Free Coursera online course: Qualitative Comparative Analysis (QCA)

Highly recommended! A well organised and very clear and systematic exposition. Available at:

About this Course

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:

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.



Representing Theories of Change: Technical Challenges and Evaluation Consequences


CEDIL – Centre for Evaluation Lecture Series
The Centre of Excellence for Development Impact and Learning (CEDIL) and the Centre for Evaluation host a lecture series addressing methods and innovation in primary studies.

Watch the live-streamed lecture here

London School of Hygiene and Tropical Medicine. Lecture Two – Wednesday 30th May 2018 – Dr Rick Davies 12:45-14:00  Jerry Morris B, LSHTM 15-17 Tavistock Place, London, WC1H 9SH

“This lecture will summarise the main points of a paper of the same name. That paper looks at the technical issues associated with the representation of Theories of Change and the implications of design choices for the evaluability of those theories. The focus is on the description of connections between events, rather the events themselves, because this is seen as a widespread design weakness. Using examples and evidence from a range of Internet sources six structural problems are described, along with their consequences for evaluation. The paper then outlines six different ways of addressing these problems, which could be used by programme designers and by evaluators. These solutions range from simple to follow advice on designing more adequate diagrams, to the use of specialist software for the manipulation of much more complex static and dynamic network models. The paper concludes with some caution, speculating on why the design problems are so endemic but also pointing a way forward. Three strands of work are identified that CEDIL and DFID could invest in to develop solutions identified in the paper.”

The paper referred to in the lecture was commissioned by CEDIL and is now pending publication in a special issue of an evaluation journal

Wiki Surveys: Open and Quantifiable Social Data Collection

by Matthew J. Salganik, Karen E. C. Levy, PLOS
Published: May 20, 2015

Abstract: In the social sciences, there is a longstanding tension between data collection methods that facilitate quantification and those that are open to unanticipated information. Advances in technology now enable new, hybrid methods that combine some of the benefits of both approaches. Drawing inspiration from online information aggregation systems like Wikipedia and from traditional survey research, we propose a new class of research instruments called wiki surveys. Just as Wikipedia evolves over time based on contributions from participants, we envision an evolving survey driven by contributions from respondents. We develop three general principles that underlie wiki surveys: they should be greedy, collaborative, and adaptive. Building on these principles, we develop methods for data collection and data analysis for one type of wiki survey, a pairwise wiki survey. Using two proof-of-concept case studies involving our free and open-source website, we show that pairwise wiki surveys can yield insights that would be difficult to obtain with other methods.

Also explained in detail in this Vimeo video: