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Hope and emotional well-being: A six-year study to distinguish antecedents, correlates, and
consequences
Joseph Ciarrochi
a
*, Philip Parker
a
, Todd B. Kashdan
b
, Patrick C.L. Heaven
c
and Emma Barkus
d
a
Institute of Positive Psychology and Education, Australian Catholic University, Strathfield, Australia;
b
Psychology, George Mason
University, Fairfax, VA, USA;
c
Institute of Positive Psychology and Education, Australian Catholic University, Sydney, Australia;
d
Department of Psychology, University of Wollongong, Wollongong, Australia
(Received 24 October 2013; accepted 12 January 2015)
Hope is a motivational factor that helps initiate and sustain action toward long-term goals, including flexible management
of obstacles that get in the way of goal attainment. Despite an abundance of research on the benefits of hope, little atten-
tion has been given to this aspect of youth development via longitudinal studies. In this study, we collected ratings of
hope and positive and negative affect from 975 adolescents over a six-year assessment period (Grades 7–12). Using cross-
lagged structural equation modeling, we found that hope led to greater positive affect, with little evidence for the reverse
direction. In contrast, hope and negative affective states were reciprocally related. Hope predicted future well-being partic-
ularly well in years when the young people where in transition (e.g. starting high school and transitioning to senior high
school). Our data support the position that hope is a malleable attribute that fosters positive youth development.
Keywords: hope; well-being; positive emotions; negative emotions; adolescence
The adolescent years are a critical developmental period
characterized by rapid biological and social changes and
challenges. Contrary to prior accounts of inevitable
‘storm and stress’, teenagers exhibit substantial variability
in their levels of emotional distress across time (Arnett,
1999). For this reason, it is crucial for researchers and
practitioners to understand the factors that are most use-
ful in predicting healthy psychological development
(Lerner & Galambos, 1998; Steinberg & Morris, 2001;
Trzesniewski et al., 2006).
Decades of research have shown that motivation-
related attitudes and states are associated with physical
and mental health, adjustment to stressful life events,
and success in work, social relationships, sports, and
other personal aspirations (Bandura, 1982; Deci & Ryan,
1985; Duckworkth, Peterson, Matthews, & Kelly, 2007;
Loch & Latham, 2002; Snyder, 2000).
Such studies appear to be based on the assumption
that beliefs of personal efficacy, setting clear goals,
developing strategies to implement goals, and believing
that setbacks can and will be overcome, reflect a healthy
pattern of coping with negative life events and an ability
to harness the benefits of positive life events. Hope is a
particularly promising individual difference factor that
helps initiate and sustain action toward long-term goals,
including flexible management of obstacles that might
interfere with accomplishments (Snyder & Lopez, 2002;
Snyder, Rand, & Sigmon, 2002). In this study, we
focused on hope and the potential contributions to the
emotional well-being of adolescents.
Unlike the majority of research that has relied on
cross-sectional, experimental, and brief prospective stud-
ies in adults (Cheavens, 2000; Snyder, 2000), we exam-
ined whether hope is an antecedent or consequence of
emotional experiences over the six years of our study.
Hope has been judged to be a psychological strength rel-
evant to the (Snyder, 2000). Development of well-being,
and developmental questions can only be addressed by
assessing repeated measures of these constructs. The
present study, with six assessment periods, extends sev-
eral studies that have examined hope in adolescents at
two time points in a single year (Ciarrochi, Heaven, &
Davies, 2007; Valle, Huebner, & Suldo, 2006).
People with high levels of hope possess a capacity to
implement goals for themselves (agency) and are adept
at discovering how to achieve them (pathways). This
second component of hope has explicit links theoretically
to psychological flexibility (Bonanno, Papa, Lalande,
Westphal, & Coifman, 2004; Kashdan & Rottenberg,
2010). People with high levels of hope flexibly adjust
implementation strategies and goal-related efforts when
thwarted (Snyder, 1996,2002). Hope can be distin-
guished from related variables such as optimism and
positive explanatory style. Both hope and optimism are
focused on the future. Optimism refers to the belief that
positive events are highly likely to occur in the future
*Corresponding author. Email: joseph.ciarrochi@acu.edu.au
© 2015 Taylor & Francis
The Journal of Positive Psychology, 2015
http://dx.doi.org/10.1080/17439760.2015.1015154
Downloaded by [Australian Catholic University] at 15:20 11 May 2015
(Scheier & Carver, 1985) and explanatory style refers to
the tendency to attribute positive events to stable and
internal factors (Peterson & Seligman, 1984). In contrast,
hope encompasses the ability to generate and implement
plans for the future (Bailey, Eng, Frisch, & Snyder,
2007) and is focused on what people can do to achieve
their goals (Magaletta & Oliver, 1999; Snyder, 1994).
Hope and optimism have been shown to load on sepa-
rate factors (Bryant & Cvengros, 2004; Magaletta & Oliver,
1999), and hope explains unique variance above and
beyond optimism when predicting mental and physical
health outcomes (Bailey et al., 2007; Kashdan et al., 2002;
Magaletta & Oliver, 1999). Likewise, hope is broader and
distinct from related variables such as global self-esteem
which reflects a positive self-evaluation (Rosenberg, 1965),
and self-efficacy, which reflects the belief that one can suc-
cessfully complete a specific behavior (Bandura, 1982).
