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Objective: We examined how personality strengths prospectively predict reactions to negative life events. Method: Participants were 797 community adults from 42 countries. At five points over the course of one year, participants completed a series of questionnaires measuring seven personality strengths (hope, grit, meaning in life, curiosity, gratitude, control beliefs, and use of strengths), subjective well-being, and frequency and severity of negative life events. Results: Using hierarchical linear modeling with assessment periods nested within participants, results from lagged analyses found that only hope emerged as a resilience factor. To illustrate the importance of using appropriate lagged analyses in resilience research, we ran non-lagged analyses; these results suggest that all seven personality strengths moderated the effect of negative life events on subjective well-being; with greater strengths associated with healthier outcomes). Conclusion: To provide evidence that personality strengths confer resilience, a prospective examination is needed with the inclusion of events and responses to them. The use of concurrent methodologies and analyses, which is the norm in psychology, often leads to erroneous conclusions. Hope, the ability to generate routes to reach goals and the motivation to use those routes, was shown to be particularly important in promoting resilience. This article is protected by copyright. All rights reserved.
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Journal of Personality 85:3, June 2017
V
C2016 Wiley Periodicals, Inc.
DOI: 10.1111/jopy.12250
Personality Strengths as Resilience:
A One-Year Multiwave Study
Fallon R. Goodman, David J. Disabato,
Todd B. Kashdan, and Kyla A. Machell
George Mason University
Abstract
We examined how personality strengths prospectively predict reactions to negative life events. Participants were 797
community adults from 42 countries. At five points over the course of 1 year, participants completed a series of
questionnaires measuring seven personality strengths (hope, grit, meaning in life, curiosity, gratitude, control beliefs, and use
of strengths), subjective well-being, and frequency and severity of negative life events. Using hierarchical linear modeling with
assessment periods nested within participants, results from lagged analyses found that only hope emerged as a resilience fac-
tor. To illustrate the importance of using appropriate lagged analyses in resilience research, we ran nonlagged analyses; these
results suggest that all seven personality strengths moderated the effect of negative life events on subjective well-being, with
greater strengths associated with healthier outcomes. To provide evidence that personality strengths confer resilience, a pro-
spective examination is needed with the inclusion of events and responses to them. The use of concurrent methodologies and
analyses, which is the norm in psychology, often leads to erroneous conclusions. Hope, the ability to generate routes to reach
goals and the motivation to use those routes, was shown to be particularly important in promoting resilience.
After encountering a potentially traumatic event, the most com-
mon response by adults is resilience (Bonanno, 2005). In sum-
marizing the field, perhaps “the great surprise of resilience
research is the ordinariness of the phenomena” (Masten, 2001,
p. 227). Researchers have discovered two key findings related to
resilience: the presence of one or more negative life events
(NLE) is not indicative of poor future functioning, and there are
multiple developmental trajectories of adaptation (Masten,
2001). Despite growing interest in resilience, there is minimal
clarity about which individual differences increase the likeli-
hood that people will show a resilient response to adversity.
Resilience factors contribute to healthy outcomes during and/
or following the onset of an adverse or NLE. To accurately
assess antecedents of resilience, resilience factors need to be
measured before an NLE occurs. Yet in the majority of studies
on resilience, personality predictors of resilience are assessed
concurrently with NLE, or worse, after they occurred (Bonanno
& Diminich, 2013). This approach prevents temporality from
being established and can lead to erroneous conclusions about
what makes a person resilient. In this article, we conducted a 1-
year multiwave study to examine how personality strengths pro-
spectively protect against the effect of NLE on subjective well-
being (SWB).
Resilience
Early research characterized resilience as a trait (e.g., “ego resil-
ience”) that reflects the ability to adaptively respond to and
bounce back from adversity (Block & Block, 1980; Block &
Kremen, 1996; Lazarus, 1993). Individuals higher in trait resil-
ience tend to habitually use effective coping strategies that func-
tion as a protective reserve to draw upon when adversity arises
(Fredrickson, Tugade, Waugh, & Larkin, 2003). There has even
been the proposition of a “resilient personality,” representing a
cluster of traits and coping strategies that contribute to healthy
adjustment when confronted with setbacks (Skodol, 2010).
Several measures of trait resilience emerged from these con-
ceptualizations (e.g., Block & Kremen, 1996; Connor &
Davidson, 2003; Friborg, Hjemdal, Rosenvinge, & Martinussen,
2003; Smith et al., 2008; Wagnild & Young, 1993). While these
measures are a tempting way to assess how resilient a person
tends to be, they are limited by biases and broad item content.
Memory does not possess a one-to-one correspondence with a
person’s past and, instead, projects a bias that only increases
over time (Walker, Skowronski, & Thompson, 2003). On aver-
age, people tend to view the past through rose-colored glasses,
such that they are more likely to remember themselves as more
resilient than they were. In addition, a person’s transient moods,
emotions, and cognitions on the day of completing a self-report
assessment can further distort their memory of prior resilience.
For example, a happy mood will lead a person to view her
responses to hardships as more successful than they actually
Correspondence concerning this article should be addressed to Todd B.
Kashdan, Department of Psychology, MS 3F5, George Mason University,
Fairfax, VA 22030. Email: tkashdan@gmu.edu.
were (Schwarz & Clore, 2003). Similarly, self-enhancement and
social desirability biases may lead a person to overestimate how
resilient they were during a prior hardship or hypothetically
when anticipating a hardship. In terms of item content, self-
report measures typically contain vague item stems (e.g., “when
something unforeseen happens” or “in difficult periods”; Fri-
borg et al., 2003) that make it impossible to discern whether a
person is responding to a traumatic event (e.g., sexual assault),
an everyday stressor (e.g., traffic jam), or the large number of
possibilities in between. Of these limitations, perhaps most
important is the assumption that resilience is constant across
time and context, an assumption that is largely unfounded. A
person may be resilient in response to the death of a close friend
but devastated after losing a family member. Thus, caution is
warranted when considering resilience as a stable trait (Bonanno
& Diminich, 2013).
