scholarly journals Component 2 (Meso): Qualitative Comparative Analysis (QCA)

2021 ◽  
pp. 233-255
Author(s):  
Jason García Portilla
Keyword(s):  
Comparative Analysis ◽  
Set Theory ◽  
Qualitative Comparative Analysis ◽  
Roman Catholic ◽  
Statistical Technique ◽  
Boolean Logic ◽  
Legal Origin ◽  
Causal Inferences ◽  
Catholic Religion ◽  
Negative Effect

AbstractThis chapter contains the meso component (Qualitative Comparative Analysis, QCA). It discusses the QCA research model, the QCA methodology, and the analysis of the QCA results.QCA is used to analyse both quantitative and qualitative data, thus enabling causal inferences. However, QCA is not a statistical technique that focuses on the likelihood of the relations among variables. Instead, it is a method based on Boolean logic, rooted in set theory, and founded on the notions of sufficiency, the necessity of conditions, and conjunctural causation.QCA results indicate, among others, that for high competitiveness, high EPI suffices if Concordats with the Vatican are low and if the Roman Catholic and Orthodox population is low. No State Religion positively affects competitiveness. Having Concordats with the Vatican negatively influences competitiveness. Additionally, factors like German, English, and Scandinavian legal origin help to increase competitiveness.Oppositely, QCA results for high corruption indicate that Concordats in combination with Roman Catholic religion adherence increase corruption. Orthodox religion has a similar negative effect. Most countries with high corruption are of French legal origin and have high Concordats. This trend is robust.Colombia and Switzerland (the two extreme cases) exhibited several consistent QCA results. The other two cases (Cuba and Uruguay) only revealed one or two consistent outcomes.

Macro-Qualitative Approaches

2019 ◽  
pp. 173-180
Author(s):  
Carsten Q. Schneider
Keyword(s):  
Comparative Analysis ◽  
Set Theory ◽  
Qualitative Data ◽  
Single Case ◽  
Qualitative Comparative Analysis ◽  
Social Conditions ◽  
Process Tracing ◽  
Macro Level ◽  
Qualitative Approaches ◽  
Set Relations

Macro-qualitative (MQ) approaches to the study of regime transformation can be defined as those that (a) in order to describe or explain macro-level phenomena (b) predominantly use qualitative data and (c) make claims about these phenomena in terms of set relations. MQ approaches can be static or dynamic and are normally used for single-case or small- to medium-N-sized studies. The set of methods employed in MQ research thus defined ranges from qualitative comparative analysis (QCA) to sequence elaboration and process tracing. Classics in the MQ transformation literature can be interpreted in terms of set theory. For instance, Lipset (1959) famously claimed that there are social conditions that are necessary for the functioning of democracy.

Qualitative Comparative Analysis (QCA) and Set Theory

2017 ◽  
Author(s):  
Claudius Wagemann
Keyword(s):  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Political Science ◽  
Set Theory ◽  
Social Scientist ◽  
A Priori ◽  
Qualitative Comparative Analysis ◽  
General Question ◽  
Explanatory Factors ◽  
The Stability

