English

Characteristic Characteristics

Applications 2011-07-07 v1 Information Retrieval Data Analysis, Statistics and Probability

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.

Keywords

Cite

@article{arxiv.1107.1229,
  title  = {Characteristic Characteristics},
  author = {Sean Brocklebank and Scott Pauls and Daniel Rockmore and Timothy C. Bates},
  journal= {arXiv preprint arXiv:1107.1229},
  year   = {2011}
}

Comments

23 pages, 5 Figures, 3 Tables

R2 v1 2026-06-21T18:33:08.925Z