Introduction to Qualitative Comparative Analysis (QCA) in R

15-16 June 2023

Dr. Elisa Volpi, coordinatrice CUSO

Dr. Ioana-Elena Oana (European University Institute)

This course provides an overview of Qualitative Comparative Analysis (QCA) and fuzzy sets, including instruction for its use within RStudio. QCA is inherently multi-method, combining within-case and cross-case analysis. Within the limitations facing empirical data, QCA is best seen as a tool for unraveling causal complexity, with different configurations of causally relevant conditions leading to the same outcome. The central aim of the Day 1 is to familiarize the participants with the formal logic of set-theoretic methods and to introduce QCA as an approach, its main assumptions, the technical environment (software) and the standard procedures and operations. Particular emphasis is put on a thorough understanding of the notions of necessity and sufficiency, as they are the nuts and bolts of QCA that set it apart from the majority of other available cross-case comparative techniques. Day 2 of the course is primarily aimed at addressing the analysis of sufficiency using truth tables and logical minimization. We elaborate on further issues that arise when neat formal logical tools and concepts, such as necessity, sufficiency, and truth tables, are applied to social science data (mainly the issues of limited diversity and the challenge to make good counterfactuals on so-called logical remainders). In the last session, we will address advanced topics in QCA such as: set-theoretic robustness and sensitivity, cluster diagnostics, and set-theoretic theory evaluation.

DAY 1 – The nuts and bolts of QCA

9:30am - 10:45am The basics of QCA

This session introduces participants to the module topic by touching upon the basics of set-theoretic methods, the epistemology of QCA, its different variants, and how it compares to other standard qualitative and quantitative social scientific research designs. The centerpiece of the first session will be a demonstration of QCA on the basis of a recently published study.

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Chapter 1

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Getting Started with R (Online Appendix available at: doi.org/10.7910/DVN/S9QPM5)

§ Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, pp. 1-20.

Recommended:

§ Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapter 1

§ Dusa, A. (2019). QCA with R. A Comprehensive Resource. Springer International Publishing, chapters 1 & 2

§ Goertz, Gary and James Mahoney (2012). A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton: Princeton University Press, chapter 2

§ Thomann, E. and M. Maggetti (2017). Designing research with Qualitative Comparative Analysis (QCA): Approaches, challenges, and tools, Sociological Methods and Research

10:45am - 11:00am – Coffee Break

11:00am - 13:00pm Calibration and Set Theory

In this session we address the question of how to prepare observational data to perform QCA, i.e., how to calibrate. Almost all cross-case evidence can be represented in terms of crisp or fuzzy sets. Unlike "variables", sets must be calibrated, and the calibration of fuzzy sets relies heavily on external knowledge, not on inductively derived statistics like means and standard deviations. This use of external knowledge provides the basis for a much tighter coupling of theoretical concepts and empirical analysis. In introducing calibration, we will cover various modes of calibrating raw data for crisp-set, multi-value and fuzzy-set QCA. Once we address the question of calibration, we turn to Boolean algebra, formal logic, and operations on complex expressions. At the end of the session, we will go through various calibration techniques using R and discuss the consequences of different calibration decisions.

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Chapter 2

§ Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, Cambridge University Press, Chapter 1 - Sets, set membership, and calibration; Chapter 2 - Notions and operations in set theory.

Recommended:

§ Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapters 4 & 5.

§ Dusa, A. (2019). QCA with R. A Comprehensive Resource. Springer International Publishing, chapters 4

13:00pm - 14:30pm – Lunch Break

14:30pm - 16:00pm Set Relations, Causal Complexity, and Parameters of fit

In this session we will start by introducing the central notions of necessity and sufficiency and discussing the so-called parameters of fit that are central to any QCA study, i.e., the measures of consistency, coverage, relevance of necessity, PRI. We further explore notions of causal complexity with a focus on INUS and SUIN causes. We then turn to ways of visualizing patterns of necessity, SUIN conditions, and some methodological issues that are related to the parameters of fit.

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Chapter 3 – Necessity; Chapter 4 – Sufficiency (Sections 4.1 and 4.2)

· Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, Cambridge University Press, Chapter 3 - Set relations; Chapter 5 - Parameters of fit.

Recommended:

§ Goertz, Gary (2006). "Assessing the Trivialness, Relevance, and Relative Importance of Necessary or Sufficient Conditions in Social Science." Studies in Comparative International Development 41(2): 88-109.

