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Unique Associations Between Big Five Personality Aspects and Multiple Dimensions of Well‐Being

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Objective: Personality traits are associated with well-being, but the precise correlates vary across well-being dimensions and within each Big Five domain. This study is the first to examine the unique associations between the Big Five aspects (rather than facets) and multiple well-being dimensions. Method: Two samples of U.S. participants (Total N = 706, Mage = 36.17, 54% female) recruited via Amazon's Mechanical Turk completed measures of the Big Five aspects and subjective, psychological, and PERMA well-being. Results: One aspect within each domain was more strongly associated with well-being variables. Enthusiasm and Withdrawal were strongly associated with a broad range of well-being variables, but other aspects of personality also had idiosyncratic associations with distinct forms of positive functioning (e.g., Compassion with positive relationships, Industriousness with accomplishment, and Intellect with personal growth). Conclusions: An aspect-level analysis provides an optimal (i.e., parsimonious yet sufficiently comprehensive) framework for describing the relation between personality traits and multiple ways of thriving in life. This article is protected by copyright. All rights reserved.
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PERSONALITY ASPECTS AND WELL-BEING
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Unique Associations Between Big Five Personality Aspects and Multiple Dimensions of Well-
Being
Jessie Sun1, 2
Scott Barry Kaufman3
Luke D. Smillie1
1Melbourne School of Psychological Sciences, The University of Melbourne, Australia
2 Department of Psychology, University of California, Davis, U.S.
3 The Imagination Institute, Positive Psychology Center, University of Pennsylvania, U.S.
Original submission date: June 16, 2016
Revised submission date: November 23, 2016
Accepted: December 21, 2016
Address all correspondence to jesun@ucdavis.edu
Sun, J., Kaufman, S. B., & Smillie, L. D. (in press). Unique associations between Big Five
personality aspects and multiple dimensions of well-being. Journal of Personality.
PERSONALITY ASPECTS AND WELL-BEING
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Abstract
Objective: Personality traits are associated with well-being, but the precise correlates vary across
well-being dimensions and within each Big Five domain. This study is the first to examine the
unique associations between the Big Five aspects (rather than facets) and multiple well-being
dimensions. Method: Two samples of U.S. participants (Total N = 706, Mage = 36.17, 54%
female) recruited via Amazon’s Mechanical Turk completed measures of the Big Five aspects
and subjective, psychological, and PERMA well-being. Results: One aspect within each domain
was more strongly associated with well-being variables. Enthusiasm and Withdrawal were
strongly associated with a broad range of well-being variables, but other aspects of personality
also had idiosyncratic associations with distinct forms of positive functioning (e.g., Compassion
with positive relationships, Industriousness with accomplishment, and Intellect with personal
growth). Conclusions: An aspect-level analysis provides an optimal (i.e., parsimonious yet
sufficiently comprehensive) framework for describing the relation between personality traits and
multiple ways of thriving in life.
Keywords: personality; aspects; Big Five; subjective well-being; psychological well-being
PERSONALITY ASPECTS AND WELL-BEING
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Unique Associations Between Big Five Personality Aspects and Multiple Dimensions of Well-
Being
[W]hen multiple positive end states are examined, it becomes apparent that aspects of
psychological well-being may be achieved by more people than just the nonneurotic,
extraverted members of society. (Schmutte & Ryff, 1997, p. 558)
The large literature describing the associations between personality traits and well-being
suggests that extraversion (the tendency to be bold, talkative, enthusiastic, and sociable) and
neuroticism (the tendency to be emotionally unstable and prone to negative emotions) are
especially strong predictors of well-being (e.g., Steel, Schmidt, & Shultz, 2008). But is well-
being only accessible to the extraverted and non-neurotic? We propose that more nuanced
insights can be revealed by examining the relation between narrower traits and a broader
spectrum of well-being dimensions. The goal of the current study is to comprehensively describe
the unique associations between personality aspects and dimensions of well-being across three
well-being taxonomies.
Personality Traits and Three Taxonomies of Well-Being
Personality traits and well-being dimensions can each be described at different levels of
resolution. The Big Five domains provide a relatively comprehensive framework for organizing
differential patterns of affect, behavior, and cognition (John, Naumann, & Soto, 2008). These
broad traits can be further broken down into anywhere between 10 (DeYoung, Quilty, &
Peterson, 2007) and 240 (Mõttus, Kandler, Bleidorn, Riemann, & McCrae, 2016) narrower
constituent traits that describe more precise subtleties of personality. Well-being can similarly be
PERSONALITY ASPECTS AND WELL-BEING
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conceptualized at different levels—as a single indicator (e.g., Disabato, Goodman, Kashdan,
Short, & Jarden, 2016), two general “types” of well-being (Keyes, Shmotkin, & Ryff, 2002), or
an array of distinct dimensions (e.g., Ryff, 1989; Seligman, 2011).
Aspects Balance the Goals of Parsimony and Comprehensiveness
The personality–well-being relation could be parsimoniously described in terms of
associations between the Big Five domains and global well-being. Alternatively, facet-level
analyses may provide a more complete description of the associations that highly-specific
personality traits have with well-being constructs (Anglim & Grant, 2016). However, as most
facet models comprise at least 30 facets (Costa & McCrae, 1995; Hofstee, de Raad, & Goldberg,
1992; but see Soto & John, 2016), the potential for more comprehensive description is
accompanied by a dramatic reduction in parsimony. In addition, because the number and content
of facets within different taxonomies have been determined somewhat arbitrarily (DeYoung et
al., 2007), a facet-level approach does not ensure comprehensiveness.
The recently-discovered aspect level of description (DeYoung et al., 2007) offers a
potential balance between the goals of parsimony and comprehensiveness. Integrative papers that
summarize the overlaps between facets across various models suggest that most of the
information within each personality domain can be captured by two to four lower-level traits
(DeYoung et al., 2007; John et al., 2008; Soto & John, 2016). Accordingly, DeYoung and
colleagues (2007) developed a revised hierarchy in which each of the five domains divide into
two distinct aspects (described in Table 1) that represent an intermediate level between facets
and domains. The number of aspects was not determined arbitrarily, but motivated by evidence
from a genetic model showing that two factors underlie the shared variance between facets
within each Big Five domain (Jang, Livesley, Angleitner, Riemann, & Vernon, 2002). The 10
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aspects may therefore offer more comprehensive description than the five domains, while being
dramatically more parsimonious than 30 or more facets.
Studies across a range of areas have demonstrated the validity and utility of an aspect-
level analysis. Such studies reveal the differential relations that aspects within a domain have
with threat processing (Cunningham, Arbuckle, Jahn, Mowrer, & Abduljalil, 2010), political
ideology (Hirsh, DeYoung, Xu, & Peterson, 2013), fairness preferences (Zhao, Ferguson, &
Smillie, 2016), and creative achievement in the arts and sciences (Kaufman et al., 2015). The
aspects may similarly capture the key within-domain divergences in predicting well-being.
PERSONALITY ASPECTS AND WELL-BEING
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Table 1
Description of Big Five Aspects and Constituent Facets and Summary of Unique Associations with Subjective and Psychological Well-
Being
Personality Trait
Description of Aspect
(Example Constituent Facets)
Subjective Well-Being
Extraversion
Positive affect, life
satisfaction3,6,8
mastery, purpose in life, self-
acceptance, personal growth8
Enthusiasm
Friendly, sociable, enjoys rewards
(Friendliness1, Warmth2, Poise1,
Gregariousness2, Positive Emotions2)
Pleasant affect, subjective
happiness (partialling
Assertiveness)4
Facets consistently related to
positive affect, life
satisfaction5-8
mastery, self-acceptance (partialling
Extraversion)8
Assertiveness
Socially dominant, motivated to attain
rewards (Leadership1, Assertiveness1,2,
Provocativeness1)
(partialling Extraversion)8
Neuroticism
(–) Positive affect, negative
affect, (–) life satisfaction3,6,8
environmental mastery, (–) positive
relations, (–) purpose in life8
Withdrawal
Susceptible to depression and anxiety,
easily discouraged and overwhelmed
(Depression2, Vulnerability2, Anxiety2,
Self-Consciousness2)
(–) Subjective happiness
(partialling Volatility)4
Facets are stronger predictors
of (–) positive affect, negative
affect, (–) life satisfaction than
Volatility facet5-7
(partialling Neuroticism)8
Volatility
Susceptible to anger and irritability,
emotionally unstable (Calmness1, Angry
hostility2, Tranquility1, Impulse control1)
acceptance (partialling Neuroticism)8
Conscientiousness
Positive affect3,6,8
personal growth, self-acceptance8
Industriousness
Achievement-oriented, self-disciplined,
efficient (Purposefulness1, Efficiency1,
Facets consistently related to
positive affect5,6,8
(partialling Conscientiousness)8
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Self-discipline2, Competence2)
Orderliness
Preference for tidiness and routine
(Orderliness1, Perfectionism1)
One facet predicted (–) purpose in life
(partialling Conscientiousness)8
Agreeableness
Positive relations, (–) autonomy8
Compassion
Feels and cares about others’ emotions
and well-being (Warmth1, Sympathy1,
Understanding1, Empathy1)
One facet had larger zero-order
correlations with positive relations,
personal growth8
Politeness
Respects others’ needs and wants
(Cooperation1, Compliance2, Morality1,
Straightforwardness2)
One facet predicted (–) autonomy
(partialling Agreeableness)8
Openness/Intellect
Personal growth, autonomy, purpose in
life8
Openness
Needs creative outlets, appreciates
beauty, daydreams (Aesthetics2,
Imagination1, Reflection1, Fantasy2,
Feelings2)
Intellect
Intellectual engagement and ability
(Quickness1, Creativity1, Intellect1,
Ideas2, Ingenuity1, Competence1)
One facet had stronger zero-order
correlations with all dimensions than
two Openness facets8
Note. Example facets are those that DeYoung et al. (2007) found to load more strongly on one aspect than the other. 1Facets from the
Abridged Big Five Circumplex Scales from the International Personality Item Pool, 2Facets from the NEO-PI-R. 3Steel et al. (2008),
4Kirkland, Gruber, & Cunningham (2015), 5Albuquerque, de Lima, Matos, & Figueiredo (2012), 6Quevedo & Abella (2011),
7Schimmack, Oishi, Furr, & Funder (2004), 8Anglim & Grant (2016).