Hope and well-being
There are a number of reasons why hope is likely to
underpin psychological well-being. First, research sug-
gests that people with higher hope cope better with stress-
ful life events (Chang, 1998; Ciarrochi et al., 2007; Horton
& Wallander, 2001; Valle et al., 2006). Second, hope has
been found to predict successful outcomes across a range
of domains. For instance, hope predicts better athletic per-
formance in college athletes beyond training and coach
ratings of natural ability (Curry, Snyder, Cook, Ruby, &
Rehm, 1997), greater academic success (Ciarrochi et al.,
2007; Snyder et al., 1991), better understanding and use of
prevention strategies when at high risk for cancer (Irving,
Snyder, & Crowson, 1998), and greater engagement and
gains in therapy (Snyder et al., 2000). In a study of pediat-
ric transplant patients, higher hope was negatively related
to emotional distress and in turn, greater adherence to an
appropriate medication regime (Maikranz, Steele, Dreyer,
Stratman, & Bovaird, 2007).
There are multiple, competing life goals aligned with
the developmental milestones of adolescence. Adoles-
cents are balancing the need for autonomy with their
dependency on caregivers and teachers, the need for
belonging with the challenge of navigating the social
rules and hierarchy of high school, and the need for com-
petence with the challenges of a daily regimen of aca-
demics, athletics, and novel social situations. A central
tenet of hope is that this attribute, psychological strength,
or motivational factor increases the likelihood of the suc-
cessful pursuit of goals (Snyder, 2000,2002). Within this
context, there is evidence that the association between
measures of hope and meaning in life is large in magni-
tude, ranging from 0.52 to 0.77 (Feldman & Snyder,
2005; Mascaro & Rosen, 2005). Furthermore, research
suggests that in adolescents and adults, the positive asso-
ciations between measures of happiness and purpose in
life is fully mediated by hope (Bronk, Hill, Lapsley,
Talib, & Finch, 2009). In attempts to distinguish who
ends up on a trajectory of positive youth development –
operationalized as children with a high sense of compe-
tence, confidence, character, connections to other people,
and caring –researchers found that hope scores were the
best predictor, outperforming other candidates such as
self-regulatory skill (Schmid et al., 2011). Additional lon-
gitudinal research revealed that baseline levels of adoles-
cent hope were positively related to life satisfaction and
negatively related to stressful life events and other indices
of maladjustment one year later (Valle et al., 2006).
Taken together, theory and research suggests that the
goal-directed nature of hope seems particularly suited to
the development and maintenance of well-being in youth.
The most widely used indicators of well-being tend to
be frequent, intense levels of positive emotions and infre-
quent, low levels of negative emotions (Diener, Suh,
Lucas, & Smith, 1999; Lyubomirsky, King, & Diener,
2005; Watson & Vaidya, 2003). This makes sense as most
of human behavior is purposeful, targeting an increase in
positive emotional states (e.g. be happy) and decrease in
negative emotional states (e.g. avoiding anything that
leads to misery or despair). Human beings, similar to other
animals, strive to attain rewards and avoid pain. Positive/
pleasant emotions tend to be preferred over negative/
unpleasant emotions. However, the value of emotions
extends beyond hedonic benefits. Emotions orient individ-
uals to best manage the demands of an existing situation
(Fredrickson & Levenson, 1998; Frijda, 1986; Tamar,
2009) .When individuals believe that they are making pro-
gress in attaining their goals, whether unimpeded or
impeded, this leads to positive emotions (Scheier &
Carver, 1985; Snyder, 2002). Likewise, negative emotions
arise from beliefs that goal-related efforts have failed or
might fail (Ortony, Clore, & Collins, 1988). Feelings of
guilt are a cue that an individual might have engaged in an
inappropriate behavior that adversely affected their social
standing and repairing the relationship should be a prior-
ity. Feelings of love signal that a relationship is worthy of
investment, promoting an increase in resource allocation.
Not surprisingly, existing psychological interventions
designed to ameliorate problems of mental and physical
health or enhance quality of life often place an
emphasis on emotional well-being as an indicator of thera-
peutic efficacy (Hayes, Luoma, Bond, Masuda, & Lillis,
2006; Hofman, Sawyer, Witt, & Oh, 2010; Suveg,
Southam-Gerow, Goodman, & Kendall, 2007).
Typically, people with higher levels of hope endorse
a greater frequency and intensity of positive emotions
and conversely, fewer and less intense negative emotions
(Snyder et al., 1996). Thus, levels of hope are tied to
particular affect, such that goal setting with a sense of
agency and flexibility is reinforced by positive emotional
states, whereas conversely, a lack of effort and progress
2J. Ciarrochi et al.
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toward personally meaningful goals is often the cause of
reductions in emotional well-being –less positive emo-
tions and more negative emotions (Deci & Ryan, 1985;
Little, Salemla-Aro, & Phillips, 2007; McKnight &
Kashdan, 2009) across the globe (Sheldon et al., 2004).
Understanding whether and how hope influences the
generation of particular emotional states offers insight
into the development and maintenance of well-being.
An assumption of cross-sectional studies is that high
hope causes well-being (Magaletta & Oliver, 1999) and
fuels increased goal achievement behavior (Snyder &
Feldman, 2000).
Some theorists suggest that hope precedes changes in
the emotional quality of a person’s life (Snyder, 2000,
2002), and other theorists argue that feeling good leads
to increased goal striving and hope (Ashby, Isen, &
Turken, 1999; Fredrickson, 2001). Researchers have
found that inducing positive emotions leads to more
effective, flexible, and creative problem-solving (Isen,
Daubman, & Nowicki, 1987; Isen & Means, 1983).
