Received: 11 June 2018 | Revised: 11 March 2019 | Accepted: 18 March 2019

DOI: 10.1002/pits.22253

RESEARCH ARTICLE

Promoting academic satisfaction and

performance: Building academic resilience
through coping strategies

Isabella Meneghel1 | Isabel M. Martínez1 | Marisa Salanova1 |

2,3
Hans de Witte

1
WANT Research Team, Department of Social
Psychology, Universitat Jaume I, Castellón de Abstract
la Plana, Spain The aim of this article is twofold: (a) to validate the
2
WOPP‐O2L, Faculty of Psychology &
psychometric properties of the Academic Resilience Scale
Educational Sciences, KU Leuven, Leuven,
Belgium, (AR‐S) in a Spanish university context; and (b) to test a
3
Optentia Research Focus Area, North West model where different coping strategies are antecedents of
University, Potchefstroom, South Africa
AR, and where academic satisfaction and performance are
Correspondence its consequences. The studies were conducted with 185
Isabella Meneghel, WANT Research Team,
Department of Social Psychology, Universitat (study 1) and 780 (study 2) students. In study 1, the
Jaume I, Avda. Sos Baynat, s/n, 12071 construct validity, reliability, and convergent and divergent
Castellón de la Plana, Spain.
Email: meneghel@uji.es validity of the AR‐S were analyzed. The hypothesized model
(study 2) was tested using structural equation modeling. The
Funding information
Fundació Caixa Castelló‐Bancaixa, Grant/ results confirmed the good psychometric properties of the
Award Number: E 2013‐31; Consellería de AR‐S in the Spanish context, providing evidence of its
Educación Generalitat Valenciana, Grant/
Award Number: PROMETEO/2013/025 validity. Furthermore, the distinctive role of different coping
strategies in resilience was confirmed, as well as the
relationship between resilience and academic satisfaction.
No direct relationship between resilience and performance
was found, highlighting the full mediation of satisfaction.

KEYWORDS
academic performance, academic resilience, academic satisfaction,
coping, scale validation

1 | INTRODUCTION

In light of the increasing need for healthy and competent workers who can cope with the current dynamic and
changing work context, it is crucial to educate and motivate university students to be valuable and contributors to
the economy and the nation in the long run (Riolli, 2012). Research has shown that psychological distress is

Psychol. Schs. 2019;1–16. wileyonlinelibrary.com/journal/pits © 2019 Wiley Periodicals, Inc. | 1

2 | MENEGHEL ET AL.

significantly higher in university students than in the general population (Stallman, 2010), and it has also been
established that stress increases as students progress in their studies (Putwain, 2007). Indeed, university students
face many challenges in the pursuit of their educational goals, including exams, demands of their academic course
work, and managing intrapersonal and interpersonal changes (Roddenberry & Renk, 2010; Ross, Niebling, &
Heckett, 1999). Although these challenges may be considered microstressors, due to their accumulated effects over
time, they are important for health (Zautra, 2003) and can have a variety of negative psychological and behavioral
effects that can reduce students’ performance. For instance, academic stress has been associated with disorders
such as depression (Dao, Lee, & Chang, 2007), school failure (McDonald, 2001), study burnout (Salanova, Schaufeli,
Martínez, & Bresó, 2010), and immune system disturbances (Vedhara & Nott, 1996).
However, some students have the capacity to successfully withstand the consequences of negative academic
experiences. They are easily encouraged following minor setbacks and generally view negative challenging events
as surmountable (e.g., Struthers & Perry, 1996). Nevertheless, little is known about the relative strength of various
factors that promote positive adaptation in the face of academic stress (Leary & DeRosier, 2012). The concept of
resilience might help to explain why some individuals who experience high levels of stress are able to withstand it
and may even thrive on it, enabling them to manage future challenges effectively (Grant & Kinman, 2012). Thus, to
account for the discrepancy in students’ responses to negative events, the present study focused on academic
resilience (AR) as a factor that promotes positive adaptation and outcomes during the academic stage.
From a stress‐coping perspective, numerous studies have highlighted the importance of coping in dealing with
adversity (e.g., Clauss‐Ehlers, 2008; Leipold & Greve, 2009), and individuals use a range of coping strategies for this
purpose. Therefore, in stressful academic‐related situations, students may have different ways to face them; that is,
they possess different coping strategies that will have different effects on well‐being and performance. In this
study, we investigate the predictive role of distinct coping strategies on AR. Moreover, we examine whether the
relationship between coping strategies and AR stimulates positive academic outcomes, assessed by two widely
recognized indicators, namely, academic satisfaction and performance. In sum, this study aims to further our
understanding about how coping strategies promote resilience, leading to greater satisfaction and better
performance.
This study extends previous research in this field in several ways. First, a validation of an AR scale in the Spanish
context is proposed to have a reliable instrument to measure resilience and test for its antecedents and outcomes.
Second, although earlier studies have examined coping strategies as predictors of resilience, they mainly combined
scales into general problem‐ and emotion‐focused categories of coping. Instead, as Carver, Scheier, and Weintraub
(1989) suggest, we use the data to determine the composition of the higher‐order factors because different
samples exhibit different patterns of relations. Third, we include the Grade Point Average (GPA) provided by the
University as an objective measure of academic performance to control for method bias, thus strengthening the
validity of our results. Moreover, academic performance (GPA) is measured 4 or 5 months after the administration
of the questionnaire with the psychosocial variables, thus allowing us to study the effect of psychosocial variables
on performance over time. Finally, we use a sample of students pursuing different degrees in different faculties to
reinforce the generalizability of the results.

1.1 | About the concept and measure of AR

It has been argued that focusing on salutogenic (or health‐enhancing) rather than pathogenic (or disease‐reducing)
factors is more successful in promoting well‐being in various life contexts (Seligman, 2003). Based on this
framework, in this study we focus on resilience because it refers to the process of positive adaptation in the face of
stress or adversity (Luthar, Cicchetti, & Becker, 2000). Applied to the academic context, resilience research focuses
on the mechanisms and interactive processes that enable students to succeed despite unfavorable circumstances
(Doll & Lyon, 1998). We refer to AR as the process of dealing with academic adversity and achieving positive
outcomes in stressful situations. A substantial body of evidence reports that resilient students are those who
MENEGHEL ET AL. | 3

