Enhancing Sensitivity Diagnostics for Qualitative Comparative Analysis: A Combinatorial Approach

Abstract

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.

Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis

“…We implement all tests using the R package QCApro, which contains extensive evaluation functions for QCA (Thiem 2016a). 17 In addition, QCApro is capable of returning the full model space of a solution, unlike other programs for configurational data analysis (Baumgartner and Thiem 2017; Thiem 2014b; Thiem and Duşa 2013a).…”

“…All our tests adhere to the inverse-search canon laid out in the second section, with one important exception: not data sets will be simulated but truth tables. The reason is that QCA transforms raw data into a truth table in its first algorithmic phase (Thiem 2017; Thiem, Spöhel, and Duşa 2016:108). One and the same truth table can represent the dependencies among exogenous and endogenous factors in an infinite number of different data sets, whereas the reverse is not true.…”

Addressing the Challenges Related to Transforming Qualitative Into Quantitative Data in Qualitative Comparative Analysis

“…The QCA literature provides several suggestions on how to assess the robustness of QCA findings using sensitivity tests. A nonexhaustive list includes (1) dropping or adding cases and conditions, (2) changing fuzzy-set membership functions, (3) altering consistency thresholds (Schneider & Wagemann, 2012;Thiem, 2014;Thiem, Spöhel, & Dus xa, 2016), (4) changing the definitions of the set values, (5) using alternative measures for a concept (Basurto & Speer, 2012), (6) changing the calibration thresholds of raw data into set membership, and (7) altering the frequency of cases linked to configurations (Skaaning, 2011). These suggestions are not specific to qualitative data.…”

“…Since studies using qualitative data often-though not always-have a relatively low number of cases, this will in many cases not be the most important sensitivity test to conduct. Some researchers conduct additional statistical analyses to assess the robustness of their findings, despite criticism about the comparability of the two methods (e.g., Thiem, Spöhel, et al, 2016). For example, (2016) Thomann (2015), Van der Heijden (2015), Verweij et al (2013), and Wang (2016) Table(s) in appendix, partial information Basurto (2013) and Hodson and Roscigno (2004) Note.…”

Sustainability startups and where to find them

“…The robustness of the results presented in this paper is tested via exhaustive numeration. This is currently the most common approach to assessing the robustness of fsQCA results (Thiem, Spoel and Dus, 2016); fsQCA results are considered robust by this method 'if they involve similar necessary and sufficient conditions and if consistency and coverage are roughly the same across different model specifications' (Schneider and Wagemann, 2012). We thus tested the robustness of the results for changes to the frequency threshold (row 2), to the number of cases analysed (row 3) and to the calibration of values of the outcome variable (row 4), as well as for the introduction of additional causal conditions (rows 5 and 6).…”