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Monday 29 July – Friday 2 August and Monday 5 – Friday 9 August
14:00–15:30 and 16:00–17:30, ending slightly earlier on the last Friday
This course introduces you to set-theoretic methods and their application in the social sciences, with a focus on Qualitative Comparative Analysis. It is complemented by an advanced course at the ECPR Winter School.
The course starts by familiarising you with the basic concepts of the underlying methodological perspective, among them the central notions of necessity and sufficiency, formal logic and Boolean algebra. From there, we move to the logic and analysis of truth tables and discuss the most important problems that emerge when this analytical tool is used for exploring social science data.
Right from the beginning, you will perform set-theoretic analyses with the relevant R software packages. When discussing set-theoretic methods, in-class debates will engage on broad, general comparative social research issues, such as case selection principles, concept formation, questions of data aggregation and the treatment of causally relevant notions of time. Examples are drawn from published applications in the social sciences.
Please bring your own raw data for in-class exercises and assignments, if you have it.
Nena (Ioana-Elena) Oana is a Research Fellow at the European University Institute, Florence, where she is currently working on developing semi-automated solutions for protest event analysis in the framework of the SOLID project.
Nena is the main developer of the R package SetMethods and has extensive experience in teaching QCA using R at various international methods schools and universities (ECPR Methods Schools, Lund University, University of Helsinki, EUI, etc).
She has also co-authored, with Carsten Q. Schneider and Eva Thomann, the book Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, forthcoming with Cambridge University Press.
Besides research methodology, Nena's main research interests include political participation and representation, political behaviour, and political psychology.
Carsten Q. Schneider is Professor of Political Science at Central European University Budapest.
His research focuses on regime transitions, autocratic regimes, the qualities of democracies, and the link between social and political inequalities. He also works in the field of comparative methodology, especially on set-theoretic methods.
Carsten has published in leading political science journals, and he is the author three books, among them Set-Theoretic Methods for the Social Sciences (Cambridge University Press, 2012).
The book Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction, co-authored with Ioana-Elena Oana and Eva Thomann, appeared in 2021 with Cambridge University Press and his book Set-Theoretic Multi-Method Research: A Guide to Combining QCA and Case Studies is forthcoming with the same publisher.
The central aim of week 1 is to familiarise you 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.
I will put particular emphasis on a thorough understanding of the notions of necessity and sufficiency, because they are the nuts and bolts of QCA that set it apart from the majority of other cross-case comparative techniques.
On day 1 I will introduce the course topic, the content and sequence of the sessions, and the resources. We touch 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 centrepiece of this first session will be a demonstration of QCA on the basis of a recently published study.
On day 2, we turn to the methodological foundations of QCA including a thorough discussion of the basic mathematical concepts of QCA, which are derived from set theory. The session begins by with an outline of sets and set membership, including the notion of fuzzy sets as opposed to crisp sets. Once these essentials are in place, we turn to Boolean and fuzzy algebra, formal logic and operations on complex expressions.
On day 3, we will start by revisiting notions of causal complexity with a focus on INUS and SUIN causes. Afterwards we address the question of how to prepare observational data to perform QCA, i.e. how to calibrate. In doing so, we will cover various modes of calibrating raw data for crisp-set, multi-value and fuzzy-set QCA. We will go through various calibration techniques using R and discuss the consequences of different calibration decisions.
On day 4, 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 analyse truth tables with respect to necessary and sufficient conditions in order to derive solution formulas. This includes the Quine-McCluskey Algorithm for the logical minimisation of the sufficiency statements in a truth table.
On day 5, we will turn to the so-called parameters of fit that are central to any QCA study, i.e. the measures of consistency and coverage for necessary and sufficient conditions. We will further discuss some methodological issues that are related to the parameters of fit. The first week concludes with an informal course evaluation and a consideration of topics that you would like to see covered in more depth in the second week.
