Comparing predictive validity in a community sample: High-dimensionality and traditional domain-and-facet structures of personality variation

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Version: Author's accepted manuscript
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Serval ID
serval:BIB_F2B7A1191D71
Type
Article: article from journal or magazin.
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Publications
Institution
Title
Comparing predictive validity in a community sample: High-dimensionality and traditional domain-and-facet structures of personality variation
Journal
European Journal of Personality
Author(s)
Saucier Gerard, Iunrion Kathryn, Thalmayer Amber Gayle
Publication state
In Press
Peer-reviewed
Oui
Language
english
Abstract
Prediction of outcomes is an important way of distinguishing, among personality models,
the best from the rest. Prominent previous models have tended to emphasize multiple internally
consistent “facet” scales subordinate to a few broad domains. But such an organization of
measurement may not be optimal for prediction. Here, we compare the predictive capacity and
efficiency of assessments across two types of personality-structure model: conventional
structures of facets as found in multiple platforms, and new high-dimensionality structures
emphasizing those based on natural-language adjectives, in particular lexicon-based structures of
20, 23, and 28 dimensions. Predictions targeted 12 criterion variables related to health and
psychopathology, in a sizeable American community sample. Results tended to favor
personality-assessment platforms with (at least) a dozen or two well-selected variables having
minimal intercorrelations, without sculpting of these to make them function as indicators of a
few broad domains. Unsurprisingly, shorter scales, especially when derived from factor analyses
of the personality lexicon, were shown to take a more efficient route to given levels of predictive
capacity. Popular 20 -century personality-assessment models set out influential but suboptimal
templates, including one that first identifies domains and then facets, which compromise the efficiency of measurement models, at least from a comparative-prediction standpoint.
Create date
27/01/2020 11:27
Last modification date
28/01/2020 7:20
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