sustain high levels of achievement motivation and performance, despite the presence of stressful events and
conditions (Leary & DeRosier, 2012). To date, the few studies that have dealt with AR have focused on ethnic‐
minority groups, learning difficulties, and extreme underachievers. However, AR is relevant to all students because
at some point they all experience challenges during their academic life (Martin & Marsh, 2006). Consequently, we
propose that the study of AR can help to further our understanding of the process through which students are able
to successfully adapt to and “bounce back” from stressful situations, as well as providing insight into possible
interventions (Steinhardt & Dolbier, 2008).
It is interesting to note that in recent years different instruments have been used to measure resilience in
student populations; however, in most cases they are not specific to the academic context (e.g., Campbell‐Sills,
Cohan, & Stein, 2006; Dolbier, Jaggars, & Steinhardt, 2010). To our knowledge, the scale proposed by Martin and
Marsh (2006) is the only one that: (a) focuses specifically on AR, and (b) has different content from previous
measures, which have generally assessed protective factors or resources involving personal characteristics (e.g.,
Connor & Davidson, 2003), framing its items as statements referring to the process of bouncing back, adapting to
stress, or thriving in the face of academic adversity. Thus, we aim to evaluate the psychometric characteristics of
the Spanish version of the AR‐S by Martin and Marsh (2006) in the first study in this paper (see study 1). After
confirming that the AR‐S is a reliable scale to measure AR in the Spanish context, it can be used as a measure of
resilience to test antecedents and outcomes.

1.2 | Coping strategies as antecedents of AR

To understand people who positively face adversity, it is undoubtedly important to identify the factors that may
promote resilience, such as coping strategies (Smith et al., 2008; Steinhardt & Dolbier, 2008). Coping has been
defined as a person’s efforts to remove, reduce, or manage threatening events or situations that are appraised as
challenging or stressful (Lazarus & Folkman, 1984). Although resilience and coping are sometimes used
interchangeably, they are in fact related, but conceptually distinct, constructs. The distinction between AR and
coping strategies is based on the definition of the constructs: whereas coping refers to the set of cognitive and
behavioral strategies used by an individual to manage the demands of stressful situations, resilience refers to
adaptive outcomes in the face of adversity (Campbell‐Sills et al., 2006).
Researchers have generally clustered coping responses into theoretically derived factors. One of the earlier
nomenclatures (Folkman & Lazarus, 1980) distinguishes two major functions of coping: problem‐focused coping and
emotion‐focused coping. Problem‐focused coping involves addressing the problem causing the distress. Effective
problem‐focused coping probably contributes to positive psychological states by allowing people to experience
some personal control and a sense of accomplishment. Emotion‐focused coping is aimed at regulating distress and
negative emotion, rather than trying to change the events themselves, through the use of strategies such as escape‐
avoidance or support‐seeking. These strategies involve thoughts and/or actions that relieve or lessen the emotional
impact of stress. Research has found that people who cope with stress by seeking social support or venting their
feelings (emotion‐focused) experience more negative outcomes than people who address the experienced stressor
directly by working on solving their problems (problem‐focused; Suldo, Shaunessy, Michalowski, & Shaffer, 2008).
Previous studies on coping strategies and resilience also confirmed these results (Campbell‐Sills et al., 2006).
Studies found that the problem‐focused and emotion‐focused distinction was a good starting point, but they
also identified meaning‐focused coping as a different type of coping where cognitive strategies are used to manage
the meaning of a situation (Folkman & Moskowitz, 2004). Meaning‐focused coping is basically aimed at regulating
positive emotions. There is considerable evidence suggesting that positive emotions occur alongside negative
emotions throughout intensely stressful periods and are of great significance for adaptation (Folkman, 2008).
Therefore, positive emotions need to be included to learn more about how people generate and sustain them, and
explore their adaptation role in relation to outcomes.
4 | MENEGHEL ET AL.

Drawing on these three different kinds of coping categories, we postulate distinct hypotheses about the
relationship between coping strategies and resilience. First, we propose that students who show high levels of
active coping and use problem‐focused strategies are more resilient. Second, we suggest that students who use
emotion‐focused strategies, which are aimed at regulating distress and negative emotions, reduce their efforts in
dealing with the problem and, thus, are less resilient. Finally, we propose that students who regulate positive
emotions by attributing meaning to the situation broaden their thought‐action repertoires and build resources,
such as resilience, that enable them to cope and manage things effectively.

1.3 | Consequences of AR (satisfaction and performance)

In studying the consequences of AR, we focus on academic satisfaction and academic performance. In this study,
academic satisfaction is considered students’ cognitive evaluations of various aspects of their academic context (i.e.,
teachers, degree, and faculty). Academic satisfaction is considered the subjective intrinsic gratification students
experience through their educational pursuits (Ryan, 2001), and its importance as an indicator of school adjustment
(Baker, Dilly, Aupperlee, & Patil, 2003) and quality in higher education (Byrne & Flood, 2003) has been recognized.
On the grounds that AR is useful in helping students to overcome the stress in the academic context, we propose
that, when students show a high level of resilience, they will experience higher satisfaction with the aspects of their
academic context. Indeed, students with higher levels of resilience, even experiencing adverse events in their
academic context, are expected to positively adapt to and successfully bounce back from these events (Leary &
DeRosier, 2012). Therefore, we postulate that this positive response enhances their academic satisfaction.
Previous research has suggested that AR leads to an increase in academic performance (Kwok, Hughes, & Wen,
2007) because it prepares individuals to rebound or bounce back from adversities, problems, and failures. Resilient
people are more flexible in responding to changing demands, and they tend to use setbacks as “springboards” or
opportunities for growth (Tugade & Fredrickson, 2004). However, we expect resilience to influence academic
performance through satisfaction as well. Specifically, we propose that, when resilience is high, students perform better
because they experience more academic satisfaction engendered by resilience. Previous evidence showed that attitudes
are proximal antecedents and guidelines for behavior (Ajzen & Fishbein, 2005), and studies have shown that a cognitive
attitude (i.e., satisfaction) is the primary source of student performance (Chambel & Curral, 2005; Rode et al., 2005).
Hence, we argue that academic satisfaction is a partial mediator of the effects of AR on academic performance.

2 | ST UD Y 1

The first study assessed the psychometric properties of a Spanish adaptation of the Academic Resilience Scale (AR‐S)
developed by Martin and Marsh (2006). We conducted a number of tests to determine the validity and reliability of the
measure. First, a confirmatory factor analysis was carried out, and construct validity was tested. Subsequently, the
reliability of the AR‐S was investigated, along with its convergent and divergent validity. Thus, we predict:

Hypothesis 1. The Spanish version of the AR‐S will demonstrate acceptable psychometric properties (i.e., validity
and reliability).

