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Predicting Psychological and Subjective Well-Being from Personality: Incremental Prediction from 30 Facets Over the Big 5

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Abstract

This study investigated the relationship between the Big 5, measured at factor and facet levels, and dimensions of both psychological and subjective well-being. Three hundred and thirty-seven participants completed the 30 Facet International Personality Item Pool Scale, Satisfaction with Life Scale, Positive and Negative Affectivity Schedule, and Ryff’s Scales of Psychological Well-Being. Cross-correlation decomposition presented a parsimonious picture of how well-being is related to personality factors. Incremental facet prediction was examined using double-adjusted r2 confidence intervals and semi- partial correlations. Incremental prediction by facets over factors ranged from almost nothing to a third more variance explained, suggesting a more modest incremental pre- diction than presented in the literature previously. Examination of semi-partial correlations controlling for factors revealed a small number of important facet-well-being correlations. All data and R analysis scripts are made available in an online repository.
FACETS AND WELL-BEING
1
Predicting Psychological and Subjective Well-Being from Personality: Incremental
Prediction from 30 Facets over the Big 5
Jeromy Anglim and Sharon Grant 1
Abstract: This study investigated the relationship between the Big 5, measured at factor
and facet levels, and dimensions of both psychological and subjective well-being. Three
hundred and thirty-seven participants completed the 30 Facet International Personality Item
Pool Scale, Satisfaction with Life Scale, Positive and Negative Affectivity Schedule, and
Ryff’s Scales of Psychological Well-Being. Cross-correlation decomposition presented a
parsimonious picture of how well-being is related to personality factors. Incremental facet
prediction was examined using double-adjusted r2 confidence intervals and semi-partial
correlations. Incremental prediction by facets over factors ranged from almost nothing to a
third more variance explained, suggesting a more modest incremental prediction than
presented in the literature previously. Examination of semi-partial correlations controlling
for factors revealed a small number of important facet-well-being correlations. All data and
R analysis scripts are made available in an online repository.
Citation Information: Anglim, J., & Grant, S. (2016). Predicting Psychological and
Subjective Well-Being from Personality: Incremental Prediction from 30 Facets over the
Big 5. Journal of Happiness Studies, 17, 59-80. https://dx.doi.org/10.1007/s10902-014-
9583-7
1. Introduction
Understanding the relationship between personality and well-being is of
fundamental importance for both theoretical and applied reasons. For one, the relationship
of personality to well-being may shed light on the temporal stability of well-being. It can
also be helpful to understand the role of personality when designing interventions targeted
at increasing well-being. However, at present, it is unclear whether facet-level or factor-
level personality analysis is superior for understanding well-being. Researchers need an
unbiased assessment of this issue, given the reduction in parsimony that results when
moving away from broad models of personality such as the Five Factor Model. In addition,
little is currently known about the relationship between psychological well-being and
personality facets. In what follows, we describe subjective well-being (SWB),
psychological well-being (PWB) and the Five Factor Model (FFM), and review previous
work on personality and well-being before outlining the objectives for the current study,
which illustrates a new approach for assessing the incremental variance of personality
facets over factors.
1 Jeromy Anglim, School of Psychology, Deakin University; Sharon Grant, Faculty of Life
and Social Sciences, Swinburne University of Technology. We thank Sue Carmen for her
assistance with data collection. Correspondence concerning this article should be addressed
to Jeromy Anglim, School of Psychology, Deakin University, 221 Burwood Highway,
Burwood, 3125 Victoria, Australia. Email: jeromy.anglim@deakin.edu.au
FACETS AND WELL-BEING
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1.1. Subjective and Psychological Well-Being
The literature on SWB is expansive (Diener & Choi, 2009; Diener, Oishi, & Lucas,
2003; Diener, Suh, Lucas, & Smith, 1999; Lucas & Diener, 2008). SWB has been defined
and measured in a variety of ways and can include happiness and quality of life measures,
but a common approach is to operationalize SWB as a composite of satisfaction with life,
high positive affect, and low negative affect (Deci & Ryan, 2008; Diener, 1984; Lucas,
Diener, & Suh, 1996). This operationalization is adopted in the current study.
The construct of PWB was developed in response to a perceived failure of SWB to
capture various humanistic concepts of well-being related to identity, meaning, and
relatedness (McGregor & Little, 1998; Ryan & Deci, 2001; Ryff & Keyes, 1995). Ryff
(1989) proposed a six dimensional model of PWB composed of autonomy, environmental
mastery, personal growth, positive relations, purpose in life, and self-acceptance. The same
author also developed a measure of these six dimensions that has subsequently been used in
several studies (for a review see, Keyes, Shmotkin, & Ryff, 2002). Studies have shown that
environmental mastery and self-acceptance overlap substantially with SWB (Compton,
1998; Keyes et al., 2002; McGregor & Little, 1998; Ryff & Keyes, 1995) but that the other
dimensions are more distinct, correlating only moderately with SWB measures (Compton,
1998; Keyes et al., 2002; McGregor & Little, 1998; Ryff & Keyes, 1995).
While situational factors lead to short-term fluctuation and in some cases long-term
change in well-being, substantial research has supported a dispositional perspective of well-
being. Building on ideas such as the "hedonic treadmill" (Brickman & Campbell, 1971),
Headey and Wearing (1992) proposed that while life events can temporarily alter well-
being, well-being has a set point which varies between individuals. Genetic and twin
studies have established a hereditary basis for the stable component of well-being
(Bouchard Jr & Loehlin, 2001; Lykken & Tellegen, 1996; Weiss, Bates, & Luciano, 2008).
Furthermore, a 17 year longitudinal study (Fujita & Diener, 2005) found that satisfaction
with life showed substantial stability over time, albeit at about half the level of personality
traits. Thus, personality traits provide an important means of understanding the stability in
well-being.
1.2. Personality
Historically, trait research began with a proliferation of traits which was later
followed by various attempts at data reduction and eventually a movement to the Big 5
(Costa & McCrae, 1992; Goldberg, 1993; McCrae & John, 1992), typically labeled
neuroticism, extraversion, openness, conscientiousness, and agreeableness. More recently,
researchers responding to the success of the Big 5 have called for even higher level factor
models (Digman, 1997; Musek, 2007) and more detailed lower level models (Paunonen &
Ashton, 2001; Paunonen & Jackson, 2000). Several test publishers have developed facet-
level models of the Big 5, which aim to capture both the Big 5 and their constituent lower-
level facets (Costa & McCrae, 1992; Goldberg, 1992; John & Srivastava, 1999). Despite
some discontent over the dominance of the Big 5, the taxonomy provides an organizing
framework for understanding different traits.
1.3. Personality Factors and Well-Being.
The relationship between personality and SWB has received substantial research
attention, with neuroticism and extraversion emerging as important correlates. DeNeve and
Cooper (1998) conducted a large meta-analysis of correlations between SWB and
personality traits. As most of the included studies were conducted prior to the emergence of
FACETS AND WELL-BEING
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the Big 5 as a unifying framework, they categorized the studies according to the Big 5.
More recently, Steel, Schmidt, and Shultz (2008) conducted an updated meta-analysis
presenting separate results using common measures of the Big 5. Both meta-analyses found
that neuroticism had the highest correlation with life satisfaction and negative affectivity
while extraversion had the highest correlation with positive affect.
Only a few studies have examined correlations between personality and PWB
(Bardi & Ryff, 2007; Butkovic, Brkovic, & Bratko, 2012; Grant, Langan-Fox, & Anglim,
2009; Keyes et al., 2002; Schmutte & Ryff, 1997). Such studies suggest that neuroticism,
extraversion and conscientiousness are the major correlates for most PWB dimensions.