Qualitative Comparative Analysis (QCA) is a method, developed by the American social scientist Charles C. Ragin since the 1980s, which has had since then great and ever-increasing success in research applications in various political science subdisciplines and teaching programs. It counts as a broadly recognized addition to the methodological spectrum of political science. QCA is based on set theory. Set theory models “if … then” hypotheses in a way that they can be interpreted as sufficient or necessary conditions. QCA differentiates between crisp sets in which cases can only be full members or not, while fuzzy sets allow for degrees of membership. With fuzzy sets it is, for example, possible to distinguish highly developed democracies from less developed democracies that, nevertheless, are rather democracies than not. This means that fuzzy sets account for differences in degree without giving up the differences in kind. In the end, QCA produces configurational statements that acknowledge that conditions usually appear in conjunction and that there can be more than one conjunction that implies an outcome (equifinality). There is a strong emphasis on a case-oriented perspective. QCA is usually (but not exclusively) applied in y-centered research designs. A standardized algorithm has been developed and implemented in various software packages that takes into account the complexity of the social world surrounding us, also acknowledging the fact that not every theoretically possible variation of explanatory factors also exists empirically. Parameters of fit, such as consistency and coverage, help to evaluate how well the chosen explanatory factors account for the outcome to be explained. There is also a range of graphical tools that help to illustrate the results of a QCA. Set theory goes well beyond an application in QCA, but QCA is certainly its most prominent variant. There is a very lively QCA community that currently deals with the following aspects: the establishment of a code of standards for QCA applications; QCA as part of mixed-methods designs, such as combinations of QCA and statistical analyses, or a sequence of QCA and (comparative) case studies (via, e.g., process tracing); the inclusion of time aspects into QCA; Coincidence Analysis (CNA, where an a priori decision on which is the explanatory factor and which the condition is not taken) as an alternative to the use of the Quine-McCluskey algorithm; the stability of results; the software development; and the more general question whether QCA development activities should rather target research design or technical issues. From this, a methodological agenda can be derived that asks for the relationship between QCA and quantitative techniques, case study methods, and interpretive methods, but also for increased efforts in reaching a shared understanding of the mission of QCA.

scholarly journals Configurations of Essential Factors Promoting China’s Commercial Health Insurance: A Qualitative Comparative Analysis Based on Technology-Organization-Environment Framework

2021 ◽  
Author(s):  
Xiuquan Huang ◽  
Xiaocang Xu ◽  
Tao Zhang
Keyword(s):  
Health Insurance ◽  
Comparative Analysis ◽  
High Performance ◽  
Social Insurance ◽  
Qualitative Comparative Analysis ◽  
Robustness Analysis ◽  
Necessary Condition ◽  
Key Factors ◽  
Negative Effect ◽  
Commercial Health Insurance

Abstract Background With the improvement in the living standards, China’s health insurance under the social security system cannot satisfy people's diversified and high-level demands. Therefore, it is necessary to promote commercial health insurance (CHI). This study identifies driving paths of CHI in China from configuration perspective. Methods This study innovatively constructs an analysis framework based on the Technology-Organization-Environment theory to investigate the driving path of China's commercial health insurance. Using the data of 31 provincial regions of China in 2018, the fuzzy-set Qualitative Comparative Analysis (QCA) is employed for configuration analysis. For the robustness analysis of necessary condition, we also adopt the Necessary Condition Analysis. Results Three main findings are discovered. First, there is no necessary relationship between any condition and high or not-high performance of CHI and any condition. Nevertheless, there are three sufficient configurations, TOE strategy, GA-EA-CD strategy, and dual EA-CD strategy, to achieve high performance, and another three, TMC-EA-CD strategy, TI-EA strategy, and TI-TMC-EA strategy, to reach not-high performance. Second, technological conditions (TI and TMC) and EA are relatively more important than other conditions. Third, it is confirmed that the financial expenditure of government departments has a negative effect on the development of commercial health insurance. Conclusion There are configurations or pathways to achieve high or not-high performance of promoting CHI and key factors are identified successfully. Each region should choose the driving path suitable for itself, instead of making homogenization policies and replicating policies of regions with high performance. Besides, TC and EA as key factors should be overcome. Finally, the governments should formulate policies to systematically evaluate social insurance and CHI simultaneously and promote their coordinated development.

