Model Ambiguities in Configurational Comparative Research

Abstract

For many years, sociologists, political scientists, and management scholars have readily relied on Qualitative Comparative Analysis (QCA) for the purpose of configurational causal modeling. However, this article reveals that a severe problem in the application of QCA has gone unnoticed so far: model ambiguities. These arise when multiple causal models fare equally well in accounting for configurational data. Mainly due to the uncritical import of an algorithm that is unsuitable for causal modeling, researchers have typically been unaware of the whole model space. As a result, there exists an indeterminable risk for practically all QCA studies published in the last quarter century to have presented findings that their data did not warrant. Using hypothetical data, we first identify the algorithmic source of ambiguities and discuss to what extent they affect different methodological aspects of QCA. By reanalyzing a published QCA study from rural sociology, we then show that model ambiguities are not a mere theoretical possibility but a reality in applied research, which can assume such extreme proportions that no causal conclusions whatsoever are possible. Finally, the prevalence of model ambiguities is examined by performing a comprehensive analysis of 192 truth tables across 28 QCA studies published in applied sociology. In conclusion, we urge that future QCA practice ensures full transparency with respect to model ambiguities, both by informing readers of QCA-based research about their extent and by employing algorithms capable of revealing them.

Event coincidence analysis for quantifying statistical interrelationships between event time series

“…considering the birthday problem [39]. The term coincidence analysis is also used in different contexts in fields such as elementary particle physics [40] or in the identification of causal dependencies in configurational data [41]. This is why we choose to use the more specific term event coincidence analysis when referring to the methodology introduced in this paper.…”

Electoral violence under different contexts. Evidence from the Western Balkans

“…At the same time, this kind of result can produce a much more robust insight based on complex overview of the whole model space without its unfounded reduction that has not theoretical as well empirical justification (Baumgartner and Thiem 2017a: 3). A good practice, adopted by this paper as well, is therefore to explicitly report all the alternative models and transparently discuss them to what degree the analysed data underdetermine the causal modelling as a result of QCA analysis (Baumgartner and Thiem 2017a).…”

Renewable Energy as a Luxury? A Qualitative Comparative Analysis of the Role of the Economy in the EU's Renewable Energy Transitions During the ‘Double Crisis’

“…QCA is a method of causal inference based on a difference--making theory of causation, and has been applied in a growing number of papers across many disciplines (Baumgartner, 2014;Baumgartner and Thiem, 2015;Ragin, 2008Ragin, , 1989Schneider and Wagemann, 2012).…”

“…This article represents the first application of QCA for testing a specific hypothesis surrounding the effect of economic conditions on renewable energy policies in times of economic crisis. To the best of our knowledge, ours is also the first study that explicitly addresses the issue of model ambiguities in QCA, a problem that has only recently been brought into focus by Thiem (2014a) and Baumgartner and Thiem (2015).…”