A comparison of generalised procrustes analysis and multiple factor analysis for projective mapping data. (July 2015)

• Record Type: Journal Article
• Authors: Tomic, O.; Berget, I.; Næs, T.
• Is Part Of: Food quality and preference. Volume 43(2015:Jul.)
• Page Start: 34
• Page End: 46
• Publication Date: 2015-07
• Subjects: Projective mapping -- Napping -- Generalised procrustes analysis -- GPA -- Multiple factor analysis -- MFA -- Consumer test -- Multiblock method -- RV coefficient; Food preferences -- Periodicals; Food -- Quality -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Préférences alimentaires -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Food industry and trade -- Quality control; Food preferences; Food -- Quality; Periodicals; 664
• DOI: 10.1016/j.foodqual.2015.02.004
• Languages: English
• ISSNs: 0950-3293

Abstract

Highlights: 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. Abstract: 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.