psychology assignment
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psy615articlefinal2.pdf
Journal of Personality 86:2, April 2018
VC 2016 Wiley Periodicals, Inc.
DOI: 10.1111/jopy.12301Unique Associations Between Big Five Personality Aspects and Multiple Dimensions of Well-Being
Jessie Sun , 1,2
Scott Barry Kaufman, 3
and Luke D. Smillie
1
1 The University of Melbourne
2 University of California, Davis
3 University of Pennsylvania
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 5 706; Mage 5 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
When 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, extra- verted members of society. (Schmutte & Ryff, 1997, p. 558)
The large literature describing the associations between person- ality 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 bro- ken down into anywhere between 10 (DeYoung, Quilty, & Peterson, 2007) and 240 (M~ottus, Kandler, Bleidorn, Riemann, & McCrae, 2017) narrower constituent traits that describe more precise subtleties of personality. Well-being can similarly be conceptualized at different levels—as a single indicator (e.g., Disabato, Goodman, Kashdan, Short, & Jarden, 2016), two gen- eral “types” of well-being (Keyes, Shmotkin, & Ryff, 2002), or an array of distinct dimensions (e.g., Ryff, 1989; Seligman, 2011).
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.
Correspondence concerning this article should be addressed to Jessie
Sun, Department of Psychology, University of California, Davis, Young Hall,
200 East Quad, Davis, CA 95616. Email: [email protected].
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 com- prise at least 30 facets (Costa & McCrae, 1995; Hofstee, de Raad, & Goldberg, 1992; but see Soto & John, 2017), the poten- tial for a more comprehensive description is accompanied by a dramatic reduction in parsimony. In addition, because the num- ber 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 mod- els 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, 2017). Accordingly, DeYoung and colleagues (2007) developed a revised hierarchy in which each of the five domains divides 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 evi- dence 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 aspects may therefore offer a more comprehensive description than the five domains, while being dramatically more parsimoni- ous 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, 2010), fairness preferences (Zhao, Ferguson, & Smillie, 2017), 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.
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 article, 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 poten- tial 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 accomplish- ment, thereby incorporating both hedonic and eudaimonic perspectives.
Just as lower-level personality traits offer a more comprehen- sive, 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 from 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 vari- ance 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 (summa- rized in Table 1) suggest that specific lower-level traits may drive these domain-level associations: Enthusiasm and With- drawal appear to be more strongly associated with SWB, relative to their complementary aspects of Assertiveness and Volatility.
Personality Aspects and Well-Being 159
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 Constit-
uent Facets) Subjective Well-Being Psychological Well-Being
Extraversion Positive affect, life satisfaction 3,6,8
Positive relations, environmental
mastery, purpose in life, self-
acceptance, personal growth 8
Enthusiasm Friendly, sociable, enjoys rewards
(Friendliness, 1
Warmth, 2
Poise, 1
Gregariousness, 2
Positive Emotions 2 )
Pleasant affect, subjective
happiness (partialing
Assertiveness) 4
Facets consistently related to
positive affect, life satisfaction 5–8
Facets predicted positive rela-
tions, mastery, self-acceptance
(partialing Extraversion) 8
Assertiveness Socially dominant, motivated to attain
rewards (Leadership, 1
Assertiveness, 1,2
Provocativeness 1 )
One facet predicted autonomy
(partialing Extraversion) 8
