The course is connected to the following study programs

  • PhD Programme in Social Sciences

Recommended prerequisites

The students must satisfy the admission requirements for the PhD programme in Social Sciences

Course contents

Over the past few years, set-theoretic methods, especially Qualitative Comparative Analysis (QCA), have gained popularity among information systems researchers who are interested in exploring alternative methods to analyze small, medium and even large number of cases. The method is applied in multiple fields, including political science, management, business, and education. Qualitative comparative analysis (QCA) is an asymmetric data analysis technique that combines the logic and empirical intensity of qualitative approaches that are rich in contextual information, with quantitative methods that deal with large numbers of cases and are more generalizable  than symmetric theory and tools.  This ability for bringing together basic concepts from both qualitative and quantitative techniques of analysis differs substantially from traditional methods of quantitative analysis that are often variance-based and employ null hypothesis significance testing (NHST). In line with this, this course is designed for (1) researchers who work with qualitative methods and are looking for a more systematic way of comparing and assessing cases, as well as (2) quantitative researchers who seek to assess alternative methods for analyzing data,  to gain deeper and richer insight into their datasets and the complexity of the phenomena under examination. Through the course, the aspects of identifying both necessary conditions and sufficient configurations would be explored. The course will also employ Necessary condition analysis (NCA) to compare necessary conditions and explore the concept of degree of necessity. During the course we will work on analytic foundations and the QCA research, work on research design and calibration, highlight the importance of analyzing the truth table, and discuss logical minimization and the interpretation of output.  The students will be employing fsQCA, learn more about the interpretation of output, and how to write-up the findings.

Learning outcomes

Upon successful completion of the course, the candidate will be able to:

  • describe the basic theoretical principles of set-theoretic research process.

  • discuss configurational analysis and counterfactuals

  • discuss the methodological foundations of QCA and NCA.

  • design a study using set-theoretic research process

  • apply the standard terminology for conducting a research study and formulate explanations in relation to his/her dissertation topic.

  • apply the tools for computing the analysis, including using R and FsQCA

  • correctly identify the suitable use, variant of, and approach of set theoretic methods to answering a research question.

  • independently carry out an analysis of necessity and sufficiency with crisp and fuzzy sets, document the analysis, assess the quality, visualize the results and finally reflect on the potential pitfalls of using set theoretic methods.

Examination requirements

Participation in compulsory lectures, group work, practical workshops, discussions and presentations as stated in the course pamphlet.

Teaching methods

Teaching and learnings methods will consist of a combination of lectures, group work, hands-on workshops, discussions, and presentations. The learning methods will be case-driven and based on the course topics and the candidates’ own research and work experiences. The course will require a combination of digital and physical presence. 

Evaluation

The PhD programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.

Offered as Single Standing Module

Yes

Admission Requirement if given as Single Standing Module

Students need to fulfill the admission requirements for the PhD Programme in Social Sciences

Assessment methods and criteria

Individual Project exam (Take-Home): Project report of about 3500 words explaining research proposal using QCA and/or NCA.

Grading scale: Pass/Fail, where pass equals the letter grade B or better. 

Other information

The course will be taught in cooperation with Kristiania University College, Oslo

Last updated from FS (Common Student System) July 18, 2024 2:21:35 AM