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Three Taxonomies of Well-Being
Compared with the relative consensus surrounding the structure of personality, there is
far less agreement about the structure and content of well-being, as reflected by the number of
theories and models that exist (for a review, see Jayawickreme, Forgeard, & Seligman 2012).
However, there is at least agreement that well-being is a complex, multidimensional construct.
In the current paper, we investigate the unique associations between personality aspects
and well-being dimensions across three well-being taxonomies (summarized in Table 2). The
first two influential models correspond to the theoretical distinction between hedonic and
eudaimonic well-being (Keyes et al., 2002). Hedonic well-being is commonly operationalized
using Diener’s (1984) tripartite model of subjective well-being (SWB): life satisfaction, positive
affect, and (low) negative affect. In contrast, eudaimonic perspectives, with roots in humanistic
and Aristotelian traditions, emphasize human potential and existential concerns (Huta &
Waterman, 2014; Maslow, 1968; Rogers, 1961). Arguing that the narrow focus of SWB on
“happiness” neglects important aspects of positive functioning, Ryff (1989) developed scales of
psychological well-being (PWB) that measure six broader, less affectively-based aspects of well-
being: autonomy, environmental mastery, personal growth, positive relations, self-acceptance,
and purpose in life. Finally, the recently-developed PERMA model (Butler & Kern, 2016;
Seligman, 2011) comprises the five “pillars” of positive emotion, engagement, relationships,
meaning, and accomplishment, thereby incorporating both hedonic and eudaimonic perspectives.
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Table 2
Description of Subjective, Psychological, and PERMA Well-Being Taxonomies
Taxonomy and Dimension
High Levels of Well-Being Involve…
Subjective Well-Being
Positive Emotions
High frequency and intensity of positive moods and emotions
(Low) Negative Emotions
Low frequency and intensity of negative moods and emotions
Life Satisfaction
A positive subjective evaluation of one’s life, using any
information the person considers relevant
Psychological Well-Being
Autonomy
Being independent and able to resist social pressures
Environmental Mastery
Ability to shape environments to suit one’s needs and desires
Personal Growth
Continuing to develop, rather than achieving a fixed state
Positive Relations
Having warm and trusting interpersonal relationships
Self-Acceptance
Positive attitudes toward oneself
Purpose in Life
A clear sense of direction and meaning in one’s efforts
PERMA
Positive Emotions
Pleasant feelings, including contentment and joy
Engagement
Being absorbed, interested, and involved in activities and life
Relationships
Feeling loved, supported, and satisfied with one’s relationships
Meaning
Having a sense of direction and purpose in life, or a connection
to something greater than oneself
Accomplishment
Goal progress and attainment, and feelings of mastery,
efficacy, and competence
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Just as lower-level personality traits offer more comprehensive, precise description, a
single well-being score may obscure meaningful variation across different dimensions of positive
functioning (Butler & Kern, 2016; Kern, Waters, Adler, & White, 2015). Simply distinguishing
between “hedonic” and “eudaimonic” constructs may not offer much more precision, as
eudaimonia is often treated as a catch-all category for any well-being-like construct that seems
different to SWB (Kashdan, Biswas-Diener, & King, 2008), and the hedonia–eudaimonia
dichotomy may not accurately reflect the higher-order factor structure of self-reported well-being
(Disabato et al., 2016). Instead, there is greater scientific precision and practical utility in
assessing specific well-being constructs (Kashdan et al., 2008; Kern et al., 2015).
Personality Traits and Subjective Well-Being
The robust links between personality and SWB were discovered decades ago (Costa &
McCrae, 1980). A recent meta-analysis estimates that the Big Five domains explain 39–63% of
the variance in SWB (Steel et al., 2008). This effect size is larger than that of demographic and
contextual factors such as gender, age, education, and income (see Diener, Suh, Lucas, & Smith,
1999, for a review). At the level of broad traits, extraversion is most strongly and robustly
associated with greater positive affect, neuroticism is linked with both greater negative affect and
slightly lower positive affect, and both independently predict higher and lower levels of life
satisfaction, respectively (Steel et al., 2008). However, aspect- and facet-level studies
(summarized in Table 1) suggest that specific lower-level traits may drive these domain-level
associations: Enthusiasm and Withdrawal appear to be more strongly associated with SWB,
relative to their complementary aspects of Assertiveness and Volatility.
Personality Traits and Psychological Well-Being
PERSONALITY ASPECTS AND WELL-BEING
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Extraversion and neuroticism also predict most dimensions of PWB (Anglim & Grant,
2016). Conscientiousness, agreeableness, and openness/intellect also have links with PWB,
despite being weaker predictors of SWB (see Table 1). However, domain-level relations may
again be driven by lower-level traits. Notable trends (see Table 1) include incremental
associations (over domains) between facets of Enthusiasm and positive relations, environmental
mastery, and self-acceptance, between facets of Withdrawal and most PWB dimensions, between
Industriousness and purpose in life, and between Compassion and positive relations as well as
personal growth. Intellect (relative to Openness) may also be more strongly associated with PWB
overall. This suggests that the Enthusiasm, Withdrawal, Industriousness, Compassion, and
Intellect aspects may have idiosyncratic associations with specific PWB dimensions.
Personality Traits and (P)ERMA Well-Being
As the PERMA taxonomy (Seligman, 2011) and its corresponding measure (Butler &
Kern, 2016) have only recently been developed, no research to our knowledge has examined its
personality correlates. Having discussed the correlates of positive emotions, and noting that
Enthusiasm and Withdrawal are associated with most well-being variables, we now consider
additional potential aspect correlates of the remaining four “(P)ERMA” dimensions.
The PERMA-Profiler operationalizes the engagement dimension in terms of absorption,
feeling excited and interested in things, and losing track of time while doing things you enjoy.
Industriousness (partialling Orderliness), Openness, and Intellect appear to be robust predictors
of components of work engagement (vigor, dedication, and absorption; Bakker, Schaufeli, Leiter,
& Taris, 2008; Douglas, Bore, & Munro, 2016; Woods & Sofat, 2013). Intellect (distinct from
Openness) also predicts more effortful cognitive engagement during a difficult cognitive task
PERSONALITY ASPECTS AND WELL-BEING
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(Smillie, Varsavsky, Avery, & Perry, 2016), whereas Openness (distinct from Intellect) has been
linked with deeper engagement in abstract art (Fayn, Tiliopoulos, & MacCann, 2015).
The remaining dimensions each have some conceptual overlap with Ryff’s (1989) PWB
dimensions: positive relationships is similar to positive relations, meaning is similar to purpose
in life, and accomplishment overlaps with both purpose in life and environmental mastery. The
personality correlates of these (P)ERMA dimensions may therefore be similar to those of their
corresponding PWB dimensions: Enthusiasm and Compassion with positive relationships, and
Industriousness with meaning and accomplishment.
Summary
To summarize, personality and well-being can each be described at different levels of
resolution that offer more or less nuanced descriptions of the personality–well-being interface.
At the broadest, most parsimonious level of description, extraversion and neuroticism are
strongly correlated with a range of well-being constructs. A closer examination of distinct well-
being dimensions reveals that the extraversion–neuroticism monopoly holds for SWB, but breaks
down when examining PWB and (P)ERMA well-being dimensions, which have idiosyncratic
correlates across all Big Five domains. Finally, the picture becomes even more nuanced when
examining narrower personality traits. Although several studies have employed a facet-level
analysis, we suggest that an aspect-level analysis would be dramatically more parsimonious
while being sufficiently comprehensive. To this end, aspect- and facet-level studies suggest that
one aspect from each domain (Enthusiasm, Withdrawal, Industriousness, Compassion, Intellect)
is more strongly associated with well-being than the other.
The Present Study
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In this study, we consolidate emerging trends by comprehensively modeling the unique
associations between the Big Five aspects and distinct dimensions of well-being. Across two
samples, we first examine whether each aspect in a given domain is independently and equally-
strongly associated with well-being variables, partialling the complementary aspect. We then
compare path models to test whether the personality–well-being relation is best-modelled at the
level of distinct personality aspects and dimensions of well-being. Finally, we present an
exploratory path model that describes these unique aspect–well-being associations.
Although our goals were exploratory, we expected our findings to align with the literature
reviewed above. This suggests that, partialling their complementary aspects, (1) Enthusiasm and
low Withdrawal will have unique positive associations with most well-being variables, whereas
(2) Industriousness, Compassion, and Intellect will have unique positive associations with
specific dimensions of PWB and (P)ERMA well-being (e.g., Industriousness with purpose in
life, environmental mastery, and accomplishment; Compassion with positive relationships; and
Intellect with personal growth and engagement). In contrast, Assertiveness, Volatility,
Orderliness, Politeness, and Openness may have more modest associations with well-being.
Method
Participants and Procedure
We recruited two samples of U.S. residents via Amazon’s Mechanical Turk (MTurk; see
Buhrmester, Kwang, & Gosling, 2011).
Sample 1. Data collection for our exploratory sample (Sample 1) occurred in two waves.
To obtain more precise and stable estimates of the effects, we added 59 observations after
running preliminary analyses on the first 142 (of 152) valid responses. Our conclusions are
robust whether we include or exclude the “top-up” participants. Data collection for the two
PERSONALITY ASPECTS AND WELL-BEING
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waves ended automatically when all allocated MTurk assignments were completed. Six
participants were excluded due to highly inconsistent responses between an original and repeated
item (i.e., differing by 2 scale points) used as an attention check. Due to multiple waves of data
collection, we had 7 duplicate participants (based on WorkerIDs), but as we did not link
WorkerIDs to survey responses, we could not exclude them. The final analyzed Sample 1
comprised 205 participants (98 female) aged 18–66 years (Mage = 34.89, SDage = 10.04).
Participants identified as White/Caucasian (n = 155), Asian (n = 18), Black/African American (n
= 13), Hispanic/Latino (n = 13), Native American/Alaskan Eskimo (n = 5), and Other (n = 1).
Half of the sample held a Bachelors degree or higher (52%), most were full- or part-time
employees (75%), and 45.8% disclosed household incomes above $40,000.
Sample 2. Sample 2 initially comprised 520 participants, again recruited via MTurk, who
completed at least one of our key measures as part of a larger survey administered by the Quiet
Revolution (http://www.quietrev.com). After excluding 19 participants with missing data on one
or more measures, the final analyzed Sample 2 comprised 501 participants (286 female) aged
18–71 (Mage = 36.77, SDage = 12.11). Participants identified as White (n = 381), Multiracial (n =
39), Black or African American (n = 29), Hispanic/Latino/Spanish (n = 21), Asian (n = 20),
Indian (n = 4), Native American/Alaskan (n = 1), or did not disclose their origin (n = 6). Half of
the sample held a Bachelors degree or higher (51%), most were engaged in full-time, part-time,
military, or self-employment (73.1%), and 57% had household incomes greater than $40,000.
