Show Summary Details
Research Methods in the Social Sciences: An A-Z of key concepts

Research Methods in the Social Sciences: An A-Z of key concepts (1st edn)

Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
Page of

Printed from Oxford Politics Trove. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 02 December 2021

Multiple Correspondence Analysis and Geometric Data Analysislocked

Multiple Correspondence Analysis and Geometric Data Analysislocked

  • Amal Tawfik
  •  and Stephan Davidshofer

Abstract

This chapter focuses on multiple correspondence analysis (MCA), which is a factor analysis statistical method used to analyse relations between a large set of categorical variables. Developed by Jean-Paul Benzécri in the early 1970s, MCA is one of the principal methods of geometric data analysis (GDA). Three different statistical methods can be identified as GDA: correspondence analysis (CA), which enables the cross-tabulation of two categorical variables; MCA for the analysis of a matrix of individuals and categorical variables; and principal component analysis (PCA), which uses numerical variables. In GDA, data is represented as a cloud of points to allow statistical interpretations. Although MCA is a relational method, it differs from social network analysis (SNA) as it focuses on the objective relations that characterize actors or groups, rather than the effective relations.

You do not currently have access to this chapter

Sign in

Please sign in to access the full content.

Subscribe

Access to the full content requires a subscription