Sign up no later than 19th of December 2025 through this page: events.ruc.dk/phdcourse-applied-multiple-correspondence-analysis-mca-for-the-social-and-human-sciences-spring2026.

Course communication: All communication from course teachers and course coordinators before the course will be in Microsoft Teams. As a confirmed participant you will automatically receive a Teams invitation.

Contact: ise-phdadmin@ruc.dk

Before the course:

The students are expected to have a basic understanding of the technique, quantitative methods, field analysis and statistical programming in R. The students should already be working on an analysis or be at the beginning of an analysis. The students should have MCA as a part of their thesis.

During course – two options:

Option 1: Active participation in the seminar is sufficient for 2 ECTS.

Option 2: Subjecting one’s paper and code to peer-review in pre-organized feedback groups combined with active participation for 4 ECTS. Students who have submitted a paper can be expected to be paired up with an opponent, who is responsible for providing feedback.

Course objectives:

The course aims to provide students with:

• A thorough understanding of the epistemological and conceptual basis of MCA.

• Insight into the historical development and sociological applications of MCA.

• Practical skills in conducting MCA and interpreting its results in R.

• The ability to integrate MCA into theory-informed empirical research.

• A critical perspective on the strengths and limitations of MCA in social science research.

Learning Outcomes:

By the end of the course, students will be able to:

• Explain the theoretical and historical foundations of MCA, including its use by Bourdieu.

• Apply MCA to complex categorical datasets and interpret the resulting dimensions in R.

• Visualize and analyze social structures, classifications, and symbolic boundaries.

• Communicate MCA results effectively in academic writing and presentations.

The course has two main objectives:

1. The course seeks to teach applied Multiple Correspondence Analysis at a PhD-level and through workshops help students already working with MCA to advance their analysis.

2. To provide a community for MCA practitioners both within RUC and between universities.

The course runs for 3 days and will take place 5th - 6th February and again 16th of March 2026

Course format:

2 + 1 Full days of seminars with both lectures and hands-on workshop - with the specific material the participants are currently working on. The students are expected to present preliminary designs and results if available. Between the two first seminar days and the last there is 1½ months where the students refine their work. Students present the current state of their work and receive comments and direct guidance. The course is passed with a short paper.

Program:

Day 1, Thursday 5th of February

09.00 – 12.00 Introduction. Plenum lecture + mini paper presentation (Troels & Anton)

12.00 - 13.00 Lunch

13.00 - 16.00 Workshop: Field construction (Anton & Jacob)

Day 2, Friday 6th of February

09.00 - 12.00 Workshop: Interpretation (Troels & Anton)

12.00 - 13.00 Lunch

13.00 - 16.00 Workshop: Analytical strategy and robustness (Anton & Jacob)

Day 3, Monday 16th of March

09.00 - 12.00 Paper presentations + comments (Troels)

12.00 - 13.00 Lunch

13.00 - 16.00 Workshop: Presentation of results (Anton & Jacob)

Format:

• The first presentation round is a mini 3 slide presentation of their project and their MCA.

• The second presentation is with an extended abstract that includes a rudimentary framing, draft of analysis and analytical appendices.

• The final paper is a polished extended abstract of at least 8 pages with appendices and working R code.

• The workshops are hands-on workshops organized around a common theme with a brief introduction. There will be plenty of time for 1:1 individual sparring.

There are two ways of completing the course:

1. Active participation (2 ECTS) Students who read the course curriculum and actively participate in the seminar will be eligible for 2 ECTS points.

2. Course paper and active participation (4 ECTS) Students who submit a paper and code after the course and actively participate in the seminar will be eligible for 4 ECTS points.

Deadlines:
Deadline for signing up is 1st of December 2025. Including ½ page statement of relevance of course to doctorial research. If there are more than 10 applicants then these statements of relevance may be used to select who will be enrolled in the course.

Deadline for handing in the working paper is 1st of April 2026.

Seminardates: 5th, 6th of February and 16th of March 2026.

2 or 4 ECTS

Multiple Correspondence Analysis (MCA) is a geometric data analysis technique designed to uncover patterns and structures in complex categorical datasets. Rooted in the tradition of French data analysis pioneered in the 1960s by French statistician Jean-Paul Benzécri, MCA represents data as clouds of points in a multidimensional space. The position and distance between these points can tell us about the relationships within the data. This way MCA allows researchers to visualize associations between multiple categorical variables in a low-dimensional space. This in turn makes MCA particularly well-suited for analyzing the relational structures of survey- as well as prosopographic data, and for developing typologies, and classifications—making it a powerful tool for social scientists working with empirical data.

Within the social and human sciences, MCA gained prominence through the work of Pierre Bourdieu, who used it extensively to map social space and different fields and analyze the distribution and composition of different forms of capital (cultural, economic, social, symbolic and in special cases academic capital) among different social groups. In seminal works such as Distinction (1984), Homo Academicus (1988), State Nobility (1996) and The Social Structures of the Economy (2005), Bourdieu employed MCA to empirically ground his theoretical concepts, demonstrating how social positions and lifestyles could be visualized and interpreted through relational data structures. His use of MCA not only spearheaded the use of the method but also showcased its potential for bridging quantitative analysis with rich sociological theory.

Students will learn how to prepare data, conduct MCA using statistical software (e.g., R), interpret graphical outputs, and critically reflect on the epistemological implications of the method. The course also explores how MCA can be used to operationalize concepts such as capital, field, social position and lifestyle, and how it supports mixed-methods research designs. The course emphasizes hands-on experience with MCA software (e.g., R) and the practical application of MCA to the phd. Students own data.

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Course curriculum:

Hjellbrekke, Johs. 2018. Multiple Correspondence Analysis for the Social Sciences. Abingdon, Oxon ; New York, NY: Routledge.

Brigitte Le Roux & Henry Rouanet (2010) Multiple Correspondence Analysis, Series in Quantitative Applications in the Social Sciences no 163, SAGE university Paper.

Duval, Julien. “Multiple Correspondence Analysis.” The SAGE Handbook of Cultural Sociology, 2016, 255.

Duval, Julien, (2017) Multiple Correspondence Analysis, Politika.

Rouanet, Henry, Wemer Ackermann, and Brigitte Le Roux. “The Geometric Analysis of Questionnaires: The Lesson of Bourdieu’s La Distinction.” Bulletin de Méthodologie Sociologique 65, no. 1 (January 1, 2000): 5–18. https://doi.org/10.1177/075910630006500103.

Kaufman, Leonard, and Peter J. Rousseeuw. 2009. Finding Groups in Data: An Introduction to Cluster Analysis.

Grolemund, Garrett, and Hadley Wickham. n.d. R for Data Science.

A written evaluation will be sent to the course participants on the last day of the course