Multivariate Methods • Multiple Correspondence Analysis

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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. Microarray studies in genetics also use MCA to identify potential relationships between genes.
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
Cross Table
Cross Table of Supplementary Rows
Cross Table of Supplementary Columns
Additional Examples of the Multiple Correspondence Analysis Platform
Example Using a Supplementary Variable
Example Using a Supplementary ID
Statistical Details for the Multiple Correspondence Analysis Platform
Details Report
Adjusted Inertia
Summary Statistics
Partial Contributions to Inertia

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Help created on 3/19/2020