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PERSONALITY AND WELL-BEING
1
Predicting Psychological and Subjective Well-Being from Personality: A Meta-Analysis
Jeromy Anglim1, Sharon Horwood1, Luke D. Smillie2, Rosario J. Marrero3, Joshua K. Wood1
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.
Keywords: HEXACO, Big Five, subjective well-being, psychological well-being,
personality facets
Citation (check publisher for updated year, volume and page numbers): Anglim, J.,
Horwood, S., Smillie, L. D., Marrero, R. J., & Wood, J. K. (2020). Predicting Psychological and
Subjective Well-Being from Personality: A Meta-Analysis. Psychological Bulletin.
https://dx.doi.org/10.1037/bul0000226
Author Note. 1. School of Psychology, Deakin University, Geelong, Australia; 2.
Melbourne School of Psychological Sciences, The University of Melbourne, Australia; 3.
Department of Clinical Psychology, Psychobiology and Methodology. Faculty of Psychology.
Universidad de La Laguna; Correspondence concerning this article should be addressed to Jeromy
Anglim, School of Psychology, Deakin University, Locked Bag 20000, Geelong, 3220 Australia.
Email: jeromy.anglim@deakin.edu.au
Data, scripts, materials, and supplementary analyses are available at https://osf.io/42rsy.
We are grateful to Jessie Sun and Ingo Zettler for their valuable feedback on an initial draft of this
manuscript.
©American Psychological Association, 2019. This paper is not the copy of record and may
not exactly replicate the authoritative document published in the APA journal. Please do not copy
or cite without author's permission. The final article is available, upon publication, at:
https://dx.doi.org/10.1037/bul0000226
PERSONALITY AND WELL-BEING
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Public Significance Statement
This meta-analysis provides a comprehensive and detailed overview of the substantial links
between personality traits and well-being. It is the first investigation to incorporate the two most
widely accepted frameworks for measuring personality (i.e., the Big Five and the HEXACO
model) as well as two of the most influential models of human well-being (i.e., subjective and
psychological well-being). Results of the meta-analysis provide important insights into the various
pathways through which people build well-being in their lives.
Introduction
Decades of research shows that personality traits play a critical role in how we experience,
approach, and appraise our lives (DeNeve & Cooper, 1998; Headey & Wearing, 1989; Steel,
Schmidt, & Shultz, 2008). Many researchers assess the "good life" in terms of subjective well-
being (SWB): a composite of life satisfaction, high levels of positive affect, and low levels of
negative affect (Diener, 1984). Whereas SWB largely avoids making assumptions about the causes
of happiness, other conceptualizations of well-being draw more strongly on eudaimonic and
humanistic perspectives in conceptualizing well-being (Waterman, 1993). In particular, the six-
dimensional model of psychological well-being (PWB) identifies a broader set of well-being
dimensions, comprising positive relations, autonomy, environmental mastery, personal growth,
purpose in life, and self-acceptance (Ryff, 1989). Previous research shows that major dimensions
of personality are robustly associated with both SWB and PWB, along with other indices of human
happiness (e.g., Anglim & Grant, 2016; Sun et al., 2018).
To date, most research examining the personality correlates of SWB has focused on the
Big Five (DeNeve & Cooper, 1998; Steel et al., 2008). These five broad ‘domains’ of personality
emerged from decades of research seeking to identify the major lines of covariation among trait
terms, and provide a robust organizing framework for personality psychology as a whole (Anglim
& O’Connor, 2019; John & Srivastava, 1999). However, the Big Five domains do not provide—
nor were they ever intended to provide—a complete description of personality. Personality traits
can be hierarchically arranged at multiple levels both above (e.g., Anusic, Schimmack, Pinkus, &
Lockwood, 2009; DeYoung, 2006; Digman, 1997; Musek, 2007; Veselka et al., 2009) and below
(e.g., Costa & McCrae, 1995; DeYoung, Quilty, & Peterson, 2007; Mõttus, Kandler, Bleidorn,
Riemann, & McCrae, 2017; Mõttus, McCrae, Allik, & Realo, 2014) the five broad domains. In
addition, a prominent alternative to the Big Five, the six-factor HEXACO model (Ashton, Lee, &
De Vries, 2014), has received increasing interest and support. Researchers have thus begun to
expand knowledge of the relation between personality and well-being by shifting to different levels
in the personality trait hierarchy within the Big Five, as well as within the HEXACO framework
(Aghababaei & Arji, 2014; Anglim & Grant, 2016; Marrero Quevedo & Carballeira Abella, 2011;
Schimmack et al., 2004; Sun et al., 2018).
To strengthen and consolidate this emerging research, we aim to address several
fundamental gaps in the literature. First, despite meta-analytic work relating the Big Five domains
to SWB (DeNeve & Cooper, 1998; Steel et al., 2008), no equivalent meta-analysis has examined
how the Big Five relates to PWB, or how the HEXACO model relates to either SWB or PWB.
Second, the meta-analysis of Steel et al. (2008) focused exclusively on the NEO and the meta-
analysis of DeNeve and Cooper (1998) largely relied on categorizing personality measures that
predated the Big Five. Third, existing research examining facets of the Big Five and their
incremental prediction of well-being above and beyond the Big Five domains suffers from several
methodological limitations, including small sample sizes, biased statistics, invalid meta-
PERSONALITY AND WELL-BEING
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analytically derived correlation matrices, and incomplete reporting (see the section below on
"Incremental Prediction" for details; for a critical review, see Anglim & Grant, 2014). Fourth, there
has been no robust examination of how facets of the HEXACO model map to dimensions of well-
being. To address these gaps, we present a meta-analysis that synthesizes the existing literature,
and a systematic examination of the datasets with the largest sample sizes that have examined
facet-level associations of Big Five and HEXACO frameworks with both SWB and PWB. We
believe this research provides the most comprehensive assessment yet of how personality traits are
linked to indices of human flourishing.
Subjective and Psychological Well-Being
Whereas previous studies have adopted a range of different perspectives on well-being
(Diener & Choi, 2009; Diener, Oishi, & Lucas, 2003; Diener, Suh, Lucas, & Smith, 1999; Lucas
& Diener, 2008), we focus on the complementary perspectives of SWB and PWB. Several decades
ago, Ed Diener and colleagues operationalized SWB as high life satisfaction combined with high
levels of positive affect and low levels of negative affect (Deci & Ryan, 2008; Diener, 1984; Lucas,
Diener, & Suh, 1996). Contrastingly, Carol Ryff and colleagues have operationalized PWB using
a six-dimensional framework comprising positive relations, autonomy, environmental mastery,
personal growth, purpose in life, and self-acceptance (McGregor & Little, 1998; Ryan & Deci,
2001; Ryff & Keyes, 1995). Definitions and example items for all of these dimensions are depicted
in Table 1. Although all nine well-being dimensions have moderate to large intercorrelations, they
each appear to capture discrete aspects of well-being (Anglim & Grant, 2016; Sun et al., 2018).
Despite the influence of situational factors on short-term fluctuation in mood, and the
longer-term impact that significant life events appear to have on well-being—e.g., marital
transition (Lucas, Clark, Georgellis, & Diener, 2003), acquiring a disability (Lucas, 2007), or
approaching death (Gerstorf et al., 2008)—measures of well-being otherwise appear very stable
over time (Fujita & Diener, 2005; Schimmack & Oishi, 2005). For example, in a recent, large
panel study, Anglim, Weinberg, and Cummins (2015) obtained 8-year test-retest correlations for
life satisfaction approaching .80. Furthermore, twin studies suggest that SWB is reasonably
heritable (Weiss, Bates, & Luciano, 2008). For example, in a large sample of Norwegian Twins,
Røysamb et al. (2018) found the twin-cotwin correlations for life satisfaction for monozygotic
twins (r = .31) was much larger than for dizygotic twins (r = .15). Grounded in the idea of the
"hedonic treadmill" (Brickman & Campbell, 1971), various “set-point” theories have been
proposed to explain these findings. From this perspective, well-being is a homeostatic process that
fluctuates around a relatively stable set-point (Cummins, 2015; Headey & Wearing, 1989; Headey
& Wearing, 1992). People differ in their set-points, and personality describes the dispositional
mechanisms that influence how people experience and perceive the world, which in turn influences
set-point dynamics (Headey & Wearing, 1989; Headey & Wearing, 1992).
Descriptive Models of Personality Traits
Personality traits describe relatively stable patterns of affect, cognition, and behavior. The
early history of research on personality traits was characterized by a huge proliferation of trait
constructs and scales to measure them. Subsequently, emerging from the lexical tradition in the
United States, the Big Five traits of neuroticism, extraversion, openness, agreeableness, and
conscientiousness has functioned as a powerful synthesizing framework (Costa & MacCrae, 1992;
Goldberg, 1993; McCrae & John, 1992). However, the Big Five is not the ‘only game in town’. In
particular, the six factor HEXACO model, derived from the same lexical approach but in different
(European and East Asian) language groups, has emerged as a prominent alternative to the Big
Five (see Ashton et al., 2004; De Raad et al., 2014; Lee & Ashton, 2004; Saucier, 2009). HEXACO
PERSONALITY AND WELL-BEING
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is an acronym for the six broad traits of honesty-humility, emotionality, extraversion,
agreeableness, conscientiousness, and openness.
There are strong similarities but also important differences between the Big Five and the
HEXACO models (Ashton & Lee, 2005; Ashton et al., 2014; Gaughan, Miller, & Lynam, 2012;
Ludeke et al., 2019). In particular, Big Five agreeableness and neuroticism are repartitioned in the
HEXACO model to form the three domains of honesty-humility, agreeableness, and emotionality.
Honesty-humility, characterized by integrity and modesty, is negatively correlated with antisocial
personality traits (e.g., within the ‘Dark Triad’ framework; Lee & Ashton, 2014) and positively
correlated with the modesty and straightforwardness facets from Big Five agreeableness (Ashton
& Lee, 2005). HEXACO agreeableness captures patience, forgiveness, and a disposition to not
experience anger towards others. Emotionality includes both the negative emotions of anxiety and
fearfulness as well as more neutral emotional tendencies such as dependence and sentimentality.
In general, conscientiousness, openness, and extraversion in the HEXACO framework are
notionally close analogues to their Big Five equivalents (e.g., cross-correlations all above .75 for
the NEO-PI R, Gaughan et al., 2012).
Both Big Five and HEXACO models are hierarchical frameworks, where each broad
domain is characterized by a set of narrower traits or "facets" (see Table 1; for discussion see
Anglim & O’Connor, 2019). In the context of the Big Five, a range of facet-level frameworks have
been proposed (e.g., Soto & John, 2017), but the most popular hierarchical framework in research
settings has been the NEO Model which characterizes the Big Five in terms of 30 facets (Costa &
McCrae, 1995). This model can be measured using the NEO PI-R, NEO PI-3, or the IPIP NEO (a
public domain equivalent). More recently, an intermediate level between facets and domains has
been proposed, whereby each Big Five domain is divided into two trait ‘aspects’ (DeYoung et al.,
2007). Unlike the facets of the Big Five, the aspects were derived empirically, informed by
quantitative genetic models and other considerations, and are thus purported to less arbitrarily cut
nature “at the joints”. The HEXACO model also has a hierarchical representation that includes 25
facets and 6 domains (4 facets for each domain and one interstitial facet) (Lee & Ashton, 2018).
Personality Traits and Well-Being: What We Know So Far
Most research on the relation between personality and well-being has focused on the Big
Five and the three dimensions of SWB (DeNeve & Cooper, 1998; Steel et al., 2008). The results
of Steel et al. (2008) were a watershed in this literature, as by this time the Big Five was sufficiently
well-established, whereas the earlier meta-analysis by DeNeve and Cooper (1998) required many
stand-alone traits to be identified by the authors as proxies of Big Five domains. Focusing
exclusively on studies using the Costa and McCrae's NEO, Steel et al. (2008) found that
neuroticism was the most consistent correlate of SWB followed by extraversion and then
conscientiousness. The research also highlighted the unique profile of correlations across the
dimensions of SWB where, for example, relatively larger correlations are seen between
neuroticism and negative affect, extraversion and positive affect, and openness and positive affect.
Although no equivalent meta-analysis exists in relation to PWB, an emerging literature of
primary studies has examined correlates with the Big Five (e.g., Grant et al., 2009; Schmutte &
Ryff, 1997; Shulman & Hemenover, 2006). Initial research has highlighted the importance of
neuroticism, extraversion, and conscientiousness in predicting PWB. Some research suggests that
the Big Five may predict PWB more strongly than SWB (Anglim & Grant, 2016). Importantly,
each of the six scales have particular Big Five traits that appear to correlate more prominently
(Anglim & Grant, 2016; Grant et al., 2009; Meléndez et al., 2019; Sun et al., 2018), for instance,
agreeableness and extraversion with positive relations, openness with personal growth, and
PERSONALITY AND WELL-BEING
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conscientiousness with purpose in life. However, meta-analytic estimates are needed to provide a
more definitive assessment of these unique cross-correlations.
More recently, researchers have correlated the six HEXACO personality domains with
dimensions of SWB and PWB (Aghababaei, 2014; Aghababaei & Arji, 2014; Aghababaei et al.,
2016; MacInnis et al., 2013; Pollock et al., 2016; Romero et al., 2015; Sibley, 2011; Visser &
Pozzebon, 2013). Perhaps the most prominent difference seen in the results of these studies,
compared to those based on the Big Five, is that HEXACO extraversion is the main correlate of
well-being, whereas emotionality has a much weaker relationship. A comparative facet-level
analysis of HEXACO and Big Five correlates would assist in understanding these differences.
Despite several existing meta-analyses mapping the Big Five domains with dimensions of
SWB (DeNeve & Cooper, 1998; Steel, Schmidt, Bosco, & Uggerslev, 2019; Steel et al., 2008),
there is a need for an updated meta-analysis of the relationship between the Big Five and SWB.
The results of Steel et al. (2008) suggested much stronger and more nuanced relationships between
personality and well-being than implied by the meta-analysis of DeNeve and Cooper (1998).
However, Steel and colleagues restricted their focus to NEO personality measures, which
represents only a fraction of the personality measures used in research. It is presently unknown
whether the results of Steel et al. (2008) generalize to a wider range of Big Five measures.
Furthermore, no meta-analysis exists relating the Big Five to the six dimensions of PWB and no
meta-analysis exists relating HEXACO domains to either SWB or PWB. Fortunately, as a result
of growing interest in these associations, there are now a sufficient number of primary studies to
make such a meta-analysis worthwhile. Such an examination would complete the mapping of
HEXACO and Big Five domains onto the dimensions of SWB and PWB and provide a more robust
assessment of the relationship between Big Five personality and SWB.
Research Question 1: What are the meta-analytic correlations of the HEXACO and Big
Five personality domains with SWB and PWB?
Beyond Domains: How Well do Narrow Traits Predict Well-being?
Several researchers have also considered the role of narrow traits of the Big Five in
predicting well-being. Some of this research has focused on life satisfaction (Schimmack et al.,
2004; Steel et al., 2019), SWB (Marrero Quevedo & Carballeira Abella, 2011; Steel et al., 2008),
or both SWB and PWB (Anglim & Grant, 2016; Marrero, Rey, & Hernández-Cabrera, 2016; Sun
et al., 2018). Such research has often highlighted facets such as depression and positive emotions
as important predictors, which in turn has highlighted how construct overlap may be relevant. This
research fits into a broader literature discussing the importance of narrow traits in providing a more
nuanced perspective on criteria of interest (Anglim & Grant, 2014; Anglim & O’Connor, 2019;
Judge, Rodell, Klinger, Simon, & Crawford, 2013; Mõttus et al., 2017; Ones & Viswesvaran,
1996; Paunonen & Ashton, 2001; Paunonen & Jackson, 2000). It also relates to several unanswered
questions about the relative predictive validity of broad and narrow traits, and the need for more
empirical evidence regarding the factors that influence the degree of incremental prediction at the
facet-level. Such factors may include personality-criteria correspondence, choice of hierarchical
personality framework, sample characteristics, criteria characteristics, and measurement
approaches.
In contrast to the Big Five, no robust facet-level analysis of the HEXACO model and well-
being has been conducted. Importantly, reliable estimation would require large samples and the
use of the 8-item per facet HEXACO 200 (Anglim & O’Connor, 2019). At present, the best
available data comes from a facet-level analysis performed by Aghababaei (2014) who correlated
the facets of the HEXACO 60 (i.e., 2 or 3 items per facet) with a single item measure of life
PERSONALITY AND WELL-BEING
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satisfaction in a sample of 288 students. They found that social self-esteem and liveliness had
notably stronger correlations than the other HEXACO extraversion facets. The agreeableness facet
of patience and the honesty-humility facet of fairness were also notably larger than other facets in
their respective HEXACO domains. Also using the HEXACO 60, Aghababaei and Arji (2014)
report correlations (n = 215) just for the honesty-humility facets with PWB dimensions and life
satisfaction. They found that sincerity and fairness tended to have slightly larger correlations with
PWB than the facets of greed-avoidance and modesty.
Although these studies have provided important insights, they have not satisfied the
methodological requirements for a robust assessment of facet-level correlations and the
incremental prediction of facets (Anglim & Grant, 2014; Anglim & O’Connor, 2019). First, facets
and domains need to be measured reliably. In particular, a valid assessment of incremental
prediction by facets requires reliable measurement of the variance in facets not shared with
personality domains. This is best achieved through the use of long-form measures of personality
such as the HEXACO 200, IPIP 300, and NEO PI R 240. Second, large samples are also required.
A comprehensive examination of the facet-level correlates of HEXACO with well-being should
also help to explain the differences between the HEXACO and Big Five frameworks. Furthermore,
relatively little research has systematically examined facet-level correlates between Big Five and
SWB / PWB. Some studies have suffered from small sample sizes, and there is a need for a
consistent data analytic approach. In particular, examining semi-partial correlations between facets
and criteria, after overlap with broad traits is removed provides a powerful way to identify which
facets provide unique prediction. Thus, there is a need for large sample studies combining different
personality frameworks including the Big Five and HEXACO perspectives.
Research Question 2: What are the correlations of the HEXACO and Big Five personality
facets with SWB and PWB?
Incremental Prediction of Facets over Domains
Beyond estimating facet-level correlates, the degree to which facets provide incremental
prediction of well-being remains a fundamental question. In particular, incremental prediction of
facets overs domains is important for justifying the loss of parsimony that results from facet-level
analyses. The degree to which facets incrementally predict well-being has been actively debated
in the literature, especially in relation to life satisfaction (Anglim & Grant, 2016; Steel et al., 2019;
Steel et al., 2008). Although some data suggests that the variance explained in life satisfaction
might double at the facet-level (Marrero Quevedo & Carballeira Abella, 2011; Steel et al., 2019;
Steel et al., 2008; Stephan, 2009), we suspect that the incremental prediction, though substantial,
may be more modest than these data suggest. First, Marrero Quevedo and Carballeira Abella
(2011) compared predictive validity of the NEO Big Five to a model that includes both the 30
facets of the NEO as well as optimism, self-esteem, and social support (i.e., variables outside the
NEO framework). When focusing only on the 30 facets, incremental prediction was around 50%.
Second, Stephan (2009) examined the incremental validity of facets only with respect to their
parent domain (i.e., the facets of openness were compared only to the domain of openness).
However, this approach does not control for overlap that facets have with all other domains. It
therefore risks over-estimating incremental variance explained by facets. Third, some early
literature using small sample sizes (e.g., < 200) compared unadjusted r-squared values of domain
versus facet regression models. As discussed in Anglim and Grant (2014), applying a correction
for the number of predictors in order to obtain unbiased estimates of population variance explained
is essential, and one reasonable approach is to use an adjusted r-squared correction. This is
particularly important in the context of domain and facet regression comparison because of the
PERSONALITY AND WELL-BEING
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large difference in the number of predictors.