Neuroticism (–) Positive affect, negative affect,
(–) life satisfaction 3,6,8
(–) Self-acceptance, (–) autonomy,
(–) environmental mastery,
(–) positive relations,
(–) purpose in life 8
Withdrawal Susceptible to depression and anxiety,
easily discouraged and overwhelmed
(Depression, 2
Vulnerability, 2
Anxiety, 2
Self-Consciousness 2 )
(–) Subjective happiness (partialing
Volatility) 4
Facets are stronger predictors of
(–) positive affect, negative
affect, (–) life satisfaction
than Volatility facet 5–7
Facets predicted (–) all
dimensions (partialing
Neuroticism) 8
Volatility Susceptible to anger and irritability,
emotionally unstable (Calmness, 1
Angry Hostility, 2
Tranquility, 1
Impulse
Control 1 )
One facet predicted autonomy,
self-acceptance (partialing
Neuroticism) 8
Conscientiousness Positive affect 3,6,8
Purpose in life, environmental
mastery, personal growth,
self-acceptance 8
Industriousness Achievement-oriented, self-disciplined,
efficient (Purposefulness, 1
Efficiency, 1
Self-discipline, 2
Competence 2 )
Facets consistently related
to positive affect 5,6,8
One facet predicted purpose in
life (partialing
Conscientiousness) 8
Orderliness Preference for tidiness and routine
(Orderliness, 1
Perfectionism 1 )
One facet predicted (–) purpose
in life (partialing
Conscientiousness) 8
Agreeableness Positive relations, (–) autonomy 8
Compassion Feels and cares about others’ emotions
and well-being (Warmth, 1
Sympathy, 1
Understanding, 1
Empathy 1 )
One facet had larger zero-order
correlations with positive
relations, personal growth 8
Politeness Respects others’ needs and wants
(Cooperation, 1
Compliance, 2
Morality, 1
Straightforwardness 2 )
One facet predicted (–) autonomy
(partialing Agreeableness) 8
Openness/Intellect Personal growth, autonomy,
purpose in life 8
Openness Needs creative outlets, appreciates
beauty, daydreams (Aesthetics, 2
Imagination, 1
Reflection, 1
Fantasy, 2
Feelings 2 )
Intellect Intellectual engagement and ability
(Quickness, 1
Creativity, 1
Intellect, 1
Ideas, 2
Ingenuity, 1
Competence 1 )
One facet had stronger
zero-order correlations with
all dimensions than two
Openness facets 8
Note. Example facets are those that DeYoung et al. (2007) found to load more strongly on one aspect than the other. 1 Facets from the Abridged Big Five Circum-
plex Scales from the International Personality Item Pool. 2 Facets from the Revised NEO Personality Inventory.
3 Steel et al. (2008).
4 Kirkland, Gruber, and
Cunningham (2015). 5 Albuquerque, de Lima, Matos, and Figueiredo (2012).
6 Quevedo and Abella (2011).
7 Schimmack, Oishi, Furr, and Funder (2004).
8 Anglim and
Grant (2016).
160 Sun, Kaufman, & Smillie
Personality Traits and Psychological
Well-Being
Extraversion and Neuroticism also predict most dimensions of PWB (Anglim & Grant, 2016). In addition, Conscientiousness, Agreeableness, and Openness/Intellect have links with PWB, despite being weaker predictors of SWB (see Table 1). Howev- er, domain-level relations may again be driven by lower-level traits. Notable trends (see Table 1) include incremental associa- tions (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 (rela- tive 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 correspond- ing measure (Butler & Kern, 2016) have only recently been developed, no research to our knowledge has examined its per- sonality correlates. Having discussed the correlates of positive emotions, and noting that Enthusiasm and Withdrawal are asso- ciated with most well-being variables, we now consider addi- tional potential aspect correlates of the remaining four “(P)ERMA” dimensions.
The PERMA-Profiler operationalizes the engagement dimen- sion in terms of absorption, feeling excited and interested in things, and losing track of time while doing things you enjoy. Industriousness (partialing Orderliness), Openness, and Intellect
appear to be robust predictors of components of work engage- ment (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 (Smillie, Varsavsky, Avery, & Perry, 2016), whereas Open- ness (distinct from Intellect) has been linked with deeper engagement in abstract art (Fayn, Tiliopoulos, & MacCann, 2015).
