A spectral clustering approach to the structure of personality: Contrasting the FFM and HEXACO models - Brocklebank, Sean; Pauls, Scott; Rockmore, Daniel; Bates, Timothy C. (Journal of research in personality, 2015)
•We use spectral clustering (SC) to test personality structure in two large datasets.•SC allows detection of connections that are strong but sparsely represented.•In five-factor model data 6-cluster solutions reveal a large Honesty–Humility dimension.•Honesty–Humility is also extracted from HEXACO data.•The psychological content of personality domains is discussed. Alternative analytic methods may help resolve the dimensionality of personality and the content of those dimensions. Here we tested the structure of personality using spectral clustering and conventional factor analysis. Study 1 analysed responses from 20,993 subjects taking the 300-item IPIP NEO personality questionnaire. For factor analysis, a five-factor solution recovered the FFM domains while the six-factor solution yielded only a small and hard to interpret sixth factor. By contrast, spectral clustering analysis yielded six-cluster solutions congruent with the HEXACO model. Study 2 analysed data from 1128 subjects taking the 100-item HEXACO-PI-R. Unambiguous support was found for a six-cluster solution. The psychological content of the 6 clusters and their relationship to the FFM domains is discussed.