There was no participant overlap between the two MTurk samples (based on WorkerID lists).
Procedure. Participants completed a battery of trait questionnaires as part of two broader
projects on personality and well-being. Both questionnaire batteries included the following
measures, administered via online survey software. Data collection for Sample 1 received ethical
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approval at the University of Melbourne, and the University of Pennsylvania’s Institutional
Review Board (IRB) determined that oversight was not required for analysis of Sample 2 data.
Measures
Big Five Aspects. Participants completed the 100-item Big Five Aspect Scales
(DeYoung et al., 2007), which measures each of the 10 aspects using 10-item subscales. Domain
scores were computed by taking the means of their two constituent aspects. Participants indicated
how much they agreed that each statement (e.g. “Carry out my plans”; “Like to solve complex
problems”) described them (1 = strongly disagree, 5 = strongly agree).
Subjective Well-Being. Participants completed the Satisfaction with Life Scale (Diener,
Emmons, Larsen, & Griffin, 1985), indicating their level of agreement with five statements (e.g.,
“I am satisfied with my life”) on 7-point (Sample 1) or 5-point (Sample 2) scales anchored by
Strongly Disagree and Strongly Agree. To measure affect, participants completed the positive
emotions and negative emotions subscales from the 23-item PERMA-Profiler (Butler & Kern,
2016), rating how often they generally feel joyful, positive, contented, anxious, angry, and sad,
on 11-point (Sample 1) or 5-point (Sample 2) scales anchored by Never and Always.
Psychological Well-being. Sample 1 participants completed the 54-item version of the
Scales of Psychological Well-Being (Ryff, 1989), whereas Sample 2 participants completed the
42-item version. The Scales of Psychological Well-Being measure six dimensions of well-being:
autonomy (e.g., “My decisions are not usually influenced by what everyone else is doing”),
environmental mastery (e.g., “I am quite good at managing the many responsibilities of my daily
life”), personal growth (e.g., “For me, life has been a continuous process of learning, changing,
and growth”), positive relations (e.g., “I know that I can trust my friends, and they know they can
trust me”), purpose in life (e.g., “I have a sense of direction and Purpose in Life”), and self-
PERSONALITY ASPECTS AND WELL-BEING
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acceptance (e.g., “In general, I feel confident and positive about myself”). Participants rated the
extent to which they agreed with statements on a 6-point (Sample 1) or 5-point (Sample 2) scale
anchored by Strongly Disagree and Strongly Agree.
(P)ERMA Well-Being. Along with the positive and negative emotions subscales
described above, the PERMA Profiler (Butler & Kern, 2016) includes 3-item measures of
engagement (e.g., “How often do you become absorbed in what you are doing?”), positive
relationships (e.g., “To what extent do you feel loved?”), meaning (e.g., “In general, to what
extent do you lead a purposeful and meaningful life?”), and accomplishment (e.g., “How often
do you achieve the important goals you have set for yourself?”). Participants rated items on 11-
point (Sample 1) or 5-point (Sample 2) scales anchored by Not at All and Completely or Never
and Always, depending on item wordings.
Data Analyses
Descriptive statistics and correlations were computed using SPSS Version 23, omega (ω)
reliability coefficients (McDonald, 1999; see Dunn, Baguley, & Brunsden, 2014) were computed
using R Version 3.3.1 (R Core Team, 2016), and path analyses were deployed via Mplus Version
7 (Muthén & Muthén, 1998–2012). Average correlations were computed by transforming raw
correlations using Fisher’s r-to-z formula, averaging these z values, and converting them back to
rs (using the inverse of Fisher’s formula). For the path analyses, having obtained highly similar
preliminary results across both samples, we combined the samples (N = 706) after transforming
well-being variables to the Proportion of Maximum Scaling (POMS) metric (Cohen, Cohen,
Aiken, & West, 1999), where 0 and 1 represent the lowest or highest possible scale scores.
Results
Descriptive Statistics and Correlations
PERSONALITY ASPECTS AND WELL-BEING
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Means, standard deviations, and omega reliability estimates are shown in Table 3. All
measures except for engagement (ωs .67) showed good internal consistency (ωs .77).
Aspects within each domain were moderately to highly correlated (rs .45–.72; see Table 4), and
all well-being variables were moderately to highly intercorrelated (rs .21–.82; see Table 5).
Zero-order cross-correlations between personality and well-being variables for Samples 1
and 2 appear in Tables S1 and S2, respectively. Despite slight differences in scale points and
number of items, the results were highly consistent. This suggested that these observed
correlations were robust and replicable, and that pooled correlations, weighted by sample size,
would be appropriate. The mean zero-order correlations (see Table 6) show that extraversion and
its aspects were most-strongly positively correlated with well-being, whereas neuroticism and its
aspects had the strongest negative correlations with well-being. Conscientiousness,
agreeableness, and openness/intellect also had moderate positive correlations with well-being.
Notably, even the zero-order correlations begin to reveal discrepancies in effect sizes within
domains: Enthusiasm, Withdrawal, Industriousness, Compassion, and Intellect had somewhat
stronger mean correlations with well-being dimensions than their complementary aspects.
We next computed pooled semipartial correlations that controlled for the complementary
aspect in each domain. These appear in parentheses below each of the zero-order correlations in
Table 6 (see Tables S1 and S2 for Sample 1 and 2 results), and reveal an even sharper divergence
within each pair of aspects in terms of their relations with wellbeing. For simplicity, we will
focus only on relatively substantial semipartial correlations greater than |.30|.
PERSONALITY ASPECTS AND WELL-BEING
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Table 3
Means, Standard Deviations, and Omega Reliability Coefficients
Sample 1 (N = 205)
Sample 2 (N = 501)
M
(POMS)
SD
(POMS)
ω
M
(POMS)
SD
(POMS)
ω
Extraversion
3.28
0.71
.92
3.24
0.74
.92
Enthusiasm
3.32
0.79
.89
3.35
0.84
.89
Assertiveness
3.23
0.84
.92
3.13
0.85
.90
Neuroticism
2.66
0.81
.94
2.49
0.82
.94
Withdrawal
2.69
0.82
.87
2.62
0.90
.90
Volatility
2.63
0.93
.93
2.37
0.87
.92
Conscientiousness
3.51
0.64
.90
3.62
0.64
.89
Industriousness
3.56
0.77
.90
3.72
0.73
.88
Orderliness
3.46
0.69
.82
3.52
0.76
.85
Agreeableness
3.75
0.61
.90
3.99
0.61
.90
Compassion
3.70
0.78
.92
3.98
0.80
.93
Politeness
3.79
0.62
.80
4.01
0.62
.80
Openness/Intellect
3.73
0.60
.88
3.85
0.61
.88
Openness
3.68
0.69
.84
3.79
0.73
.84
Intellect
3.78
0.72
.87
3.90
0.70
.87
SWB
Life Satisfaction
4.44 (.57)
1.65 (.27)
.94
3.17 (.54)
1.09 (.27)
.92
Positive Emotions
6.59 (.66)
2.24 (.22)
.90
3.55 (.64)
1.07 (.27)
.90
Negative Emotions
3.76 (.38)
2.22 (.22)
.80
2.33 (.33)
1.01 (.25)
.77
PWB
Autonomy
4.41 (.68)
0.86 (.17)
.85
3.64 (.66)
0.74 (.19)
.78
Environmental Mastery
4.11 (.62)
0.99 (.20)
.89
3.55 (.64)
0.90 (.23)
.89
Personal Growth
4.41 (.68)
0.83 (.17)
.82
3.89 (.72)
0.72 (.18)
.79
Positive Relations
4.10 (.62)
1.06 (.21)
.90
3.72 (.68)
0.84 (.21)
.84
Self-Acceptance
3.85 (.57)
1.12 (.22)
.93
3.40 (.60)
1.02 (.25)
.92
Purpose in Life
4.24 (.65)
0.99 (.20)
.88
3.64 (.66)
0.79 (.20)
.82
(P)ERMA
Engagement
7.03 (.70)
1.76 (.18)
.67
3.89 (.72)
0.74 (.18)
.60
Relationships
7.32 (.73)
2.34 (.23)
.91
3.69 (.67)
1.03 (.26)
.86
Meaning
6.83 (.68)
2.47 (.25)
.91
3.60 (.65)
1.08 (.27)
.91
Accomplishment
6.81 (.68)
1.96 (.20)
.85
3.83 (.71)
0.83 (.21)
.80
Note. SWB = Subjective Well-Being; PWB = Psychological Well-Being; POMS = Proportion of
Maximum Scale (0 = lowest possible score, 1 = highest possible score).
PERSONALITY ASPECTS AND WELL-BEING
19
Table 4
Zero-Order Correlations Among Big Five Domains and Aspects for Sample 1 (below the diagonal) and Sample 2 (above the diagonal)
E
E–E
E–A
N
N–W
N–V
C
C–I
C-O
A
A–C
A–P
O
O–O
O–I
Extraversion (E)
.87
.87
-.51
-.61
-.34
.31
.51
.03
.27
.47
-.07
.48
.32
.50
Enthusiasm (EE)
.85
.52
-.54
-.59
-.40
.27
.46
.01
.48
.59
.18
.38
.31
.33
Assertiveness (EA)
.87
.49
-.36
-.47
-.20
.28
.44
.05
.00
.23
-.29
.46
.25
.54
Neuroticism (N)
-.57
-.53
-.45
.93
.93
-.36
-.62
-.02
-.30
-.28
-.24
-.27
-.08
-.39
Withdrawal (NW)
-.65
-.57
-.55
.92
.72
-.40
-.65
-.04
-.23
-.26
-.11
-.28
-.07
-.40
Volatility (NV)
-.42
-.42
-.30
.94
.72
-.28
-.50
.01
-.34
-.27
-.33
-.22
-.07
-.31
Conscientiousness (C)
.48
.42
.41
-.47
-.50
-.38
.86
.87
.30
.28
.23
.22
.07
.31
Industriousness (CI)
.59
.51
.51
-.65
-.68
-.54
.89
.48
.34
.34
.23
.34
.11
.48
Orderliness (CO)
.23
.21
.19
-.14
-.16
-.10
.86
.53
.17
.14
.17
.04
.00
.06
Agreeableness (A)
.28
.48
.01
-.37
-.27
-.40
.45
.42
.37
.90
.83
.41
.43
.26
Compassion (AC)
.45
.60
.20
-.35
-.29
-.35
.42
.42
.30
.90
.50
.50
.49
.36
Politeness (AP)
-.03
.19
-.23
-.28
-.17
-.35
.36
.29
.34
.83
.51
.17
.22
.07
Openness/Intellect (O)
.35
.30
.31
-.29
-.30
-.25
.27
.32
.14
.45
.55
.19
.86
.85
Openness (OO)
.19
.21
.12
-.08
-.10
-.05
.08
.09
.05
.40
.49
.17
.85
.46
Intellect (OI)
.40
.29
.39
-.42
-.40
-.37
.37
.44
.19
.37
.45
.15
.86
.45
Note. Correlations |.13| for Sample 1 or |.11| for Sample 2 are significant at p < .05.
