Adrian Duşa

Crisp-Set Qualitative Comparative Analysis (csQCA), Contradictions and Consistency Benchmarks for Model Specification

The purpose of this paper is to address and test two assumptions on which csQCA is based, namely that csQCA will generate contradictions and low consistency scores if models are ill-specified. The first part of the paper introduces csQCA in general and as a stepwise approach. In a second part a real-life example is introduced with the purpose of illustrating how csQCA operates and as an input for a simulation in the subsequent part. The third part introduces contradictions, consistency, their interrelatedness and the assumptions which are made with regard to contradictions and consistency. Subsequently the assumptions are tested via a simulation on the basis of a csQCA analysis of over 5 million random datasets. The paper argues that researchers cannot always assume that csQCA will generate contradictions or low consistency scores when models are ill-specified. Such an assumption is only justified when csQCA applications take limitations with regard to model specification (the number of conditions and the number of cases) into account. Benchmark tables for model specification purposes are developed. Since these tables are based on a probability value of 0.5 the paper also tests the results for contradictions and consistency for the probabilities which were present in a real-life example. This test shows that the 0.5 probability generates an appropriate measure for the occurrence of contradictions and consistency indicating that the benchmark tables can be used for different applications with different distributions of 0s and 1s in the conditions and outcomes. The paper ends with a conclusion.

Qualitative Comparative Analysis with R

1.Loading Neccessary Packages.- 2. Reading Data into R.- 3. Testing for Neccessity.- 4. Testing for Sufficiency 5.- Sufficiency: Parameters of Fit 6.- Plotting Results

Boolean Minimization in Social Science Research: A Review of Current Software for Qualitative Comparative Analysis (QCA)

Besides an increase in the number of empirical applications, the widening landscape of tailored computer programs attests to the success of qualitative comparative analysis (QCA) as a social research method. Users now have the choice between three graphical user interface (GUI) and three command line interface (CLI) solutions. In addition to different functional foci, each program possesses several technical particularities, some of which the vast majority of end users remain unaware of. Since these particularities may influence results and in turn substantive conclusions, this review is a timely undertaking. More specifically, we compare the two most common GUIs fs/QCA and Tosmana as well as the CLI QCA. By reanalyzing data from a sociological study on rural grassroots associations in Norway, major differences and similarities with respect to truth table construction, minimization algorithms, and prime implicant chart management are illustrated.

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.

Fuzzy-Set QCA

This section explains how to perform QCA using fuzzy sets, commonly referred to as fsQCA. Since the publication of Ragin (2000), fsQCA has become increasingly popular because continuous base variables need not be dichotomized. After a short theoretical introduction to the concept of fuzzy-set calibration, we introduce the two most popular calibration methods: direct assignment and transformational assignment. While the former is quickly dealt with, more time will be spent on the latter as its mechanisms and implications have so far received little attention. In the remainder of the chapter, the results from the study by Emmenegger (2011) on job-security regulations in Western democracies are replicated.

Crisp-Set QCA

A QCA which uses only binary-value crisp-set data is referred to as a crisp-set QCA (csQCA). In this chapter, the findings of Krook’s (2010) csQCA study on women’s representation in national parliaments are replicated. We first show how to calibrate crisp sets from categorical and continuous base variables, using both external criteria as suggested by theoretical knowledge and internal criteria as arrived at through empirical data analysis. We then proceed to the testing of necessity relations. The analysis of sufficiency relations is the next step, including the derivation of complex, parsimonious and intermediate solutions. Finally, we also demonstrate how to plot results for both types of relation as Venn diagrams.

Less Known Features

This final chapter is a more practical and applied one, describing in detail the rest of the graphical user interface as well as the most used functions in package QCA. Although widely used, there are still some not so obvious features that remain unemployed in most of the cases. In a way, this is a chapter about all the little secrets that each function has, that in many situations are capable of making the usage experience as straightforward as possible.

Pseudo-Counterfactual Analysis

The word “assumption” has been used and overused, across all these sections referring to the conservative, parsimonious and intermediate solutions of all kinds. The time is ripe to discuss yet another assumption, perhaps the biggest myth of all in terms of scope.

The Truth Table

The truth table is the main analytical tool needed to perform the minimization process that was invented in the engineering, and which Charles Ragin adapted for the social sciences. It is well known the electric engineering procedure was pioneered by Shannon (1940) at MIT, after having been earlier exposed to the work of George Boole, during his studies at the University of Michigan. However it is less known that something extremely similar was developed even earlier by the American sociologist Lazarsfeld (1937) who introduced the concept of “attribute space”, later to become what is known today as the concept of “property space”.

The Logical Minimization

The logical, or Boolean minimization process is the core of the QCA methodology, which seeks to find the simplest possible expression that is associated with the explained value of an output. The term expression, here, is a synonym for sums of products, or unions of intersections, or disjunctions of conjunctions (of causal conditions). It will also be used as a synonym for a causal configuration, since that is a conjunction (a product) of causal conditions.