Running head: Stress, Resilience and Depression
Stress and Depression in University Students: The Mediating Role of Resilience
[masked title page]
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Running head: Stress, Resilience and Depression
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Abstract
Objective: We examined the extent to which the relationship between adverse stress and
depression is mediated by university students’ perceived resilience.
Participants: Students were randomly sampled at a Canadian university in 2006 (N = 2,147) and
2008 (N = 2,292).
Methods: Data about students’ stress (1 item), depression (4 items), resilience (4 items), and
their demographics were obtained via the online National College Health Assessment survey and
analyzed using confirmatory factor analysis and latent variable mediation modeling.
Results: Greater resilience was associated with lower depression scores for students whose stress
impeded their academic performance, irrespective of their sex and age (total R2depression = 40%).
The relationship between stress and depression was partially mediated by resilience (37% to 55%
mediation depending on the severity of stress).
Conclusions: Identifying students with limited resilience and providing them with appropriate
supportive services may help them to manage stress and prevent depression.
Keywords: resilience, depression, stress, student
Running head: Stress, Resilience and Depression
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Stress and Depression in University Students: The Mediating Role of Resilience
The National College Health Assessment (NCHA), sponsored by the American College
Health Association, has collected data about students’ health status, behavior, and attitudes since
2000. The participation has increased annually and in 2007 more than 90,000 students from
about 150 postsecondary institutions completed the survey (up-to-date survey participation rates
are available at: http://www.acha-ncha.org/partic_history.html). Topics covered via the
approximately 300 questions include: risk and protective behavior, health information access,
injury and violence prevention, tobacco, alcohol and other substance use, sexual health, nutrition,
and mental health. The NCHA is used by many academic institutions to gain information about
the status of students’ mental and physical health. This helps inform the planning of student
health services and is of interest to nurses who often play an important role in the delivery of
these services.
Concerns about students’ mental health status have arisen as a result of the data collected
and compiled through the NCHA. The percentage of North American students that reported
having ever been diagnosed with depression appears to be increasing: 10.3% and 11.8% in the
spring surveys of 2000 and 2001, respectively, and 15.3% and 14.9% in spring 2007 and 2008
(The American College Health Association, 2001, 2008, 2009). Of the many risk factors for
depression, students’ experience of stress is of particular concern to many college health
professionals involved in health promotion and counseling services, including nurses and nurse
practitioners. For example, the most frequently reported impediment to academic performance is
stress, which out ranks viral infections, sleep disturbances, concerns about family members and
friends, and relationship problems (The American College Health Association, 2003, 2009).
Running head: Stress, Resilience and Depression
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Given the evidence indicating that an association exists between the experience of stress
and subsequent risk for major depressive episodes in adults (Hammen, 2005; Kendler,
Karkowski, & Prescott, 1999; Kessler, 1997), researchers have sought to examine the causes and
consequences of stress experienced by university students. Students must manage multiple
demands including the transition from adolescence to adulthood, adapting to a new environment,
establishing new social networks, independently managing the demands of daily life, and
meeting their personal goals. Although these potentially stressful transitions are expected, they
can contribute to symptoms of depression (Dyson & Renk, 2006).
Whereas reducing the number and intensity of stressors experienced by students might be
viewed as the most efficient means of improving the mental health of students, recent research
about resilience indicates that the experience and successful management of stress represent a
critical component of adolescent development (Campbell-Sills, Cohan, & Stein, 2006; Haglund
et al., 2009; Nrugham, Holen, & Sund, 2010; Steinhardt & Dolbier, 2008). The scientific
literature contains several definitions of resilience offered by researchers working in a wide
variety of socio-cultural settings and with populations ranging from adult Holocaust survivors to
children living in extreme poverty. For example, in their research on the development of
competence in children and adolescents, Masten and Coatsworth (1998) defined resilience as
“manifested competence in the context of significant challenges to adaptation or development”
(p. 206). Similarly, resilience has been defined as “the positive counterpart[s] to both
vulnerability, which denotes an individual’s susceptibility to a disorder, and risk factors”
(Werner & Smith, 1992, p. 3), and as “a dynamic process wherein individuals display positive
adaptation despite experiences of significant adversity or trauma” (Luthar, Cicchetti, & Becker,
2000, p. 543). Luthar et al. elaborated that there are “two critical conditions: (1) exposure to
Running head: Stress, Resilience and Depression
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significant threat or severe adversity; and (2) the achievement of positive adaptation despite
major assaults on the developmental process” (Luthar et al., 2000, p. 543). Resilience, then, can
be conceptualized as a process as well as a personal characteristic that can be developed over
time and in response to the exposure to and subsequent effects of stressors (sometimes referred
to as the “steeling effect”) (Rutter, 2006).