More generally, dimensions of PWB tend to be better predicted by personality than are
SWB dimensions, and PWB tends to correlate with more of the Big 5 dimensions.
In order to compare Big 5 correlates of SWB versus PWB, Grant et al. (2009) used
a model constraints approach. After reversing neuroticism and negative affect, they found
support for a model where personality traits varied in their average correlation with well-
being, and well-being dimensions varied in their average correlation with personality.
Average well-being correlations were largest for neuroticism (r=-.44), followed by
extraversion (r=.31), conscientiousness (r=.29), openness (r=.12), and agreeableness
(r=.11). PWB variables tended to correlate more with personality than did SWB variables.
There were also several unique combinations of personality factors and well-being
variables that had larger correlations than would be expected from their component
averages. These were neuroticism with negative affect, extraversion with positive affect
and positive relations, conscientiousness with personal growth and purpose in life,
agreeableness with positive relations, and openness with personal growth. In the opposite
direction, autonomy correlated less with agreeableness than would be expected by
component averages.
1.4. Personality Facets and Well-Being
While the Big 5 provides a useful organizing framework for personality research,
several researchers have raised concerns that a more detailed model of personality is
required to adequately predict criteria of interest such as well-being (Paunonen & Ashton,
2001; Paunonen & Jackson, 2000). To provide a richer model of personality, many
personality tests include both high-level factors, such as the Big 5, and nested lower-level
facets. For example, the NEO-PI-R (Costa & McCrae, 1992) includes six facets for each
factor of the Big 5. So for example, the neuroticism factor is composed of the facets of
anxiety, hostility, depression, self-consciousness, impulsiveness, and vulnerability to stress.
As a result, many studies have examined facet-level correlations with a range of outcome
variables (e.g., Paunonen & Ashton, 2001), and a few have examined facet-level
correlations with well-being (e.g., Quevedo & Abella, 2011; Schimmack, Oishi, Furr, &
Funder, 2004).
Initial research on facet-level prediction of SWB suggested that facets enable a
dramatic increase in prediction of SWB (Quevedo & Abella, 2011; Schimmack et al., 2004;
Steel et al., 2008), yet critical analysis suggests that incremental prediction may be more
modest. For example, Quevedo and Abella (2011) examined prediction of SWB by NEO
Big 5 and 30 facets as well as additional scales of optimism, self-esteem, and perceived
social support. Using stepwise regression predicting life satisfaction they found that
adjusted r-squared was .16 with the NEO Big 5 as predictors, .22 with NEO 30 facets as
predictors, and .29 with NEO 30 facets and additional non-NEO scales including self-
FACETS AND WELL-BEING
4
esteem and perceived social support as predictors. They interpreted their results as
indicating that facets explain double the variance of factors. However, an alternative
interpretation is that self-esteem and perceived social support are not personality traits in
the traditional sense, and thus the adjusted r-squared comparison of .16 for factors versus
.22 (i.e., 37.5% increase) for facets is a more reasonable estimate. A second example is
provided in the meta-analysis of Steel et al. (2008), based on pooled correlations, which
suggested very large incremental prediction for extraversion and neuroticism facets over
corresponding factors. However, the authors acknowledged that the obtained estimates of
incremental prediction were unreasonably large, suggesting that the process of pooling
correlations across studies may have led to unreliable estimates.
In relation to PWB, to our knowledge, Siegler and Brummett (2000) provide the
only facet-level analysis to date. They used data from a pre-existing study, and although
this included items for the 30 NEO facets, dimensions of PWB were approximated based
on available items rather than established PWB scales. The study reported facet-level
correlations with the constructed indices of PWB. No estimate of incremental prediction of
facets over factors was provided.
1.5. The Current Study
There are several problems with existing approaches to performing facet-level
analysis. First, much of the broader facet-analysis literature has relied on small samples
with fewer than 200 respondents, which has produced uncertain estimates of incremental
prediction and increased the biasing effect that can result from having many more facets
than factors as predictors. Second, methods for assessing the incremental prediction of
facets have been employed without explicit articulation of the population parameter being
estimated. Thus, it has been difficult to evaluate the potential bias and uncertainty in
parameter estimates due to stepwise regression with different p-entry rules, adjusted or non-
adjusted r-squared, and use of only some or all factors. Third, existing research has
involved different types of factor-facet comparisons. Specifically, studies vary in their use
of facet-level test inventories, the number of Big 5 factors included, and their inclusion of
variables that are arguably not personality traits. Finally, many studies have only reported
zero-order correlations between facets and criteria, instead of controlling for variance
explained by factors. This leads to a dramatic loss of parsimony without evidence of
whether a facet-level analysis is superior.
In summary, there is a need to provide a realistic picture of the value of a facet-level
analysis for understanding the relationship between personality and SWB and PWB. Some
existing studies suggest that facets may explain double the variance of factors, yet the
combination of methods used and minimal research suggest that this may be an
overestimate, at least when limited to facets within a Big 5 inventory.
The aim of the current study was to examine the relationship between personality
and well-being, focusing particularly on the degree to which 30 personality facets provide
incremental prediction of well-being over and above Big 5 personality factors. To provide a
more comprehensive perspective, both subjective well-being (SWB) and psychological
well-being (PWB) were examined. To provide more accurate estimates, we applied new
methods for obtaining unbiased estimates and confidence intervals of incremental facet
prediction.
Using a moderately large sample, we measured the Big 5 factors and 30 facets of
personality, SWB, and Ryff's (1989) six dimensions of PWB. To overcome issues with
FACETS AND WELL-BEING
5
previous studies, we applied methods to get both point estimates and confidence intervals
for incremental prediction of facets over factors for SWB and PWB. We also assessed
incremental facet prediction using semi-partial correlations controlling for the Big 5. In
general, we predicted a more modest prediction of facets over factors in the range of almost
none to a third more variance explained. We expected facet-level semi-partial correlations
to highlight a small number of meaningful incremental facets, with a factor-level
explanation capturing most of the main story.
Specifically, to overcome previous limitations, we define the parameter of interest
as the population incremental variance explained,
Δ
ρ
2
, by facets,
ρ
facets
( )
2
, over factors,
ρ
factors
( )
2
. Thus,
Δ
ρ
2=
ρ
facets
( )
2
ρ
factors
( )
2
. Since adjusted r-squared is designed to provide an
unbiased estimate of
ρ
2
, we recommend using
Radj facets
( )
2Radj factors
( )
2
as the estimator for
Δ
ρ
2
. We use a double-adjusted-r-squared bootstrap procedure for providing confidence
intervals on the incremental population prediction of facets. Finally, we examine semi-
partial correlations between facets and criteria, where facets are adjusted for factors, in
order to assess incremental contribution of facets in a more parsimonious way than only
reporting zero-order correlations.
In addition to the facet-level analysis, we also examined the relationship between
personality factors and well-being. We decomposed cross-correlations between personality
factors and well-being in order to identify the unique profile of personality correlations for
each type of well-being, thereby replicating and extending previous work by Grant et al.
(2009), who included only four dimensions of PWB. We examined cross-correlations using
all six PWB dimensions. In particular, we were interested in whether the unique
combinations of correlations between personality factors and well-being would replicate.
2. Method
2.1. Participants and Procedure
The sampling method was based on convenience sampling and, as such, participants
were mostly undergraduate psychology students drawn from two Australian universities.
The final cleaned sample for this study included 337 participants (24% male, 76% female).