The purpose of week two is fourfold:
On day 6, we will start by briefly reviewing what we learned in week 1, above all with regard to the basics of the analysis of necessary and sufficient conditions and how truth tables are used to reveal the latter. Putting everything together, we explain how the Truth Table algorithm, the standard mode for analysing crisp and fuzzy sets in QCA, works. We will recap the notions of parameters of fit, problematise some of their properties, and elaborate on potential improvements of these formulas. Since several of the problems have their roots in what could be called skewed set membership scores, we will be looking into this issue more closely.
On day 7, we will discuss the second problem of incomplete truth tables: logical remainder rows. We will explain how this phenomenon of limited diversity arises and which basic strategies are at the researcher’s disposal to mitigate its impact on drawing inferences. Above all, we will show how counterfactual thinking can be used to resolve problems of limited diversity. This leads to the development of 'intermediate solutions' in a so-called standard analysis.
On day 8, we continue with the issue of limited diversity and introduce several amendments to the standard analysis. In addition to distinguishing between easy and difficult counterfactuals, we introduce the notion of tenable and untenable assumptions on remainders.
On day 9, we introduce 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). Along these lines, we also discuss strategies for confronting situations when the data at hand contains potentially relevant clusters. 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.
On day 10, we put together the material of the entire course by spelling out the Truth Table algorithm, i.e. the process from turning the data matrix into a truth table, then logically minimising the table, allowing for different strategies vis-à-vis the logical remainders, and calculating the parameters of fit for each solution formula. On this day, I will ask you to apply the knowledge gained during this course to different published data sets and/or your own data.
Throughout the course, we will analyse fake and real data in the computer lab, using the R software environment and its QCA package.
In addition to prepared datasets, which will be made available, we encourage you to bring your own raw data (even if still tentative), for lab exercises and project work.
Instructors and teaching assistants will be available for individual appointments to discuss your research projects, questions regarding the design of a QCA study, the ‘formatting' of raw data to be compatible with QCA, and similar issues.
You don't need any prior knowledge of QCA or the R software environment and the package relevant for set-theoretic methods.
However, you would profit from prior empirical-comparative training and we strongly encourage you to read the recommended literature in advance, to familiarise yourself with the basic principles of the method.
Knowledge of the basic functions of R and RStudio would be useful to start working with the software from day one.
To get off to the best start, we strongly recommend signing up for the short course Introduction to Case-Based Research and Set-Theoretic Thinking, which includes a brief introduction to set-theoretic methods, and to R.
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.
Day | Topic | Details |
---|---|---|
Week 1: Goals | - Address fundamental issues in comparative research - Introduce set-theoretic principles and formal logic - Become familiar with the terminology and principles of QCA - Practice using the QCA software package for the R environment | |
Week 2: Goals | - Deepen the skills and knowledge on QCA - Apply QCA to real data and learn how to address typical challenges of applied QCA - Become better acquainted with standards of good practice - Explore the limitations and pitfalls of set-theoretic methods | |
9 | Robustness and Sensitivity (1 hour) - Robustness tests - Cluster diagnostics |
Exercises (1.5 hours)
|
10 | Advanced Standard Analysis (1 hour) - From data matrix to truth table - From truth table to solution formula |
Exercises (2 hours)
|
1 | Course Introduction (2 hours) - Detailed course overview - Set-theoretic methods - Causal complexity (Introduction) - Rationale for applying QCA - Empirical demonstration |
Introduction (1 hour)
|
2 | Set Theory (1.5 hours) - Methodological foundations: set theory, Boolean and fuzzy algebra, formal logic - Set operations and set relations |
Exercises (1.5 hours)
|
3 | Causal Complexity and Calibration (1.5 hours) - Causal complexity - Measurement and calibration - Calibration techniques - Differences in calibration and their consequences |
Exercises (1.5 hours)
|
4 | Truth Table Analysis (1.5 hours) - From data matrix to truth table - Analysing truth tables - Quine-McCluskey Algorithm |
Exercises (1.5 hours)
|