2.1 | Method
2.1.1 | Sample and procedures
The first study involved 185 students from a Spanish university. The sample size fits all the criteria suggested by
Muthén and Muthén (2002) for determining sample size through a Monte Carlo study: (a) parameter and standard
MENEGHEL ET AL. | 5

error biases did not exceed 10% for any parameter in the model; (b) the standard error biases for parameters that
are the specific focus of the power analysis (e.g., the factor covariance of resilience and self‐efficacy) did not exceed
5%; and (c) coverage, that is, the confidence intervals, was between 0.91 and 0.98. In addition to these criteria, an
appropriate sample size was determined because the power of the salient model parameters was above 0.80.
Participants (57.5% females) were stratified and belonged to the four faculties of the University, that is: Faculty
of Humanities and Social Sciences (56.5%), School of Technology and Experimental Sciences (17.4%), Faculty of
Health Sciences (14.7%), and Faculty of Law and Economics (11.4%). Ages ranged from 18 to 44 years (M = 22.8;
SD = 5.06 years). Most of them (95.6%) were studying a bachelor’s degree lasting 4 years, whereas the others were
studying five‐year degrees from the previous curriculum (4.4%). Regarding the course they were taking, 13.7%
were in their first year, 50.5% in the second, 27.5% in the third, 7.1% in the fourth, and 1.1% in their fifth year.
Eighty percent of them were not working at that moment.
The data were collected through a paper and pencil questionnaire that includes all the study variables. Students
received a brief presentation of the study by the researchers during class time, and they were invited to individually
fill out a questionnaire. On the cover page of the questionnaire, they read and signed an individual informed
consent. The data collected by the questionnaire and the data on academic performance (GPA provided by the
University) were matched through the National Identity document of each student.

2.1.2 | Measures
All the items on the measures used were scored on a 7‐point scale ranging from 0 (strongly disagree) to 6 (strongly
agree). On the questionnaire, the order of the items on the four measures was alternated.

The Academic Resilience Scale

The Spanish version of the AR‐S was a translation of the Martin and Marsh (2006) scale. It is one‐dimensional and
includes 6 items, each framed in a positive direction. An example item is: “I think I’m good at dealing with
schoolwork pressures”. The AR score was obtained by computing the mean of the 6 items on the scale. The original
α for this scale was 0.89 (Martin & Marsh, 2006). The translation was performed by two researchers who were
fluent in Spanish and English, and then two other researchers were asked to compare the Spanish and English
versions of the scale.

Other measures
A number of existing measures were used in this study to assess the convergent, divergent, and construct validity
of the AR‐S. For each of them, the score was obtained by computing the mean of the items that make up each scale.

Resilience Another measure used to assess resilience was the short 12‐item version of the Psychological Capital
Questionnaire (PCQ‐12; see Luthans, Avey, Smith, & Li, 2008), adapted to the academic context. In this instrument,
resilience was measured with a three‐item scale. An example item is: “I usually take stressful things in my stride
with regard to my studies”. Cronbach’s α was 0.60.

Self‐efficacy It was measured with a five‐item scale developed by Midgley and colleagues (2000) and used with a
sample of Spanish students (Salanova, Bresó, & Schaufeli, 2005). An example item is: “I can do even the hardest
work in this class if I try”. Cronbach’s α was 0.74.

Proving goal orientation Proving goal orientation was assessed with a four‐item scale from the Goal Orientation
scale developed by Vandewalle (1997). An example item is: “It’s important for me to prove that I am better than
others in class”. Cronbach’s α was 0.75.
6 | MENEGHEL ET AL.

2.2 | Results
2.2.1 | Construct validity
With the purpose of confirming the one‐factor structure of the AR‐S and its relatedness to other constructs, we
performed a confirmatory factor analysis, including a theoretically relevant concept, self‐efficacy. From a
theoretical point of view, self‐efficacy has been proposed as an important supportive characteristic of AR. Bandura
(2011) suggested that people who are high in self‐efficacy view impediments as surmountable by developing
required competencies and making a perseverant effort, and so they are able to face difficulties and remain
resilient to adversity. Thus, it is proposed that students’ beliefs about themselves and their academic capacities
influence their behaviors and emotional reactions, allowing them to perform successfully in the face of obstacles or
adverse experiences.
To shed more light on the relationship between these variables, structural equation modeling (SEM) was used to
test the fit of the structural models. The analysis was performed using AMOS 21.0 (Arbuckle, 2005), and various fit
indices were used to assess the model fit. Goodness of fit can be determined with the following indices (Hu &
Bentler, 1999): the chi‐square test (χ2); Comparative Fit Index (CFI ≥ 0.90); the Normed Fit Index (NFI ≥ 0.90); the
Tucker–Lewis Index (TLI ≥ 0.90); and the Root Mean Square Error of Approximation (RMSEA ≤ 0.08). SEM results
were used to compare a single‐factor model (which tested all the items of resilience and self‐efficacy loading in one
factor) with a two‐factor model (which tested all the items loading in two different factors) to determine which
model fit better. Confirmatory factor analyses (CFA) conducted on the single‐factor model revealed inadequate fit
indices, χ2(44) = 248.59, CFI = 0.76, NFI = 0.73, TLI = 0.64, and RMSEA = 0.16, whereas the two‐factor model fit the
data well, χ2(43) = 90.03, CFI = 0.95, NFI = 0.90, TLI = 0.92, and RMSEA = 0.08. The comparison of χ2 statistics
indicated that the two‐factor model fit the data significantly better than the one‐factor model Δχ2(a) = 158.56,
p < 0.001. All items had significant loadings in their intended latent factors. Moreover, as expected, self‐efficacy was
positively and significantly related to AR: β = 0.37, p < 0.001. Based on these findings, we can empirically confirm
the factor structure of the resilience measure, and that self‐efficacy is significantly related to AR, but they are
conceptualized in different ways, thus providing empirical support for the construct validity of the AR‐S.

2.2.2 | Reliability
An important step in establishing the psychometric characteristics of a scale is to determine its reliability. We used
Cronbach’s α as a measure of the internal consistency reliability of the scale because it reflects the extent to which
the items are homogeneous. The AR‐S showed a Cronbach’s α = 0.90 (Kline, 2011), which is excellent.

