Characteristic Characteristics
Sean Brocklebank, Scott Pauls, Daniel Rockmore, Timothy C. Bates

TL;DR
This study applies spectral clustering to a large personality dataset to explore its structure, finding support for five and six clusters, with the six-cluster solution aligning with the HEXACO model, offering an alternative to factor analysis.
Contribution
The paper introduces spectral clustering as a robust alternative to factor analysis for understanding personality structure, revealing new cluster solutions.
Findings
Support for five- and six-cluster solutions
Six-cluster solution aligns with HEXACO model
Spectral clustering offers a new perspective on personality data
Abstract
While five-factor models of personality are widespread, there is still not universal agreement on this as a structural framework. Part of the reason for the lingering debate is its dependence on factor analysis. In particular, derivation or refutation of the model via other statistical means is a worthwhile project. In this paper we use the methodology of spectral clustering to articulate the structure in the dataset of responses of 20,993 subjects on a 300-item item version of the IPIP NEO personality questionnaire, and we compare our results to those obtained from a factor analytic solution. We found support for five- and six-cluster solutions. The five-cluster solution was similar to a conventional five-factor solution, but the six-cluster and six-factor solutions differed significantly, and only the six-cluster solution was readily interpretable: it gave a model similar to the…
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Taxonomy
TopicsFace and Expression Recognition · Personality Traits and Psychology · Mental Health Research Topics