PERSONALITY ASPECTS AND WELL-BEING
20
Table 5
Zero-Order Correlations Among Well-Being Variables for Sample 1 (below the diagonal) and Sample 2 (above the diagonal)
Subjective Well-Being
Psychological Well-Being
(P)ERMA
Mean r
Sample 2
SWL
PE
NE
AU
EM
PG
PR
SA
PU
E
R
M
A
Satisfaction with Life
.75
-.53
.25
.72
.40
.60
.79
.59
.37
.67
.69
.66
.61
Positive Emotions
.75
-.63
.38
.77
.49
.72
.81
.62
.52
.72
.77
.74
.68
Negative emotions
-.46
-.60
-.42
-.71
-.40
-.58
-.68
-.56
-.26
-.48
-.55
-.52
-.54
Autonomy
.21
.39
-.35
.52
.58
.45
.51
.48
.37
.31
.44
.46
.44
Environmental Mastery
.70
.76
-.68
.46
.56
.73
.86
.74
.39
.66
.74
.75
.70
Personal Growth
.33
.43
-.39
.56
.56
.62
.60
.63
.47
.45
.58
.58
.53
Positive Relations
.57
.70
-.60
.36
.74
.55
.73
.66
.42
.75
.67
.61
.64
Self-Acceptance
.82
.79
-.61
.44
.83
.52
.76
.76
.45
.66
.79
.75
.72
Purpose in Life
.46
.57
-.52
.44
.73
.76
.66
.68
.41
.51
.80
.72
.64
Engagement
.43
.66
-.28
.31
.47
.43
.47
.45
.45
.36
.50
.52
.42
Relationships
.69
.80
-.52
.34
.68
.41
.77
.73
.57
.57
.62
.60
.58
Meaning
.73
.81
-.49
.35
.76
.52
.65
.80
.68
.55
.69
.79
.68
Accomplishment
.65
.72
-.48
.44
.79
.55
.59
.72
.66
.56
.62
.78
.65
Mean r Sample 1
.59
.68
-.51
.39
.70
.51
.63
.70
.61
.47
.63
.67
.64
Note. All correlations are significant at p < .01 for Sample 1 and p < .001 for Sample 2. Mean correlations were computed with
negative emotions reversed. SWL = Satisfaction with Life; PE = Positive Emotions; NE = Negative Emotions; AU = Autonomy; EM
= Environmental Mastery; PG = Personal Growth; PR = Positive Relations; SA = Self-Acceptance; PU = Purpose in Life; E =
Engagement; R = Relationships; M = Meaning; A = Accomplishment.
PERSONALITY ASPECTS AND WELL-BEING
21
Table 6
N-Weighted Zero-Order and Semipartial Cross-Correlations (in parentheses) Between Personality and Well-Being Variables
Subjective Well-Being
Psychological Well-Being
(P)ERMA
Mean
r
SWL
PE
NE
AU
EM
PG
PR
SA
PU
E
R
M
A
Extraversion
.45
.61
-.43
.53
.61
.55
.68
.61
.56
.45
.55
.59
.55
.56
Enthusiasm
.50
.66
-.49
.35
.62
.51
.77
.63
.56
.44
.58
.60
.51
.56
(.41)
(.53)
(-.41)
(.07)
(.46)
(.33)
(.65)
(.48)
(.40)
(.30)
(.46)
(.44)
(.33)
(.41)
Assertiveness
.29
.41
-.26
.57
.45
.45
.41
.43
.41
.35
.37
.43
.44
.41
(.04)
(.08)
(-.02)
(.45)
(.15)
(.22)
(.02)
(.13)
(.15)
(.15)
(.09)
(.14)
(.21)
(.14)
Neuroticism
-.50
-.66
.81
-.54
-.75
-.52
-.64
-.72
-.59
-.34
-.49
-.61
-.60
-.61
Withdrawal
-.57
-.70
.79
-.59
-.79
-.54
-.65
-.76
-.62
-.35
-.55
-.64
-.64
-.64
(-.44)
(-.47)
(.39)
(-.43)
(-.53)
(-.34)
(-.38)
(-.52)
(-.40)
(-.22)
(-.42)
(-.42)
(-.43)
(-.42)
Volatility
-.37
-.52
.72
-.41
-.59
-.42
-.54
-.56
-.48
-.28
-.36
-.49
-.47
-.49
(.06)
(-.02)
(.21)
(.02)
(-.03)
(-.05)
(-.10)
(-.02)
(-.04)
(-.03)
(.05)
(-.03)
(-.01)
(-.03)
Conscientiousness
.36
.39
-.31
.30
.57
.32
.44
.46
.53
.25
.38
.46
.56
.41
Industriousness
.47
.54
-.50
.49
.73
.49
.59
.62
.64
.35
.48
.59
.69
.56
(.45)
(.54)
(-.56)
(.55)
(.70)
(.53)
(.58)
(.61)
(.58)
(.36)
(.45)
(.57)
(.65)
(.55)
Orderliness
.15
.14
-.03
.03
.25
.06
.17
.17
.28
.09
.18
.20
.27
.16
(-.10)
(-.14)
(.25)
(-.25)
(-.13)
(-.21)
(-.14)
(-.16)
(-.05)
(-.10)
(-.07)
(-.11)
(-.09)
(-.14)
Agreeableness
.19
.30
-.25
.17
.34
.43
.54
.29
.42
.29
.34
.39
.27
.33
Compassion
.23
.35
-.22
.22
.36
.51
.61
.35
.46
.33
.41
.44
.32
.38
(.21)
(.33)
(-.13)
(.23)
(.30)
(.47)
(.53)
(.33)
(.40)
(.30)
(.40)
(.39)
(.30)
(.34)
Politeness
.08
.13
-.22
.05
.20
.21
.30
.13
.24
.15
.14
.21
.12
.17
(-.03)
(-.05)
(-.12)
(-.07)
(.02)
(-.06)
(-.01)
(-.05)
(.00)
(-.02)
(-.08)
(-.02)
(-.05)
(-.02)
Openness/Intellect
.13
.27
-.12
.45
.27
.60
.35
.28
.38
.45
.23
.33
.35
.33
Openness
.03
.17
.02
.25
.10
.44
.23
.13
.25
.37
.13
.21
.18
.19
(-.06)
(.04)
(.14)
(.02)
(-.08)
(.19)
(.08)
(-.03)
(.08)
(.22)
(.01)
(.06)
(-.02)
(.03)
Intellect
.19
.30
-.23
.51
.37
.58
.37
.35
.40
.40
.26
.35
.43
.37
(.20)
(.25)
(-.27)
(.45)
(.37)
(.43)
(.29)
(.33)
(.32)
(.26)
(.23)
(.28)
(.39)
(.31)
Mean domain r
.33
.46
-.43
.41
.53
.49
.54
.49
.50
.36
.40
.48
.47
.46
Note. Semipartial correlations greater than |.30| are marked in bold. SWL = Satisfaction with Life; PE = Positive Emotions; NE =
Negative Emotions; AU = Autonomy; EM = Environmental Mastery; PG = Personal Growth; PR = Positive Relations; SA = Self-
Acceptance; PU = Purpose in Life; E = Engagement; R = Relationships; M = Meaning; A = Accomplishment. Mean domain r was
computed with neuroticism correlations reversed.
PERSONALITY ASPECTS AND WELL-BEING
22
In the extraversion domain, Enthusiasm (partialling Assertiveness) was substantially
positively correlated with all indicators of well-being, except for autonomy. In contrast,
Assertiveness (partialling Enthusiasm) only had a substantial semipartial correlation with
autonomy, and much weaker associations with all other well-being variables. Overall, the
average semipartial correlation for Enthusiasm (mean sr = .41) was nearly three times the
magnitude of the average semipartial correlation for Assertiveness (mean sr = .14).
In the domain of neuroticism, Withdrawal (partialling Volatility) had substantial negative
semipartial correlations with nearly all well-being variables, with a similar absolute magnitude of
association with well-being variables (mean sr = -.42) as Enthusiasm. In contrast, the average
effect size for Volatility (partialling Withdrawal) was close to zero (mean sr = -.03).
For the conscientiousness domain, Industriousness was substantially, positively
associated with all indicators of positive well-being, and had the largest average semipartial
correlation out of all 10 aspects (mean sr = .55). In contrast, Orderliness was only weakly—and
negatively—associated with well-being variables overall (mean sr = -.14).
Turning to the agreeableness and openness/intellect domains, we can see that
Compassion (partialling Politeness; mean sr = .34) and Intellect (partialling Openness; mean sr =
.31) generally had moderate and similar positive semipartial correlations with well-being
variables, whereas their sister aspects of Politeness (mean sr = -.02) and Openness (mean sr =
.03) had essentially no notable unique associations with well-being variables.
In sum, Enthusiasm, Withdrawal, Industriousness, Compassion, and Intellect (controlling
for their complementary aspects) had strong unique associations with well-being variables. In
contrast, their counterpart aspects generally had weak positive (Assertiveness), negligible
(Volatility, Politeness, and Openness), or even weak negative (Orderliness) unique associations
PERSONALITY ASPECTS AND WELL-BEING
23
with well-being variables. Therefore, even though the extraversion, low neuroticism,
conscientiousness, agreeableness, and openness/intellect domains were generally associated with
greater well-being, these associations were largely driven by one aspect in each domain.
How Specific is the Relation Between Personality and Well-Being?
Although the semipartial correlations presented above partialled out variance explained
by the other aspect in the focal domain, aspects across domains are also correlated (see Table 4).