2.2.3 | Convergent validity

Convergent validity of the scale was checked in two ways. First, we inspected whether our measures of AR were
related to another (criterion) resilience measure. The convergent validity of the AR‐S was examined by correlating
it with the resilience scale from the PCQ‐12, adapted to the academic context (Luthans et al., 2008; Luthans,
Youssef, Sweetman, & Harms, 2013). Convergent validity is assumed when the scales of the tested instrument
correlate positively and moderately to highly with the criterion instrument (Kline, 2011). Pearson’s correlation
coefficient between the AR‐S and the resilience scale from the PCQ‐12 showed that the correlation was high and
significant (r = 0.58, p < 0.01).
We then computed the average variance extracted (AVE; Fornell & Larcker, 1981). The AVE represents the
extent to which items of a specific construct “converge” or share a high proportion of variance in common. A model
can be considered to have good convergent validity if at least 50% of the measurement variance is captured by the
construct (AVE > 0.50). The estimated AVE of the AR‐S is 0.61. Taken together, the results support the convergent
validity of the AR‐S.
MENEGHEL ET AL. | 7

2.2.4 | Divergent validity

Finally, the divergent validity was checked by demonstrating that AR is different from another (criterion) variable.
We investigated the divergent validity of the AR‐S by correlating it with the proving dimension of goal orientation.
A proving goal orientation is a focus on demonstrating one’s competence and gaining favorable judgments or
positive evaluations from others (Vandewalle, Cron, & Slocum, 2001). Previous evidence showed that proving goal‐
oriented students typically do not increase their efforts following failures, and that when confronted with obstacles,
they exhibit decreased problem‐solving and readily disengage from goals, even if they were performing adequately
previously (Snyder et al., 2002). Thus, they are not expected to develop resilience when faced with an adverse
situation.
Divergent validity is assumed when the scales of the tested instrument show a low‐to‐moderate correlation
with the criterion instrument (Kline, 2011). Pearson’s correlation coefficient between the AR‐S and the proving goal
orientation scale showed that the correlations were not significant (r = −0.01, p = 0.95). These results provide
evidence for good divergent validity of the AR‐S.

3 | ST UD Y 2

In the second study, the hypothesized model was explored through the following hypotheses:

Hypothesis 2. Problem‐focused coping will be positively related to AR.

Hypothesis 3. Emotion‐focused coping will be negatively related to AR.

Hypothesis 4. Meaning‐focused coping will be positively related to AR.

Hypothesis 5. AR will be positively related to academic satisfaction.

Hypothesis 6. Academic satisfaction will partially mediate the relationship between AR and performance.

3.1 | Method
3.1.1 | Sample and procedures
The second study involved 780 university students from a Spanish university. The sample size was adequate to
carry out the proposed analyses as it exceeded the minimum of 754 observations needed for a statistical power of
0.80 and eight degrees of freedom (MacCallum, Browne, & Sugawara, 1996), the degrees of freedom of our
proposed model.
Participants (59.7% females) were stratified and belonged to the four faculties of the University, namely:
Faculty of Humanities and Social Sciences (33.2%), School of Technology and Experimental Sciences (25%), Faculty
of Law and Economics (24.5%), and Faculty of Health Sciences (17.3%). Ages ranged from 18 to 61 years (M = 22.7;
standard deviation [SD] = 5.68 years). Most of them (93.4%) were studying a bachelor’s degree lasting 4 years,
whereas the others were studying 5‐year degrees from the previous curriculum (6.6%). Regarding the year they
were studying, 35.7% were in the first year, 37.2% were in the second, 19.6% were in the third, 6.5% were in the
fourth, and 9% were in their fifth year. Moreover, 84.1% were not working at the time.
The data were collected through a paper and pencil questionnaire that includes all the study variables. Students
received a brief presentation of the study by the researchers during class time, and they were invited to individually
8 | MENEGHEL ET AL.

fill out a questionnaire. On the cover page of the questionnaire, they read and signed an individual informed
consent. The data collected by the questionnaire and the data on academic performance (GPA provided by the
University) were matched through the National Identity document of each student.

3.1.2 | Measures
The students completed the AR‐S described in Study 1 to assess their resilience. Moreover, three other measures
were used in this study to test our hypotheses.

Coping strategies
The Spanish version of the Brief COPE inventory was used to assess coping strategies (Perczek, Carver, Price, &
Pozo, Kaderman, 2000). It includes 14 two‐item subscales: active coping, acceptance, emotional support,
instrumental support, positive reframing, planning, self‐distraction, denial, behavioral disengagement, venting, self‐
blame, religion, humor, and substance use. An example item is: “In the presence of difficult situations related to my
studies, I’ve been taking action to try to make the situation better” (active coping subscale). For each of the items,
respondents indicated the extent to which they used the strategy to deal with stressful situations on a 4‐point
Likert scale ranging from 0 (not at all) to 3 (a lot). Cronbach’s α ranged between 0.62 and 0.94 (mean = 0.77). The
order of the coping strategy items was alternated between different subscales.

Academic satisfaction
Academic satisfaction was measured with a three‐item scale (Salanova et al., 2005) that takes three salient aspects
into consideration: their professors, the degree they are studying for, and the faculty to which they belong. An
example item is: “How satisfied are you with your professors?”, and students indicated the extent of their
satisfaction on a 5‐point Face scale ranging from 1 (frowning) to 5 (smiling). The academic satisfaction score was
obtained by computing the mean of the three items.

Academic success
It was assessed by using objective performance as reflected by the GPA, provided by the University at the end of
the examination period after the distribution of the questionnaire, that is, 4–5 months later. In accordance with the
Spanish system of qualifications, the GPA ranged from 5 (poor) to 10 (excellent).

3.1.3 | Fit indices

Our data were analyzed by means of SEM techniques using the AMOS 21.0 (Arbuckle, 2005). The covariance
matrix was analyzed using the maximum‐likelihood estimation method. To reduce the complexity of the models
examined, we used manifest variables for all constructs (Jöreskog & Sörbom, 1993). To use scores for our coping
manifest variables that encapsulate the factor loadings of their underlying subscales, we calculated their weighted
factor scores. Because AR, satisfaction, and GPA are one‐dimensional constructs, we used their standardized scores
as manifest variables (Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009). Goodness of fit was determined as in
Study 1.

