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Exploring the Relationship between Emotion Regulation and Stress Resilience: Is there an Impact on the Occurrence of Depression and Anxiety in Adults?

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The aim of this study was to explore the relationship between emotion regulation and resilience, and to determine what impact this relationship has on depression and anxiety in adults. Participants´ prevalence of depressive and anxiety symptoms, emotion regulation skills and resilience were measured using the validated Emotion Regulation Questionnaire (ERQ), the Resilience Scale (RS), the revised Beck Depression Inventory (BDI-II), and the Beck Anxiety Inventory (BAI). Correlational analysis was conducted to explore the relationship between emotion regulation and resilience, depression and anxiety. In a second step, multiple regression analyses were performed to assess how this relationship impacts the occurrence of depression and anxiety in adults. The analyses showed a positive correlation between both depression and anxiety with emotion regulation strategy expressive suppression, and a negative correlation between resilience components personal competence and acceptance of self and life. Furthermore, expressive suppression and acceptance of self and life were positively correlated. The regression analyses further showed, that emotion regulation strategy cognitive reappraisal and resilience personal competence predicted lower depressive symptoms, and resilience acceptance of self and life predicted lower anxiety. Keywords: anxiety, depression, emotion regulation, mental health, resilience
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Running head: THE RELATIONSHIP BETWEEN EMOTION REGULATION AND
STRESS RESILIENCE
Exploring the Relationship between Emotion Regulation and Stress Resilience:
Is there an Impact on the Occurrence of Depression and Anxiety in Adults?
Tiziana Osel
University of Liverpool
28.02.2016
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Declaration
No portion of this work has been submitted in support of an application, for degree or
qualification of this or any other university or institute of learning.
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Abstract
The aim of this study was to explore the relationship between emotion regulation and
resilience, and to determine what impact this relationship has on depression and
anxiety in adults. Participants´ prevalence of depressive and anxiety symptoms,
emotion regulation skills and resilience were measured using the validated Emotion
Regulation Questionnaire (ERQ), the Resilience Scale (RS), the revised Beck
Depression Inventory (BDI-II), and the Beck Anxiety Inventory (BAI). Correlational
analysis was conducted to explore the relationship between emotion regulation and
resilience, depression and anxiety. In a second step, multiple regression analyses were
performed to assess how this relationship impacts the occurrence of depression and
anxiety in adults. The analyses showed a positive correlation between both depression
and anxiety with emotion regulation strategy expressive suppression, and a negative
correlation between resilience components personal competence and acceptance of
self and life. Furthermore, expressive suppression and acceptance of self and life were
positively correlated. The regression analyses further showed, that emotion regulation
strategy cognitive reappraisal and resilience personal competence predicted lower
depressive symptoms, and resilience acceptance of self and life predicted lower
anxiety.
Keywords: anxiety, depression, emotion regulation, mental health, resilience
Word count abstract: 178
Word count dissertation: 9,747
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Acknowledgements
First and foremost, I would like to thank my Dissertation Advisor Dr. Lauren
Mondshein, who has been of great support and help throughout the development of
my dissertation. Working through a tight time and work schedule herself, she has been
more than understanding and supportive of my own demanding work schedule, which
at times had me in the wildest time zones and jet lags. She helped me prevail and keep
my head focused, for which I am sincerely grateful. Her brilliant thoughts and remarks
very much helped me capture and critically analyze my own direction of thought and
work. In addition, I would like to thank Dr. Rachel Dolan and Dr. Kathrine Ethridge
for helping me focus and improve the development and successful completion of my
research paper proposal. Furthermore, I would like to thank the faculty and
administrative staff of the University of Liverpool and Laureate Online for always
being available to me, no matter what my requests or necessities.
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List of Abbreviations
AC – Attention Control
APA – American Psychological Association
BAI – Beck Anxiety Inventory
BDI-II – Revised Beck Depression Inventory
CD-RISC – Connor Davidson Resilience Scale
CERQ – Cognitive Emotion Regulation Questionnaire
CES-D – Center for Epidemiological Studies Depression Scale
CI – Confidence Interval
DASS – Depression, Anxiety and Stress Scale
DSM-5 - The Diagnostic and Statistical Manual of Mental Disorders, fifth revision
df – Degrees of freedom
ERCR – Emotion Regulation Cognitive Reappraisal
ERES – Emotion Regulation Expressive Suppression
ERT – Emotion Regulation Therapy
ERQ – Emotion Regulation Questionnaire
F– Ratio of two mean square values
- Effect size
GAD – Generalized Anxiety Disorder
ICD–10 - International Statistical Classification of Diseases and Related Health
Problems, 10th revision
LEQ – Life Events Questionnaire
MDD – Major Depressive Disorder
MHI – Mental Health Inventory
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M– Mean
MLRA –Multiple Linear Regression Analysis
N– Total sample size
n– Subsample size
p– Probability
PANAS – Positive and Negative Affect Scale
PMER – Process Model of Emotion Regulation
PSWQ – Penn State Worry Questionnaire
r– Coefficient of correlation
- Coefficient of determination
RS – Resilience Scale
RSASL – Resilience Acceptance of Self and Life
RSPC – Resilience Personal Competence
SAD – Social Anxiety Disorder
SCT – Social Cognitive Theory
SD – Standard Deviation
SPSS – Statistical Package for the Social Sciences
t– Statistical test based on the t distribution
VIF – Variance Inflation Factor
WHO – World Health Organization
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TABLE OF CONTENTS
Table of contents…………………………………………………………..........
vii
List of tables…………………………………………………………………….
ix
Introduction……………………………………………………………………..
1
Literature Review……………………………………………………………….
8
Methods…………………………………………………………………………
17
Participants……………………………………………………………….
18
Sample Size…………………………………………………………........
19
Design……………………………………………………………………
20
Data collection/Materials………………………………………………...
21
Beck Depression Inventory…………………………………………
21
Beck Anxiety Inventory……………………………………….........
22
Emotion Regulation Questionnaire…………………………………
22
Resilience Scale……………………………………………….........
23
Procedure…………………………………………………………………
23
Inferential Statistics…………………………………………….......
24
Ethics……………………………………………………………………..
25
Results…………………………………………………………………………..
26
Demographics……………………………………………………………
26
Analyses………………………………………………………………….
26
Discussion………………………………………………………………………
31
Limitations……………………………………………………………………...
36
Conclusion………………………………………………………………………
39
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References………………………………………………………………………
41
Tables………………………………………………………………………..
63
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LIST OF TABLES
Table 1………………………………………………………………………...
28
Table 2………………………………………………………………………...
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Table 3………………………………………………………………………...
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Table 4………………………………………………………………………...
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Table 5………………………………………………………………………...
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Table 6………………………………………………………………………...
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Table 7………………………………………………………………………...
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Table 8………………………………………………………………………...
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Table 9………………………………………………………………………...
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Table 10……………………………………………………………………….
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Table 11……………………………………………………………………….
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Table 12……………………………………………………………………….
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Table 13……………………………………………………………………….
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Table 14……………………………………………………………………….
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Table 15……………………………………………………………………….
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Table 16……………………………………………………………………….
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Table 17……………………………………………………………………….
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Table 18……………………………………………………………………….
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Exploring the Relationship between Emotion Regulation and Stress Resilience: Is
there an Impact on the Occurrence of Depression and Anxiety in Adults?
Introduction
Mental disorders have a severe impact on individuals´ quality of life, and may
result in social and economic disadvantage (Kring, Johnson, Davison & Neale, 2010).
The prevalence of individuals suffering from mental disorders, such as depression and
anxiety, has increased significantly in past years (Substance Abuse and Mental Health
Services Administration, 2013), with the percentage for any mental illness registered
in the United States of America, rising from 17.7% in 2008, to 18.1% in 2014,
according to the Center for Behavioral Health Statistics and Quality (2015).