Rather than being a stable trait of a person, resilience has
more recently been conceptualized as an interaction between an
individual’s unique resources and the events he or she experien-
ces (Norris, Stevens, Pfefferbaum, Wyche, & Pfefferbaum,
2008). Resilience is dynamic and depends on the context of life
events and responses to them (Shiner & Masten, 2012). In this
way, resilience is a series of mechanisms leading a person to be
minimally impacted by adversity and, in turn, experience mini-
mal functional impairment (Bonanno, 2005; Fletcher & Sarkar,
2013). A person is resilient when despite the presence of NLE,
there is no sustainable decline in his or her well-being. Resil-
ience factors, therefore, are best captured prior to NLE, with
both predicting subsequent changes in SWB.
Resilience and Personality
The proposition that personality characteristics can be a source
of resilience has roots in developmental research. A primary
goal of developmental research is to identify what risk factors
(e.g., traumatic experiences, chronic adversities) predict future
psychopathology and related maladaptive outcomes. Accord-
ingly, some researchers have turned their attention to strength-
based models to examine the individual characteristics that, over
time, increase the degree to which people will be resilient. Previ-
ously identified resilience factors include positive emotions
(Fredrickson et al., 2003), flexible coping strategies (Bonanno,
Pat-Horenczyk, & Noll, 2011), and self-enhancement biases
(Bonanno, Field, Kovacevic, & Kaltman, 2002). Nonetheless,
most conceptualizations suggest that at least some variance in
resilient outcomes is attributable to personality (Bonnano &
Diminich, 2013; Fletcher & Sarkar, 2013; Mancini & Bonanno,
2009).
As support for this proposition, in three studies, the Big Five
personality traits Extraversion and Neuroticism strongly corre-
lated with a measure of trait resilience (Campbell-Sills, Cohan,
& Stein, 2006; Friborg, Barlaug, Martinussen, Rosenvinge, &
Hjemdal, 2005; Peng et al., 2012). However, each of these stud-
ies contains important limitations. First, resilience was measured
with a single self-report questionnaire, which fails to capture
individuals’ responses to ongoing NLE. Second, in studies of
adults,
1
there has been minimal use of longitudinal designs (for
exceptions, see Gupta & Bonanno, 2010; Weems et al., 2007).
Instead, personality characteristics and resilience are typically
measured simultaneously at a single time point. A positive rela-
tionship is thought to imply a causal link between a given per-
sonality characteristic and the resilient response, based on the
assumption that personality is defined by temporal stability.
Challenging this assumption, although personality tends to sta-
bilize in early adulthood, changes occur across the life span in
response to intentional activity and environmental stimuli (e.g.,
Roberts, Walton, & Viechtbauer, 2006).
Researchers have also been interested in the influence of per-
sonality on reactions to life events. At least one longitudinal
study failed to find support for personality traits as a moderator
of individuals’ responses to life events (Yap, Anusic, & Lucas,
2012). In general, existing longitudinal studies tend to assess
individuals once per year (or longer; e.g., L
ockenhoff,
Terracciano, Patriciu, Eaton, & Costa, 2009; Ludkte, Roberts,
Trautwein, & Nagy, 2011; Specht, Egloff, & Schmukle, 2011).
The problem with this strategy is that the data are unable to pro-
vide insight on the proximal effects of negative life events and
the potential resiliency influence of personality. Studies that
have examined the proximal effects of personality and life
events on adjustment, such as the daily diary work of Longua,
DeHart, Tennen, and Armeli (2009), suggest that certain person-
ality traits buffer against the adverse effects of NLE. While
promising, the studies reviewed above focused only on the
higher-order Big Five dimensions (not the lower-order facets)
and have yet to explore other personality characteristics that
might influence people’s responses to life events.
One subset of personality characteristics that appear promis-
ing as resilience factors is personality strengths. Several promi-
nent models of personality strengths have emerged in the past
decade (Buckingham & Clifton, 2001; King & Trent, 2013;
Peterson & Seligman, 2004). Although terminology differs
between models,
2
scholars agree on core features of a
“personality strength”—they are positive trait-like features of
personality, embodied in thought, feeling, and behavior, that
promote adjustment and adaptation. When used, personality
strengths increase the likelihood of desirable outcomes, but
these strengths are valued in their own right, irrespective of out-
comes associated with their usage. That is, the possession of a
personality strength is a valued asset. In considering where per-
sonality strengths fit into a larger taxonomy of personality, we
offer insights from a three-level model (McAdams, 1995). At
Level 1 are the personality traits or general behavioral tenden-
cies that individuals possess (“having”; e.g., Big Five). At Level
2 are the life projects or personal strivings that guide an individ-
ual’s daily behavior and effort (“doing”). At Level 3 are the
overarching life narratives with the attributions and mental inter-
pretations about one’s past, present, and future that are an indi-
vidual’s life story (“being”). Personality strengths are best
conceptualized as a Level 1 construct in that they tend to be
424 Goodman, Disabato, Kashdan, et al.
stable over time and represent the central, healthy trait-like char-
acteristics of a person. A case could be made for placing person-
ality strengths at Level 2, in that strengths are motivational
factors that lead to positive outcomes when used, but insufficient
research is available to establish the link between the possession
of strengths and their use and development (Biswas-Diener,
Kashdan, & Minhas, 2011).
It is impractical to examine all purported personality
strengths simultaneously. Researchers must identify a subset of
personality strengths relevant to their research questions. Per-
sonality strengths in the current study were included if they met
the criteriaof trait-like features that promote adjustment, embod-
ied in thoughts, feelings, and behaviors. For example, we did
not include trait measures of one’s search for meaning and rumi-
nation because they are both consistently linked with maladjust-
ment; we did not include one’s subjective level of happiness
because although linked to adjustment, it does not represent a
trait-like quality. The personality strengths included were as fol-
lows: hope—the ability to generate routes to reach goals (path-
ways) and the motivation to use those routes (agency; Snyder
et al., 1991); grit—passion and perseverance toward consistent,
long-term goals (Duckworth, Peterson, Matthews, & Kelly,
2007); meaning in life—the feeling that one’s life has a purpose
and is significant (Steger, Frazier, Oishi, & Kaler, 2006); curios-
ity—the desire to seek out new knowledge and experiences
(Kashdan et al., 2009); gratitude—the tendency to feel apprecia-
tive of benefits in one’s life (McCullough, Emmons, & Tsang,
2002); and control beliefs—the extent to which a person feels in
control of whether or not good things happen to him or her
(Haidt & Rodin, 1999). In addition to these measures of the pos-
session of personality strengths, we directly assessed strength
use—awareness and regular use of one’s unique capacities in
everyday life, which shows trait-like features (Seligman, Steen,
Park, & Peterson, 2005).