These conceptual foundations suggest that the experience of stress and its consequences
can either enhance or constrain the development of resilience. For example, in college or
university students, stress can affect academic performance (e.g., students may withdraw from or
fail a course), and consequently can contribute to a more general perceived inability to manage
stress (i.e., reduced resilience). Students with limited resilience are vulnerable to adverse
psychological outcomes including depression. In the context of statistical modeling, this
intervening role of resilience can be represented as a mediating effect of the relationship between
adverse stress and depression (see Figure 1). The purpose of our investigation was to examine
the plausibility that resilience is a mediator of the relationship between adverse stress (as
manifested through its reported effect on academic performance) and depression, and, if so, to
quantify the degree of mediation.
Method
Participants
The data were obtained via one Canadian university’s spring 2006 and 2008 NCHA
surveys. This university administers the full NCHA survey every two years and adds a few
university-specific questions, including items to measure resilience, disabilities, and Canadianrelevant indicators of ethnicity. Students were randomly selected within three strata: (a)
undergraduate students at Campus I, (b) graduate students at Campus I, and (c) undergraduate
Running head: Stress, Resilience and Depression
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and graduate students combined at Campus II. In addition, all international students and students
living in residence at Campus I were invited to complete the questionnaires. All analyses were
first conducted with the 2006 data and subsequently validated with the 2008 data. The results
presented here are for 2006 data unless otherwise stated.
Ethical approval was provided by the university’s Behavioural Research Ethics Board.
The students at the two campuses were invited via email to participate in the web-based survey.
Invited students received information about the purpose and content of the survey, the expected
time required, how privacy would be protected, and referrals to health services. They were
informed that by accessing the survey website their consent to participate was inferred.
Measures
The NCHA questionnaire includes seven items measuring depressive symptoms and
suicidal ideation and attempts. The respondents were asked to indicate the number of times,
within the last school year, they had: (a) “felt things were hopeless,” (b) “felt overwhelmed by all
you had to do,” (c) “felt exhausted (not from physical activity),” (d) “felt very sad,” (e) “felt so
depressed that it was difficult to function,” (f) “seriously considered attempting suicide,” and (g)
“attempted suicide.” The response options were: (a) “never,” (b) “1-2 times,” (c) “3-4 times,” (d)
“5-6 times,” (e) “7-8 times,” (f) “9-10 times,” and (g) “11 or more times” (coded as 1 to 7,
respectively). Researchers have used these items as an index to compare levels of depressive
symptomology across groups of students (e.g., men vs. women, younger vs. older students, oncampus vs. off-campus residents, fraternity/sorority members vs. not) (Office for Survey
Research, 2002). However, the construct validity of these items as a unidimensional measure of
depression has not been shown. Other researchers have used only some of the items to measure
depression. For example, Adams, Moore, and Dye (2007) conducted a principal components
Running head: Stress, Resilience and Depression
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analysis of five of the indicators (items a-e) and produced a 3-item scale (items a, d, and e).
Considering the limited evidence of measurement validity, we conducted a psychometric
analysis with the 2006 data to determine whether the 7 depression items could be represented by
a single factor. We found support for a model with 4 items (a, d, e, f), which was validated via a
multi-group CFA, using the 2008 data, with the loadings, thresholds and variances constrained to
be equal across the two study periods (further described in the Results).
