In this article, you’ll learn how MFA (Multiple Factor Analysis) works, as well as, how to easily compute and interpret MFA in R using the FactoMineR package.
Recall that MFA is a multivariate data analysis method for summarizing and visualizing a complex data table in which individuals are described by several sets of variables (quantitative and /or qualitative) structured into groups. The grouping can be due to information coming from different sources.
Basics and key concepts
This video describes the data format and the type of questions that can be investigated by the multiple factor analysis.
Weighting and global PCA
MFA can be considered as a type of PCA on a weighted matrix. The aim of the weighting is to balance the information provided by the different groups of variables. You’ll learn here how and why it’s important to balance the influence of each group of variables in the analysis.
Study of the groups of variables
MFA provides results on individuals and variables just like PCA does for quantitative variables, and MCA does for qualitative variables. the most important feature of MFA is that it can take into account several groups of variables.
Here, you’ll learn:
- how to compare information provided by each of these groups,
- what information is common to several groups,
- and what information is specific to certain groups.
Complements: qualitative groups, frequency tables
In this video, you’ll learn how to take into account groups of qualitative variables. Then, you’ll see what to do when one or more groups of variables correspond to one or more contingency tables. And lastly, you’ll see which interpretation aids are useful for interpreting the results of an MFA.