Fourth, Steel and colleagues (Steel et al., 2019; Steel et al., 2008) have conducted meta-
analytic regression models to estimate facet-level prediction. However, because researchers rarely
report facet-level intercorrelations, these meta-analytic facet-level regressions have to rely on
sources other than the primary studies (e.g., test manuals). Facet-level correlations vary from study
to study and the inability to accurately represent multicollinearity can dramatically inflate or distort
variance explained in regression equations. This is already problematic for meta-analytic
regression involving the Big Five domains, and is of more serious concern for regressions
comprising 30 highly correlated facet predictors.
Finally, the few studies that have compared domain and facet regression models predicting
life satisfaction using the NEO framework and reasonable sample sizes have obtained the
following domain and facet adjusted r-squared values, respectively: .40 versus .52 with n = 337
(Anglim & Grant, 2016); .16 versus .22 with n = 554 (based on stepwise facet regression, Marrero
Quevedo & Carballeira Abella, 2011); and .24 versus .32 with n = 1,516 (Røysamb et al., 2018).
Thus, an increase in prediction by facets relative to domains of between 20% and 60% seems more
likely for life satisfaction. Beyond life satisfaction, Anglim and Grant (2016) also examined
incremental prediction in relation to the nine SWB and PWB variables. Although their sample size
was too small to yield precise estimates, they found some evidence for levels of incremental
prediction varying across outcomes whereby life satisfaction, autonomy, purpose in life, and self-
acceptance had relatively more incremental prediction.
In summary, the question of incremental prediction of facets over domains in relation to
well-being remains unanswered, and methods for synthesizing research findings regarding
incremental prediction are still in their infancy. We propose that in addition to measuring criteria
of interest, primary studies need to measure reliable full-length hierarchical measures of
personality (i.e., typically 8 or more items per facet), and they need to provide (a) raw data, (b) a
full inter-correlation matrix between facets, domains, and criteria, or (c) a valid estimate of
incremental variance explained consistent with the approach adopted in the meta-analysis; i.e.,
typically this would be the difference in adjusted r-squared between domain and facet regression
models, but other approaches such as bifactor models also have merit (Anglim, Morse, De Vries,
MacCann, & Marty, 2017; Chen et al., 2012). In addition, particularly large samples are needed
when estimating incremental prediction of facets with the necessary precision. By obtaining such
data, it would be possible to estimate incremental prediction of facets in each sample, and
synthesize these findings. Such research could examine how incremental prediction of facets varies
across well-being scales (e.g., SWB and PWB scales), personality questionnaires (e.g., IPIP NEO
versus NEO PI), personality frameworks (Big Five versus HEXACO), and target populations.
Research Question 3: What is the relative prediction of broad and narrow personality
traits in relation to SWB and PWB and how does this vary across the Big Five and
HEXACO?
The Present Research
In seeking to answer these three research questions, the overall objective of this research
is to thoroughly describe relations that the domains and facets of HEXACO and Big Five
personality have with the dimensions of SWB and PWB. To achieve these aims, we conducted a
set of comprehensive analyses of published domain-level correlations and facet-level datasets. To
understand domain-level correlations (RQ1), we conducted a meta-analysis of the domain-level
correlates of HEXACO and Big Five personality with the dimensions of SWB and PWB.
To provide a systematic assessment of facet-level correlations (RQ2) and incremental
PERSONALITY AND WELL-BEING
8
prediction of facets-over-domains (RQ3) across well-being measures and various Big Five and
HEXACO frameworks, we adopted a multi-pronged approach. This included collecting new data,
re-analyzing partially reported raw-data, merging datasets where equivalent measures were used,
and analyzing complete correlation matrices where these were reported. All of the datasets
involved included (a) the nine well-being variables, (b) reliable, full-length personality measures,
and (c) moderate to large sample sizes. Importantly, the combined sample size of these datasets is
an order of magnitude larger than previous attempts to estimate incremental prediction of facets,
and will thus provide the first robust examination of that question.
Method
All data, scripts, materials, and supplementary analyses are available on the Open Science
Framework: https://osf.io/42rsy
Meta-Analysis
Our meta-analysis served to estimate cross-sectional self-report relations that the
HEXACO and Big Five Domains have with SWB and PWB.
Literature search. The literature search sought to identify any study that reported a
correlation between Big Five or HEXACO Personality and the dimensions of SWB or PWB. The
final literature search reported in this study was conducted in August 2019. Keyword searches
were conducted in Scopus and PsycInfo, which included dissertations and foreign language
articles. The primary search sought to identify articles that included (a) at least one personality-
related keyword indicating that the Big Five or HEXACO was used, which included any
personality domain name (e.g., extraversion, neuroticism, honesty-humility) or a common test or
framework name (e.g., BFI, NEO, HEXACO, Big Five, Big 5, FFM, Five Factor Model, etc.) (b)
the word "personality", and (c) a well-being related term (e.g., SWB, PWB, subjective well-
being, life satisfaction, satisfaction with life, positive affect, negative affect, etc.). Second, a
search for well-being related terms was performed on the more than 600 HEXACO-related
references listed on http://hexaco.org/references. Third, references from key meta-analyses on
personality and well-being were included (i.e., DeNeve & Cooper, 1998; Heller, Watson, & Ilies,
2004; Lucas & Fujita, 2000; Steel et al., 2019; Steel et al., 2008).
After merging the above sources and removing obvious duplicates, the combined dataset
consisted of 2472 articles. Based on title and abstracts screening, the full-text was examined for
60.5% of these articles.
In addition to the articles that met the inclusion criteria, a further 249 articles were
identified where relevant variables were measured but the correlations were not reported or not
completely reported. The corresponding author of each of these articles was sent an email inviting
them to provide either the correlation matrix or the data from which we could compute the
correlation matrix. When a working corresponding author's email could not be found, another
author or Doctoral supervisor was emailed. Contacted authors also provided several additional
studies that met the inclusion criteria of our meta-analysis. Several of these additional studies were
unpublished or from articles where the correlations were not reported. This process of contacting
authors resulted in 68 additional studies being included in the meta-analysis (11 supplied data; 57
supplied correlation matrices).
Several additional sources of correlations were as follows: We obtained correlations from
6 studies where the correlation matrices were not otherwise published that were reported in the
meta-analysis on personality and various forms of satisfaction by Heller et al. (2004). We included
the domain-level correlations from the two facet-level studies reported in the current paper that
have not previously been reported (i.e., the Combined Dataset and the NEO Dataset). We also
PERSONALITY AND WELL-BEING
9
computed correlations for six studies that did not report correlation matrices but included a dataset
with the publication (e.g., data on the OSF, PlosOne, other data repository).
After collating the studies, 17 studies were excluded for one of the following reasons. First,
studies were excluded if they reported correlations that used a sample that overlapped with another
study. This was common with large panel studies such as the GSOEP, HILDA, BHPS, and MIDUS
as well as some individual small-scale studies. In these cases, we sought to retain the article that
provided the most comprehensive study in terms of sample and measurement. Second, several
studies were excluded because they used non-standard measurement of personality or well-being
that was not initially excluded by our exclusion rules, but were flagged because they produced
outlier correlations (e.g., IPIP HEXACO, asking about life satisfaction in the past, etc.). Third, we
excluded studies that had outlier correlations combined with other concerns about data integrity.
In several studies, there were strong indicators that a large proportion of participants were not
completing the study conscientiously as evidenced by use of samples such as Mechanical Turk,
very large average correlations between the Big Five (e.g., above .6), exclusion of large numbers
of participants due to failing attention checks combined with attention checks that would not be
sufficient to identify all non-conscientious responders, and relatively undifferentiated personality–
well-being correlations. Other indicators of concern included correlations close to zero between
well-being variables and poorly written manuscripts.
The final cleaned database consisted of 377 articles and 462 studies. Note that in six
samples both HEXACO and Big Five personality were measured and these were treated as two
separate studies. Likewise, some articles reported correlations separately for different groups (e.g.,
males and females; patients and controls) and these were also treated as separate studies. Articles
were retained if they reported a correlation between a relevant personality variable (i.e., HEXACO
or Big Five) and a relevant well-being variable. In order to focus our primary meta-analytic
estimates on studies that used reliable measures, we classified correlations into core and noncore.
If the personality trait was measured with eight or more items and the well-being dimension was
measured with five or more items, the correlation was classified as core. For reporting purposes,
we classified a study as core if it had one or more core correlations. Sixteen studies had a mix of
core and non-core correlations.
Importantly, in recent years there has been a proliferation of short-form measures of
personality (e.g., TIPI, BFI 10, Mini-IPIP, etc.). There are also a wide range of short-form
adaptations used in individual studies. In contrast, studies classified as core tended to use well-
validated and well-established measures of personality and well-being. The focus on these core
studies also makes results more comparable across the Big Five and HEXACO, where HEXACO
personality is typically measured with 60, 100 and 200 item formats. It also enables more direct
comparison with the meta-analysis by Steel et al. (2008) which focused exclusively on the NEO
where the most common formats involve 12 (NEO FFI) and 48 (NEO PI R) items per factor,
respectively. It also reduces the need to rely on problematic assumptions related to estimating
reliability and correcting for measurement error. Nonetheless, we do report results for the full set
of studies in the section on moderator analysis.
Eligibility criteria and data coding procedures. Several criteria needed to be satisfied
for correlations to be retained in the meta-analysis. For consistency, the study needed to involve
self-report measurement of both personality and well-being. Second, personality needed to be
measured with either a standard measure of the HEXACO (e.g., HEXACO 60, 100, 192, 200,
etc.) or a measure explicitly designed to assess the Big Five. We excluded the one study by
Churchyard, Pine, Sharma, and Fletcher (2014) that used the IPIP HEXACO, largely because
PERSONALITY AND WELL-BEING
10
this is based on an early model of HEXACO that excluded social self-esteem. This also resulted
in the exclusion of studies that used the Eysenck Personality Inventory (EPI) or the Eysenck
Personality Questionnaire (EPQ). Detailed meta-analysis of the EPI and EPQ are already
available in Steel et al. (2008) and we wanted to focus on measures that were explicitly designed
to partition personality trait variance into the Big Five or HEXACO. We similarly excluded
measures that can be scored to derive a Big Five measure but were not designed to measure the
Big Five.
Third, the well-being measure needed to be designed to measure satisfaction with life,
positive affect, negative affect (i.e., SWB) or the six scales of Ryff's measure of PWB. In relation
to life satisfaction, we sought to only include pure measures of life satisfaction. Life satisfaction
was typically (82%) measured using Diener's Satisfaction with Life Scale (Diener, Emmons,
Larsen, & Griffin, 1985). We also included single-item measures of life satisfaction, composite
measures of life satisfaction that sum satisfaction with various life domains (e.g., Personal Well-
Being Index), modified versions of the Satisfaction with Life Scale, and a few other focused scales.
We excluded any life satisfaction measure which included a broader set of well-being indicators.
To be included, positive affect and negative affect needed to be measured as the sum of
items asking about the frequency of experiencing a set of positive and negative emotions,
respectively. The vast majority (86%) of studies used the PANAS (Watson, Clark, & Tellegen,
1988) or a variant of the PANAS. We excluded studies that measured affect using experience
sampling methods because there was a lack of standardization in how affect was measured and
aggregated to the person-level. We also excluded measures of affect that were obtained following
experimental manipulation or that were in response to stimuli.
To be included, PWB needed to be measured using an official measure of Ryff's conception
of the six dimensions of PWB. This mostly included 42-, 54-, and 84-item versions of Ryff's scales
and their translations. We focused exclusively on the six scales and not overall measures of PWB.
Data extraction. For each included study, we extracted the following study features:
sample size, personality measure, life satisfaction measure, positive affect measures, PWB
measure, proportion female, mean age, country of sample, type of sample (e.g., university students,
Mechanical Turk, Workers, Community, etc.), the source of the correlations (e.g., from the article,
provided following correspondence with author, etc.), reference details, and additional notes.
Correlations were extracted by copying the correlation matrix into Excel, extracting the
correlations in the order they appeared in the correlation matrix and then using data transformations
to convert into a standardized order. All study feature and correlation extraction was performed by
the first- and fifth-author of this paper. All correlations were extracted by one author and checked
for accuracy by the other. To further identify data entry errors, reporting errors by original authors,
and problematic studies, we obtained z-scores for all correlations by correlation type (i.e., there
were 99 different types of correlations based on the 11 personality traits and 9 well-being
variables). We closely examined correlations with absolute z-scores larger than 2.5. In a few cases,
researchers had made an error in reporting their correlations (e.g., omitting the minus sign on
correlations with neuroticism) and this was corrected. In other cases, we examined the study more
carefully and identified indicators that the study was problematic (non-conscientious participants;
failure to exhibit universal features of correlations in this area such as correlations between well-
being), and these studies were excluded as described earlier.
Data analytic approach. Meta-analytic correlations were estimated using a random-
effects model using the metafor package in R (Viechtbauer, 2010). The standard deviation of
true effect sizes (i.e., t) was estimated using restricted maximum-likelihood estimation. Meta-
PERSONALITY AND WELL-BEING
11
analytic estimates were obtained using both observed correlations and correlations corrected for
measurement error. Relatively few studies provided scale-level reliability information, so we
relied on more general sources based on the test used, and where this was not available we
estimated reliability as the average reliability for tests in the database with equivalent numbers of
items per factor.
Facet-Level Analysis
Identifying datasets. In order to provide a comprehensive assessment of facet-level
correlates and incremental prediction, we sought to identify all studies that had included a
hierarchical measure of personality that enabled reliable facet-level measurement, and that
included measurement of SWB and PWB. In order to estimate incremental prediction, we needed
to have either (a) the raw data, (b) the full correlation matrix between facets, domains, and criteria,
or (c) the adjusted r-squared values for the domain and facet regression equations. Based on these
criteria, we identified three existing datasets that could be analyzed: the NEO Dataset (Marrero et
al., 2016), the IPIP NEO Dataset (Anglim & Grant, 2016), and the Big Five Aspects Dataset (Sun
et al., 2018). We also conducted an additional study that measured 200-item HEXACO PI R, 300-
item IPIP NEO, and well-being. Importantly, this study provided a facet-level assessment using
the HEXACO model, and substantially increased the sample size for the IPIP NEO. The resulting
four datasets each provide the large samples needed for assessment of incremental variance
explained by facets over domains.
We note that the identification of the above datasets was based on a systematic search of
studies measuring personality facets with any measure of SWB or PWB. Common issues included
(a) very small sample sizes for estimating incremental prediction (e.g., under 200), (b) only partial
measurement of facets, (c) focus on a limited set of well-being measures (e.g., only life satisfaction
was common), (d) use of non-standard measures of PWB, (e) the study was a meta-analysis, (f)
the study was a re-analysis of existing data, or (g) the personality assessment had poor facet-level
psychometric properties. We briefly note two relevant datasets that did involve large samples.
First, Røysamb et al. (2018) does provide a valid estimate of incremental prediction of life
satisfaction by the NEO PI-R. However, they did not measure any other well-being indicators.
Second, Romero et al. (2015) reported domain-level correlations (but nothing at the facet-level)
between personality (HEXACO 100 and NEO PI-R) and dimensions of SWB and PWB. However,
we were unable to obtain the data or full facet-level correlations needed to estimate incremental
prediction in this dataset.
Datasets.
NEO Dataset. Participants were 1,673 Spanish adults (52% female; age in years M = 38.9,
SD = 13.3, range: 17 to 89). Participants were recruited by university students instructed to target
participants of different ages and professions. Participants completed Spanish translations of the
NEO PI R and well-being measures, administered individually. Although a subset of this data was
analyzed in Marrero et al. (2016), facet-level correlations and incremental prediction by facets
were not reported. Thus, the analyses presented here are novel. Moreover, this is the largest sample
yet reported examining a hierarchical measure of personality in combination with a full set of SWB
and PWB measures. This large sample is particularly crucial for deriving precise estimates of
incremental prediction.
Combined Dataset. We conducted a new study where me measured the HEXACO PI R,
the IPIP NEO, and both SWB and PWB. This enabled (a) the first rigorous estimate of HEXACO
correlates of SWB and PWB at the facet-level, (b) a more robust assessment of the correlates of
the IPIP NEO with SWB and PWB, (c) clarity regarding the similarities and differences between
PERSONALITY AND WELL-BEING
12
the HEXACO and IPIP NEO frameworks, and (d) an opportunity to examine the combined
prediction of HEXACO and the IPIP NEO. The final sample consisted of 465 Australian university
students (79% female; age in years M = 25.1, SD = 7.8, range: 18 to 56), based on an initial sample
of 578, from which 113 cases were dropped because of incomplete data. Due to the large number
of items, data was collected online over two sessions. In the first session, participants completed
demographics, the 300-item IPIP personality measure, the well-being measures, and measures that
did not form part of this study (i.e., problematic smartphone usage, reported in Horwood &
Anglim, 2018; Horwood & Anglim, 2019). In the second session, completed on average 28 days
later, participants completed the 200-item HEXACO PI R.
IPIP Dataset. This sample (n = 903) combines data from three related sources. First, it
uses the IPIP NEO data from the Combined Dataset (n = 465). Second, it includes cases from the
Combined Dataset that were excluded because they did not have matching HEXACO data (n =
102). Finally, 336 cases were obtained from Anglim and Grant (2016), which was also based on
an Australian university student sample and used identical measures of personality (i.e., the 300
item IPIP NEO Inventory) and well-being to those used in the Combined Study.
HEXACO Dataset. This is the Combined Dataset focusing on the HEXACO-PI-R data (n
= 465).
Big Five Aspects Dataset. A study by Sun et al. (2018) examined the The Big Five Aspects
in relation to SWB and PWB across two samples (n1 = 205, n2 = 501). We pooled the correlations
across the two datasets by weighting correlations by their respective sample sizes, giving a final
sample size of 706. Although Sun et al. (2018) reported the variance explained by the 10 aspects,
they did not report the variance explained by the Big Five. Thus, we sought to compute this value
and thereby assess the incremental prediction of the 10 aspects over and above the Big Five. We
calculated adjusted r-squared using the setCor function in the psych package in R (Revelle,
2018) which enables regression analyses to be performed on correlation matrices.
Measures.
Satisfaction with Life Scale. This well-established 5-item measure (Diener et al., 1985)
provides a measure of overall life satisfaction. Items were rated on a 7-point scale (1 = strongly
disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5 = slightly agree, 6 =
agree, 7 = strongly agree). The scale score was the mean of items. The NEO Dataset used the
Spanish version of the measure (Vázquez, Duque, & Hervás, 2013), and the English version was
used in all other datasets.
Positive and Negative Affect. The IPIP, HEXACO, and NEO datasets measured positive
and negative affect using the PANAS (Watson et al., 1988). The PANAS consists of two scales
that measure the frequency with which positive and negative affect is experienced. In the current
study, participants were asked about how frequently they had experienced the emotions in "the
past few weeks". The 20 items each concerned a different emotion and were rated on a 5-point
scale (1 = very slightly or not at all, 2 = a little, 3 = moderately, 4 = quite a bit, 5 = extremely).
Scales were scored as the mean of items. The NEO Dataset used a version of the measure translated
into Spanish by Marrero et al. (2016). The Big Five Aspects Dataset measured positive and
negative emotions using six-items from the PERMA-Profiler (Butler & Kern, 2016).
Psychological Well-Being. Ryff's (1989) scales were used to measure the six proposed
dimensions of psychological well-being. Items were rated on a 6-point scale (1 = strongly disagree,
2 = disagree somewhat, 3= disagree slightly, 4 = agree slightly, 5 = agree somewhat, 6 = strongly
agree). The scale consisted of positively and negatively worded items, and scale scores were the
mean after item reversal. The NEO Dataset used the 84-item Spanish translation of Ryff's PWB
PERSONALITY AND WELL-BEING
13
measure (Díaz et al., 2006). The IPIP and HEXACO datasets used the standard 84-item version.