Ages ranged from 16 to 55 (M=21, SD=8.8). The study was completed online using
Inquisit 3.0 (2011). After reading a plain language statement and providing informed
consent for participation, participants completed demographics, the IPIP-NEO, SWLS,
PANAS, and PWB. The final sample was drawn from an initial sample of 420 participants.
Participants were excluded if any of the following criteria were met: (1) they took less than
500 milliseconds to respond to 10 or more items out of the 409 personality and well-being
items (n= 72), or (2) they failed to answer one or more personality or well-being items
(n=14).
2.2. Instruments
International Personality Item Pool (IPIP) Scales Measuring Constructs
Similar to 30 NEO-PI-R Facet Scales. This inventory provides a measure of both Big 5
personality factors (neuroticism, extraversion, conscientiousness, agreeableness, openness)
as well as 30 facets representing six facets per factor (Goldberg, 1999; Goldberg et al.,
2006). The 30 facets are closely aligned with those of the 30 item NEO-PI-R (Costa &
FACETS AND WELL-BEING
6
McCrae, 2008). The IPIP measure has the advantage of being in the public domain
permitting full disclosure of item content and sharing of raw data. The test is composed of
300 items, 10 items per facet, and 60 items per factor. Each item is rated on a 5-point scale
measuring the degree to which it accurately describes the participant (1 = very inaccurate, 2
= moderately inaccurate, 3 = neither inaccurate nor accurate, 4 = moderately accurate, 5 =
very accurate). Scales were computed as the mean of items after any required item-reversal.
Initial evidence regarding the reliability and predictive validity of the IPIP scales is
favorable (Goldberg, 1999). The scales have an average coefficient alpha of .80 and an
average correlation with corresponding NEO-PI-R scales of .73, or .94 when corrected for
attenuation due to the unreliability of the scales in each pair (Goldberg, 1999). The IPIP
scales show good predictive utility for health-related criterion variables. Johnson’s (2000)
factor analysis (principal components) of the IPIP facet-level scales showed that a five-
factor solution accounted for 64.9% of the variance. Facets generally loaded as expected,
and the five factors were clearly defined by the five sets of six facet scales, with the facet
scales within a given domain showing primary loadings on the domain factor in 27 out of
30 cases.
Scales of Psychological Well-being (Ryff, 1989). This inventory measures six
dimensions of PWB: positive relations, autonomy, environmental mastery, personal
growth, purpose in life, and self-acceptance. Each item was rated on a 6-point Likert-style
response scale (1 = strongly disagree, 2 = disagree somewhat, 3= disagree slightly, 4 =
agree slightly, 5 = agree somewhat, 6 = strongly agree). Responses were scored as the mean
after any required item-reversal. The 14-item per scale version was used to ensure
reliability for high quality measurement. Specifically, for the 14-item version, Ryff and
Essex (1992) report internal consistency alpha coefficients ranging from .86 to .93. Factor
analytic evidence suggests that (a) self-acceptance and environmental mastery are closely
related to traditional SWB measures, (b) personal growth, positive relations with others and
purpose in life share a higher order factor, and (c) autonomy is more distinct, being more
related to variables concerned with power and control (Ryff, 1989).
Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). This
well-established 5-item scale was used to measure global life satisfaction. Each item was
rated on a 7-point Likert-style response scale (1 = strongly disagree, 2 = disagree, 3 =
slightly disagree, 4 = neither agree nor disagree, 5 = slightly agree, 6 = agree, 7 = strongly
agree). The scale scores were computed as the mean of the items. Diener et al. (1985)
reported high internal consistency and high temporal reliability for the scale. The two-
month test-retest reliability in their study was .82 with a Cronbach’s alpha of .87. Item
loadings ranged from .61 to .84, with a single factor accounting for 66% of the variance. In
addition, the scale correlated significantly with related measures (e.g., personality, self-
esteem, symptom checklist) and was uncontaminated by social desirability.
Positive and Negative Affectivity Schedule (PANAS; Watson, Clark, &
Tellegen, 1988). The PANAS consists of two subscales that measure positive and negative
affect. In the current study, the instrument was administered using "past few weeks" time
instructions. Participants rated the extent to which they had experienced each of 20
emotions over the past few weeks on a 5-point scale (1 = very slightly or not at all, 2 = a
little, 3 = moderately, 4 = quite a bit, 5 = extremely). Scales were scored as the mean of the
items. Watson et al. (1988) reported that reliabilities (Cronbach’s alpha) were within an
acceptable range for both positive and negative affect (.86 to .90) and were unaffected by
FACETS AND WELL-BEING
7
the time instructions used. Both subscales demonstrated satisfactory test-retest reliability
over a two-month period. The same authors reported a low (negative) correlation between
positive and negative affect, with adjectives loading on the appropriate factor. The
subscales showed good external validity, correlating significantly with measures of anxiety,
depression, and distress.
2.3. Data Analysis
Data was analyzed using R 3.0.1 (R Core Team, 2013). In the interests of
reproducible research, all code used to perform the analysis and all data and metadata is
available from figshare.com (Analysis for "Predicting Psychological and Subjective Well-
Being"; http://dx.doi.org/10.6084/m9.figshare.972885 ).
3. Results
3.1. Descriptive Statistics, Reliabilities, and Factor Analysis
Table 1 and Table 2 report descriptive statistics and reliability for all scales used in
the study. Reliability was generally very good with mean Cronbach's alpha of .81 for
personality factors, .80 for personality facets, and .88 for well-being scales.
Table 1
Descriptive Statistics and Cronbach's Alpha Reliability for Personality Factors and Well-
Being Scales
Variable
α
M
SD
Neuroticism
.85
2.91
0.59
Extraversion
.84
3.37
0.54
Openness
.72
3.64
0.41
Agreeableness
.79
3.64
0.45
Conscientiousness
.84
3.49
0.51
Satisfaction with Life
.88
4.47
1.44
Positive Affect
.89
3.47
0.79
Negative Affect
.87
2.21
0.80
Positive Relations
.89
4.42
0.90
Autonomy
.85
4.08
0.83
Environmental Mastery
.88
4.07
0.87
Personal Growth
.86
4.85
0.69
Purpose in Life
.89
4.36
0.91
Self-Acceptance
.94
4.06
1.07
FACETS AND WELL-BEING
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Table 2
Descriptive Statistics and Cronbach's Alpha Reliability for Personality Facets
Variable
α
M
SD
N1: Anxiety
.83
3.10
0.73
N2: Anger
.88
2.85
0.80
N3: Depression
.90
2.61
0.88
N4: Self-consciousness
.83
2.94
0.77
N5: Immoderation
.77
3.24
0.70
N6: Vulnerability
.85
2.73
0.75
E1: Friendliness
.88
3.60
0.78
E2: Gregariousness
.87
3.32
0.84
E3: Assertiveness
.84
3.24
0.74
E4: Activity Level
.72
3.01
0.57
E5: Excitement Seeking
.82
3.30
0.72
E6: Cheerfulness
.82
3.75
0.67
O1: Imagination
.82
3.73
0.71
O2: Artistic Interests
.75
3.96
0.62
O3: Emotionality
.77
3.76
0.62
O4: Adventurousness
.76
3.47
0.59
O5: Intellect
.79
3.70
0.64
O6: Liberalism
.65
3.21
0.60
A1: Trust
.85
3.43
0.70
A2: Morality
.77
3.85
0.62
A3: Altruism
.79
4.03
0.57
A4: Cooperation
.75
3.55
0.67
A5: Modesty
.80
3.28
0.70
A6: Sympathy
.73
3.68
0.59
C1: Self-Efficacy
.80
3.69
0.57
C2: Orderliness
.84
3.33
0.78
C3: Dutifulness
.69
3.94
0.50
C4: Achievement Striving
.84
3.67
0.70
C5: Self-Discipline
.89
3.01
0.83
C6: Cautiousness
.80
3.26
0.67
Exploratory factor analysis was performed on facet scale scores to examine whether
the 30 facets loaded on the proposed five factors. Five factors were extracted using
maximum likelihood estimation with Promax rotation. Overall, the factor solution showed
good correspondence to the theorized structure. There was a clear drop in the scree plot
after five factors and the parallel analysis also suggested five factors. Five factors explained
58.9% of the variance. Of the 30 facets, 28 facets loaded above .35, and 25 loaded
maximally on their theorized factor. Out of the 120 cross-loadings of facets on non-
FACETS AND WELL-BEING
9
theorized factors, only 12 (10%) loaded above .35. Prominent cross-loadings included self-
consciousness (-.51), trust (.56), and altruism (.50) on extraversion; activity level (.59) on
conscientiousness; dutifulness (.51) on agreeableness; and emotionality (.51) on
neuroticism.
