All you need to know to analyse a survey with MCA …

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.

Multiple Correspondence Analysis with FactoMineR

How to analyse of categorical data? Here is a course with videos that present Multiple Correspondence Analysis in a French way. The most well-known use of Multiple Correspondence Analysis is: surveys.

Four videos present a course on MCA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement MCA in FactoMineR, to deal with missing values in MCA thanks to the package missMDA and lastly a video to draw interactive graphs with Factoshiny. And finally you will see that the new package FactoInvestigate allows you to obtain automatically an interpretation of your MCA results.

With this course, you will be stand-alone to perform and interpret results obtain with MCA.

 

• Course
  1. Data – issues
  2. Visualizing the point cloud of individuals
  3. Visualizing the cloud of categories
  4. Interpretation aids
  5. Material on the course videos: the slides, the transcription
• Tutorial in R
  • MCA in practice with FactoMineR
  • Handling missing values in MCA with missMDA and FactoMineR
  • Interactive graphs with Factoshiny
• Automatic interpretation
  • The package FactoInvestigate allows you to obtain a first automatic description of your MCA results.

For more information, you can see the book blow. Here are some reviews on the book and a link to order the book.