The academic consequences resulting from stress were inferred from one of the 25
potential impediments to academic performance enumerated in the NCHA questionnaire. The
respondents were prompted to select one of the following five options to indicate whether stress
had adversely affected their academic performance: (a) “this [stress] did not happen to me/not
applicable,” (coded as “0”) (b) “I have experienced this issue [stress] but my academics have not
been affected,” (coded as “1”) (c) “received a lower grade on an exam or important project,”
(coded as “2”) (d) “received a lower grade in the course,” (coded as “3”) and (e) “received an
incomplete or dropped the course” (coded as “3”). Response options (d) and (e) were collapsed
and response option (a) served as the referent in the dummy variable, which was created to
accommodate the ordinality of the measure.
Four resilience indicators were developed by one of the authors (CW), a registered
psychologist and director of university counselling services. These indicators were derived from
the work of Lazarus and Folkman (1984), which was based on the theory that the experience of
stress is a product of an individual’s evaluation of the importance of a stressor combined with the
perceived ability to cope with the stressor. Based on this theory and the notion of resilience as
an evolving characteristic, students’ perceived ability to recognize and manage their stress is
conceptualized as reflective of resilience to stress and reduces the extent to which stress
Running head: Stress, Resilience and Depression
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contributes to the onset of depression. The items are: “Please indicate the degree to which the
following statements are true: (a) I believe I have the ability to cope with the demands of my life,
(b) I know when I’m starting to experience too much stress, (c) I know how to cope with stress
when it comes, and (d) I am usually able to successfully deal with my stress levels.” Responses
were labeled: “agree strongly,” “agree somewhat,” “disagree somewhat,” or “disagree strongly”
(coded as “1” to “4,” respectively). These items were reverse coded in the statistical models
such that a higher score was indicative of more resilience. A one-factor CFA was conducted to
examine the measurement validity of these items with the 2006 data, and a validation assessment
was carried out using the 2008 data (see Results for further information).
Age was represented as three categories that were dummy-coded: less than or equal to 20
years (referent, coded as “0”), greater than 20 and less than or equal to 30 years, and greater than
30 years. Sex was represented as a binary variable (female coded as “1”).
Analytical Approaches
Frequency distributions were produced for each of the observed variables. A latent
variable mediation model (Mackinnon, 2008) was specified to examine the relationships among
the variables, academic consequences of stress, depression, and resilience, as shown in Figure 1,
while controlling for age and sex (not shown in the figure). Depression and resilience were
specified as latent variables with one of the loadings for each variable fixed at one for purposes
of identification. The manifest, polytomous variable, academic consequences of stress, along
with age and sex, were specified to directly affect depression and to indirectly affect it through
the mediating variable, resilience. The indirect effect of academic consequences of stress on
depression, via resilience, was calculated as the product of the coefficients of these relationships
(MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). The estimation of the indirect
Running head: Stress, Resilience and Depression
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effects for the three levels of academic consequences of stress was done simultaneously in the
latent variable mediation model. Similar to the commonly used Sobel (1990) test (which tests
whether a mediator carries an effect from an explanatory variable to an outcome variable), the
delta method was used to estimate the standard errors of the indirect effects (Mackinnon &
Dwyer, 1993). For each level of academic consequences of stress, the percent mediation was
determined by dividing the indirect effect by the total effect (the sum of the indirect and direct
effects) (Mackinnon, 2008).
The MPlus 5.2 software (Muthén & Muthén, 2008) was used to conduct exploratory and
confirmatory factor analyses of the latent variable measurement models, and structural equation
modeling (SEM) was conducted to estimate the parameters of the mediation model. Two-group
SEM (Schumacker & Lomax, 2010) was conducted with the 2006 and 2008 data to validate the
consistency of the measurement model parameters across the two years. We applied mean and
variance adjusted weighted least squares estimation (WLSMV) to accommodate the ordered
categorical data (Finney & DiStefano, 2006). Model fit was assessed by scrutinizing the residual
correlations and the model fit indices. Indicators suggestive of acceptable fit included: a root
mean square error of approximation (RMSEA) of less than .06 and a comparative fit index (CFI)
greater than .95 (Beauducel & Herzberg, 2006).