The Big Five Aspects datasets included two samples, where Sample 1 used the 54-item version
and Sample 2 used the 42-item version.
NEO Personality. The NEO Dataset measured the Big Five and 30 Facets of the NEO
model of personality using the official Spanish translation of the 240-item Revised NEO
Personality Inventory. Four items were excluded because of low corrected-item-total correlations
(< .20).
IPIP NEO Personality. The IPIP and Combined Datasets measured the 30 facets and five
domains of the NEO model (Costa & McCrae, 2008) using the 300 item IPIP-NEO Inventory
(Goldberg, 1999; Goldberg et al., 2006). Items were rated on a 5-point scale (1 = very inaccurate,
2 = moderately inaccurate, 3 = neither inaccurate nor accurate, 4 = moderately accurate, 5 = very
accurate). Scale scores were the mean after any item reversal. The scales have an average
correlation with corresponding NEO-PI-R scales of .73, or .94 when corrected for measurement
error (Goldberg, 1999).
HEXACO Personality. The HEXACO Dataset measured personality traits using the full-
length 200-item version of the HEXACO PI-R (Ashton et al., 2014; Lee & Ashton, 2004, 2006).
The measure consists of six domain scales and 25 facet scales. Each domain scale consists of four
facet scales, and there is one interstitial facet, altruism. Participants responded to items on a scale
from 1 = strongly disagree to 5 = strongly agree. Scale scores were obtained as the mean of items
after any necessary item reversal. To increase comparability with the Big Five, a HEXACO
Neuroticism factor was computed as weighted composite facets as set out in Lee and Ashton
(2013): HEXACO Neuroticism = Fearfulness + 3 * (Anxiety) + Dependence + 3 * (6 – Social
Self-Esteem) + (6 – Liveliness) + (6 – Patience) + (6 – Prudence).
Big Five Aspects Personality. In the Big Five Aspects Dataset, the 5 domains and 10
aspects were measured using the 100-item Big Five Aspect Scales (DeYoung et al., 2007). The
Big Five Aspect Scales were developed using items from the IPIP. The response scale ranged from
1 = strongly disagree to 5 = strongly agree.
Data analytic approach. We broadly followed the methodology for reporting facet-level
correlations and incremental prediction set out in Anglim and Grant (2014). For each personality
measure we report zero-order correlations between facets and the dimensions of SWB and PWB.
In the supplement, we report semi-partial correlations that remove the shared variance between
the facet and the five domain-level personality factors. They provide an estimate of the unique
prediction provided by the facets over and above the domains. The square of the semi-partial
correlation is equivalent to the percentage of incremental variance explained by a regression
model that adds the facet of interest (e.g., gregariousness) as a predictor to one with only the
domains (e.g., the Big Five). Incremental prediction of facets over domains was obtained by
taking the difference in the adjusted r-squared values for a regression model with domains as
predictors to one with facets as predictors.
Results
Summary of the Literature
A summary of the studies included in the meta-analysis is provided in Table 2 with further
details provided in the OSF repository. In total, the meta-analysis included 4,153 correlations
(3,246 core; 907 noncore). Table 3 provides an overview of the included studies for the combined,
core, and noncore samples. The combined sample consisted of 462 studies and a total sample of
334,567 participants. Most scales of personality measures involved 8 to 15 items. The most
common personality frameworks were the NEO and the BFI. The number of studies that met the
PERSONALITY AND WELL-BEING
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inclusion criteria has grown dramatically since the meta-analysis by Steel et al. (2008). More
studies were from the five-year period from 2010-to-2014 than from before 2010, and in the last
4.5 years the number of studies per year has increased even further. This may reflect the general
growth in science, the expanding number of journals, the accessibility of international journals and
PhD theses, and the increasing popularity of the Big Five, the PANAS, and life satisfaction
measurement.
Meta-Analytic Correlations
Table 4 provides an overall summary of the meta-analytic correlations between personality
and well-being based on the core studies. Detailed reporting of the meta-analytic observed and
reliability-corrected correlations between Big Five and SWB (Table 5), Big Five and PWB (Table
6), HEXACO and SWB (Table 7), and HEXACO and PWB (Table 8) are presented for the core
studies.
Overall, the average correlation between personality domains and well-being was .28. If
negative affect is reversed, the mean meta-analytic correlation averaged over the nine well-being
indicators for the Big Five domains were -.46 (neuroticism), .37 (extraversion), .19 (openness),
.25 (agreeableness), and .36 (conscientiousness). The corresponding values for HEXACO domains
were .16 (honesty-humility), -.16 (emotionality), .48 (extraversion), .18 (agreeableness), .28
(conscientiousness), and .16 (openness). Thus, for the Big Five, neuroticism was the strongest
correlate followed by extraversion and conscientiousness; correlations for openness and
agreeableness were more moderate. For HEXACO, extraversion was clearly the strongest
correlate. As discussed earlier, although the content of HEXACO emotionality has some similarity
with Big Five neuroticism, it also has important differences, and thus it is perhaps not surprising
that it had a much weaker correlation with well-being. HEXACO conscientiousness and openness
exhibited similar correlations with well-being to their Big Five analogues. The average correlations
with well-being for honesty-humility and HEXACO agreeableness were also similar to the
correlation for Big Five agreeableness. Results also showed that the variance in observed
correlations was greater for the Big Five than for the HEXACO; this is consistent with the greater
variability in questionnaires used to measure the Big Five.
To assess which combinations of personality and well-being dimension were uniquely
related, we performed a marginalization procedure on the meta-analytic corrected correlation
matrix (see Online Supplement). Specifically, we reversed negative affect, neuroticism, and
emotionality so that all variables were positively aligned with well-being. We then subtracted the
overall mean correlation, and the row and column marginal means from the correlation matrix (for
further details of the procedure see, Anglim & Grant, 2016). Large residual cross-correlations (e.g.,
above .10 or .15) highlight the unique profile of the personality-well-being relationship, where
positive residuals indicate that the pair of variables is more related than expected, and negative
residuals indicate that the pair of variables is less related than expected. Absolute residuals greater
than .12 for the Big Five were reversed neuroticism with reversed negative affect (.14), and
personal growth (-.15); openness with personal growth (.22); agreeableness with positive relations
(.13) and autonomy (-.13), and conscientiousness with purpose in life (.13). For HEXACO, these
were reversed emotionality with reversed negative affect (.19), positive relations (-.18), autonomy
(.22), and purpose in life (-.14); agreeableness with autonomy (-.13); conscientiousness with
purpose in life (.18); and openness with autonomy (.12) and personal growth (.15).
Table 9 presents the meta-analytic estimate of the correlations between the Big Five and
SWB across various moderators (i.e., core and non-core studies, item length, and personality
measurement type) and compares results with past meta-analyses. It also reports the mean and
PERSONALITY AND WELL-BEING
15
standard deviation of correlations after reversing the negative correlations (i.e., N with PA, N with
SWL, and E, O, A, C with NA). The mean correlation indexes the extent to which personality is
related to well-being. The standard deviation of correlations indexes the degree to which a nuanced
profile of personality correlates is provided as opposed to a more homogenous set of correlations.
Overall, the pattern of correlations is fairly robust across different types of measures and different
item lengths. Nonetheless, consistent with reduced reliability of measurement and potentially
validity, noncore studies and extra-short measures had weaker correlations with well-being.
In general, there was a high degree of consistency across the different personality
frameworks, although the TIPI was notably less consistent. The BFAS had somewhat stronger
average correlations and the TIPI had weaker average correlations. The NEO and BFAS had larger
standard deviations. To quantify the consistency across frameworks, we created a data frame that
had 15 rows for the 15 absolute SWB correlations and 7 columns for the 7 personality frameworks.
We then computed the average correlation each framework had with the other six frameworks.
These correlations were .88 (NEO), .88 (IPIP), .90 (BFAS), .87 (BFI), .74 (TIPI), .90 (Adjectives),
and .84 (Other).
Table 9 also compares meta-analytic correlations of the current study with that of previous
meta-analyses. A major conclusion of Steel et al. (2008) was that personality is more strongly
related to well-being than was found in the meta-analysis of DeNeve and Cooper (1998). Whereas
DeNeve and Cooper (1998) synthesized a mostly pre-Big Five literature, Steel et al. (2008) focused
exclusively on the NEO framework. The current meta-analysis found meta-analytic correlations
between personality and well-being that were slightly larger than Steel et al. (2008). Importantly,
the current results indicate that this finding is not limited to the NEO framework, but is shared
across a broad range of personality measures that are intended to measure the Big Five.
The pattern of correlations in the current meta-analysis was almost identical to that
obtained in Steel et al. (2008), but quite different to that of DeNeve and Cooper (1998). To quantify
this, we first treated the 15 absolute correlations between Big Five personality and SWB (i.e.,
SWL, PA, NA) for the three meta-analyses (i.e., current study, Steel et al., and DeNeve & Cooper)
as a vector. The correlation between the 15 Big Five–SWB-absolute-correlations was r =. 991
(Current study with Steel), r = .689 (Current study with DeNeve), and r = .679 (DeNeve with
Steel). Thus, it seems that categorizing historical measures of personality into Big Five frameworks
as was done by necessity in DeNeve and Cooper (1998) only provides an approximation of how
Big Five personality actually correlates with well-being.
Finally, a publication bias analysis was conducted. There are several reasons to expect
publication biases to be minimal in this context. First, the majority of primary studies have a high
degree of power to detect the main correlations between personality and well-being. For example,
a study with n = 200 has 99% statistical power to detect a population correlation of .30 at a .05
significance threshold. Second, many studies measure personality and well-being incidentally as
part of broader studies of individual differences and there is no obvious incentive to show a specific
pattern of correlations between personality and well-being. Nonetheless, we examined funnel plots
for the 99 correlation types (i.e., 11 personality traits by 9 well-being variables) and calculated the
rank test for funnel asymmetry (Begg & Mazumdar, 1994). After reversing neuroticism,
emotionality, and negative affect, none of the correlations examined exhibited significant positive
asymmetry.
Well-Being Intercorrelations
In order to contextualize the meta-analytic and facet-level analyses, we present estimates
of the intercorrelations between dimensions of well-being. Table 10 presents correlations among
PERSONALITY AND WELL-BEING
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the nine well-being scales for the Combined and the NEO Datasets. Reflecting a general well-
being factor, the average correlation between well-being variables was .51 in the Combined
Dataset. Consistent with the focus on the scale-level, when factor analysis is performed and two
factors are extracted, loadings for the nine scales do not align with higher-order PWB and SWB
dimensions. Life satisfaction shared the greatest overlap with self-acceptance, although
correlations were relatively large for most other well-being scales, with the exception of autonomy
and personal growth.
Facet-Level Correlations
We first examined the degree to which the domain correlations between personality and
well-being in the facet-level datasets were consistent with the core meta-analytic estimates. In
general, there was very strong convergence with the pattern of domain correlations for all the facet-
level datasets: NEO (r = .94), IPIP (r = .95), HEXACO (r = .96), Big Five Aspects (r =.89)
datasets (see Supplement for details). Average correlations between personality and well-being
were higher (mean difference study and meta-analytic correlations in parentheses) than meta-
analytic estimates for the IPIP (M = .06) and Big Five Aspects (M = .12), but similar for HEXACO
(M = .03) and NEO (M = -.03).
Zero-order correlations between personality facets and well-being are presented for NEO
(Table 11), IPIP NEO (Table 12), and HEXACO (Table 13). Domain-level correlations for the
NEO and IPIP NEO datasets are reported in the supplement. Semi-partial correlations that
involved removing overlap between each facet and the corresponding domain scores are also
reported in the supplement. For the NEO, the strongest average correlations with well-being are
seen for depression (-.46), vulnerability (-.44), and competence (.41). For the IPIP NEO, semi-
partial correlations frequently highlighted depression as an incremental predictor over and above
the Big Five. Positive emotions was also a prominent incremental predictor in relation to
satisfaction with life, positive affect, and self-acceptance. Various other semi-partial correlations
emerged consistent with the unique profile of the well-being variable (e.g., purpose in life with
achievement striving and autonomy with angry hostility (+), self-consciousness (-), and
assertiveness (+)). For the HEXACO, social self-esteem and liveliness emerged as the strongest
average predictors of well-being. Differential correlations of emotionality facets highlight why
emotionality correlated much less with well-being overall. Specifically, anxiety and to a lesser
extent fearfulness had strong negative correlations with well-being whereas dependence and
sentimentality did not. Similarly, with regards to conscientiousness, it was mostly diligence that
had the stand-out correlations.
Incremental Prediction of Facets over Domains
In order to examine the variance explained by broad and narrow traits across the four
datasets, regression models were estimated predicting each well-being variable from either the
broad or the narrow traits for the given personality measure. The variance explained by broad and
narrow traits (adjusted r-squared) for each measure is shown in Table 14. Two measures of
incremental prediction of narrow traits are also provided: raw incremental prediction by narrow
over broad traits and proportional increase of narrow traits relative to broad traits.
On average, broad traits explained 46% of variance and narrow traits explained 53% for an
average proportional increase of facets over domains of 18% (21% if you exclude the Big Five
Aspects data). Despite differences in the overall magnitude of prediction (i.e., Big Five Aspects
and IPIP NEO explained more than HEXACO and NEO), the general pattern of well-being
predicted by domains and facets/aspects was similar across NEO, IPIP NEO, and HEXACO, but
distinct for the Big Five Aspects. On average, PWB variables were better predicted by personality
PERSONALITY AND WELL-BEING
17
than SWB variables. IPIP NEO and HEXACO had larger incremental prediction than the NEO
and Big Five Aspects, although the difference for the NEO was reduced when incremental
prediction was defined as a proportion, due to the relatively lower levels of prediction in the NEO
sample. Overall, the greatest proportional increase in variance explained by facets was seen for
life satisfaction, autonomy, self-acceptance, and purpose in life.
HEXACO versus Big Five Comparison
In order to contextualize the meta-analytic finding and frame a comparison of HEXACO
and Big Five, Table 15 presents the correlations between HEXACO and Big Five domains using
the Combined Dataset. All analogous scales between HEXACO and Big Five correlated greater
than .50. Interestingly—though unsurprisingly, given the rotational differences between the two
models—honesty-humility correlated more with Big Five agreeableness than did HEXACO
agreeableness. Of relevance to understanding correlations with well-being, HEXACO extraversion
correlated more with neuroticism than did HEXACO emotionality.
Table 16 presents the domain-level correlations for HEXACO and IPIP NEO Domains
with well-being dimensions in the combined dataset. The pattern of correlations is broadly similar
to the meta-analytic findings, albeit the correlations are slightly stronger on average. This may
reflect the use of particularly reliable personality and well-being measures in this study. We also
computed the HEXACO Neuroticism domain score using the weighted facet-composite described
in the method. This yielded a pattern of correlations that was very similar to IPIP NEO
Neuroticism.
In order to compare the HEXACO and Big Five models of personality in terms of the
prediction of well-being dimensions, regression models were estimated (using the Combined
Dataset) predicting each well-being variable from various sets of personality predictors: i.e.,
HEXACO Domains, NEO Domains, HEXACO Facets, NEO Facets, and the different
combinations of Domains and Facets from both instruments. The variance in well-being explained
by each set of predictors, using adjusted r-squared to penalize for overfitting, is shown in Table
17. On average, NEO Domains explained more variance than HEXACO Domains and NEO facets
explained more variance than HEXACO facets. HEXACO facets explained about 22% more
variance (mean increase of adjusted r-squared of .09) than HEXACO domains, and NEO Facets
explained about 18% more variance than NEO domains (mean increase of adjusted r-squared of
.12). Satisfaction with life showed the largest relative increase in prediction when moving from
domains to facets: 52% for HEXACO and 41% for NEO, although in terms of absolute increase,
self-acceptance showed similar increases. Whereas the HEXACO facets improved prediction
when added to a model with NEO Domains, adding HEXACO Domains or HEXACO Facets to a
model with NEO Facets led to almost no improvement in prediction.
Discussion
The present study provides a comprehensive examination of the links between self-reported
personality and well-being, using both the HEXACO and Big Five frameworks of personality,
broad and narrow traits within each of these frameworks, and both evaluative (i.e., SWB) and
eudaimonic (i.e., PWB) conceptualizations of well-being. Whereas previous meta-analyses have
either relied on pre-Big-Five measures or a single Big Five personality framework, the current
study incorporated a broad range of Big Five measures and synthesized the large body of research
that has emerged in recent years. Whereas previous meta-analyses have examined the relationship
between the Big Five and SWB, none have examined the Big Five in relation to PWB, and none
have examined the HEXACO framework at all. The study also provides the first robust assessment
of incremental prediction by facets across both SWB and PWB and two major personality
PERSONALITY AND WELL-BEING
18
frameworks.
Several important findings emerged from this investigation. First, the research confirms
that the overlap between basic personality traits and well-being dimensions is substantial. Second,
whereas (lower) neuroticism is the strongest correlate of well-being within the Big Five
framework, extraversion is the strongest correlate within the HEXACO framework. Conversely,
conscientiousness—which previous research has rarely highlighted in relation to well-being—is a
notable correlate within both frameworks. Third, correlations with personality mirror the unique
characteristics of different dimensions of well-being. For example, notably strong correlations
were observed between openness and personal growth, between conscientiousness and purpose in
life, and between neuroticism and negative affect. Fourth, examination of facet-level correlates
highlighted the unique importance of particular facets (e.g., depression and positive emotions in
the Big Five framework and social self-esteem in the HEXACO framework) as well as explaining
differences between the HEXACO and Big Five frameworks. Fifth, facets provided moderate
levels of incremental prediction over and above domains when predicting well-being. Across
multiple measures of the Big Five and HEXACO frameworks there were moderate levels of
consistency in the degree of incremental prediction by facets. These findings have fundamental
implications for understanding well-being, in terms of the role that both broad and narrow
personality traits may play in human flourishing.
Personality and Well-Being
According to effect size guidelines in individual differences research (e.g., Gignac &
Szodorai, 2016), the relationship between personality and well-being is strong. The average
correlation between personality domains and well-being was r = .28, considerably higher than the
average correlation in individual differences research as a whole (i.e., r ~ .20). The strongest
average correlations with well-being were -.46 for Big Five neuroticism and .48 for HEXACO
extraversion. Regression models indicated that about half the observed variance in well-being
scales can be explained by personality domains (46%) and facets (53%).
The domain-level correlations between Big Five personality and SWB were very similar
to those reported in the meta-analysis by Steel et al. (2008) and larger and more nuanced than those
reported in the meta-analysis by DeNeve and Cooper (1998). There are several reasons for this.
First, DeNeve and Cooper (1998) included many studies that predated the Big Five and also used
a mixture of different well-being measures. In contrast, Steel et al. (2008) focused on a small
number of high-quality personality questionnaires such as the NEO and a limited set of reliable
measures of SWB. Similar to Steel et al. (2008), we focused the core meta-analysis on a limited
set of reliable personality and well-being measures. Our research extends that of Steel et al. (2008)
by showing that the magnitude and pattern of correlations observed in Steel et al. (2008) is not
limited to the NEO. A broadly similar magnitude and pattern of well-being correlations was found
across a diverse range of Big Five measures. Second, the HEXACO and the Big Five frameworks
have a strong focus on affect, well-being, and psychological functioning. In general, it seems likely
that measures based on the Big Five and related lexical approaches, such as the HEXACO, will
generally exhibit strong correlations with well-being.
Broad and Narrow Personality Traits of the Big Five and HEXACO
Overall, both the HEXACO and Big Five models are similarly effective in predicting well-
being. For the Big Five model, neuroticism is a very strong predictor, extraversion and
conscientiousness are fairly strong, and openness and agreeableness are more moderate. For the
HEXACO model, extraversion is a very strong predictor (even stronger than Big Five
neuroticism), conscientiousness is fairly strong, and honesty-humility, emotionality,
PERSONALITY AND WELL-BEING
19
agreeableness, and openness are more modest.