To clarify the unique profile of aspect–well-being associations, we therefore conducted path
analysis, using the combined sample, to simultaneously model the associations between all
personality aspects and well-being variables.
We first examined the utility of modeling the personality–well-being relation in terms of
associations between specific personality aspects (vs. domains) and distinct (vs. global) well-
being variables. The results of the semipartial correlations (see Table 6) strongly suggest that the
model will fit substantially better when the personality–well-being relation is modelled at the
level of distinct personality aspects. The somewhat distinct profile of semipartial correlations
across the 13 well-being variables also suggests at least some utility to distinguishing between
different well-being variables when modeling the personality–well-being relation. To formally
assess whether the relation between personality and well-being is substantially better-described
at a fine-grained level, we compared the fit of four candidate models that varied whether (1) the
two aspects within each domain were free to have different associations with well-being
variables, and whether (2) each well-being variable was free to have different associations with
personality traits (see Grant, Langan-Fox, & Anglim, 2009).
In Model 1, we allowed personality–well-being associations to vary between personality
domains, but constrained personality–well-being associations to be equivalent for the two aspects
PERSONALITY ASPECTS AND WELL-BEING
24
within each domain, and for all well-being variables. This model assumes that the personality–
well-being relation differs across the trait domains, but does not vary appreciably between the
two aspects within each domain, or across different well-being variables (with negative emotions
reverse-scored). Unsurprisingly, this model fit poorly, with Comparative Fit Index (CFI), Root
Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual
(SRMR) values nowhere near traditional > .95, < .06, and < .08 cut-offs values (Hu & Bentler,
1999; see Table 7). However, the purpose of Model 1 was only to provide a frame of reference
for the relative improvement in three subsequent models that freed some of these constraints.
In Model 2, we allowed personality–well-being associations to vary between the two
aspects within each domain, but not across the 13 well-being variables. This model assumes that
the personality–well-being relation will differ across the 10 personality aspects, but will not vary
appreciably across different well-being variables (again with negative emotions reverse-scored).
As Model 1 was nested within Model 2, we conducted a χ2 difference test, which revealed that
Model 2 had significantly better fit than Model 1, χ2(5) = 86.413, p < .001. Despite this
improvement, Model 2 still had unsatisfactory fit on all other indices (see Table 7). Therefore,
modeling the personality–well-being relation at the level of distinct personality aspects (but not
well-being variables) did not provide a good fit to the data.
In Model 3, we once again constrained the personality–well-being associations within
each domain, but this time, allowed these associations to vary across the 13 well-being variables.
This model assumes that the relation between personality and well-being differs across the five
trait domains and different well-being dimensions, but will not vary appreciably between aspects
within each domain. A χ2 difference test revealed that Model 3 had substantially better fit than
Model 1, χ2(56) = 789.453, p < .001. However, Model 3 still had unsatisfactory fit on all other fit
PERSONALITY ASPECTS AND WELL-BEING
25
indices (see Table 7). Thus, modeling the personality–well-being relation at the level of distinct
well-being variables (but not personality aspects) also did not adequately describe the data.
Finally, in Model 4, we allowed the model to freely estimate most of the aspect–well-
being associations. We needed to constrain at least a few parameters to allow the model to be
overidentified, so that we could obtain model fit statistics. We therefore constrained 24 paths
where the semipartial correlations were nonsignificant in both Samples 1 and 2 (see Tables S1
and S2) to zero. For example, as the relation between Volatility (partialling Withdrawal) and
Autonomy was near zero in both samples, these associations were constrained to zero in Model
4. In stark contrast to the previous models, Model 4 showed excellent fit on the CFI and the
SRMR, whereas the RMSEA approached the standard .06 cutoff (see Table 7). This suggests that
the personality–well-being relation is best described as associations between distinct personality
aspects and distinct well-being variables. Given the exploratory nature of this final model (see
Table 8), we focus on interpreting coefficients that meet a conservative p < .001 significance
threshold.
Unique Associations Between Personality Aspects and Dimensions of Well-Being
The R2 values (see Table 8) reveal that aspects of personality explained an average of
56% of the variance across a broad range of well-being variables. Overall, with few exceptions,
Enthusiasm and Withdrawal were consistently the two strongest predictors of each well-being
variable. Industriousness and Compassion also had several notable unique well-being
associations. The remaining aspects had fewer and weaker (Assertiveness, Volatility, Openness)
or no notable associations with well-being variables (Orderliness, Politeness).
PERSONALITY ASPECTS AND WELL-BEING
26
Table 7
Path Model Descriptions and Fit Statistics
Model
Description
Free
Parameters
χ2 (df)
CFI
RMSEA
SRMR
1
Personality–well-being associations free to vary across
personality domains but not aspects or well-being variables
109
1807.946
(125)
.844
.138
.104
2
Personality-well-being associations free to vary across
personality aspects but not well-being variables
114
1721.533
(120)
.851
.137
.107
3
Personality-well-being associations free to vary across
personality domains and well-being variables, but not
personality aspects
165
1018.493
(69)
.912
.140
.167
4
Personality-well-being associations free to vary across
personality aspects and well-being variables
Paths corresponding to null semipartial cross-correlations in
both samples (see Tables S1 and S2) were constrained to zero
210
90.546
(24)
.994
.063
.010
PERSONALITY ASPECTS AND WELL-BEING
27
Table 8
Standardized Beta Coefficients for Final Path Model 4
Subjective Well-Being
Psychological Well-Being
(P)ERMA
SWL
PE
NE
AU
EM
PG
PR
SA
PU
E
R
M
A
Enthusiasm
0.34
0.40
-0.12
-0.17
0.21
0.03
0.42
0.27
0.12*
0.25
0.30
0.21
0.16
Assertiveness
-0.07
-0.01
0.09*
0.33
-0.02
0.08
-0.02
0.03
0.04
0.05
0.07
0.02
Withdrawal
-0.41
-0.43
0.60
-0.38
-0.49
-0.25
-0.16
-0.52
-0.32
-0.04
-0.28
-0.34
-0.24
Volatility
0.09*
-0.05
0.28
-0.15
-0.02
Industriousness
0.14
0.03
0.00
0.11
0.32
0.11
0.13
0.13*
0.26
0.11
0.08
0.18
0.40
Orderliness
0.05
0.07
0.02
-0.10*
0.05
-0.09*
0.02
0.05
0.08*
-0.01
0.08
0.04
0.03
Compassion
-0.09
-0.04
0.03
0.04
-0.01
0.22
0.25
-0.01
0.15
-0.03
0.14*
0.13
-0.03
Politeness
0.02
-0.04
0.02
-0.06
Openness
-0.04
0.06
0.15
-0.02
0.07
0.18
0.01
Intellect
-0.04
-0.03
0.06
0.19
-0.03
0.24
-0.03
0.00
0.00
0.17
-0.07
-0.04
0.09*
R2
.39
.59
.70
.51
.74
.56
.73
.66
.55
.30
.42
.53
.56
Note. Coefficients in bold are statistically significant at p < .001; * p < .01. Blank cells represent coefficients constrained to zero. SWL
= Satisfaction with Life; PE = Positive Emotions; NE = Negative Emotions; AU = Autonomy; EM = Environmental Mastery; PG =
Personal Growth; PR = Positive Relations; SA = Self-Acceptance; PU = Purpose in Life; E = Engagement; R = Relationships; M =
Meaning; A = Accomplishment
PERSONALITY ASPECTS AND WELL-BEING
28
Predictors of Subjective Well-Being. As shown in Table 8, SWB variables were most
strongly associated with Enthusiasm and Withdrawal. Withdrawal had substantial relations with
all three variables, whereas Enthusiasm was more strongly associated with positive emotions and
life satisfaction than negative emotions. Volatility also independently predicted increased
negative emotions, but the effect of Withdrawal was twice as strong. The remaining associations
between other personality aspects and SWB variables did not meet the p < .001 threshold.
Predictors of Psychological and (P)ERMA Well-Being. As shown in Table 8, the
effects of Enthusiasm and Withdrawal extended to the PWB and (P)ERMA dimensions of well-
being. Enthusiasm had particularly notable relations with both measures of positive relationships,
whereas Withdrawal had particularly strong negative associations with environmental mastery
and self-acceptance. In contrast, Assertiveness and Volatility only had unique associations with
greater autonomy and worse positive relations, respectively.
Beyond the extraversion and neuroticism domains, Industriousness, Compassion, and
Intellect were uniquely associated with a range of PWB and (P)ERMA dimensions (see Table 8).
Controlling for other significant predictors, Industriousness had notable positive associations
with environmental mastery, positive relations, purpose in life, accomplishment, and meaning. In
contrast, Orderliness had negligible associations with all well-being dimensions. Compassion
was one of the strongest predictors of personal growth and both measures of positive
relationships, and also had notable positive associations with purpose in life and meaning.
Finally, there was less of a divergence between Openness and Intellect, which both predicted
greater personal growth and engagement. However, Intellect (relative to Openness) had a
stronger effect on personal growth, and also independently predicted greater autonomy.
PERSONALITY ASPECTS AND WELL-BEING
29
Overall, the final path model presents a parsimonious yet relatively comprehensive
picture of the unique associations between personality aspects and a breadth of well-being
variables. By modeling the simultaneous effects of all 10 aspects, this model bolsters and
expands on the semipartial correlational finding that one aspect within each personality domain
is more strongly associated with overall well-being. Together, these findings demonstrate that
although Enthusiasm and Withdrawal have the strongest unique associations with nearly all well-
being variables, other aspects (especially Industriousness, Compassion, and Intellect) also have
notable idiosyncratic associations with specific PWB and (P)ERMA well-being variables.
Discussion
The present research provided the first aspect-level analysis of the associations between
personality traits and well-being variables featured in three taxonomies of well-being. Given that
two to four lower-level traits capture most of the personality information within each domain
(DeYoung et al., 2007; Soto & John, 2016), we proposed that ten personality aspects would
similarly capture the major within-domain divergences in predicting well-being, while offering
dramatically greater parsimony than a facet-level analysis (e.g., Anglim & Grant, 2016). In light
of calls for multidimensional well-being assessment (Butler & Kern, 2016; Kern et al., 2015), we
examined associations with specific well-being constructs. We showed that one aspect within
each domain (Enthusiasm, Withdrawal, Industriousness, Compassion, Intellect) was generally
more strongly associated with well-being variables, relative to its complementary aspect
(Assertiveness, Volatility, Orderliness, Politeness, Openness). Model comparisons then
confirmed that the personality–well-being association varies substantially not only across
personality aspects within each domain but also for specific well-being variables. Finally, a path
model revealed idiosyncratic associations between personality aspects and well-being variables.
