Multivariate Methods > Multiple Correspondence Analysis
Publication date: 08/13/2020

Multiple Correspondence Analysis

Identify Associations between Levels of Categorical Variables

Multiple Correspondence Analysis (MCA) takes multiple categorical variables and seeks to identify associations between levels of those variables. MCA extends correspondence analysis from two variables to many. It can be thought of as analogous to principal component analysis for quantitative variables. Similar to other multivariate methods, it is a dimension reducing method; it represents the data as points in 2- or 3-dimensional space.

Multiple correspondence analysis is frequently used in the social sciences particularly in France and Japan. It can be used in survey analysis to identify question agreement. It is also used in consumer research to identify potential markets for products.

Figure 7.1 Multiple Correspondence Analysis 

Contents

Example of Multiple Correspondence Analysis

Launch the Multiple Correspondence Analysis Platform

The Multiple Correspondence Analysis Report

Multiple Correspondence Analysis Platform Options

Correspondence Analysis Options
Show Plot
Show Detail
Show Adjusted Inertia
Show Coordinates
Show Summary Statistics
Show Partial Contributions to Inertia
Show Squared Cosines
Cochran’s Q Test
Cross Table

Additional Examples of the Multiple Correspondence Analysis Platform

Example Using a Supplementary Variable
Example Using a Supplementary ID
Example of Cochran’s Q Test

Statistical Details for the Multiple Correspondence Analysis Platform

Details Report
Adjusted Inertia
Summary Statistics
Partial Contributions to Inertia
Cochran’s Q Statistic
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