Standards of Good Practice and the Methodology of Necessary Conditions in Qualitative Comparative Analysis

2016 ◽  
Vol 24 (4) ◽  
pp. 478-484 ◽  
Author(s):  
Alrik Thiem
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Necessary Conditions ◽  
Meta Analysis ◽  
Good Practice ◽  
Qualitative Comparative Analysis ◽  
Boolean Minimization ◽  
The Social ◽  
Truth Tables

The analysis of necessary conditions for some outcome of interest has long been one of the main preoccupations of scholars in all disciplines of the social sciences. In this connection, the introduction of Qualitative Comparative Analysis (QCA) in the late 1980s has revolutionized the way research on necessary conditions has been carried out. Standards of good practice for QCA have long demanded that the results of preceding tests for necessity constrain QCA's core process of Boolean minimization so as to enhance the quality of parsimonious and intermediate solutions. Schneider and Wagemann's Theory-Guided/Enhanced Standard Analysis (T/ESA) is currently being adopted by applied researchers as the new state-of-the-art procedure in this respect. In drawing on Schneider and Wagemann's own illustrative data example and a meta-analysis of thirty-six truth tables across twenty-one published studies that have adhered to current standards of good practice in QCA, I demonstrate that, once bias against compound conditions in necessity tests is accounted for, T/ESA will produce conservative solutions, and not enhanced parsimonious or intermediate ones.

scholarly journals Systematische kwalitatieve vergelijkende analyse

KWALON ◽  
2012 ◽  
Vol 17 (3) ◽  
Author(s):  
Stefan Verweij ◽  
Lasse M. Gerrits
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Spatial Planning ◽  
Qualitative Data ◽  
Qualitative Comparative Analysis ◽  
The Social

Systematic Qualitative Comparative Analysis Systematic Qualitative Comparative Analysis Qualitative Comparative Analysis (QCA) was introduced in the social sciences by Charles Ragin in 1987. Literature on and applications of QCA show the method as a way to systematically organize, summarize and compare qualitative data to discover and analyze patterns occurring over cases. Although the literature stresses the importance of iterating between theory and data in its procedures, its grounded nature remains relatively underexposed. In this article we illustrate the principles of QCA by means of a qualitative comparative analysis of fourteen Dutch spatial planning projects, thereby also articulating the method’s grounded nature.

scholarly journals Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets

2010 ◽  
Vol 9 (3) ◽  
pp. 397-418 ◽  
Author(s):  
Carsten Q. Schneider ◽  
Claudius Wagemann
Keyword(s):  
Data Analysis ◽  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Good Practice ◽  
Qualitative Comparative Analysis ◽  
Research Approach ◽  
Analytical Technique ◽  
Work In Progress ◽  
Methodological Tool

AbstractAs a relatively new methodological tool, QCA is still a work in progress. Standards of good practice are needed in order to enhance the quality of its applications. We present a list from A to Z of twenty-six proposals regarding what a “good” QCA-based research entails, both with regard to QCA as a research approach and as an analytical technique. Our suggestions are subdivided into three categories: criteria referring to the research stages before, during, and after the analytical moment of data analysis. This listing can be read as a guideline for authors, reviewers, and readers of QCA.

Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA

2016 ◽  
Vol 46 (2) ◽  
pp. 242-251 ◽  
Author(s):  
Bear F. Braumoeller
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Qualitative Comparative Analysis ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Aggregation Bias ◽  
Data Sets ◽  
The Social ◽  
Necessary And Sufficient ◽  
Causal Complexity

Fuzzy-set qualitative comparative analysis (fsQCA) has become one of the most prominent methods in the social sciences for capturing causal complexity, especially for scholars with small- and medium- N data sets. This research note explores two key assumptions in fsQCA’s methodology for testing for necessary and sufficient conditions—the cumulation assumption and the triangular data assumption—and argues that, in combination, they produce a form of aggregation bias that has not been recognized in the fsQCA literature. It also offers a straightforward test to help researchers answer the question of whether their findings are plausibly the result of aggregation bias.

The origins, development, and application of Qualitative Comparative Analysis: the first 25 years

2013 ◽  
Vol 6 (1) ◽  
pp. 115-142 ◽  
Author(s):  
Axel Marx ◽  
Benoît Rihoux ◽  
Charles Ragin
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Quantitative Research ◽  
Comparative Method ◽  
Qualitative Comparative Analysis ◽  
Research Approach ◽  
Quarter Century ◽  
Quantitative Research Methods ◽  
Qualitative And Quantitative ◽  
The Social

A quarter century ago, in 1987, Charles C. Ragin published The Comparative Method, introducing a new method to the social sciences called Qualitative Comparative Analysis (QCA). QCA is a comparative case-oriented research approach and collection of techniques based on set theory and Boolean algebra, which aims to combine some of the strengths of qualitative and quantitative research methods. Since its launch in 1987, QCA has been applied extensively in the social sciences. This review essay first sketches the origins of the ideas behind QCA. Next, the main features of the method, as presented in The Comparative Method, are introduced. A third part focuses on the early applications. A fourth part presents early criticisms and subsequent innovations. A fifth part then focuses on an era of further expansion in political science and presents some of the main applications in the discipline. In doing so, this paper seeks to provide insights and references into the origin and development of QCA, a non-technical introduction to its main features, the path travelled so far, and the diversification of applications.

