scholarly journals B. Le Roux and H. Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Dordrecht, Kluwer, 2004, pp. xi + 475.

2008 ◽  
Vol 25 (1) ◽  
pp. 137-141 ◽  
Author(s):  
Fionn Murtagh
Keyword(s):  
Data Analysis ◽  
Correspondence Analysis ◽  
Structured Data ◽  
Geometric Data ◽  
Geometric Data Analysis

Multiple Correspondence Analysis and Geometric Data Analysis

2021 ◽  
pp. 177-184
Author(s):  
Amal Tawfik ◽  
Stephan Davidshofer
Keyword(s):  
Data Analysis ◽  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Categorical Variables ◽  
Large Set ◽  
Geometric Data ◽  
Jean Paul ◽  
Geometric Data Analysis ◽  
Or Groups

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.

Combinatorial Inference in Geometric Data Analysis

2019 ◽  
Author(s):  
Brigitte Le Roux ◽  
Solène Bienaise ◽  
Jean-Luc Durand
Keyword(s):  
Data Analysis ◽  
Geometric Data ◽  
Geometric Data Analysis
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