§ Schneider, C.Q. (2018). Realists and Idealists in QCA. Political Analysis, 26(2), 246-254.

16:00pm - 16:30pm – Coffee Break

16:30pm - 17:30pm – Group R exercises

DAY 2 – The analytic moment and an overview of advanced QCA

9:30am - 11:30am Truth Tables and Logical Minimization

In this session we focus on introducing the standard analysis of sufficiency. We will define the notion of a truth table in crisp-set and fuzzy-set QCA and how it differs from a data matrix. We will show how to analyze truth tables with respect to sufficient conditions in order to derive solution formulas. This includes the Quine-McCluskey Algorithm for the logical minimization of the sufficiency statements in a truth table.

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Chapter 4 – Sufficiency (Sections 4.3).

§ Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, Cambridge University Press, Chapter 4 – Truth Tables.

Recommended:

§ Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapters 7.

§ Dusa, A. (2019). QCA with R. A Comprehensive Resource. Springer International Publishing, chapter 7.

11:30am - 11:45am Coffee Break

11:45pm - 13:00pm Limited Diversity and the (Enhanced) Standard Analysis

In this session we will discuss the problem of limited diversity that arises from incomplete truth tables. We will discuss different types of logical remainders and which basic strategies are at the researcher's disposal to mitigate the impact of limited diversity on drawing inferences. Above all, we will show how counterfactual thinking can be used to resolve problems of limited diversity. Based on this, we introduce the "standard analysis" and the "enhanced standard analysis" by distinguishing between easy and difficult counterfactuals, and between tenable and untenable assumptions on remainders.

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Chapter 4 – Sufficiency (Sections 4.4, 4.5.).

§ Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, Cambridge University Press; Chapter 6 – Limited Diversity and Logical Remainders & Chapter 8.2

Recommended:

§ Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapters 8 & 9.

§ Dusa, A. (2019). QCA with R. A Comprehensive Resource. Springer International Publishing, chapter 8.

13:00pm - 14:30pm Lunch Break

14:30pm - 16:00pm Group R exercises

16:00pm - 16:30pm Coffee Break

16:30pm - 17:30pm Wrapping up and overview of advanced QCA tools: Robustness Tests, Cluster Diagnostics, and Theory Evaluation

This session introduces a series of advanced topics in QCA. In terms of robustness tests, we will start by introducing various perspectives on the 'robustness' or 'sensitivity' of results obtained with QCA. We discuss against which analytic decisions a result ought to be robust and how we see if and when a result can be considered robust (enough). We condense all this into a QCA robustness check protocol. We will also discuss strategies for confronting situations when the data at hand contains clusters that are potentially analytically relevant but have not been captured during the truth table analysis. These clusters can be of any kind, such as temporal, geographic, or substantive clusters, and we explain how to probe whether the result obtained for the pooled (i.e., across clusters) data holds for all clustered separately. Finally, we discuss set-theoretic theory evaluation. It intersects theoretical expectations with empirical results generated with QCA. The findings from this procedure can be used to identify areas in which theory find empirical support and where it does not. Theory evaluation can also be used to identify most-likely and least-likely cases that are or are not confirmed by our QCA, information that can be used for selecting cases for further empirical scrutiny.

§ Ioana-Elena Oana and Carsten Q. Schneider. A Robustness Test Protocol for Applied QCA: Theory and R Software Application. Sociological Methods & Research, https://doi.org/10.1177/00491241211036158

§ Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann (2021). Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, Chapter 5 & Chapter 6.2.

§ Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, Cambridge University Press; Chapter 11.3.

Recommended:

§ Arel-Bundock, Vincent. 2019. "The Double Bind of Qualitative Comparative Analysis." Sociological Methods & Research: 1–20.

§ Rohlfing, Ingo. 2018. "Power and False Negatives in Qualitative Comparative Analysis : Foundations, Simulation and Estimation for Empirical Studies." Political Analysis 26(1): 72–89.

§ Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press, Chapter 9

§ Garcia-Castro, Roberto and Ariño, Miguel A., A General Approach to Longitudinal Set-Theoretic Research in Management (October 30, 2013). Available at SSRN: https://ssrn.com/abstract=2347340

Lausanne

The workshop will take place at the University of Lausanne (Amphipôle, room 338).

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