This course is discontinued

UV9114 – Qualitative Comparative Analysis

Course content

On the one hand, comparative social science is defined by the existence (or at least the presumption) of meaningful "cases." Comparativists treat cases as whole entities purposefully selected (e.g., the French Revolution), not as homogeneous observations drawn haphazardly or randomly from a large pool of equally plausible selections (e.g., a random selection of cases from the population of all revolutions—assuming such a population could be constructed). This gives comparative work a special focus on cases as “meaningful” in their own right. On the other hand, however, one of the primary goals of comparative social science (and social science in general) is to derive general statements about theoretically important relationships. Making general statements requires using general concepts. At the level of cases, concepts are most often represented through observable variables. Concepts and variables permeate almost all social scientific discussion of cases, no matter how much or how little homage is paid to their singularity, specificity, or meaningfulness as cases. Thus, comparative social scientists (especially) need tools that link case-oriented and variable-oriented discourse—tools that help them construct a rich dialogue of ideas and evidence.

The analytic challenge of case-oriented research is not simply that the number of cases is limited, but that researchers gain useful in-depth knowledge of cases that is difficult to represent using conventional forms of analysis (e.g., representations that emphasize the “net effects” of “independent variables”). The researcher is left wondering how to represent knowledge of cases in a way that is meaningful and compact, but which also does not deny their complexity.

Set-theoretic methods such as Qualitative Comparative Analysis (QCA) offer a solution. QCA is fundamentally a case-oriented method that can be applied to small-to-moderate size Ns. It is most useful when researchers have knowledge of each case included in an investigation, there is a relatively small number of such cases (e.g., 10-50), and the investigator seeks to compare cases as configurations. With these methods it is possible to construct representations of cross-case patterns that allow for substantial causal heterogeneity and case diversity.

Fuzzy set analysis can work in tandem with QCA.  The use of fuzzy sets is gaining popularity in the social sciences today because of the close connections it enables between verbal theory, substantive knowledge (especially in the assessment of degree of set membership), and the analysis of empirical evidence. Fuzzy sets are especially useful in case-oriented research, where the investigator has substantial familiarity with the cases included in the investigation and seeks to understand cases configurationally, that is, as specific combinations of aspects or elements. Using fuzzy-set methods, case outcomes can be examined in ways that allow for causal complexity, where different configurations of causally relevant conditions combine to generate the outcome in question. Also, with fuzzy-set methods it is a possible to evaluate arguments that causal conditions are necessary or sufficient. Examinations of this type are outside the scope of conventional variable-oriented analysis.

Learning outcome

The candidate should be able to:

  • get insights into the different research strategies associated with Qualitative Comparative Analysis
  • identify how Qualitative Comparative Analysis can be used in designing research studies within the fields of social and educational sciences.
  • examine and discuss the research reports applying Qualitative Comparative Analysis


Open lectures for all signed up for the seminars. Closed workshops for course participants. PhD-candidates enrolled in NATED will be given priority, but it is also possible for other PhD-candidates to apply for the course. Candidates admitted to a PhD-program at UiO: Apply by Studentweb. Other applicants: apply through registration form


Recommended previous knowledge

Students should have previous exposure to social research methods, including basic training in quantitative methods, at the post-baccalaureate level. The course will include instruction in the use of the software package fsQCA which can be downloaded from

Recommended Reading:

  • Goertz, Gary and James Mahoney. 2012. A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton, NJ: Princeton University Press.
  • Lauri, Triin, & Põder, Kaire. (2013). School Choice Policy: seeking to balance educational efficiency and equity. A Comparative Analysis of 20 European Countries. European Educational Research Journal, 12(4), 534-552.
  • Ragin, Charles C. 1987. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies, Berkeley, CA: University of California Press.
  • Ragin, Charles C. 2000. Fuzzy-Set Social Science, Chicago, IL: University of Chicago Press.
  • Ragin, Charles C. 2008. Redesigning Social Inquiry: Fuzzy-Sets and Beyond. Chicago, IL: University of Chicago Press.
  • Rihoux, Benoit and Charles C. Ragin (eds.) 2008.  Configurational Comparative Methods. Thousand Oaks, CA: Sage.
  • Schneider, Carsten and Claudius Wagemann. 2012. Set-Theoretic Methods for the Social Sciences: A Guide to QCA. New York: Cambridge.
  • Sivesind, Karl Henrik. (1999). Structured, qualitative comparison. Quality and Quantity, 33(4), 361-380.
  • Sivesind, Karl Henrik, & Selle, Per. (2009). Does public spending “crowd out” nonprofit welfare. Comparative social research, 26, 105-134.