PERSONALITY ASPECTS AND WELL-BEING
30
Specificity in the Personality–Well-Being Relation
By measuring constructs at high resolution, we can describe broad patterns as well as the
nuances. Although zero-order correlations revealed that all five personality domains were
associated with well-being, semipartial correlations revealed that one aspect within each domain
drove these associations. Enthusiasm (partialling Assertiveness), Withdrawal (partialling
Volatility), Industriousness (partialling Orderliness), Compassion (partialling Politeness), and
Intellect (partialling Openness) each had moderate to strong average semipartial correlations
with well-being, whereas their complementary aspects had smaller or even inverse semipartial
correlations with well-being. This illustrates how domain-level analyses can obscure important
details about the specific traits that may underlie overall associations with well-being. This
qualifies previous conclusions about the roles of extraversion and neuroticism in well-being:
well-being is indeed higher for the extraverted and non-neurotic, but only to the extent that they
possess the enthusiastic, non-withdrawn aspects of these traits. These findings also
parsimoniously summarize trends emerging from facet-level analyses (summarized in Table 1).
Model comparisons, which assessed the value of modeling specific personality–well-
being relations, revealed that models fit poorly when (1) the two aspects within a domain were
assumed to have equal associations with well-being variables, or (2) all well-being variables
were assumed to have equal associations with personality traits. The only model that fit well was
one where personality–well-being associations were free to vary across all 10 aspects and 13
well-being variables. This implies that both personality and well-being need to be described in
sufficient detail to enable an adequate description of the associations between the two.
Idiosyncratic Links between Aspects and Well-Being Variables
By simultaneously modeling all personality aspects and well-being variables, our
PERSONALITY ASPECTS AND WELL-BEING
31
exploratory path model sheds more light on the idiosyncratic links that remain. Consistent with
previous research (Anglim & Grant, 2016), aspects of extraversion and neuroticism were
associated with nearly all well-being variables. However, for the less affectively-based PWB and
(P)ERMA dimensions, aspects of conscientiousness, agreeableness, and openness/intellect
emerged from the shadows—sometimes even out-predicting aspects of extraversion and
neuroticism. This supports Schmutte and Ryff’s (1997) argument that when well-being is
conceptualized in terms of multiple end states, there is more than one personality profile that
predicts greater well-being.
For SWB dimensions, Enthusiasm and low Withdrawal were the strongest unique
predictors of high life satisfaction and positive emotions. Withdrawal was the strongest predictor
of negative emotions, whereas Volatility had an effect size that was about half the magnitude.
Interestingly, Enthusiasm and Assertiveness had divergent associations with negative emotions,
which may explain why the extraversion domain tends to not be uniquely associated with
negative emotions.
For dimensions of PWB and (P)ERMA well-being, Enthusiasm and Withdrawal were
again unique and strong predictors of nearly all well-being dimensions (see Table 8), whereas
Assertiveness and Volatility had negligible roles. One notable exception was that Enthusiasm
predicted lower autonomy, whereas Assertiveness had a positive effect. This suggests that a
previous domain-level analysis, which revealed that extraversion (controlling for the other Big
Five domains) did not significantly predict autonomy (Anglim & Grant, 2016), may have
masked the divergent effects of lower-level traits. This divergence makes sense in light of the
theoretical distinction between Enthusiasm (social warmth and enjoyment of interpersonal
bonds) and Assertiveness (social dominance and behaviors oriented towards attaining rewards;
PERSONALITY ASPECTS AND WELL-BEING
32
DeYoung, Weisberg, Quilty, & Peterson, 2013). To this end, Enthusiastic people may be less
likely to go against social consensus if this makes social interactions less enjoyable, whereas
Assertive individuals may be comfortable with boldly voicing their opinions if this helps them to
attain rewards such as status, even to the possible detriment of other forms of adjustment.
Consistent with trends that emerged from Anglim and Grant’s (2016) facet-level analysis,
Industriousness had notable associations with environmental mastery, purpose in life, meaning,
and accomplishment. In other words, those who are self-disciplined and hard-working are more
likely to report feeling competent, purposeful, and accomplished. In contrast, Orderliness was
essentially unrelated to these dimensions, and even predicted lower levels of personal growth.
Also as expected, Compassion was associated with both measures of positive relationships
(although the effect of Enthusiasm was two to three times greater in magnitude). Compassion
was also uniquely associated with greater levels of personal growth, meaning, and purpose in
life. These diverse correlates align with the perspective that prosocial behavior may be one route
to well-being (e.g., Nelson, Layous, Cole, & Lyubomirsky, 2016). In contrast, Politeness
(partialling Compassion) was the only aspect to have no significant independent associations
with any well-being dimensions. This suggests that the tendency to be fair and considerate, in
and of itself, may be largely unrelated to well-being. Finally, Intellect and Openness showed less
divergence in their prediction of well-being: both aspects had independent associations with
personal growth and engagement. Intellect only had a slightly stronger association with personal
growth, as well as a unique association with increased autonomy. For the latter finding, it is
plausible that intellectual individuals are more confident in their beliefs because they have
engaged more deeply and thoughtfully with their ideas.
Limitations, Strengths, and Future Directions
PERSONALITY ASPECTS AND WELL-BEING
33
There were several limitations to the current study. First, given the exploratory nature of
our final path model, it needs to be replicated. Second, our use of MTurk samples potentially
limits generalizability; however, our well-being intercorrelations (see Table 5) were very similar
to previous studies that used a range of samples (Anglim & Grant, 2016; Butler & Kern, 2016;
Schmutte & Ryff, 1997). Third, as we only obtained self-reports, personality–well-being
associations may have been inflated by item content overlap. Informants may also be more
accurate judges of evaluative traits (e.g., Intellect; Vazire, 2010), and could provide useful
external perspectives on well-being dimensions that have an objective or interpersonal
component (e.g., accomplishment, positive relationships; Jayawickreme et al., 2012). Finally,
although we conceptualized personality as the predictor, causal direction is of course ambiguous
in cross-sectional data, and personality and well-being may influence each other (Soto, 2015).
This study is nevertheless the first to describe the associations between the Big Five
aspects and a comprehensive range of well-being variables across hedonic (Diener, 1984) and
existential–humanistic conceptions of well-being (Ryff, 1989). As the Big Five aspects and
PERMA are both relatively new taxonomies, we lay a necessary descriptive foundation to guide
future investigations. We further suggest that an aspect-level analysis provides an optimal (i.e.,
parsimonious yet relatively comprehensive) framework for describing how personality traits
relate to well-being. From an applied perspective, improving “well-being” is a vague goal;
instead, interventions that target specific well-being dimensions may be more useful (Kern et al.,
2015). Our model homes in on patterns of affects, behaviors, and cognitions that may be most
relevant for enhancing positive emotions, meaning, and other specific elements of well-being.
For example, hard work (Industriousness) may be a more effective route to accomplishment than
following rules (Orderliness), and practicing kindness (Compassion), not Politeness, may
PERSONALITY ASPECTS AND WELL-BEING
34
strengthen relationships. Personality trait (Hudson & Fraley, 2016) or state change (Blackie,
Roepke, Forgeard, Jayawickreme, & Fleeson, 2014) interventions could test these possibilities.
In this study, we chose to analyze the 13 well-being variables as presented in their
respective measures to enable comparisons with other studies that employ these measures and to
examine the consistency of personality correlates across different measures of conceptually-
similar constructs. However, as there is considerable conceptual and empirical overlap between
many of these measures (see Table 5), the number of dimensions could certainly be reduced.
Despite several psychometric efforts (e.g., Chen, Jing, Hayes, & Lee, 2013; Gallagher, Lopez, &
Preacher, 2009), there is still little consensus about the structure of well-being, perhaps in part
due to normative and theoretical debates about what constitutes the “good life”. Yet, from the
perspective that the Big Five aspects represent variation in basic cybernetic (i.e., goal-directed,
self-regulating) mechanisms that support or disrupt psychological functioning (DeYoung, 2015),
the structure of self-reported well-being may not be fundamentally different to the structure of
personality. Supporting this possibility, psychopathological traits and symptoms have already
been successfully integrated with the Big Five model (Krueger & Markon, 2014; Markon, 2010).
As the Big Five taxonomy represents the major dimensions of covariation among all
traits, the current study also has implications beyond traits explicitly named within Big Five
hierarchies (e.g., DeYoung et al., 2007). For example, findings relating to Enthusiasm have
implications for related traits such as zest (Park, Peterson, & Seligman, 2004), and likewise for
traits related to Withdrawal (e.g., experiential avoidance; Hayes et al., 2004), Industriousness
(e.g., grit; Credé, Tynan, & Harms, 2016), Compassion (e.g. empathic concern; Habashi,
Graziano, & Hoover, 2016), and Intellect (e.g., need for cognition; Cacioppo, Petty, Feinstein, &
Jarvis, 1996). Yet, although our findings have broad trait-level implications, we also recognize
PERSONALITY ASPECTS AND WELL-BEING
35
that personality and its relations with well-being extend beyond the Big Five (e.g., Sheldon,
Cheng, & Hilpert, 2011). Thus, investigations featuring additional frameworks such as personal
projects (Little, 2015) and narrative identity (Bauer, McAdams, & Sakaeda, 2005) would provide
a more holistic understanding of the ways that various levels of personality and interactions
between these levels (e.g., McGregor, McAdams, & Little, 2006) contribute to well-being.
Conclusions
Extraverted and non-neurotic individuals experience higher well-being—but this headline
is imprecise and incomplete. Instead, the personality–well-being relation varies appreciably
across personality aspects and distinct dimensions of well-being. Not all aspects of extraversion
and neuroticism are equally predictive, and aspects of conscientiousness, agreeableness, and
openness/intellect also have idiosyncratic, meaningful associations with distinct forms of
positive functioning. This study therefore extends current knowledge on the breadth of
associations between personality traits and multiple ways of thriving in life.
PERSONALITY ASPECTS AND WELL-BEING
36
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship,
and/or publication of this article: Preparation of this manuscript was partially supported by a
Melbourne Research Grant Support Scheme awarded to the last author.
