SOS9033 – Multiple Correspondence Analysis
Schedule, syllabus and examination date
Course content
The course offers an introduction to Multiple Correspondence Analysis, via simple Correspondence Analysis, and covers certain extensions and integration into the wider framework of Geometric Data Analysis.
Multiple Correspodence Analysis (MCA) is a powerful technique that helps uncover patterns in categorical data and visualize them in a multidimensional space. For instance, when studying themes like political attitudes or lifestyles, which involve many different indicators (such as preferences, behaviors, or opinions), MCA can show the main lines of division within those responses. It does this extracting underlying or latent dimensions or axes, where each represents a key way the responses differ from one another. This allows researchers to analyze complex data and see how various categories relate to each other in a broader context.
In social sciences, MCA became widely known through the work of Pierre Bourdieu, especially in books like Distinction (1984), Homo Academicus (1988), and The State Nobility (1996). Bourdieu used MCA to explore and map social relationships in a way that reveals hidden structures of power and domination.
MCA can be thought of as the counterpart of Principal Component Analysis (PCA) for categorical data. While PCA deals with continuous variables, MCA uncovers patterns in data where the values are grouped into categories.
Developed 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 technique, when combined with methods that analyze variance and make inferences, forms a broader approach known as Geometric Data Analysis (GDA), offering a rich framework for interpretation.
By using MCA, researchers can better visualize and make sense of the complex relationships in categorical data, which helps in understanding the social world more deeply.
Course leaders and lectures: Johs. Hjellbrekke, University of Bergen; Magne Flemmen, University of Oslo; Maren Toft, University of Oslo.
Learning outcome
Upon completing the course, students will have a comprehensive understanding of the foundational concepts, procedures, and applications of Multiple Correspondence Analysis (MCA) and its related techniques. They will gain insight into how MCA is used to explore and interpret social structures, with particular attention to social spaces structured by inequalities along multiple forms of capital. Through this course, students will acquire the skills to critically engage with MCA and related techniques, and apply them to their own research projects, enabling them to make informed methodological choices.
Participants will learn to apply MCA and its variations to empirical research, using a combination of:
- Lectures delivered by experts in the field,
- Mandatory readings of preselected texts that introduce key concepts and techniques,
- Laboratory exercises that provide hands-on experience with MCA software (e.g., SPAD, R),
- Presentation and discussion of their own research in relation to MCA and the construction of social spaces, with opportunities for peer feedback and instructor guidance, and
- Preparation of a final 4000-word paper that integrates the concepts learned during the course with their own PhD research.
Throughout the course, students will receive mentoring in relation to their ongoing PhD projects, helping them critically reflect on the application of MCA to their empirical work.
Admission to the course
The course is open to all PhD students who want to work with MCA ?in their thesis and/or seek an up-to-date introduction to its method and the broader approach.
PhD students at the Department of Sociology and Human Geography register for the course in?StudentWeb.?
Participants outside the Department of Sociology and Human Geography should fill out this?application form. Since registration is binding, if you are not entirely sure that you are able to attend, please mark for waiting list in the application form.
The application deadline is four?weeks prior the course, December 16th 2024.
Teaching
Course dates: 13-16. January 2025.
In a combination of lectures and laboratory excercises, this course will introduce students to the fundamental properties, procedures and rules of interpretation of the most commonly used forms of correspondence analysis, i.e. simple correspondence analysis (CA) and MCA, and also to the most commonly used software.
Particular attention will be paid to how MCA can be used in the construction of social spaces, i.e. spaces structured by social inequalities along multiple forms of capital.
A reading list is going to be sent to the participants before of the course. Participants are expected to read all obligatory readings prior to meeting in class.
Place: Seminar room 114, 1st floor (and 035 for lab, -1st floor), Harriet Holters Building, Moltke Moes vei 31. /om/finn-fram/omrader/blindern/bl11/?[Open URL]
Examination
The course requires active participation all days, reading, and submission of a paper. 5 ECTS will be awarded when the participant has received a pass grade based on activity and completing the paper.
The paper should focus on the application of MCA to issues related to the candidate's research. Ideally, it would be a first draft of an article or a thesis chapter. It should be approx. 4000 words, plus references. The paper deadline is?approx. six weeks after the course's end - date is to be communicated.?The paper needs to be sent to?katalin.varga@sosgeo.uio.no.
Examination support material
All exam support materials are allowed during this exam.?
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
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