3.2. Correlations between Big 5 Personality and Well-Being
The full correlation matrix between factors, facets, and well-being measures is
available from the online repository mentioned in the Method. Table 3 shows the
correlations between personality factors and well-being scales. To better understand the
cross-correlations between personality factors and well-being, a decomposition was
performed. First, neuroticism and negative affect were reversed, so that all variables were
positively framed. Second, cross-correlations were obtained between personality factors
and well-being variables denoted by
rij
where
i=1,,I
, and
j=1,,J
indexing the
I=5
personality factors and
J=9
well-being variables respectively. Then, the overall
average cross-correlation was obtained as
r
.. =1
IJ i
j
rij
,
as well as the average deviation for cross-correlations for each well-being variable
r
.j=1
Ii
rij
and personality factor
ri.=1
Jj
rij
.
Thus, observed correlations can be decomposed into the overall average correlation,
average deviation of the personality correlations, average deviation of the well-being
correlations, and a residual.
rij =r
.. +(r
.jr
.. )+(ri.r
.. )+uij
.
So for example, the expected correlation between openness and personal growth was the
grand mean (.39) plus the deviation from the grand mean of the average openness
correlation (-.16) plus the deviation from the grand mean of the average personal growth
correlation (.03) which equals .26, but the obtained correlation was .55; the residual was
therefore .55 - .26 = .29. Table 4 reports this analysis. Large positive residual correlations
indicate that the two variables correlate more with each other than would be expected based
on how much the variables correlate generally with other variables. Thus, such correlations
help to highlight the unique personality profile of each well-being variable.
FACETS AND WELL-BEING
10
Table 3
Correlations between Big 5 Personality and Well-Being Scales
Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
1.
Neuroticism
---
2.
Extraversion
-.47
---
3.
Openness
-.07
.26
---
4.
Agreeableness
-.21
.02
.23
---
5.
Conscientiousness
-.54
.14
.04
.36
---
6.
Satisfaction with Life
-.57
.51
.13
.11
.35
---
7.
Positive Affect
-.52
.56
.22
.20
.45
.51
---
8.
Negative Affect
.76
-.40
-.06
-.22
-.42
-.51
-.35
---
9.
Positive Relations
-.48
.58
.25
.38
.28
.55
.43
-.45
---
10.
Autonomy
-.53
.26
.27
.04
.34
.33
.27
-.49
.32
---
11.
Environmental Mastery
-.75
.56
.13
.22
.60
.69
.58
-.68
.64
.48
---
12.
Personal Growth
-.42
.43
.55
.31
.40
.44
.49
-.39
.56
.51
.55
---
13.
Purpose in Life
-.58
.50
.25
.28
.65
.63
.62
-.49
.63
.43
.78
.69
---
14.
Self-Acceptance
-.74
.58
.20
.18
.46
.78
.56
-.64
.68
.54
.80
.60
.76
Note. |r|.11 indicates p<.05; |r| .15 indicates p<.01 and are bolded.
The average correlation between personality and well-being was moderately large
(.39). On the well-being side, there was not a lot of variation in average correlations with
personality, although autonomy was lower than the others. The average absolute cross-
correlation was larger for psychological well-being scales (.41) than for subjective well-
being scales (.36). In terms of personality, the common ordering emerged of neuroticism
being most important by some margin followed by extraversion and conscientiousness, and
then with much weaker average cross-correlations for openness and agreeableness. In
addition to these general patterns, there were several notable residual cross-correlations
shown in Table 4. For example, the following correlated substantially more than was
implied by average cross-correlations for constituent variables: personal growth with
openness, positive relations with agreeableness, neuroticism with negative affect, and
purpose in life with conscientiousness. Interestingly, there were several negative residual
cross-correlations: openness with negative affect, personal growth with neuroticism,
environmental mastery with openness, and positive relations with conscientiousness. Thus,
for example, the profile of correlations for positive relations indicates stronger relations
with agreeableness and weaker relations with conscientiousness than is typically the case
for well-being variables.
FACETS AND WELL-BEING
11
Table 4
Decomposition of Cross-Correlations between Well-Being and Big 5 Personality
Personality
Mean Well-Being
Deviation
N-
E
O
A
C
Residual Correlations
Satisfaction with Life
.04
.08
-.04
-.04
-.03
-.06
Positive Affect
-.07
.08
-.01
-.01
.02
.00
Reversed Negative Affect
.19
-.06
-.15
.02
.00
-.02
Positive Relations
-.12
.10
.02
.17
-.16
.00
Autonomy
.04
-.12
.14
-.08
.01
-.10
Environmental Mastery
.09
.02
-.16
-.06
.10
.06
Personal Growth
-.21
-.09
.29
.07
-.07
.03
Purpose in Life
-.07
-.05
-.04
.01
.15
.06
Self-Acceptance
.10
.06
-.06
-.08
-.02
.04
Mean Personality
Deviation
.20
.09
-.16
-.18
.05
.39a
Note. Mean deviations are the average cross-correlation for cross-correlations containing
the focal variable minus the overall mean of cross-correlations. Residual correlations equal
the actual cross-correlation minus the correlation predicted by summing the overall mean
cross-correlations and the two mean deviations for the constituent variables. Absolute
residual cross-correlations greater than or equal to 0.15 are bolded.
a Overall mean cross-correlations between personality and well-being variables
3.3. Facet-level correlations with Well-Being
Table 5 reports zero-order correlations between personality facets and well-being
scales. and semi-partial correlations between personality facets and well-being scales (see
parentheses), where facets have been adjusted for their shared variance with personality
factors. The zero-order correlations present a complex pattern with many large correlations
often consistent with patterns at the factor-level. The semi-partial correlations focus purely
on the incremental prediction of facets over factors. Notable semi-partial correlations are
depression with satisfaction with life (r = -.28), positive relations (r = -.20), purpose in life
(r = -.24), and self-acceptance (r = -.37); self-consciousness with autonomy (r = .20);
assertiveness with autonomy (r =.18); excitement seeking with positive relations (r = -.19);
cheerfulness with satisfaction with life (r = .26), cooperation with autonomy (r = -.19); and
achievement striving with purpose in life (r = .21). The table also reports the proportion of
variance in the facet that is not explained by factors. The mean proportion of unique
variance was 35.6 percent.