According to the 10th revision of the International Statistical Classification of Diseases
and Related Health Problems (ICD-10) (World Health Organization,2016b; WHO,
2016c) and the fifth revision of The Diagnostic and Statistical Manual of Mental
Disorders (DSM-5) (American Psychiatric Association, 2013), depressive disorders
can be described as a mental state of continuous negative affect, lack of joy and
interest in life and others, mood irritability, and reduced feeling of worth and
motivation. Individuals suffering from this affective disorder, experience changes in
both cognitive and somatic nature, according to symptoms, symptom duration and
severity (APA, 2013), and often have difficulties pursuing daily tasks and routines
(Evans, Iverson, Yatham & Lan, 2014), such as getting ready for work or school, or
upholding interpersonal relationships . The repercussions on emotional, professional,
economic and psychological welfare can be devastating, or even life threatening
(Cuijpers, de Beurs, van Spijken, Berking, Andersson & Kerkhoff, 2013; Oliffe,
Ogrodniczuk, Bottorff, Johnson, & Hoyak, 2012). Anxiety disorders according to the
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2
DSM-5 (APA, 2013) and ICD-10 (WHO, 2016a), are mental disorders, which include
excessive worry, fear and restlessness, and induces dysfunctional and disturbed
behavior. Individuals suffering from these disorders are severely limited on a
biological, social and psychological level, as the disorders place them in a continuous
condition of vigilance of anticipated dangers and threats, with consecutive avoidance
or hostility, as well as dysfunctional behavior, as for instance, panic attacks (APA,
2013). Persons suffering from anxiety disorders may suffer quietly out of feelings of
shame and guilt, and exhibit avoidant behavior, somatic symptoms (Campo, 2012;
Simms, Prisciandaro, Krueger & Goldberg, 2012), or even social isolation (Cacioppo,
Cacioppo, Capitanio & Cole, 2015). Recent research has identified inappropriate and
dysfunctional emotions, to play a significant role in the formation and maintenance of
these disorders (Marganska, Gallagher & Miranda, 2013; Michl, McLaughlin,
Shepherd & Nolen-Hoeksema, 2012). Emotions can be triggered deliberately or
automatic, implicit or explicit, according to Gross (1998) and Gyurak, Gross and
Etkin (2011). When confronted with external cues, individuals respond with emotion,
which can be positive, or negative, according to past experience, natural instinct, or
learned behavior (Gross & John, 2003), and can be regulated according to the
situational context. Poor emotion regulation skills seem not only to sustain, but also
impact the development of anxiety and depression (Berking et al., 2008; Kret &
Ploeger, 2015), which affect 7.3% (Baxter, Scott, Vos & Whiteford, 2013) and 4.7%
(Ferrari et. al., 2013) of the world population, respectively. Deliberate emotional
reaction and consequential regulation can for instance take place, when we experience
an emotion after thorough analysis, as becoming angry after finding out a co-worker
has spoken ill of us, but deciding to suppress this anger, seen that the working-
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relationship will continue and therefore require the feasibility of collaboration.
Automatic emotional response on the other hand might emerge, if one were afraid of
dogs and finds himself confronted with a stray dog on the street. This would be
considered an implicit emotion, that is, an emotion arising within the individual
himself and without any conscious manipulation (Gyurak, Gross & Etkin, 2011).
Explicit emotional responses on the other hand, result from external cues, as would be
fear for one´s life, following imminent threats to health or safety. For example,
experiencing fear while being held hostage in a bank robbery, would constitute
explicit emotion, that is, resulting from an external cue. According to the requirements
for survival or social expectancies, emotions can be up- or down regulated, which can
occur either consciously or sub-consciously, in order to adapt to the correspondent
needs (Gross, 2014). Emotion regulation in other words, is the process of consciously
or subconsciously modifying experienced mood, emotions and affect which influence
consequential behavior (Gross, 1998; Gyurak, Gross & Etkin, 2011), which following
the conceptual framework of Gross (2014), involves enhancing or decreasing
different aspects of emotions according to the exigencies of particular situations.
Sharing experiences with others which generate positive emotions, or developing
gratitude for positive experiences, for instance, is a way to up-regulate and thus
enhance positive emotion (Gross, 2014; Sheldon & Lyubomirsky, 2006). The process
of down-regulating emotions, takes place through cognitive reappraisal and
expressive suppression, and accordingly decreases unwanted emotions when they
emerge (Gross, 2014; Sheldon & Lyubomirsky, 2006). Cognitive reappraisal describes
the process of mentally re-evaluating situations, in order to change the experienced
emotion, while expressive suppression describes the process of barring incurring
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4
behaviors, which result from emerging emotions (Gross & John, 2003). Dysfunctional
behavior thus occurs, when inadequate up- or down-regulation of emotion takes place,
for instance by either not regulating at all, regulating to the extremes, or regulating in
the wrong direction (Gross, 1998; Gross & Munoz, 1995). Once these dysfunctional
up- and down-regulations of emotion are performed automatically and continuously,
mental disorders are often the consequence (Gross, 1998; Gross & Munoz, 1995; Kret
& Ploeger, 2015). According to Gross (2014), individuals with anxiety disorders for
instance, fail to down-regulate negative emotion, but rather tend to up-regulate
negative emotion when confronted with adverse or threatening conditions, hence
remaining trapped in a vicious cycle of anxiety. In individuals with depression on the
other hand, up-regulation of positive emotion no longer takes place, while negative
emotions are either intensified, or positive emotions are suppressed altogether -
excessive use of suppression of positive emotion, and up-regulation of negative
emotion, have been found to underlie most depressive symptoms (Gross, 2014). The
Process Model of Emotion Regulation (PMER) which was developed by Gross (2014),
posits that emotion regulation takes place following situation selection (approach or
avoid), situation modification, attentional deployment, and cognitive change. The
direction implied by this framework, is that cognition and the appraisal of external
cues which trigger emotion, precede every act of emotion regulation. Regulating
emotions in a direction, which is beneficial for mental health and stability, therefore
poses a great challenge for many individuals, specifically when resilience is low (Kret
& Ploeger, 2015; Troy & Mauss, 2011).
Resilience is the ability to psychologically withstand or handle adversity
or trauma (Gloria & Steinhardt, 2014; Troy & Mauss, 2011), and appears to
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5
stand in direct relationship with an individuals´ capabilities to assess external
cues in a manner beneficial to one´s mental state, or to regulate maladaptive and
negative emotions in a manner, which prevents harmful psychopathological
manifestation (Tugade, Fredrickson & Barrett, 2014). It involves the ability to
cope and adapt to traumatic and stressful life events, and adverse conditions, as
to maintain and foster psychological, emotional and physical well-being (Gloria
& Steinhardt, 2014; Sheldon & Lyubomirsky, 2006). Furthermore, Fredrickson,
Cohn, Coffey, Pek and Finkel (2008) found, that intentional cultivation of
positive emotions greatly increases positive affect and personal resources from
which individuals can draw, such as social support and seeing a purpose in life.
According to Lo, Schutte and Thorsteinsson (2014), positive affect plays an
important role in the formation and maintenance of resilience. Constituents of
resilience further comprise personal competence, which is the belief in one´s
abilities to succeed and prevail, and acceptance of self and life, and appear to
promote psychological well-being, and hence protect against the development of
mental disorders (Gloria & Steinhardt, 2014; Robinson, Larson & Cahill, 2014;
Zlomke & Hahn, 2010). According to Johnson and Tottenham (2015), and
Tugade, Fredrickson and Barrett (2004), elevated stress resilience is noted in
individuals, who approach adverse and general life events with positive emotion
regulation skills, specifically, cognitive reappraisal. Coherent cognitive
appraisal of environmental cues as well as up-regulating of positive emotions
and down-regulating of negative emotions, seem to function in a manner, which
fosters psychological well-being and mental health (Gross, 2014; Troy & Mauss,
2011), while negative emotions might impair cognitive functioning, as all
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attention is focused on the perceived threat, or the avoidance of that threat
(Fredrickson & Losada, 2005). Cognitive reappraisal, that is, reinterpreting
experiences and acceptance of one´s self and life are both factors which
contribute to human agency (Bandura, 2001; Chambon, Filevich & Haggard,
2014), and may hence play an important mediating role in the development and
maintenance of depressive symptoms, as the results from this study suggest.
Human agency is a concept, which originates from Bandura´s social cognitive
theory (SCT), and describes, how individuals have the ability to take influence
and control of their lives by making conscious and willing choices (Bandura,
1982; Bandura, 1989). This requires individuals to understand, analyze, and
become active in pursuing happiness and well-being (Bandura, 1982; Bandura,
1993; Dogan, Totan & Sapmaz, 2013; Hu, Zhang & Wang, 2015). This “agentic
perspective” (Bandura, 2001, p.1) further proposes, that life experiences,
observations, personality factors, and specifically cognition, significantly
contribute to learning and behavior, which may also include emotional reaction
(Bandura, 1989; Bandura, 2001), and includes adequate emotional responses. In
fact, Chambon, Filevich and Haggard (2014) point to what they refer to as the
“delusions of control” (p.322), which describes the underlying process of
misjudging one´s own level of agency, that is, the extent of control over
behavior, and that can lead to mental disorder. Resilience component personal
competence, could hence further inter-relate with the ability of cognitive
reappraisal, seen that both are found to be high in individuals with low levels of
depressive symptoms, as the belief in one´s abilities is suggested to influence the
frequency and perhaps intensity of thought processes (Bandura, 1993). In fact,
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7
assertiveness training is a widely applied component of cognitive behavior
therapy, for instance in the treatment of anxiety, but also for mood disorders
(Cuijpers, Donker, van Straten, Li & Andersson, 2010; Dobson, 1989; Finucane
& Mercer, 2006; Leahy, Holland & McGinn, 2011; Linehan, Goldfried &
Goldfried, 1979). These and other findings suggest that there is a relationship
between resilience and emotion regulation, yet the nature of this relationship
with regard to depression and anxiety has not been sufficiently addressed in
current literature.