The remaining dimensions each have some conceptual over- lap 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 monopo- ly holds for SWB, but breaks down when examining PWB and (P)ERMA well-being dimensions, which have idiosyncratic cor- relates 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,
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
Personality Aspects and Well-Being 161
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
In this study, we consolidate emerging trends by comprehensive- ly 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 indepen- dently and equally strongly associated with well-being variables, partialing the complementary aspect. We then compare path models to test whether the personality–well-being relation is best modeled at the level of distinct personality aspects and dimen- sions of well-being. Finally, we present an exploratory path mod- el that describes these unique aspect–well-being associations.
Although our goals were exploratory, we expected our find- ings to align with the literature reviewed above. This suggests that, partialing their complementary aspects, (a) Enthusiasm and low Withdrawal will have unique positive associations with most well-being variables, whereas (b) Industriousness, Com- passion, 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 mas- tery, and accomplishment; Compassion with positive relation- ships; 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). Although one other paper has used the Sample 1 dataset (Sun, Stevenson, Kabbani, Richardson, & Smillie, 2017), the results here have not been reported elsewhere, aside from descriptive statistics for the Extraversion scale and subscales.
Sample 1. Data collection for our exploratory sample (Sample 1) occurred in two waves. To obtain more precise and stable esti- mates of the effects, we added 59 observations after running pre- liminary 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 waves ended auto- matically when all allocated MTurk assignments were complet- ed. Six participants were excluded due to highly inconsistent responses between an original and a repeated item (i.e., differing by 2�scale points) used as an attention check. Due to multiple waves of data collection, we had seven duplicate participants (based on worker IDs), but as we did not link worker IDs to
survey responses, we could not exclude them. The final ana- lyzed Sample 1 comprised 205 participants (98 female) aged 18–66 years (Mage 5 34.89, SD 5 10.04). Participants identified as White/Caucasian (n 5 155), Asian (n 5 18), Black/African American (n 5 13), Hispanic/Latino (n 5 13), Native American/ Alaskan Eskimo (n 5 5), and Other (n 5 1). Half of the sample held a bachelor’s 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 mea- sures as part of a larger survey administered by the Quiet Revolu- tion (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 5 36.77, SD 5 12.11). Participants identified as White (n 5 381), Multiracial (n 5 39), Black or African American (n 5 29), Hispanic/Latino/Spanish (n 5 21), Asian (n 5 20), Indi- an (n 5 4), Native American/Alaskan (n 5 1), or did not disclose their origin (n 5 6). Half of the sample held a bachelor’s degree or higher (51%); most were engaged in full-time, part-time, mili- tary, 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 worker ID lists).
Procedure. Participants completed a battery of trait question- naires as part of two broader projects on personality and well- being. Both questionnaire batteries included the following mea- sures, administered via online survey software. Data collection for Sample 1 received ethical approval at the University of Melbourne, and the University of Pennsylvania’s Institutional Review Board 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 measure 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 5 strongly disagree, 5 5 strongly agree).
Subjective Well-Being. Participants completed the Satisfac- tion 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,
162 Sun, Kaufman, & Smillie
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, chang- ing, 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-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 (Sam- ple 2) scale anchored by Strongly Disagree and Strongly Agree.
(P)ERMA Well-Being. Along with the Positive Emotions and Negative Emotions subscales described above, the PERMA- Profiler (Butler & Kern, 2016) includes three-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., “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 (x) 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 (Muth�en & Muth�en, 1998– 1998). 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 5 706) after transforming well-being variables to the proportion of maximum scaling (POMS) metric (Cohen, Cohen, Aiken, & West, 1999), where 0 and 1 represent the low- est or highest possible scale scores, respectively.
RESULTS
Descriptive Statistics and Correlations
Means, standard deviations, and omega reliability estimates are shown in Table 3. All measures except for engagement
(xs� .67) showed good internal consistency (xs� .77). Aspects within each domain were moderately to highly correlat- ed (rs 5 .45–.72; see Table 4), and all well-being variables were moderately to highly intercorrelated (rs 5 .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 num- ber 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 posi- tively 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, Industri- ousness, Compassion, and Intellect had somewhat stronger mean correlations with well-being dimensions than their com- plementary aspects.
