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Analyzing multilevel data with QCA: yet another straightforward procedure


A significant body of social-scientific literature has developed contextual theories. In a recent contribution to Quality & Quantity, Denk and Lehtinen (Qual Quant 48(6):3475–3487, 2014) present Comparative Multilevel Analysis (CMA) as an innovative method whereby the effects of contexts on outcomes of interest can be studied configurationally if combined with Qualitative Comparative Analysis (QCA). In contradistinction, I argue that CMA is neither innovative in nor necessary for ascertaining the influence of context in a configurational-comparative manner. QCA is appreciably more powerful than the authors acknowledge and provides all required functionality. In repetition of Rohlfing’s (Int J Soc Res Methodol 15(6):497–506, 2012) verdict on Denk’s (Int J Soc Res Methodol 13(1):29–39, 2010) earlier version of CMA, I conclude that QCA need not be extended in the direction proposed by Denk and Lehtinen. Researchers interested in the contextual analysis of configurational data are well-served by the existing toolbox of QCA.

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  1. 1.

    Both mvQCA and fsQCA are generalizations of csQCA, the former on the dimension of factor levels, the latter on the dimension of set membership scores. A fourth variant called generalized-set QCA (gsQCA) has recently been introduced by Thiem (2014b) to allow sets formed by multivalent factors to be fuzzy. Thus, gsQCA generalizes mvQCA and fsQCA.

  2. 2.

    Despite mvQCA’s capabilities, it has been applied relatively rarely in comparison with csQCA and fsQCA. See the website for an extensive bibliography of QCA-related publications.

  3. 3.

    The religious context in which a minority is embedded is operationalized as the country’s dominant religion.

  4. 4.

    The QCA package to be used later is the only software so far that can process outcome factors with multiple levels directly. In Tosmana, the other software for mvQCA, such factors have to be dichotomized before the analysis can commence (Cronqvist and Berg-Schlosser 2009, 84).

  5. 5.

    Note that not all minimization algorithms in QCA follow this pairwise elimination procedure since it is not very efficient. For example, both the QCA and Tosmana software packages use different algorithms.

  6. 6.

    The R replication script is available in the article’s online appendix or from the author on request.

  7. 7.

    Parsimonious solutions are generated by not confining the analysis to observed configurations only. Some researchers argue that they are the only legitimate solution type for purposes of causal data analysis with QCA (Baumgartner 2014).


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Correspondence to Alrik Thiem.

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Thiem, A. Analyzing multilevel data with QCA: yet another straightforward procedure. Qual Quant 50, 121–128 (2016).

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  • Comparative Multilevel Analysis
  • Configurational comparative methods
  • Contextual analysis
  • csQCA
  • fsQCA
  • mvQCA
  • QCA
  • Qualitative Comparative Analysis