Both anxiety and depression have a high level of comorbidity
(Hirschfeld, 2001; Zhighuo & Yiru, 2014), which seems to indicate an existing
inter- and intra-relationship between factors which influence the development of
these disorders. Consistently, both emotion regulation skills and the level of
stress resilience in individuals appear to be determinants in the occurrence of
depression and anxiety (Gross, 1998; Gross & Munoz, 1995), which generates
the question of the nature of this relationship, and how it possibly impacts the
occurrence of these disorders. Although the positive effects of both resilience
and functional emotion regulation skills on mental health have been explored
extensively (Cal, Ribeiro de Sá, Glustak & Barreto, 2015; D´Avanzato,
Joormann, Siemer & Gotlieb, 2013; Gross, 2015; Gross & John, 2003; Haddadi
& Besharat, 2010; Herrman, 2012; Hu, Zhang, Wang, 2015; Karreman &
Vingerhoets, 2012), there is a gap in the knowledge regarding the impact of this
relationship on the occurrence of depression and anxiety in adults, therefore
leaving room for further research, in order to understand the nature of this
phenomenon. The purpose of this study is to understand the underlying
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8
relationship between emotion regulation and resilience, which could help
develop treatment and prevention plans to decelerate a further rise in mental
illness, such as depression and anxiety in adults. Specifically, the elements of
emotion regulation cognitive reappraisal and expressive suppression, as well as
resilience acceptance of self and life, and personal competence, will be
illuminated using a quantitative research method, with regard to their
relationship, and how this relationship might have impact on depressive and
anxiety symptoms in adult individuals.
Literature Review
The relationship between emotion regulation strategies and mental health was
illuminated by Hu, Zhang, Wang, Mistry, Ran & Wang (2014), who conducted a
meta-analysis, which included 48 studies with 21,150 participants. They found that
cognitive reappraisal correlated positively with indicators of positive mental health,
and negatively with indicators of negative mental health, while expressive suppression
correlated negatively with indicators of positive mental health, and positively with
indicators of negative mental health (Hu et al., 2014). The analysis further implies that
while expressive suppression was a distinct contributor to psychological well-being
and mental health, cognitive reappraisal seemed to be more active and predictable in
presenting protective traits against depression by avoiding long-term negative
emotions (Hu et al., 2014). Furthermore, although culture had no apparent mediating
effect on the relationship between emotion regulation and mental health, the effect of
emotion regulation subset expressive suppression did exhibit varying levels of
association to mental health in relation to the cultural values participants in the
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9
respective studies lived in. While expressive suppression was strongly associated with
higher levels of depressive symptoms in studies, which mainly involved western
cultures, this was not the case for studies involving Asian cultures. The findings of
this study present important results in the field of emotion regulation, although
generalizability is limited due to the emphasis on Chinese and English Journals, which
had been chosen for research. The meta-analysis incorporated a relatively small
number of studies, which reduced the amplitude of performed analysis. Furthermore,
cross-sectional research data fails to deliver inferences throughout different points in
time (Sedlmeier & Renkewitz, 2008). In addition, the applied selection criteria were
limited to cognitive reappraisal and expressive suppression, hence omitting the
Attentional Deployment Phase of Gross´ (2014) PMER. In this phase, individuals
choose to direct their attention towards environmental cues or not, and it involves
essential actions, such as distraction from emotional cues towards other cues,
rumination on distressing environmental cues, worry, and thought suppression, where
individuals re-direct their attention of distressing thoughts to other, less distressing
matters. Berking, Wirtz, Svaldi and Hofmann (2014) conducted a longitudinal study,
which assessed the reciprocal association between emotion regulation and depression
twice within five years, using an online self-report survey. The findings suggest, that
depression may result from deficits in emotion regulation capacities, as the severity of
depressive symptoms, was cross-sectionally associated with successfully applied
emotion regulation skills at both times of measurement (Berking et al., 2014).
Although the findings deliver important associations between the successful
application of emotion regulation skills, the questionnaire did not address whether
participants had deliberately refused to apply emotion regulation skills or had been
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unable to acquire them. Furthermore, the study addressed the impact of negative
emotions, excluding the cognitive components of emotion regulation, and included
only broad classifications of emotion regulation. Nonetheless, the findings presented
by Berking et al. (2014) strongly suggest, that consistent application of emotion
regulation strategies have on the alleviation of depressive symptoms in individuals. To
this effect, D´Avanzato, Joormann, Siemer and Gotlieb (2013) investigated the
varying frequency of applied emotion regulation strategies in individuals suffering
from major depressive disorder (MDD), social anxiety disorders (SAD) and formerly
depressed participants in a total of 551 participants, who were divided into groups
according to the diagnosed disorder, as well as a control group. The applied measures
were the emotion regulation questionnaire for emotion regulation strategies
reappraisal and suppression, the ruminative responses scale, the revised Beck
depression inventory and the state-trait anxiety inventory-trait (D´Avanzato et al.,
2013). D´Avanzato et al., (2013) examined the relative specificity of applied emotion
regulation strategies according to disorder. Furthermore, the study explored the
consistency and stability of using emotion regulation strategies during the recovery
phases of depressive episodes, and whether both the frequency and stability of
strategy use could be associated with the level of symptom severity for participants
with depression and anxiety symptoms. The results showed, that for participants with
MDD, rumination was predominant, while the emotion regulation strategy cognitive
reappraisal was lower than in the SAD group (D´Avanzato et al., 2013). In addition,
the study revealed, that the level of emotion regulation strategy expressive suppression
was significantly higher in participants with SAD, than in the MDD group, and that
the level of emotion regulation strategy cognitive reappraisal, increases, once
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depressive episodes are overcome (D´Avanzato et al., 2013). D´Avanzato et al., (2013)
deliver important insight on how emotion regulation strategies are applied by
individuals suffering from MDD and SAD, yet it remains to mention, that the
collected data was based on self-report questionnaires, which may not provide
accurate measures of emotion regulation strategies, due to participants´ possible
restricted capability or perception of own emotions and behavior (Wilson & MacLean,
2011). Furthermore, the conducted research was cross-sectional, hence possible
differences in the MDD, SAD or the control group, could not be measured
(D´Avanzato et al., 2013). In addition, the accuracy of SAD measurements may be
limited, seen that the STAI was primarily developed to measure general anxiety
(D´Avanzato et al., 2013). Nonetheless, the study presents a great amount of valuable
knowledge on the intensity and duration of applying emotion regulation strategies in
individuals with SAD and MDD, which expand the understanding of both the
processes underlying the development and the nature of these disorders. Affect seems
to play a significant part in bot depression and anxiety disorders. Hofmann, Sawyer,
Fang and Asnaani (2012) found, that both emotion dysregulation of negative affect
and deficits in positive affect resulted in anxiety and mood disorders, with an
underlying diathesis in combination with a triggering event, leading to either negative
or positive affect. This review implies, that an individuals´ capability to revert to
different emotion regulation strategies in varying situations, affects mental health, and
that a dysregulation of negative affects promotes the development of mental disorders.
