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Qualitative Comparative Analysis (QCA): State of the Art and Prospects
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Secondly, it is advisable to draw on the different functions of the software. Many of these functions are still under-used, such as the " hypothesis testing " function (Watanabe 2003, Yamasaki 2003), which can be exploited in different ways.
Thirdly, technical and reference concepts should be used with precision, in order not to induce the reader in error. It is so that several misunderstandings -- and misplaced critiques vis-à -vis QCA -- stem from the use of inappropriate technical terms. One of the most frequent examples is the reference to “independent variables” (instead of “conditions”, as potential explanatory factors are referred to in QCA jargon). The problem is that conditions are not “independent variables” in the statistical sense (Rihoux et.al. 2004).
Fourthly, one should never forget the fundamentally configurational logic of QCA (Ragin 2003b; 2004b; Nomiya 2004). Hence one should never consider the influence of such or such condition in an isolated manner, especially in the interpretation of minimal formulae.
Fifth, QCA should never be used following a " press button " logic, but instead as a tool which requires iterative steps, frequent moves back and forth, between the QCA analysis proper (use of the software), cases and theories. Bottom line : a QCA analysis should be both case-informed (relying on “case-based knowledge”; Ragin 2003b) and theory-informed. When the researcher encounters difficulties, he/she should not try to conceal them; instead, he/she should explain, as transparently as may be, how they have been solved or by-passed. This often implies being transparent about trade-offs, “pragmatic” choices which may at times be somewhat arbitrary (“rules of thumb”) in real-life research --but at least, then, the reader is informed about the choices that have been made and their justifications.
Sixth, one should be careful in the interpretation of the minimal formula (reached at the end of the Boolean minimization procedure with QCA). In particular, it is advisable to be cautious before interpreting a minimal formula in terms of “causality”. Technically speaking, minimal formulae express, more modestly, co-occurrences. It is then up to researcher to decide (relying on his substantive [case-based] and theoretical knowledge) how far he/she can go in the interpretation of the minimal formula in terms of “causality”.
Finally, in the research process, it is most often more fruitful to exploit different methods. No researcher should become a “QCA monomaniac” -- indeed we would argue that the same is true (or at least should be true!) for any other method, whether QCA is included in the research process or not. At different stages of one’s research, it is very often the case that different methods suit different needs. Thus is it advisable to use QCA in some stages of the research, while exploiting other methods (be they qualitative or quantitative) at other stages of the research. This is not to say that QCA should necessarily only be used in a “modest” way. Indeed, we believe that, in some research situations, QCA can be used as the main data processing method.
4. Best QCA Practices II : Technical Aspects and Procedure
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| | Authors: Rihoux, Benoît. and Ragin, Charles. |
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9
Secondly, it is advisable to draw on the different functions of the software. Many of these
functions are still under-used, such as the " hypothesis testing " function (Watanabe 2003,
Yamasaki 2003), which can be exploited in different ways.
Thirdly, technical and reference concepts should be used with precision, in order not to
induce the reader in error. It is so that several misunderstandings -- and misplaced critiques
vis-Ă -vis QCA -- stem from the use of inappropriate technical terms. One of the most
frequent examples is the reference to “independent variables” (instead of “conditions”, as
potential explanatory factors are referred to in QCA jargon). The problem is that conditions
are not “independent variables” in the statistical sense (Rihoux et.al. 2004).
Fourthly, one should never forget the fundamentally configurational logic of QCA (Ragin
2003b; 2004b; Nomiya 2004). Hence one should never consider the influence of such or
such condition in an isolated manner, especially in the interpretation of minimal formulae.
Fifth, QCA should never be used following a " press button " logic, but instead as a tool
which requires iterative steps, frequent moves back and forth, between the QCA analysis
proper (use of the software), cases and theories. Bottom line : a QCA analysis should be
both case-informed (relying on “case-based knowledge”; Ragin 2003b) and theory-
informed. When the researcher encounters difficulties, he/she should not try to conceal
them; instead, he/she should explain, as transparently as may be, how they have been solved
or by-passed. This often implies being transparent about trade-offs, “pragmatic” choices
which may at times be somewhat arbitrary (“rules of thumb”) in real-life research --but at
least, then, the reader is informed about the choices that have been made and their
justifications.
Sixth, one should be careful in the interpretation of the minimal formula (reached at the end
of the Boolean minimization procedure with QCA). In particular, it is advisable to be
cautious before interpreting a minimal formula in terms of “causality”. Technically
speaking, minimal formulae express, more modestly, co-occurrences. It is then up to
researcher to decide (relying on his substantive [case-based] and theoretical knowledge)
how far he/she can go in the interpretation of the minimal formula in terms of “causality”.
Finally, in the research process, it is most often more fruitful to exploit different methods.
No researcher should become a “QCA monomaniac” -- indeed we would argue that the
same is true (or at least should be true!) for any other method, whether QCA is included in
the research process or not. At different stages of one’s research, it is very often the case
that different methods suit different needs. Thus is it advisable to use QCA in some stages
of the research, while exploiting other methods (be they qualitative or quantitative) at other
stages of the research. This is not to say that QCA should necessarily only be used in a
“modest” way. Indeed, we believe that, in some research situations, QCA can be used as the
main data processing method.
4. Best QCA Practices II : Technical Aspects and Procedure
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