3.2 | Results
3.2.1 | Preliminary analyses
Before testing our hypotheses, we conducted an exploratory factor analysis (EFA) to examine how the coping
subscales grouped together to form broader coping constructs in university students. In fact, the theory proposes that
the 14 coping strategies assessed by these subscales are part of several larger constructs (e.g., problem‐focused,
MENEGHEL ET AL. | 9

emotion‐focused, and meaning‐focused); however, researchers have warned against the practice of assuming that
certain coping strategies are always grouped in the same way across different contexts (Lazarus & Folkman, 1984).
Therefore, we standardized the total score of each lower‐order coping subscale and conducted a principal axis
factoring (PAF) analysis with varimax rotation on the 14 coping subscales. The advantage of PAF is that it takes into
account the degree to which each lower‐order factor contributes to the overall factor (i.e., factor loadings) when
calculating factor scores (Xanthopoulou et al., 2009). Subscales with a loading of less than 0.40 (self‐blame, religion,
substance use, and self‐distraction) were sequentially deleted, which resulted in 10 subscales loading in 4 factors.
Factor loadings of the lower‐order coping subscales are presented in Table 1. Factor 1 included the planning and
active coping subscales, called problem‐solving coping. Factor 2 consisted of the emotional support, instrumental
support, and venting subscales, called social coping. Factor 3 consisted of the denial and behavioral disengagement
subscales, called avoidant coping. Factor 4 included the positive reframing, humor, and acceptance subscales, referred
to as positive‐acceptance coping. All 4 factors accounted for 45.03% of the total variance explained. In accordance
with the coping literature, Factor 1 (problem‐solving coping) is consistent with the problem‐focused category, Factors
2 and 3 (social and avoidant coping) are representative of the emotion‐focused category, and Factor 4 (positive‐
acceptance coping) is coherent with the meaning‐focused category.
To confirm the structure of our coping factors, we also conducted a series of subscale‐level CFA. We compared
a model where the 14 original subscales from the COPE load in one general coping factor (M1) to a model where
the final 10 subscales from the EFA load in one general coping factor (M2), and a model where the final 10
subscales from the EFA load in the respective underlying covariate factors (M3). Results supported the use of 10
coping subscales rather than the 14 originally proposed (Δχ2M1−M2(42) = 243.80, p < 0.001), and that they belong to
the four factors (Δχ2M2−M3(6) = 553.28, p < 0.001).

3.3 | Descriptive analyses

Table 2 presents the correlations among the study variables, as well as Cronbach’s α on the diagonal. Given that the
variables used are standardized factor scores, means and standard deviations are not reported. Correlations
between the 10 separate coping subscales will be provided by the first author upon request.
To mitigate problems with common method bias, we implemented two procedural remedies, as suggested in
Podsakoff, MacKenzie, and Podsakoff (2012). First, we obtained the measures from different sources, specifically

T A B L E 1 Results of principal axis factoring analyses: Standardized factor loadings of lower‐order coping
strategies on higher‐order factors (N = 780)

Higher‐order Factors
Lower‐order coping strategies Problem‐solving Social Avoidance Positive acceptance
Active coping 0.811 – – –
Planning 0.550 – – –
Emotional support – 0.873 – –
Venting – 0.606 – –
Instrumental support – 0.548 – –
Disengagement – – 0.655 –
Denial – – 0.642 –
Positive reframing – – – 0.561
Humor – – – 0.550
Acceptance – – – 0.544
10 | MENEGHEL ET AL.

T A B L E 2 Correlations for the study variables (N = 780)

1 2 3 4 5 6 7
1. Social coping (0.81) – – – – – –
2. Problem‐solving coping −0.06 (0.68) – – – – –
3. Positive acceptance coping −0.04 −0.11** (0.69) – – – –
4. Avoidance coping −0.08* 0.10** −0.03 (0.69) – – –
5. Academic resilience −0.26** 0.16** 0.33** −0.14** (0.89) – –
6. Academic satisfaction 0.01 0.20** 0.01 −0.08* 0.16** (0.72) –
7. Academic performance 0.12** 0.12** −0.06 −0.10** 0.03 0.14** –
Note. Cronbach’s α reliability estimates are listed in the diagonal in parentheses.
*p < 0.05.
**p < 0.01.

the predictor and mediator measures from students and the criterion measure from an objective source. Second,
we differentiated the scale properties shared by the measures of the predictor and mediator variables. Finally, the
correlations between the study variables were examined, and no significant relationships were found between any
of them. When common method bias is present, all of the relationships should be significant; otherwise, the
common method bias is so small that it is meaningless (Spector, 2006).

3.3.1 | Test of hypotheses: path analyses

When the model was tested, social and avoidant coping negatively and significantly predicted resilience (β = −0.25,
p < 0.001, and β = −0.17, p < 0.001, respectively), whereas problem‐solving and positive‐acceptance coping
positively and significantly predicted resilience (β = 0.20, p < 0.001, and β = 0.34, p < 0.001, respectively). Resilience
positively and significantly predicted satisfaction (β = 0.16, p < 0.001), which in turn positively and significantly
predicted GPA (β = 0.14, p < 0.001). The relationship between resilience and GPA was not significant (β = 0.01,
p = 0.933), which suggests full mediation by satisfaction. The results of the Sobel test confirmed the fully mediated
relationship (z = 2.99, p < 0.01). However, our hypothesized model (M1) showed a poor fit to the data: χ2(8) = 60.85,
RMSEA = 0.09, NFI = 0.82, IFI = 0.84, and CFI = 0.83.
The so‐called modification indices indicated that the model fit could be improved significantly by including
direct paths from problem‐solving coping to academic satisfaction and GPA, as well as a direct path from social
coping to GPA. The results of the SEM analyses showed that the modified model (M2) fit the data reasonably well,
χ2(6) = 16.15, RMSEA = 0.05, NFI = 0.95, IFI = 0.97, and CFI = 0.97. M1 and M2 were compared using the χ2
difference test, and results supported the modified model (Δχ2M1−M2(3) = 44.7, p < 0.001). The relationships
between the variables were almost invariant between M1 and M2, but the inclusion of these three paths
significantly improved the model. Moreover, these additional relationships in M2 were significant, showing that
problem‐solving coping has a direct impact on academic satisfaction and that both problem‐solving and social
coping have a direct impact on GPA. Figure 1 shows the final model with the standardized coefficients.

4 | D IS C U S S IO N

The lack of specific tools to measure resilience in academic contexts in Spain makes it important to validate suitable
instruments for studying AR. These instruments allow resilience to be evaluated and related to other variables. In
this regard, the purpose of the present study was twofold: (a) to evaluate the psychometric characteristics of the
AR‐S by Martin and Marsh (2006) in the Spanish context, checking for its validity and reliability; and (b) to
MENEGHEL ET AL. | 11

Self-reported Objective
T1 T2

.13
Social coping
-.25

Avoidance coping -.17

Academic .14 Academic .12 Academic
resilience satisfaction performance
Positive
.34
acceptance coping

.20
Problem-solving .18
coping .10

FIGURE 1 The final model with standardized path coefficients (N = 780)

investigate the relationships between coping strategies, AR, and academic satisfaction, as well as the impact on
academic performance over time. To achieve these objectives, we conducted two studies. The findings of the first
study suggest that the Spanish version of the AR‐S demonstrated acceptable psychometric properties. Regarding
the second study, the results revealed that students who used emotion‐focused coping strategies showed less AR,
whereas resilience was higher when problem‐ and meaning‐focused coping strategies were used. Moreover,
students with a high level of AR tended to be more satisfied with their academic context, and students who
displayed more satisfaction tended to perform better over time. Furthermore, results indicated that AR was only
indirectly related to performance, through satisfaction. Finally, the study revealed three unpredicted significant
paths linking coping strategies to outcomes, namely, problem‐solving coping to both academic satisfaction and
performance, and social coping to academic performance.