5 | Parameters of Fit (1.5 hours) - Consistency and coverage measures for necessary and sufficient conditions - Issues related to the parameters of fit |
Exercises (1.5 hours)
|
6 | Recap of Week 1 and Alternative Parameters of Fit (2 hours) - Calibration - Set relations - Truth table analysis - Parameters of fit - Skewed set membership scores |
Exercises (1 hour)
|
7 | Limited Diversity I (1.5 hours) - Origins of remainders - Types of remainders - Types of assumptions on remainders - The Standard Analysis |
Exercises (1.5 hours)
|
8 | Limited Diversity II (1.5 hours) - Untenable versus tenable assumptions - Enhanced Standard Analysis |
Exercises (1.5 hours)
|
11 (optional) | Exam (for additional credits) |
Day | Readings |
---|---|
General note |
Mandatory reading Schneider, Carsten Q. and Claudius Wagemann (2012) Below, we specify which chapter(s) of this book are to be read and which additional optional literature you may want to consult In addition, we will assign selected draft chapters from the book project Oana/Schneider/Thomann “An Introduction to Applied QCA through R” * = compulsory text |
1 |
*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. |
2 |
*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. 21-31; 42-90. 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. Ragin, Charles C. (1987). The Comparative Method. Moving Beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press, chapter 6. |
3 |
*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. 32-41, 76-90. Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapters 4 & 5. Rohlfing, Ingo (2012). Case Studies and Causal Inference: An Integrative Framework. Basingstoke: Palgrave Macmillan, chapter 2 |
4 |
*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. 91-115. Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press chapter 7. |
5 |
*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. 119-150. Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapter 3. Thomann, E. and M. Maggetti (2017). Designing research with Qualitative Comparative Analysis (QCA): Approaches, challenges, and tools, Sociological Methods and Research |
6 |
*Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 9.2. 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. Haesebrouck, T. (2015). Pitfalls in QCA’s Consistency Measure, Journal of Comparative Politics 2:65-80. |
7 |
*Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 6. Ragin, Charles C. (1987). The Comparative Method. Moving Beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press, chapter 7. Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapters 8 & 9. |
8 |
*Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 8. Emmenegger, Patrick (2012). How Good Are Your Counterfactuals? Assessing Quantitative Macro-Comparative Welfare State Research. Journal of European Social Policy, 21 (4): 365-80 |
9 |
*Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 11.2 Oana, I.-E., & Schneider, C. Q. (2018). SetMethods: an Add-on R Package for Advanced QCA. The R Journal, XX, 1–27 Oana, I.-E., & Schneider, C. Q. (2018). Fit-Oriented and Case-Oriented Robustness in QCA. Mimeo |
10 |
*Schneider & Wagemann chapters 11.1 and conclusion *Schneider, Carsten Q. and Ingo Rohlfing (2013) Rohlfing, Ingo and Carsten Q. Schneider (2013) |
10 |
*Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 7 Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press, chapter 9. |
R, RStudio, packages QCA, SetMethods and all their dependencies.
Please bring your own laptop.
Mandatory reading
Schneider, Carsten Q. and Claudius Wagemann (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press.
Recommended books
Goertz, Gary and James Mahoney (2012). A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton: Princeton University Press.
Rihoux, Benoît and Charles C. Ragin, eds. (2009). Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and Related Techniques. Thousand Oaks and London: Sage.
Ragin, Charles C. (1987). The Comparative Method: Moving Beyond Quantitative and Qualitative Strategies. Berkeley: University of Berkeley Press.
Ragin, Charles C. (2000). Fuzzy-Set Social Science. Chicago: University of Chicago Press.
Ragin, Charles C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago: University of Chicago Press.
Further literature
This list is necessarily selective. Further references are provided during the lecture sessions
Baumgartner, Michael (2008). “Uncovering Deterministic Causal Structures: A Boolean Approach.” Synthese 170(1): 71-96.
Baumgartner, Michael (2009). “Inferring Causal Complexity.” Sociological Methods & Research 38 (1): 71-101.
Braumoeller, Bear F. (2003). “Causal Complexity and the Study of Politics.” Political Analysis 11 (3): 209-33.
Goertz, Gary and Harvey Starr (2003). Necessary Conditions: Theory, Methodology, and Applications. Lanham: Rowman & Littlefield.
Goertz, Gary (2006). Social Science Concepts: A User’s Guide. Princeton: Princeton University Press.