Results
Sample Description
Of 11,781 randomly sampled undergraduate and graduate students registered at the two
campuses, 2,147 (18.2%) completed the questionnaire in 2006. In 2008, 11,490 students were
randomly sampled and 2,292 (20.0%) completed the questionnaire. A comparison of
demographic characteristics of the samples and the corresponding populations is provided in
Running head: Stress, Resilience and Depression
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Table 1. The sample shows over representation of females, full-time students and graduate
students. The frequency distributions of the seven depression items and four resilience items are
provided in Figures 2 and 3, respectively. Almost all students responded to the academic
consequences of stress item (n = 2,103): 38% reported experiencing stress that affected their
academic performance (25% reported a lower grade on an exam/project and 13% reported a
lower course grade or an incomplete/dropped course); 44% experienced stress that did not affect
their academic performance; and 16% reported experiencing no stress.
Psychometric Analyses
In establishing the measurement structure for the seven depression items, we first excluded
“item g” (attempted suicide) because only 1.3% of the respondents answered affirmatively. The
remaining six items were included as ordinal variables in a one-factor CFA. This model did not
fit well (WLSMV χ2(6) = 589.8, RMSEA = .216, CFI = .955). The results of a subsequent EFA
indicated that a two-factor solution would provide better fit to the data. We then conducted a
two-factor CFA with items a, d, e, and f loading on factor 1 (these items were interpreted as
indicative of depression) and items b and c loading on factor 2 (overwhelmed or exhausted). The
fit of this model was significantly better than the one-factor CFA model (∆WLSMV χ2(1) =
311.6, WLSMV χ2(6) = 72.3, RMSEA = .073, CFI = .995). Factor 2 was omitted from further
analyses because of its non-specificity. The one-factor CFA of the remaining four items (a, d, e,
f) resulted in good model fit (WLSMV χ2(2) = 15.5, RMSEA = .057, CFI = .999). The ordinal
alpha coefficient of reliability (Zumbo, Gadermann, & Zeisser, 2007) for the four items was .92,
with standardized factor loadings ranging from .78 to .91. The validation of this model (with
2008 data) provided evidence of parameter invariance across the two study periods (WLSMV
χ2(8) = 29.7, RMSEA = .035, CFI = .999).
Running head: Stress, Resilience and Depression
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A 1-factor CFA of the four resilience items resulted in a well-fitting model (WLSMV χ2(2)
= 17.8, RMSEA = .061, CFI = .999). The standardized factor loadings ranged from .43 for item
b (“I know when I’m starting to experience too much stress”) to .92 for item d (“I am usually
able to successfully deal with my stress levels”). The ordinal alpha coefficient was .86.
Evidence of parameter invariance was shown in the validation assessment with the 2008 data,
when the parameters were constrained to be equal across the two years (WLSMV χ2(8) = 12.6,
RMSEA = .016, CFI = 1.000).
Structural Equation Model
The mediation model as shown in Figure 1 resulted in acceptable fit (WLSMV χ2(32) =
248.3, RMSEA = .057, CFI = .982). Similar results were obtained when the model was
validated with the 2008 data by constraining the measurement model parameters (loadings and
thresholds) and the structural parameters to be equivalent across the two study periods (WLSMV
χ2(60) = 409.59, RMSEA = .052, CFI =.986). The 2006 model explained 40% of the variance in
depression and 23% of the variance in resilience. Depression was significantly explained by
academic consequences of stress and resilience but not by the covariates of age and gender; a
relative increase in the consequences of stress and a decrease in resilience were associated with
greater depression scores. Resilience was significantly explained by academic consequences of
stress and by the covariates of age and gender; a relative increase in academic consequences of
stress was associated with reduced resilience, students in the oldest age group had greater
resilience scores relative to those in the youngest age group, and female students had lower
resilience scores compared with male students’ scores.
