Characteristic Characteristics

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 HEXACO model. We suggest that spectral clustering provides a robust alternative view of personality data.