We next computed pooled semipartial correlations that con- trolled 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 well-being. For simplicity, we will focus only on relatively substantial semipartial correlations greater than |.30|.
In the Extraversion domain, Enthusiasm (partialing Asser- tiveness) was substantially positively correlated with all indica- tors of well-being, except for autonomy. In contrast, Assertiveness (partialing Enthusiasm) only had a substantial semipartial correlation with autonomy, and much weaker associ- ations with all other well-being variables. Overall, the average semipartial correlation for Enthusiasm (mean sr 5 .41) was nearly three times the magnitude of the average semipartial cor- relation for Assertiveness (mean sr 5 .14).
In the domain of Neuroticism, Withdrawal (partialing Vola- tility) had substantial negative semipartial correlations with nearly all well-being variables, with a similar absolute magni- tude of association with well-being variables (mean sr 5 –.42) as Enthusiasm. In contrast, the average effect size for Volatility (partialing Withdrawal) was close to zero (mean sr 5 –.03).
For the Conscientiousness domain, Industriousness was sub- stantially, positively associated with all indicators of well-being and had the largest average semipartial correlation out of all 10 aspects (mean sr 5 .55). In contrast, Orderliness was only weak- ly—and negatively—associated with well-being variables over- all (mean sr 5 –.14).
Turning to the Agreeableness and Openness/Intellect domains, we can see that Compassion (partialing Politeness; mean sr 5 .34) and Intellect (partialing Openness; mean sr 5 .31) generally had moderate and similar positive semipar- tial correlations with well-being variables, whereas their sister
Personality Aspects and Well-Being 163
Table 3 Means, Standard Deviations, and Omega Reliability Coefficients
Sample 1 (N 5 205) Sample 2 (N 5 501)
M (POMS) SD (POMS) x M (POMS) SD (POMS) x
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 5 subjective well-being; PWB 5 psychological well-being; POMS 5 proportion of maximum scale (0 5 lowest possible score, 1 5 highest possible score).
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 (E–E) .85 .52 –.54 –.59 –.40 .27 .46 .01 .48 .59 .18 .38 .31 .33
Assertiveness (E–A) .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 (N–W) –.65 –.57 –.55 .92 .72 –.40 –.65 –.04 –.23 –.26 –.11 –.28 –.07 –.40
Volatility (N–V) –.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 (C–I) .59 .51 .51 –.65 –.68 –.54 .89 .48 .34 .34 .23 .34 .11 .48
Orderliness (C–O) .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 (A–C) .45 .60 .20 –.35 –.29 –.35 .42 .42 .30 .90 .50 .50 .49 .36
Politeness (A–P) –.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 (O–O) .19 .21 .12 –.08 –.10 –.05 .08 .09 .05 .40 .49 .17 .85 .46
Intellect (O–I) .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.
164 Sun, Kaufman, & Smillie
aspects of Politeness (mean sr 5 –.02) and Openness (mean sr 5 .03) had essentially no notable unique associations with well-being variables.
In sum, Enthusiasm, Withdrawal, Industriousness, Compas- sion, 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 Open- ness), or even weak negative (Orderliness) unique associations with well-being variables. Therefore, even though Extraversion, low Neuroticism, Conscientiousness, Agreeableness, and Open- ness/Intellect 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 partialed 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 per- sonality 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 modeled at the level of distinct personality aspects. The somewhat distinct
profile of semipartial correlations across the 13 well-being varia- bles 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 (a) the two aspects within each domain were free to have different associations with well- being variables, and whether (b) 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 we constrained personality–well-being associations to be equivalent for the two aspects within each domain, and for all well-being variables. This model assumes that the personality–well-being relation dif- fers 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 their traditional > .95, < .06, and < .08 cut-off 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 person- ality aspects, but will not vary appreciably across different well- being variables (again with negative emotions reverse-scored).