Although the incorporated studies were generated in laboratory settings, thus
controlling for confounding variables, which affects the external validity of the study
results, the review suggests important links between affective styles and emotion
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12
regulation strategies, and the development and maintenance of mood and anxiety
disorders. Zlomke and Hahn (2010) further expand these findings, by investigating the
gender differences in making use of emotion regulation skills in the development and
maintenance of inordinate worry and generalized anxiety disorder (GAD). In their
study, Zlomke and Hahn (2010) examined the differences in applied cognitive
emotion regulation strategies of 1,080 young adults, and the imminent effects these
had on worry and anxiety, using internet self-report questionnaires. Psychometric
measures were made using the Cognitive Emotion Regulation Questionnaire (CERQ)
to measure cognitive emotion regulation strategies, the Life Events Questionnaire
(LEQ) to measure life stressors, the Depression, Anxiety, and Stress Scale (DASS) to
measure depressive and anxiety symptoms, and the Penn State Worry Questionnaire
(PSWQ) to assess the level of worry in participants. The results showed, that the level
of worry and anxiety was related to the type and occurrence of applied cognitive
emotion regulation strategies for both men and women, although there was a gender
difference in the incidence of applying cognitive emotion regulation strategies
(Zlomke & Hahn, 2010). Furthermore, cognitive regulation strategies proved to be
predictive in the severity of worry (28%), stress (40%) and anxiety symptoms (39%)
in men, while in women, they accounted for a 27% change in worry, 38% change in
stress and 24% change in anxiety symptoms (Zlomke & Hahn, 2010). Specifically,
excessive rumination and catastrophizing were predictive of elevated stress and worry
in men, while self-blame and catastrophizing were predictive for women, according to
Zlomke and Hahn (2010). In contrast, cognitive reappraisal and acceptance were
negatively correlated to excessive worry in both genders (Zlomke & Hahn, 2010).
This study proposes meaningful findings on the effect cognitive emotion regulation
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13
strategies have on the development and maintenance of anxiety and worry. However,
all participants were university students, which limits the generalizability of the
findings. Further studies would be necessary to expand the gathered knowledge on a
heterogeneous population. In addition, the study applied self-repost measures, hence
the responses on the questionnaires may be biased, due to a lack of understanding
one´s own emotions, responding in accordance with perceived social desirability, or a
restricted understanding of administered questions (Wilson & MacLean, 2011,
Sedlmeier & Renkewitz, 2008). Finally, the study only focused on one partition of
emotion regulation strategies, that is, cognitive emotion regulation, disregarding
emotion regulation strategy expressive suppression, which has shown to exert a
significant impact on mental health, specifically anxiety disorders (D´Avanzato et al.,
2013). Nonetheless, the exploration of the different cognitive strategies to regulate
emotion, and which outcome they have on excessive worry and anxiety disorder,
delivers important conclusions on the nature of the disorder and enhance treatment
pathways, specifically with regard to gender differences. Emotion Regulation Therapy
(ERT) for instance, has shown effectiveness in the treatment of anxiety disorders
(Fresco et al., 2013). Albeit the results remain preliminary and require further testing,
Fresco et al. (2013) found, that measures of GAD severity, trait anxious, depression
symptoms and worry, were significantly reduced after 18-20 therapy sessions and
maintained for 9 months following treatment in two case studies. These and other
findings suggest that emotion regulation has an impact on the development and
maintenance of depression and anxiety, and can be considered as a mediating
mechanism in the psychopathology of these disorders (Marroquín, 2011). Marroquín
(2011) further posits that emotion regulation skills are affected by interpersonal
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
14
influences, which would account for the significance of social support on the
development and severity of depression. Nonetheless, further research is required to
establish the relevance of emotion regulation and the influence of interpersonal and
intra-personal processes to psychopathology.
Depression and anxiety further seem to be associated with low resilience,
according to Haddadi and Besharat (2010), and Hu, Zhang and Wang (2015).
Ong, Bergeman, Bisconti and Wallace (2006) in addition posit, that resilience
can be considered both as a trait, but also an acquired skill vested through life
events (Fredrickson, Tugade, Waugh & Larkin, 2003), suggesting that resilience
can be enhanced though targeted interventions. In their meta-analysis of 60
studies, Hu, Zhang and Wang (2015) found, that trait resilience predicts mental
health by promoting general satisfaction and psychological wellbeing, and
reduces negative criteria. The analysis shows a positive correlation between
positive mental health and trait resilience, and a negative correlation between
negative mental health and resilience. Their review delivers important
knowledge of the relationship between resilience and mental health in general,
yet specific investigation between resilience and the prevalence of depression
and anxiety, was not undertaken. In their study, Haddadi and Besharat (2010)
evaluated the Mental Health Inventory (MHI), the Connor-Davidson Resilience
Scale (CD-RISC), the Beck Depression Inventory (BDI), and the Beck Anxiety
Inventory (BAI) which 256 college students completed, in order to explore the
relationship between resilience, psychological distress, psychological well-being,
general health, anxiety and depression. Although not generalizable because the
study focuses on a specific population, their results present a positive correlation
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
15
between psychological well-being and resilience, and a negative correlation
between resilience and psychological distress, anxiety and depression (Haddadi
& Besharat, 2010). According to Troy and Mauss (2011), attention control (AC)
and cognitive reappraisal, as components of cognitive emotion regulation, can
be regarded mediators in the adaptation process to stress, thus suggesting, that
cognitive emotion regulation skills impact the formation of resilience.
Nonetheless, the study omits emotion regulation component expressive
suppression, which is a significant constituent of emotion regulation model.
Consistently, Lo, Schutte and Thorsteinsson (2014) found, that depression was
associated with resilience, and that the resilience level was significantly
impacted by positive affect. In their longitudinal study, Lo, Schutte and
Thorsteinsson (2014) investigated the mediating role resilience plays in the
relationship between depressive symptoms and positive affect. To assess this
relationship, 107 first year university students were recruited, who completed
the Center for Epidemiological Studies-Depression Scale (CES-D) to assess the
prevalence of depressive symptoms, the Positive and Negative Affect Scales
(PANAS) to assess positive and negative affect, and the Perceived Stress Scale
(PSS) to measure stress level (Lo, Schutte & Thorsteinsson, 2014), twice within
three months. The results showed that a high level of positive affect and
resilience were found in participants who exhibit low depressive symptoms,
while low resilience and negative affect levels could be measured for those who
had high depression scores. Furthermore, negative affect appears to be the
strongest predictor of depression, and the level of resilience suggests to
precipitate the effects positive affect had on the change of depressive symptoms,
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
16
while negative affect only partly mediated the level of depressive symptoms (Lo,
Schutte & Thorsteinsson, 2014). These findings suggest that over time, negative
affect negatively impacts depression, while positive affect appears to enhance
resilience, and thus, have a beneficial effect on depression severity. Limitations
of this study include the difficulty to establish a causal relationship between
positive affect, resilience and severity of depressive symptoms. While the
findings strongly lean towards this direction, a causal relationship cannot be
established. Furthermore, all participants were first semester university students,
hence the generalizability of the findings is limited. In addition, the applied
time-span of three months between tests could be considered insufficient to
reveal meaningful results, specifically when taking into consideration, that
factors such as the students´ adaptation to new challenges and surroundings
might play an important role in the amelioration of depressive symptoms.
Furthermore, longitudinal studies typically require more long-term time frames
in order to make valid inferences regarding effects (Sedlmeier & Renkewitz,
2008). None the less the importance of positive affect in the conceptualization,
enhancement and maintenance of resilience has been highlighted, which delivers
significant knowledge on both possible factors contributing to the development
of depression, but also the expansion of knowledge applied in the treatment and
prevention of depression. Consistent with the findings of the above-mentioned
studies, which focus on emotion regulation or resilience as influencing factors to
the prevalence of depression and anxiety, is the implication of a relationship
between emotion regulation and resilience in the development and maintenance
of psychopathology. While the relationship between resilience and mental health,
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
17
and emotion regulation and mental health has been extensively investigated, the
current literature does not address the implicit nature of the relationship between
these variables, and how this relationship affects the development of depression
and anxiety. Identifying factors, which may influence the development of
depression and anxiety in adults, could provide valuable insight, and promote
future research, treatment and preventive strategies. This study will therefore
address the nature of the relationship between emotion regulation and resilience,
and how it impacts the occurrence of depression and anxiety in adults. To
explore this phenomenon, participants´ level of depression and anxiety
symptoms, as well as their individual level of distinct emotion regulation skills
and components of resilience will be measured using validated psychometric
questionnaires. Then, statistical analyses will be performed to explore the
individual relationship between particular domains of resilience and emotion
regulation, and whether there is an impact this relationship has on the
occurrence of depression and anxiety in adults.
Methods
The purpose of this study was to explore the relationship between emotion
regulation and resilience, and which effect this relationship has on the prevalence of
depression and anxiety in adults. This explorative research applied a cross-sectional,
self-administered online survey (Wilson & MacLean, 2011), in order to collect
quantitative data from which to assess the level of resilience and emotion regulation,
as well as anxiety and depression in adults, using validated psychometric
questionnaires. Data from every survey was used for statistical analyses, hence
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
18
controlling for sampling bias (Sedlmeier & Renkewitz, 2008; Wilson & MacLean,
2011). Correlational analyses were applied to measure the relationship between
emotion regulation, resilience, depression and anxiety (Sedlmeier & Renkewitz, 2008;
Wilson & MacLean, 2011). In a second step, multiple regression analyses were
performed to assess the impact the relationship between emotion regulation expressive
suppression and cognitive reappraisal, and resilience personal competence and
acceptance of self and life, have on the development of anxiety and depression in
adults (Sedlmeier & Renkewitz, 2008; Wilson & MacLean, 2011).