Differences in well-being correlations between the Big Five and HEXACO may largely
result from how these models partition personality trait variance (for a review, see Ashton & Lee,
2018; Ashton et al., 2014). These differences can be readily appreciated by examining (a) the
correlations between the HEXACO and the Big Five (see Table 15 in the current paper and Table
1 in Gaughan et al., 2012), (b) the item content of relevant HEXACO and Big Five scales, and (c)
the correlations between personality and well-being at the facet-level for HEXACO and the Big
Five. For instance, HEXACO extraversion (a) correlates at -.65 with IPIP NEO neuroticism, (b)
has many (reversed) items that relate to low self-esteem and depression (e.g., ‘I sometimes feel
that I am a worthless person’), and (c) shows correlations with well-being most prominently for
the facets of social self-esteem and liveliness. In contrast, HEXACO emotionality (a) correlated
only .56 with IPIP NEO neuroticism, and (b) combines traditional neuroticism facet scales such
as fearfulness and anxiety (which correlate negatively with well-being) with more neutral
emotional tendencies such as dependence (which is relatively uncorrelated with well-being) and
prosocial tendencies such as sentimentality (which correlate positively with some aspects of well-
being). HEXACO honesty-humility and HEXACO agreeableness both correlate most strongly
with Big Five agreeableness, although HEXACO honesty-humility has a secondary correlation
with Big Five conscientiousness, whereas HEXACO agreeableness has a secondary correlation
with neuroticism, reflecting its content related to lower anger and hostility.
Although organized differently across the Big Five and HEXACO frameworks, the
tendency to experience low levels of negative emotions and high levels of positive emotions
accounts for much of the effect of personality on well-being. In the Big Five model, neuroticism
captures the broad set of tendencies to experience negative emotions, whereas facets related to
positive emotions form only part of extraversion. Facets such as depression, positive emotions,
and social self-esteem are particularly strong predictors of well-being. It is not surprising that these
characteristic ways of experiencing the world—viewing life through a more negative lens,
ruminating on negative experiences, and emphasizing what's wrong rather than what's right with
the world—translate into lower levels of well-being. On the other hand, Big Five extraversion may
operate both through the tendency to experience positive emotion as well as the more instrumental
pathways paved by the behavioral components of extraversion, such as facilitating positive social
connections and actively engaging with environmental rewards (Smillie, Cooper, Wilt, & Revelle,
2012; Smillie, Wilt, Kabbani, Garratt, & Revelle, 2015; Sun et al., 2017).
Whereas most previous research has emphasized only neuroticism/emotionality and
extraversion in relation to well-being (e.g., Diener et al., 1999; Schimmack et al., 2004; Smillie,
Kern, & Uljarevic, 2018), the present research reveals that conscientiousness is not far behind, and
is perhaps even on par with extraversion. For instance, the average correlation for Big Five
extraversion was .37 versus .36 for Big Five conscientiousness (.28 for HEXACO
conscientiousness). Conscientiousness emerged as particularly important for purpose in life and
environmental mastery, although was somewhat less related to negative affect and positive
relations. Several processes described by conscientiousness could account for its positive
implications for well-being. First, conscientiousness is related to a sense of competence in life, and
the competence facet of conscientiousness was a particularly strong predictor of well-being.
Second, conscientiousness describes effective self-regulation, as when one forgoes short-term
pleasures for the attainment of longer-term goals, whether they be related to family, education,
finance, or health (Roberts, Lejuez, Krueger, Richards, & Hill, 2014). Third, achievement striving
and diligence can connect people with a sense of purpose and meaning, that can facilitate a deeper
PERSONALITY AND WELL-BEING
20
sense of life satisfaction. However, as a small counterpoint, we note that a desire for order and
perfection generally showed much weaker correlations with well-being. Consistent with
highlighting the shortcomings of one's achievements relative to demanding expectations,
perfectionism showed small negative semi-partial correlations with some well-being dimensions
after controlling for personality domains (for further discussion of the benefits and costs of
perfectionism, see Stoeber & Otto, 2006; Stoeber & Stoeber, 2009).
Both the Big Five and HEXACO conceptions of agreeableness, as well as HEXACO
honesty-humility, had relatively modest correlations with well-being. Each of these ‘prosocial’
traits may plausibly improve well-being by reducing interpersonal conflict and helping to foster
positive relations with others. Status seeking, manipulativeness, and greed (captured by honesty-
humility and some facets of Big Five agreeableness) may also create instability of social networks,
with negative consequences for well-being. Although self-interest may bring short-term benefits,
excessive self-interest may, in the long term, damage one’s reputation, social relationships, and
sense of meaning in life. Furthermore, placing substantial value on status symbols and power
places more weight on zero-sum aspects of life (Headey & Wearing, 1992). As a counterpoint, we
note that the modesty facet in both the Big Five and HEXACO models tended to be unrelated or
negatively related to well-being. This may suggest that an inability or unwillingness to compare
oneself favorably to others—whether this be in terms of income, wealth, health, physical
attractiveness, or even popularity on social media—may have negative implications for well-being.
Indeed, it is well-established that most people perceive their lives to be “better than average”
(Alicke, Klotz, Breitenbecher, Yurak, & Vredenburg, 1995; Headey & Wearing, 1992), and that
this rationalization may promote well-being.
Finally, openness to experience was also a modest but nevertheless meaningful predictor
of well-being, with correlations approximating the average effect size in individual differences
research. Openness comprises such characteristics as intellectual curiosity, an ability to adapt to
change, and the tendency to seek novel experiences (Schmutte & Ryff, 1997). Consistent with this,
the current study revealed that openness was particularly related to personal growth, autonomy,
and positive emotions. Whereas Stephan (2009) found openness to feelings and ideas to be the
most important facets in relation to life satisfaction, our current findings varied somewhat across
the different datasets. Openness to actions was a salient predictor to emerge in our data, particularly
in relation to personal growth. Openness appears to reflect an orientation towards well-being that
involves valuing novelty and non-conformity, and viewing life as a process of growth and change.
This is reflected in the strong correlation between values and openness for the Big Five (Parks-
Leduc, Feldman, & Bardi, 2015) and the HEXACO (Anglim, Knowles, Dunlop, & Marty, 2017),
whereby people who are high on openness tend to value self-direction, stimulation, and universalist
values and are less interested in power and conformity. Given that openness is relatively unrelated
to life satisfaction, it may provide an example of a personality trait that influences not just the
experience of well-being, but the process through which a person achieves the good life. For those
high on openness to experience, variety and growth are important, for those low in openness to
experience, stability, safety and maintaining tradition may be more critical.
Well-Being Dimensions
One of the main insights revealed by the present study concerns the differential patterns of
correlations between personality and well-being as one shifts between SWB and PWB. Whereas
SWB focuses on the evaluation of the good life, PWB is more strongly reflective of eudaimonic
perspectives. It is important to note, however, that this distinction is theoretical and conceptual,
whereas the empirical differences between these models are less clear cut. All nine dimensions of
PERSONALITY AND WELL-BEING
21
well-being are positively intercorrelated (after reversing negative affect), despite each capturing
important unique variance. Additionally, the nine scales do not segregate into distinct SWB and
PWB factors. Thus, it is important to consider both the broad and the scale-specific patterns of
personality correlates.
First, and in line with recent research (e.g., Anglim & Grant, 2016), many PWB scales
showed a much stronger overlap with personality compared to SWB scales. In the meta-analysis,
correlations were larger for environmental mastery, personal growth, and self-acceptance, and
smaller for life satisfaction, although the PWB scale of autonomy also had smaller correlations. In
the domain- and facet-level regression models this pattern was also observed, although positive
and negative affect were also predicted somewhat less well. These differences may partially be
methodological. PWB is often measured with a 14-item per scale format whereas the standard life
satisfaction measure (Diener et al., 1985) involves only 5 items. Nonetheless, as we discuss below,
there are several theoretical reasons why some PWB scales overlap more with particular
personality traits.
Second, of the three components of SWB, life satisfaction was less well predicted by
personality compared with positive and negative affect. This is perhaps unsurprising given that the
tendency to experience positive and negative emotions is part of the core content of personality
scales (Pytlik Zillig, Hemenover, & Dienstbier, 2002). In contrast, life satisfaction is a cognitive
appraisal, influenced both by expectations and evaluations, and the individual’s choice of what
factors are relevant to that judgment. It is therefore a step removed from summaries of a person's
typical behavior and experience. Such factors may help explain why life satisfaction shows a much
more modest overlap with personality compared to other dimensions of well-being. Interestingly,
the facets of modesty and perfectionism showed negative semi-partial correlations with life
satisfaction. Thus, whether through objective circumstance, arrogance, or pleasant self-deception,
very high life satisfaction is often related to seeing oneself and one's life as superior to those around
you. Furthermore, perfectionism may lead people to focus on ways that their life could conceivably
be better.
At a more general level, it was apparent that each well-being dimension was characterized
by a coherent pattern of personality correlates. Specifically, positive affect, unsurprisingly, was
well-predicted by extraversion and facets related to the tendency to experience positive emotions.
Negative affect was strongly related to neuroticism, and most prominently with the facet of
depression. Positive relations showed close connections with agreeableness and to some extent
extraversion. Autonomy combined common well-being correlates with a fairly unique set of
personality correlates that combine impulsiveness, non-compliance, and low trust, with
assertiveness and social boldness. Environmental mastery correlated fairly uniformly across
personality traits although it did show some elevation for conscientiousness. Personal growth was
characterized most uniquely by openness with some amplification for diligence and achievement
striving. Purpose in life was particularly well characterized by conscientiousness and especially
diligence and achievement striving. Finally, self-acceptance showed a somewhat similar pattern
of correlations to that of life satisfaction albeit at much greater levels. Although self-acceptance
and life satisfaction are highly correlated, self-acceptance places relatively less emphasis on the
external conditions of life. This emphasis on liking or loathing oneself brings it very close to
several dimensions of personality, as seen by the particularly large correlation with the facet of
depression. Some of these cross-correlations have already been noted in previous research (e.g.,
Anglim & Grant, 2016; Grant et al., 2009; Sun et al., 2018), and the current study consolidates
these observations through the first comprehensive, large sample assessment.
PERSONALITY AND WELL-BEING
22
Incremental Prediction by Narrow Traits
One of the most critical contributions of the present study concerns estimation of the
proportional increase in variance explained by facets above and beyond domains. Average
incremental variance explained by facets was 17%, 22%, and 24% for NEO, IPIP NEO and
HEXACO taxonomies, respectively. The amount of incremental prediction showed some
systematic variation across these three measures, although much less consistency was observed
for the Big Five Aspect Scales. In particular, life satisfaction, autonomy, and self-acceptance
showed the greatest incremental prediction. These scales are not obviously broader or narrower
than other well-being dimensions. Rather they may exhibit a complexity that means that several
facets are important as is the case with autonomy. Equally, there may be a particular facet that
aligns very closely, perhaps as can be seen with depression and social self-esteem in relation to
self-acceptance.
A major focus of the literature on incremental facet prediction has been on life satisfaction
(Røysamb et al., 2018; Schimmack et al., 2004; Steel et al., 2019), and this exhibited somewhat
greater increases of between 24% and 51% depending on the personality framework. This estimate
is broadly consistent with the largest study to report incremental facet prediction to date, albeit
limited to life satisfaction, which obtained 33% incremental prediction (Røysamb et al., 2018).
Steel et al. (2019) reported a 78% increase based on a meta-analytic correlation matrix, but it is
important to note that meta-analytic regression is problematic. In particular, estimating a
regression model with 30 highly correlated predictors, where facet-level intercorrelations are not
provided in the primary studies leads to unreliable and often inflated estimates of variance
explained.
More generally, we consider the proportional increase of 10% to 50% when using
hierarchical instruments as noteworthy. Even though much of the perceived value of narrow traits
is owing to the idea that facets might double prediction, more modest incremental prediction is
still of practical and theoretical importance. Facets also provide a richer profile of how and why
different domains correlate with relevant criteria, and provide a more nuanced picture of the
personality–well-being interface.
Interestingly, the HEXACO model was characterized by larger incremental facet prediction
(as a proportion) than the Big Five, both in terms of the NEO and IPIP NEO. This is striking, given
that the NEO model has fewer domains and more facets than does the HEXACO model, which
should lead the NEO model to have stronger incremental prediction. The IPIP NEO also has more
items per facet, which should yield more reliable measurement of the unique aspects of each facet.
On the other hand, the HEXACO model incudes the interstitial trait of altruism, which is not used
in scoring the domains, whereas all of the items of the Big Five facets/aspects are used to compute
the domain scores. Critically, none of the HEXACO domains capture the general tendency to
experience negative emotions in the same way as Big Five neuroticism (Gaughan et al., 2012).
Rather, the HEXACO model distributes content from Big Five neuroticism over various domains
including extraversion (r = -.50), emotionality (r = .52), and agreeableness (r = -.38) (Gaughan et
al., 2012). The most salient observation regarding incremental facet prediction within the
HEXACO concerned the emotionality facet of anxiety and the extraversion facets of social self-
esteem and liveliness, all of which seem to capture the most affect-related influences on well-
being.
Limitations and Future Research
Because the current meta-analysis is based on self-report measures of personality and well-
being, some care is required when generalizing the findings to the latent constructs. Participants
PERSONALITY AND WELL-BEING
23
vary in the degree to which social desirability influences their responses, and items and scales vary
in their degree of socially desirable content (Anglim, Morse, et al., 2017; McCrae & Costa, 1983;
Wiggins, 1968). Person- and item-level variance in socially desirable responding can lead to
elevated correlations between personality and well-being. This is particularly evident in the
minority of studies using low-paid participant samples where many participants engage in
satisficing and semi-random responding. We observed that in such studies, correlations between
broad personality traits were often elevated, which presumably translates to elevated correlations
between personality and well-being. As a consequence, care is needed when evaluating personality
measures in terms of how much variance they explain in self-reported well-being. One measure
might predict self-reported well-being better because it has more socially desirable items. This
may partially explain why the IPIP NEO predicted well-being better than the HEXACO PI R.
Similarly, if one sample has more evaluative variance, then this may lead to elevated correlations
between personality and well-being. For example, the greater prediction of well-being in the Big
Five Aspects dataset may partially be explained by the use of a Mechanical Turk sample. While
several studies have examined other-reports of personality and well-being (Dobewall, Realo, Allik,
Esko, & Metspalu, 2013; Schimmack et al., 2004), more research is needed in this area, particularly
involving large samples, full hierarchical measures of personality, and multidimensional models
of well-being.
Finally, it is worth considering the degree to which the correlations between personality
and well-being are due to artefactual measurement overlap (Anglim & Grant, 2016; Schmutte &
Ryff, 1997). Theoretically, the concepts of personality and well-being can be distinguished in
terms of temporal frame-of-reference, implied stability, and degree of attribution to the person
versus the situation. Whereas personality is defined as relatively stable and originating more from
the person, well-being captures the experience and appraisal of life at a given moment.
Nonetheless, it is unsurprising that an individual's general approach to acting in and experiencing
the world (i.e., their personality) predicts his or her momentary emotional experiences and
evaluations of life. Importantly, the correlations between personality and well-being index the
extent and nature of this relationship. So, for example, to remove negative affect from neuroticism,
or positive affect from extraversion is to fundamentally change the nature of these personality
traits. However, many important research questions remain regarding the causal processes that
relate personality and well-being. Facet-level analysis provides some perspective about which
aspects of a given trait are more or less important in predicting different dimensions of well-being.
Nonetheless, the literature would benefit from more experimental and experience sampling
research exploring these questions (e.g., Jacques-Hamilton et al., 2019).
Conclusion
The current research re-affirms that personality is critical to the experience of well-being.
This is consistent with set-point theories of well-being (Cummins, 2015; Headey & Wearing, 1989;
Headey & Wearing, 1992), and the idea that well-being is relatively stable despite short-term
fluctuations in response to many transient events. However, it is also important to remember that
personality traits are not ‘set like plaster’, but malleable, with a wealth of evidence that traits
change across the lifespan (Ashton & Lee, 2016; McCrae et al., 1999; Soto, John, Gosling, &
Potter, 2011), after specific experiences (e.g., Zimmermann & Neyer, 2013) or interventions (e.g.,
Roberts et al., 2017), and even according to one's trait change-goals (e.g., Hudson & Fraley, 2015).
It would therefore be inappropriate to interpret the strong relation between personality and well-
being as indicative of the immutability of human happiness. Rather, efforts to improve well-being
might target the most critical aspects of one's habitual or characteristic patterns of behavior and
PERSONALITY AND WELL-BEING
24
experience, as reflected in basic personality traits.
In summary, we have provided the most comprehensive assessment yet of the relations
between personality traits and dimensions of well-being. Our study expands the mapping of
personality to well-being by encompassing both the Big Five and the increasingly popular
HEXACO model of personality, and also both Diener’s SWB perspective as well as Ryff's PWB
perspective on well-being. Moreover, our analyses span domain-level traits and narrower aspects
and facets within the personality trait hierarchy, while contributing more broadly to methods for
synthesizing facet-level research. Taken together, the findings reported here expand and enrich our
understanding of the role that personality traits play in pathways to the good life.
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PERSONALITY AND WELL-BEING
38
Table 1
Components and Sample Items for Personality, SWB, and PWB
Construct
Components / Sample items
Big Five
Neuroticism
Facets: Anxiety, Hostility, Depression, Self-consciousness, Impulsiveness,
Vulnerability to Stress
Aspects: Withdrawal, Volatility
Extraversion
Facets: Warmth, Gregariousness, Assertiveness, Activity, Excitement Seeking,
Positive Emotion
Aspects: Enthusiasm, Assertiveness
Openness
Facets: Fantasy, Aesthetics, Feelings, Actions, Ideas, Values
Aspects: Openness/Creativity, Intellect
Agreeableness
Facets: Trust, Straightforwardness, Altruism, Compliance, Modesty,
Tendermindedness
Aspects: Politeness, Compassion
Conscientiousness
Facets: Competence, Order, Dutifulness, Achievement Striving, Self-Discipline,
Deliberation
Aspects: Orderliness, Industriousness
HEXACO
Honesty-humility
Sincerity, Fairness, Geed Avoidance, Modesty
Emotionality
Fearfulness, Anxiety, Dependence, Sentimentality
Extraversion
Social Self-Esteem, Social Boldness, Sociability, Liveliness
Agreeableness
Forgiveness, Gentleness, Flexibility, Patience
Conscientiousness
Organization, Diligence, Perfectionism, Prudence
Openness
Aesthetic Appreciation, Inquisitiveness, Creativity, Unconventionality
Interstitial Traits
Altruism
SWB
Satisfaction with life
e.g., "In most ways my life is close to my ideal", "I am satisfied with my life"
Positive Affect
Frequency of experiencing positive emotions in the last few weeks/months/etc.: e.g.,
"interested", "excited", "strong",
"enthusiastic"
Negative Affect
Frequency of experiencing negative emotions in the last few weeks/months/etc.: e.g.,
"depressed", "upset", "guilty",
"scared"
PWB
Positive relations
e.g., "Most people see me as loving and affectionate"; "I enjoy personal and mutual
conversations with family members or friends"
Autonomy
e.g., "Sometimes I change the way I act or think to be more like those around me";
"My decisions are not usually influenced by what everyone else is doing"
Environmental mastery
e.g., "In general, I feel I am in charge of the situation in which I live"; "The demands
of everyday life often get me down"
Personal growth
e.g., "I am not interested in activities that will expand my horizons"; "In general, I feel
that I continue to learn more about myself as time goes by"
Purpose in life
e.g., "I feel good when I think of what I've done in the past and what I hope to do in
the future"; "I live life one day at a time and don't really think about the future"
Self-acceptance
e.g., "When I look at the story of my life, I am pleased with how things have turned
out"; "I feel like many of the people I know have gotten more out of life than I have"
Note. Sample items are from Satisfaction with Life Scale (Diener et al., 1985), PANAS (Watson
et al., 1988), and Ryff's measure of PWB (Ryff & Keyes, 1995).