FACETS AND WELL-BEING
12
Table 5
Zero-Order Correlations between Facets and Well-being (Semi-partial Correlations with
Variance Explained by Factors Removed from Facets Shown in Parentheses)
SWL
PA
NA
PR
AU
EM
PG
PL
SA
Unique
Meanb
N1: Anxiety
-.47 (.02)
-.39 (.00)
.64 (-.01)
-.35 (.07)
-.41 (.06)
-.58 (.02)
-.29 (-.01)
-.38 (.05)
-.57 (.09)
.21
.03
N2: Anger
-.31 (.05)
-.29 (-.03)
.54 (-.02)
-.27 (.08)
-.25 (.17)
-.43 (.05)
-.22 (.04)
-.30 (.04)
-.39 (.13)
.33
.06
N3: Depression
-.67 (-.28)
-.56 (-.09)
.70 (.12)
-.59 (-.20)
-.45 (-.03)
-.76 (-.14)
-.48 (-.13)
-.70 (-.24)
-.86 (-.37)
.27
-.18
N4: Self-consciousness
-.46 (.14)
-.45 (.11)
.56 (-.02)
-.47 (-.02)
-.53 (-.20)
-.59 (.08)
-.40 (-.01)
-.48 (.05)
-.63 (.04)
.24
.02
N5: Immoderation
-.16 (.09)
-.16 (.05)
.32 (-.09)
-.07 (.07)
-.29 (.01)
-.32 (.07)
-.10 (.12)
-.24 (.12)
-.24 (.10)
.49
.08
N6: Vulnerability
-.49 (.00)
-.46 (-.03)
.65 (.03)
-.37 (.02)
-.49 (-.04)
-.68 (-.08)
-.37 (-.02)
-.50 (-.01)
-.61 (.04)
.27
-.02
E1: Friendliness
.44 (-.03)
.49 (-.03)
-.41 (.00)
.67 (.17)
.22 (-.02)
.55 (.04)
.37 (-.07)
.49 (.04)
.56 (.03)
.23
.01
E2: Gregariousness
.34 (-.05)
.34 (-.11)
-.27 (-.01)
.46 (-.01)
.03 (-.15)
.36 (-.04)
.19 (-.12)
.28 (-.06)
.37 (-.07)
.27
-.07
E3: Assertiveness
.39 (-.08)
.46 (-.03)
-.31 (.03)
.39 (.01)
.39 (.18)
.48 (-.03)
.41 (.10)
.45 (.00)
.50 (.01)
.29
.01
E4: Activity Level
.33 (-.02)
.49 (.08)
-.30 (.01)
.35 (.01)
.26 (.05)
.51 (.04)
.38 (.07)
.53 (.06)
.44 (.03)
.55
.03
E5: Excitement Seeking
.21 (-.03)
.23 (.01)
-.08 (.05)
.15 (-.19)
.05 (-.07)
.10 (-.12)
.15 (-.03)
.04 (-.12)
.16 (-.13)
.38
-.08
E6: Cheerfulness
.57 (.26)
.52 (.09)
-.41 (-.08)
.57 (.06)
.25 (.02)
.54 (.12)
.46 (.07)
.49 (.11)
.59 (.16)
.30
.11
O1: Imagination
.06 (.07)
.07 (.01)
.06 (.00)
.07 (-.04)
.08 (-.10)
-.04 (.00)
.25 (-.09)
.04 (-.02)
.02 (-.04)
.41
-.02
O2: Artistic Interests
.06 (-.01)
.19 (.06)
-.05 (-.02)
.16 (-.04)
.08 (-.10)
.05 (-.06)
.32 (-.08)
.16 (-.06)
.13 (.01)
.54
-.03
O3: Emotionality
.01 (.04)
.13 (.02)
.16 (.03)
.25 (.14)
.10 (.14)
-.01 (.02)
.41 (.12)
.22 (.12)
.08 (.13)
.40
.08
O4: Adventurousness
.27 (-.01)
.29 (-.02)
-.27 (.01)
.30 (-.07)
.27 (-.04)
.31 (.00)
.51 (.11)
.32 (.02)
.34 (-.04)
.53
.00
O5: Intellect
.18 (-.06)
.25 (-.04)
-.19 (.04)
.21 (.00)
.44 (.14)
.31 (.02)
.50 (.05)
.33 (-.04)
.26 (-.08)
.45
.00
O6: Liberalism
-.10 (-.03)
-.08 (-.03)
.06 (-.04)
-.02 (.02)
.07 (-.02)
-.10 (.03)
.13 (-.07)
-.06 (-.02)
-.04 (.02)
.60
-.01
A1: Trust
.35 (.05)
.36 (.01)
-.38 (.00)
.59 (.14)
.13 (-.06)
.44 (.05)
.37 (.01)
.39 (.03)
.45 (.07)
.43
.03
A2: Morality
.05 (-.02)
.09 (-.07)
-.20 (-.06)
.21 (-.03)
.08 (.11)
.22 (.07)
.18 (-.02)
.28 (.06)
.15 (.07)
.31
.02
A3: Altruism
.26 (-.01)
.41 (.07)
-.24 (.07)
.52 (.03)
.15 (.04)
.37 (-.02)
.47 (.07)
.45 (.02)
.35 (.02)
.24
.02
A4: Cooperation
.07 (.03)
.08 (.00)
-.22 (-.03)
.17 (-.07)
-.06 (-.19)
.13 (-.04)
.14 (-.06)
.17 (-.01)
.11 (.00)
.33
-.03
A5: Modesty
-.32 (-.11)
-.22 (.01)
.14 (-.03)
-.17 (-.11)
-.15 (.09)
-.26 (-.01)
-.15 (-.04)
-.24 (-.09)
-.38 (-.16)
.43
-.04
A6: Sympathy
.09 (.07)
.14 (.00)
-.01 (.09)
.33 (.04)
.03 (.02)
.05 (-.07)
.36 (.06)
.20 (.01)
.11 (.04)
.32
.01
C1: Self-Efficacy
.49 (.02)
.51 (-.05)
-.52 (.01)
.44 (.07)
.53 (.14)
.70 (.05)
.56 (.11)
.70 (.07)
.64 (.06)
.26
.05
C2: Orderliness
.07 (-.09)
.22 (.01)
-.17 (-.02)
.02 (-.05)
.06 (-.10)
.25 (-.09)
.11 (-.05)
.30 (-.12)
.13 (-.06)
.36
-.06
C3: Dutifulness
.14 (-.09)
.25 (-.07)
-.26 (.01)
.22 (-.05)
.23 (.11)
.37 (-.01)
.28 (-.02)
.41 (-.06)
.26 (-.01)
.35
-.02
C4: Achievement Striving
.42 (.13)
.54 (.16)
-.31 (.07)
.30 (.01)
.33 (.04)
.57 (.04)
.47 (.12)
.72 (.21)
.48 (.08)
.29
.08
C5: Self-Discipline
.41 (.05)
.49 (.08)
-.44 (.00)
.27 (-.04)
.30 (-.12)
.62 (.05)
.34 (-.06)
.60 (-.02)
.47 (-.03)
.25
-.01
C6: Cautiousness
.06 (-.01)
.03 (-.15)
-.22 (-.05)
.05 (.08)
.17 (.01)
.23 (-.01)
.11 (-.06)
.26 (-.04)
.12 (-.01)
.35
-.02
Note. SWL = Satisfaction with life, PA = Positive affect, NA = Negative affect, PR =
Positive relations, AU = Autonomy, EM = Emotional mastery, PG = Personal growth, PL =
Purpose in life, SA = Self-acceptance.
Significant semi-partial correlations (p<.001) are bolded.
a Proportion of personality variance not predicted by Big 5 factors of personality.
b Average semi-partial correlation for facet across well-being variables, where negative
affect has been reversed.
