Publication date: 07/08/2024
Multiple Factor Analysis
Analyze Agreement among Panelists
Multiple factor analysis (MFA) is an analytical method closely related to principal components analysis (PCA). MFA uses eigenvalue decomposition to transform multiple measurements on the same items into orthogonal principal components. These components can help you understand how the items are similar and how they are different. MFA uses multiple table or consensus PCA techniques.
Figure 7.1 Consensus Map in Multiple Factor Analysis
Contents
Overview of the Multiple Factor Analysis Platform
Example of Multiple Factor Analysis
Launch the Multiple Factor Analysis Platform
Data Format
The Multiple Factor Analysis Report
Summary Plots
Consensus Map
Multiple Factor Analysis Platform Options
Statistical Details for the Multiple Factor Analysis Platform
Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).