WWW sites:


Wednesday Sept 23 (Georg Sverdrups Hus, Blindern Campus, room 2531 (Stort møterom))

  • 09.15 – 09.30: Welcome by Jorunn Møller
  • 09.30 – 11.30: Open lecture by Professor Charles Ragin, University of California, Irvine

Part 1: Background

Social research as a process of constructing empirically grounded representations

The case-oriented/variable oriented distinction

The distinctiveness of configurational comparative research

What is QCA?

  • 11.30 – 12.30: Lunch
  • 12.30 – 14.45: Lecture by Professor Charles Ragin, University of California, Irvine

Part 2: Basics

Introduction to Boolean algebra and set theoretic methods

Set-theoretic analysis vs. correlational analysis

Necessity and sufficiency

Consistency, coverage, coincidence

Case-oriented research strategies for theory building

  • 15.00 – 17.00: PhD-presentations and feedback
  • 1900: Dinner at “Brunos Proseccheria”, Rådhusgata 30 b


Thursday Sept 24 (Helga Eng's Building, room 231)

  • 09.15 – 11.30: Lecture by Professor Charles Ragin, University of California, Irvine

Part 3: Crisp Set Analysis

Overview of crisp-set QCA (csQCA)

Examples of crisp set analyses

A set-theoretic approach to counterfactual analysis

The three solutions—complex, parsimonious, and intermediate

Easy versus difficult counterfactuals

The impact of assumptions on counterfactual analysis

Consistency and coverage in crisp-set truth table analysis

  • 11.30 – 12.30: Lunch
  • 12.30 – 14.00: Lecture by Research Professor Karl Henrik Sivesind, Institute for Social Research, Norway

Part 4: Examples

Qualitative Comparative Analysis – Examples from Research on Welfare Policy and the Civil Society

  • 14.00 – 15.30: PhD presentations and feedback in groups (including break)
  • 15.30 – 16.00: Plenary discussion/ Summing up
  • 1900: Dinner (private)


Friday Sept 25 (Helga Eng's Building, room 231)

  • 09.15 – 11.30: Lecture by Professor Charles Ragin, University of California, Irvine

Part 5: Fuzzy Set Analysis

Fuzzy sets and fuzzy set relations

Calibrating fuzzy sets

Fuzzy set consistency, coverage, and coincidence

Fuzzy-set coverage: raw versus unique versus solution coverage

Limited diversity, fuzzy sets, and counterfactual analysis

The fuzzy-set truth table algorithm

  • 11.30 – 12.30: Lunch
  • 12.30 – 14.00: PhD presentations and feedback in groups (including break) continued.
  • 14.15 – 15.00: Plenary discussion/ Summing up


UV9114: November 10 (Helga Eng's Building, PC Lab 241)

Lecturer: Research Professor Karl Henrik Sivesind, Institute for Social Research, Norway

10.00-11.30: Use of different type of data in comparative research

11.30 – 12.30: Lunch

12.30 – 13.15 PhD presentations and feedback (QCA papers)

13.30 – 15.30: Workshop/guiding activities for course participants at the Datalab (QCA) (including break)

15.45 – 17.15: PhD presentations and feedback on the papers (cont.)


Paper for seminar: 5000-6000 words. Send paper for oral presentation, no later than November 5, 2015 to e-mail address:

Please send a paper for final evaluation no later than December 10.

Facts about this course






Autumn 2015


Autumn 2015

Teaching language