Qualitative comparative analysis for reviewing evidence and making decisions

2016 ◽  
Author(s):  
Matt Ryan
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Qualitative Comparative Analysis ◽  
Policy Learning ◽  
The Social ◽  
Fuzzy Set Analysis

This chapter looks at another technique that has grown in popularity – qualitative comparative analysis – in the social sciences to analyse situations with comparative rigour through access to medium-N. In this, the author explores the logic of Boolean and fuzzy set analysis and then explores any applications to policy learning identified. This chapter will offer an assessment of the limitations to the technique but also the enormous potential it holds.

scholarly journals Analyzing Relationships of Necessity Not Just in Kind But Also in Degree

2016 ◽  
Vol 47 (4) ◽  
pp. 872-899 ◽  
Author(s):  
Barbara Vis ◽  
Jan Dul
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Applied Research ◽  
Qualitative Comparative Analysis ◽  
Necessary Condition ◽  
Specific Level ◽  
Limited Ability ◽  
The Social ◽  
Research Questions

Analyzing relationships of necessity is important for both scholarly and applied research questions in the social sciences. An often-used technique for identifying such relationships— fuzzy set Qualitative Comparative Analysis (fsQCA)—has limited ability to make the most out of the data used. The set-theoretical technique fsQCA makes statements in kind (e.g., “a condition or configuration is necessary or not for an outcome”), thereby ignoring the variation in degree. We propose to apply a recently developed technique for identifying relationships of necessity that can make both statements in kind and in degree, thus making full use of variation in the data: Necessary Condition Analysis (NCA). With its ability to also make statements in degree (“a specific level of a condition is necessary or not for a specific level of the outcome”), NCA can complement the in kind analysis of necessity with fsQCA.

Boolean Algebra

2021 ◽  
pp. 30-32
Author(s):  
Jasmin Hasić
Keyword(s):  
Comparative Analysis ◽  
Boolean Algebra ◽  
Binary Data ◽  
Qualitative Comparative Analysis ◽  
Boolean Logic ◽  
Boolean Minimization ◽  
The Social ◽  
Boolean Equation ◽  
Basic Features ◽  
Causal Complexity

This chapter addresses Boolean algebra, which is based on Boolean logic. In the social sciences, Boolean algebra comes under different labels. It is often used in set-theoretic and qualitative comparative analysis to assess complex causation that leads to particular outcomes involving different combinations of conditions. The basic features of Boolean algebra are the use of binary data, combinatorial logic, and Boolean minimization to reduce the expressions of causal complexity. By calculating the intersection between the final Boolean equation and the hypotheses formulated in Boolean terms, three subsets of causal combinations emerge: hypothesized and empirically confirmed; hypothesized, but not detected within the empirical evidence; and causal configurations found empirically, but not hypothesized. This approach is both holistic and analytic because it examines cases as a whole and in parts.

scholarly journals Beyond the Facts: Limited Empirical Diversity and Causal Inference in Qualitative Comparative Analysis

2019 ◽  
pp. 004912411988246 ◽  
Author(s):  
Alrik Thiem
Keyword(s):  
Social Sciences ◽  
Comparative Analysis ◽  
Causal Inference ◽  
Qualitative Comparative Analysis ◽  
Causal Discovery ◽  
Artificial Data ◽  
Methodological Research ◽  
Solution Type ◽  
The Social ◽  
Intermediate Solution

Qualitative Comparative Analysis (QCA) is a relatively young method of causal inference that continues to diffuse across the social sciences. However, recent methodological research has found the conservative (QCA-CS) and the intermediate solution type (QCA-IS) of QCA to fail fundamental tests of correctness. Even under conditions otherwise ideal for causal discovery, both solution types frequently committed causal fallacies by presenting inferences that were in direct disagreement with the underlying data-generating structure to be discovered by QCA. None of these problems affected the parsimonious solution type (QCA-PS). These findings conflict with conventional wisdom in the QCA literature, which has it that QCA-CS uses empirical information only and that QCA-IS is preferable to both QCA-CS and QCA-PS. The present article resolves these contradictions. It shows that QCA-CS and QCA-IS systematically supplement empirical data with matching artificial data. These artificial data, however, regularly induce causal fallacies of severe magnitude. Researchers who employ QCA-CS or QCA-IS in empirical analyses thus always risk moving further away from the truth rather than closer to it.

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