FACETS AND WELL-BEING
13
3.4. Prediction of Well-Being from Personality Factors and Facets
A set of linear multiple regressions (direct entry) was conducted predicting each
well-being variable using the Big 5 as predictors. Table 6 reports the obtained standardized
regression coefficients. Table 7 reports the corresponding estimate of population variance
explained (i.e.,
Radj
2(factors)
. In general, the Big 5 explained substantial variance. In all cases,
the Olkin-Pratt formula for adjusted r-squared was used as it aligns with the assumption
that the predictor variables are a random sample from a population. Average
Radj
2(factors)
was
.55 for PWB variables and .48 for SWB variables.
Table 6
Standardized Regression Coefficients Predicting Well-Being Scales from Big 5 Personality
DV
Standardized Beta
N
E
O
A
C
Satisfaction with Life
-.37
.31
.02
-.01
.11
Positive Affect
-.14
.43
.08
.03
.31
Negative Affect
.71
-.07
.03
-.07
.00
Positive Relations
-.18
.49
.03
.33
-.01
Autonomy
-.51
-.07
.29
-.18
.13
Environmental Mastery
-.42
.31
.00
.01
.32
Personal Growth
-.13
.21
.45
.08
.25
Purpose in Life
-.13
.33
.13
.03
.52
Self-Acceptance
-.52
.29
.09
.00
.14
Note. Significant coefficients (p < .01) are bolded.
Table 7 reports the adjusted r-squared predicting each well-being scale, first with
the five personality factors as predictors, and then with the 30 personality facets as
predictors. Estimates of incremental population variance explained,
Δ
ρ
2
, were obtained by
subtracting
Radj
2
for factors from
Radj
2
for facets. Confidence intervals were obtained using a
double-adjusted-r squared bootstrap method. This method involves first sampling with
replacement from the data to generate K bootstrap samples of equal size as the raw data.
For each bootstrap sample,
Rfactors
( )
2
and
Rfacets
( )
2
is obtained. Then for both
R2
values, the
formula for adjusted r-squared is applied twice
Radj
2=f f R2
( )
( )
where
f.
( )
is the formula for adjusted r-squared. The adjustment formula is applied twice,
first to adjust for the bias associated with the bootstrap treating the sample as the
population, and second to adjust for the standard bias in estimating
ρ
2
from sample data.
Finally the estimate is obtained for the particular bootstrap sample as
Δˆ
ρ
2=
Radj(facets)
2
Radj(factors)
2
.To obtain 95 percent confidence intervals, the .025 and .975
sample quantiles are obtained from the K bootstrap estimates.
FACETS AND WELL-BEING
14
Table 7
Variance Explained in Well-Being Scales by Big 5 Personality and 30 Facets of Personality
DV
Radj
2(factors)
Radj
2(facets)
Δ
ˆ
ρ
2
(95% CI)
Satisfaction with Life
.40
.52
.12 (.06 to .18)
Positive Affect
.47
.51
.04 (.00 to .09)
Negative Affect
.58
.59
.01 (.00 to .05)
Positive Relations
.50
.57
.07 (.02 to .13)
Autonomy
.36
.50
.14 (.07 to .21)
Environmental Mastery
.68
.73
.04 (.01 to .08)
Personal Growth
.51
.59
.07 (.02 to .13)
Purpose in Life
.62
.72
.11 (.07 to .15)
Self-Acceptance
.63
.78
.15 (.10 to .20)
Note.
Δ
ˆ
ρ
2=Radj
2(facets) Radj
2(factors)
The mean ratio of facets to factors adjusted r-squared was 1.17. The mean
Δˆ
ρ
2
was
.08. In general,
Δˆ
ρ
2
was larger for psychological well-being (mean
Δˆ
ρ
2
=.10) than for
subjective well-being (mean
Δˆ
ρ
2
=.06). In particular, negative affect, which correlated very
highly with neuroticism, showed minimal incremental facet prediction. Positive affect and
environmental mastery showed small amounts of incremental facet prediction. Autonomy
and self-acceptance showed the largest amounts of incremental facet prediction.
4. Discussion
This study aimed to examine the relationship between personality and well-being. In
particular, it examined the incremental prediction of personality facets over Big 5 factors.
In general, personality and well-being showed substantial correlation. Facets accounted for
additional population variance in well-being but the increase was often modest, ranging
from almost no additional variance explained to around a third more variance explained.
The subsequent discussion elaborates, first, on factor-level relationships, then on facet-level
relationships, and finally on broader theoretical and methodological issues.
4.1. Well-being and the Big 5
There were several general patterns in the cross-correlations between Big 5
personality and well-being. First, neuroticism was clearly the largest and most consistent
correlate of well-being; then came extraversion, closely followed by conscientiousness.
These findings are generally consistent with DeNeve and Cooper (1998) and Steel et al.
(2008), whose meta-analytic studies focused on subjective well-being, and are consistent
with Grant et al. (2009) and Keyes et al. (2002). While agreeableness and openness still had
meaningful correlations, these were less consistent and generally smaller. Second, PWB
dimensions showed a slightly stronger relationship with the Big 5 than did SWB
dimensions. Butkovic et al. (2012) likewise reported that personality explained more
variance in PWB than SWB. Third, consistent with Schmutte and Ryff (1997), PWB
showed a more diverse relationship with personality than did SWB. In broad terms, SWB
dimensions were often well predicted by neuroticism and extraversion, whereas
agreeableness, openness, and conscientiousness were important correlates of several PWB
FACETS AND WELL-BEING
15
dimensions (c.f. Grant et al., 2009). In addition, residual cross-correlations and
standardized betas highlighted several relationships between PWB and the Big 5 that shed
light on the nature of the PWB construct contributing to broader discussion regarding the
meaning of PWB (e.g., Ryff & Singer, 1998, 2006). These points are elaborated in more
detail below. Taken together, the results reinforce the notion that the key dispositional
influences on well-being vary across well-being dimensions (Grant et al., 2009).
There were no significant residual cross-correlations for SWL, indicating that there
were no personality variables that correlated more strongly with SWB than expected based
on their correlations with other well-being variables. Neuroticism had the strongest
standardized beta for SWL, which was also predicted by extraversion and to a lesser extent
conscientiousness. In contrast, extraversion was the strongest predictor of PA, which was
also predicted by conscientiousness and neuroticism. There were no significant residual
cross-correlations for PA, but neuroticism and openness showed significant residual cross-
correlations for NA. Neuroticism was the only significant predictor of NA. The finding that
SWB dimensions were well predicted by neuroticism and extraversion is consistent with
meta-analytic studies (DeNeve & Cooper, 1998; Steel et al., 2008).
Personal growth had a positive residual cross-correlation with openness and a
negative residual cross-correlation with neuroticism. Bardi and Ryff (2007) similarly
reported that individuals who were higher on openness and lower on neuroticism reported
higher personal growth. Standardized betas showed that personal growth was predicted by
all five traits, with openness emerging as the strongest predictor. This strong relationship
between personal growth and openness is consistent with Schmutte and Ryff (1997).
Personal growth items include the perception that the individual is growing, a belief that
change is possible, and valuing of change (Ryff, 1989). Thus, beyond the pure well-being
elements, the measure of personal growth also captures a disposition to the concept that
growth and change is positive, which helps to explain the relationship with openness.
Arguably, these more attitudinal elements go beyond pure well-being and actually suggest a
humanistic value system regarding what is the good life.
