How to Do Multiple Correspondence Analysis in Displayr

Whereas traditional correspondence analysis analyzes a table, multiple correspondence analysis analyzes the variables themselves; for example, a multi-response question with 11 categories is analyzed as 11 categorical variables. It is essentially a form of factor analysis for categorical data. You should use it when you want a general understanding of how categorical variables are related. This article describes how to run a Multiple Correspondence Analysis in Displayr.

Requirements

Multiple categorical variables to use as inputs to the Multiple Correspondence Analysis. As an example, we'll use 5 different variables from a political survey: voting in the 2008 and 2012 US elections, approval of President Trump, age, and gender.

Method

From the toolbar, select Anything > Advanced Analysis > Dimension Reduction > Multiple Correspondence Analysis.
Select the categorical variable inputs from the Input Variables dropdown in the object inspector.
Click the Calculate button to generate the output.

Additional Options

Output - How the analysis results should be displayed. The choices are:
- Scatterplot - A labelled scatterplot showing associations between variables
- Text - A text representation of the analysis
Maximum number of labels to plot - Limits the number of labels shown in the scatterplot. The remaining points are shown without labels. This can be useful with large data sets to avoid overlapping labels.
Chart title - Title of the scatterplot
Color palette - Controls the colors of the points in the Scatterplot output
Missing data - Method for dealing with missing data. See Missing Data Options.
Variable names - Displays Variable Names in the output instead of labels.