4.1 | Theoretical contributions

The present study represents a step forward with respect to previous research on AR in several ways. First, the
validation of the Spanish version of the AR‐S by Martin and Marsh (2006) was successful. To provide solid evidence
for the psychometric characteristics of the scale, we carried out a number of analyses, rigorously following well‐
established validation procedures, and we can conclude that the Spanish version of the AR‐S demonstrates good
psychometric properties. A contribution of the study is the use of a heterogeneous sample, and so the Spanish
translation of the AR‐S can be used by scholars as a valid scale to investigate resilience in the academic context.
Second, we advance the theoretical understanding of the relationship between different kinds of coping
strategies and AR. One strength of the current study is that we factor analyzed the subscales to allow student
responses to determine subscale categorization. However, the factors found in this study are strongly supported by
previous theoretical conceptualizations (Folkman & Moskowitz, 2004), thus providing support for the validity of our
categories. Specifically, we have identified two coping strategies, problem‐solving and positive acceptance, that are
positively related to resilience. Within the academic context, problem‐solving coping should assist students in
adjusting to the many challenges posed by the environment as it involves taking active steps to alter the
circumstances or address the problem. Therefore, students who tend to change the situation when they are faced
with stressful events have higher levels of resilience. Positive‐acceptance coping underlies the cognitive strategies
used to manage the meaning of a situation and regulate positive emotions, which is consistent with the emerging
12 | MENEGHEL ET AL.

focus on meaning‐focused coping (Folkman, 2008). In turn, meaning‐focused coping is said to generate positive
emotions and the underlying appraisals, and these emotions and appraisals influence the resilience process.
Therefore, students who tend to generate positive emotions by attributing meaning to the situation show higher
resilience. By contrast, we have identified two coping strategies, social and avoidance coping, that are negatively
related to resilience. Both strategies belong to the traditional emotion‐focused category and have to do with the
regulation of negative emotions, although each of them refers to a definite type of strategy. Through social coping,
students look for social support and vent emotions. Although the strategy can help to reduce stress at first,
symptom reduction can be dysfunctional if students mentally focus too much on the situation that created the
stress when venting. Therefore, students who use more social coping strategies show lower resilience. Finally,
avoidance is a coping strategy students that use to try to ignore a stressful situation by not thinking about it.
Ignoring a stressful situation can be positive as it might reduce stress, but avoiding the reality of a stressful
situation can allow the situation to worsen, thus increasing stress in the long run (Belanger, Lewis, Kasper, Smith, &
Harrington, 2007). Hence, students who tend to avoid the problem have lower levels of resilience.
Third, in studying the positive outcomes of AR, we further extend current theory in at least two ways. On the one
hand, past research on resilience outcomes focused predominantly on successful performance as an indicator of positive
adjustment. However, students’ attitudes and beliefs are more proximal outcomes (Baker et al., 2003) and may be
better able to predict eventual academic‐related behaviors, such as performance. Thus, in this study, we included
academic satisfaction as a measure of positive academic outcomes, and we found that AR is positively related to
satisfaction. The role of AR in predicting satisfaction seems particularly encouraging because it suggests that a higher
level of resilience is associated with increased satisfaction with their academic context. On the other hand, in contrast to
our expectations, we found that resilience is only associated with performance via satisfaction. Although further
investigation is needed, this result seems to refute the recognized statement that higher resilience is a predictor of
better performance (Luthar, 1991). Some possible explanations can be provided for this unexpected result. Although
resilience may influence academic performance, so do many other circumstances and student characteristics, such as
performance self‐efficacy, grade goal, and effort regulation (Ouweneel, Schaufeli, & Le Blanc, 2013; Richardson,
Abraham, & Bond, 2012). Moreover, AR might not have a direct impact on performance because the process of
bouncing back is not necessarily directly related to an increase in performance. For instance, a psychological process
engendered by resilience—through an increase in attitudes (i.e., satisfaction)—affected performance.
Finally, unexpected results revealed the direct effects of the selected coping strategies on outcomes. Specifically, we
found a direct relationship that links problem‐solving coping to both academic satisfaction and performance, thereby
providing evidence that the cumulative effect of using problem‐solving strategies across a range of stressful situations
results in better outcomes. Because problem‐solving coping involves strategies to alter or diminish a stressful event, it
seems plausible that it would be positively related to satisfaction and performance. For instance, if the stressor is an
impending assignment, focusing on the assignment should result in getting the assignment handed in on time and getting
a good grade (MacCann, Fogarty, Zeidner, & Roberts, 2011). Furthermore, we found a direct relationship between social
coping and performance. There are several possible pathways through which social coping strategies might influence
academic performance. First, students who are able to regulate their negative emotions through venting or support
seeking could be less impaired in assessment and learning situations. Second, in the Spanish context, academic
performance requires not only passing examinations, but also collaboration and teamwork, for instance, in the form of
group projects (Ahles & Bosworth, 2004). Social coping has been linked to better social relationships, suggesting that
individuals who use this kind of coping might be better able to maintain the social relationships required for effective
group work and, thus, achieve higher grades (MacCann et al., 2011).

4.2 | Practical implications

In terms of practical implications, we can conclude that the AR‐S is a valid and reliable instrument for measuring
resilience in the Spanish academic context. Additionally, this questionnaire contains only six items to measure
MENEGHEL ET AL. | 13

resilience and, therefore, is short and practical. Thus, the AR‐S can be considered a solid tool for conducting
research on AR.
Another important point underpinning the present study is related to the proposition that both coping
strategies and AR can be influenced in a direct way. Thus, a practical implication of the study is that, in the
university context, both coping strategies and resilience can be used as tools to boost students’ outcomes,
especially for those who are underperformers or have low satisfaction. Given that our results suggest that AR is
enhanced by specific coping strategies, interventions aimed at encouraging problem‐solving and positive‐
acceptance coping strategies seem to be crucial. In this regard, there are several interventions designed to teach
individuals how to cope with adversity. They typically include techniques that help the individual to deal with and
manage stress (see MacCann et al., 2011).
Moreover, evidence shows that resilience can also be developed through interventions. For example, repeated
exposure to positive emotions may help to strengthen an individual’s resilience capacity (Fredrickson, Tugade,
Waugh, & Larkin, 2003). Specifically, positive emotions may take the form of laughter or smiling, and such emotions
may reinforce or strengthen resilience (Bonanno, Noll, Putnam, O’Neill, & Trickett, 2003). Although these types of
positive emotions may seem simplistic, their effects can be important. Taken together, these guidelines suggest that
coping and resilience could be modifiable, leading to enhanced positive academic outcomes.