Participants
Participants for the study were invited using research study invites to
complete the online survey via different social media platforms such as
Facebook, LinkedIn, and Xing. Furthermore, emails were distributed within the
research student´s contacts. Additionally, numerous research study invite
postings were placed within multiple online student communities at the
University of Liverpool. All research invites contained a supplementary
Participant Information Sheet (PIS), which contained the purpose of the study,
mention of the approval through the University of Liverpool International
Online Research Ethics Committee, the anonymity of research participation and
data, privacy, as well as voluntary participation and the right to abort
participation at any point during the survey. This form of convenience sampling
provides a large spectrum of potential adult participants pertaining to varying
gender, age, ethnicity, educational level, occupation and social context, in order
to reflect a representative cross-section of the target population (Wilson &
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
19
MacLean, 2011). The inclusion criteria was constituent of adult participants (18
years and older), who completed the survey. The exclusion criteria consisted of
surveys, which had not been completed in full, seeing that fragmentary data
does not allow for meaningful inferences (Sedlmeier & Renkewitz, 2008).
Sample size
Although the total target population size was unknown, using a 95%
confidence level (CI), and a 5% margin of error with a response rate of 20%, a
total of 385 samples had originally been calculated to provide reliable insight to
the research question (Lwanga & Lemeshow, 1991; Sedlmeier & Renkewitz,
2008; Van Dessel, 2013). A total of 2,798 emails were sent to prospective
participants and approximately 9,900 students had access to the research study
invite through different online student communities. Initially, 214 participants
followed the research invite and filled out the online survey, which corresponds
to a 7.6% response rate with regard to all sent invitations. In a first revision
through the research student, 122 surveys were discharged from data analysis, as
informed consent had either been rejected by participants, or personal
information, such as age spectrum, gender or employment status, had not been
provided. The following 92 surveys were then reviewed a second time to
confirm that all criteria for formal integrity in all survey sub-sections, which
comprised answering all questions of the relevant sections in full, had been
satisfied. This lead to the discharge of further 33 surveys, as some participants
had omitted answering single questions in the survey, thus falling under the
exclusion criteria. After a final revision, a total of 59 surveys fulfilled the
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
20
required inclusion criteria, and were hence admitted as data for this study.
Although the final quantity of viable survey responses was much lower than
originally hoped for, the study was none the less carried out. This decision was
based on Cohen´s rules of effect sizes for calculating the sample size. After
applying the formula for Cohen´s rules of effect sizes (f²= R²/1-R²) on the study
parameters, the minimum sample size required to deliver meaningful insight
based on an anticipated effect size of (f²) = 0.3, with a statistical power level of
80%, four predictor variables, and a probability level of 0.05, resulted in a
minimum of 45 surveys, which fulfilled the inclusion criteria (Cohen, 1988;
Cohen, Cohen, West & Aiken, 2003). A larger number of samples was not able
to be obtained due to limited financial resources and a restricted time frame to
conduct the study.
Design
This research applied a cross-sectional study design with a self-
administered, online survey (Wilson & MacLean, 2011). Although the quality of
the gathered data could not be monitored by the researcher, and research
participation was subject to restricted time and sampling availability, this form
of data collection was both cost-efficient and convenient (Wilson & MacLean,
2011). Data from every completed survey, which fulfilled the inclusion criteria,
was used for statistical analysis, hence controlling for sampling bias. Sampling
error was controlled for through the high number of mailed research study
invites (Sedlmeier & Renkewitz, 2008; Wilson & MacLean, 2011).
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
21
Data Collection/Materials
Prior to completing the online psychometric questionnaires, participants
were asked to provide general demographic information regarding their gender,
age spectrum, marital status, academic achievements, employment status and
whether they had children. Furthermore, participants were asked if they had
previously received emotion regulation training, and if they had been diagnosed
with depression and anxiety by a mental health professional. The administered
self-report psychometric questionnaires consisted of the Emotion Regulation
Questionnaire (ERQ) and Resilience Scale (RS), the revised Beck Depression
Inventory (BDI-II), as well as the Beck Anxiety Inventory (BAI). Participants,
who had indicated a diagnosis of depression, omitted the BDI-II, and
participants who had been diagnosed with anxiety, the BAI, which reduced the
overall time spent on the survey significantly. Participants, who had received
both depression and anxiety diagnosis, completed only the ERQ and RS, and
were statistically classified as positively diagnosed accordingly. Participants
could choose to complete all self-report questionnaires in either English or
German language.
Beck Depression Inventory. The BDI-II was developed by Beck, Steer &
Brown (1996) to measure the level of depressive symptoms an individual presents.
Responses to the 21 items range from 0 (no symptoms) to 3 (severe symptoms).
Questions include: “sadness, pessimism, past failure, loss of pleasure, guilty feelings,
punishment feelings, self-dislike, self-criticalness, suicidal thoughts or wishes, crying,
agitation, loss of interest, indecisiveness, worthlessness, loss of energy, changes in
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
22
sleeping pattern, irritably, changes in appetite, concentration difficulty, tiredness or
fatigue and loss of interest in sex” (Beck, Steer & Brown, 1996). Internal consistency
of the BDI-II was measured between α= .89 and α= .94 using Cronbach´s Alpha
(Beck, Steer & Brown, 1996). The German Version of the BDI-II showed high
internal consistency ranging between α= .90 and .93 (Hautzinger, Keller & Kühner,
2009).
Beck Anxiety Inventory. The BAI is a 21-item questionnaire, which assesses
an individual´s level of anxiety. Responses range from 0 (not at all) to 3 (severely, it
bothered me a lot). The BAI includes the following questions: “numbness or tingling,
feeling hot, wobbliness in legs, unable to relax, fear of worst happening, dizzy or
lightheaded, heart pounding/racing, unsteady, terrified or afraid, nervous, feeling of
choking, hands trembling, shaky/unsteady, fear of losing control, difficulty in
breathing, fear of dying, scared, indigestion, faint/lightheaded, face flushed and
hot/cold sweats” (Beck & Steer, 1993). Internal consistency of the BAI was measured
using Cronbach´s Alpha and ranged between α=.85 and α=.92 (Beck & Steer, 1993),
the German BAI, presented a range between α=.85 and α=.90 (Margraf & Ehlers,
2012).
Emotion Regulation Questionnaire. The ERQ was developed by Gross and
John (2003) in order to assess the individual level of emotion regulatory strategies
according to the Process Model of Emotion Regulation (Gross & John, 2003). This
10-item questionnaire differentiates between two main regulatory strategies, cognitive
reappraisal (ERCR), e.g. “when I want to feel more positive emotion, I change the
way I’m thinking about the situation, and expressive suppression” (ERES), e.g. “I
control my emotions by not expressing them” (Gross & John, 2003, p.351).
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
23
Respondents indicate, whether they strongly disagree (1) or strongly agree (7) on a 7-
point Likert-scale. Higher scores suggest a higher level of regulatory capacity and use
of strategy. The questionnaire shows high internal consistency (Cronbach´s Alpha) for
ERCR α=.79 and ERES α=.73 (Gross & John, 2003). The German version of the ERQ
was published by Abler & Kessler (2009), and shows high internal consistency for
ERCR α=.82 and ERES α=.76 (Wiltink et al., 2011).
Resilience Scale. The RS is a 25-item questionnaire, developed by Wagnild &
Young (1993) in order to assess individual levels of resilience. The questionnaire
differentiates between personal competence (RSPC), e.g. “When I make plans, I
follow through with them, and acceptance of self and life” (RSASL), e.g. “I usually
take things in stride” (Wagnild & Young, 1993, p. 169). A 7-point Likert scale is
applied to measure whether respondents strongly disagree (1) or strongly agree (7).
Internal consistency (Cronbach´s Alpha) ranges between α= .73 to α=.91 (Wagnild,
2009). The German version was developed by Leppert, Strauß, & Young (2000) and
shows internal consistency between α=.82 and α=.95 (Schumacher, Gunzelmann,
Leppert, Strauß & Brähler, 2004).