A common criticism of well-being research is that strengths
are often studied in isolation, which prevents meaningful com-
parisons. Such an approach may lead to isolated strands without
a coherent framework of how strengths relate to well-being. An
alternative approach is to compare multiple strengths at once,
clarifying which personality strengths relate to SWB and to
what degree. In this way, researchers can identify which person-
ality strengths most strongly or uniquely relate to important out-
comes, a simultaneous approach that has been used elsewhere
(Sheldon, Elliot, Kim, & Kasser, 2001; Sheldon, Jose, Kashdan,
& Jarden, 2015). Although the personality strengths included in
this study differ in important ways, they can be compared based
on their effectiveness as resilience factors that promote adjust-
ment following adversity.
The Present Study
The goal of the present study was to advance resilience research
by exploring whether certain personality strengths acted as pro-
tective factors when individuals experienced NLE. Using a large
international sample and multiwave study, we tested the extent
to which possessing a personality strength buffered the effect of
NLE on a person’s SWB. In an effort to demonstrate temporal
relationships between personality and resilience, we moved
beyond single time point assessments to test within time point
and 3-month lagged models.
We first made broad hypotheses about the set of personality
strengths. When personality strengths were measured concur-
rently with SWB, we hypothesized that nearly all of them would
be positively associated with our operationalization of resilience.
On the contrary, when appropriately measured prior to SWB
and NLE, we hypothesized fewer personality strengths would
prospectively promote resilience.
Prior studies have found that the personality strengths
included in this study relate to greater SWB at a global level, but
not all may be relevant to resilience. That is, there are many
pathways through which personality strengths can promote
SWB, but not all may do so by mitigating the effects of NLE.
Specifically, we expected the presence of hope, grit, and mean-
ing in life before NLE to promote resilience. Past research has
found that each of these three personality strengths mitigates the
influence of NLE on several indicators of positive functioning,
including life satisfaction (hope; Valle, Huebner, & Suldo,
2006), suicidal ideation (grit; Blalock, Young, & Kleiman,
2015), immune functioning (meaning in life; Bower, Kemeny,
Taylor, & Fahey, 2003), and psychological well-being (meaning
in life; Park, Edmondson, Fenster, & Blank, 2008).
We offer hypothesized theoretical mechanisms for each of
these three strengths. A hopeful person believes she can achieve
goals and find workable solutions around obstacles. The hopeful
person might interpret NLE as another obstacle and harness her
energy and problem-solving skills to prevent decreases in SWB.
Grit is similar to hope, with a future orientation and a behavioral
tendency to persist when obstacles occur. Gritty individuals are
defined by their ability to work through hardships, and grit might
be harnessed as a source of stability. We expected the Persever-
ance of Effort subscale to have a stronger influence than the
Consistency of Interests subscale due to recent research compar-
ing their relative effects on well-being (Bowman, Hill, Denson,
& Bronkema, 2015). As for meaning in life, ascribing meaning
to an NLE can help people manage the negative impact and
dampen the impact on their SWB (Park, 2010). Individuals who
score high on trait measures of resilience report finding more
meaning in daily stressors and traumatic incidents (Tugade &
Fredrickson, 2004). Taken together, we hypothesized that hope,
grit, and meaning in life would act as prospective resilience fac-
tors by buffering the harmful effects of NLE.
METHOD
Participants and Procedures
Data were collected from 797 adults from the community who
completed the International Wellbeing Study (IWBS; www.
wellbeingstudy.com). Recruitment for the IWBS included
Personality Strengths and Resilience 425
emailed and printed advertisements that were distributed to busi-
nesses, charitable organizations, various university departments,
listservs, and online forums. Participants were from 42 coun-
tries, with most being from New Zealand (n5258), the United
States (n5127), Hungary (n590), and Australia (n569). All
other nationalities had fewer than 39 participants. Participants
were 661 females and 136 males, with ages ranging from 18 to
81 (M539 years, SD 514.31).
Upon entering the IWBS website, participants were asked to
complete a series of questionnaires five times, every 3 months
over the course of 1 year. The full battery of 20 scales contained
235 questions, and average completion time was 29 minutes.
Participants were compensated with an entry into a drawing for
one of 15 $100 (USD) vouchers, and they were provided with a
summary of their scores compared to others who completed the
assessment.
Measures
Eleven scales (94 items) from the total assessment battery were
used for the present analyses.
3
Most participants completed the
assessment battery in English (71%). Where scales were already
available in one of the desired 15 languages, that language trans-
lation was used. Where no translation was available from the
English version to the required language, scales were translated
by a native speaker of that language who had a bachelor’s degree
in psychology or higher (most translators were master’s or PhD
students in psychology familiar with psychometrics). Scales
were independently cross-checked after translation by a second
translator, and areas of disagreement were identified and
resolved.
Negative Life Events (NLE). An abbreviated measure of
NLE was created for the purposes of the study. Although valid
adult NLE measures exist, their length increases participant bur-
den (e.g., Sarason, Johnson, & Siegel, 1978). With an already
lengthy assessment battery, a brief measure was desired. Crea-
tors of the IWBS generated five broad NLE items that sought to
capture the diversity of NLE experienced by adults. Participants
indicated whether one of five NLE occurred during the past 3
months: (a) You had a serious disagreement with another per-
son,(b)You were injured or ill,(c)You experienced a significant
financial loss or lost your job,(d)Someone you care about expe-
rienced a significant problem,or(e)You didn’t achieve some-
thing or obtain something that you wanted. Each event that
occurred was rated from 1 (none)to4(alot;05the event did
not occur) on “how much of a problem” it was. A composite
score was created by summing the five items at baseline
(M57.10, SD 54.30) and each subsequent time point.
Subjective Well-Being (SWB). A composite score of SWB
was created by using the five-item Satisfaction With Life Scale
(SWLS; Diener, Emmons, Larsen, & Griffin, 1985), the four-
item Subjective Happiness Scale (SHS; Lyubomirsky & Lepper,
1999), and the 20-item Center for Epidemiological Studies–
Depression Scale (CES-D; Radloff, 1977). Validity has been
established in prior studies for the SWLS (Diener, Kahneman,
& Helliwell, 2010), SHS (Shimai, Otake, Utsuki, Ikemi, &
Lyubomirsky, 2004), and CES-D (Simon, Fleck, Lucas,
Bushnell, & LIDO Group, 2004).