Greckhamer, Thomas, Vilmos Misangyi and Peer Fiss (2013). “The Two QCAs: From A Small-N To A Large-N Set Theoretic Approach.” In: Configurational Theory and Methods in Organizational Research, ed. Peer Fiss, Bart Cambré and Axel Marx. Bingley: Emerald Group, 49-75.
Hino, Airo (2009). “Time-Series QCA.” Sociological Theory and Methods, 24 (2): 247-265.
Mahoney, James, Erin Kimball, and Kendra L. Koivu (2009). “The Logic of Historical Explanation in the Social Sciences.” Comparative Political Studies 42 (1): 114-46.
Rihoux, Benoît and Axel Marx (2013). “QCA, 25 Years after ‘The Comparative Method’: Mapping, Challenges, and Innovations: Mini-Symposium.” Political Research Quarterly, 66 (1): 167-235.
Rohlfing, Ingo (2012). Case Studies and Causal Inference: An Integrative Framework. Basingstoke: Palgrave Macmillan.
Schneider, Carsten Q. and Claudius Wagemann (2007). Qualitative Comparative Analysis (QCA) und Fuzzy Sets. Ein Lehrbuch für Anwender und jene, die es werden wollen. Opladen & Farmington Hills: Barbara Budrich. [for German speakers]
Schneider, Carsten Q. and Claudius Wagemann (2006). “Reducing Complexity in Qualitative Comparative Analysis (QCA): Remote and Proximate Factors and the Consolidation of Democracy.” European Journal of Political Research 45 (5): 751-86.
Smithson, Michael and Jay Verkuilen (2006). Fuzzy Set Theory: Applications in the Social Sciences. Thousand Oaks: Sage.
Thiem, Alrik (2013). “Clearly Crisp, and Not Fuzzy: A Reassessment of the (Putative) Pitfalls of Multi-Value QCA.” Field Methods. 25(2): 197-207.
Thiem, Alrik and Adrian Duşa (2013). Qualitative Comparative Analysis with R: A User’s Guide. New York: Springer.
Thiem, A. and Adrian Duşa (2013). “Boolean Minimization in Social Science Research: A Review of Current Software for Qualitative Comparative Analysis (QCA).” Social Science Computer Review 31(4): 505–21.
Vink, Maarten P., and Olaf van Vliet. (2009). “Not Quite Crisp, Not Yet Fuzzy? Assessing the Potentials and Pitfalls of Multi-Value QCA.” Field Methods 21(3): 265–89.
Wagemann, Claudius and Carsten Q. Schneider (2010). “Qualitative Comparative Analysis (QCA) and Fuzzy-Sets: Agenda for a Research Approach and a Data Analysis Technique.” Comparative Sociology 9 (3): 376-96.
Examples of empirical studies (some used during course)
Bara, C. (2014). “Incentives and opportunities A complexity-oriented explanation of violent ethnic conflict”, Journal of Peace Research 51(6): 696-710.
Emmenegger, P. (2011) “Job-security regulations in Western democracies”, European Journal of Political Research 50: 336-364.
Hinterleitner, M., Sager, F. and E. Thomann (2016). “The Politics of External Approval: Explaining the IMF’s Evaluation of Austerity Programs”, European Journal of Political Research 55(3): 549–567.
Sager, F. and E. Thomann (2016). “Multiple streams in member state implementation: politics, problem construction and policy paths in Swiss asylum policy”, Journal of Public Policy.
Schneider, C. Q. and Makszin, K. (2014). “Forms of Welfare Capitalism and Education-Based Participatory Inequality”, Socio-Economic Review 12(2):437– 462.
Schneider, M. R., Schulze-Bentrop, C., Paunescu, M. (2010) “Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance”, Journal of International Business Studies 41:246-266.
Schneider, M. R., Schulze-Bentrop, C., Paunescu, M. (2010) “Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance”, Journal of International Business Studies 41:246-266.
Introduction to R
Comparative Research Design
Advanced Topics in Set-Theoretic Methods and QCA
Advanced Multi-Method Research
Case Study Research – Method and Practice
Process Tracing Methodology I and II