Procedure
All data was obtained through a self-administered, anonymous online
survey in German or English language provided by the researcher, which
inquired about the participants´ prevalence of depression and anxiety, emotion
regulation skills and self-perceived resilience, via the online survey software
provider Survey Monkey. Participants were informed about the research
question, the confidentiality of all data, privacy, their voluntary participation and
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
24
their right to withdraw from the survey at any point (Wilson & MacLean, 2011).
The survey could then be accessed after informed consent had been given, and
took approximately 25 minutes if completed in full.
Inferential Statistics. Correlation Analysis using Pearson´s r was applied to
assess the relationship between ERQ (ERCR, ERES), RS (RSPC, RSASL), depression
and anxiety. The product-moment correlation analysis quantifies the relationship
between two normally distributed, and is a standardized measure of variables´
covariance (Wilson & MacLean, 2011). The strength of the direction of the
relationship is reflected through the correlation coefficient (r), which can lie between -
1 and 1. Values of zero reflect no correlation, while values of -1 and 1 reflect perfect
negative or positive correlation, respectively (Wilson & MacLean, 2011). Values
between +/-.30 and +/-.50 are considered weak to moderate, while values above +/-.70
are regarded high in correlation (Sedlmeier & Renkewitz, 2008). Multiple linear
regression analyses were performed to investigate the impact ERCR and ERES, RSPC
and RSASL as predictor variables, had on the level of depressive and anxiety
symptoms, as outcome variables. Multiple linear Regression analysis (MLRA) allows
for inferences on the level of influence at least two predictor variables have on a
continuous outcome variable, as well as the association between the variables
(Sedlmeier & Renkewitz, 2008). The advantage of MLRA compared to other
statistical analyses, is that the level of influence for every predictor variable on the
outcome variable can be measured individually, therefore identifying the best group of
predictor variables, which influence the outcome variable (Sedlmeier & Renkewitz,
2008; Wilson & MacLean, 2011). Requirements to perform MLRA are the
assumptions of normal distribution of data, a linear relationship between predictor and
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
25
outcome variable, homoscedasticity of data, and that the predictor variables do not
present a collinear relationship among each other (Wilson & MacLean, 2011).
Preliminary analyses were performed, to ascertain that the assumptions of normality,
linearity and multicollinearity (VIF < 10) were met (Sedlmeier & Renkewitz, 2008;
Wilson & MacLean, 2011). Although MLRA does not investigate the causal
relationship between variables, it was nonetheless chosen as a statistical analysis for
this research, seen that the relationship between subsets of emotion regulation and
resilience and their impact on depression and anxiety, are the aim of this study.
Ethics
This research is based on anonymous data gathered using an online
questionnaire. The survey was distributed via research invite, which contained a
brief description of the study, a reference explaining the anonymity of
participation and gathered data, highlighting voluntary participation and the
right to withdraw from the study at any time. Informed consent was obtained by
clicking on a link to the survey. The inclusion criterion was an age of 18 years
and above, hence participants were neither vulnerable nor fell under special
ethical attention. This research abides to the American Psychological
Association (2010), the European Federation of Psychologists´ Associations
(2005), the Association of German Professional Psychologists (1999) and The
British Psychological Society (2010) Codes of Ethics, and attained expedited
approval by the University of Liverpool International Online Research Ethics
Committee. All data was exclusively accessed by the student researcher and
dissertation advisor, and is further stored on a password protected personal
computer for the duration of five years.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
26
Results
Demographics
SPSS software was applied for statistical analysis. The majority of all
respondents was male (59.3%) and aged 36-45 years (37.3%), was married
(69.5%), had a graduate degree (47.5%), was employed (71.2%) and had at least
one child (68.7%). Approximately one-fifth (20.3%) of all participants had
learned emotion regulation strategies in the past. Of all respondents, 13.6% had
been diagnosed with depression, 5.1% with anxiety, and 1.7% with both
depression and anxiety by a mental health specialist. 79.7% of all respondents
had not been diagnosed with either depression or anxiety (Table 1).
Analyses
Survey scores on depressive symptoms showed that 65.3% of all
respondents presented no (scores 0-8), 20.4% minimal (scores 9-13), 5.1% mild
(scores 14-19), 3.4% moderate (scores 20-28%), and 1.7% (scores 29-63) severe
level of depressive symptoms according to the BDI-II (Table 2). Anxiety
measures showed that 69% of all respondents presented minimal (scores 0-7),
18.7% mild (scores 8-15), 1.7% moderate (scores 16-25), and 10.2% severe
(scores 26-63) level of anxiety (Table 3). The ERQ showed that 58.5% of
respondents presented low to medium (scores 10-40), and 41.5% medium to
high (41-70) scores (Table 4), with 83% of respondents scoring low to medium
(scores 4-16) on ERES (Table 5) and 50.9% low to medium (scores 6-24) on
ERCR (Table 6). The RS showed that 59.3% of respondents scored moderately
high to high (scores 145-175) (Table 7) with 71.2% scoring moderately high to
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
27
high on RSPC and 57.6% scoring low-moderate RSASL (Table 9). Pearson´s
Product-Moment Correlation (r) was applied to assess the relationship between
depression and anxiety, ERQ and RS. The data analysis showed a positive
correlation between depression and anxiety (r (47) =.579, p=.000). Depression
and overall ERQ scores (r (43) =.420, p=.004), specifically ERES (r (43) =.409,
p=.005), further showed a positive correlation, while a negative correlation
between depression and overall RS scores (r (47) =-.595, p=.000), was found.
Resilience sub-divisions RSPC (r (47) =-.586, p=.000), and RSASL (r (47) =-
.518, p=.000), both presented a negative correlation with depression scores.
Anxiety was positively correlated with ERES (r (51) =.390, p=.004), and
negatively correlated with overall RS scores (r (56) =-.523, p=.000), and each
sub-division of resilience RSPC (r (56) =-.422, p=001), and RSASL (r (56) =-
.623, p=.000). For ERES a negative correlation with RSASL (r (51) =-.291,
p=.034) was identified (Table 10).
As shown in Table 11, the mean BDI-II score lay at M=7.13 (SD= 6.973,
N=45) in the regression analysis. The regression analysis further pointed to a
low positive correlation between BDI-II scores and ERES (r (44) =.409, p=.003),
while the correlation between depression and RSPC (r (44) =-.590, p=.000) as
well as RSASL (r (44) =-.526, p=.000), was negative (Table 12). The variance
in BDI-II scores, which is accounted for by the adjusted R² value indicated, that
53.1% of the variance in depression, is attributed to all variables of resilience
RSASL, RSPC, as well as emotion regulation ERES and ERPC. In the
prediction of BDI-II scores, the regression equation proved a significantly better
predictor (F=11.312, df= 4, p=.000)
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
28
Table 1
Demographic Data (N=59)
Variable
Percent (%)
Gender
Male
59,3
Female
40,7
Age
26-35
18,6
36-45
37,3
46-55
33,9
66-older
10,2
Marital Status
Single
10,2
In a marriage-like
relationship
16,9
Married
69,5
Separated/Divorced
3,4
Academics
No academic degree
5,1
Elementary School
1,7
High School
11,9
Undergraduate Degree
23,7
Graduate Degree
47,5
Higher
10,2
Employment
Employed
71,2
Unemployed
5,1
Retired
11,9
Student
5,1
Other
6,8
Children
Yes
67,8
No
32,2
Emotion Regulation Strategies
Yes
20,3
No
79,7
Diagnosis Depression and Anxiety
No
79,7
Yes, Depression
13,6
Yes, Anxiety
5,1
Yes, Depression and
Anxiety
1,7
Note: Demographic data (variables) are represented in percentage (%) of total
sample population (N).
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
29
Mean
Std. Deviation
N
BDI-II Score
7,13
6,973
45
ER Expressive Suppression
13,07
5,666
45
ER Reappraisal
28,49
8,284
45
RS Personal Competence
102,40
13,356
45
RS Acceptance
44,56
8,114
45
Note: Score means, standard deviations (SD) and the total sample population (N) are
represented in columns.
than the score mean (Table 13). The analysis suggests, that ERES (Beta=.216,
t(45)=1.84, p=.073) and RSASL (Beta=.051, t(45)=-.300, p=.766) do not present
statistical significance in predicting BDI-II scores, while ERCR (Beta=.305,
t(45)=2.653, p=.011) and RSPC (Beta=-.554, t(45)=-3.281, p=.002) can be considered
significant predictors of BDI-II scores (Table 14).