The effect of stress on depression was partially mediated by resilience. That is, the degree
to which the academic consequences of stress influenced depression could be partially attributed
Running head: Stress, Resilience and Depression
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to the respondents’ reported resilience. Relative to “no stress” (the referent), 37% of the total
effect of “stress that does not affect academic performance versus no stress” on depression was
mediated by resilience. The percentages of mediation were 55% for “stress resulting in a lower
grade on an assignment or project” and 51% for “stress resulting in a lower or incomplete course
grade versus no stress.” The corresponding indirect, direct and total effects are reported in Table
2.
Discussion
The mediation model presented here was found to be a plausible explanation of the
relationships among the variables of interest. Similar to other published literature, we found that
the stress students experience is directly related to increased levels of depressive symptoms. In
addition, we found that a substantial portion of this relationship appears to be mediated through
students’ perceived ability to recognize and manage their stress (i.e., their resilience) when the
stress is an impediment to academic performance. Approximately one half of the relationship
between stress and depression was mediated through the resilience of students whose stress
affected their academic performance.
The latent variable approach used for the measurement of depression and resilience is both
a strength and limitation of this study. We undertook a rigorous psychometric evaluation of the
items with particular attention paid to the ordinal nature of the response options thereby
accounting for some sources of measurement error. The results of the psychometric analyses of
the depression items suggest that significant error would have been unaccounted for if an index
of depression were to be used based on the summed responses to these items. We recommend
that researchers use a relatively sophisticated data analytic approach based on a latent variable
single factor structure of the four relevant items identified in our analyses. With respect to the
Running head: Stress, Resilience and Depression
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resilience items, it is acknowledged that they have not been used or examined in other studies so
far, and therefore require further examination of their validity. A further limitation arises from
the cross-sectional nature of the survey data; any implied causal inferences must be interpreted
with caution and other plausible models exist. In addition, the low response rate raises concerns
about the representativeness of the sample. Nevertheless, these limitations are partially mitigated
by having replicated the analyses in another sample (i.e., in two time periods). Relative to the
population, female and full-time students were overrepresented in the sample. This pattern is
commonly found in university-based voluntary surveys. The extent to which this
overrepresentation biases the conclusions is unknown. The field would be advanced further by
the availability of prospective studies about resilience development in students.
The finding of the substantial mediating effect of resilience is congruent with the work of
others in the field. Although not examined in the current study, it is hypothesized that, through
the development of resilience, students could learn to effectively recognize and cope with the
stressors that they encounter and, in so doing, avoid transitioning into states of depression
(Schaefer & Moos, 1992). Resilience development refers to the notion that low to moderate
levels of stress exposure provide students with the opportunity to practice coping skills (Fergus
& Zimmerman, 2005). Conversely, the inability to cope with even low to moderate levels of
stress could impair the development of resilience and thereby increase vulnerability to
depression. An important implication relevant for nurses and other health professionals involved
in college health promotion is that interventions focused on developing resilience may reduce the
risk of depression. Specifically, although potentially stressful transitions are expected in
university students, referrals to and the provision of counselling services that focus on enhancing
Running head: Stress, Resilience and Depression
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students’ resilience, may help to mitigate undesirable consequences of stress and reduce the risk
of depression.
Stress is ubiquitous and many students may require support in developing resilience.
Rather than focussing all efforts on removing stressors, programs designed to enhance students’
ability to cope are needed. For example, research has shown that active coping (seeking social
support, reframing stressors in a positive light) is associated with improved well-being, fewer
psychological symptoms, and the ability to manage stressful circumstances (Valentiner, Holahan,
& Moos, 1994). Promising programs have been developed for both school-aged children and
university or college students to develop their ability to cope with stress (Gillham, Hamilton,
Freres, Patton, & Gallop, 2006; Steinhardt & Dolbier, 2008). Identifying students with limited
resilience and providing them with appropriate supportive services may help them to manage
stress and thus prevent it from impeding their academic performance.
