Introduction to fsQCA using R.

Name: Introduction to fsQCA using R.
Rating: 4.3 (38 reviews)

Hands on causal relationships

Created byPedro Carmona Ibáñez

Last updated 4/2026

English

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What you'll learn

fsQCA as an analysis of set relations. Sets are usually composed of numbers, or other sets of things.
In fsQCA, the variables are transformed into sets. Then, it is analyzed what combination of causal sets (dependent variables) produces the outcome set.
There are many advantages fsQCA has over traditional correlational analysis like regression: Relationships are asymmetrical, Equifinality and Causal complexity
FsQCA differs from regression analysis in the way it focuses on problems. The focus is on what conditions lead to a given outcome

Course content

4 sections • 35 lectures • 4h 36m total length

Introduction5:03
Learn how to apply fsQCA in R to uncover causal configurations in comparative analysis. Convert variables into sets and identify combinations that produce a given outcome (the dependent variable).
Naming of variables and objective6:05
Learn how fsQCA, or qualitative comparative analysis, names variables as outcomes and conditions, compare it with regression, and identify how multiple condition combinations produce a given outcome.
Necessity4:45
Explain necessity in fsQCA: the outcome is a subset of the condition, indicating a necessary relationship. For example, operating legally in Europe is necessary to have losses in Europe.
Sufficiency4:43
Examine sufficiency in fsQCA by contrasting it with necessity and show how a sufficient condition, a subset of outcome, produces the outcome, illustrated by an unfair dismissal severance pay example.
Set membership5:44
Convert variables into sets for fsQCA by transforming continuous variables into fuzzy sets, while binary variables remain binary, and distinguish crisp from fuzzy sets.
Calibration4:00
Calibrate real variables into set memberships by crisp or fuzzy rules; crisp yields 1 or 0, while fuzzy maps to 0–1, with ambiguity near 2.5.
Negation of sets4:44
Examine the negation of sets in fsQCA using R, showing how to denote original sets with uppercase notation and calculate non-rich membership as 1 minus the rich score.
Example (Causal combinations)8:26
Explore a full fsQCA example with fuzzy, calibrated conditions and an outcome, and learn to build a truth table to identify sufficient causal combinations.
Example (truth table)8:11
Compute membership scores for all causal combinations to complete the fsQCA truth table using R, illustrating with rich and educated versus not rich and not educated cases.
Example (consistency)6:57
Compute the consistency metric for sufficiency in fsQCA using the sum of the minimums of the causal combination and outcome; values range 0–1, with higher scores indicating stronger causality.
Example (include)6:42
this fsQCA using R example calculates consistency scores for causal combinations, builds a truth table, and applies a 0.8 consistency cutoff to identify a sufficient configuration that yields liberal outcome.
Example (coverage)8:13
Learn fsQCA in R, focusing on consistency (inclusion) and coverage, and understand how to calculate coverage for causal configurations and outcomes.
Example (final solution)9:55
Slides

Introduction and working directory5:05
Learn how to use fsQCA in R to manage the working directory via the console and GUI, including changing and verifying directories and loading files.
Workspace2:35
Explore the R workspace by creating a single-value object with the assignment operator, viewing it in the global environment, and removing it with rm, then verify via ls.
Type of objects11:35
Examine the main object types in R by building vectors (numeric and character), creating matrices with row and column names, and constructing data frames using c() and seq().
Indexing and subsetting11:01
Learn how to index and subset data in r using vectors and data frames, employing square brackets, the dollar operator, and the subset function to filter rows and columns.
RStudio5:51
RECOMMENDED READINGS0:07

Data loading13:34
Learn to load European bank data for fsQCA in R, including downloading the file, setting the working directory, and reading a semicolon-delimited, comma-decimal dataset into a data object named data.
Missing values and variables9:57
Prepare fsQCA data in r by checking structure, removing missing values, and selecting numeric variables, including liquidity ratios and bank failure as the dependent variable.
Row names3:47
Assign row names to each observation in your fsQCA analysis with R to map observations to causal configurations and support correct identification of observations across configurations.
Outcome calibration12:13
Calibrate the variables for fsQCA in R by installing and loading the QCA package, and calibrate the outcome FRAC as a binary 0/1 bank failure, using uppercase names.
Condition calibration R2015:04
Condition calibration R22 and R1110:43
Calibrate fsQCA conditions in R by determining cutoffs for R22 and R11, using manual or automatic thresholds (125, 251, 667), and create fuzzy sets in the data.
Condition calibration R14, R15 and LOGA11:40
Calibrate conditions 14, 15, and the log of assets using graphical representations to identify three cutoff points and create fuzzy sets in fsQCA with R.
Selection of fuzzy set variables5:45
Select fuzzy set variables from the dataset and subset the necessary columns for fsQCA in R. Calibrate the variables and build the truth table to study sufficiency and causal configurations.
Truth table12:36
Explore constructing a truth table in fsQCA using R, linking fuzzy sets to a binary bank failure outcome and interpreting causal configurations.
Complex solution9:09
Derive the complex solution from a truth table with the minimize function in fsQCA to identify the causal configuration that produces the outcome, and assess inclusion, consistency, and coverage.
Complex solution clarification5:58
Explore how to derive complex solutions in fsQCA using R by applying minimize to a complex object. Interpret the truth table and identify a single causal configuration through minimization.
Parsimonious solution13:08
Explore the parsimonious solution, obtained with the minimize function and logical remainders, to simplify results. It links low liquidity, high operations ratio, and log assets as size to bankruptcy risk.
Intermediate solution14:08
Discover how the intermediate solution in fsQCA using R is built by specifying researcher expectations and comparing it to complex and parsimonious solutions for bank failure.

Requirements

You will learn everything. No previous experience is needed about R programming language.

Description

This course is oriented to everyone that wants to study causal relationships using fuzzy set qualitative comparative analysis (fsQCA). That is, the identification of causal configurations or recipes that lead to a certain outcome.

We use the R programming language although no previous experience is required in its use.

The course ends with a practical example to identify the conditions or configurations that lead a bank to a possible bankruptcy situation.

Once the example is understood, the fsQCA methodology can be applied to a large variety of different scenarios.

The structure of the course is the following:

- INTRODUCTION TO FSQCA: includes the explanation of necessity, sufficiency, set membership, calibration, the negation of sets, consistency, coverage, truth table, or different causal solutions

- R INSTALLATION: procedure to install R programming language on a Windows PC and on an Apple computer.

- INTRODUCTION TO R: introduction to R ecosystem, the importance of the working directory, type of R objects, indexing and subsetting data, and introduction to RStudio IDE.

- EXAMPLE OF fsQCA APPLICATION TO BANK FAILURE: data loading, dealing with missing values, the importance of row names, calibration of the outcome and the conditions, selection of fuzzy set variables, truth table construction, and extraction of the different types of solutions: complex, parsimonious and intermediate.

Who this course is for:

Everybody with an interest in causal relationships that lead to a given output.

Introduction to fsQCA using R.

What you'll learn

Explore related topics

Course content

INTRODUCTION TO FSQCA13 lectures • 1hr 23min

R INSTALLATION3 lectures • 19min

INTRODUCTION TO R6 lectures • 36min

EXAMPLE OF fsQCA APPLICATION TO BANK FAILURE13 lectures • 2hr 18min

Requirements

Description

Who this course is for: