Multiple Correspondence Analysis: a multidimensional and relational technique

Multiple correspondence analysis can be conceived as the counterpart of Principal Component Analysis for categorical data (i.e. with a finite number of response categories). MCA is a geometric approach that represents and models data sets as clouds of points in a multidimensional Euclidean space. The interpretation of the data is based on these clouds of points. As will be shown in the empirical example, the results from a MCA can be combined with other inferential techniques and variance analysis in order to get an integrated framework of interpretation. MCA has become known through the work of Pierre Bourdieu, in particular his maps of social space in France in the Distinction (1984).

Resources:

Celine Teney & Laurie Hanquinet (2012) 'High political participation, high social capital? A relational analysis of youth social capital and political participation', forthcoming in: Social Science Research(2012)

Mustafa Emirbayer (1997) 'Manifesto for a Relational Sociology', American Journal of Sociology 103(2): 281-317