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15th ECPR General Conference, University of Innsbruck

WD204 - Advanced Topics in Set-Theoretic Methods and QCA

Instructor Details

Instructor Photo

Carsten Q. Schneider

Institution:
Central European University

Instructor Bio

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 articles in several peer-reviewed journals, and three books, among them Set-Theoretic Methods for the Social Sciences (Cambridge University Press, 2012) co-authored with Claudius Wagemann.

  @CarstenQSchneid
 

Instructor Details

Instructor Photo

Ioana-Elena Oana

Institution:
Central European University

Instructor Bio

Nena I.E. Oana is a Research Fellow at the European University Institute, Florence, where she is currently working on the POLCON project, directed by Hanspeter Kriesi. She finished her PhD in Comparative Politics at the Central European University, Budpapest, on policy responsiveness to collective mobilisation.

Sheis the main developer of the R package SetMethods and has extensive experience in teaching applied QCA using R, having assisted and taught for the ECPR Summer and Winter School QCA courses for the past 6 years.

Besides research methodology, Nena's main research interests include political participation and representation, political behavior, and political psychology.

  @NenaOana


Course Dates and Times

Monday 17 – Friday 21February 2019, 14:00 – 17:30 (finishing slightly earlier on Friday)
15 hours over five days

Prerequisite Knowledge

You should have a firm command of basic formal logic, Boolean algebra, and set-theory.

You must be familiar with the basic protocol of Qualitative Comparative Analysis (QCA), including:

  • the difference between sets and variables
  • the notion of set calibration
  • the meaning of set relations (sufficiency, necessity, INUS, SUIN)
  • the construction and logical minimisation of a truth table
  • the calculation and interpretation of the parameters of fit (consistency and coverage)
  • the treatment of logical remainders as done by the Standard Analysis.

Check whether you are in command of all the questions addressed in Schneider/Wagemann (2012) Set-Theoretic Methods for the Social Sciences, chapters 1–7.

You should be familiar with the basics of the R software environment because we will use R packages relevant for performing set-theoretic analyses.

If you attended the two-week course on Set-Theoretic Methods and QCA at the ECPR Summer School, you are well prepared for this advanced course.

Short Outline

This course addresses advanced issues that arise if and when scholars embrace notions of sets and their relations. While it is a course about set-theoretic methods writ large, most of the time, we will discuss issues specific to QCA.

We will try and address all the following topics but, depending on participants' needs and interests, we can put more emphasis on some:

  • set-theoretic multi-method research
  • robustness and sensitivity
  • set-theoretic theory evaluation
  • enhanced Standard Analysis
  • data structures and set-theoretic methods, including temporal ordering and two-step QCA
  • model ambiguity
  • multi-value QCA.

Tasks for ECTS credits

2 credits (pass/fail grade) Attend at least 90% of the course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, classes.

3 credits (to be graded) As above, plus complete daily assignments which involve performing data analysis in R, using the functions and concepts learned in class.

4 credits (to be graded) As above, plus complete a take-home paper of roughly 15 pages. You will receive a published QCA study plus its data. You must, first, replicate and, second, expand the analysis. Deadline for submission of the paper, along with clean R code, is three weeks after the end of the course.

Long Course Outline

If you have good knowledge of all the elements listed under 'Prerequisite knowledge', above, this course will deepen your understanding of the potentials and pitfalls of set-theoretic methods. The skills you gain will enable you to be more critical and assertive if and when you choose or reject set-theoretic methods as the most appropriate research method for your research project.

By the end of this course, you will be able to produce QCA studies of a quality and level of sophistication beyond the current mainstream and thus yield substantive results that are more compelling for you and for your (critical) audience.

Much of the course explores the boundaries of the still-relatively-young family of set-theoretic methods. Unavoidably, some of our debates will remain inconclusive. You won't always get ready-made, foolproof answers and procedures for all the issues you will face when trying to implement a high-quality QCA. Rather, this course invites you to think critically about set-theoretic methods, and, by extension, also about other data analysis techniques you will have to choose when doing empirical comparative research.


Day 1: Refresher Potpourri
We refresh our knowledge and go through the standard protocol of a QCA, using the relevant R packages. We cover a set of relatively unrelated, yet interesting and important issues. We focus on one or two of the following topics in more detail, depending on participants' interest:

  • Enhanced Standard Analysis
  • skewed sets and their analytic consequences
  • multi-value QCA.

Day 2: SMMRI I
We introduce set-theoretic multi-method research as an attempt at specifying just how QCA should be combined with within-case process tracing. We define the meaning of typical and deviant cases after a QCA, spell out the different rationales for studying each of them, and provide formulas for selecting the best available cases for (comparative) within-case analysis after a QCA. We discuss the principles and computer-assisted practice of set-theoretic theory evaluation.

Day 3: SMMR II, plus Theory Evaluation

Day 4: Sensitivity Analysis and Data Structures I
We engage with the notion of robustness in set-theoretic methods and try to systematise the debate by specifying the analytic decisions against which QCA results should be expected to be robust. Along these lines, we aim to formulate criteria for meaningful robustness tests and practice recent software implementations for robustness tests.  

Day 5: Sensitivity Analysis and Data Structures II
As part of the sensitivity analysis, we focus on different structures in the data. We discuss various strategies for detecting such structures via calibration, temporal QCA (tQCA), cluster diagnostics, Coincidence Analysis (cna), and the updated version of the two-step QCA approach.


This is an advanced course. Don't expect a general introduction to the basics of set-theoretic methods and QCA, or an introduction to the basics of the R software environment.

Day-to-Day Schedule

Day-to-Day Reading List

Software Requirements

R, R packages QCA, QCAGUI, SetMethods, and all their dependencies

RStudio

Hardware Requirements

Bring your laptop

Literature

Goertz, Gary, and James Mahoney. 2012
A Tale of Two Cultures: Contrasting Qualitative and Quantitative Paradigms
Princeton, N.J: Princeton University Press

Ragin, Charles C. 2008
Redesigning Social Inquiry: Fuzzy Sets and Beyond
Chicago: University of Chicago Press

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

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

Summer School

Set-Theoretic Methods: Qualitative Comparative Analysis and Related Approaches
Introduction to R

Winter School

Comparative Research Designs

Recommended Courses After

Summer School

Case Study Research – Method and Practice
Machine Learning

Winter School

Machine Learning
Advanced Multi-Method Research

Additional Information

Disclaimer

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 in due time.

Note from the Academic Convenors

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, contact the instructor before registering.


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