21 August 2015
Multiple Correspondence Analysis for the Social Sciences
This course offers an introduction to MCA, which is a method that allows researchers to observe the patterning of complex data sets through representing categorical variables as points in N-dimensional space. Although it was developed from the later 1960s, MCA has not previously had a large Anglophone following, but it is an increasingly popular method because of (a) its association with Pierre Bourdieu’s high profile sociology, (b) its capacity to lend itself to visualisation of clusters and (c) its potential for mixed methods research.
Issues covered include mathematical principles of geometric data analysis, the difference between the active space of modalities and the use of supplementary variables, coding issues, working with the cloud of modalities and the cloud of individuals, clustering methods within MCA, and the use of inferential statistics within MCA.
The course is designed to allow the beginner to grasp basic mathematical principles of geometric data analysis. The course will be delivered by a series of lectures by leading international experts in MCA in the morning, with practical sessions in a computer lab in the afternoons. Comprehensive training is provided in using SPAD software, the most accessible and flexible package to use when carrying out MCA.
Professor Johs Hjellbrekke (University of Bergen), Professor Mike Savage (Department of Sociology, LSE); Dr Daniel Laurison (Department of Sociology, LSE)
This course is suitable for: PhD students, post-doctoral fellows and academic staff in the social sciences, interested in one of the main methods for the clustering of categorical data; those interested in learning about the methods used by Pierre Bourdieu for the analysis of cultural fields and social relations; and market researchers, other commercial researchers, and public sector professionals wishing to learn MCA as a means of clustering complex data sets, and presenting attractive and intuitive visualisations.
This course offers an introduction to MCA, which is a method that allows researchers to observe the patterning of complex data sets through representing categorical variables as points in N-dimensional space.
The decision to award credits is at the discretion of the student's home institution. Students should always check with their home institution to confirm the number of credits that can be awarded.
GBP 935: Student rate - available to current university (including PhD) students.
Academic staff and staff of UK charities are eligible for a reduced rate of £1,250.
GBP 1575: Standard rate
Previous students of LSE may be eligible for a 15% discount on their tuition fees.