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Different algorithms, different models

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  • Martyna Daria Swiatczak

    (Department of Philosophy)

Abstract

This study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

Suggested Citation

  • Martyna Daria Swiatczak, 2022. "Different algorithms, different models," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 1913-1937, August.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:4:d:10.1007_s11135-021-01193-9
    DOI: 10.1007/s11135-021-01193-9
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    References listed on IDEAS

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    1. Eva Thomann & Martino Maggetti, 2020. "Designing Research With Qualitative Comparative Analysis (QCA): Approaches, Challenges, and Tools," Sociological Methods & Research, , vol. 49(2), pages 356-386, May.
    2. Michael Baumgartner & Alrik Thiem, 2020. "Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis," Sociological Methods & Research, , vol. 49(2), pages 279-311, May.
    3. Hug, Simon, 2013. "Qualitative Comparative Analysis: How Inductive Use and Measurement Error Lead to Problematic Inference," Political Analysis, Cambridge University Press, vol. 21(2), pages 252-265, April.
    4. Ragin, Charles C., 2000. "Fuzzy-Set Social Science," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226702773, December.
    5. Ragin, Charles C., 2006. "Set Relations in Social Research: Evaluating Their Consistency and Coverage," Political Analysis, Cambridge University Press, vol. 14(3), pages 291-310, July.
    6. Carsten Q. Schneider, 2019. "Two-step QCA revisited: the necessity of context conditions," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(3), pages 1109-1126, May.
    7. Schneider, Carsten Q., 2018. "Realists and Idealists in QCA," Political Analysis, Cambridge University Press, vol. 26(2), pages 246-254, April.
    8. Tim Haesebrouck, 2019. "An alternative update of the two-step QCA procedure," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(6), pages 2765-2780, November.
    9. Krogslund, Chris & Choi, Donghyun Danny & Poertner, Mathias, 2015. "Fuzzy Sets on Shaky Ground: Parameter Sensitivity and Confirmation Bias in fsQCA," Political Analysis, Cambridge University Press, vol. 23(1), pages 21-41, January.
    10. Wagemann, Claudius & Buche, Jonas & Siewert, Markus B., 2016. "QCA and business research: Work in progress or a consolidated agenda?," Journal of Business Research, Elsevier, vol. 69(7), pages 2531-2540.
    11. repec:ucp:bkecon:9780226702766 is not listed on IDEAS
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