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Analyzing Multilevel Data with QCA – A Straightforward Procedure

Authors: Rohlfing, Ingo;

Analyzing Multilevel Data with QCA – A Straightforward Procedure

Abstract

The social sciences are witnessing a growing body of multilevel theories and debates about the proper methodological tools for the analysis of multilevel data. In a recent contribution to this journal, Thomas Denk (2010) proposes multilevel Qualitative Comparative Analysis (QCA) as a new methodological tool for discerning set-relational patterns in multilevel data. I argue that the presentation of multilevel QCA is erroneous in two respects. First, multilevel QCA ignores the fact that equifinal solutions entail diversity and therefore leads one to overestimate the complexity of QCA solutions. Second, the ordinary minimization procedure of truth tables that contain multilevel data yields the same solutions as multilevel QCA, but is much easier to implement. I conclude that the established inventory of QCA does not need to be extended by a special multilevel approach.

{"references": ["Rohlfing, Ingo (2011): Analyzing Multilevel Data with Qca: A Straightforward Procedure. International Journal of Social Research Methodology 15 (6): 497-506."]}

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Keywords

QCA, algorithm, equifinality, Quine-McCluskey, minimization, set relations, diversity

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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