PERSONALITY AND WELL-BEING
39
Table 2
Summary of Studies Included in Meta-Analysis
Study
N
Framewor
k
Ite
m
s
S
W
L
P
A
N
A
P
W
B
F
A
ge
Cou
ntry
Co
re
Sou
rce
Aghababaei & Arji (2014) Big 5 Study 3
215
IPIP
10
D
W
61
22
IR
C
FA
Aghababaei & Arji (2014) HEXACO
Study 3
215
HEXACO
10
D
W
61
22
IR
C
FA
Aghababaei et al. (2016) Sample 1
422
HEXACO
10
D
70
23
IR
C
FA
Aghababaei et al. (2016) Sample 2
221
HEXACO
10
D
77
22
PL
C
FA
Aghababaei et al. (2016) Sample 3
255
HEXACO
10
D
76
24
MY
C
FA
Aghababaei et al. (2016) Sample 4
251
HEXACO
10
D
W
68
22
IR
C
FA
Aghababaei et al. (2016) Sample 5
226
HEXACO
10
D
W
91
20
PL
C
FA
Ahadi & Puente-Diaz (2011) Study 1
107
NEO
36
D
P
P
50
20
US
C
FA
Ahadi & Puente-Diaz (2011) Study 2
88
NEO
36
D
P
P
62
21
US
C
FA
Albrecht et al. (2014)
913
NEO
48
D
32
37
C
FA
Albuquerque et al. (2012)
398
NEO
48
D
P
P
72
41
PT
C
FA
Alfonsi et al. (2011)
341
NEO
12
P
53
59
CA
C
FA
Anand et al. (2015)
756
NEO
12
D
58
39
US
C
FA
Anglim & Grant (2016)
337
NEO
60
D
P
P
W
76
21
AU
C
FA
Anglim & Horwood (2019) Big 5
465
NEO
60
D
P
P
W
79
25
AU
C
FA
Anglim & Horwood (2019) HEXACO
465
HEXACO
32
D
P
P
W
79
25
AU
C
FA
Anwar (2017)
274
BFI
9
P
P
22
47
PK
C
FA
Austin et al. (2010)
475
Adjectives
8
D
P
P
70
21
CA
C
FA
Aykac et al. (2011)
131
HEXACO
32
D
51
32
GB
C
FA
Baltes et al. (2010)
289
IPIP
10
P
61
38
US
C
FA
Barr (2018)
142
BFI
9
P
P
98
AU
C
FA
Baselmans et al. (2019)
8622
NEO
12
D
36
42
NL
C
FA
Baudin et al. (2011)
313
NEO
48
D
26
23
FR
C
FA
Bauer & McAdams (2010)
145
BFI
9
D
P
P
74
20
US
C
CA
Beer et al. (2013)
395
BFI
9
P
P
50
32
US
C
DA
Belsky et al. (1995) Fathers
69
NEO
36
P
P
0
31
US
C
FA
Belsky et al. (1995) Mothers
69
NEO
36
P
P
100
28
US
C
FA
Benet-Martínez & Karakitapoğlu-Aygün
(2003) Asian
199
BFI
9
D
59
20
US
C
FA
Benet-Martínez & Karakitapoğlu-Aygün
(2003) European
122
BFI
9
D
59
20
US
C
FA
Benotsch et al. (2000)
198
BFI
9
P
P
52
54
US
C
CA
Bianchi et al. (2018) Men
222
NEO
12
D
0
43
FR
C
FA
Bianchi et al. (2018) Women
941
NEO
12
D
100
43
FR
C
FA
Biderman et al. (2018) Big 5
1195
NEO
12
P
P
76
20
US
C
FA
Biderman et al. (2018) HEXACO
1195
HEXACO
16
P
P
76
20
US
C
FA
Blatný et al. (2015)
138
NEO
12
D
61
40
CZ
C
FA
Bogin (2018)
283
Adjectives
8
D
67
18
US
C
FA
Boland & Cappeliez (1997)
113
NEO
36
D
100
73
CA
C
FA
Bono (2011)
228
NEO
12
D
US
C
FA
Boudreau et al. (2001) Americans
1885
NEO
12
D
10
47
US
C
FA
Boudreau et al. (2001) Europeans
1871
NEO
12
D
6
42
C
FA
Brajša-Žganec et al. (2011)
392
IPIP
10
D
P
P
50
20
HR
C
FA
Bratko & Sabol (2006)
1166
IPIP
10
D
66
26
HR
C
FA
Brenner et al. (2011) Community
29
NEO
12
D
29
28
CA
C
FA
Brenner et al. (2011) Schizophrenia
30
NEO
12
D
30
20
CA
C
FA
Burles et al. (2014)
179
NEO
60
P
P
75
20
CA
C
CA
Burton et al. (2015) Study 1
619
BFAS
20
D
55
32
US
C
FA
Burton et al. (2015) Study 2
700
BFAS
20
D
52
33
US
C
FA
Bye & Pushkar (2009)
385
NEO
12
P
P
52
60
CA
C
FA
Cabrera-Darias & Marrero-Quevedo
(2015) Online
108
NEO
48
D
P
P
71
36
ES
C
FA
PERSONALITY AND WELL-BEING
40
Cabrera-Darias & Marrero-Quevedo
(2015) Paper
45
NEO
48
D
P
P
71
36
ES
C
FA
Caprara et al. (2002) Females
300
Other
12
D
100
17
IT
C
FA
Caprara et al. (2002) Males
292
Other
12
D
0
17
IT
C
FA
Caprara et al. (2012) Study 3
3589
Other
12
D
58
39
IT
C
FA
Caprara et al. (2012) Study 5 Italy
689
Other
12
D
56
19
IT
C
FA
Caprara et al. (2012) Study 5 Japan
281
Other
12
D
60
20
JP
C
FA
Caprara et al. (2012) Study 5 Spain
302
Other
12
D
64
28
ES
C
FA
Carmona-Halty & Rojas-Paz (2014)
235
Other
19
D
34
21
CL
C
FA
Carrillo et al. (2012)
356
BFI
9
D
24
24
ES
C
FA
Castro Solano & Cosentino (2018)
302
BFI
9
D
52
39
AR
C
CA
Cellini et al. (2017)
498
BFI
9
P
P
71
27
IT
C
FA
Chambers (2004)
238
NEO
12
D
P
P
0
30
C
FA
Chan et al. (2018)
349
BFI
9
D
P
P
55
62
C
CA
Chen & Carey (2009)
113
NEO
12
D
54
20
HK
C
FA
Chen (2011)
107
NEO
48
D
63
35
US
C
FA
Chen et al. (2012)
383
NEO
48
D
P
P
58
19
US
C
FA
Chen (2015)
371
NEO
12
D
P
P
75
21
CN
C
FA
Choi & Lee (2014)
373
IPIP
10
D
23
33
KR
C
FA
Clark et al. (2010)
322
IPIP
10
P
P
73
24
US
C
FA
Clifton et al. (2019) Study 2
562
BFI
9
D
O
O
51
37
US
C
CA
Compton et al. (1996)
338
NEO
36
D
39
26
US
C
FA
Costa & MacCrae (1992)
364
NEO
48
O
O
C
FA
Cotter & Fouad (2011)
172
NEO
12
D
67
21
US
C
FA
Courneya et al. (2000)
56
NEO
12
D
O
O
41
60
CA
C
FA
Cowan (2019)
159
NEO
12
D
64
56
US
C
FA
Crouch (2016)
562
NEO
12
D
41
21
US
C
FA
Crowe et al. (2016)
914
IPIP
12
D
P
P
62
34
US
C
CA
de Frias et al. (2003)
528
NEO
36
O
O
67
68
CA
C
FA
De Gucht et al. (2004)
377
NEO
12
P
P
73
44
C
FA
Delfabbro et al. (2011)
2266
NEO
12
O
60
15
AU
C
CA
Di Fabio & Saklofske (2014)
164
Other
12
D
56
18
IT
C
FA
Di Fabio & Palazzeschi (2015)
168
Other
12
D
P
63
20
IT
C
FA
Di Fabio et al. (2017)
258
Other
12
D
41
46
IT
C
FA
Di Fabio & Kenny (2018)
241
Other
12
D
P
P
63
24
IT
C
FA
Di Nuovo (2009)
1080
Other
12
D
50
IT
C
FA
Dimotakis et al. (2012)
112
NEO
48
P
39
21
US
C
FA
Donofrio (2005)
138
NEO
48
D
75
33
US
C
FA
Drezno et al. (2019)
379
IPIP
10
D
34
36
PL
C
FA
Drobnjaković et al. (2017) Study 1
400
HEXACO
16
P
P
74
RS
C
DA
Drobnjaković (2019)
377
HEXACO
10
P
P
49
33
RS
C
DA
Dumitrache et al. (2015)
400
NEO
12
D
W
62
75
ES
C
CA
Egan et al. (2014)
860
IPIP
10
D
69
30
I
C
CA
Etxeberria et al. (2019) 65 to 84
155
NEO
12
D
P
P
58
74
ES
C
FA
Etxeberria et al. (2019) 85 to 104
102
NEO
12
D
P
P
61
94
ES
C
FA
Fagley (2012)
243
BFI
9
D
63
23
US
C
CA
Fagley (2018)
236
BFI
9
P
P
64
19
US
C
FA
FitzMedrud (2009)
119
NEO
12
D
P
P
82
35
US
C
FA
Fortunato (2002)
206
Adjectives
8
D
34
50
US
C
FA
Fossum & Barrett (2000) Sample 1
205
NEO
48
P
P
71
US
C
FA
Fossum & Barrett (2000) Sample 2
241
NEO
48
P
P
65
US
C
FA
Fowler et al. (2018)
448
BFI
9
D
75
29
CA
C
FA
Fox & Moore (2019)
142
NEO
12
P
P
70
21
I
C
CA
Froehlich (2005)
350
NEO
12
D
0
US
C
FA
Furr & Funder (1998)
146
NEO
36
D
56
US
C
FA
Galea (2014)
121
BFI
9
D
65
MT
C
FA
Ganginis Del Pino (2012)
305
BFI
9
D
100
38
US
C
FA
Gannon & Ranzijn (2005)
191
NEO
12
D
67
36
AU
C
FA
Garcia & Erlandsson (2011)
151
NEO
48
D
67
23
SE
C
FA
Garcia (2011)
98
NEO
48
D
P
P
68
17
SE
C
FA
Goldberg et al. (2017)
156
BFI
9
P
P
W
62
19
US
C
DA
Golden (2002)
321
Adjectives
16
D
19
51
US
C
FA
PERSONALITY AND WELL-BEING
41
Gore et al. (2014) Study 2
260
IPIP
10
D
71
US
C
FA
Grady (1996)
140
NEO
48
P
P
100
39
CA
C
FA
Graham (2012) Entrepreneurs
88
NEO
12
D
25
US
C
FA
Graham (2012) Students
102
NEO
12
D
54
17
US
C
FA
Grant et al. (2009)
211
NEO
12
D
P
P
W
58
36
AU
C
FA
Guilera et al. (2018)
364
BFI
9
D
60
38
ES
C
AD
Gutiérrez et al. (2005)
236
NEO
12
O
O
86
35
ES
C
FA
Habarth (2009)
576
Adjectives
8
D
55
45
US
C
FA
Halama & Dědová (2007)
148
NEO
12
D
51
17
SK
C
FA
Halama (2010)
451
NEO
12
D
52
20
SK
C
FA
Harris (2002)
147
BFI
9
D
P
P
74
22
US
C
FA
Hart (1999) Wave 1
282
NEO
48
D
10
34
AU
C
FA
Hayes & Joseph (2003)
129
NEO
12
D
58
38
GB
C
FA
Hébert & Weaver (2014)
270
HEXACO
10
D
62
25
I
C
FA
Heller et al. (2002)
159
NEO
12
D
P
P
US
C
FA
Heller (2004)
76
BFI
9
D
P
P
80
US
C
FA
Hemenover (2001)
236
NEO
48
P
P
71
20
US
C
FA
Hengartner et al. (2017)
831
IPIP
48
O
O
66
34
CH
C
FA
Henriett (2018)
421
BFI
9
D
61
24
HU
C
FA
Herringer (1998)
162
NEO
48
D
65
22
US
C
FA
Hill & Allemand (2011)
962
BFI
9
D
O
O
57
52
CH
C
FA
Hirsh et al. (2010)
137
BFI
9
P
P
72
20
CA
C
CA
Hofer et al. (2008)
131
NEO
12
D
W
55
25
DE
C
FA
Hogan (2006)
318
IPIP
10
P
P
85
60
US
C
FA
Holder et al. (2015)
437
NEO
12
D
P
P
69
20
CA
C
CA
Hossack (1997)
520
NEO
12
D
50
CA
C
FA
Howell (2006)
314
BFI
9
D
62
19
US
C
FA
Hudson & Roberts (2014)
264
BFI
9
D
53
19
US
C
FA
Hutz et al. (2014) American
179
NEO
48
D
P
P
63
25
US
C
FA
Hutz et al. (2014) Brazilian
168
Other
25
D
P
P
60
22
BR
C
FA
Ioannidis & Siegling (2015)
203
BFI
9
P
P
71
23
GB
C
FA
Isaacowitz & Smith (2003)
516
NEO
36
P
P
85
DE
C
FA
Işık & Üzbe (2015)
335
Adjectives
8
P
P
57
46
TR
C
FA
Jacques-Hamilton et al. (2019)
223
BFAS
20
D
P
P
68
23
AU
C
AD
Jaksic et al. (2015)
319
IPIP
10
D
58
44
HR
C
CA
James et al. (2012)
150
IPIP
20
D
53
21
AU
C
FA
Jensen et al. (2019)
259
NEO
12
D
44
DK
C
FA
Jibeen (2014)
251
NEO
12
D
39
30
PK
C
FA
Johnson (2003)
140
NEO
48
P
P
US
C
FA
Jokela et al. (2015)
56019
BFI
9
D
63
33
GB
C
FA
Jones et al. (2015)
207
Other
12
W
59
ZA
C
FA
Joshanloo & Afshari (2011)
235
BFI
9
D
74
21
IR
C
FA
Jovanovic (2011)
225
Other
10
D
56
24
RS
C
FA
Jovanović (2014)
380
Other
10
D
P
P
59
22
RS
C
CA
Jovanović (2019)
500
BFI
9
D
68
17
RS
C
FA
Kahlbaugh & Huffman (2017)
49
BFI
9
P
P
65
74
US
C
FA
Kahn & Hessling (2001)
278
NEO
12
P
P
52
20
US
C
FA
Kampfe & Parriaux (2010) Sample 1
467
NEO
12
D
56
26
DE
C
FA
Kampfe & Parriaux (2010) Sample 3
679
NEO
12
D
P
P
69
28
DE
C
FA
Kaynak (2018) Older
61
Other
15
P
P
48
78
TR
C
FA
Kaynak (2018) Younger
64
Other
15
P
P
52
21
TR
C
FA
Kirkland et al. (2015) Sample 1 Students
352
BFAS
20
P
P
61
19
US
C
FA
Kirkland et al. (2015) Sample 2 MTurk
459
BFAS
20
P
P
62
33
US
C
FA
Kirkland et al. (2015) Sample 3 MTurk
178
BFAS
20
P
P
58
34
US
C
FA
Kjell et al. (2013) Iranian
122
BFI
9
D
P
P
W
59
15
IR
C
FA
Kjell et al. (2013) Swedish
109
BFI
9
D
P
P
W
65
17
SE
C
FA
Kluemper (2008)
180
NEO
12
D
42
27
US
C
FA
Kokinda (2011)
108
Adjectives
8
D
73
38
US
C
FA
Kong et al. (2015)
274
NEO
24
D
54
CN
C
CA
Kong et al. (2019)
136
NEO
12
D
40
CN
C
CA
Kovacs (2007)
450
NEO
12
D
57
22
US
C
FA
Koydemir & Schütz (2012) German
101
BFI
9
D
P
P
68
24
DE
C
FA
PERSONALITY AND WELL-BEING
42
Koydemir & Schütz (2012) Turkey
86
BFI
9
D
P
P
55
22
TR
C
FA
Krick & Felfe (2019)
259
NEO
12
P
P
21
26
DE
C
CA
Kwan et al. (1997) American
184
NEO
12
O
71
22
US
C
FA
Kwan et al. (1997) Hong Kong
194
NEO
12
O
55
22
HK
C
FA
Lang et al. (2001)
480
BFI
9
P
P
56
DE
C
FA
Langvik et al. (2016)
372
NEO
12
P
P
76
22
NO
C
FA
Lee et al. (2013)
1584
BFI
9
P
0
26
CA
C
FA
Letrzing (2019)
206
BFI
9
D
P
P
W
68
39
US
C
DA
Letzring (2015)
152
IPIP
10
D
P
P
64
25
US
C
DA
Lightsey et al. (2013)
199
BFI
9
P
P
69
24
US
C
FA
Lodewyk (2018)
300
HEXACO
16
P
51
CA
C
FA
Lönnqvist & große Deters (2016) Study 1
153
BFI
9
D
P
P
61
20
US
C
FA
Lönnqvist & große Deters (2016) Study 2
187
BFI
9
D
79
24
DE
C
FA
Lopez et al. (2015)
1643
NEO
12
P
P
55
55
NL
C
AD
Lounsbury et al. (1999)
249
NEO
12
O
67
22
US
C
HM
Lucas & Fujita (2000) Study 2
142
NEO
36
P
73
US
C
FA
Lucas & Fujita (2000) Study 3
212
NEO
12
P
62
US
C
FA
Lucas & Fujita (2000) Study 5
221
NEO
36
P
61
US
C
FA
MacCann et al. (2012)
354
IPIP
24
O
52
16
US
C
FA
MacInnis et al. (2013)
245
HEXACO
10
O
P
P
88
20
CA
C
FA
Mangino (2018)
220
IPIP
20
D
56
US
C
FA
Marcionetti & Rossier (2016)
437
NEO
12
D
47
13
CH
C
FA
Margolis et al. (2018) Study 1
504
BFI
12
D
P
P
W
51
35
C
CA
Margolis et al. (2018) Study 2
303
BFI
12
D
P
P
W
45
32
I
C
CA
Margolis & Lyubomirsky (2019)
129
BFI
12
D
O
O
69
19
US
C
CA
Marrero Quevedo & Carballeira Abella
(2011)
554
NEO
48
D
P
P
64
28
ES
C
FA
Marrero (2019)
1673
NEO
48
D
P
P
W
52
39
ES
C
FA
Marshall et al. (1992) Sample 1
346
NEO
12
P
P
0
20
US
C
FA
Marshall et al. (1992) Sample 2
543
NEO
12
P
P
0
19
US
C
FA
Martin et al. (2013)
969
Other
8
D
48
14
AU
C
FA
McCrae & Costa (1991)
364
NEO
36
O
O
O
47
US
C
FA
McCullough et al. (2002) Study 2
1179
Adjectives
8
D
84
45
I
C
HM
McKay (2017) Big 5
127
IPIP
24
D
P
P
61
22
US
C
FA
McKay (2017) HEXACO
127
HEXACO
10
D
P
P
61
22
US
C
FA
Meléndez et al. (2019)
618
NEO
12
D
P
P
W
64
70
CO
C
FA
Mellor et al. (2003)
45
NEO
12
O
96
45
AU
C
FA
Michel & Clark (2013)
380
IPIP
10
P
P
54
36
US
C
FA
Miciuk, Jankowski, & Oleś (2016)
130
NEO
12
D
62
25
PL
C
FA
Miciuk, Jankowski, Laskowska, et al.
