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Contextual analyses with QCA-methods

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  • Thomas Denk
  • Sarah Lehtinen

Abstract

Contextual analyses are essential in comparative research, as they investigate the importance of contextual conditions for causal relationships. During the last decades, an increasing number of comparative studies have also focused on how contextual conditions affect causal relationships. At the same time, new comparative methods have been developed based on set-theoretical logics. Two of the most prominent methods are csQCA and fsQCA, which are used in comparative studies with increasing frequency. However, the conventional design for contextual analysis is still based on quantitative methods and the use of interaction-factors. This article discusses why the use of interaction-factors is not suitable together with QCA-methods. Instead of the conventional design, the article presents an alternative design for contextual analyses with QCA-methods grounded on subgroup-design. Based on one recently-developed methodology comparative multilevel analysis (CMA), some guidelines for performing contextual analyses with two set-theoretical methods (csQCA and fsQCA) are presented. As illustrated with examples, the combination of CMA and QCA provides opportunities to use QCA for contextual analysis. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Thomas Denk & Sarah Lehtinen, 2014. "Contextual analyses with QCA-methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3475-3487, November.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:3475-3487
    DOI: 10.1007/s11135-013-9968-4
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    References listed on IDEAS

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    1. Ragin, Charles C., 2000. "Fuzzy-Set Social Science," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226702773, November.
    2. 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.
    3. Brambor, Thomas & Clark, William Roberts & Golder, Matt, 2006. "Understanding Interaction Models: Improving Empirical Analyses," Political Analysis, Cambridge University Press, vol. 14(1), pages 63-82, January.
    4. repec:ucp:bkecon:9780226702766 is not listed on IDEAS
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    Cited by:

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    2. Anh Thi Nguyen & Alrence S. Halibas & Robert McClelland & Nguyen Hoang Thuan, 2024. "Configurational analysis of conditions influencing customers’ channel switching intention in omnichannel retailing: a fuzzy-set analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 141-178, February.
    3. Kun-Huang Huarng & Tiffany Hui-Kuang Yu, 2017. "Using qualitative approach to forecasting regime switches," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2035-2048, September.
    4. Sascha Kraus & Domingo Ribeiro-Soriano & Miriam Schüssler, 2018. "Fuzzy-set qualitative comparative analysis (fsQCA) in entrepreneurship and innovation research – the rise of a method," International Entrepreneurship and Management Journal, Springer, vol. 14(1), pages 15-33, March.
    5. Victoria Finn, 2022. "A qualitative assessment of QCA: method stretching in large-N studies and temporality," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3815-3830, October.
    6. Alrik Thiem, 2016. "Analyzing multilevel data with QCA: yet another straightforward procedure," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 121-128, January.
    7. Eva Thomann & Anita Manatschal, 2016. "Identifying context and cause in small-N settings: a comparative multilevel analysis," Policy Sciences, Springer;Society of Policy Sciences, vol. 49(3), pages 335-348, September.

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