Acknowledgements
We thank Susan Cain, Mike Erwin, and Jeff Bryan from the Quiet Revolution, and Spencer
Greenberg and Aislinn Pluta from GuidedTrack, for their invaluable assistance in collecting and
preparing the Sample 2 data for analysis.
PERSONALITY ASPECTS AND WELL-BEING
37
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PERSONALITY ASPECTS AND WELL-BEING
46
Supplementary Materials for Sun, Kaufman, & Smillie (2016)
Table S1
Zero-Order and Semipartial Cross-Correlations (in parentheses) Between Personality and Well-Being Variables for Sample 1
Subjective Well-Being
Psychological Well-Being
(P)ERMA
Mean
r
SWL
PE
NE
AU
EM
PG
PR
SA
PU
E
R
M
A
Extraversion
.45
.64
-.45
.50
.59
.53
.65
.58
.55
.52
.54
.59
.57
.55
Enthusiasm
.44
.65
-.46
.28
.56
.46
.76
.57
.53
.50
.57
.56
.49
.53
(.31)
(.49)
(-.36)
(.01)
(.39)
(.27)
(.66)
(.41)
(.37)
(.35)
(.45)
(.39)
(.29)
(.37)
Assertiveness
.34
.46
-.31
.57
.45
.46
.38
.44
.42
.40
.36
.46
.49
.43
(.15)
(.16)
(-.10)
(.49)
(.21)
(.27)
(.01)
(.19)
(.19)
(.18)
(.09)
(.21)
(.29)
(.20)
Neuroticism
-.51
-.67
.76
-.51
-.75
-.51
-.67
-.70
-.56
-.35
-.52
-.62
-.60
-.61
Withdrawal
-.55
-.73
.72
-.58
-.75
-.52
-.64
-.72
-.59
-.39
-.57
-.63
-.63
-.63
(-.38)
(-.49)
(.32)
(-.44)
(-.41)
(-.30)
(-.30)
(-.41)
(-.37)
(-.28)
(-.42)
(-.36)
(-.41)
(-.38)
Volatility
-.41
-.54
.69
-.38
-.65
-.44
-.60
-.60
-.47
-.27
-.40
-.53
-.49
-.50
(-.01)
(-.02)
(.24)
(.06)
(-.15)
(-.09)
(-.20)
(-.12)
(-.06)
(.02)
(.03)
(-.10)
(-.04)
(-.07)
Conscientiousness
.43
.47
-.35
.26
.65
.35
.49
.53
.59
.33
.48
.51
.58
.47
Industriousness
.50
.58
-.48
.42
.77
.49
.60
.64
.65
.38
.55
.63
.70
.58
(.46)
(.55)
(-.49)
(.49)
(.69)
(.51)
(.55)
(.58)
(.55)
(.34)
(.47)
(.59)
(.64)
(.54)
Orderliness
.22
.22
-.11
.01
.34
.10
.25
.27
.36
.18
.29
.25
.30
.23
(-.05)
(-.10)
(.16)
(-.25)
(-.08)
(-.19)
(-.08)
(-.08)
(.01)
(-.02)
(-.01)
(-.11)
(-.09)
(-.09)
Agreeableness
.12
.28
-.30
.13
.37
.39
.52
.29
.50
.28
.33
.36
.27
.32
Compassion
.15
.31
-.25
.18
.38
.50
.57
.34
.54
.35
.35
.41
.32
.36
(.14)
(.27)
(-.14)
(.20)
(.30)
(.49)
(.49)
(.32)
(.45)
(.34)
(.29)
(.36)
(.29)
(.32)
Politeness
.05
.15
-.26
.02
.25
.14
.29
.13
.30
.11
.20
.19
.13
.17
(-.03)
(-.01)
(-.16)
(-.08)
(.06)
(-.13)
(.01)
(-.05)
(.04)
(-.08)
(.03)
(-.02)
(-.03)
(-.01)
Openness/Intellect
.16
.24
-.19
.38
.30
.56
.30
.31
.44
.36
.16
.30
.35
.32
Openness
.04
.10
.02
.23
.11
.39
.15
.12
.28
.30
.05
.16
.17
.16
(-.07)
(-.04)
(.20)
(.05)
(-.08)
(.17)
(-.02)
(-.06)
(.07)
(.18)
(-.05)
(.00)
(-.02)
(-.01)
Intellect
.23
.30
-.35
.42
.40
.55
.36
.40
.47
.31
.22
.36
.42
.37
(.24)
(.29)
(-.40)
(.36)
(.39)
(.42)
(.33)
(.39)
(.39)
(.20)
(.22)
(.32)
(.39)
(.33)
Note. Semipartial correlations partial out the other aspect in the domain. Semipartial correlations greater than |.30| are marked in bold. Zero-order
correlations |.14| and semipartial correlations |.12| are statistically significant at p < .05. Mean r was computed with negative emotions reverse-
scored. SWL = Satisfaction with Life; PE = Positive Emotions; NE = Negative Emotions; AU = Autonomy; EM = Environmental Mastery; PG =
Personal Growth; PR = Positive Relations; SA = Self-Acceptance; PU = Purpose in Life; E = Engagement; R = Relationships; M = Meaning; A =
Accomplishment.
PERSONALITY ASPECTS AND WELL-BEING
47
Table S2
Zero-Order and Semipartial Cross-Correlations (in parentheses) Between Personality and Well-Being Variables for Sample 2
Subjective Well-Being
Psychological Well-Being
(P)ERMA
Mean
r
SWL
PE
NE
AU
EM
PG
PR
SA
PU
E
R
M
A
Extraversion
.46
.60
-.42
.54
.62
.56
.69
.62
.56
.42
.55
.59
.54
.56
Enthusiasm
.53
.66
-.50
.37
.64
.54
.78
.65
.57
.41
.59
.61
.52
.58
(.46)
(.54)
(-.43)
(.09)
(.48)
(.35)
(.65)
(.51)
(.42)
(.28)
(.46)
(.46)
(.35)
(.43)
Assertiveness
.27
.39
-.24
.57
.45
.45
.43
.43
.41
.33
.38
.42
.42
.40
(-.01)
(.05)
(.02)
(.44)
(.13)
(.20)
(.03)
(.11)
(.13)
(.13)
(.08*)
(.12)
(.18)
(.12)
Neuroticism
-.50
-.65
.83
-.55
-.75
-.52
-.63
-.72
-.61
-.33
-.48
-.60
-.60
-.61
Withdrawal
-.57
-.69
.81
-.60
-.81
-.55
-.66
-.78
-.64
-.34
-.54
-.64
-.64
-.65
(-.46)
(-.46)
(.42)
(-.42)
(-.58)
(-.35)
(-.41)
(-.56)
(-.41)
(-.20)
(-.42)
(-.44)
(-.44)
(-.43)
Volatility
-.35
-.52
.73
-.43
-.57
-.42
-.52
-.55
-.49
-.28
-.34
-.47
-.46
-.48
(.09)
(-.02)
(.20)
(.01)
(.03)
(-.03)
(-.06)
(.03)
(-.04)
(-.05)
(.07)
(.00)
(.00)
(-.01)
Conscientiousness
.33
.37
-.29
.32
.54
.30
.41
.43
.51
.22
.34
.44
.55
.39
Industriousness
.45
.52
-.51
.52
.72
.49
.59
.61
.64
.34
.46
.58
.69
.56
(.45)
(.54)
(-.58)
(.57)
(.70)
(.53)
(.60)
(.63)
(.59)
(.37)
(.45)
(.56)
(.65)
(.56)
Orderliness
.12
.11
.00
.03
.21
.04
.14
.13
.24
.05
.14
.18
.26
.13
(-.12)
(-.16)
(.28)
(-.25)
(-.15)
(-.22)
(-.17)
(-.19)
(-.07*)
(-.14)
(-.10)
(-.11)
(-.09)
(-.16)
Agreeableness
.22
.30
-.23
.19
.32
.45
.56
.29
.38
.29
.34
.40
.27
.33
Compassion
.26
.37
-.21
.24
.35
.52
.63
.35
.43
.32
.44
.45
.33
.38
(.24)
(.35)
(-.13)
(.24)
(.30)
(.46)
(.55)
(.33)
(.38)
(.28)
(.45)
(.40)
(.31)
(.34)
Politeness
.10
.13
-.20
.06
.18
.23
.30
.13
.21
.16
.11
.21
.12
.17
(-.04)
(-.07)
(-.10)
(-.06)
(.01)
(-.03)
(-.02)
(-.06)
(-.01)
(.00)
(-.13)
(-.02)
(-.05)
(-.03)
Openness/Intellect
.12
.28
-.09
.47
.26
.61
.37
.28
.36
.49
.26
.34
.36
.34
Openness
.03
.19
.02
.26
.09
.46
.27
.14
.24
.40
.16
.23
.18
.21
(-.06)
(.07)
(.12)
(.01)
(-.08*)
(.21)
(.11)
(-.02)
(.08)
(.23)
(.03)
(.08*)
(-.02)
(.04)
Intellect
.18
.29
-.18
.55
.36
.59
.37
.33
.37
.44
.28
.34
.43
.37
(.19)
(.23)
(-.21)
(.49)
(.36)
(.43)
(.28)
(.30)
(.29)
(.28)
(.24)
(.27)
(.39)
(.31)
Note. Semipartial correlations partial out the other aspect in the domain. Semipartial correlations greater than |.30| are marked in bold. Zero-order
and semipartial correlations |.09| and those marked with * are statistically significant at p < .05. Mean r was computed with negative emotions
reverse-scored. SWL = Satisfaction with Life; PE = Positive Emotions; NE = Negative Emotions; AU = Autonomy; EM = Environmental
Mastery; PG = Personal Growth; PR = Positive Relations; SA = Self-Acceptance; PU = Purpose in Life; E = Engagement; R = Relationships; M =
Meaning; A = Accomplishment.
... Different theoretical approaches have tried to explain processes that link personality to SWB and have suggested a rather complex explanatory path from personality to SWB (for an overview, Diener et al., 1999), for example, that personality traits are moderators of wellbeing-related adaptation following life events (Diener et al., 2006). For a more nuanced perspective on predicting SWB with personality, it has been suggested that it is necessary to NEED FOR COGNITION, SUBJECTIVE WELL-BEING, AND BURNOUT examine narrower traits (Anglim & Grant, 2016;Sun et al., 2018). When specifying each Big Five dimension with two aspects (e.g., Withdrawal and Volatility for Neuroticism), associations with SWB dimensions were mainly driven by one aspect (e.g., Withdrawal;Sun et al., 2018). ...