Flexible, creative problem-solving is related to Snyder’s
(2000) pathways dimension of hope or the capacity to
find alternative routes to obtaining goals under challeng-
ing circumstances. Positive emotional states often inspire
people to set new, meaningful goals and devote the effort
to attain them and grow as a person (Garland et al.,
2010; Thrash, Elliot, Maruskin, & Cassidy, 2010). Thus,
there is reason to believe that hope might serve as an
antecedent or consequence of emotional states.
The present study
The adolescent years are characterized by mean level
changes in the major personality dimensions (Roberts &
DelVecchio, 2000), as well as lower-order dimensions
such as hope. To date, little longitudinal research has
been conducted to assess the stability of hope and the
direction of influence between hope and well-being
among adolescents. In this study, we conducted an
empirical test of whether and how hope is linked to emo-
tional well-being during the adolescent phase of human
development. Over the course of six years, capturing the
totality of adolescence, we examined the extent that hope
served as an antecedent to changes in emotional well-
being, a consequence, or both (reciprocal influence
model). Of additional interest was whether any emo-
tional benefits linked to hope differed between boys and
girls. In the relative absence of multi-wave longitudinal
studies, gender differences in the stability and benefits of
hope and affect should be considered exploratory.
Method
Participants and procedure
Participants were students at five high schools from a
Catholic Diocese of New South Wales, Australia.
Catholic schools in Australia tend to be government-
subsidized, charge low fees, and accept a proportion of
students from other faiths. The Diocese was in a regional
city with a population of approximately 250,000 that
included small coastal towns, rural districts, and outer
south-western suburbs of Sydney. Data were collected
from participants each year for the six years of their sec-
ondary education. The total sample consisted of 975 par-
ticipants (499 male, 474 female, 2 unknown). 237
participants completed all six waves, 484 completed at
least five waves, 695 completed at least four waves, and
828 completed at least three waves. Mean waves com-
pleted was 4.2 (SD = 1.48). Participants completed mea-
sures at approximately the same time each year, from
Grade 7 (mean Age = 12.41, SD = 0.53) to Grade 12
(mean age = 17.37, SD = 0.50).
The occupations of the fathers of our participants at
Time 1 resembled national distributions (Australian
Bureau of Statistics, ABS, 2004): Professionals com-
prised 20.4% of the sample (whereas nationally the
figure was 16.5%); associated professionals, 15.1%
(12.7%); intermediate production and transport, 11.2%
(13.4%); tradespersons, 34.3% (21%); managers, 4.8%
(9.7%); laborers, 3.3% (10.8%); advanced clerical, 1.2%
(0.9%); intermediate clerical, 5.5% (8.8%); and elemen-
tary clerical, 4.3% (6.1%).
We obtained university ethics clearance for each year
of the study as well as clearance from the Diocesan
Schools Authority, parents, and schools. Students were
invited to participate in a study on ‘Youth Issues’and
both students and parents provided consent prior to com-
pleting the questionnaire each year. At all times, admin-
istration of the questionnaires took place during regular
classes under the supervision of either a teacher or a
member of the research team. Students completed the
questionnaires without any discussion. At the conclusion
of each testing session, students were thanked for their
participation and debriefed.
Measures
Children’s hope scale (Snyder et al., 2002)
This scale comprises six items that participants
responded to on a Likert scale with endpoints that ran-
ged from 1 (none of the time)to6(all of the time). The
scale items assess the agency aspects of hope (e.g. ‘I
think I am doing pretty well’) as well as pathways hope
(e.g. ‘I can think of ways to get the things in life that
are most important to me’). This measure has demon-
strated validity (Snyder, 2000; Snyder et al., 1997) and
is known to be correlated to measures of adolescent
adjustment (Valle et al., 2006). Hope has been shown to
be distinguishable from other related constructs, such as
optimism, hopelessness, self-esteem, and emotional
The Journal of Positive Psychology 3
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awareness (Bryant & Cvengros, 2004; Heaven,
Ciarrochi, & Hurrell, 2010; Magaletta & Oliver, 1999;
Snyder et al., 1997). It has shown criterion related
validity, linking to positive parenting style (Heaven &
Ciarrochi, 2008) school grades (Ciarrochi et al., 2007),
standardized test performance (Snyder et al., 1997), and
positive psychological outcomes (Kashdan et al., 2002).
On the present occasion, alpha coefficients from Grade 7
to 12 were 0.82, 0.85, 0.86, 0.90, 0.88, and 0.89,
respectively.
Positive and negative affect schedule –expanded form
(PANAS-X; Watson & Clark, 1994)
The PANAS-X was used to assess a number of affective
states, namely, fear (6 items), sadness (5 items), hostility
(6 items), and joviality (8 items). Students were
asked to describe their feelings and emotions over the
past month. Evidence shows strong convergence
between trait and state indices of affect when using the
PANAS-X (Watson & Clark, 1994). Alpha coefficients
ranged from 0.82 (hostility; Grade 8) to 0.95 (joviality;
Grades 10, 11, and 12). These instruments have been
well validated and are the most widely employed mea-
sures of subjective well-being (Linley, Matlby, Wood,
Osborne, & Hurling, 2009).
The hope and PANAS scales were treated as latent
variables in SEM, in order to explicitly represent mea-
surement error in the models (Weston & Gore, 2006).
Due to the large number of parameters to be estimated,
we used a parceling procedure in accordance with Little,
Cunningham, and Shahar (2002). For models where
items measure a single construct, parcels can be created
through random assignment of items to individual par-
cels (Coffman & MacCallum, 2005). Numerous authors
have noted the advantages of using item parcels since
they are more normally distributed, reliable, less influ-
enced by unique characteristics of individual items and
require the estimation of fewer parameters (Bagozzi &
Heatherton, 1994; Coffman & MacCallum, 2005;
Kishton & Widaman, 1994; West, Finch, & Curran,
1995). The latent hope and positive affect factors were
specified as a one-factor model, based on three item par-
cels, each consisting of randomly selected items from
each questionnaire. The smaller negative affect scales
were also specified as one-factor models with each parcel
consisting of two items.
