A robust data-driven approach identifies four personality types across four large data sets

Martin Gerlach, Beatrice Farb, William Revelle, Luís A. Nunes Amaral*

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

47 Scopus citations

Abstract

Understanding human personality has been a focus for philosophers and scientists for millennia1. It is now widely accepted that there are about five major personality domains that describe the personality profile of an individual2,3. In contrast to personality traits, the existence of personality types remains extremely controversial4. Despite the various purported personality types described in the literature, small sample sizes and the lack of reproducibility across data sets and methods have led to inconclusive results about personality types5,6. Here we develop an alternative approach to the identification of personality types, which we apply to four large data sets comprising more than 1.5 million participants. We find robust evidence for at least four distinct personality types, extending and refining previously suggested typologies. We show that these types appear as a small subset of a much more numerous set of spurious solutions in typical clustering approaches, highlighting principal limitations in the blind application of unsupervised machine learning methods to the analysis of big data.

Original languageEnglish (US)
Pages (from-to)735-742
Number of pages8
JournalNature human behaviour
Volume2
Issue number10
DOIs
StatePublished - Oct 1 2018

ASJC Scopus subject areas

  • Social Psychology
  • Experimental and Cognitive Psychology
  • Behavioral Neuroscience

Fingerprint

Dive into the research topics of 'A robust data-driven approach identifies four personality types across four large data sets'. Together they form a unique fingerprint.

Cite this