(2016)
200
NEO
12
D
50
23
PL
C
FA
Mongrain et al. (2018)
648
BFI
9
D
67
32
I
C
FA
Morris et al. (2015)
337
NEO
48
D
P
P
66
20
US
C
FA
Morrison (1997)
307
NEO
12
D
12
US
C
FA
Murray (2002)
7133
IPIP
10
D
50
52
AU
C
HM
Musek (2007)
301
BFI
9
D
P
P
40
37
SI
C
FA
Navarro-Prados et al. (2018)
342
NEO
12
D
66
68
ES
C
FA
Neff et al. (2007)
177
NEO
12
D
P
P
71
20
US
C
FA
Ng et al. (2019)
507
IPIP
10
O
O
O
51
43
SG
C
FA
Novak et al. (2017)
117
BFI
9
P
P
43
57
US
C
FA
Novakov & Popovic-Petrovic (2017)
40
BFI
9
P
P
100
55
RS
C
FA
Novoa & Barra (2015)
353
BFI
9
D
53
20
CL
C
FA
O'Rourke (2004)
192
NEO
12
D
100
61
I
C
CA
O'Rourke (2005)
208
NEO
12
D
O
O
54
64
CA
C
FA
Odacı & Cikrikci (2018)
620
BFI
9
D
74
21
TR
C
FA
Oken et al. (2017)
134
NEO
12
P
P
80
60
US
C
CA
Olesen et al. (2015)
1181
NEO
12
D
P
P
59
22
DK
C
FA
Osma et al. (2018)
428
NEO
12
P
P
ES
C
CA
Panaccio & Vandenberghe (2012)
181
BFI
9
P
P
52
36
CA
C
FA
Parker et al. (2008)
523
NEO
12
D
70
22
AU
C
FA
Paulson & Leuty (2015)
270
IPIP
10
P
P
42
33
US
C
FA
Pavani et al. (2017)
78
NEO
60
O
O
62
45
FR
C
FA
PERSONALITY AND WELL-BEING
43
Pazda & Thorstenson (2018)
262
NEO
12
P
P
68
US
C
FA
Petrides et al. (2007)
274
Other
40
D
66
26
GR
C
FA
Kandler et al. (2017)
576
NEO
48
D
58
37
US
C
AD
Plopa et al. (2017)
359
NEO
12
D
81
39
PL
C
FA
Pollock et al. (2016)
149
HEXACO
10
D
P
P
47
34
US
C
FA
Pratt (2006)
305
IPIP
10
P
P
62
36
US
C
FA
Purvis et al. (2011) Sample 1
1858
Adjectives
8
D
P
P
73
29
US
C
FA
Purvis et al. (2011) Sample 2
1065
BFI
9
D
56
41
I
C
FA
Pychyl & Little (1998)
81
NEO
36
D
O
O
56
35
CA
C
FA
Qing-Guo et al. (2011)
818
BFI
9
O
44
34
CN
C
FA
Ramanaiah et al. (1995)
245
NEO
36
D
55
23
US
C
HM
Ro (2011) Study 1
429
BFI
9
D
W
65
25
US
C
FA
Ro (2011) Study 2
181
BFI
9
W
75
41
US
C
FA
Robinson et al. (2006) Study 1
246
IPIP
10
P
P
74
US
C
FA
Robinson et al. (2006) Study 2
68
IPIP
10
P
P
72
US
C
FA
Romero et al. (2002)
324
NEO
48
P
P
36
16
ES
C
FA
Romero et al. (2009)
405
NEO
48
D
P
P
61
32
ES
C
FA
Romero et al. (2012)
583
NEO
48
D
P
P
72
35
ES
C
FA
Romero et al. (2015)
876
HEXACO
16
D
P
P
W
57
41
ES
C
FA
Røysamb et al. (2018)
1516
NEO
48
D
65
57
NO
C
FA
Ryan & Frederick (1997) Study 3
102
NEO
36
P
P
59
21
US
C
FA
Rzeszutek et al. (2018)
530
NEO
12
D
P
P
16
40
PL
C
FA
Sadiković et al. (2018) Dizygotic
122
NEO
48
D
63
25
RS
C
FA
Sadiković et al. (2018) Monozygotic
242
NEO
48
D
76
25
RS
C
FA
Saeed Abbasi et al. (2018)
819
BFI
9
P
62
27
US
C
FA
Saklofske et al. (2012)
216
Adjectives
8
D
P
P
78
20
GB
C
FA
Salter et al. (2013) Control
36
NEO
48
P
P
US
C
FA
Salter et al. (2013) Spinal Cord Injury
36
NEO
48
P
P
US
C
FA
Schimmack et al. (2004) Study 1
136
NEO
48
D
74
20
US
C
FA
Schimmack et al. (2004) Study 2
124
NEO
60
D
71
21
US
C
FA
Schimmack et al. (2004) Study 3
143
NEO
48
D
US
C
FA
Schimmack et al. (2004) Study 4
344
BFI
9
D
74
CA
C
FA
Schmutte & Ryff (1997) Sample 1
215
NEO
12
O
O
W
53
54
US
C
FA
Schmutte & Ryff (1997) Sample 2
139
NEO
12
W
47
US
C
FA
Schneider et al. (2012)
152
IPIP
10
P
P
72
20
US
C
FA
Schwartz et al. (2018)
541
NEO
12
W
76
44
US
C
CA
Selnes et al. (2004)
131
NEO
12
D
O
O
W
52
44
NO
C
FA
Sheu et al. (2016)
849
Adjectives
10
D
58
20
US
C
FA
Sheu et al. (2017)
757
Adjectives
10
D
70
21
CN
C
FA
Shi et al. (2019) Study 2
208
IPIP
10
D
54
20
CN
C
FA
Shulman & Hemenover (2006)
112
NEO
12
W
47
19
US
C
FA
Sibley (2011) Study 3
148
HEXACO
10
O
64
20
NZ
C
FA
Şimşek (2011) Study 4
106
BFI
9
D
P
P
45
22
TR
C
FA
Şimşek & Koydemir (2013)
721
BFI
9
D
P
P
66
29
TR
C
CA
Şimşek & Kocayörük (2013) Study 4
SWB
99
BFI
9
D
P
P
54
19
TR
C
FA
Singh & Shejwal (2017) Females
98
NEO
12
P
P
100
18
IN
C
CA
Singh & Shejwal (2017) Males
102
NEO
12
P
P
0
18
IN
C
CA
Sirianni Molnar (2011) Ill
773
Adjectives
8
D
P
P
93
49
US
C
FA
Sirianni Molnar (2011) Student
538
Adjectives
8
D
P
P
78
22
US
C
FA
Skomorovsky & Sudom (2011)
200
Other
15
D
19
CA
C
FA
Sliter et al. (2015)
708
IPIP
10
P
P
72
21
US
C
FA
Sobocko & Zelenski (2015) Study 1
154
BFI
9
D
P
P
68
22
CA
C
CA
Sobocko & Zelenski (2015) Study 2
118
BFI
9
P
P
63
20
CA
C
CA
Sorondo (2017) Public Services
25
BFI
9
P
P
62
45
US
C
FA
Sorondo (2017) Technical Services
21
BFI
9
P
P
62
45
US
C
FA
Soto & John (2017) Study 3
179
BFI
12
W
US
C
FA
Soubelet & Salthouse (2011)
1175
IPIP
10
D
P
P
63
C
FA
Spörrle et al. (2010)
200
NEO
12
D
50
28
DE
C
FA
Stamatopoulou et al. (2016)
602
Other
15
D
62
34
GR
C
FA
Stanton et al. (2016) Big 5
293
NEO
48
D
71
46
US
C
CA
Stanton et al. (2016) HEXACO
293
HEXACO
16
D
71
46
US
C
CA
PERSONALITY AND WELL-BEING
44
Stanton et al. (2017) Students
381
BFI
9
D
P
P
67
19
US
C
CA
Steca et al. (2005) Females
549
Other
12
D
100
43
IT
C
FA
Steca et al. (2005) Males
601
Other
12
D
0
45
IT
C
FA
Stimson (2010)
89
BFI
9
D
79
18
US
C
FA
Stolarski (2016)
265
NEO
12
D
54
23
PL
C
FA
Suh et al. (1996)
115
NEO
24
D
O
O
63
22
US
C
FA
Sulaiman et al. (2013)
315
NEO
12
D
P
P
41
19
MY
C
FA
Suldo et al. (2015)
624
Other
23
O
63
16
US
C
FA
Sun et al. (2017)
205
BFAS
20
P
48
35
US
C
FA
Sun et al. (2018)
706
BFAS
20
D
O
O
W
54
36
US
C
FA
Szcześniak et al. (2019)
213
NEO
12
D
72
32
PL
C
FA
Tan et al. (2017)
330
NEO
12
D
100
69
AU
C
FA
Tanksale (2015)
183
NEO
12
D
P
P
51
35
IN
C
FA
Teachman et al. (2007)
325
IPIP
10
P
P
64
US
C
CA
Terracciano (2003)
575
NEO
48
P
P
63
28
IT
C
FA
Tett et al. (2005)
152
Adjectives
8
D
P
P
66
22
US
C
FA
Thingujam (2011)
300
NEO
12
D
P
P
49
23
IN
C
FA
Thomas (2011)
176
IPIP
10
P
P
54
31
US
C
FA
Thoresen (2000)
440
NEO
12
D
P
P
39
40
US
C
FA
Thorpe (2015)
197
BFI
9
O
58
34
US
C
FA
Tov (2012) Study 1
206
IPIP
10
O
O
O
59
22
SG
C
FA
Tov (2012) Study 2
139
IPIP
10
D
O
O
66
21
SG
C
FA
Trankle & Haw (2009)
157
BFI
9
P
P
83
22
AU
C
FA
Tuce & Fako (2014) Boys
225
Other
10
O
0
18
BA
C
FA
Tuce & Fako (2014) Girls
200
Other
10
O
100
18
BA
C
FA
van Allen & Zelenski (2018)
221
IPIP
24
D
P
P
W
75
22
CA
C
DA
Vilhena et al. (2014)
729
NEO
48
O
71
42
PT
C
FA
Villieux et al. (2016)
403
BFI
9
D
P
P
86
23
FR
C
FA
Vittersø (2001)
264
Other
12
D
O
O
19
NO
C
FA
Vorkapić & Lončarić (2013)
290
BFI
9
D
99
37
HR
C
FA
Wahl et al. (2012) Hearing Impaired
116
NEO
12
P
P
42
83
DE
C
FA
Wahl et al. (2012) Sensory Unimpaired
150
NEO
12
P
P
49
82
DE
C
FA
Wahl et al. (2012) Visually Impaired
121
NEO
12
P
P
59
83
DE
C
FA
Watson & Clark (1992) Sample 1
532
Adjectives
16
P
P
US
C
FA
Watson & Clark (1992) Sample 2
236
Adjectives
16
P
P
US
C
FA
Watson & Clark (1992) Sample 3
224
NEO
36
P
P
US
C
FA
Watson & Clark (1992) Sample 4
325
NEO
12
P
P
US
C
FA
Watson et al. (2000) Dating females
136
NEO
12
D
100
US
C
HM
Watson et al. (2000) Dating males
136
NEO
12
D
0
US
C
HM
Watson et al. (2000) Friends
558
BFI
9
D
P
P
US
C
CA
Watson et al. (2002) Study 2
287
BFI
9
P
P
51
US
C
FA
Watson et al. (2002) Study 3
346
NEO
48
P
P
61
US
C
FA
Watson et al. (2004)
576
BFI
9
P
P
50
28
US
C
CA
Watson et al. (2007) Study 2
370
BFI
9
P
P
67
39
US
C
CA
Watson et al. (2007) Study 3 Patients
329
BFI
9
P
P
68
42
US
C
CA
Watson et al. (2007) Study 3 Students
306
BFI
9
P
P
63
US
C
CA
Watson et al. (2015) Community
372
BFI
9
P
P
74
37
US
C
CA
Watson et al. (2015) Iowa
554
BFI
9
P
P
67
19
US
C
CA
Watson et al. (2015) Notre Dame
493
BFI
9
P
P
60
19
US
C
CA
Watson et al. (2017)
448
BFI
12
P
P
53
36
US
C
CA
Webb et al. (2013)
65
NEO
48
P
P
49
30
US
C
FA
Weber & Huebner (2015)
344
Other
23
O
55
12
US
C
FA
West (2007)
148
Other
23
O
US
C
FA
White (2011) Dating
262
BFI
9
P
P
63
19
US
C
FA
White (2011) Married
202
BFI
9
P
P
50
39
US
C
FA
Williams & Wiebe (2000)
140
NEO
48
P
55
21
US
C
FA
Williams & Simms (2018)
336
NEO
24
D
68
40
US
C
FA
Wilt et al. (2016) Community
965
BFI
9
D
62
35
US
C
FA
Wilt et al. (2016) University Student
418
BFI
9
D
70
US
C
FA
Shyh Shin et al. (2009) Australian
189
Adjectives
8
D
69
19
AU
C
FA
Shyh Shin et al. (2009) Singaporean
243
Adjectives
8
D
66
18
SG
C
FA
Wong et al. (2015)
401
NEO
12
P
58
44
CN
C
FA
PERSONALITY AND WELL-BEING
45
Wood et al. (2010)
259
BFI
9
D
US
C
FA
Woyciekoski et al. (2014)
274
Other
25
D
P
P
69
27
BR
C
FA
Wu et al. (2019) Husband
587
BFI
9
D
0
42
CN
C
FA
Wu et al. (2019) Wife
587
BFI
9
D
100
41
CN
C
FA
Xu et al. (2017)
2357
Other
8
O
58
16
CN
C
FA
Yeo (2015)
260
IPIP
10
D
W
51
37
ID
C
FA
Yilmaz & Kafadar (2019)
100
Other
9
P
P
59
20
TR
C
DA
Zeidner & Olnick-Shemesh (2010)
203
Other
12
D
58
16
IL
C
FA
Zellars et al. (2006)
188
NEO
12
P
P
90
40
US
C
FA
Zhai et al. (2010)
413
BFI
9
O
59
31
CN
C
FA
Zhai et al. (2013)
818
BFI
9
O
56
34
CN
C
FA
Zhang et al. (2010)
139
BFI
9
D
52
25
DE
C
FA
Zhang & Howell (2011)
754
Adjectives
8
D
70
25
US
C
FA
Zhang & Tsingan (2014)
238
BFI
9
P
P
71
19
CN
C
FA
Zhu et al. (2013)
309
BFI
9
D
58
19
US
C
FA
Agbo & Ngwu (2017)
238
TIPI
2
O
O
48
22
NG
N
FA
Aghababaei & Tabik (2013)
256
IPIP
4
D
49
23
IR
N
FA
Aghababaei (2014)
288
HEXACO
10
O
64
21
IR
N
FA
Aghababaei & Arji (2014) Big 5 Study 1
183
IPIP
10
O
68
21
IR
N
FA
Aghababaei & Arji (2014) HEXACO
Study 1
183
HEXACO
10
O
68
21
IR
N
FA
Aghababaei & Arji (2014) Study 2
109
HEXACO
10
O
59
20
IR
N
FA
Antunes et al. (2017) Sample 1
542
IPIP
4
P
P
56
33
PT
N
FA
Balgiu (2018)
496
BFI
2
D
O
O
W
39
19
RO
N
FA
Blatný et al. (2018)
2229
BFI
2
D
43
42
CZ
N
FA
Brailovskaia & Margraf (2016) Facebook
non-users
155
BFI
2
D
64
25
DE
N
FA
Brailovskaia & Margraf (2016) Facebook
users
790
BFI
2
D
71
23
DE
N
FA
Brailovskaia & Margraf (2018)
633
BFI
2
D
66
22
DE
N
AD
Brailovskaia et al. (2019)
438
BFI
2
D
66
22
DE
N
CA
Carciofo & Song (2019)
767
BFI
2
O
P
P
20
CN
N
CA
Chopik & Lucas (2019) Men
2578
BFI
3
O
0
51
DE
N
FA
Chopik & Lucas (2019) Women
2578
BFI
3
O
100
51
DE
N
FA
Cikrikci (2019)
292
TIPI
2
D
66
20
TR
N
FA
Correa et al. (2010)
959
TIPI
2
O
33
46
US
N
FA
Csarny (1998)
386
NEO
12
O
58
52
US
N
FA
Datu (2014)
210
TIPI
2
D
63
18
PH
N
FA
Datu et al. (2018)
356
TIPI
2
O
O
O
67
14
PH
N
FA
Denovan & Michael (2018)
306
TIPI
2
D
P
P
82
20
GB
N
FA
Deventer et al. (2019)
896
BFI
9
O
29
18
DE
N
FA
Dijkstra & Barelds (2009)
3626
Adjectives
2
D
P
P
100
46
NL
N
FA
Duckworth et al. (2012)
9649
Other
6
D
O
O
58
68
US
N
FA
Eakman & Eklund (2012)
224
TIPI
2
D
54
28
US
N
FA
Ebner et al. (2018) Study 2
322
BFI
4
O
67
30
DE
N
FA
Freund & Baltes (1998)
200
NEO
6
P
51
84
DE
N
FA
Furler et al. (2013) Men
1608
BFI
2
O
0
52
CH
N
FA
Furler et al. (2013) Women
1608
BFI
2
O
100
19
CH
N
FA
Gibson (2007) Study 1
240
TIPI
2
D
73
US
N
DA
Glidden et al. (2006)
295
NEO
12
O
62
43
US
N
DA
Goldstein & Flett (2009)
138
TIPI
2
P
P
70
19
CA
N
FA
Gore et al. (2014) Study 1
2566
Other
5
P
P
70
US
N
FA
Goswami (2014)
893
IPIP
5
O
61
12
GB
N
FA
Grevenstein & Bluemke (2015)
1842
BFI
5
D
86
28
DE
N
FA
Grevenstein et al. (2018)
1033
BFI
3
D
75
42
DE
N
FA
Halama et al. (2010) Hungarian
249
Adjectives
6
D
62
22
HU
N
FA
Halama et al. (2010) Slovak
274
Adjectives
6
D
53
22
SK
N
FA
Hengartner et al. (2016)
1125
BFI
3
P
P
50
30
CH
N
CA
Jennings (2004)
794
Adjectives
7
D
P
P
30
72
US
N
FA
Joshanloo & Nosratabadi (2009)
227
BFI
9
O
W
49
23
IR
N
FA
Kashdan & Steger (2007)
97
Other
5
D
66
20
US
N
FA
Kim et al. (2016) American
174
BFI
9
O
80
19
US
N
CA
PERSONALITY AND WELL-BEING
46
Kim et al. (2016) Hong Kong
97
BFI
9
O
76
20
HK
N
CA
Knöpfli et al. (2016)
2508
BFI
2
D
58
60
CH
N
DA
Lai (2018)
13424
Adjectives
6
O
47
44
AU
N
FA
Augusto Landa et al. (2010)
228
NEO
12
W
84
21
ES
N
FA
Leffel et al. (2018)
499
NEO
3
D
45
US
N
FA
Levinson & Rodebaugh (2011)
323
IPIP
4
P
68
19
US
N
FA
Lönnqvist & Itkonen (2014)
4701
Adjectives
6
D
66
33
FI
N
FA
Losoncz (2007)
10512
Adjectives
6
O
53
44
AU
N
FA
Luhmann et al. (2014)
414
BFI
2
D
P
P
64
35
US
N
FA
Margolis et al. (2018) Study 3
407
BFI
3
O
O
O
62
36
I
N
CA
Martinez-Molina & Arias (2018)
278
IPIP
4
D
P
P
71
22
ES
N
AD
McMahan et al. (2013)
464
TIPI
2
D
P
P
W
65
21
US
N
FA
Montasem et al. (2013)
218
TIPI
2
D
P
P
58
22
GB
N
FA
Morsunbul (2014)
793
Other
6
D
64
18
TR
N
FA
Naukkarinen et al. (2016)
187
TIPI
2
D
FI
N
FA
Ng (2015)
1972
BFI
2
O
55
42
SG
N
FA
Nishimura & Suzuki (2016)
463
Other
5
D
36
19
JP
N
FA
Oishi et al. (2012) African American
33
Other
5
D
O
O
76
US
N
FA
Oishi et al. (2012) Asian American
46
Other
5
D
O
O
76
US
N
FA
Oishi et al. (2012) European American
41
Other
5
D
O
O
76
US
N
FA
Oishi et al. (2018)
1546
BFI
2
O
52
61
JP
N
CA
Pavot et al. (1998) Study 3
66
NEO
12
O
61
79
US
N
FA
Rammstedt et al. (2018)
1338
BFI
6
O
50
43
DE
N
FA
Reich et al. (2019)
223
TIPI
2
D
77
21
US
N
FA
Rigby & Huebner (2005)
211
Other
5
O
51
16
US
N
FA
Robinson et al. (2010) Approaching
Retirement
86
TIPI
2
D
54
61
GB
N
FA
Robinson et al. (2010) In Retirement
279
TIPI
2
D
54
64
GB
N
FA
Rodgers et al. (2018)
244
TIPI
2
D
W
77
25
I
N
CA
Ryan et al. (2017)
716
Other
6
P
P
55
62
US
N
FA
Saeki et al. (2014)
404
BFI
4
O
O
O
43
20
JP
N
FA
Saiz et al. (2011)
655
Other
12
O
ES
N
CA
Schimmack et al. (2008)
1053
BFI
3
O
DE
N
FA
Schoeps et al. (2016) Female
182
BFI
2
D
100
42
ES
N
FA
Schoeps et al. (2016) Male
182
BFI
2
D
0
44
ES
N
FA
Seder & Oishi (2012) Study 1
48
Other
1
D
58
US
N
FA
Seder & Oishi (2012) Study 2
36
Other
1
D
64
US
N
FA
Selvarajan et al. (2016)
1130
Adjectives
7
P
51
50
US
N
FA
Sibley et al. (2011)
21219
IPIP
4
O
59
47
NZ
N
CA
Sodermans & Matthijs (2014)
506
BFI
9
O
49
18
BE
N
FA
Soto & Luhmann (2013) BHPS
13825
BFI
3
O
55
48
GB
N
CA
Tartaglia et al. (2017)
600
Other
1
D
40
22
IT
N
FA
Tian & Zheng (2007)
1151
Other
5
O
48
CN
N
FA
Vollmann et al. (2016)
158
BFI
2
O
68
56
DE
N
FA
Wang et al. (2017)
545
IPIP
4
D
28
20
CN
N
CA
Whisman et al. (2006) Female
416
NEO
12
O
100
68
US
N
FA
Whisman et al. (2006) Male
416
NEO
12
O
0
72
US
N
FA
Wicker (2016)
183
TIPI
2
D
80
US
N
FA
Wigert (2002)
125
NEO
12
O
57
53
US
N
FA
Note. Items indicates the rounded mean number of items per personality factor. SWL indicates
whether life satisfaction was measured using either D = Diener's Satisfaction with Life Scale or
O = other measure. PA and NA indicates whether the positive and negative affect measures were
measured with either P = PANAS or O = other measure. PWB is W when PWB was measured in
the study. A blank cell for SWL, PA, NA, or PWB indicates that the construct was not measured
in the study in a way that met inclusion criteria for this meta-analysis. F indicates the percentage
of females in the sample. Age is the mean age of the sample. Country is the 2-digit ISO country
code, and "I" indicates a multi-country English-speaking Internet sample. Core is coded C =
Core and N = Noncore, where core studies included at least one correlation involving a
PERSONALITY AND WELL-BEING
47
personality scale with at least 8 items per factor and a well-being measure with at least 5 items.