Results
Preliminary results
Missing value analyses revealed only two significant dif-
ferences between those who completed all waves and
those who did not. Completers had slightly higher hope
in Grades 8 and 9 (M
8
= 4.57, SD = 0.94; M
9
= 4.43;
SD = 0.94) than non-completers (M
8
= 4.39, SD = 0.96;
M
9=
4.26; SD = 1.00), p< 0.05, Cohen’sd< 0.20.
Completers and non-completers were similar on mea-
sures of positive and negative affect, with no significant
differences and Cohen’sd not larger than 0.12. Thus,
while some selectivity effects were observed for hope,
the effects were typically small. Nevertheless, in all anal-
yses, we dealt with missing data using full-information
maximum likelihood estimation (FIML). Traditional
approaches to missing data (e.g. listwise or pairwise
deletion) can lead to considerable bias in parameter
estimates. In contrast, FIML provides a superior
approach to dealing with missing data which uses all the
available information for parameter estimation (Enders &
Bandalos, 2001).
We found no associations between any of our vari-
ables and mothers and fathers employment status, all
ps > 0.05. We next examined bivariate correlations
between hope, negative affect, and positive affect across
the school years. Correlations between positive and nega-
tive affect were moderate in magnitude with larger effect
sizes when both variables were measured in the same
year (rvaried between −.44 in Grade 12 and −0.25 in
Grade 8). Correlations were lower across years (cross-
grade rvaried between −0.08 [Grade 7 and 11] and
−0.29 [Grade 10 and 11]). Bivariate correlations are pre-
sented in Table 1and indicated strong relationships
between the variables of interest over time.
Table 1. Intercorrelations for hope, joviality, and negative
affective states across six high school years.
Hope7 Hope8 Hope9 Hope10 Hope11 Hope12
Fear7 −0.21
**
−0.16
**
−0.15
**
−0.19
**
−0.09 −0.19
**
Fear8 −0.08
*
−0.12
**
−0.18
**
−0.19
**
−0.13
**
−0.18
**
Fear9 −0.07 −0.06 −0.16
**
−0.23
**
−0.19
**
−0.19
**
Fear10 −0.12
**
−0.09
*
−0.16
**
−0.25
**
−0.24
**
−0.19
**
Fear11 −0.11
*
−0.15
**
−0.21
**
−0.29
**
−0.37
**
−0.25
**
Fear12 −0.05 −0.03 −0.14
**
−0.20
**
−0.14
**
−0.31
**
Hos7 −0.35
**
−0.28
**
−0.21
**
−0.22
**
−0.12
*
−0.20
**
Hos8 −0.22
**
−0.22
**
−0.21
**
−0.21
**
−0.18
**
−0.19
**
Hos9 −0.17
**
−0.18
**
−0.23
**
−0.21
**
−0.17
**
−0.24
**
Hos10 −0.12
**
−0.04 −0.15
**
−0.21
**
−0.18
**
−0.22
**
Hos11 −0.14
**
−0.16
**
−0.26
**
−0.30
**
−0.33
**
−0.33
**
Hos12 −0.18
**
−0.16
**
−0.25
**
−0.23
**
−0.14
**
−0.36
**
Sad7 −0.27
**
−0.21
**
−0.18
**
−0.25
**
−0.14
**
−0.23
**
Sad8 −0.15
**
−0.29
**
−0.28
**
−0.33
**
−0.25
**
−0.27
**
Sad9 −0.15
**
−0.19
**
−0.33
**
−0.39
**
−0.31
**
−0.30
**
Sad10 −0.17
**
−0.13
**
−0.26
**
−0.40
**
−0.28
**
−0.24
**
Sad11 −0.17
**
−0.17
**
−0.26
**
−0.35
**
−0.42
**
−0.30
**
Sad12 −0.15
**
−0.14
**
−0.25
**
−0.26
**
−0.24
**
−0.40
**
Jov7 0.40
**
0.25
**
0.25
**
0.25
**
0.27
**
0.26
**
Jov8 0.33
**
0.50
**
0.34
**
0.32
**
0.32
**
0.25
**
Jov9 0.27
**
0.34
**
0.50
**
0.41
**
0.35
**
0.32
**
Jov10 0.23
**
0.26
**
0.35
**
0.48
**
0.37
**
0.26
**
Jov11 0.19
**
0.19
**
0.32
**
0.39
**
0.49
**
0.27
**
Jov12 0.24
**
0.24
**
0.35
**
0.30
**
0.34
**
0.51
**
Note: *p< .05; **p< .01.
4J. Ciarrochi et al.
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Structural equation modeling strategy
We utilized Mplus 6.1 and structural equation modeling
to represent the relationships between hope and affective
states across the six years of the study. We utilized maxi-
mum likelihood parameter estimates with standard errors
and a chi-square test statistic that are robust to non-nor-
mality.
The data for the current study had a nested data
structure in which students were nested within schools.
If this complex data structure is not accounted for, stan-
dard errors, chi-square, and log-likelihood values may be
biased (Hox, 2010). Therefore, we utilized the
TYPE = COMPLEX option in Mplus, which adjusts
standard errors for the effects of clustered data and hence
provides appropriate statistical significance tests.
