PLS model building: A multivariate approach to personality test data | Zendy

Henningsson Mikael | Zendy; Sundbom Elisabet | Zendy; Armelius Bengt–Åke | Zendy; Erdberg Philip | Zendy

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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.

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