Source indicates the source of the correlations using the following codes: FA = From article, AD
= Accompanying dataset, CA = Correlations provided following contact with the author, DA =
Data was provided following contact with the author, HM = otherwise unpublished correlations
taken from the Heller et al. (2004) Meta-Analysis. Further details about the nature of the sample
in each study are provided in the online repository that accompanies this paper. Samples where
HEXACO and Big Five were measured are treated as two separate studies for reporting
purposes.
PERSONALITY AND WELL-BEING
48
Table 3
Combined Sample Sizes and Number of Studies across Study Features
Combined
Core
Noncore
Category
n
k
n
k
n
k
Total
334567
462
206364
370
128203
92
Personality Items
Extra Short 1 to 3
47941
45
47941
45
Short 4 to 7
75012
30
75012
30
Standard 8 to 15
180646
292
175396
275
5250
17
Long 16 or more
30968
95
30968
95
Measure Type
HEXACO
7146
22
6566
19
580
3
NEO
64398
170
61767
161
2631
9
IPIP
44359
43
20120
35
24239
8
BFAS
3442
8
3442
8
BFI
131342
125
87251
93
44091
32
TIPI
4847
17
4847
17
Adjectives
45290
28
10580
20
34710
8
Other
33743
49
16638
34
17105
15
Year
Pre-2000
7256
30
6604
27
652
3
2000-2004
23903
49
22984
47
919
2
2005-2009
30664
51
12282
39
18382
12
2010-2014
106176
146
42598
112
63578
34
2015-2019
166568
186
121896
145
44672
41
Sample Size
Under 100
2239
36
1689
27
550
9
100-199
16288
111
14329
99
1959
12
200-299
23904
99
19230
80
4674
19
300-499
38454
102
32344
87
6110
15
500-999
47609
70
37520
56
10089
14
1000 or more
206073
44
101252
21
104821
23
Mean Age
Under 18
13722
29
10753
23
2969
6
18 to 29
65597
192
49522
155
16075
37
30 to 59
213033
147
127288
122
85745
25
60 or over
21082
29
4406
18
16676
11
Note. Correlations between a trait and a well-being variable were classified as core if the
personality trait was measured with 8 or more items and the well-being variable was measured
with five or more items. Studies were classified as core if they had one or more core correlation.
PERSONALITY AND WELL-BEING
49
Table 4
Meta-Analytic Correlations of Big Five and HEXACO Personality with SWB and PWB
!!
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Mea
n
NEO
Neuroticism
-.39
-.34
.56
-.43
-.45
-.58
-.34
-.45
-.60
-.46
Extraversion
.32
.44
-.21
.47
.26
.38
.39
.39
.43
.37
Openness
.08
.24
-.05
.20
.24
.11
.44
.21
.16
.19
Agreeableness
.20
.19
-.25
.39
.10
.28
.31
.28
.28
.25
Conscientiousness
.27
.35
-.25
.32
.30
.51
.32
.50
.44
.36
HEXACO
Honesty-Humility
.11
.07
-.15
.20
.19
.20
.21
.18
.14
.16
Emotionality
-.09
-.12
.31
.01
-.36
-.19
-.11
-.03
-.24
-.16
Extraversion
.43
.55
-.39
.57
.39
.52
.45
.41
.61
.48
Agreeableness
.17
.14
-.25
.27
.02
.22
.16
.13
.23
.18
Conscientiousness
.22
.32
-.17
.18
.23
.41
.31
.47
.23
.28
Openness
.10
.15
-.01
.14
.25
.10
.34
.14
.18
.16
Note. SWL = satisfaction with life, PA = positive affect, NA = negative affect, PR = positive
relations, AU = autonomy, EM = environmental mastery, PG = personal growth, PL = purpose in
life, SA = self-acceptance. Absolute correlations above .30 are bolded. Mean is the mean
correlation between the personality trait and well-being variables, where the correlation with
negative affect (NA) is reversed.
PERSONALITY AND WELL-BEING
50
Table 5
Detailed Meta-Analytic results for Big Five Domains and Subjective Well-being
k
n
𝑟
𝜏#
Lower
95% CI
𝑟
Upper
95% CI
𝑟
𝜌
𝜏%
Lower
95% CI
𝜌
Upper
95% CI
𝜌
Satisfaction with Life
Neuroticism
224
158934
-.39
.10
-.41
-.38
-.46
.13
-.48
-.44
Extraversion
219
158905
.32
.08
.31
.33
.38
.11
.36
.39
Openness
194
146668
.08
.08
.07
.10
.10
.11
.08
.12
Agreeableness
188
145623
.20
.07
.19
.21
.24
.10
.23
.26
Conscientiousness
196
149681
.27
.07
.26
.28
.31
.09
.30
.33
Positive Affect
Neuroticism
167
54816
-.34
.11
-.36
-.32
-.39
.13
-.41
-.36
Extraversion
157
51731
.44
.10
.42
.46
.51
.13
.49
.53
Openness
123
41406
.24
.13
.21
.26
.28
.15
.25
.31
Agreeableness
122
40714
.19
.13
.16
.21
.22
.16
.19
.25
Conscientiousness
128
43497
.35
.10
.33
.37
.40
.12
.38
.43
Negative Affect
Neuroticism
172
55495
.56
.11
.55
.58
.65
.13
.63
.67
Extraversion
152
49212
-.21
.10
-.22
-.19
-.24
.12
-.26
-.22
Openness
121
39538
-.05
.08
-.07
-.03
-.06
.10
-.08
-.04
Agreeableness
120
39023
-.25
.11
-.28
-.23
-.30
.14
-.33
-.28
Conscientiousness
128
42358
-.25
.11
-.27
-.22
-.29
.14
-.31
-.26
Note. Only core studies using at least 8 items per personality factor and at least 5 items for well-
being were included. k is the number of studies. 𝑟 is mean observed correlation estimated from
random-effects model and inverse-variance weighting. 𝜌 is the equivalent correlation estimated
using correlations corrected for measurement error. 𝜏# and 𝜏% are the estimated standard
deviations of true unadjusted and corrected correlations, respectively.
PERSONALITY AND WELL-BEING
51
Table 6
Detailed Meta-Analytic results for Big Five Domains and Psychological Well-being
k
n
𝑟
𝜏#
Lower
95% CI
𝑟
Upper
95% CI
𝑟
𝜌
𝜏%
Lower
95% CI
𝜌
Upper
95% CI
𝜌
Positive relation with others
Neuroticism
18
6440
-.43
.11
-.49
-.37
-.51
.14
-.57
-.44
Extraversion
19
6840
.47
.12
.41
.53
.56
.15
.49
.63
Openness
17
6233
.20
.09
.15
.25
.24
.12
.17
.30
Agreeableness
17
6233
.39
.09
.34
.44
.47
.12
.41
.53
Conscientiousness
18
6440
.32
.12
.26
.38
.38
.16
.30
.46
Autonomy
Neuroticism
17
6309
-.45
.08
-.50
-.41
-.54
.11
-.60
-.49
Extraversion
17
6309
.26
.10
.20
.32
.31
.13
.25
.38
Openness
16
6102
.24
.09
.18
.29
.29
.13
.23
.36
Agreeableness
16
6102
.10
.11
.04
.16
.13
.14
.05
.20
Conscientiousness
17
6309
.30
.05
.27
.34
.36
.07
.32
.41
Environmental mastery
Neuroticism
16
6160
-.58
.11
-.64
-.52
-.69
.13
-.76
-.63
Extraversion
16
6160
.38
.14
.31
.45
.45
.16
.37
.53
Openness
15
5953
.11
.11
.04
.17
.13
.15
.04
.21
Agreeableness
15
5953
.28
.10
.22
.34
.35
.13
.27
.42
Conscientiousness
16
6160
.51
.10
.45
.56
.61
.11
.55
.67
Personal growth
Neuroticism
16
5920
-.34
.11
-.40
-.28
-.41
.15
-.49
-.33
Extraversion
16
5920
.39
.09
.34
.44
.47
.12
.41
.54
Openness
15
5713
.44
.10
.39
.50
.55
.12
.48
.61
Agreeableness
15
5713
.31
.10
.25
.36
.38
.12
.31
.45
Conscientiousness
16
5920
.32
.06
.28
.36
.40
.08
.35
.44
Purpose in life
Neuroticism
15
5699
-.45
.12
-.51
-.38
-.53
.14
-.61
-.46
Extraversion
15
5699
.39
.10
.33
.45
.47
.13
.40
.54
Openness
14
5492
.21
.09
.15
.26
.25
.13
.18
.33
Agreeableness
14
5492
.28
.06
.24
.32
.35
.09
.29
.40
Conscientiousness
15
5699
.50
.10
.44
.55
.60
.10
.54
.66
Self-acceptance
Neuroticism
14
5488
-.60
.13
-.67
-.53
-.69
.15
-.77
-.61
Extraversion
14
5488
.43
.11
.37
.49
.50
.13
.43
.57
Openness
13
5281
.16
.10
.10
.23
.19
.13
.11
.27
Agreeableness
13
5281
.28
.06
.24
.32
.35
.09
.29
.41
Conscientiousness
14
5488
.44
.05
.40
.47
.51
.08
.46
.56
PERSONALITY AND WELL-BEING
52
Table 7
Detailed Meta-Analytic Results for HEXACO Domains and Subjective Well-being
k
n
𝑟
𝜏#
Lower
95% CI
𝑟
Upper
95% CI
𝑟
𝜌
𝜏%
Lower
95% CI
𝜌
Upper
95% CI
𝜌
Satisfaction with Life
Honesty-Humility
14
4049
.11
.00
.08
.14
.13
.00
.10
.16
Emotionality
14
4049
-.09
.07
-.14
-.04
-.11
.09
-.16
-.05
Extraversion
14
4049
.43
.07
.39
.48
.51
.09
.46
.56
Agreeableness
14
4049
.17
.06
.13
.22
.21
.08
.15
.26
Conscientiousness
14
4049
.22
.00
.19
.25
.27
.02
.24
.30
Openness
14
4049
.10
.12
.03
.17
.11
.14
.03
.19
Positive Affect
Honesty-Humility
8
3834
.07
.05
.02
.13
.09
.06
.03
.14
Emotionality
8
3834
-.12
.05
-.17
-.06
-.15
.09
-.22
-.08
Extraversion
8
3834
.55
.04
.51
.58
.63
.05
.59
.67
Agreeableness
8
3834
.14
.09
.07
.21
.17
.10
.09
.25
Conscientiousness
8
3834
.32
.10
.25
.40
.38
.12
.29
.47
Openness
8
3834
.15
.04
.10
.20
.17
.05
.13
.22
Negative Affect
Honesty-Humility
9
4134
-.15
.05
-.20
-.11
-.18
.06
-.23
-.13
Emotionality
9
4134
.31
.09
.24
.37
.36
.11
.28
.44
Extraversion
9
4134
-.39
.11
-.47
-.32
-.46
.13
-.55
-.37
Agreeableness
9
4134
-.25
.07
-.31
-.19
-.30
.09
-.36
-.23
Conscientiousness
9
4134
-.17
.09
-.24
-.10
-.20
.11
-.28
-.12
Openness
9
4134
-.01
.02
-.04
.03
-.01
.04
-.05
.03
PERSONALITY AND WELL-BEING
53
Table 8
Detailed Meta-Analytic results for HEXACO Domains and Psychological Well-being
k
n
𝑟
𝜏#
Lower
95% CI
𝑟
Upper
95% CI
𝑟
𝜌
𝜏%
Lower
95% CI
𝜌
Upper
95% CI
𝜌
Positive relation with others
Honesty-Humility
5
2033
.20
.00
.16
.24
.24
.00
.20
.28
Emotionality
5
2033
.01
.09
-.08
.09
.00
.12
-.11
.12
Extraversion
5
2033
.57
.04
.52
.61
.68
.00
.66
.70
Agreeableness
5
2033
.27
.04
.21
.32
.33
.06
.26
.40
Conscientiousness
5
2033
.18
.00
.14
.22
.22
.02
.17
.27
Openness
5
2033
.14
.00
.10
.19
.18
.05
.12
.25
Autonomy
Honesty-Humility
5
2033
.19
.05
.13
.25
.24
.06
.17
.31
Emotionality
5
2033
-.36
.00
-.40
-.32
-.45
.00
-.48
-.41
Extraversion
5
2033
.39
.00
.36
.43
.49
.02
.45
.53
Agreeableness
5
2033
.02
.07
-.05
.10
.03
.09
-.06
.12
Conscientiousness
5
2033
.23
.05
.17
.29
.29
.06
.22
.36
Openness
5
2033
.25
.05
.19
.32
.32
.07
.24
.39
Environmental mastery
Honesty-Humility
5
2033
.20
.02
.15
.25
.26
.06
.19
.32
Emotionality
5
2033
-.19
.09
-.28
-.10
-.23
.10
-.33
-.13
Extraversion
5
2033
.52
.08
.44
.61
.64
.09
.56
.72
Agreeableness
5
2033
.22
.07
.14
.30
.27
.09
.18
.37
Conscientiousness
5
2033
.41
.07
.34
.49
.51
.11
.41
.61
Openness
5
2033
.10
.08
.01
.19
.12
.11
.01
.23
Personal growth
Honesty-Humility
5
2033
.21
.07
.13
.29
.27
.10
.17
.37
Emotionality
5
2033
-.11
.00
-.15
-.06
-.14
.05
-.20
-.07
Extraversion
5
2033
.45
.04
.40
.50
.56
.00
.53
.59
Agreeableness
5
2033
.16
.04
.10
.21
.20
.05
.14
.26
Conscientiousness
5
2033
.31
.02
.26
.35
.40
.05
.35
.46
Openness
5
2033
.34
.05
.28
.41
.43
.09
.35
.52
Purpose in life
Honesty-Humility
5
2033
.18
.00
.13
.22
.24
.06
.17
.31
Emotionality
5
2033
-.03
.04
-.09
.03
-.03
.05
-.10
.04
Extraversion
5
2033
.41
.08
.33
.49
.52
.06
.46
.59
Agreeableness
5
2033
.13
.07
.05
.21
.17
.09
.08
.27
Conscientiousness
5
2033
.47
.00
.43
.50
.60
.04
.55
.64
Openness
5
2033
.14
.00
.10
.19
.19
.02
.15
.24
Self-acceptance
Honesty-Humility
5
2033
.14
.02
.10
.19
.18
.03
.12
.23
Emotionality
5
2033
-.24
.00
-.29
-.20
-.31
.06
-.37
-.24
Extraversion
5
2033
.61
.03
.57
.64
.74
.03
.71
.78
Agreeableness
5
2033
.23
.06
.17
.30
.29
.07
.21
.37
Conscientiousness
5
2033
.23
.07
.15
.30
.27
.09
.18
.36
Openness
5
2033
.18
.10
.08
.27
.22
.14
.09
.35
PERSONALITY AND WELL-BEING
54
Table 9
Meta-Analytic Correlations between Big Five Personality and Subjective Well-Being by Study
Type, Number of Personality Items, Personality Measure Type, and Comparison with Past Meta-
Analyses
SWL
PA
NA
Personality Items
N
E
O
A
C
N
E
O
A
C
N
E
O
A
C
Mean
SD
Study Status
Core Studies
-.39
.32
.08
.20
.27
-.34
.44
.24
.19
.35
.56
-.21
-.05
-.25
-.25
.28
.13
Noncore Studies
-.32
.24
.09
.18
.21
-.36
.40
.27
.24
.26
.53
-.20
-.08
-.14
-.24
.25
.12
Personality Items
Extra Short 1 to 3
-.31
.22
.08
.15
.20
-.34
.33
.20
.12
.23
.46
-.20
-.05
-.13
-.21
.22
.11
Short 4 to 7
-.32
.27
.14
.19
.23
-.32
.45
.36
.33
.28
.55
-.18
-.10
-.12
-.23
.27
.12
Standard 8 to 15
-.38
.31
.09
.21
.26
-.34
.43
.25
.22
.36
.57
-.20
-.07
-.27
-.26
.28
.13
Long 16 or more
-.42
.33
.06
.18
.29
-.35
.46
.19
.11
.31
.57
-.22
-.01
-.20
-.22
.26
.15
Measure Type
NEO
-.42
.34
.05
.17
.28
-.32
.44
.18
.10
.36
.56
-.20
-.02
-.20
-.21
.26
.15
IPIP
-.38
.28
.09
.19
.25
-.36
.38
.20
.23
.33
.54
-.21
-.05
-.23
-.28
.27
.12
BFAS
-.43
.37
.06
.14
.31
-.41
.57
.27
.24
.42
.65
-.34
-.12
-.24
-.27
.32
.16
BFI
-.34
.27
.09
.20
.23
-.37
.43
.28
.24
.34
.57
-.20
-.06
-.31
-.29
.28
.13
TIPI
-.31
.22
.10
.14
.19
-.32
.38
.27
.09
.19
.39
-.26
-.16
-.01
-.22
.22
.11
Adjectives
-.35
.26
.06
.21
.23
-.29
.46
.33
.23
.33
.57
-.22
-.10
-.19
-.24
.27
.13
Other
-.34
.31
.17
.25
.25
-.34
.46
.31
.26
.27
.58
-.17
-.09
-.15
-.12
.27
.13
Meta-Analyses
Current (core)
-.39
.32
.08
.20
.27
-.34
.44
.24
.19
.35
.56
-.21
-.05
-.25
-.25
.28
.13
DeNeve (1998)
-.24
.17
.14
.16
.22
-.14
.20
.14
.17
.14
.23
-.07
.05
-.13
-.10
.15
.07
Steel (2008)
-.38
.28
.03
.14
.22
-.30
.44
.20
.12
.27
.54
-.18
-.02
-.20
-.20
.23
.14
Heller (2004)
-.48
.28
.08
.29
.31
Note. Current (core) k = 120 to 224, n = 39,023 to 158,934; Heller et al. (2004) k = 19, n =
12,092; Steel et al. (2008) k = 22 to 57, n = 6,040 to 16,764; DeNeve and Cooper (1998) k = 38
to 102, n is a subset of 42,171. Mean and SD is the mean and standard deviation of correlation
after reversing N with PA, N with SWL, and E, O, A, C with NA.
