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Cited by 427

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Personality and Life Satisfaction: A Facet-Level Analysis

Schimmack, Ulrich, Oishi, Shigehiro, Furr, R. Michael, Funder, David C.·2004·Personality and Social Psychology Bulletin

Abstract

At the global level of the Big Five, Extraversion and Neuroticism are the strongest predictors of life satisfaction. However, Extraversion and Neuroticism are multifaceted constructs that combine more specific traits. This article examined the contribution of facets of Extraversion and Neuroticism to life satisfaction in four studies. The depression facet of Neuroticism and the positive emotions/cheerfulness facet of Extraversion were the strongest and most consistent predictors of life satisfaction. These two facets often accounted for more variance in life satisfaction than Neuroticism and Extraversion. The findings suggest that measures of depression and positive emotions/cheerfulness are necessary and sufficient to predict life satisfaction from personality traits. The results also lead to a more refined understanding of the specific personality traits that influence life satisfaction: Depression is more important than anxiety or anger and a cheerful temperament is more important than being active or sociable.

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Metadata

Authors
Schimmack, U., Oishi, S., Furr, R. M., & Funder, D. C.
Venue
Personality and Social Psychology Bulletin
Published
2004-08
Volume
30
Issue
8
Pages
1062-1075
ISSN
  • 0146-1672 (ISSN-L)
  • 1552-7433
OpenAlex
W2121914490
Cited by
427
References
9
FWCI
8.83
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Personality and Life Satisfaction: A Facet-Level Analysis. https://app.cochat.ai/papers/10.1177/0146167204264292
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Schimmack, U., Oishi, S., Furr, R. M., & Funder, D. C. (2004). Personality and Life Satisfaction: A Facet-Level Analysis. Personality and Social Psychology Bulletin. https://doi.org/10.1177/0146167204264292

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.