Conventional models of SWB (e.g., Diener et al., 1985)
include positive affect, negative affect, and life satisfaction. As
the IWBS did not contain measures of positive and negative
affect, we followed prior research (e.g., Sheldon et al., 2015)
and used happiness in place of positive affect, and depressive
symptoms in place of negative affect. Both pairs show strong
correlations with the other (Watson, Clark, & Tellegen, 1988).
In nonclinical samples, the CES-D measures general psycholog-
ical distress rather than symptoms of clinical depression (Wood,
Taylor, & Joseph, 2010). Therefore, the CES-D is an appropriate
indicator of SWB and not mental disorder in our sample.
Exploratory factor analysis using principal-axis factoring
was performed at each time point to confirm the unidimensional-
ity of our SWB composite. At each time point, the first initial
eigenvalue was greater (2.15) than the second and third (
0.45). The standardized factor loading magnitudes for satisfac-
tion with life (.71 to .78), subjective happiness (.79 to .83), and
depression (20.73 to –.77) were similar across the five time
points. The initial eigenvalues clearly suggest a one-factor solu-
tion, and the standardized factor loading magnitudes justify our
unit-weighted composite scores. A single SWB score at baseline
and each subsequent time point was taken from the average of
each scale after reversing the CES-D scores and standardizing
all three variables (as5.82, .84, .80, .81, .81).
Hope. The 12-item Adult Hope Scale (AHS; Snyder et al.,
1991) measures a positive motivational state oriented toward
achieving goals. The AHS assesses two facets of hope: Agency,
or goal-directed energy (e.g., “I energetically pursue my goals”),
and Pathways, or planning to meet goals (e.g., “I can think of
many ways to get the things in life that are important to me”).
Positive associations with optimism and self-esteem, and nega-
tive associations with depression and anxiety provide evidence
for construct validity (Holleran & Snyder, 1990). Items were
rated from 1 (definitely false)to8(definitely true) and summed
to create a total scale score at baseline (M549.49, SD 58.49)
and each subsequent time point (as5.87, .88, .88, .89, .89).
4
Grit. The 12-item Grit Scale (GS; Duckworth et al., 2007)
measures a person’s perseverance in the face of challenges, and
his ambition and passion in the pursuit of long-term goals, with
two subscales: Perseverance of Effort (e.g., “Setbacks don’t dis-
courage me”) and Consistency of Interests (e.g., “I often set a
goal but later choose to pursue a different one”; reverse coded).
The negligible association between grit and IQ demonstrates its
discriminant validity as a measure of effort, not ability. Grit’s
predictive validity is supported by relationships with achieve-
ment in academic and military settings (Duckworth et al., 2007).
Items were rated from 1 (not at all like me)to5(very much like
426 Goodman, Disabato, Kashdan, et al.
me). Recent research suggests that each subscale may differen-
tially relate to well-being and goal attainment (e.g., Bowman
et al., 2015). We therefore calculated baseline subscale scores
for Perseverance of Effort (M53.68, SD 50.72) and Consis-
tency of Interests (M53.56, SD 50.84) and at each subsequent
time point (as5.73, .76, .75, .79, .77, and as5.82, .85, .85,
.86, .88, respectively).
5
Meaning in Life. The 10-item Meaning in Life Questionnaire
(MLQ; Steger et al., 2006) contains two five-item subscales.
The Presence subscale measures the degree to which a person
feels her life is meaningful (e.g., “I understand my life’s mean-
ing”). The Search subscale measures the degree to which a per-
son is actively searching for meaning in her life (e.g., “I am
seeking a purpose or mission for my life”). Negligible associa-
tions with conformity, universalism, hedonism, and achieve-
ment demonstrate discriminant validity. The MLQ-Search
subscale was excluded because prior research has shown that
the MLQ-Search subscale is negatively associated with well-
being (Steger et al., 2006), and it is meant to be a dynamic set of
cognitive and behavioral acts as opposed to a personality
strength (e.g., Steger, Kashdan, Sullivan, & Lorentz, 2008).
Items from the Presence subscale were rated from 1 (absolutely
untrue)to7(absolutely true) and summed to create a total scale
score at baseline (M525.21, SD 56.74) and each subsequent
time point (as5.90, .92, .92, .93, .92).
Curiosity. The 10-item Curiosity and Exploration Inventory-II
(CEI-II; Kashdan et al., 2009) measures tendencies to seek out
new knowledge and experience. Two 5-item subscales measure
Stretching (seeking out new knowledge and experiences; e.g., “I
actively seek as much information as I can in new situations”)
and Embracing (willingness to embrace novelty and uncertainty;
e.g., “I am the type of person who really enjoys the uncertainty
of everyday life”). Construct validity is supported by positive
associations with psychological flexibility and openness to
experience (e.g., Kashdan et al., 2013). Items were rated from 1
(very slightly or not at all)to5(extremely) and summed to create
a total scale score at baseline (M531.81, SD 57.64) and each
subsequent time point (as5.88, .88, .89, .89, .89).
Gratitude. The six-item Gratitude Questionnaire (GQ-6;
McCullough et al., 2002) was used to assess the tendency to feel
grateful and appreciate benefits in life. An example item is “I
have so much in life to be thankful for.” Construct validity is
supported by predictive validity of well-being above and beyond
the Big Five personality traits (Wood, Joseph, & Maltby, 2009)
and positive associations with positive affect and meaning in
life. Items were rated from 1 (strongly disagree)to7(strongly
agree) and summed to create a total scale score at baseline
(M535.98, SD 55.53) and each subsequent time point
(as5.83, .83, .84, .84, and .83).
Control Beliefs. The four-item Control Beliefs Scale (CB;
Bryant & Veroff, 2007) measures the degree to which a person
believes that he or she can generate positive future outcomes.
An example item is “In general, how much control do you feel
that you personally have over whether or not good things hap-
pen to you?” Items were answered on varying 4-, 5-, and 7-point
Likert scales. The four items were summed to create a total scale
score at baseline (M516.90, SD 53.18) and each subsequent
time point (as5.73, .77, .76, .78, .78).