Empirical Tests with Fuzzy-Set Qca

2020 ◽  
pp. 159-172
Author(s):  
Rosemary A. Kelanic
Keyword(s):  
Comparative Analysis ◽  
Set Theory ◽  
Fuzzy Set ◽  
Qualitative Comparative Analysis ◽  
The State ◽  
Great Powers ◽  
Causal Relationships ◽  
Analysis Methods ◽  
Empirical Tests ◽  
Necessary And Sufficient

This chapter examines the book's theory further by using fuzzy-set qualitative comparative analysis methods, which test for necessary and sufficient causal relationships using the logic of set theory. The data confirm the hypothesis that both the petroleum deficit and the threat to imports must be substantial to trigger anticipatory strategies. Thus, the results reinforce the findings from the previous chapters that coercive vulnerability, as determined by the petroleum deficit and import disruption threat, spurs great powers to adopt anticipatory strategies to reduce the danger of oil coercion. Moreover, the severity of the strategy chosen is consistent with the level of coercive vulnerability faced by the state. The more extreme the deficit and import disruption threat, the more extreme the strategy chosen; the less extreme the deficit and threat, the less extreme the strategy chosen.

The Many Faces of Authoritarian Persistence: A Set-Theory Perspective on the Survival Strategies of Authoritarian Regimes

2018 ◽  
Vol 55 (1) ◽  
pp. 64-87 ◽  
Author(s):  
Seraphine F. Maerz
Keyword(s):  
Comparative Analysis ◽  
Set Theory ◽  
Fuzzy Set ◽  
Qualitative Comparative Analysis ◽  
Authoritarian Regimes ◽  
Survival Strategies ◽  
Causal Relations ◽  
The Many ◽  
Authoritarian Survival ◽  
Causal Complexity

AbstractThis article examines how authoritarian regimes combine various strategies of repression, co-optation and legitimation to remain in power. The contribution of the article is two-fold. First, I conceptualize the hexagon of authoritarian persistence as a framework to explain how authoritarian regimes manage to survive. The hexagon is based on Gerschewski’s (2013) three pillars of stability but proposes some crucial modifications. In contrast to the model of the three pillars, the hexagon can grasp the causal complexity of autocratic survival because it is rooted in set theory and accounts for asymmetric causal relations, conjunctural causation and equifinality. Based on this, it illuminates how authoritarian regimes use multiple, mutually non-exclusive survival strategies. The second contribution is an empirical exploration which applies the hexagon and provides a case-oriented analysis of 62 persistent and non-persistent authoritarian regimes (1991–2010). By using fuzzy-set qualitative comparative analysis, the findings of this assessment illustrate five configurations of the hexagon – called hegemonic, performance-dependent, rigid, overcompensating and adaptive authoritarianism – as those combinations of strategies which facilitate authoritarian survival.

scholarly journals Influential Pathways of Employees’ Career Growth: Linkage of Psychological and Organizational Factors Based on Qualitative Comparative Analysis

2022 ◽  
Vol 12 ◽  
Author(s):  
Bailin Ge ◽  
Zhiqiang Ma ◽  
Mingxing Li ◽  
Zeyu Li ◽  
Ling Yang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Professional Identity ◽  
Achievement Motivation ◽  
Set Theory ◽  
Self Efficacy ◽  
Qualitative Comparative Analysis ◽  
Organizational Factors ◽  
Service Value ◽  
Career Growth ◽  
High Level

Implementing the “hierarchical diagnosis and treatment” system highlights the important role of general practitioners as “residents’ health gatekeepers.” Still, the low level of career growth always limits the realization of their service value. Inertial thinking uses a single factor to explain the complexity of career growth in previous studies; in fact, it isn’t easy to assess whether the factor is a sufficient and necessary condition for a high level of career growth. Herein, we have used a set theory perspective to analyze the mechanism of influencing high-level career growth by combining psychological and organizational factors. This research aims to analyze causal complexity relationship between these conditions and results is analyzed in detail. We choose fuzzy-set qualitative comparative analysis (fsQCA) with a sample of 407 GPs to test 5 antecedent conditional variables that can affect their career growth. The variables include professional identity, self-efficacy, achievement motivation, training mechanism, and incentive mechanism. To ensure the universality and diversity of data, the samples were selected from community medical institutions in different regions of China. The results show that three pathways can affect the high career growth of GPs, and the optimal pathway A2 is the linkage matching of high incentive mechanism, high professional identity, high achievement motivation, and high self-efficacy. At the same time, we find that professional identity plays an alternative role in the three pathways. When professional identity is at a high level, as long as achievement motivation and self-efficacy are superior, or achievement motivation, self-efficacy, and achievement motivation are superior, a high level of career growth can be achieved. We broke the shackles of previous studies that only focused on the impact of single factors on the career growth of GPs. From the perspective of set theory, we use configurational thinking to construct Influential pathways of high career growth of GPs by integrating antecedents. The results can provide effective support for improving GPs’ service ability and realizing their service value to protect residents’ health.

