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PLS model building: A multivariate approach to personality test data
Author(s) -
Henningsson Mikael,
Sundbom Elisabet,
Armelius Bengt–Åke,
Erdberg Philip
Publication year - 2001
Publication title -
scandinavian journal of psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 72
eISSN - 1467-9450
pISSN - 0036-5564
DOI - 10.1111/1467-9450.00252
Subject(s) - multivariate statistics , partial least squares regression , personality , structural equation modeling , psychology , latent variable , test (biology) , complement (music) , multivariate analysis , artificial intelligence , statistics , data mining , machine learning , computer science , social psychology , mathematics , paleontology , biochemistry , chemistry , complementation , gene , biology , phenotype
The aim of this study was to demonstrate how personality test data can be plotted with a multivariate method known as Partial Least Squares of Latent Structures (PLS). The basic methodology behind PLS modeling is presented and the example demonstrates how a PLS model of personality test data can be used for diagnostic prediction. Principles for validating the models are also presented. The conclusion is that PLS modeling appears to be a powerful method for extracting clinically relevant information from complex personality test data matrixes. It could be used as a complement to more hard modeling methods in the process of examining a new area of interest.