4.3 | Limitations and research directions

This study has several limitations that highlight important avenues for future research. With the exception of
academic performance rates (i.e., GPA), perhaps the clearest limitation is the use of self‐reported data, which
increases the risk of common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Although the use of
self‐reports in this study could be justified by the nature of the constructs because students are the most accurate
source of their own strategies and feelings or attitudes, it introduces the possibility of response acquiescence and
precludes discussion of causality. We tried to minimize these errors by differentiating the response scales for each
of these variables, as suggested in Podsakoff et al. (2012).
Another limitation of the present study is that the data are cross‐sectional, with the exception of the GPA,
which is collected at a different point in time. Although SEM analysis provides some information about the possible
direction of the relationships, cross‐sectional study designs do not allow us to draw firm conclusions about the
causal ordering among the variables studied. Clearly, there is a need for longitudinal studies to strengthen causal
inferences about the relationships among coping strategies, resilience, and a range of academic outcomes.
In addition, although the sample came from four different faculties and several degrees, our results are based
on a sample from the same university. Thus, the results need to be replicated to generalize our findings to different
academic contexts.

4.4 | Conclusions
Although research on AR is usually focused on students who encounter greater adversity, it is important to
recognize its importance for university students in general because they all have to deal with adverse and stressful
situations during their academic experience. The results of the current study indicate that the Spanish translation
of the AR‐S can be used in a valid and reliable way to measure AR. Thus, this measure can be used to investigate the
relationship between AR and relevant antecedents and outcomes. In this regard, our results offer new insights into
the way university students can benefit from using adequate coping strategies to support the development of their
resilience and achieve positive outcomes, such as satisfaction and performance. These findings have implications
for researchers investigating the processes students use to deal with everyday setbacks, and for practitioners
seeking to assist students in dealing with the highs and lows that characterize academic life.
14 | MENEGHEL ET AL.

A C K N O W L E D GM E N T S

This study was supported by the Fundació Caixa Castelló‐Bancaixa under Grant E 2013‐31 and Consellería de
Educación Generalitat Valenciana under Grant PROMETEO/2013/025.

CO NFLICT OF I NTERE STS

The authors declare that there are no conflict of interests.

OR CID

Isabella Meneghel http://orcid.org/0000-0001-9455-7317

Hans de Witte http://orcid.org/0000-0002-6691-517X

REFERENC ES

Ahles, C. B., & Bosworth, C. C. (2004). The perception and reality of student and workplace teams. Journalism & Mass
Communication Educator, 59, 41–59.
Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.),
The handbook of attitudes (pp. 173–221). Mahwah, NJ: Erlbaum.
Arbuckle, J. L. (2005). Amos 6.0 User’s Guide. Chicago, IL: SPSS Inc.
Baker, J. A., Dilly, L. J., Aupperlee, J. L., & Patil, S. A. (2003). The developmental context of school satisfaction: Schools as
psychologically healthy environments. School Psychology Quarterly, 18, 206–221.
Bandura, A. (2011). A social cognitive perspective on positive psychology. Revista de Psicología Social, 26, 7–20.
Belanger, F., Lewis, T., Kasper, G. M., Smith, W. J., & Harrington, K. V. (2007). Are computing students different? An analysis
of coping strategies and emotional intelligence. IEEE Transactions on Education, 50, 188–196.
Bonanno, G. A., Noll, J. G., Putnam, F. W., O’Neill, M., & Trickett, P. K. (2003). Predicting the willingness to disclose
childhood sexual abuse from measures of repressive coping and dissociative tendencies. Child Maltreatment, 8,
302–318.
Byrne, M., & Flood, B. (2003). Assessing the teaching quality of accounting programmes: An evaluation of the Course
Experience Questionnaire. Assessment & Evaluation in Higher Education, 28, 135–145.
Campbell‐Sills, L., Cohan, S. L., & Stein, M. B. (2006). Relationship of resilience to personality, coping, and psychiatric
symptoms in young adults. Behaviour Research and Therapy, 44, 585–599.
Carver, C. S., Scheier, M. F., & Weintraub, J. K. (1989). Assessing coping strategies: A theoretically based approach. Journal
of Personality and Social Psychology, 56, 267–283.
Chambel, M. J., & Curral, L. (2005). Stress in academic life: Work characteristics as predictors of student well‐being and
performance. Applied Psychology, 54, 135–147.
Clauss‐Ehlers, C. S. (2008). Sociocultural factors, resilience, and coping: Support for a culturally sensitive measure of
resilience. Journal of Applied Developmental Psychology, 29, 197–212.
Connor, K. M., & Davidson, J. R. T. (2003). Development of a new resilience scale: The Connor‐Davidson Resilience Scale
(CD‐RISC). Depression and Anxiety, 18, 76–82.
Dao, T. K., Lee, D., & Chang, H. L. (2007). Acculturation level, perceived English fluency, perceived social support level, and
depression among Taiwanese international students. College Student Journal, 41, 287–295.
Dolbier, C. L., Jaggars, S. S., & Steinhardt, M. A. (2010). Stress‐related growth: Pre‐intervention correlates and change
following a resilience intervention. Stress and Health, 26, 135–147.
Doll, B., & Lyon, M. A. (1998). Risk and resilience: Implications for the delivery of educational and mental health services in
schools. School Psychology Review, 27, 348–363.
Folkman, S. (2008). The case for positive emotions in the stress process. Anxiety, Stress & Coping: An International Journal, 21,
3–14.
Folkman, S., & Lazarus, R. S. (1980). An analysis of coping in a middle‐aged community sample. Journal of Health and Social
Behavior, 21, 219–239.
Folkman, S., & Moskowitz, J. T. (2004). Coping: Pitfalls and promise. Annual Review of Psychology, 55, 745–774.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement
error. Journal of Marketing Research, 18, 39–50.
MENEGHEL ET AL. | 15

