Alrik Thiem

Still Lost in Translation! A Correction of Three Misunderstandings Between Configurational Comparativists and Regressional Analysts:

Even after a quarter-century of debate in political science and sociology, representatives of configurational comparative methods (CCMs) and those of regressional analytic methods (RAMs) continue talking at cross purposes. In this article, we clear up three fundamental misunderstandings that have been widespread within and between the two communities, namely that (a) CCMs and RAMs use the same logic of inference, (b) the same hypotheses can be associated with one or the other set of methods, and (c) multiplicative RAM interactions and CCM conjunctions constitute the same concept of causal complexity. In providing the first systematic correction of these persistent misapprehensions, we seek to clarify formal differences between CCMs and RAMs. Our objective is to contribute to a more informed debate than has been the case so far, which should eventually lead to progress in dialogue and more accurate appraisals of the possibilities and limits of each set of methods.

Navigating the Complexities of Qualitative Comparative Analysis Case Numbers, Necessity Relations, and Model Ambiguities

Background: In recent years, the method of Qualitative Comparative Analysis (QCA) has been enjoying increasing levels of popularity in evaluation and directly neighboring fields. Its holistic approach to causal data analysis resonates with researchers whose theories posit complex conjunctions of conditions and events. However, due to QCA’s relative immaturity, some of its technicalities and objectives have not yet been well understood. Objectives: In this article, I seek to raise awareness of six pitfalls of employing QCA with regard to the following three central aspects: case numbers, necessity relations, and model ambiguities. Most importantly, I argue that case numbers are irrelevant to the methodological choice of QCA or any of its variants, that necessity is not as simple a concept as it has been suggested by many methodologists, and that doubt must be cast on the determinacy of virtually all results presented in past QCA research. Method: By means of empirical examples from published articles, I explain the background of these pitfalls and introduce appropriate procedures, partly with reference to current software, that help avoid them. Conclusion: QCA carries great potential for scholars in evaluation and directly neighboring areas interested in the analysis of complex dependencies in configurational data. If users beware of the pitfalls introduced in this article, and if they avoid mechanistic adherence to doubtful “standards of good practice” at this stage of development, then research with QCA will gain in quality, as a result of which a more solid foundation for cumulative knowledge generation and well-informed policy decisions will also be created.

Back to Square One A Reply to Munck, Paine, and Schneider

We thank the editors of Comparative Political Studies for having invited us to join this symposium. Rather than addressing separate points made by Munck, Paine, and Schneider, we focus on two related problems that unite their pieces, that are of high relevance beyond this symposium, and that we have addressed only indirectly in our original article. The first problem concerns the over-inflation of the Boolean concept of necessity in Qualitative Comparative Analysis (QCA), the second one ignorance about the formalities of the theory of causation which QCA rests on.

Enhancing the Minimization of Boolean and Multivalue Output Functions With eQMC

Configurational comparative methods have gained in popularity among sociologists and political scientists. In particular, Qualitative Comparative Analysis (QCA) has attracted considerable attention in recent years. The process of Boolean minimization by means of the Quine-McCluskey algorithm (QMC) is the central procedure in QCA, but QMCs exactitude renders it memory intensive and slow in processing complex output functions. In this article, we introduce the enhanced QMC algorithm (eQMC) to alleviate these problems. eQMC is equally exact but, unlike QMC, capable of processing multivalent condition and outcome factors. Instead of replacing QMC, however, eQMC acts as an optimizing complement in contexts of limited empirical diversity. We demonstrate its speed and computer memory performance through simulations.

Using Qualitative Comparative Analysis for Identifying Causal Chains in Configurational Data A Methodological Commentary on Baumgartner and Epple (2014)

In a recent contribution to Sociological Methods & Research, Baumgartner and Epple (B&E) employ Coincidence Analysis (CNA) to explain the outcome of the vote on the Swiss minaret initiative of 2009. Although the authors also present a substantive argument, their principal objective is to prove the superiority of CNA over Qualitative Comparative Analysis (QCA) due to the former’s capability of identifying causal chains in configurational data without resort to Quine–McCluskey (QMC) optimization, whereby logical contradictions are allegedly introduced into the latter’s minimization process that trivialize the results. In this methodological commentary, I demonstrate that CNA does not challenge QCA per se but merely seeks to find fault with QMC. However, the link between QCA and QMC has never been inextricable, and alternative algorithms not beset by the one-difference restriction B&E consider problematic have long been in use. Hence, it follows that CNA introduces a new algorithm but does not perforce offer a superior method. To support this argument, I showcase the untapped potential of QCA for identifying causal chains in data that even incorporate multivalent factors. In employing the eQMC algorithm, whose general approach to Boolean minimization resembles that of CNA in decisive parts, I extend the authors’ original analysis in several directions, without generating logical contradictions along the way. I conclude that future research should continue to explore the methodological implications of the issues which CNA’s introduction has raised for QCA. Ultimately, however, the integration of their individual strengths represents one of the most promising avenues for the further development of configurational comparative methods.

Modeling Causal Irrelevance in Evaluations of Configurational Comparative Methods

In volume 44 of Sociological Methodology, Lucas and Szatrowski argued that the method of qualitative comparative analysis (QCA) suffers from a built-in confirmation bias due to a proclivity for including conditions in its output models whose corresponding factors are not systematically correlated with the endogenous factor. The authors therefore urged that QCA be abandoned. With this comment, we pursue four related objectives: first, we explain why correlation-based evaluation designs for testing QCA’s power of discrimination with respect to causally irrelevant factors are unsuitable; second, we show how appropriate tests must be constructed; third, we offer an R function that implements a routine for such tests; and fourth and finally, we conduct three series of comprehensive tests, all of whose results indicate that QCA does not suffer from the kind of confirmation bias criticized by Lucas and Szatrowski.

Conducting Configurational Comparative Research With Qualitative Comparative Analysis A Hands-On Tutorial for Applied Evaluation Scholars and Practitioners

The search for necessary and sufficient causes of some outcome of interest, referred to as configurational comparative research, has long been one of the main preoccupations of evaluation scholars and practitioners. However, only the last three decades have witnessed the evolution of a set of formal methods that are sufficiently elaborate for this purpose. In this article, I provide a hands-on tutorial for qualitative comparative analysis (QCA)—currently the most popular configurational comparative method. In drawing on a recent evaluation of patient follow-through effectiveness in Lynch syndrome tumor-screening programs, I explain the search target of QCA, introduce its core concepts, guide readers through the procedural protocol of this method, and alert them to mistakes frequently made in QCA’s use. An annotated replication file for the QCApro extension package for R accompanies this tutorial.

Burden Sharing in CSDP Military Operations

Abstract Military burden sharing has been a subject of repeated debates in NATO and the UN. Despite more modest goals, the European Union’s (EU) Common Security and Defense Policy (CSDP) has experienced no fewer difficulties in garnering men, money, and materiel. While this may not come as a surprise, the fact that some EU member states have carried disproportionate shares of the burden of CSDP operations is a puzzle that remains unaccounted for. We address this gap by analyzing determinants of contribution levels to CSDP operations. In employing an innovative multi-method design that combines insights from collection action theory with those from integrated theories of military burden sharing, our results indicate that EU countries tend to contribute in positive disproportion with their capabilities when they have a strong peacekeeping tradition and elections are distant. In contrast, they undercontribute when small trade volumes with the area of operations combine with a weak peacekeeping tradition.