Models were considered to fit the data well if a) the
solution was well-defined, b) parameter estimates were
consistent with the theory proposed, and c) the fit indices
were acceptable, giving emphasis to fit indices which are
appropriate for larger sample sizes (Marsh, Balla, &
McDonald, 1988; McDonald & Marsh, 1990).
pecifically, we provide two additional fit indices in
addition to chi-square, considering its sensitivity to
sample size (all chi-squares for the models were, as
expected, significant). The comparative fix index (CFI) ≥
0.97 and root mean square error of approximation
(RMSEA) < 0.05 were considered to provide evidence
of model fit in accordance with commonly accepted
criteria.
Testing measurement invariance across time and gender
Before testing the relationship between hope and affect,
we first considered whether the instruments operated in a
similar manner across time and gender. This is important
as differences in the way gender respond to a question-
naire or, indeed, differences in the way individuals
respond over the course of high school represents a
potentially concerning confound for this research (Parker,
Dowson, & McInerney, 2007). For this reason, we
employed multi-group (across gender) and longitudinal
invariance testing. Invariance testing consists of first fit-
ting a baseline model (M1) which holds only the model
structure the same across groups and across time (i.e.
configural invariance). Should this model provide an
acceptable fit to the data, it is used as a baseline from
which increasingly restrictive nested models are then
compared. Multiple models are used as different invari-
ance assumptions underlying various analyses. The pres-
ent study is particularly focused on the covariance-based
models (e.g. the cross-lagged models) that assume weak
factorial invariance (M2; i.e. configural and indicator fac-
tor loadings are held to have the same weight for the
same indicators across each year in the study ). In addi-
tion, the comparison of latent means across time and
gender assumes strong factorial invariance (M3: i.e. con-
figural and factorial variance, and intercepts for matching
items are held invariant across the different years).
Evidence of invariance comes from comparing a
well-fitting baseline model to alternate nested models.
Table 2. Gender and longitudinal measurement invariance statistics.
χ
2
dfΔχ
2
CFI RMSEA
Hope
Model 1: Configural 173.77 156 –0.997 0.016
Model 2: Weak invariance 212.988 183 39.2 0.995 0.019
Model 3: Strong invariance 251.965 193 78.20** 0.991 0.026
Sadness
Model 1: Configural 199.780 156 –0.991 0.025
Model 2: Weak invariance 236.119 183 36.34 0.990 0.026
Model 3: Strong invariance 273.277 193 73.50* 0.984 0.031
Joviality
Model 1: Configural 201.837 156 0.994 0.026
Model 2: Weak invariance 227.742 183 36.34 0.994 0.023
Model 3: Strong invariance 252.117 193 50.28** 0.992 0.026
Hostility
Model 1: Configural 202.039 156 0.988 0.026
Model 2: Weak invariance 225.299 183 23.26 0.989 0.023
Model 3: Strong invariance 278.037 193 76.00** 0.978 0.032
Fear
Model 1: Configural 243.502 156 0.989 0.036
Model 2: Weak invariance 310.037 183 66.54** 0.985 0.040
Model 3: Strong invariance 320.191 193 76.70** 0.985 0.039
Notes: Best model highlighted in gray. The configural model estimates measurement structure freely across time and gender; the weak invariance
model constraints factor loadings to be equivalent across time and gender; the strong invariance model constrains loadings and intercepts to be
equivalent across time and gender.
*p< .05; **p< .01.
The Journal of Positive Psychology 5
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Invariance sensitivity to sample size of the chi-square
that underlies the widespread use of fit indices (e.g.
RMSEA, CFI, and tucker lewis index (TLI)) does not
merely relate to model fit but additionally log-likeli-
hood ratio tests that are often used to conduct such
model comparisons. Therefore, in this study, we use
the criteria by Cheung and Rensvold (2002) who sug-
gest invariance between nested models if ΔCFI is
≤0.01 (we utilize the same criteria for the TLI) and
the criteria described by Chen (2007) who suggest
invariance between nested models if ΔRMSEA is
≤0.015.
As can be seen in Table 2, the configural invariance
model (M1) was supported in all analyses. Assuming
weak (M2) or strong (M3) factorial invariance did not
result in a change in CFI and RMSEA for the longitudi-
nal analyses beyond the criteria used in this research
(though the assumption of strong invariance would be
rejected if the χ
2
-difference test was used). The latent
means and standard errors from these analyses are pre-
sented in Figure 1. Girls experienced more sadness and
fear than boys, but not more hostility. Girls also experi-
enced higher joviality than boys. Finally, girls started out
higher in hope than boys, then dropped well-below boys
in Grade 10, and bounced back somewhat in Grades 11
and 12.
Cross-lag models
We next sought to evaluate the likely causal ordering of
hope and affect. We were particularly interested in the
extent that hope acted as an antecedent to the development
of affect. We tested a series of increasingly restrictive struc-
tural models in order to identify the most parsimonious
model concerning these longitudinal relationships. Given
the strong invariance model did show a significant decrease
in chi-square (though not in CFI and RMSEA), we report
the results for the weak invariance models below, and the
strong invariance models in the supplementary materials.
Assuming strong or weak invariance had no significant
effects on the autocorrelations and cross-lags.
Model 1 allowed the cross-lag and autoregression
estimates to vary across year, and estimated autoregres-
sion effects across all lags. Model 2 was the same as
Model 1 except that autoregression paths were con-
strained to single year spans (e.g. from Time 1 to Time 2
but not Time 1 to Time 3). Finally, Model 3 constrained
all autoregression and cross-lag effects from Model 2 to
be consistent across time. This model was thus a test of
whether the relationship between hope and affect had
consistent developmental effects across high school. To
compare these models, as with gender and longitudinal
invariance above, we used the criteria described above.