Fredrickson, B. L., Tugade, M. M., Waugh, C. E., & Larkin, G. R. (2003). What good are positive emotions in crises? A
prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th,
2001. Journal of Personality & Social Psychology, 84, 365–376.
Grant, L., & Kinman, G. (2012). Enhancing wellbeing in social work students: Building resilience in the next generation.
Social Work Education, 31, 605–621.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus
new alternatives. Structural Equation Modeling: Multidisciplinary Journal, 6, 1–55.
Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL:
Scientific Software International.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.
Kwok, O. M., Hughes, J. N., & Wen, L. (2007). Role of resilient personality on lower achieving first grade students’ current
and future achievement. Journal of School Psychology, 45, 61–82.
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY: Springer Publishing.
Leary, K. A., & DeRosier, M. E. (2012). Factors promoting positive adaptation and resilience during the transition to college.
Psychology, 3, 1215–1222.
Leipold, B., & Greve, W. (2009). Resilience. European Psychologist, 14, 40–50. https://doi.org/10.1027/1016‐9040.14.1.40
Luthans, F., Avey, J. B., Smith, R. C., & Li, W. (2008). More evidence on the value of Chinese workers’ psychological capital: A
potentially unlimited competitive resource? The International Journal of Human Resource Management, 19, 818–827.
Luthans, F., Youssef, C. M., Sweetman, D. S., & Harms, P. D. (2013). Meeting the leadership challenge of employee well‐
being through Relationship PsyCap and Health PsyCap. Journal of Leadership & Organizational Studies, 20, 118–133.
Luthar, S. S. (1991). Vulnerability and resilience: A study of high‐risk adolescents. Child Development, 62, 600–616.
Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future
work. Child Development, 71, 543–562.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for
covariance structure modeling. Psychological Methods, 2, 130–149.
MacCann, C., Fogarty, G. J., Zeidner, M., & Roberts, R. D. (2011). Coping mediates the relationship between emotional
intelligence (EI) and academic achievement. Contemporary Educational Psychology, 36, 60–70.
Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct
validity approach. Psychology in the Schools, 43, 267–281.
McDonald, A. S. (2001). The prevalence and effects of test anxiety in school children. Educational Psychology, 21, 89–101.
Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., & Urdan, T. (2000). Manual for Patterns
of Adaptive Learning Scales, Ann Arbor, MI: University of Michigan Press.
Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo Study to decide on sample size and determine power.
Structural Equation Modeling: A Multidisciplinary Journal, 9, 599–620.
Ouweneel, E., Schaufeli, W. B., & Le Blanc, P. M. (2013). Believe, and you will achieve: Changes over time in self‐efficacy,
engagement, and performance. Applied Psychology: Health and Well‐Being, 5, 225–247.
Perczek, R., Carver, C. S., Price, A. A., & Pozo–Kaderman, C. (2000). Coping, mood, and aspects of personality in Spanish
translation and evidence of convergence with English versions. Journal of Personality Assessment, 74, 63–87.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A
critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and
recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
Putwain, D. (2007). Researching academic stress and anxiety in students: Some methodological considerations. British
Educational Research Journal, 33, 207–219.
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A
systematic review and meta–analysis. Psychological Bulletin, 138, 353–387.
Riolli, L. (2012). Psychological capital as a buffer to student stress. Psychology, 3, 1202–1207.
Roddenberry, A., & Renk, K. (2010). Locus of control and self‐efficacy: potential mediators of stress, illness, and utilization
of health services in college students. Child Psychiatry & Human Development, 41, 353–370.
Rode, J. C., Arthaud–Day, M. L., Mooney, C. H., Near, J. P., Baldwin, T. T., Bommer, W. H., & Rubin, R. S. (2005). Life
Satisfaction and Student Performance. Academy of Management Learning & Education, 4, 421–433.
Ross, S. E., Niebling, B. C., & Heckett, T. M. (1999). Sources of stress among college students. College Student Journal, 33,
316–318.
Ryan, A. M. (2001). The peer group as a context for the development of young adolescents’ motivation and achievement.
Child Development, 72, 1135–1150.
Salanova, M., Bresó, E., & Schaufeli, W. B. (2005). Hacia un modelo espiral de las creencias de eficacia en el estudio del
burnout y del engagement (Towards a spiral model of efficacy beliefs on the burnout and engagement research).
Ansiedad y Estrés, 11, 215–231.
16 | MENEGHEL ET AL.

Salanova, M., Schaufeli, W., Martínez, I., & Bresó, E. (2010). How obstacles and facilitators predict academic performance:
The mediating role of study burnout and engagement. Anxiety, Stress & Coping, 23, 53–70.
Seligman, M. (2003). Authentic happiness: Using the new positive psychology to realize your potential for deep fulfilment. London:
Nicholas Brealey.
Smith, B. W., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The brief resilience scale: Assessing the
ability to bounce back. International Journal of Behavioral Medicine, 15, 194–200.
Snyder, C. R., Shorey, H. S., Cheavens, J., Pulvers, K. M., Adams, V. H., & Wiklund, C. (2002). Hope and academic success in
college. Journal of Educational Psychology, 94, 820–826.
Spector, P. E. (2006). Method variance in organizational research: Truth or urban legend? Organizational Research Methods,
9, 221–232.
Stallman, H. M. (2010). Psychological distress in university students: A comparison with general population data. Australian
Psychologist, 45, 249–257.
Steinhardt, M., & Dolbier, C. (2008). Evaluation of a resilience intervention to enhance coping strategies and protective
factors and decrease symptomatology. Journal of American College Health, 56, 445–453.
Struthers, C. W., & Perry, R. P. (1996). Attributional style, attributional retraining, and inoculation against motivational
deficits. Social Psychology of Education, 1, 171–187.
Suldo, S. M., Shaunessy, E., Michalowski, J., & Shaffer, E. J. (2008). Coping strategies of high school students in an
International Baccalaureate program. Psychology in the Schools, 45, 960–977.
Tugade, M. M., & Fredrickson, B. L. (2004). Resilient individuals use positive emotions to bounce back from negative
emotional experiences. Journal of Personality and Social Psychology, 86, 320–333.
Vandewalle, D. (1997). Development and validation of a work domain goal orientation instrument. Educational and
Psychological Measurement, 57, 995–1015.
VandeWalle, D., Cron, W. L., & Slocum, J. W. (2001). The role of goal orientation following performance feedback. Journal of
Applied Psychology, 86(4), 629–640. https://doi.org/10.1037/0021‐9010.86
Vedhara, K., & Nott, K. H. (1996). Psychosocial vulnerability to stress: A study of HIV‐positive homosexual men. Journal of
Psychosomatic Research, 41, 255–267.
Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Reciprocal relationships between job resources,
personal resources, and work engagement. Journal of Vocational Behavior, 74, 235–244.
Zautra, A. J. (2003). Emotions, stress, and health. New York, NY: Oxford University Press.

How to cite this article: Meneghel I, Martínez IM, Salanova M, de Witte H. Promoting academic satisfaction
and performance: Building academic resilience through coping strategies. Psychol. Schs. 2019;1–16.
https://doi.org/10.1002/pits.22253