Anglim, Jeromy, Sharon Horwood, Luke Smillie, Rosario J. Marrero, and Joshua K. Wood. 2019. “Predicting Psychological and Subjective Well-being from Personality: A Meta-analysis.” PsyArXiv. December 14. doi:10.1037/bul0000226
Abstract: This study reports the most comprehensive assessment to date of the relations that the domains and facets of Big Five and HEXACO personality have with self-reported subjective well- being (SWB: life satisfaction, positive affect, and negative affect) and psychological well-being (PWB: positive relations, autonomy, environmental mastery, purpose in life, self-acceptance, and personal growth). It presents a meta-analysis (n = 334,567, k = 462) of the correlations of Big Five and HEXACO personality domains with the dimensions of SWB and PWB. It provides the first meta-analysis of personality and well-being to examine (a) HEXACO personality, (b) PWB dimensions, and (c) a broad range of established Big Five measures. It also provides the first robust synthesis of facet-level correlations and incremental prediction by facets over domains in relation to SWB and PWB using four large datasets comprising data from prominent, long-form hierarchical personality frameworks: NEO PI-R (n = 1,673), IPIP-NEO (n = 903), HEXACO PI- R (n = 465), and Big Five Aspect Scales (n = 706). Meta-analytic results highlighted the importance of Big Five neuroticism, extraversion, and conscientiousness. The pattern of correlations between Big Five personality and SWB was similar across personality measures (e.g., BFI, NEO, IPIP, BFAS, Adjectives). In the HEXACO model, extraversion was the strongest well- being correlate. Facet-level analyses provided a richer description of the relationship between personality and well-being, and clarified differences between the two trait frameworks. Prediction by facets was typically around 20% better than domains, and this incremental prediction was larger for some well-being dimensions than others.
See https://osf.io/42rsy/ for Data and R scripts for the meta-analysis and facet-level data analyses of the above paper.
No comments:
Post a Comment