Figure 1. Male (solid line) and female (dotted line) levels of hope, and positive and negative affective state across six years of high
school.
Note: Male time 1 was fixed to 0 in order to allow the model to be identified.
6J. Ciarrochi et al.
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Table 3shows that Model 3 in which cross-lags
and autoregression coefficients were constrained to be
equal across all time lags (e.g. the relationships between
Time 1 and 2 are constrained to be equal to those from
Time 2 to 3) was within our criteria for invariance (i.e.
there was little change in CFI and RMSEA fit from
Model 1). This suggests that there was some consistency
in the effects of hope and affect over time. However,
there was significant drop in chi-square, which suggests
that our assumption of temporal invariance may not be
entirely accurate. To ere on the conservative side, we
report both the detailed results from Model 1 (Table 4)
and the averaged results from Model 3 (Figure 2).
As illustrated in Table 4, hope was a significant pre-
dictor of the development of joviality in three of the five
cross-lags, with the other cross-lags being positive and
in the expected direction. In contrast, only two of the
joviality to affect cross-lags were significant, with one in
the expected direction and one in the unexpected
direction. Thus, we had reliable evidence for the
hope-as-antecedent hypothesis, but not for the hope-
as-consequence model. In contrast to joviality, the
cross-lags involving hope and the negative affective
states were not as reliably significant, and when they
were significant, tended to support the reciprocal influ-
ence model, with negative affect predicting decreases in
hope, and hope predicting decreases in negative affect.
The constrained results of Model 4 are presented in
Figure 2and indicate that hope had a significant effect
on change in positive affect over the course of high
Table 3. Assessment of the consistency of cross-lag effects across time.
χ
2
dfΔχ
2
CFI RMSEA
Hope and sadness
Model 1: Multiple time span, autoregressive model 809.102 484 –0.976 0.026
Model 2: Single time span autoregressive model 948.756 504 139.65** 0.968 0.030
Model 3: Time span invariant model 983.213 520 174.11** 0.966 0.030
Hope and joviality
Model 1: 893.745 484 –0.974 0.029
Model 2: 1036.863 504 143.11** 0.967 0.033
Model 3: 1076.814 520 189.07** 0.965 0.033
Hope and hostility
Model 1: 658.059 484 –0.985 0.019
Model 2: 786.381 504 128.32** 0.975 0.024
Model 3: 816.968 520 158.901** 0.974 0.024
Hope and fear
Model 1: 660.939 484 –0.989 0.019
Model 2: 807.107 504 146.17** 0.981 0.025
Model 3: 848.758 520 187.82** 0.979 0.025
Note: Best model highlighted in gray. Model 1: Cross-lags and autoregression paths allowed to differ at each time point, and autoregression effects
estimated at all time lags. Model 2: Identical to Model 1 except that autoregression effects estimated at lags of one year. Model 3: Identical to model
to except that cross-lags and auto-correlations assumed to be equal across all time points.
**p< .01.
Table 4. Standardized coefficients (standard errors) at one year lag for Model 1.
Joviality Sadness Hostility Fear
G7→G8
Hope→Affect 0.43 (0.061)*** −0.10 (0.045)* −.020 (0.045)*** −0.06(0.029)*
Affect→Hope 0.00 (0.034) −0.08 (0.034)* −0.07 (0.055) −0.04 (0.031)
G8→G9
Hope→Affect 0.22 (0.085)* −0.08 (0.022)** −0.05 (0.054) −0.022 (0.036)
Affect→Hope 0.023(0.042) −0.14 (0.031)*** −0.09 (0.066) −0.11 (0.039)**
G9→G10
Hope→Affect 0.09 (0.061) −0.05 (0.028) −0.01 (0.073) −0.09 (0.063)
Affect→Hope 0.13 (0.018)*** −0.26 (0.03)*** −0.07 (0.06) −0.16 (0.041)***
G10→G11
Hope→Affect 0.17 (0.024)*** −0.12 (0.051)* −0.21 (0.053)*** −0.17 (0.094)*
Affect→Hope 0.03 (0.024) −0.06 (0.045) −0.04 (0.048) −0.10 (0.021)***
G11→G12
Hope→Affect 0.09 (0.064) 0.01 (0.049) 0.05 (0.084) 0.04(0.061)
Affect→Hope −0.07 (0.025)** −0.007(0.062) −0.09 (0.048)* −0.03 (0.044)
Note: *p< 0.05; **p< 0.01; ***p< 0.001; Model 1, single time span, multiple span autocorrelation.
The Journal of Positive Psychology 7
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school (β= 0.23, p< 0.001), whereas positive affect only
had a weak effect on hope (β= 0.047, p< 0.01). Using
the delta method, these parameters were significantly dif-
ferent (β
diff
= 0.185, p< 0.001). In contrast, the negative
states had consistent, reciprocal influences, with no sig-
nificant difference in the cross-lags. Hope predicted
lower sadness (β=−0.12), fear (β=−0.10), and hostility
(β=−0.10), ps < 0.001,and sadness, fear, and hostility
predicted lower hope, β=−0.11, β=−0.09, β=−0.08,
respectively, ps < 0.001.