Running head: Stress, Resilience and Depression
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Running head: Stress, Resilience and Depression
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Table 1
Sample and Population Distributions
2006
2006
2008
2008
Sample
Population
Sample
Population
(N = 2,147)
(N=49,683)
(N = 2,292)
(N=50,706)
Gender
Female
67%
57%
67%
56%
Male
32%
43%
32%
46%
2%
-
1%
-
92%
67%
88%
69%
No
6%
33%
10%
31%
Missing
2%
-
1%
-
16%
17%
16%
18%
2nd year undergrad
15%
15%
15%
16%
3rd year undergrad
16%
19%
15%
20%
4th year undergrad
11%
18%
11%
17%
5%
5%
6%
5%
34%
20%
34%
18%
adult special / other
1%
6%
2%
5%
Missing
3%
-
2%
-
Age
18 - 20
33%
30%
32%
31%
21 - 29
46%
51%
48%
50%
30 - 68
19%
20%
19%
19%
Missing
2%
-
1%
-
Variable
Missing
Full time students
Yes
Year in school
1st year undergrad
5th year or more undergrad
graduate
Running head: Stress, Resilience and Depression
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Table 2
Percent of Mediation and Unstandardized Effects
Direct Effects
Indirect Effecta
Total Effectb
Mediation
B (SE)
B (SE)
B (SE)
%
Stress
Stress
Stress
Resilience
Depression
Resilience
Depression
Stress 1
.22 (.05)
-.23 (.06)
-.55 (.03)
.13 (.03)
.35 (.06)
37%
Stress 2
.35 (.06)
-.78 (.06)
-.55 (.03)
.43 (.04)
.78 (.07)
55%
Stress 3
.64 (.08)
-1.21 (.07)
-.55 (.03)
.66 (.05)
1.30 (.08)
51%
Note. Unstandardized coefficients are reported here. The corresponding standardized
coefficients are found in Figure 1. All coefficients are adjusted for sex and age. Regression
parameters for depression on age and gender are: .08 and .02 for the two age variables (>20
and <=30 vs. <= 20 and >30 vs. <=20, respectively), and -.02 for gender. Regression parameters
for resilience on age and gender are: .04 and .13 for the two age variables, and .08 for gender.
a
At each level of stress, the indirect effect is the product of the effects of stress on resilience
and resilience on depression (i.e., mediated through resilience).
b
At each level of stress, the total effect is the sum of the indirect effect (mediated through
resilience) and the direct effect of stress on depression.
Running head: Stress, Resilience and Depression
19
Figure 1. Structural Equation Model
e
Academic
S1 = .12
Depressive
consequences
S2 = .17
symptoms
of stress (S1 - S3)
S3 = .24
R2 = .41*
S1 = -.13
S2 = -.39
.84 .89 .91 .77
-.52
dep e
dep f
R2 = .23
dep d
Resilience**
dep a
e
S3 = -.47
e
e
e
e
resil a
resil b
resil c
resil d
.80 .45 .94 .92
e
e
e
e
Figure 1. Model fit: WLSMV χ2(32) = 248.3, RMSEA = .057, CFI = .982 (N = 2,044). All shown
coefficients are standardized and statistically significant. The variable “academic consequences
of stress” was dummy coded with “no stress” as the referent (coded 0). S1: Stress that does not
affect academic performance versus (coded 1), S2: Stress resulting in a lower grade on an
assignment or project versus (coded 2), S3: Stress resulting in a lower or incomplete course
grade versus (coded 3). Regressions on resilience and academic consequences of stress are
adjusted for age and sex (these parameters are not shown here). * Standardized regression
parameters for depression on age and gender are: .04 and .01 for the two age variables (>20
and <=30 vs. <= 20 and >30 vs. <=20, respectively), and -.01 for gender. ** Standardized
regression parameters for resilience on age and gender are: .02 and .05 for the two age
variables, and .04 for gender.
Running head: Stress, Resilience and Depression
Figure 2. Frequency Distributions for Depression Items (Prior to Collapsing)
20
Running head: Stress, Resilience and Depression
Figure 3. Frequency Distributions for Resilience Items
Figure 3. These items were reverse coded in the statistical models.
21