Abstract
Qualitative comparative analysis allows for the breadth of analysis associated with quantitative data and the depth of case knowledge provided by qualitative analysis. In particular, fuzzy-set qualitative analysis (fsQCA) provides a nuanced analysis of data and offers actionable insights. Despite this, fsQCA has been underutilized in the social sciences. This chapter explores the application of fsQCA in the social sciences. The authors advance an argument for its wider adoption due to fsQCA's ability to disentangle the causal complexity involved in person- and policy-based contexts by applying a set-theoretic understanding of causation. The chapter provides an introduction to fsQCA for readers unfamiliar with the approach and advocates for its suitability within a broad range of social science studies.
TopIntroduction
Qualitative Comparative Analysis (QCA) was developed to bridge case based and traditional quantitative approaches to research, and harness the best of both approaches (Ragin, 2014). QCA is commonly used to guide public policy and has been applied to multiple policy contexts. Examples include intersectional (Ragin & Fiss, 2017) and multidimensional (Neff, 2013) understandings of poverty, organizational structures (Florea, et al. 2019), outcomes of social organizations (Cress & Snow, 2000), pro-environmental behaviors (Schmitt, Grawe & Woodside, 2017), and entrepreneurial decision making (Pappas & Brown, 2020). It has been used to assess policy performance in relation to issues such as policy accountability (Pérez-Durán, 2013), funding (Vis, 2011), service delivery (Gasparro & Walters, 2017) public opposition to policy proposals (Kirchherr, Charles & Walton, 2016), and the development of new policy approaches (Feldman, 2021). It can also be used to conduct cross-jurisdictional comparisons which can generate policy learnings and contribute to effective policy transfer (Ingrams, 2018).
Despite its continued uptake in public policy application, QCA has largely been underutilized in the social sciences. Probability-based null hypothesis significance testing remains the dominant approach (Woodside, 2017). This chapter explores the application of fuzzy-set Qualitative Comparative Analysis (fsQCA) to the social sciences and advances an argument for its wider adoption. This chapter provides an overview of fsQCA and its application as both a methodology and analysis technique. The authors argue that fsQCA should be more widely adopted within the social sciences due to i) the prevalence of set-theoretic rather than correlational relations within social phenomena (Ragin, 2014), and ii) the ability of fsQCA to unravel the causal complexity often involved in person- and policy-based contexts (Rihoux, Rezöhazy & Bol, 2011). In advancing this argument the chapter explores the key underpinning principles of fsQCA, namely; conjunctural causation, equifinality, asymmetry and multifinality (Gerrits & Verweij, 2013). Using illustrative examples, the utility of fsQCA is demonstrated. The chapter includes a discussion of the key characteristics and benefits of fsQCA analysis with comparisons made to linear/correlational techniques and the dominance of symmetric theory construction and null hypothesis statistical testing in the social sciences. To assist those intending to use fsQCA, the chapter also describes the analytical steps required to perform fsQCA analysis, including calibration of set memberships, truth table construction, and analyses of necessity and sufficiency. Finally, the chapter considers the strengths and limitations of fsQCA. The aim is to provide a middle ground between journal articles that explore a single aspect of QCA, and books that provide highly detailed discussions.
Key Terms in this Chapter
Equifinality: The observation that a given end state or goal can be reached by many potential means or ways, thus a diversity of pathways may lead to the same outcome.
SUIN Condition: A S ufficient but U nnecessary part of a condition set that is itself I nsufficient but N ecessary.
Necessity: A situation in which the presence of an outcome condition is only attained with the presence of a condition. Seen when the outcome is a subset of the antecedent condition, or values of the outcome are less than values of the cause.
Fuzzy Set: A class of objects with a continuum of grades of membership ranging between 0 and 1. Differ from Boolean or crisp sets which are limited to only values of 1 or 0.
Multifinality: The observation that one component may function differently depending on the organization of the system in which it operates thus leading to different outcomes. Challenges the concept of causality in relationships.
Causal Asymmetry: The observation that antecedent configurations sufficient for the presence or high scores of an outcome are not necessarily the opposite of the configurations sufficient for the absence or low scores of an outcome.
INUS Condition: An I nsufficient but N ecessary part of a condition set which is itself U nnecessary but S ufficient
Qualitative Comparative Analysis (QCA): An analytical technique developed by Charles Ragin that is a means to analyse causal contribution of conditions to an outcome using a configurational approach.
Sufficiency: A situation in which the presence of a causal condition always, or almost always, results in the presence of the outcome condition. Seen when the causal condition is a subset of the outcome condition, or values of the cause are equal to or less than values of the outcome.