Strengths Use. The 10-item Strengths Use and Knowledge
Scale (SUK; Govindji & Linley, 2007) measures the extent to
which people are aware of and regularly use personal strengths.
Table 1 Time 1 Zero-Order Correlations, Means, and Standard Deviations
Variable 1 2 3 4 5 6 7 8 9 10 11 12
1. NLE
SWB 2. SWLS –.35
3. SHS –.24 .62
4.CES-D .35 –.57 –.63
Person-ality
Trait
5. AHS –.15 .50 .58 –.47
6. Grit-POE –.05 .30 .35 –.32 .59
7. Grit-COI –.12 .07 .11 –.21 .08 .23
8. MLQ-Pres. –.06 .43 .50 –.43 .50 .42 .15
9. CEI –.03 .23 .37 –.24 .55 .42 –.14 .30
10. GQ-6 –.12 .46 .54 –.45 .45 .27 .02 .49 .27
11. CBS –.12 .40 .53 –.45 .54 .37 .07 .44 .38 .48
12. SUK –.13 .44 .50 –.46 .68 .54 .11 .48 .46 .42 .45
Mean 7.10 22.34 4.89 13.01 49.49 3.68 3.56 25.21 31.81 35.98 16.90 54.48
SD 4.30 7.37 1.30 10.45 8.49 0.72 0.84 6.74 7.64 5.53 3.18 8.68
ICC .46 .72 .80 .57 .82 .81 .79 .80 .81 .77 .74 .78
Note. SWB 5subjective well–being; NLE 5negative life events; SWLS 5Satisfaction With Life Scale; SHS 5Subjective Happiness Scale; CES-D 5Center for Epidemi-
ological Study of Depression; AHS 5Adult Hope Scale; POE 5perseverance of effort; COI 5consistency of interests; MLQ-Pres. 5Meaning in Life Questionnaire–
Presence subscale; CEI 5Curiosity and Exploration Inventory; GQ-6 5six-item Gratitude Questionnaire; CBS 5Control Beliefs Scale; SUK 5Strengths Use and
Knowledge.
All correlations |.07| are statistically significant (p<.05).
Personality Strengths and Resilience 427
Example items include “I know my strengths well” and “I
always try to use my strengths.” Strengths use has been shown
to predict vitality, positive affect, and self-esteem over time. In a
comparison of positive psychology interventions, participants
who used their “signature strengths” every day showed the larg-
est and most stable increases in happiness over a 6-month period
(Seligman et al., 2005). Items were rated from 1 (strongly dis-
agree)to7(strongly agree) and summed to create a total scale
score at baseline (M554.48, SD 58.68) and each subsequent
time point (as5.89, .89, .86, .91, .91).
RESULTS
Analytic Overview
To take full advantage of our multiple repeated measures over
time, data were analyzed using multilevel modeling in SPSS
19.0. This procedure nests the five measurement time points
within individuals to account for the dependencies inherent with
repeated measures. We standardized all variables to z-scores to
allow regression coefficients to be interpreted as standardized
coefficients. This resulted in grand-mean centering and a combi-
nation of both between- and within-person associations between
variables. We used an autocorrelated error structure to allow
measurements at closer time points to be more highly correlated
(Bolger & Laurenceau, 2013). We estimated only a random
effect for intercepts, as none of our hypotheses concerned ran-
dom effects for slopes.
The primary predictor (NLE) and personality strength moder-
ators were grand mean centered to enhance interpretation of the
main effects after the product term is included (Preacher, Curran,
& Bauer, 2006). In multilevel modeling, random effects from
less complex models can be used to calculate variance explained
in the outcome variable (La Huis, Hartman, Hakoyama, &
Clark, 2014). To calculate total variance explained, we used the
equation from Snijders and Bosker (1994) that combines the
Level 1 and Level 2 variance explained. As suggested, any nega-
tive variance explained was reported as zero.
We first tested a simple multilevel model where the fre-
quency and intensity of past NLE predicted SWB. We
expected to find a significant negative relationship. Next, we
computed grand-mean centered product terms between the
NLE variable and each personality strength. Because there
are benefits (e.g., statistical control) and costs (e.g., multicol-
linearity) to analyzing each strength separately, or in tandem,
we performed both analyses. In the first stage of analyses,
separate multilevel models were tested with three predictors:
the main effect for NLE, the main effect for the personality
Table 2 Lagged and Nonlagged Personality Strengths Moderating the Effect of NLE on Subjective Well-being
Personality Lagged Analyses Nonlagged Analyses
Strength Std. Coef. 95% CI Var. Comp. DR
2
Std. Coef. 95% CI Var. Comp. DR
2
NLE –.218*** [–.243, –.193] 0.122 –.149
[–.169, –.129] 0.126
Hope .234*** [.199, .270] 0.248 .483
[.453, .512] 0.203
Hope 3NLE .028* [.007, .050] 0.455 0.005 .042
[.025, .059] 0.37 0.007
NLE 20.212
[–.237, –.187] 0.149 –.162
[–.183, –.141] 0.165
Strengths use .133
[.099, .167] 0.248 .356
[.327, .386] 0.224
Strengths Use 3NLE 0.022 [–.000, .044] 0.547 0.002 .030** [.011, .048] 0.442 0.003
NLE 20.213
[–.238, –.188] 0.155 –.180
[–.201, –.160] 0.141
MIL-Presence .147
[.113, .182] 0.248 .359
[.329, .389] 0.219
MIL-Presence 3NLE 0.021 [–.001, .044] 0.546 0.002 .038
[.020, .057] 0.47 0.006
NLE –.212
[–.237, –.187] 0.144 –.178
[–.198, –.157] 0.137
Gratitude .155
[.122, .188] 0.248 .363
[.334, .391] 0.22
Gratitude 3NLE 0.02 [–.002, .042] 0.526 0.002 .021* [.003, .039] 0.43 0.002
NLE –.215
[–.240, –.190] 0.135 –.170
[–.190, –.149] 0.132
Control beliefs .142
[.110, .173] 0.247 .353
[.326, .380] 0.218
Control Beliefs 3NLE 0.014 [–.007, .173] 0.529 0.002 .036
[.018, .053] 0.419 0.008
NLE –.209
[–.234, –.184] 0.178 –.185
[–.206, –.165] 0.169
Curiosity .067
[.032, .103] 0.248 .291
[.261, .322] 0.227
Curiosity 3NLE 0.013 [–.010, .035] 0.616 0.001 .035
[.017, .054] 0.559 0.004
NLE –.212
[–.236, –.187] 0.177 –.180
[–.202, –.159] 0.169
Grit-POE .093
[.057, .128] 0.249 .271
[.240, .302] 0.232
Grit-POE 3NLE 0.011 [–.012, .034] 0.596 0 .033
[.015, .052] 0.542 0
NLE –.209
[–.234, –.184] 0.192 –.187
[–.209, –.166] 0.171
Grit-COI .045* [.010, .080] 0.249 0.032 [–.000, .063] 0.243
Grit-COI* NLE 0.004 [–.019, .027] 0.644 0 .024* [.005, .044] 0.656 0.004
Note. NLE 5negative life events; POE 5perseverance of effort; COI 5consistency of interests; MIL-Presence 5Meaning in Life Questionnaire–Presence subscale;
Var. Comp. 5variance components, with the first row the autoregressive Level 1 variance, second row nonautoregressive Level 1 (i.e., residual) variance, and third
row Level 2 (i.e., intercept) variance.
