A comparison of generalised procrustes analysis and multiple factor analysis for projective mapping data

- The RV coefficient should be accompanied with scatter plots of scores.
- Constructed data can be used to highlight important method properties.
- Plots of individual differences can be used for visual validation.
- MFA and GPA give in many cases similar consensus configurations.

Generalised procrustes analysis and multiple factor analysis are multivariate statistical methods that belong to the family of multiblock methods. Both methods are often used for analysis of data from projective mapping (a.k.a. Napping). In this study, generalised procrustes analysis and multiple factor analysis are compared for a number of simulated and real data sets. The type of data used in this study were (I) random data from Monte Carlo simulations; (II) constructed data that were manipulated according to some specific criteria; (III) real data from nine Napping experiments. Focus will be on similarities of the consensus solutions. In addition we considered interpretation of the RV coefficient and individual differences between assessors.