PERSONALITY AND WELL-BEING
55
Table 10
Correlation Among Well-Being Scales for Combined Dataset (Lower Diagonal) and NEO
Dataset (Upper Diagonal)
Variable
1
2
3
4
5
6
7
8
9
SWB
1. Life Satisfaction
.36
-.29
.41
.25
.51
.27
.52
.65
2. Positive Affect
.52
-.09
.31
.23
.40
.32
.37
.36
3. Negative Affect
-.44
-.39
-.32
-.29
-.43
-.21
-.33
-.40
PWB
4. Positive Relations
.49
.53
-.41
.45
.57
.53
.58
.63
5. Autonomy
.16
.26
-.42
.25
.55
.46
.48
.56
6. Environmental Mastery
.58
.60
-.59
.61
.42
.47
.72
.74
7. Personal Growth
.36
.51
-.38
.53
.44
.58
.53
.49
8. Purpose in Life
.55
.60
-.49
.53
.38
.76
.69
.73
9. Self-Acceptance
.74
.63
-.58
.60
.44
.77
.60
.77
Note. N = 903 for Combined Dataset; N = 1,673 for NEO Dataset.
PERSONALITY AND WELL-BEING
56
Table 11
Correlations of NEO Facets with Well-Being Measures in NEO Dataset
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Mean
N1. Anxiety
-.28
-.16
.31
-.21
-.28
-.34
-.06
-.15
-.38
-.23
N2. Angry hostility
-.23
-.14
.35
-.39
-.28
-.39
-.20
-.29
-.39
-.29
N3. Depression
-.48
-.32
.41
-.46
-.41
-.57
-.27
-.49
-.66
-.46
N4. Self-consciousness
-.31
-.27
.26
-.40
-.41
-.43
-.22
-.34
-.50
-.36
N5. Impulsiveness
-.15
-.07
.19
-.05
-.14
-.23
.04
-.15
-.21
-.12
N6. Vulnerability
-.39
-.35
.36
-.36
-.44
-.60
-.28
-.48
-.59
-.44
E1. Warmth
.22
.27
-.13
.59
.24
.32
.35
.31
.32
.33
E2. Gregariousness
.19
.17
-.07
.40
.04
.14
.24
.18
.18
.19
E3. Assertiveness
.23
.28
-.04
.31
.23
.28
.22
.23
.32
.26
E4. Activity
.18
.29
.02
.22
.19
.25
.23
.30
.25
.24
E5. Excitement seeking
.00
.12
.05
.07
-.05
-.06
.25
-.07
-.03
.03
E6. Positive emotions
.34
.31
-.14
.49
.22
.36
.42
.34
.40
.36
O1. Fantasy
-.02
.07
.06
.09
.03
-.05
.30
.01
.00
.05
O2. Aesthetics
.00
.10
.06
.10
.02
-.02
.30
.01
-.03
.06
O3. Feelings
.07
.17
.04
.25
.14
.13
.41
.18
.12
.18
O4. Actions
.08
.13
-.03
.19
.12
.07
.43
.08
.12
.15
O5. Ideas
.01
.19
-.01
.09
.14
.08
.37
.09
.07
.13
O6. Values
.02
.06
-.11
.25
.23
.12
.40
.16
.13
.17
A1. Trust
.22
.16
-.15
.41
.12
.25
.17
.24
.27
.23
A2. Straightforwardness
.02
-.05
-.15
.11
.13
.08
.05
.11
.07
.07
A3. Altruism
.18
.14
-.16
.43
.22
.28
.24
.30
.26
.26
A4. Compliance
.05
-.04
-.15
.11
-.06
.07
-.03
.04
.08
.03
A5. Modesty
-.09
-.13
-.04
.05
.03
-.06
.02
.00
-.09
-.03
A6. Tender-mindedness
.07
.05
-.11
.27
.22
.17
.27
.23
.18
.18
C1. Competence
.37
.33
-.24
.35
.35
.55
.28
.54
.51
.41
C2. Order
.15
.14
-.04
.06
.11
.30
.09
.30
.17
.17
C3. Dutifulness
.17
.16
-.15
.17
.31
.41
.17
.39
.28
.26
C4. Achievement striving
.24
.33
-.02
.18
.24
.39
.23
.46
.31
.30
C5. Self-discipline
.28
.29
-.19
.26
.34
.55
.19
.52
.43
.36
C6. Deliberation
.15
.11
-.14
.04
.09
.24
-.04
.26
.18
.13
Note. N = 1,673. SWL = satisfaction with life, PA = positive affect, NA = negative affect, PR =
positive relations, AU = autonomy, EM = environmental mastery, PG = personal growth, PL =
purpose in life, SA = self-acceptance. Correlations .30 or above are in bold. Correlations equal
to or larger than .05, .07 and .09 are significant at .05, .01, and .001 respectively.
PERSONALITY AND WELL-BEING
57
Table 12
Correlations between IPIP NEO Facets and Well-Being Measures in Combined Dataset
SWL
PA
NA
PR
AU
EM
PG
PL
SA
mean
N1. Anxiety
-.38
-.38
.59
-.33
-.43
-.56
-.31
-.36
-.53
-.43
N2. Angry hostility
-.32
-.35
.54
-.32
-.29
-.45
-.30
-.33
-.43
-.37
N3. Depression
-.65
-.58
.70
-.59
-.45
-.76
-.50
-.69
-.83
-.64
N4. Self-consciousness
-.36
-.43
.49
-.45
-.56
-.56
-.42
-.44
-.55
-.47
N5. Impulsiveness
-.20
-.22
.36
-.13
-.34
-.36
-.14
-.27
-.31
-.26
N6. Vulnerability
-.41
-.43
.62
-.36
-.53
-.65
-.42
-.49
-.57
-.50
E1. Warmth
.42
.50
-.40
.69
.25
.52
.44
.47
.53
.47
E2. Gregariousness
.30
.36
-.24
.46
.07
.33
.25
.24
.33
.29
E3. Assertiveness
.34
.44
-.30
.42
.42
.47
.44
.46
.47
.42
E4. Activity
.28
.41
-.22
.29
.25
.49
.38
.51
.38
.36
E5. Excitement seeking
.14
.23
-.03
.17
.03
.09
.20
.04
.12
.12
E6. Positive emotions
.50
.53
-.37
.59
.23
.48
.49
.47
.55
.47
O1. Fantasy
.00
.11
.08
.09
.06
-.06
.21
.03
.01
.04
O2. Aesthetics
.08
.24
-.06
.23
.15
.11
.42
.22
.16
.19
O3. Feelings
.01
.09
.19
.19
.02
-.04
.35
.20
.05
.08
O4. Actions
.20
.30
-.26
.27
.29
.29
.54
.32
.32
.31
O5. Ideas
.12
.28
-.17
.20
.41
.29
.48
.35
.26
.28
O6. Values
-.04
-.04
.02
.01
.06
-.08
.17
-.04
-.01
.00
A1. Trust
.35
.32
-.37
.54
.10
.40
.34
.37
.42
.36
A2. Straightforwardness
.08
.09
-.25
.22
.15
.22
.21
.27
.17
.18
A3. Altruism
.26
.36
-.25
.52
.15
.34
.47
.43
.34
.35
A4. Compliance
.13
.11
-.21
.19
-.04
.12
.17
.17
.15
.13
A5. Modesty
-.30
-.26
.16
-.22
-.18
-.27
-.17
-.26
-.39
-.25
A6. Tender-mindedness
.10
.15
-.07
.31
.07
.07
.33
.22
.14
.16
C1. Competence
.41
.47
-.48
.42
.52
.66
.56
.68
.60
.53
C2. Order
.10
.15
-.14
.02
.12
.25
.10
.28
.13
.14
C3. Dutifulness
.21
.23
-.34
.27
.30
.40
.34
.43
.32
.32
C4. Achievement striving
.34
.45
-.27
.29
.34
.54
.49
.67
.45
.43
C5. Self-discipline
.34
.42
-.37
.26
.33
.61
.33
.58
.45
.41
C6. Deliberation
.09
.06
-.26
.09
.21
.26
.11
.30
.17
.17
Note. N = 903. SWL = satisfaction with life, PA = positive affect, NA = negative affect, PR =
positive relations, AU = autonomy, EM = environmental mastery, PG = personal growth, PL =
purpose in life, SA = self-acceptance. Correlations .30 or above are in bold. Correlations equal to
or larger than .07, .09 and .11 are significant at .05, .01, and .001 respectively.
PERSONALITY AND WELL-BEING
58
Table 13
Correlations Between HEXACO Facets and Well-Being Measures in HEXACO Dataset
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Mean
H1: Sincerity
.14
.10
-.25
.21
.27
.24
.23
.19
.21
.20
H2: Fairness
.19
.21
-.22
.25
.16
.21
.18
.25
.23
.21
H3: Greed-Avoidance
.08
.04
-.14
.11
.23
.03
.15
.07
.10
.11
H4: Modesty
-.05
.00
-.09
.11
.03
.01
.10
.01
-.06
.03
E1: Fearfulness
-.04
-.16
.19
-.15
-.37
-.27
-.22
-.14
-.17
-.19
E2: Anxiety
-.26
-.22
.47
-.23
-.35
-.43
-.23
-.26
-.40
-.32
E3: Dependence
.09
.05
.25
.17
-.30
-.19
.01
-.08
-.05
-.06
E4: Sentimentality
.13
.17
.11
.25
-.14
.04
.22
.18
.07
.09
X1: Social Self-Esteem
.57
.56
-.55
.62
.37
.70
.50
.62
.75
.58
X2: Social Boldness
.27
.35
-.27
.39
.44
.38
.40
.38
.40
.36
X3: Sociability
.27
.33
-.20
.51
.09
.32
.30
.24
.31
.29
X4: Liveliness
.52
.59
-.46
.60
.29
.66
.50
.58
.64
.54
A1: Forgiveness
.21
.21
-.18
.29
.09
.21
.19
.15
.23
.20
A2: Gentleness
.17
.17
-.15
.18
.06
.10
.13
.07
.13
.13
A3: Flexibility
.14
.14
-.19
.23
-.02
.16
.14
.10
.17
.14
A4: Patience
.22
.27
-.34
.20
.16
.27
.20
.19
.27
.24
C1: Organization
.11
.19
-.12
.07
.16
.33
.14
.31
.18
.18
C2: Diligence
.26
.44
-.29
.24
.36
.52
.44
.62
.41
.40
C3: Perfectionism
-.02
.13
-.03
.02
.16
.15
.20
.27
.10
.12
C4: Prudence
.17
.24
-.35
.15
.27
.34
.17
.33
.27
.25
O1: Aesthetic Appreciation
.09
.20
-.06
.12
.22
.11
.33
.16
.13
.16
O2: Inquisitiveness
.06
.21
-.16
.10
.29
.21
.30
.16
.16
.18
O3: Creativity
.05
.23
-.06
.08
.25
.08
.28
.13
.17
.15
O4: Unconventionality
.00
.14
.05
.02
.22
-.04
.25
.05
.07
.07
I: Altruism
.14
.21
-.06
.28
.00
.12
.32
.25
.18
.17
Note. N = 465; SWL = satisfaction with life, PA = positive affect, NA = negative affect, PR =
positive relations, AU = autonomy, EM = environmental mastery, PG = personal growth, PL =
purpose in life, SA = self-acceptance. Correlations .30 or above are in bold.
PERSONALITY AND WELL-BEING
59
Table 14
Variance Explained by Broad and Narrow Traits across Measures
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Mean
Broad: adjusted R2
NEO
.25
.23
.21
.47
.27
.51
.41
.44
.50
.36
IPIP NEO
.32
.43
.52
.50
.38
.65
.54
.58
.57
.50
HEXACO
.25
.37
.35
.47
.39
.52
.39
.46
.45
.41
Big Five Aspects
.32
.54
.67
.44
.69
.53
.67
.61
.53
.56
Mean
.29
.39
.44
.47
.43
.56
.50
.52
.51
.46
Narrow: adj R2
NEO
.30
.25
.24
.54
.38
.55
.48
.51
.56
.42
IPIP NEO
.47
.48
.58
.59
.52
.71
.62
.70
.74
.60
HEXACO
.38
.44
.44
.51
.44
.63
.45
.58
.61
.50
Big Five Aspects
.39
.59
.69
.52
.73
.55
.72
.65
.55
.60
Mean
.39
.44
.49
.54
.52
.61
.56
.61
.61
.53
Adj R2 Change
NEO
.06
.02
.03
.06
.11
.04
.07
.07
.06
.06
IPIP NEO
.15
.05
.06
.10
.14
.06
.07
.12
.17
.10
HEXACO
.13
.07
.09
.04
.05
.11
.06
.11
.16
.09
Big Five Aspects
.07
.06
.03
.08
.03
.02
.05
.04
.02
.04
Mean
.10
.05
.05
.07
.08
.06
.06
.09
.10
.07
Adj R2 Prop Increase
NEO
.24
.09
.16
.13
.43
.08
.16
.16
.13
.17
IPIP NEO
.47
.12
.11
.19
.37
.09
.14
.21
.30
.22
HEXACO
.51
.19
.26
.09
.13
.20
.14
.24
.36
.24
Big Five Aspects
.21
.11
.04
.18
.05
.03
.07
.06
.03
.09
Mean
.36
.13
.14
.15
.24
.10
.13
.17
.20
.18
Note. SWL = satisfaction with life, PA = positive affect, NA = negative affect, PR = positive
relations, AU = autonomy, EM = environmental mastery, PG = personal growth, PL = purpose in
life, SA = self-acceptance.
PERSONALITY AND WELL-BEING
60
Table 15
Correlations Among HEXACO and IPIP NEO Personality Domains from Combined Dataset
Variable
1
2
3
4
5
6
7
8
9
10
HEXACO
1. Honesty-Humility
2. Emotionality
.06
3. Extraversion
.01
-.21
4. Agreeableness
.37
-.18
.31
5. Conscientiousness
.31
-.11
.21
.22
6. Openness
.13
-.18
.19
.19
.17
IPIP NEO
7. Neuroticism
-.19
.56
-.65
-.46
-.36
-.26
8. Extraversion
-.09
-.08
.83
.17
.11
.13
-.49
9. Agreeableness
.67
.22
.12
.53
.26
.05
-.17
.08
10. Conscientiousness
.32
-.14
.28
.19
.84
.09
-.48
.19
.32
11. Openness
.16
.06
.23
.14
.15
.71
-.19
.30
.20
.14
Note. N = 465; Cross-correlations between personality measures greater than .50 are shown in
bold.
PERSONALITY AND WELL-BEING
61
Table 16
Correlations between HEXACO and IPIP NEO Domains and Well-Being Measures for
Combined Dataset
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Mean
IPIP NEO
Neuroticism
-.45
-.52
.69
-.46
-.55
-.70
-.47
-.56
-.68
-.56
Extraversion
.42
.55
-.30
.63
.30
.53
.53
.49
.52
.47
Openness
.09
.32
-.04
.26
.28
.15
.57
.31
.24
.25
Agreeableness
.15
.20
-.21
.35
.04
.19
.29
.24
.19
.21
Conscientiousness
.27
.39
-.37
.26
.39
.59
.38
.61
.45
.41
HEXACO
Honesty-Humility
.12
.12
-.23
.22
.23
.16
.21
.17
.16
.18
Emotionality
-.03
-.07
.37
.00
-.41
-.31
-.09
-.12
-.20
-.18
Extraversion
.49
.56
-.45
.64
.37
.62
.52
.55
.64
.54
Agreeableness
.24
.26
-.28
.29
.10
.24
.21
.17
.26
.23
Conscientiousness
.17
.33
-.26
.16
.30
.44
.31
.50
.31
.31
Openness
.07
.25
-.08
.11
.31
.12
.37
.17
.17
.18
HEXACO Neuroticism
-.48
-.49
.64
-.48
-.45
-.70
-.44
-.55
-.68
-.55
Note. N = 465; SWL = satisfaction with life, PA = positive affect, NA = negative affect, PR =
positive relations, AU = autonomy, EM = environmental mastery, PG = personal growth, PL =
purpose in life, SA = self-acceptance. Correlations equal to or larger than .10, .12 and .16 are
significant at .05, .01, and .001 respectively. Mean is the mean correlation between the
personality trait and well-being variables, where the correlation with negative affect (NA) is
reversed. Correlations .30 or above in bold.
PERSONALITY AND WELL-BEING
62
Table 17
Adjusted R Squared for Regression Models Predicting Well-Being Measures in Combined
Dataset
Predictors
k
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Mean
HEXACO Domains
6
.25
.37
.35
.47
.39
.52
.39
.46
.45
.41
NEO Domains
5
.26
.43
.49
.50
.36
.63
.55
.55
.53
.48
HEXACO Facets
25
.38
.44
.44
.51
.44
.63
.45
.58
.61
.50
NEO Domains + HEXACO Domains
11
.31
.45
.50
.53
.45
.64
.55
.57
.57
.51
NEO Domains + HEXACO Facets
30
.41
.50
.52
.59
.49
.70
.57
.64
.67
.57
NEO Facets
30
.44
.50
.57
.59
.52
.70
.64
.70
.70
.60
HEXACO Domains + NEO Facets
36
.46
.50
.57
.59
.54
.71
.64
.71
.71
.60
HEXACO Facets + NEO Facets
55
.48
.52
.56
.61
.56
.72
.64
.70
.73
.61
Note. n = 465. NEO = IPIP NEO, SWL = satisfaction with life, PA = positive affect, NA =
negative affect, PR = positive relations, AU = autonomy, EM = environmental mastery, PG =
personal growth, PL = purpose in life, SA = self-acceptance. k is number of predictors. Mean
represents the average variance explained for the predictor set over the 9 well-being measures.
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