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Qualitative Comparative Analysis (QCA): State of the Art and Prospects
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Qualitative Comparative Analysis (or QCA; Ragin 1987; Drass and Ragin 1994) and its successor, Fuzzy-Set Qualitative Comparative Analysis (fsQCA; Ragin 2000; Ragin, Drass, and Davey 2004), were both developed for the analysis of small- and moderate-N data sets, typical of those used by researchers in comparative politics and related disciplines. These techniques are designed to unravel causal complexity by applying set-theoretic methods to cross-case evidence. Their central goal is to mimic some of the basic analytic procedures that comparative researchers use when making sense of their cases. The key difference between QCA and traditional case-oriented methods is that with QCA it is possible to extend these basic analytic procedures to the examination of more than a handful of cases, for example, to more than 10.
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In fact, there is no procedural limit on the number of cases
that can be studied using QCA (see below). This limit is set instead by the researcher’s degree of interest in maintaining familiarity with each case and his or her tolerance for complexity.
This paper offers (1) an advanced, conceptually oriented introduction to QCA, (2) an overview of the current state of the art, and (3) a discussion of best practices in the use of QCA, both general and more technical, with a special focus on QCA and counterfactual analysis.
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We begin by examining two analytic procedures commonly used by comparative researchers (and by qualitative researchers, more generally) and contrast these techniques with correlational analysis, the main analytical engine of mainstream quantitative social science. We then discuss the state of the art of existing QCA applications, by trying to map the diversity and scope of these applications. Following this, we first lay out some more general guidelines to engage in a “good” QCA analysis. This is followed by a list of more specific, technical guidelines in the form of “best practices”. One of these guidelines has to do with the exploitation of “remainders”, i.e. non-observed configurations. This leads us to discuss more in-depth one of the most powerful features of QCA : counterfactual analysis. We conclude by sketching several of QCA's future prospects.
1. The Distinctiveness of Comparative Research
Researchers in comparative politics and related fields often seek to identify commonalities across cases, focusing on a relatively small number of purposefully selected cases. There are two analytic strategies central to this type of research. The first strategy is to examine cases sharing a given outcome (e.g., consolidated third-wave democracies) and attempt to identify their shared causal conditions (e.g., the possibility that they share presidential
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Hereafter, QCA is used generically to refer to both QCA and fsQCA, as well as to the multi-value
QCA (MVQCA) variant (see http://www.compasss.org ; “software” section, and Cronqvist (2004)).
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Practical descriptions of the technique are presented in De Meur and Rihoux (2002) and in De
Meur, Rihoux, and Ragin (forthcoming).
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| | Authors: Rihoux, Benoît. and Ragin, Charles. |
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2
Qualitative Comparative Analysis (or QCA; Ragin 1987; Drass and Ragin 1994) and its
successor, Fuzzy-Set Qualitative Comparative Analysis (fsQCA; Ragin 2000; Ragin, Drass,
and Davey 2004), were both developed for the analysis of small- and moderate-N data sets,
typical of those used by researchers in comparative politics and related disciplines. These
techniques are designed to unravel causal complexity by applying set-theoretic methods to
cross-case evidence. Their central goal is to mimic some of the basic analytic procedures
that comparative researchers use when making sense of their cases. The key difference
between QCA and traditional case-oriented methods is that with QCA it is possible to
extend these basic analytic procedures to the examination of more than a handful of cases,
for example, to more than 10.
2
In fact, there is no procedural limit on the number of cases
that can be studied using QCA (see below). This limit is set instead by the researcher’s
degree of interest in maintaining familiarity with each case and his or her tolerance for
complexity.
This paper offers (1) an advanced, conceptually oriented introduction to QCA, (2) an
overview of the current state of the art, and (3) a discussion of best practices in the use of
QCA, both general and more technical, with a special focus on QCA and counterfactual
analysis.
3
We begin by examining two analytic procedures commonly used by comparative
researchers (and by qualitative researchers, more generally) and contrast these techniques
with correlational analysis, the main analytical engine of mainstream quantitative social
science. We then discuss the state of the art of existing QCA applications, by trying to map
the diversity and scope of these applications. Following this, we first lay out some more
general guidelines to engage in a “good” QCA analysis. This is followed by a list of more
specific, technical guidelines in the form of “best practices”. One of these guidelines has to
do with the exploitation of “remainders”, i.e. non-observed configurations. This leads us to
discuss more in-depth one of the most powerful features of QCA : counterfactual analysis.
We conclude by sketching several of QCA's future prospects.
1. The Distinctiveness of Comparative Research
Researchers in comparative politics and related fields often seek to identify commonalities
across cases, focusing on a relatively small number of purposefully selected cases. There
are two analytic strategies central to this type of research. The first strategy is to examine
cases sharing a given outcome (e.g., consolidated third-wave democracies) and attempt to
identify their shared causal conditions (e.g., the possibility that they share presidential
2
Hereafter, QCA is used generically to refer to both QCA and fsQCA, as well as to the multi-value
QCA (MVQCA) variant (see http://www.compasss.org ; “software” section, and Cronqvist (2004)).
3
Practical descriptions of the technique are presented in De Meur and Rihoux (2002) and in De
Meur, Rihoux, and Ragin (forthcoming).
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