Exploring the persome: The power of the item in understanding personality structure

William Revelle*, Elizabeth M. Dworak, David M. Condon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We discuss methods of data collection and analysis that emphasize the power of individual personality items for predicting real world criteria (e.g., smoking, exercise, self-rated health). These methods are borrowed by analogy from radio astronomy and human genomics. Synthetic Aperture Personality Assessment (SAPA) applies a matrix sampling procedure that synthesizes very large covariance matrices through the application of massively missing at random data collection. These large covariance matrices can be applied, in turn, in Persome Wide Association Studies (PWAS) to form personality prediction scores for particular criteria. We use two open source data sets (N=4,000 and 126,884 with 135 and 696 items respectively) for demonstrations of both of these procedures. We compare these procedures to the more traditional use of “Big 5” or a larger set of narrower factors (the “little 27”). We argue that there is more information at the item level than is used when aggregating items to form factorially derived scales.

Original languageEnglish (US)
Article number109905
JournalPersonality and Individual Differences
Volume169
DOIs
StatePublished - Feb 1 2021

Keywords

  • Open Source
  • Persome, Persome Wide Association Studies, Synthetic Aperture Personality Assessment (SAPA), Massively Missing Completely at Random (MMCAR), Scale construction, Factor analysis, Item analysis

ASJC Scopus subject areas

  • Psychology(all)

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