Model
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R
Square
Change
F
Change
df1
df2
Sig. F
Change
1
,729a
,531
,484
5,009
,531
11,312
4
40
,000
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
30
Table 15
Mean
Std. Deviation
N
BAI Score
7,57
10,028
53
ER Expressive Suppression
13,40
5,329
53
ER Reappraisal
28,04
7,852
53
RS Personal Competence
99,83
15,300
53
RS Acceptance
42,74
9,676
53
Note: Score means, standard deviations (SD) and the total sample population (N) are
represented in columns.
The BAI score had a mean of M=7.57 (SD=10.028, N=53) (Table 15). A low
positive correlation was noted between BAI and ERES (r (52) =.390, p=.002), while
resilience RSPC (r (52) =.-.420, p=.001) and RSASL (r (52) =.-.629, p=.000),
exhibited a low negative and negative correlation, respectively (Table 16). The
adjusted R² value showed that 47% of the variance in BAI scores was accounted for
through ERCR, ERES, RSPC and RSASL. The regression equation proved a better
predictor, than the BAI score mean (F=10.627, df=4, p=.000) (Table 17).
Table 17
Model
R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R
Square
Change
F
Change
df1
df2
Sig. F
Change
1
,685a
,470
,425
7,601
,470
10,627
4
48
,000
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
31
The analysis shows, that ERES (Beta=.239, t(53)=2.095, p=.042), ERCR (Beta=-.068,
t(53)=-.611, p=.544) and RSPC( Beta=.289, t(53)=1.556, p=.126) were not significant
predictors of BAI scores, however RSASL significantly influenced the measured level
of anxiety (Beta=-.788, t(53)=-4.23, p=.000) (Table 18).
Discussion
The aim of this research was to investigate the distinct relationship
between emotion regulation and resilience, and how this relationship would
impact the occurrence of depression and anxiety in adults. Specifically, emotion
regulation strategies expressive suppression and cognitive reappraisal were
assessed using the ERQ, and resilience components personal competence and
acceptance of self and life, were assessed using the RS. The level of depressive
and anxiety symptoms were measured using the BDI-II and BAI, respectively.
The statistical analyses comprised correlation analyses between all variables of
emotion regulation strategies and resilience and depression, as well as anxiety.
Furthermore, the relationship between depression and anxiety was measured
using correlation analysis. The results showed, that both depression and anxiety
scores were positively correlated with each other (r (47) =.579, p=.000).
Depression further correlated positively with the total scores of emotion
regulation strategies (r (43) =.420, p=.004), and specifically with expressive
suppression (r (43) =.409, p=.005). In addition, depression was found to
correlate negatively with the total score of measured resilience in participants
(r (47) =-.595, p=.000), as well as with each sub-division of resilience, personal
competence (r (47) =-.586, p=.000) and acceptance of self and life
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
32
(r (47) =-.518, p=.000). These findings suggest that emotion regulation
strategies, specifically the level of expressive suppression, impact the
occurrence of depressive symptoms in adults. In other words, participants who
presented high expressive suppression scores tended to exhibit higher depression
scores as measured in the survey. In contrast, participants who scored high on
the resilience scale in the sections, which measured personal competence and
acceptance of self and life, presented rather low depressive symptom scores on
the BDI-II. The study further found a positive correlation between anxiety
symptoms and expressive suppression scores (r (51) =.390, p=.004), which
indicates, that individuals who tend to down-regulate their emotions by resorting
to expressive suppression, are more inclined to show overall higher anxiety
scores on the BAI. This finding is substantiated by research conducted by
Campbell-Sills, Barlow, Brown and Hofmann (2006), who found that
suppression showed exacerbating effects on displayed emotions of individuals
with anxiety and mood disorders, compared to acceptance. Moore, Zoellner and
Mollenholt (2008), further found, that the effects of suppression on
psychopathology were not only less effective as a regulative strategy of emotion,
but also more detrimental in the development of anxiety. In contrast, the level of
measured anxiety decreased, when the overall resilience score (r (56) = -.523,
p=.000) increased, specifically regarding the measurements on acceptance of
self and life (r (56) =-.623, p=.000), but also for scores on personal competence
(r (56) =-.422, p=.001), hence they displayed a negative relationship. This
implies, that participants who achieved higher resilience scores in total, tended
to present less predominant symptoms of anxiety. Specifically notable was the
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
33
impact, which the measurements of acceptance of self and life appeared to have
on overall anxiety scores. Apparently, participants who tend to be more content
with themselves, their individual life situation and life in general, scored lowest
on the anxiety scale. However, this may also include the ability to control for
unwanted emotional outbursts or harmful behaviors at times, which in fact, was
indicated by a positive correlational relationship between emotion regulation
strategy expressive suppression and resilience factor acceptance of self and life
(r (51)=-.291, p=.034). This might indicate, that these participants also appeared
to down-regulate unwanted or inadequate emotions through expressive
suppression more often, in order to regulate for dysfunctional or unwanted
behavior and adapt to perceived social and environmental requirements. In fact,
Soto, Perez, Kim, Lee and Minnick (2014) found, that the impact of suppression
of emotion on well-being and psychopathology differed according to the cultural
context individuals lived in. Their study revealed, that while general
contentment and well-being was more often impaired for European and
American individuals who consistently made use of suppression to regulate
experienced emotion, and could even manifest in psychopathological disorders,
this was not the case for Chinese individuals (Soto et al., 2011). Seen that the
targeted participants in this study belonged to various ethnic, religious and
cultural groups, the positive relationship between expressive suppression and
acceptance of self and life found in this study, could be explained for. The
multiple regression analyses further showed, that the regression equation was a
better predictor of the level of depressive symptoms than the score mean. In
other words, 53.1% of the noted variance (R²) in depression scores according to
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
34
the BDI-II can be accounted for by emotion regulation strategies and resilience.
Specifically, emotion regulation cognitive reappraisal (Beta=.305, t(45)=2.653,
p=.011) and resilience personal competence (Beta=-.554, t(45)=-3.281, p=.002)
indicated statistical significance in predicting depression scores, while resilience
acceptance of self and life (Beta=.051, t(45)=-.300, p=.766) as well as emotion
regulation expressive suppression (Beta=.216, t(45)=1.84, p=.073) did not. This
directs to the assumption, that participants who scored high in emotion
regulation strategy cognitive reappraisal and resilience component personal
competence, showed lower depressive symptoms (Garnelfski & Kraaij, 2006),
due to the ability to re-evaluate external cues, which elicit emotions in such a
manner, that is beneficial to emotional well-being (Gross, 2014). This is further
substantiated by a study conducted by Troy, Wilhelm, Shallcross and Mauss
(2010), who found, that cognitive reappraisal was protective of depressive
symptoms in women, when high levels of stress were experienced, but also in
men (Granefski, Teerds, Kraaij, Legerstee & van den Kommer, 2004). In
accordance with this assumption, resilience also suggests to be protective in the
development of anxiety, seen that anxiety scores were lower when personal
competence scores (r (56) =-.422, p=001) and acceptance of self and life scores
(r (56) =-.623, p=.000) were high. In contrast, high expressive suppression
scores
(r (51) =.390, p=.004) were found in participants who presented higher levels of
anxiety according to the BAI. This again implies that individuals appear to
display lower levels of anxiety, when personal competence, that is, relative
autonomy, and a distinct level of control over one´s life and a sense of purpose,
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
35
is given (Bandura, 1989; Hu, Zhang & Wang, 2015). The regression analysis
furthermore indicated, that the predictor variables accounted for 47% in variance
of the anxiety scores, with acceptance of self and life (Beta=-.788, t (53) =-4.23,
p=.000), specifically presenting as a significant predictor of anxiety score
variance. These findings suggest, that a high level of personal competence and
acceptance of self and life are the most important protective factors in the
development depression and anxiety in adults, while personal competence and
cognitive reappraisal best predict depression, and acceptance of self and life best
predicts anxiety. It appears that individuals with a healthy feeling of self-worth
and agency, reflected in the resilience trait personal competence, tend to believe
more in themselves and their own capacities, which is reflected in the emotion
regulation strategy cognitive reappraisal (Bandura, 1989; Gross, 2014). Both
traits are found to be strongly presented in individuals who show no or low
levels of depressive symptoms. In contrast, suppression as an emotion regulation
strategy, appears to enhance both depressive and anxiety symptoms. This could
be accounted for by the fact, that the down-regulation of experienced emotions
on a continuous and dysfunctional basis, leads to self-criticism and self-doubt,
and loss of self-worth, seen that individuals internalize negative affect (Gross,
2014). Interventions could hence work in the direction of strengthening
individual´s feeling of self-worth and agency, hence stimulating cognitive
reappraisal of external cues and beliefs, and thus enhancing personal
competence (Fredrickson & Tugade, 2003; Troy & Mauss, 2011). Furthermore,
enhancing assertiveness and learning to accept one self and life situations, might
help develop gratitude and contentment for positive emotional experiences, thus
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
36
up-regulating positive emotions, strengthening resilience and improving general
well-being, as well as mediating negative cognitive and emotional processes,
which influence the development of mental disorders, such as depression and
anxiety.