Finally, we conducted multigroup analyses to exam-
ine the extent that the effects described above were con-
sistent across gender (gender invariance). Past research
suggests that males and females may decline more rap-
idly in hope that males from Grade 7 to 10 (Heaven &
Ciarrochi, 2008). We compared a model where the
effects of gender were allowed to vary to a model where
gender effects were fixed to be the same. Significant
drop in log-likelihood would indicate that gender was
not invariant. While in previous analyses, we used
changes in fit indices rather than fit statistics as measures
of invariance, in these analyses, we use chi-square differ-
ences test given the small degrees of freedom difference
between these models. Invariance was supported for sad-
ness, joviality, and hostility, p> 0.1. However, there was
a significant effect for fear (Δχ
2
(4) = 14.8, p< 0.01). We
then used the delta method to determine which parame-
ters were different across gender. Interestingly, the gen-
der effects were not in the cross-lags but in the stability
of fear. On average, fear was less stable in boys
(β= 0.36 p< 0.001) then in girls (β= 0.49, p< 0.001).
Discussion
We tested how a brief and reliable measure of hope –
reflecting goal-directed behavior, self-efficacy, persever-
ance, and flexibility in response to impediments (Snyder,
2000)–was related to measures of positive and negative
affect over the six years that characterize adolescence.
Adding to the findings of previous research that has been
mostly cross-sectional or limited to adult samples, we
found clear support for hope being an antecedent to posi-
tive affect, rather than a mere consequence or concomi-
tant. Hope predicted changes in positive affect, whereas
there was only a weak, and significantly smaller, effect
of positive affect predicting hope. In contrast, we found
evidence that hope and the negative affective states were
reciprocally related: hope predicted decreasing negative
affect (antecedent effect), negative affect predicted
decreasing hope (consequence effect), and there was no
significant difference between the size of the antecedent
and consequence effects.
We also found some evidence that hope may be par-
ticularly important at transition points. Specifically, hope
most reliability predicted all forms of well-being at two
transition points. Grade 7 and 10. Grade 7 is when the
young Australians begin high school. Grade 10 is when
they transition into the last two critical years of high
school (similar to senior high school in the United
States). At the end of these two years, students take a
series of standardized exams which are the major deter-
minant of whether they will get into university. Transi-
tion points in education are generally externally
mandated and are associated with a conglomeration of
developmental tasks that require channeling of engage-
ment efforts (Dietrich, Parker, & Salmela-Aro, 2012;
Masten & Curtis, 2000; Savickas, 2011). Adaptive transi-
tions are associated with well-being (Roisman, Masten,
Coatsworth, & Tellegen, 2004; Schulenberg, Bryant, &
O’Malley, 2004; Zarrett & Eccels, 2006).
Given hope is associated with developing and suc-
cessfully pursuing goals, it is likely that hope proceeding
the transition is associated with more adequate engage-
ment and as a result higher well-being, explaining the
particularly strong link between these constructs
observed here. Indeed, Litalien, Lüdtke, Parker, and
Trautwein (2013) showed that goal motivation was a
positive predictor of well-being for the transition from
school to university consistent with the current research.
Future research is needed to replicate this finding and to
examine the possibility that hope is of particular impor-
tance during transitional periods.
Our longitudinal data support claims that positive
emotional states and well-being are a consequence of
goal-directed thought and flexible, determined goal pur-
suit. As Snyder (2000) theorized, there is good evidence
that hopeful thoughts drive positive feelings and that
there is very little evidence of the reverse. Hopeful
thoughts help build positive emotions, which have been
shown to be building blocks leading to other dimensions
of well-being. Both theoretical and empirical work
indicate that positive emotions foster positive evaluations
of the self and other people (Waugh & Fredrickson,
2006), social activity and the development of healthy
relationships (Algoe, Haidt, & Gable, 2008; Keltner &
Figure 2. Average autocorrelation and cross-lagged effects for
hope and affective state (Time T) predicting across a year
(Time T+ 1) for six waves of data (Model 3).
Note: *p< 0.01; **p< 0.001; effects are reported in the order
of joviality/fear/sadness/hostility.
8J. Ciarrochi et al.
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Bonanno, 1997), flexible thinking and problem-solving
(Estrada, Isen, & Young, 1994,1997; Isen et al., 1987),
resilience to stressful life events (Ong, Bereman,
Bisconit, & Wallace, 2006; Zautra, Johnson, & Davis,
2005), the ability to extract rewards from positive life
events (Catalino & Fredrickson, 2011), greater mindful-
ness and purpose in life (Fredrickson, Cohn, Coffey, Pek,
& Finkel, 2008; King, Hicks, Krull, & Del Gaiso, 2006),
and physical health ranging from stronger immunological
functioning to greater life longevity (Lyubomirsky, King,
& Diener, 2005; Pressman & Cohen, 2005). The broad
benefits of positive emotions imply the need for further
inquiry into any disposition that increases the propensity
to experience positive emotions –in this case, hope.
There was a general drop in well-being and hope
across the high school years, a result that is generally
consistent with past research (Larson, Moneta, Richards,
& Wilson, 2002). This result may be explainable in
terms of the new and sometimes stressful challenges that
occur as young people progress (Larson et al., 2002;
Smetana, Campione-Barr, & Metzger, 2006; Steinberg &
Morris, 2001). Based on past research, we would expect
mean levels of well-being to improve once the young
people leave high school, and for variability in emotional
experience to decrease (Larson et al., 2002; Williams,
Ciarrochi, & Heaven, 2014).
