Challenges to capture the big five personality traits in non-WEIRD populations

Rachid Laajaj, Karen Macours, Daniel Alejandro Pinzon Hernandez, Omar Arias, Samuel D. Gosling, Jeff Potter, Marta Rubio-Codina and Renos Vakis
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Daniel Alejandro Pinzon Hernandez: UNIANDES - Universidad de los Andes [Bogota]
Samuel D. Gosling: University of Texas at Austin [Austin], University of Melbourne
Jeff Potter: Atof Inc.
Marta Rubio-Codina: Inter-American Development Bank - Inter-American Development Bank
Renos Vakis: The World Bank - The World Bank - The World Bank
Authors registered in the RePEc Author Service: Marta Rubio Codina ()

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Abstract: Can personality traits be measured and interpreted reliably across the world? While the use of Big Five personality measures is increasingly common across social sciences, their validity outside of western, educated, industrialized, rich, and democratic (WEIRD) populations is unclear. Adopting a comprehensive psychometric approach to analyze 29 face-to-face surveys from 94,751 respondents in 23 low- and middle-income countries, we show that commonly used personality questions generally fail to measure the intended personality traits and show low validity. These findings contrast with the much higher validity of these measures attained in internet surveys of 198,356 self-selected respondents from the same countries. We discuss how systematic response patterns, enumerator interactions, and low education levels can collectively distort personality measures when assessed in large-scale surveys. Our results highlight the risk of misinterpreting Big Five survey data and provide a warning against naïve interpretations of personality traits without evidence of their validity.

Date: 2019-07
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Citations: View citations in EconPapers (14)

Published in Science Advances , 2019, 5 (7), ⟨10.1126/sciadv.aaw5226⟩

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Working Paper: Challenges to capture the big five personality traits in non-WEIRD populations (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-02306945

DOI: 10.1126/sciadv.aaw5226

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