects. She is fostering the use of mixed-methods research among statistical researchers. She has work experience in the community as a school governor, Salford Women’s Centre management committee member, and treasure or auditor of other bodies. She currently holds a grant about Indian women and their work, funded by the Global Challenges Research Fund.

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