Autonomy was one of the least well-predicted well-being dimensions. There were
no significant residual cross-correlations for this dimension, although standardized betas
indicated that the dimension was predicted by greater openness and conscientiousness and
less neuroticism and agreeableness, with neuroticism being the strongest predictor.
Previous studies have also primarily identified an association between autonomy and
agreeableness or neuroticism (Grant et al., 2009; Schmutte & Ryff, 1997), perhaps
reflecting the focus of autonomy items on a lack of care for what others think or low self-
consciousness. However, there is also arguably an implicit assumption that autonomy
involves some degree of independent thinking. Items capture self-confidence and as well as
a spectrum of not being excessively influenced by others to more extreme independence of
thought. Emotional stability (the inverse of neuroticism) and antagonism (the inverse of
agreeableness) capturinquie elements of self-confidence and independent thinking
respectively. A readiness to not conform can go against being agreeable. The Ryff scale
measures a relatively social conception of autonomy. While much of the autonomy
construct captures positive aspects, there is an aspect that might actually result in less well-
being. For instance, not listening to the views of others, never sacrificing one's needs for
the needs of others, or an inability to accept the rituals and values of a society could have a
range of negative consequences. Similarly, some individuals may place less value on
FACETS AND WELL-BEING
16
independence of thought thereby further reducing the relationship between autonomy and
well-being.
Positive relations had a positive residual cross-correlation with agreeableness and a
negative residual cross-correlation with conscientiousness and it was predicted particularly
by extraversion, agreeableness and neuroticism. Items for positive relations capture not
only whether a person has good friends, but also whether the person values interactions
with others and sees him or herself as capable of being a good friend. In this sense,
extraversion relates to both social engagement and a desire to be social, and agreeableness
captures many aspects related to being friendly and accommodating. Consistent with this,
previous studies have primarily linked positive relations to agreeableness and extraversion
(Grant et al., 2009; Schmutte & Ryff, 1997). However, the Ryff measure of positive
relations goes beyond measuring presence of or satisfaction with interpersonal
relationships, also measuring evaluative judgments about the importance of friendship and
skills in friendship formation, suggesting that other personality dimensions are also
important. Indeed, Siegler and Brummett (2000) linked positive relations with select facets
of all Big 5 traits.
Purpose in life had a strong positive residual cross-correlation with
conscientiousness. Conscientiousness also had the strongest standardized beta, followed by
extraversion while neuroticism and openness showed a weaker relationship with purpose in
life. The strong association between purpose in life and conscientiousness is consistent with
previous work (Grant et al., 2009; Schmutte & Ryff, 1997), and others have also
documented the associations between this dimension and extraversion and neuroticism
(Schmutte & Ryff, 1997; Siegler & Brummett, 2000). Purpose in life items focus on having
longer term projects, getting pleasure from moving towards goals, and aspects of life
satisfaction.
Self-acceptance and environmental mastery tended to have similar patterns to
satisfaction with life, with significant betas for neuroticism, extraversion and
conscientiousness (self-acceptance was also predicted by openness, though to a lesser
extent). Environmental mastery had a significant residual cross-correlation with openness;
there were no significant residual cross-correlations for self-acceptance. Both of these PWB
dimensions have been flagged (Bouchard Jr & Loehlin, 2001) as more reflective of SWB
than PWB. Self-acceptance items largely focus on self-esteem, positive comparison of self
versus others, and elements of life satisfaction. Environmental mastery focuses on a sense
control, with elements of life satisfaction.
It is noteworthy that the Big Five predicted self-acceptance and environmental
mastery more strongly than they predicted satisfaction with life. Once again, this is
consistent with previous work supporting a stronger relationship between personality and
well-being for PWB than SWB (Butkovic et al., 2012; Grant et al., 2009) and reinforces the
distinctiveness of these dimensions.
At present, the Ryff scales seem to incorporate more than just whether the well-
being aspects are present; they also embody a range of assumptions about what constitutes
the good life. Of course, psychological theory underpins the importance of such
dimensions, but each dimension captures a unique flavor of the concept of PWB and also
seems to measure the degree to which that dimension is characteristically valued by the
individual. Thus, open people may search for personal growth. Disagreeable people may be
more willing to assert their opinion in defiance of what a group thinks. And conscientious
FACETS AND WELL-BEING
17
people may value purpose in life and seek to achieve projects and plans. While any measure
of well-being will have a particular orientation, there is a risk of imposing a humanistic
value system on to people by labeling such dimensions as well-being rather than using the
more theoretically neutral SWB dimensions.
4.2. Incremental Prediction of Well-Being by Facets
Overall, personality facets provided a meaningful increase in the variance explained
in well-being over and above the personality factors. However, there was no doubling of
explained variance as eluded to in the extant literature (see Quevedo & Abella, 2011).
Instead, increases ranged from almost nothing to around a third more. Well-being
dimensions varied substantially in the size of this increase. Positive and negative affect
both showed minimal increases, which is inconsistent with the very large incremental
prediction achieved for facets over factors for some traits in meta-analytic research (Steel et
al., 2008). In contrast, self-acceptance, autonomy, satisfaction with life, and purpose in life
showed fairly large increases. However, this is the first study to estimate the incremental
prediction of facets over factors for PWB and the results await replication. Furthermore, the
cause of the variation in incremental variance is not entirely clear and warrants exploration
in future research.
Examination of the semi-partial correlations between facets and well-being helped
to explain the incremental prediction by facets. For example, autonomy was associated with
more anger and assertiveness, and less with self-consciousness, cooperation, and
gregariousness, reinforcing the above notion that this dimension reflects the degree of
importance placed on what others think and independent thought. Purpose in life had a
strong link with achievement striving, reinforcing the goal-directed emphasis of this
dimension. More generally, depression and cheerfulness emerged as incremental correlates
for many well-being variables. In many cases, these correlates seemed to be related to
overlap in the conceptual nature of the constructs (for further discussion of construct
overlap in this context, see Schmutte & Ryff, 1997).
Overall, the bootstrapping and the semi-partial correlations helped to explain the
incremental contribution of facets. First, bootstrapping highlighted the uncertainty around
estimates of incremental variance explained. While the size of the confidence intervals
varied, the sample size of approximately 300 was sufficient for 95% confidence intervals to
provide a good understanding of the ‘ball park’ of the effect size. Also, the semi-partial
correlations helped to yield a more parsimonious view of the incremental role of facets.
Compared to zero-order correlations, semi-partial correlations flagged only a select few
facets, taking factor correlations as a starting point and presenting a more parsimonious
view. Compared to stepwise regression, the results were less binary in terms of inclusion of
predictors.
4.3. Incremental Facet Prediction
Conclusions about incremental facet prediction in the present study are based on the
inclusion of nested facets. As Quevedo and Abella (2011) found, inclusions of non-nested
facets can substantially increase the incremental prediction of facets. There are several
reasons for this. First, by construction, factors capture some of the variance of nested facets.
So for instance, when comparing facets to the Big 5 from a given test, incremental
prediction should be greater when facets come from a different test. However, by taking
facets from a different test, some of the incremental variance would be obtained by the
slightly different measurement of the Big 5. Second, the selection of facets in a personality
FACETS AND WELL-BEING
18
test may be partially constrained by the need to fit within a Big 5 theoretical framework.
Thus, personality traits not captured by the Big 5 might be omitted. However, alternatively,
there is the potential to include variables that are not typically considered personality traits,
or that get even closer to well-being related constructs.
This raises questions about what is a natural or useful way of framing incremental
prediction of well-being from personality facets. It also relates to issues of how personality
tests should be constructed in order to both reliably measure the Big 5 but also capture
diverse facets that assist with incremental prediction. At the very least, it is necessary to be
clear when describing estimates of incremental prediction as to what class of facets is being
included.
