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

TY - JOUR

T1 - A comparison of generalised procrustes analysis and multiple factor analysis for projective mapping data

AU - Tomic, O.

AU - Berget, I.

AU - Næs, Tormod

PY - 2015/7/1

Y1 - 2015/7/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.foodqual.2015.02.004

DO - 10.1016/j.foodqual.2015.02.004

M3 - Journal article

SN - 0950-3293

VL - 43

SP - 34

EP - 46

JO - Food Quality and Preference

JF - Food Quality and Preference

ER -