*p<.05. **p<.01. ***p<.001.
428 Goodman, Disabato, Kashdan, et al.
strength, and the associated product term.
6
Therefore, eight
multilevel models were run (with separate models for the two
grit subscales). In the second stage, a single multilevel model
was tested using simultaneous entry of all predictors and
moderation effects (17 total) to determine which predictors
had the strongest unique moderation effect of NLE on SWB.
We probed significant moderation effects by calculating sim-
ple slopes at low and high levels of the personality strength
according to Preacher et al. (2006).
Of critical importance to note is that we conducted lagged
analyses, such that (a) personality strengths were measured 3
months prior to SWB and (b) participants reported on NLE that
occurred in between the personality and SWB assessments. This
set up the proper temporal sequence (personality strengths,
NLE, SWB), allowing for a true test of the resilience process. To
illustrate the importance of establishing temporal precedence
when studying resilience, we conducted seven identical analyses
where we did not use lagged analyses (i.e., we used measures all
from the same time point). In this case, the personality strength
predictors were measured at the same time as NLE and SWB
outcome. Although these analyses make less conceptual sense,
they are relatively commonplace in the resilience literature. We
present them to show how misleading nonlagged or cross-
sectional tests of resilience can be.
Descriptive Statistics
Means, standard deviations, and correlations at Time 1 are pre-
sented in Table 1. These descriptive statistics were similar across
the five time points. In addition, the intra-class correlations are
reported from null multilevel models. The SWB and personality
strength variables all show strong stability over time, with most
of the variance between persons.
Main Effect of Negative Life Events
At baseline, 93.6% of participants endorsed at least one of
the NLE; on average, 52.3% of participants endorsed any
given NLE. These relative frequencies were similar across
all five time points. As expected, greater frequency and
intensity of negative life events over the past 3 months pro-
spectively predicted worse SWB (Std. Coef.5–.190,
t5217.38, p<.001; variance explained 5.032). We pro-
ceeded to test which personality strengths weakened this
relationship.
Moderating Effects of Lagged Personality
Strengths
When each of the seven personality strengths was tested sepa-
rately in different models (i.e., Stage 1), only hope emerged as a
significant moderator. Hope attenuated the harmful effects of
negative life events on SWB (Std. Coef. 5.028, t52.57,
p<.01). Variance explained by the product terms is displayed
in the DR
2
column in Table 2. Table 2 also presents the fixed
effects and their associated significance for each personality
strength and product term.
To probe the significant moderating effect of hope, we
calculated the slope between NLE and SWB at one stand-
ard deviation above and below the sample mean of hope
(i.e., simple slopes; Preacher et al., 2006). The effect of
NLE was statistically significant at all three levels of hope;
however, the effect was smaller in magnitude at higher lev-
els of hope. At one standard deviation below the mean of
hope, the NLE slope was strongest (Std. Coef.5–.246,
t5217.1, p<.001). At the mean of hope, the NLE slope
was weaker (Std. Coef.5–.218, t5215.1, p<.001). At
one standard deviation above the mean, the NLE slope was
even weaker (Std. Coef.5–.190, t5210.9, p<.001). Fig-
ure 1 presents the simple slopes at one standard deviation
above and below the mean of hope.
As a conservative test of hope’s significance, all personality
strengths and their associated product terms were entered into a
single model predicting SWB (i.e., Stage 2). Hope’s moderating
effect remained statistically significant after controlling for the
moderating influences of other strengths (Std. Coef.5.045,
t52.34, p<.05). No other personality strength product terms
were significant, indicating no suppression effects (Tzelgov &
Henik, 1991).
Nonlagged Analyses
In the nonlagged analyses, personality strengths were
measured after the NLE had occurred and at the same time
as the outcome SWB (i.e., misleading analysis). Results
showed that each strength significantly moderated the
effects of NLE on SWB, such that NLE were less harmful.
The results are presented in identical fashion to the non-
lagged results (Table 2).
Figure 1 Simple slopes of negative life events on subjective well-being at
low and high levels of lagged hope.
Personality Strengths and Resilience 429
Correction for Multiple Comparisons
The large number of null hypothesis significance tests in the
present study, 14, indicates the high risk for false positive (i.e.,
Type I) error. When one hypothesis test is conducted with an
alpha level of .05, the probability of an incorrect rejection of the
null hypothesis is 5%; this probability increases as the number
of hypothesis tests increases.
7
Because controlling for the false
positive rate via the Bonferroni correction reduces statistical
power, we controlled for the false discovery rate instead via the
Benjamini-Hochberg (B-H) correction (Benjamini & Hochberg,
1995). The false discovery rate ensures—of the hypothesis tests
that were statistically significant—the probability of an incorrect
rejection of the null hypothesis is 5%. The false discovery rate
preserves statistical power while preventing a large number of
erroneous significant effects. However, the procedure has a
greater probability of Type I error compared with Bonferroni
correction. With this new analytic approach, the following
effects were no longer statistically significant: nonlagged effects
for consistency of interests and gratitude. Accordingly, these
effects should be interpreted with caution.
