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Qualitative Comparative Analysis (QCA)

Approx. 27 hours to complete

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Course Summary

Learn how to use Qualitative Comparative Analysis (QCA) to identify patterns and analyze complex data sets in this comprehensive course.

Key Learning Points

Understand the principles of QCA and how it can be used in social science research
Learn how to conduct and interpret data from QCA using software tools such as R or fsQCA
Explore real-world case studies to see how QCA can be applied in different contexts

Learning Outcomes

Ability to conduct QCA analyses using software tools
Understanding of how QCA can be applied in different contexts
Experience with real-world case studies

Prerequisites or good to have knowledge before taking this course

Basic understanding of social science research methods
Familiarity with statistical analysis

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Real-world case studies
Hands-on exercises

Similar Courses

Introduction to Quantitative Methods
Advanced Data Analysis

Notable People in This Field

Dr. Wendy Olsen
Dr. Gary Goertz

Related Books

Description

Welcome to this massive open online course (MOOC) about Qualitative Comparative Analysis (QCA). Please read the points below before you start the course. This will help you prepare well for the course and attend it properly. It will also help you determine if the course offers the knowledge and skills you are looking for.

What can you do with QCA? • QCA is a comparative method that is mainly used in the social sciences for the assessment of cause-effect relations (i.e. causation). • QCA is relevant for researchers who normally work with qualitative methods and are looking for a more systematic way of comparing and assessing cases. • QCA is also useful for quantitative researchers who like to assess alternative (more complex) aspects of causation, such as how factors work together in producing an effect. • QCA can be used for the analysis of cases on all levels: macro (e.g. countries), meso (e.g. organizations) and micro (e.g. individuals). • QCA is mostly used for research of small- and medium-sized samples and populations (10-100 cases), but it can also be used for larger groups. Ideally, the number of cases is at least 10. QCA cannot be used if you are doing an in-depth study of one case. What will you learn in this course? • The course is designed for people who have no or little experience with QCA. • After the course you will understand the methodological foundations of QCA. • After the course you will know how to conduct a basic QCA study by yourself. How is this course organized? • The MOOC takes five weeks. The specific learning objectives and activities per week are mentioned in appendix A of the course guide. Please find the course guide under Resources in the main menu. • The learning objectives with regard to understanding the foundations of QCA and practically conducting a QCA study are pursued throughout the course. However, week 1 focuses more on the general analytic foundations, and weeks 2 to 5 are more about the practical aspects of a QCA study. • The activities of the course include watching the videos, consulting supplementary material where necessary, and doing assignments. The activities should be done in that order: first watch the videos; then consult supplementary material (if desired) for more details and examples; then do the assignments. • There are 10 assignments. Appendix A in the course guide states the estimated time needed to make the assignments and how the assignments are graded. Only assignments 1 to 6 and 8 are mandatory. These 7 mandatory assignments must be completed successfully to pass the course. • Making the assignments successfully is one condition for receiving a course certificate. Further information about receiving a course certificate can be found here: https://learner.coursera.help/hc/en-us/articles/209819053-Get-a-Course-Certificate About the supplementary material • The course can be followed by watching the videos. It is not absolutely necessary yet recommended to study the supplementary reading material (as mentioned in the course guide) for further details and examples. Further, because some of the covered topics are quite technical (particularly topics in weeks 3 and 4 of the course), we provide several worked examples that supplement the videos by offering more specific illustrations and explanation. These worked examples can be found under Resources in the main menu. • Note that the supplementary readings are mostly not freely available. Books have to be bought or might be available in a university library; journal publications have to be ordered online or are accessible via a university license. • The textbook by Schneider and Wagemann (2012) functions as the primary reference for further information on the topics that are covered in the MOOC. Appendix A in the course guide mentions which chapters in that book can be consulted for which week of the course. • The publication by Schneider and Wagemann (2012) is comprehensive and detailed, and covers almost all topics discussed in the MOOC. However, for further study, appendix A in the course guide also mentions some additional supplementary literature. • Please find the full list of references for all citations (mentioned in this course guide, in the MOOC, and in the assignments) in appendix B of the course guide.

Knowledge

After the course you will understand the methodological foundations of QCA.
After the course you will know how to conduct a basic QCA study by yourself.

Outline

Introduction, analytic foundations and the QCA research process
1.1. Objectives and agenda of the course
1.2. QCA vs other approaches
1.3. Set theory and complex causality
1.4. The QCA research field
Course guide
Readings in course guide

Research design and calibration
2.1. Orientation and focal points
2.2. Cases, outcomes and conditions
2.3. Crisp vs fuzzy sets
2.4. Calibration with quantitative, qualitative and secondary data
Readings in course guide
Assignment 1. Main terms and background

The truth table
3.1. The purpose and construction of a truth table
3.2. Raw consistency
3.3. Resolving contradictory configurations
Readings in course guide
Assignment 3. Make a truth table for crisp data
Assignment 4. Make a truth table for fuzzy data

Logical minimization and the interpretation of output
4.1. What is logical minimization?
4.2. The minimal formula
4.3. Parameters of fit
Readings in course guide
Assignment 5. Minimize a truth table

Using FsQCA, more about the interpretation of output, and the write-up
5.1. Using FsQCA 3, part 1
5.2. Using FsQCA 3, part 2
5.3. Do's and don'ts for the write-up
Readings in course guide
Assignment 6. Using the program fsQCA for the analysis of crisp data (PLUS bonus assignment 7; is not mandatory)
Assignment 8. Using the program fsQCA for the analysis of fuzzy data (PLUS bonus assignment 9; is not mandatory)

Summary of User Reviews

Learn Qualitative Comparative Analysis through Coursera's online course. Students praise the course for its comprehensive content and easy-to-follow structure.

Key Aspect Users Liked About This Course

Comprehensive content

Pros from User Reviews

Easy-to-follow structure
Engaging course material
Great for beginners
Good value for money
Excellent instructor

Cons from User Reviews

Limited interaction with peers
Slow pace for advanced learners
Outdated examples
Needs more practical exercises
No certificate of completion for free version

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Qualitative Comparative Analysis (QCA)

Welcome to this massive open online course (MOOC) about Qualitative Comparative Analysis (QCA). Please read the points below before you start the course....

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