... For a more nuanced perspective on predicting SWB with personality, it has been suggested that it is necessary to NEED FOR COGNITION, SUBJECTIVE WELL-BEING, AND BURNOUT examine narrower traits (Anglim & Grant, 2016;Sun et al., 2018). When specifying each Big Five dimension with two aspects (e.g., Withdrawal and Volatility for Neuroticism), associations with SWB dimensions were mainly driven by one aspect (e.g., Withdrawal;Sun et al., 2018). Concerning Openness, Intellect was the aspect that was mainly associated with SWB (|r| ≈ .2 ...
... Concerning Openness, Intellect was the aspect that was mainly associated with SWB (|r| ≈ .2 -.3; Sun et al., 2018). ...
Preprint
Subjective well-being is both an indicator of and the essential foundation for accomplishing tasks or mastering challenges at university, work, and other areas of life. Hence, much research has addressed questions about variables that may be beneficial or detrimental to subjective well-being. A personality trait that has increasingly been addressed by such research is Need for Cognition (NFC). This article reports two studies that were aimed at deepening our understanding of how NFC is linked to different facets of SWB in three samples. Study 1 provided initial insights using short scales and examining a population-representative sample of N = 200 participants. Study 2 extended that research by examining NFC’s relationships with affective well-being, both general and domain-specific life satisfaction, and burnout via online self-reports. Domain-specific analyses were applied to two subsamples consisting of students and working adults, respectively. Participants were 489 adults of which 256 were students and 198 were working. The results confirmed previous results in which higher NFC was weakly to moderately associated with higher positive affect and life satisfaction. Additionally, students with higher NFC levels were more satisfied with the content of their studies, and working individuals reported increased job satisfaction. For both studying and working individuals, NFC predicted lower burnout symptoms. We found evidence both for generalizable associations of NFC with higher order constructs and for the necessity to distinguish between different facets of subjective well-being and burnout. Our results indicate that NFC should be considered a resource for subjective well-being and mental health.
... Empirically, limited studies exist that combines the three constructs (flexible wellbeing, organizational creativity, personality traits) and most of the results have been inconclusive. For instance, Sun et al. (2017) found that a link existed between personality traits and wellbeing, but the precise correlates vary across wellbeing dimensions and within each Big Five domain. Also, Chau et al. (2018) postulated that educators' creative personality has a significant positive effect on individual innovation behavior. ...
... Additional studies revealed that while conscientious workers were found to be more empowered, extraverts were capable of handling their work-related tasks and competencies as service workers (Yazdi & Mustamil, 2015). Supporting Yazdi and Mustami (2015) submission, Sun et al. (2017) found that extraverted and non-neurotic individuals experienced higher wellbeing but, the degree of the experience is imprecise and incomplete since individuals respond differently to the dimensions of wellbeing. Summarily, Baer and Oldham (2006) proposed that there could be a link between organizational creativity, personal and contextual factors to increase creativity. ...
... However, Abdullah et al. (2016) maintained that based on personality traits and the intricacies among the BIG Five traits, the integration with the concept of creativity and wellbeing is more complex than initially perceived. In line with previous comments, Sun et al. (2017) found that personality traits are associated with wellbeing and creativity, but the precise strength and direction of the correlates vary across wellbeing dimensions and within each Big Five domain. Likewise, other researchers opined that individual characteristics, work environment, and other contextual factors either supports or hamper workers creativity (Gorondutse & John, 2018;Zhou & Shalley, 2018). ...
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... Research indicates personality is moderately heritable, relatively stable over time, and relates strongly to well-being (Sun et al., 2018); however, personality relates differentially to well-being outcomes when traditional variable-centered approaches are employed. Most studies examine the relations among personality and hedonic well-being (i.e., life satisfaction, positive/negative affect). ...
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... However, Lucas et al. (2000) argue that the core of extraversion is not sociability but reward sensitivity, while Ashton et al. (2002), p. 245) describe extraversion as a tendency to "behave in ways that attract social attention." Extraversion is linked to numerous -generally positive -outcomes, such as a more active social life and closer relationships (Lucas et al., 2008) and general well-being (Sun et al., 2018). Openness to experience (autonomy) refers to curiosity and cognitive exploration in terms of gathering and engaging with new information (DeYoung, 2015;Schwaba, 2019). ...
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Following the relational-developmental systems approach, this three-wave study examines whether acute stress (T2) mediates the relationship between the development of personality traits from the beginning of 8th grade (T1, M age = 15.63, SD = 0.59; 22 girls) to the end of 9th grade (T3). Using the Montréal Imaging Stress Task, which is a task that provokes acute social stress by negative social feedback, this study combined the functional magnetic resonance imaging (fMRI), heart rate, and longitudinal survey data of 41 adolescents. Mediation analysis revealed that stress-induced left insula activation partially mediates the longitudinal stability of conscientiousness. These results highlight the impact of negative social feedback during stress on students’ personality development.
... Individer som är mer extraverta, alltså utåtriktade och sociala, är helt enkelt lyckligare, mer tillfredsställda med livet och upplever högre nivåer av PA än introverta (som utgör motpolen till extraverta) enligt otaliga studier, översikter samt metaanalyser (ex. Costa & McCrae, 1980;Diener m.fl., 1999;Diener, Oishi & Lucas, 2003;Diener & Seligman, 2002;Furnham & Brewin, 1990;Hayes & Joseph, 2003;Heller, Watson & Ilies, 2004;Hills & Argyle, 2001;Keyes, Shmotkin & Ryff, 2002;Lucas, 2007b;McCrae & Costa, 1991;Nave, Sherman & Funder, 2008;Schmutte & Ryff, 1997;Smillie, DeYoung & Hall, 2015;Sun, Kaufman & Smillie, 2018;. Detta samband förekommer då man mäter E och lycka på varierande sätt i olika åldrar samt i olika kulturer och länder runt om i världen (Fulmer m.fl., 2010;Kokko, Tolvanen & Pulkkinen, 2013;Lucas, Diener, Grob, Suh & Shao, 2000;Lucas & Fujita, 2000;Schimmack, Radhakrishnan, Oishi, Dzokoto & Ahadi, 2002;Steel & Ones, 2002;Wilson & Gullone, 1999 (Suoninen, Pirttilä-Backman, Lahikainen & Ahokas, 2011, 12). ...
Thesis
Full-text available
Ett av de mest robusta fynden inom personlighets- och välbefinnandeforskning är det starka sambandet mellan personlighetsdraget extraversion och positiva emotioner, lycka samt subjektivt och psykologiskt välbefinnande. Vad som kunde förklara varför extraverta är lyckligare har i årtionden ingående undersökts, om än osystematiskt och från skilda utgångspunkter. Detta har även noterats på fältet, och för att underlätta fortsatt forskning belyser denna litteraturöversikt hur frågeställningen undersökts till dags dato. Utifrån McCraes och Costas (1991) ursprungliga uppdelning i instrumentella och temperamentella modeller samt Hampsons (2012) indelning av medierande och modererande personlighetsprocesser identifieras, systematiseras och presenteras de huvudsakliga förklaringarna som förekommer i litteraturen för sambandet mellan extraversion och lycka. Resultatet består av ett konceptuellt diagram (se Figur 1 s. 20–21) med två övergripande förklaringsmodeller, sex distinkta mekanismer, tio personlighetsprocesser och tretton hypoteser som redovisas med tillhörande forskningslitteratur. Förutom en historisk överblick över tillvägagångssätt i forskningen presenteras även aktuell metodik för personlighetsprocesser. Vidare behandlas även hur resultaten är symptomatiska för den rådande problematiken kring konceptualisering, operationalisering samt metodologi inom personlighets- och lyckoforskning, samt resultatens och socialpsykologins relevans för fortsatt forskning och befrämjande av lycka och välbefinnande. [One of the most robust findings in personality and well-being research is the strong relationship between the personality trait extraversion and positive emotions, happiness, and subjective and psychological well-being. The factors explaining why extraverts are happier has been investigated in depth for decades, albeit unsystematically and from different points of view. This has also been noted in the field, and to facilitate further research, this literature review highlights how the issue has been investigated to date. Based on the original division into instrumental and temperamental models by McCrae and Costa (1991), and the division of mediating and moderating personality processes by Hampson (2012), the main explanations that appear in the literature for the relationship between extraversion and happiness are identified, systematized, and presented. The result consists of a conceptual diagram (see Figure 1, pp. 20–21) with two overall explanatory models, six distinct mechanisms, ten personality processes, and thirteen hypotheses, which are reported with associated research literature. In addition to a historical overview of research approaches, current methodology for personality processes is also presented. Furthermore, the issue of how the results are symptomatic of the prevailing problems around conceptualization, operationalization, and methodology in personality and happiness research is also discussed, as well as the relevance of the results and social psychology for continued research and the promotion of happiness and well-being.]
... Recent work by Ryff (2019) calls for a closer examination of the well-being construct in entrepreneurship. Episodic experiences of self-employment coupled with personality and temperament may latently coalesce to drive an individual's eudaimonic wellbeing (Grant et al., 2009;Huta, 2017;Mann et al., 2021;Sun et al., 2018). According to Rothbart and Bates (2006), temperament refers to individual differences in prevailing affect and activity, whereas the trait-based view of personality refers to the stable patterns of behaviors less bounded by sociocultural contexts (Wilt & Revelle, 2009;Zillig et al., 2002). ...
Article
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Considerable research finds that entrepreneurs enjoy higher subjective well-being than wage-earning employees. At the same time, entrepreneurship is uniquely stressful for founders, who generally have high levels of personal commitment to the business and often higher workloads than wage employees. This highlights a tension in entrepreneurship research where it is unclear how self-employment influences well-being. This research seeks to resolve some existing tensions by tackling complex constellations of well-being profiles among both entrepreneurs and wage employees. Our latent profile analysis and commentary suggest the multifaceted nature of self-employment experiences, straddling both personal and business goals that may not always be hedonic, as an important consideration for future research on entrepreneurial well-being.
... The theme like vividness is hedonic ways of achieving well-being, but themes related to uncertainty reduction, online disinhibition and perceived autonomy are individual trait-based. Thus, our findings corroborate other studies that postulate that significant relationships exist between personality traits and well-being (Nikbin et al., 2020;Sun et al., 2018). Human psychographics influence activities, interests and opinions of individuals', and well-being from consumption experiences requires meeting certain preferences formed from customer psychographics. ...
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