DISCUSSION
There is good reason to suggest that the most common reaction
to adverse life events is resilience (e.g., Bonanno, 2005; Masten,
2001). Less is known about individual differences that increase
the likelihood of resilient responses. Cross-sectional designs are
a good first step, but alone they are insufficient to test scientific
theories. Failure to properly account for temporal precedence
can lead to erroneous conclusions about how personality
strengths promote resilience in response to NLE. Results from
the nonlagged analyses in the present study would lead readers
to conclude that increasing almost any personality strength can
promote resilience.
Lagged analyses, however, yielded a different conclusion
about the effects of personality strengths on the relationship
between NLE and SWB. In prospective models testing seven
personality strengths, only hope emerged as a statistically signif-
icant resilience factor. Hope remained the only significant pre-
dictor when we tested all personality strengths simultaneously.
Results from our longitudinal study suggest that for individuals
low on hope, NLE were especially detrimental to future SWB;
for individuals high on hope, NLE still decreased SWB, but to a
lesser degree.
The Hopeful Person Is Resilient
We hesitate to emphasize the resilience power of hope over all
the other personality traits. The moderation effect sizes for other
personality traits (i.e., gratitude, meaning in life, strengths use)
are similar to hope, and hope’s statistical significance may be
partially attributable to the large sample size. More favorable
than the significant versus nonsignificant dichotomy, the confi-
dence intervals for each product term provided demonstrate how
similar the moderation effects are for each personality trait
(Cumming, 2013). Nonetheless, hope had the largest moderat-
ing effect and was the only statistically significant personality
strength, and thus we offer suggestions for why hope might act
as a resilience factor.
Hope is not synonymous with optimism, which is limited to
the expectation of positive future outcomes (Bryant & Cven-
gros, 2004). Hope, in contrast, captures the belief that multiple
paths can be taken to flexibly manage obstacles, and there is suf-
ficient vitality to put these plans into action to make progress
toward meaningful goals (e.g., Snyder et al., 1991); in some
ways, the operationalization of hope is similar to conceptual
descriptions of psychological flexibility (i.e., the ability to pur-
sue valued aims despite the presence of pain and other obstacles;
Kashdan & Rottenberg, 2010). Our results extend prior cross-
sectional research that suggests hope attenuates the relationship
between NLE and depression symptoms (Visser, Loess, Jeglic,
& Hirsch, 2013), as well as research into the treatment of post-
traumatic stress disorder that suggests hope can buffer the most
severe events of loss and adversity (Gilman, Schumm, & Chard,
2012).
How might hope act as a resilience factor? Researchers sug-
gest that a hopeful person does four things: positively interprets
failures, identifies goals, identifies resources for goal attainment,
and addresses barriers to goal attainment (Snyder, 1995; Snyder
et al., 1991). Each of these strategies may promote resilience.
Positively interpreting failures allows a hopeful person to use
external attributions to explain and persist through setbacks. For
example, many NLE (including an item from our measure:
“You didn’t achieve something or obtain something that you
wanted”) involve thwarted goal attainment. The hopeful person
might attribute this failure to poor strategy rather than lack of
ability. In the face of setbacks, hopeful individuals use their
goal-oriented flexibility to discover and implement new strat-
egies to find success.
The Other Personality Strengths
Contrary to expectations, grit and meaning in life did not act as
significant resilience factors against NLE. We expected gritty
individuals to persist through NLE with smaller dips in SWB
because of their ability to persevere through adversity, but this
was not borne out in our results. Gritty people have high rates of
goal attainment and success, but research is less clear on how
grit promotes SWB. It may be that the relationship between
SWB and grit is best explained by goal attainment (which is bet-
ter captured by the pursuit within the construct of hope), or that
grit is most helpful for SWB when paired within particular per-
sonality configurations. Our measure captured a broad range of
NLE, with only one item related to thwarted goal attainment. It
is possible that the protective effects of grit may be strongest in
the context of NLE that are related to goal attainment.
The hypothesis that meaning in life would be a resilience fac-
tor was not supported. We expected individuals high in trait
430 Goodman, Disabato, Kashdan, et al.
meaning in life to be better equipped to find meaning in negative
life experiences. One possibility for the presence of small effects
is that the NLE occurred in the very domains of life that individ-
uals tended to derive meaning from. Rather than protect against
NLE, their sense of meaning may instead have been compro-
mised by the setback. Further, meaning in life was measured
irrespective of the NLE, which makes it unclear whether indi-
viduals cultivated a sense of meaning from their life experiences
or derived meaning from something else. Global judgments of
meaning in life (as used in this study) may be less effective than
more contextualized approaches (e.g., daily diary studies) that
offer explanations for what experiences individuals derive
meaning from or how life events interact with people’s sense of
global meaning in life (e.g., Heine, Proulx, & Vohs, 2006; Park,
2010).
Although the other personality strengths tested were non-
significant resilience factors, these should not be discarded as
irrelevant to resilience. Most research on resilience and person-
ality has used the Big Five traits, and there is value in moving
beyond these higher-level factors to include personality
strengths, which have been robustly linked to SWB. Addition-
ally, the effect of a given personality strength is likely context
dependent, such that its effectiveness depends on the specific
situation in which it is deployed. Further, people possess
different combinations of strengths. Future research should
examine how certain strength profiles influence resilience.
Certain combinations have been shown to predict resilience
better than any single strength (e.g., Kleiman, Adams, Kash-
dan, & Riskind, 2013), including personality strengths not
included in the current study that have been linked to effec-
tively managing NLE, such as optimism (Scheier, Weintraub,
&Carver,1986).
LIMITATIONS
Several study limitations warrant discussion. Resilience is a
broad construct that can be measured in several ways. The pres-
ent study measured resilience as a person’s SWB in response to
NLE, which differs from models that measure resilience as
change or loss in functioning. Future research could include
occupational, social, and physical functioning as outcomes in
addition to SWB (McKnight & Kashdan, 2009). Our measure of
adversity was limited to a pre-established list of NLE, and it is
possible that other important NLE occurred throughout the
course of the study. Most measures of NLE include events that
are assumed to be severe (e.g., illness, job loss), but responses to
daily stressors also contribute to resilience (Davis, Luecken, &
Lemery-Chalfant, 2009) by inoculating individuals for future,
potentially severe stressors. Additionally, the emphasis on NLE
excludes the possibility that seemingly positive events influence
resilience (e.g., a job promotion with increased responsibilities
and expectations).
