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

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 A...

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Bibliographic Details
Published in:Personality and individual differences 2021-02, Vol.169, p.109905, Article 109905
Main Authors: Revelle, William, Dworak, Elizabeth M., Condon, David M.
Format: Article
Language:English
Subjects:
Astronomy
Data collection
Genomics
Health status
Matrices
Open Source
Persome, Persome Wide Association Studies, Synthetic Aperture Personality Assessment (SAPA), Massively Missing Completely at Random (MMCAR), Scale construction, Factor analysis, Item analysis
Personality
Personality tests
Power
Radio
Self evaluation
Smoking
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Summary: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.
ISSN:0191-8869
1873-3549
DOI:10.1016/j.paid.2020.109905