All you need to do with MCA to analyse a survey is in Factoshiny!

MCA – Multiple Correspondence Analysis – is a method for exploring and visualizing data obtained from a survey or a questionnaire, i.e. datasets with categorical variables.

The function Factoshiny of the package Factoshiny allows you to perform MCA in a really easy way. You can include extras information such as quantitative variables, manage missing data, draw and improve the graphs interactively, draw confidence ellipses, have several numeric indicators as outputs, perform clustering on the MCA results, and even have an automatic interpretation of the results. Finally, the function returns the lines of code to parameterize the analysis and redo the graphs, which makes the analysis reproducible.

Implementation with R software

See this video and the audio transcription of this video:

The lines of code to do a MCA:

install.packages(Factoshiny)
library(Factoshiny)
data(tea)
result <- Factoshiny(tea)

Course videos

Theorectical and practical informations on Multiple Correspondence Analysis are available in these 4 course videos:

Data – issues

Visualizing the point cloud of individuals

Visualizing the cloud of categories

Interpretation aids

Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

• Hobbies data: the data set– Rmarkdown – the script with the outputs
• Tea data: the data set  –  Rmarkdown– the script with the outputs

Management of missing data

This video gives more information on the management of missing data in MCA.

If you want to see more methods on Exploratory Data Analysis, follow this link.