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Personality and Life Satisfaction: A Facet Level Analysis

Personality and Life Satisfaction: A Facet Level Analysis. Schimmack, Oishi, Furr and Funder 2004. Main aim:. Previous research focused on examining the relationship between the global domains of the big 5 and life satisfaction.

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Personality and Life Satisfaction: A Facet Level Analysis

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  1. Personality and Life Satisfaction: A Facet Level Analysis Schimmack, Oishi, Furr and Funder 2004

  2. Main aim: • Previous research focused on examining the relationship between the global domains of the big 5 and life satisfaction. • E & N prove to be the strongest predictors, but both are multifaceted constructs. • This study examined the role of the facets within the factors of E & N in life satisfaction.

  3. Subjective well-being • High SWB refers to a happy life with few unpleasant experiences, many pleasant ones, and a high life satisfaction. • It has been shown to be moderately stable over time, and influenced by personality traits. • 2 components: • Affective component: strong influence of personality traits • Cognitive component: in theory should be influenced by a variety of traits

  4. Personality & Life satisfaction • N & E emerge consistently as predictors in the literature but: • There is a over-reliance on self-report measures • Studies assess the relationship at the broad factor level of personality • Possible that more specific facets are more correlated with life satisfaction than the global dimensions. • Paunonen – ‘aggregating personality traits into their underlying personality factors could result in decreased predictive validity’ • Study addressed this by examining relation between the ‘lean thirty’ and life satisfaction.

  5. Predictions are based on the mediator model of personality influences on Life satisfaction: • Assumes E & N have a strong influence on affective component of SWB, and that people rely on their hedonic balance to judge life satisfaction. • Postulates the influence of E &N is almost completely mediated by hedonic balance. • Study has several hypotheses: • Facets of E & N which are dispositions to experience emotions (either pleasant or unpleasant) will be most closely related to life satisfaction. • For N, these facets are anger, anxiety and depression, with a further prediction that depression will be the strongest predictor of life satisfaction. • For E, the facets of positive emotions (NEO-PI-R) and cheerfulness (IPIP), with additional predication that excitement-seeking will not predict life-satisfaction

  6. Study 1 - Examining the contribution of Extraversion and Neuroticism facets to Life Satisfaction • 100 women/36 male, average age 20, during course on personality • Took NEO-PI-R (Costa and McCrae, 1992) in first couple of weeks (6 facets of each factor, 8 items per facet) as well as Satisfaction With Life Scale (SWLS; Diener et al. 1985) which doesn’t share content with the items of NEO-PI-R. The two measures were tested at separate times. • Participants repeated the SMLS questionnaire 2 further times during the semester and distributed the SWLS questionnaires to family and friends to complete about the participants. This provided self-reports and informant reports.

  7. Study 1 - Results E and N were significant predictors of life satisfaction (N: r= 0.46, p<0.05; E: r=0.30, p<0.05). Depression and Positive Emotions were both more highly correlated with life satisfaction than N and E respectively.

  8. Study 1 - Results • Stepwise regression analyses show that depression is the best predictor of variance in both Self-reports (Mean R²= 0.26) and Informant reports (R²= 0.14). • Positive Emotions is the next highest predictor of variance in self reports (Mean R²= 0.06) but not in informants reports where warmth is next highest (R²= 0.04).

  9. Study 1 - Results • Other Big 5 dimensions did not explain variance after controlling for influences of Positive Emotions and Depression.

  10. Study 1 - Discussion • Depression and Positive emotions are better predictors of life satisfaction than N or E. • Other negative emotions such as anxiety and hostility are also detrimental to life satisfaction. Anxiety and hostility are linked to depression. • According to theories, extraversion enhances well-being as it is a disposition to have a more cheerful temperament.

  11. Study 2 - Replicating Study 1’s results using a different Personality Measure. • 88 women/36 male, average age 21, during course on personality. • Took the IPIP (Goldberg, 1997) test at same times as participants in study one did, and they also completed and distributed the SWLS to parents and peers.

  12. Study 2 - Results • Findings of study 1 were replicated. • E and N sig. predictors of life satisfaction (E: r= 0.39, p<0.05; N: r=-0.47, p< 0.05). • Cheerfulness and depression best predictors. • Cheerfulness(r= 0.49) and depression(r=-0.56) only subscales to correlate more highly with life satisfaction than do E(r= 0.39) and N(r= -0.46).* • *Average of correlations from self reports

  13. Study 2 - Discussion • Findings in study 1 were replicated in study 2 using different personality measure. IPIP as opposed to NEO-PI-R. • More predictors = Higher risk of type1 error (Paunonen, 1998). • The findings rule out this possibility as the same two facets were the best predictors of life satisfaction.

  14. Study 3 - : Examining whether previous findings (from Study’s 1 and 2) could generalize to personality based on Informant reports and to allow examination of sex differences in life satisfaction. • 82 women/64 men • Completed NEO-PI-R (Costa and McCrae, 1992) and the Satisfaction With Life Scale (SWLS; Diener et al. 1985) • Parents and friends completed the NEO-PI-R about each participant to provide Informant reports on participants personality.

  15. Study 3 - Results • Findings for depression and positive emotions again replicate those of studies 1 and 2. • In parent reports, highest correlation with life satisfaction for assertiveness (r= 0.36, p<0.05) and self-consciousness (r= -0.28, p< 0.05). • No significant correlation between depression and life satisfaction in parent reports.

  16. Study 3 - Discussion • Study 3 further investigated the relation between personality and life satisfaction using multiple personality measures via self-reports, peers and parents. • “Self-reports of personality replicated once more that depression and positive emotions predict unique variance in life satisfaction above and beyond the influence of other factors and the global traits” • There was no significant difference found between the sexes.

  17. Study 4 - Comparing the predictive validity of positive emotion and depression facets on Extraversion and Neuroticism using a shortened measure. • 255 women/89 men, participated for course credit • Life satisfaction measured using SWLS at beginning of course and Personality measured over the next 6 months using the 44-item Big Five Inventory (John, Donahue et al., 1991). • Too few depression facets were found on the neuroticism scale and there were no positive emotion facets on the extraversion scale, therefore the test was adapted and these were added in.

  18. Study 4 - Results • E and N more highly correlated with life satisfaction than other dimensions. • Regression analyses show that depression and positive emotion are the only personality traits that explained unique variance in life satisfaction. • Depression was more highly correlated with life satisfaction (r= -0.34, p<0.05) than the N scale that didn’t include the depression item (r= -0.27, p<0.05).

  19. Limitations Extraversion and Neuroticism are weaker predictors in individualistic cultures (Schimmack et al., 2002) Use of a single life satisfaction scale – another measure may have provided different results Abstraction of the Life Satisfaction measure Influence of Social Desirability Results not entirely consistent over the studies

  20. Summary Positive emotions and depression are better predictors of life satisfaction than Extraversion and Neuroticism. Which implies that studying facets could be a better method for studying life satisfaction than broader domains. It allows non-personality researchers to control for the influence of personality factors on third variables.

  21. Benefits of the study for future research: Using the ‘lean thirty’ and a facet level analysis: • Necessary for more refined theories of personality influence on SWB. • the main facet/s predicting LS matter because it can lead to different conceptualizations and theories of SWB. • Or can allow the interpretation of different aspects of a global factor as predicting different aspects of SWB. • Lean predictors allow more economical assessment: • And therefore more breadth of research? • If a few facets are adequate to capture the variance in LS, even researchers whose primary interest lies outside personality could assess them. Controlling for personality’s influence would address the 3rd variable problem in correlational research.

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