scholarly journals A Unifying Framework for Causal Analysis in Set-Theoretic Multimethod Research

2016 ◽  
Vol 47 (1) ◽  
pp. 37-63 ◽  
Author(s):  
Ingo Rohlfing ◽  
Carsten Q. Schneider
Keyword(s):  
Comparative Analysis ◽  
Causal Inference ◽  
Empirical Research ◽  
Set Theory ◽  
Single Case ◽  
Qualitative Comparative Analysis ◽  
Causal Analysis ◽  
Process Tracing ◽  
Case Method ◽  
Multimethod Research

The combination of Qualitative Comparative Analysis (QCA) with process tracing, which we call set-theoretic multimethod research (MMR), is steadily becoming more popular in empirical research. Despite the fact that both methods have an elected affinity based on set theory, it is not obvious how a within-case method operating in a single case and a cross-case method operating on a population of cases are compatible and can be combined in empirical research. There is a need to reflect on whether and how set-theoretic MMR is internally coherent and how QCA and process tracing can be integrated in causal analysis. We develop a unifying foundation for causal analysis in set-theoretic MMR that highlights the roles and interplay of QCA and process tracing. We argue that causal inference via counterfactuals on the level of single cases integrates QCA and process tracing and assigns proper and equally valuable roles to both methods.

scholarly journals The Double Bind of Qualitative Comparative Analysis

2019 ◽  
pp. 004912411988246 ◽  
Author(s):  
Vincent Arel-Bundock
Keyword(s):  
Comparative Analysis ◽  
Methodological Approach ◽  
Qualitative Comparative Analysis ◽  
Sufficient Conditions ◽  
Data Entry ◽  
Large Data ◽  
Boolean Logic ◽  
Double Bind ◽  
Data Sets ◽  
The One

Qualitative comparative analysis (QCA) is an influential methodological approach motivated by set theory and boolean logic. QCA proponents have developed algorithms to analyze quantitative data, in a bid to uncover necessary and sufficient conditions where causal relationships are complex, conditional, or asymmetric. This article uses computer simulations to show that researchers in the QCA tradition face a vexing double bind. On the one hand, QCA algorithms often require large data sets in order to recover an accurate causal model, even if that model is relatively simple. On the other hand, as data sets increase in size, it becomes harder to guarantee data integrity, and QCA algorithms can be highly sensitive to measurement error, data entry mistakes, or misclassification.

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 The Double Bind of Qualitative Comparative Analysis

2021 ◽  
Author(s):  
Vincent Arel-Bundock
Keyword(s):  
Comparative Analysis ◽  
Methodological Approach ◽  
Qualitative Comparative Analysis ◽  
Sufficient Conditions ◽  
Data Entry ◽  
Large Data ◽  
Boolean Logic ◽  
Double Bind ◽  
Data Sets ◽  
The One

Qualitative comparative analysis (QCA) is an influential methodological approach motivated by set theory and boolean logic. QCA proponents have developed algorithms to analyze quantitative data, in a bid to uncover necessary and sufficient conditions where causal relationships are complex, conditional, or asymmetric. This article uses computer simulations to show that researchers in the QCA tradition face a vexing double bind. On the one hand, QCA algorithms often require large data sets in order to recover an accurate causal model, even if that model is relatively simple. On the other hand, as data sets increase in size, it becomes harder to guarantee data integrity, and QCA algorithms can be highly sensitive to measurement error, data entry mistakes, or misclassification.

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