Introduction to fsQCA using R.

Hands on causal relationships

4.25 (22 reviews)
Udemy
platform
English
language
Social Science
category
Introduction to fsQCA using R.
70
students
4.5 hours
content
Dec 2021
last update
$49.99
regular price

What you will 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

Why take this course?

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.

Screenshots

Introduction to fsQCA using R. - Screenshot_01Introduction to fsQCA using R. - Screenshot_02Introduction to fsQCA using R. - Screenshot_03Introduction to fsQCA using R. - Screenshot_04

Reviews

Aditya
August 14, 2023
Well explained for beginners. Examples were good. I found the instructor's delivery to be a bit on the slower slide, but I sped up the playback and it was fine
Ramani
March 7, 2022
It is indeed an opportunity to learn fsQCA in such a lucid and easy manner. The entire course was divided into step by step and made the learning easy and comprehensive. I really thank Sir for introducing the lesson. Thanks Sir.

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4454692
udemy ID
12/21/2021
course created date
1/2/2022
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