Our results converge with dominant theories and
research on the relative independence of positive and
negative affect (Watson & Clark, 1994), as we found that
hope has a more robust impact on positive compared
with negative affect. This might be because people with
high hope might show a higher sensitivity to the rewards
of goal effort and progress; people with high hope may
also create and pursue increasingly challenging life goals
that inevitably induce negative emotions such as anxiety
and frustration. In this context, negative emotions might
be unpleasant but simultaneously necessary and desir-
able. Pursuing goals that are aligned with one’s deepest
interests and values can be anxiety provoking because of
their meaningfulness and desirability (Hayes, Strosahl, &
Wilson, 2011).When asked, most people would choose a
life imbued with meaningful life pursuits, even if distress
arises, rather than an easy, simple life (King & Napa,
1998; Scollon & King, 2004). In the absence of contex-
tually sensitive measures, negative affect might be less
useful as an outcome variable compared with positive
affect in evaluating well-being (See Emmons [1986];
Little et al. [2007] for strategies to measure affect in the
context of goal pursuit). Future research should examine
the possibility that high hope is associated with the
pursuit of more emotionally challenging goals.
Hope can influence one’s emotions –indeed, emo-
tions are a consequence of goal-directed behaviors and
thoughts (Snyder, 2000). Scheier and Carver (1985) and
Snyder (2002) suggested that, when individuals perceive
that they are making progress in attaining a goal, or have
overcome impediments in achieving goals, this leads to a
positive emotional state. Our data converge with these
claims as it appears to be the case that higher levels of
hope are predictive of positive dispositions or mindsets.
The current research extends prior work by the attention
to a critical phase of human development when people’s
personalities are highly sensitive to biological and
environmental stimuli and thus malleable. If we are to
understand personality development, the six years of
adolescence might be the ideal time frame to explore sta-
bility, change, and associated benefits and costs. To our
knowledge, this is the first study to examine hope over
the entire stage of adolescence.
Gender differences
We found substantial stability in hope, but also substan-
tial change. Compared to boys, girls started out with
higher levels of hope, and then experienced a precipitous
reduction in hope by Grade 10. Girls appeared to
rebound with an increase in hope from Grade 10 to the
end of high school. We also found mean differences in
affective states, with girls experiencing higher fear and
sadness across the years, but also slightly higher
joviality. These findings are consistent with previous
findings that suggest females experience emotions more
intensely than males (Fujita, Diener, & Sandvik, 1991).
Finally, we found that fear was less stable in boys than
in girls, suggesting that boys’level of fear may be more
driven by environmental demands.
Hope and negative affect
Compared to the prediction of positive affect, hope was
not as strong a predictor of the development of sadness,
fear, and hostility. We can only speculate about this pat-
tern, but we would suggest that more hopeful people
engage in more goal striving. Such striving increases the
opportunities to contact reinforcers, but also may
increase contact with adversity, such as those associated
with failure (sadness) and future risks (fear). For exam-
ple, striving to get the best possible grades may lead to
anxiety about getting poor results. There are risks to goal
striving. Even with these risks, however, hope did pre-
dict somewhat less negative affect.
Implications and future directions
Prior theorists have argued that regardless of therapy
type, all therapies seek to increase hope (Grencavage &
Norcross, 1990; Snyder, Michael, & Cheavens, 1999;
Stotland, 1969; Waddington, 2002). It is ‘the’common
factor. It is also assumed to be the common antecedent,
or mediator, of positive changes in mental health. In
order to assess this hypothesis, one needs a reliable
The Journal of Positive Psychology 9
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measure of hope, a measure that has certain properties. It
should be brief to be useful in practice. It should not be
redundant with mental health and affective measures.
And, in keeping with the notion of hope as a mediator,
this process should be shown to be an antecedent to the
development of well-being. The measure of hope used in
the present study satisfies all these criteria. It is brief (six
items), reliable, and predicts future positive affect when
controlling for baseline levels of positive affect (and so
is not redundant with it).
Future research should evaluate the hope mediational
hypotheses. A past meta-analysis suggests that hope
enhancement interventions may be of most benefit
among community samples and may have their largest
effects on positive states such as life satisfaction, while
having little effect on psychological distress (Weis &
Speridakos, 2011). There are a wide variety of types of
hope intervention, varying from ones that use positive
music, guided imagery, narratives, and/or memory recall
(Weis & Speridakos, 2011). Future research is needed to
identify the hope enhancement strategies that are best at
increasing hope and positive outcomes in specific
populations.
Despite three decades of research on hope as defined
by Rick Snyder, little work has addressed the question
of how hope operates during the developmental transi-
tion of adolescence. Our results suggest that hope is a
useful personality dimension in understanding the devel-
opment of well-being. However, we need to acknowl-
edge some limitations. Our results focus on a general
population of high school students and may not general-
ize to clinical populations. We utilized parcels which has
the benefit of reducing the number of parameters that
need to be estimated and tends to make the indicators
more normally distributed, but parceling can also hide
sources of model misspecification (Marsh, Lüdtke,
Nagengast, & Morin, 2013). Future research with larger
samples should replicate the findings using individual
items rather than parcels. In addition, the present study
does not assess features of the context that might support
hope or buffer negative affect in the presence of low
hope (e.g. adolescents’relationships with their parents,
peers, teachers, etc.). Moreover, it is possible that hope
is linked indirectly to positive affect by successful goal
attainment or through the engagement of in positive
behaviors/avoiding negative behaviors, such as selecting
supportive peers and avoiding drug use. Future research
is needed to tease apart the relations between hope and
positive and negative affect by assessing features of the
context as well as individual behaviors across the adoles-
cent period. Answers to questions on the etiology of
hope, and the various strategies that lead to high, sus-
tainable levels of hope and well-being will offer guid-
ance on how to improve existing efficacious
interventions for positive youth development.
Supplementary data
Supplementary data for this article can be accessed here: http://
dx.doi.10.1080/17439760.2015.1015154.
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