We used a global measure of well-being (Diener and col-
leagues’ [1985] model of SWB). Results may have differed if
specific facets of well-being were analyzed separately, such as
Ryff’s psychological well-being model (1989) or measures of
psychological dysfunction (e.g., anxiety); notably, evidence
suggests that these various dimensions tend to load strongly on a
single dimension of well-being (e.g., Disabato, Goodman,
Kashdan, Short, & Jarden, in press).
CONCLUSION
Among seven candidate personality strengths, this study found
that only hope operates prospectively as a resilience factor. We
offered multiple explanations related to goal pursuit, flexibility,
and attainment to explain why hope buffers against NLE. Every
human being seeks to handle adversity with aplomb such that
their quality of life is only temporarily disrupted before returning
to normalcy or even improving from lessons learned. This work
offers new insights into personality dimensions, with the poten-
tial to increase the probability of desired outcomes.
The current study demonstrates that researchers, practi-
tioners, and policy makers must carefully attend to measurement
and analytical strategies when interpreting resilience research.
Research must move beyond trait resilience questionnaires and
cross-sectional designs. Misleading findings could result in
resource expenditures with minimal gains in understanding and/
or improving desirable outcomes.
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:
The Center for the Advancement of Well-being at George
Mason University funded Fallon Goodman as a doctoral fellow
and Todd B. Kashdan as Senior Scientist.
Notes
1. Research on child and adolescent resilience is more often longitu-
dinal in nature, likely because of the assumed changes in personality
over that developmental period.
2. One common term is character strengths; however, character
strengths are defined by consideration of morality and virtue
(Peterson & Seligman, 2004). For instance, why would perseverance,
one of the character strengths in the Peterson and Seligman taxon-
omy, be considered a moral virtue, whereas the dimension of Consci-
entiousness in the Big Five is not? We believe this taxonomy of
character strengths reflects a narrow framework, as there are plenty
of adaptive personality traits that are central to the field that appear to
Personality Strengths and Resilience 431
be strengths (e.g., Emotional Stability, Openness to Experience,
Agreeableness).
3. Copies of all scales and items used in the International Wellbeing
Study are available at the study website: http://www.wellbeingstudy.com.
4. We conducted identical analyses using the Agency and Pathways
subscales separately and found similar results. In nonlagged analyses,
Hope-Agency (b5.002, t54.75, p<.001) and Hope-Pathways
(b5.002, t54.39, p<.001) both moderated the effect of NLE on
SWB. In lagged analyses, Hope-Agency (b5.001, t52.62, p<.01)
and Hope-Pathways (b5.001, t52.42, p<.05) both moderated the
effect of NLE on SWB.
5. We conducted identical analyses using a total scale score for grit
and found similar results. In nonlagged analyses, grit moderated the
effect of NLE on SWB (b5.012, t53.84, p<.001). In lagged anal-
yses, grit was not a significant moderator (b5.003, t5.82, p5.41).
6. Although the main effect of each personality strength was not of
substantive interest, Cohen (1978) showed it is important to control
for the main effect of the moderating variable. This prevents arbitrary
scale dependency from impacting the effect size and statistical signif-
icance of the product term.
7. If all 19 hypothesis tests were completely independent, the study-
wide false positive rate would be 1 – (1 – .05)
19
5.62 (Tabachnick
& Fidell, 2001). When hypothesis tests are highly dependent, as in
the present study, the rate is much lower. Unfortunately, an exact rate
is never known due to the unknown dependency parameter.
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... For instance, resilience has been grounded in the psychological domain, in neuroscientific and physiologic disciplines [3,4], and related to a socio-ecological view in terms of personal characteristics, outcomes, or processes [3,4]. Resilience has also been associated with the interaction between risk factors and inner strength [4,5]. On the other hand, resilience reflects a dynamic process that derives and is influenced by context and human responses to life events [6]. ...
... On the other hand, resilience reflects a dynamic process that derives and is influenced by context and human responses to life events [6]. Resilience has also been described to have a fundamental role in the quality of life [7] and wellbeing [5,8]. A synthesis of concept analysis studies identified resilience as an "ability to recover from perceived adverse or changing situations, through a dynamic process of adaptation, influenced by personal characteristics, family and social resources, and manifested by positive coping, control, and integration" [9]. ...
... Additionally, such positive adaptation benefits were associated with low psychological and mental manifestations such as anxiety and depression [5,[11][12][13][14]. Shiner and Masten (2012) and McGowan and collaborators (2018) claimed that resilience improved with age and with recurrent and repeated experiences in handling Int. ...
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... In contrast, current depression status-the most consistently significant risk factor in the comprehensive models-was only significantly associated with PWB in two cohorts, and accounted for ≤13 % of variance in PWB. Character strengths confer a degree of resilience against adverse life events (Goodman et al., 2017;Isaacs et al., 2017), and may operate similarly for reestablishing PWB post-suicide attempt. Critically, pending replication of these results, character strengths among suicide attempt survivors may prove to be a more important factor for PWB than traditional suicide risk factors. ...
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... Research suggests that most of the top five resource factors, including energy, personal growth, mild-tono forgetfulness, and life purpose, can be improved through behavioral and lifestyle interventions [63][64][65]. The psychological resources have been characterized in the literature and our theoretical model as "personality strengths" that can be used to promote positive adjustments, embodied thoughts, feelings, and behaviors [3,66]. For example, coaching and educational interventions can have a positive influence on one's personal growth and life purpose [67]. ...
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... It is predictive of adaptive functioning in an educational setting, even when controlling for intelligence (Snyder et al., 1997), prior academic history (Gallagher et al., 2017), and self-esteem . There is also evidence that hope is particularly important in predicting student resilience, especially in response to uncertainty (Goodman et al., 2017). Indeed, as studies begin to emerge from the recent global pandemic, we can see the role that hope played in navigating some of the extraordinary challenges facing students. ...
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... In such cases, we followed the recommendations of Schmidt and Hunter (2015) in using the inter-correlations among indicators to estimate the composite correlation and reliability. Two studies (Burrow et al., 2020;Disabato et al., 2018) did not report the correlations among the indicators, so we used the correlations from Jiang, Jiang, et al. (2019) and Goodman et al. (2017), respectively. At the same time, the sign of negative affect and depression correlations were reversed as well. ...
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