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 2SWL 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 5 satisfaction with life; PE 5 positive emotions; NE 5 negative emotions; AU 5 autonomy; EM 5 environmental mastery; PG 5 personal growth; PR 5 positive relations; SA 5 self-acceptance; PU 5 purpose in life; E 5 engagement; R 5 relationships; M 5 meaning; A 5 accomplishment.
Personality Aspects and Well-Being 165
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166 Sun, Kaufman, & Smillie
As Model 1 was nested within Model 2, we conducted a v2 dif- ference test, which revealed that Model 2 had significantly better fit than Model 1, v2(5) 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, we allowed these associations to vary across the 13 well-being vari- ables. 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 v2 difference test revealed that Model 3 had substantially better fit than Model 1, v2(56) 5 789.453, p < .001. However, Model 3 still had unsatisfactory fit on all other fit 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 ade- quately describe the data.
Finally, in Model 4, we allowed the model to freely estimate most of the aspect–well-being associations. We needed to con- strain at least a few parameters to allow the model to be overi- dentified, 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 (par- tialing Withdrawal) and autonomy was near zero in both sam- ples, 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 cut-off (see Table 7). This suggests that the personality–well-being relation is best described as associations between distinct personality aspects and distinct well-being
Table 7 Path Model Descriptions and Fit Statistics
Model Description Free Parameters v2 (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) constrained to zero
210 90.546 (24) .994 .063 .010
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 20.12 20.17 0.21 0.03 0.42 0.27 0.12* 0.25 0.30 0.21 0.16 Assertiveness 20.07 20.01 0.09* 0.33 20.02 0.08 20.02 0.03 0.04 0.05 0.07 0.02
Withdrawal 20.41 20.43 0.60 20.38 20.49 20.25 20.16 20.52 20.32 20.04 –0.28 20.34 20.24 Volatility 0.09* 20.05 0.28 20.15 20.02 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 20.10* 0.05 20.09* 0.02 0.05 0.08* 20.01 0.08 0.04 0.03
Compassion 20.09 20.04 0.03 0.04 20.01 0.22 0.25 20.01 0.15 20.03 0.14* 0.13 20.03
Politeness 0.02 20.04 0.02 20.06
Openness 20.04 0.06 0.15 20.02 0.07 0.18 0.01
Intellect 20.04 20.03 0.06 0.19 20.03 0.24 20.03 0.00 0.00 0.17 20.07 20.04 0.09*
R 2
.39 .59 .70 .51 .74 .56 .73 .66 .55 .30 .42 .53 .56
Note. Coefficients in boldface are statistically significant at p < .001; *p < .01. Blank cells represent coefficients constrained to zero. SWL 5 satisfaction with life; PE 5 positive emotions; NE 5 negative emotions; AU 5 autonomy; EM 5 environmental mastery; PG 5 personal growth; PR 5 positive relations; SA 5 self- acceptance; PU 5 purpose in life; E 5 engagement; R 5 relationships; M 5 meaning; A 5 accomplishment.
Personality Aspects and Well-Being 167
variables. Given the exploratory nature of this final model (see Table 8), we focus on interpreting coefficients that meet a con- servative 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 associa- tions. The remaining aspects had fewer and weaker (Assertive- ness, Volatility, Openness) or no notable associations with well- being variables (Orderliness, Politeness).
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 associat- ed with positive emotions and life satisfaction than negative emotions. Volatility also independently predicted increased neg- ative 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 Vol- atility only had unique associations with greater autonomy and worse positive relations, respectively.
Beyond the Extraversion and Neuroticism domains, Industri- ousness, 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, posi- tive relations, purpose in life, accomplishment, and meaning. In contrast, Orderliness had negligible associations with all well- being dimensions. Compassion was one of the strongest predic- tors of personal growth and both measures of positive relation- ships, 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 it also independently predicted greater autonomy.
