Conditions of intergovernmental armaments cooperation in Western Europe, 1996-2006

Defence cooperation between Western European countries has increased considerably since the end of the Cold War. An analytical distinction can be made between political and economic cooperation, the latter having been neglected by political scientists. This study advances the debate on economic cooperation by identifying sources of variation in the European Union (EU)-15 countries' membership rate in cooperative armaments fora aimed at restructuring the demand side of European defence from 1996 to 2006. By combining six models from three different schools of thought, the risk of confirmation bias through intra-paradigmatic reasoning is reduced. At the same time, fuzzy-set analysis opens up the space for data-driven combination effects. Two distinct combinations form sufficient paths leading to high rates of membership. Most importantly, intentions to create collective defence technological and industrial benefits combine with trust in partners' ability and integrity to form an essential combination of conditions for governments to pursue cooperation on armament

Algorithmic bias in social research : a meta-analysis

Both the natural and the social sciences are currently facing a deep “reproducibility crisis”. Two important factors in this crisis have been the selective reporting of results and methodological problems. In this article, we examine a fusion of these two factors. More specifically, we demonstrate that the uncritical import of Boolean optimization algorithms from electrical engineering into some areas of the social sciences in the late 1980s has induced algorithmic bias on a considerable scale over the last quarter century. Potentially affected are all studies that have used a method nowadays known as Qualitative Comparative Analysis (QCA). Drawing on replication material for 215 peer-reviewed QCA articles from across 109 high-profile management, political science and sociology journals, we estimate the extent this problem has assumed in empirical work. Our results suggest that one in three studies is affected, one in ten severely so. More generally, our article cautions scientists against letting methods and algorithms travel too easily across disparate disciplines without sufficient prior evaluation of their suitability for the context in hand

Coincidence analysis: a new method for causal inference in implementation science

Background Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches and can reveal new empirical findings related to implementation that might otherwise have gone undetected. Methods We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings. Results The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage. Conclusions CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.publishedVersio

Correction to: Coincidence analysis: a new method for causal inference in implementation science

An amendment to this paper has been published and can be accessed via the original article.http://deepblue.lib.umich.edu/bitstream/2027.42/173844/1/13012_2020_Article_1079.pd

Parameters of fit and intermediate solutions in multi-value Qualitative Comparative Analysis

Multi-value Qualitative Comparative Analysis (mvQCA) is a variant of QCA that continues to exist under the shadow of crisp and fuzzy-set QCA. The lack of support for parameters of fit and intermediate solutions has contributed to this undeserved status. This article introduces two innovations that put mvQCA on a par with its two sister variants. First, consistency and coverage as the two most important parameters of fit are generalized. Second, the concepts of easy and difficult counterfactuals for deriving intermediate solutions are imported. I demonstrate how to leverage these features in the QCA software package for the R environment. For researchers who do not use QCA, I explain how to exploit Veitch-Karnaugh maps instead for solving set-theoretic minimization problems of low to moderate complexity

Replication Data for: Standards of Good Practice and the Methodology of Necessary Conditions in Qualitative Comparative Analysis

The file replication.R allows the replication of all analyses carried out in the article. The file supplement_S1.doc lists all 21 studies included in the meta-analysis and presents detailed results

Supplemental Material, replication_Thiem_2018_SMR - The Logic and Methodology of “Necessary but Not Sufficient Causality”: A Comment on Necessary Condition Analysis (NCA)

<p>Supplemental Material, replication_Thiem_2018_SMR for The Logic and Methodology of “Necessary but Not Sufficient Causality”: A Comment on Necessary Condition Analysis (NCA) by Alrik Thiem in Sociological Methods & Research</p

Case-to-Factor Ratios and Model Specification in Qualitative Comparative Analysis (QCA)

Replication matrial for Thiem, Alrik and Lusine Mkrtchyan. forthcoming. "Case-to-factor ratios and model specification in Qualitative Comparative Analysis (QCA)." Field Methods. Abstract: Qualitative Comparative Analysis (QCA) is an empirical research method that has gained some popularity in the social sciences. At the same time, the literature has long been convinced that QCA is prone to committing causal fallacies when confronted with non-causal data. More specifically, beyond a certain case-to-factor ratio, the method is believed to fail in recognizing real data. To reduce that risk, some authors have proposed benchmark tables that put a limit on the number of exogenous factors given a certain number of cases. Many applied researchers looking for methodological guidance have adhered to these tables. We argue that fears of inferential breakdown in QCA due to an “unfavorable” case-to-factor ratio are without foundation. What is more, we demonstrate that these benchmarks induce more fallacious inferences than they prevent. For valid causal inference, researchers are better off relying on the current state of knowledge in their respective fields

Enhancing sensitivity diagnostics for Qualitative Comparative Analysis : A combinatorial approach.

Sensitivity diagnostics has recently been put high on the agenda of methodological research into Qualitative Comparative Analysis (QCA). Existing studies in this area rely on the technique of exhaustive enumeration, and the majority of works examine the reactivity of QCA either only to alterations in discretionary parameter values or only to data quality. In this article, we introduce the technique of combinatorial computation for evaluating the interaction effects between two problems afflicting data quality and two discretionary parameters on the stability of QCA reference solutions. In this connection, we challenge a hitherto unstated assumption intrinsic to exhaustive enumeration, show that combinatorial computation permits the formulation of general laws of sensitivity in QCA, and demonstrate that our technique is most efficient