Overall, the results of this study support the value of a facet-level analysis, but
suggest that the contribution is more modest than some previous studies have suggested.
The increases in estimated population prediction seen in this study are of a magnitude that
justifies the increased complexity. Furthermore, in contrast to the complexity of zero-order
correlation matrices, the semi-partial correlation analysis helps to provide a parsimonious
picture of the relevant facets that support the incremental prediction.
4.4. Construct Overlap and Causal Pathways
Beyond identifying the correlations between personality and well-being, there is the
broader issue of the degree to which such relationships are based on construct overlap or
some form of causal relationship. Examination of item content strongly supports the idea
that construct overlap explains many of the observed correlations. Neuroticism measures
the tendency to experience a range of negative emotions, and clearly negative affect is
almost synonymous with this tendency. In the case of extraversion, there is a mixture of
items, some of which pertain directly to the experience of positive emotions whereas others
pertain more to experiences that often elicit positive emotions. However, personality traits
can be seen as more stable than well-being and thus as the cause of well-being. Arguments
can also be made for how personality traits influence the motivation, environment, and
interpretive lens of the individual, which in turn influences well-being. A recent study by
Soto (2014) of the longitudinal relationship between the Big 5 and the subjective well-
being dimensions supported the notion that personality traits and well-being dimensions
influence one another reciprocally over time.
In some respects a facet-level analysis provides greater scope for both forms of
prediction, but perhaps greater construct overlap is particularly likely. The Big 5 is
necessarily broad, yet the chance that a well-being scale is going to overlap substantively
with a specific facet scale increases. For example, depression seems to be the aspect of
neuroticism that most directly relates to a wide range of well-being measures. Likewise,
specific facets like achievement striving overlap substantively with valuing an orientation
to life that emphasizes personal growth (Ryff & Singer, 2006).
The research also raises issues regarding the position of PWB in the causal and
definitional system that contains personality and SWB. For example, Diener et al. (2003)
proposed that there are a multiple pathways to well-being that may differ between people
and across cultures. Generally, environmental mastery, self-acceptance and, to some extent,
purpose in life substantially overlap with satisfaction with life. Satisfaction with life seems
to be the more ‘pure’ measure of well-being in that the individual is free to evaluate their
life on their own terms. Autonomy, positive relations, and personal growth seem to capture
important pathways to SWB. Even if they are viewed as an essential part of well-being,
FACETS AND WELL-BEING
19
care is needed when designing measures to ensure attitude to the dimension is not
confounded with status on the dimension.
4.5. Conclusion, Limitations, and Future Research
This study has provided a more complete picture of the relationship between
personality, SWB, and PWB. The results provide a balance between calls that only the Big
5 is necessary and claims that facets substantially improve prediction. In addition, our
methodological approach provided a parsimonious explanation to the complex patterns of
cross-correlations. By making available all data and data analysis code, others are
encouraged to further explore the data to generate additional insights.
In terms of limitations, the research was conducted on a young adult sample,
predominantly consisting of university students. Such a sample may have particular
priorities and values in life, which may have influenced the pattern of correlations
observed. Clearly more research is required to explore incremental facet prediction with
different personality tests, and different kinds of facets. Furthermore, while the Ryff scales
have proven very useful in advancing understanding of PWB, there may be a need to
further refine measures of PWB to minimize inappropriate measurement of values and
unnecessary confounding with life satisfaction and related measures.
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Book
The Collected Works of Ed Diener, in 3 volumes, present the major works of the leading research scientist studying happiness and well-being. Professor Diener has studied subjective well-being, people’s life satisfaction and positive emotions, for over a quarter of a century, and has published 200 works on the topic, many more than any other scholar. He has studied hundreds of thousands of people in over 140 nations of the world, and the Collected Works present the major findings from those studies. Diener has made many of the major discoveries about well-being, which are outlined in the chapters. The first volume presents the major theory and review papers of Ed Diener. These publications give a broad overview of findings in the field, and the theories of well-being. As such, the first volume is an absolute must for beginning scholars in this area, and offers a clear tutorial to the history of the field and major findings. The second volume focuses on culture. This volume is most unique, and could sell on its own, as it should appeal to cultural psychologists and anthropologists. The findings in the culture area are mostly all derived from the Diener laboratory and his students. Thus, the papers in this volume represent most of the major publications on culture and well-being. Furthermore, this is the area that is least well-known by most scholars. The third volume on measurement is the most applied and practical one because it discusses all the measures used, and presents new measures. Even for those who do not want to study well-being per se, but want to use some well-being measures in their research, this volume will be of enormous help. Volume 1: Gives a broad overview of findings and theories on subjective well-being. Volume 2: Presents most of the major papers on well-being and culture, and the international differences in well-being Volume 3: Presents discussions of measures of well-being and new measures of well-being, and is thus of great value to those who want to select measurement scales for their research Endorsements Over the past several decades Professor Diener has contributed more than any other psychologist to the rigorous research of subjective well-being. The collection of this work in this series is going to be of invaluable help to anyone interested in the study of happiness, life-satisfaction, and the emerging discipline of positive psychology. Mihaly Csikszentmihalyi, Professor of Psychology And Management, Claremont Graduate University Ed Diener, the Jedi Master of the world's happiness researchers, has inspired and informed all of us who have studied and written about happiness. His life's work epitomizes a humanly significant psychological science. How wonderful to have his pioneering writings collected and preserved for future students of human well-being, and for practitioners and social policy makers who are working to promote human flourishing. David G. Myers, Hope College, and author, The Pursuit of Happiness. Ed Diener's work on life satisfaction -- theory and research -- has been ground-breaking. Having his collected works available will be a great boon to psychologists and policy-makers alike. Christopher Peterson, Professor of Psychology, Univ. of Michigan By looking at happiness and well-being in many different cultures and societies, from East to West, from New York City to Calcutta slums, and beyond, Ed Diener has forever transformed the field of culture in psychology. Filled with bold theoretical insights and rigorous and, yet, imaginative empirical studies, this volume will be absolutely indispensable for all social and behavioral scientists interested in transformative power of culture on human psychology. Shinobu Kitayama, Professor and Director of the Culture and Cognition Program, Univ. of Michigan Ed Diener is one of the most productive psychologists in the world working in the field of perceived quality of life or, as he prefers, subjective wellbeing. He has served the profession as a researcher, writer, teacher, officer in professional organizations, editor of leading journals, a member of the editorial board of still more journals as well as a member of the board of the Social Indicators Research Book Series. As an admirer of his work and a good friend, I have learned a lot from him, from his students, his relatives and collaborators. The idea of producing a collection of his works came to me as a result of spending a great deal of time trying to keep up with his work. What a wonderful public and professional service it would be, I thought, as well as a time-saver for me, if we could get a substantial number of his works assembled in one collection. In these three volumes we have not only a fine selection of past works but a good number of new ones as well. So, it is with considerable delight that I write these lines to thank Ed and to lend my support to this important publication. Alex C. Michalos, Ph.D., F.R.S.C., Chancellor, Director, Institute for Social Research and Evaluation, Professor Emeritus, Political Science, Univ. of Northern British Columbia
Chapter
The force of culture on child development will be highlighted. Based on the literature, it will be shown how two prototypical environments (rural subsistence-based and urban Western environment) favor either the independent or interdependent self-model, which in turn have implications on what competencies are valued in a specific culture and what determines subjective well-being or happiness. A full understanding of child well-being will be viewed against the background of culture.