Overall, the final path model presents a parsimonious yet rel- atively 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 cor- relational finding that one aspect within each personality domain is more strongly associated with overall well-being. Together, these findings demonstrate that although Enthusiasm and With- drawal have the strongest unique associations with nearly all well-being variables, other aspects (especially Industriousness, Compassion, and Intellect) also have notable idiosyncratic asso- ciations 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 varia- bles featured in three taxonomies of well-being. Given that two to four lower-level traits capture most of the personality infor- mation within each domain (DeYoung et al., 2007; Soto & John, 2017), we proposed that 10 personality aspects would sim- ilarly 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 vari- ables. Finally, a path model revealed idiosyncratic associations between personality aspects and well-being variables.
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 cor- relations revealed that all five personality domains were associ- ated with well-being, semipartial correlations revealed that one aspect within each domain drove these associations. Enthusiasm (partialing Assertiveness), Withdrawal (partialing Volatility), Industriousness (partialing Orderliness), Compassion (partialing Politeness), and Intellect (partialing Openness) each had moder- ate 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
168 Sun, Kaufman, & Smillie
aspects of these traits. These findings also parsimoniously sum- marize 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 (a) the two aspects within a domain were assumed to have equal associations with well-being variables, or (b) all well-being variables were assumed to have equal associa- tions 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 suf- ficient detail to enable an adequate description of the associa- tions between the two.
Idiosyncratic Links Between Aspects and Well- Being Variables
By simultaneously modeling all personality aspects and well- being variables, our 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 varia- bles. However, for the less affectively based PWB and (P)ERMA dimensions, aspects of Conscientiousness, Agree- ableness, and Openness/Intellect emerged from the shadows— sometimes even out-predicting aspects of Extraversion and Neu- roticism. 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 posi- tive emotions. Withdrawal was the strongest predictor of nega- tive emotions, whereas Volatility had an effect size that was about half the magnitude. Interestingly, Enthusiasm and Asser- tiveness 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, Enthusi- asm and Withdrawal were again unique and strong predictors of nearly all well-being dimensions (see Table 8), whereas Asser- tiveness and Volatility had negligible roles. One notable excep- tion was that Enthusiasm predicted lower autonomy, whereas Assertiveness had a positive effect. This suggests that a previous domain-level analysis, which revealed that Extraversion (con- trolling for the other Big Five domains) did not significantly pre- dict 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 toward attaining rewards; 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, mean- ing, 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, Orderli- ness 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 rela- tionships (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 perspec- tive that pro-social behavior may be one route to well-being (e.g., Nelson, Layous, Cole, & Lyubomirsky, 2016). In contrast, Politeness (partialing 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 con- siderate, 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 associa- tions 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 find- ing, 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
There were several limitations to the current study. First, given the exploratory nature of our final path model, it needs to be rep- licated. 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).
Personality Aspects and Well-Being 169
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 existen- tial–humanistic conceptions of well-being (Ryff, 1989). As the Big Five aspects and PERMA are both relatively new taxono- mies, 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 compre- hensive) 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 cogni- tions that may be most relevant for enhancing positive emotions, meaning, and other specific elements of well-being. For exam- ple, hard work (Industriousness) may be a more effective route to accomplishment than following rules (Orderliness), and prac- ticing kindness (Compassion), not Politeness, may 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 mea- sures of conceptually similar well-being 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 on 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 well-being may not be fundamentally different from 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 impli- cations beyond traits explicitly named within Big Five hierar- chies (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; Cred�e, Tynan, & Harms, 2017), 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 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, Agree- ableness, and Openness/Intellect also have idiosyncratic, mean- ingful 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.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors 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, and a Templeton Foundation grant (#50696) awarded to the second author.
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article at the publisher’s web-site:
Table S1 Zero-Order and Semipartial Cross-Correlations (in parentheses) Between Personality and Well-Being Variables for Sample 1.
Table S2 Zero-Order and Semipartial Cross-Correlations (in parentheses) Between Personality and Well-Being Variables for Sample 2.
172 Sun, Kaufman, & Smillie
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