Limitations
In alignment with the findings of recent literature, the findings of the
present study imply that resilience factors personal competence and acceptance
of self and life seem to have an impact on mental health (Davydov et al., 2010;
Hu, Zhang & Wang, 2015; Nygren, Aléx, Jonsén, Gustafson, Norberg Lundman,
2005; Ong et al., 2006; Troy & Mauss, 2011), specifically depression and
anxiety (Dumont & Provost, 1999; Haddadi & Besharat, 2010; Hjemdal, Vogel,
Solem, Hagen & Stiles, 2011). In fact, results showed that lower rates of
depressive and anxiety symptoms were found in participants who scored higher
in resilience components personal competence and acceptance of self and life.
Furthermore, while emotion regulation strategy cognitive reappraisal was
predictive of depression scores on the BDI-II, resilience trait acceptance of self
and life, predicted BAI scores. However, the present findings should be
evaluated within the limitations of this research. The primary limitation of this
study consists in the effective number of viable surveys, which fulfilled the
study´s inclusion criteria, and were hence admitted for analysis. The initial aim
to generate 385 surveys was most likely undermined by the fact, that the
researched topic is commonly perceived as an intrusive and sensitive subject of
inquiry (Yale University Institutional Review Boards, 2013). Although it was
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
37
emphasized in the research study invite, as well as the participant information
sheet, that the participation in the survey and the given responses remained
anonymous at all times, the assumption can nonetheless be made, that
inhibitions to partake in the survey were predominant for most prospective
participants. Albeit this fact, 59 of the initially submitted 214 surveys fulfilled
the inclusion criteria after an extensive review by the research student. When re-
evaluating the viable account of participation under the premise, that a medium
effect size of 30% and a statistical power of 80% was desired, the actual return
rate of feasible data was sufficient to make insightful inferences (Cohen, 1988;
Cohen, Cohen, West & Aiken, 2003). Furthermore, it must be pointed out, that
the conducted study was descriptive, that is, none of the variables were
manipulated at any point. Instead, the aim of the study was to investigate the
relationship of variables, therefore a smaller sample size than initially
anticipated, was deemed acceptable (Karimollah, 2011). Another important
factor, which might have had a negative effect on the frequency of survey
participation, was that participants were not compensated in any form for
completing the survey. In other words, the respondents who partook in the
survey and spent a considerable amount of time in doing so, acted upon the basis
of good will. Taking into consideration furthermore, that most participants hold
an employment (71.2%) and have family (68.7%), the time factor should not be
underestimated. Still, a larger sample size would have been desirable in order to
enhance the power of the study and make the results more generalizable
(Sedlmeier & Renkewitz, 2008; Wilson & MacLean, 2011). A further limitation
regarding the representativeness of the results lay in the demographic
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
38
characteristics of the participants. The survey invites had been mailed to a wide
range of individuals pertaining to the network of the researcher, which mostly
includes individuals with a higher level of education. In fact, more than half
(57.7%) of all respondents held a graduate degree or higher. These numbers
appear not to be representative of the total target population, as this would
comprise individuals with different academic backgrounds, hence diminishing
the representativeness of the findings. The research study further applied an
online self-report questionnaire to collect psychometric data from participants.
While this type of data collection facilitates the receipt of a large sample size
within a short amount of time, without having to resort to large financial
commitments, there are some limitations, which are worth mentioning (Wilson
& MacLean, 2011). While the researcher must trust that participants respond
truthfully to survey questions, this cannot be ascertained, as many factors might
influence the level of honesty in given responses, such as shame or perceived
social desirability (Wilson & MacLean, 2011). Furthermore, even if respondents
approach self-report survey with the intention to give truthful responses, the
individual level of introspective capacity, understanding, and the inclination to
interpret the meaning of any given point in a rating scale, may vary to such an
extent, that responses do not reflect the full truth (Fan, 2006; Wilson &
MacLean, 2011). None the less, taking into consideration, that there was a
limited amount of time and funds available to perform this research, the applied
data collection methods seems justified. Furthermore, it may be assumed, that
those participants, who completed the online survey, relied on the anonymity of
their participation and responses, thus responding as truthful as possible
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
39
Conclusion
This research investigates the relationship between emotion regulation and
resilience, and how this relationship impacts the occurrence of anxiety and depression
in adults. Although the study only sought to ascertain the relationship between these
variables, and inferences regarding a causal relationship cannot be made, the gathered
insight hopefully contributes to further research in this regard, and aids in the
development of therapeutic measures and intervention in not only the development of
therapeutic measures and intervention not only in the treatment, but also the
prevention of depression and anxiety. The findings showed that resilience personal
competence and acceptance of self and life seem to constitute the greatest influences
on the prevalence of depression and anxiety, but also a high level of emotion
regulation skill cognitive reappraisal was found in individuals with low depressive
symptoms. This could direct further research in focusing on these inter-relating and
beneficial factors to mental health, and could contribute to the expansion of existing
interventions for depression and anxiety. Furthermore, these protective factors seem to
be connected to human agency and assertiveness. This might present an ancillary
emphasis in prevention programs for these disorders. Another question, which arises
when looking at the results of this research, is why the rate of depressive symptoms in
this study differs from the global rate of depression, which lies at 7.3% (Baxter, Scott,
Vos & Whiteford, 2013), and that of anxiety at 4.7% (Ferrari et. al., 2013), compared
to the sample, which revealed a rate of 13.6% for depression and 5.1% for anxiety.
According to the Center for Behavioral Health Statistic and Quality (2015), the
prevalence of mental disorders decreases with academic achievement, yet the results
of this study appear to show the contrary. One assumption could be, that higher
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
40
academic achievements imply more challenging professional careers, which might
more often demand the containment of a behavioral expression of emotions the
professional context, and hence produce the tendency to suppress emerging emotions.
This could be illuminated in further research and help understand different aspects of
factors, which in the current literature. It remains to say, that this study presents a
further step in the direction of highlighting the significance of the inter- and intra-
relation of resilience factor personal competence and emotion regulation skill
cognitive reappraisal, in the prevention of depressive symptoms, while emphasizing
resilience factor acceptance of self and life, as a protective factor in the occurrence of
anxiety. Further research with specific focus on these variables, might help understand
and develop measures to treat and prevent the severe impairments these disorders have
on individuals.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
41
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Tables
Table 2
Beck Depression Inventory (BDI-II)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
6
10,2
12,2
12,2
1
3
5,1
6,1
18,4
2
5
8,5
10,2
28,6
3
2
3,4
4,1
32,7
4
4
6,8
8,2
40,8
5
3
5,1
6,1
46,9
6
2
3,4
4,1
51,0
7
4
6,8
8,2
59,2
8
3
5,1
6,1
65,3
9
4
6,8
8,2
73,5
10
4
6,8
8,2
81,6
11
2
3,4
4,1
85,7
13
2
3,4
4,1
89,8
16
1
1,7
2,0
91,8
18
1
1,7
2,0
93,9
19
1
1,7
2,0
95,9
26
1
1,7
2,0
98,0
35
1
1,7
2,0
100,0
Total
49
83,1
100,0
Missing
999
10
16,9
Total
59
100,0
Note: Interpretation of depression scores took place according to the BDI-II Manual
(Hautzinger, Keller & Kühner, 2009). BDI-II scores in this study range from 0-35.
Scores under total value of 9 reflect no depressive symptoms, between 9-13 classify as
minimal depression, between 14-19 as mild depression, between 20-28 as moderate
depression, and scores between 29-63 as severe depression (Beck, Steer & Brown,
1996). Missing values indicate participants with a depression diagnosis.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
64
Table 3
Beck Anxiety Inventory (BAI)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
11
18,6
19,0
19,0
1
4
6,8
6,9
25,9
3
11
18,6
19,0
44,8
4
5
8,5
8,6
53,4
5
2
3,4
3,4
56,9
6
6
10,2
10,3
67,2
7
1
1,7
1,7
69,0
9
4
6,8
6,9
75,9
10
2
3,4
3,4
79,3
11
2
3,4
3,4
82,8
12
2
3,4
3,4
86,2
14
1
1,7
1,7
87,9
17
1
1,7
1,7
89,7
29
3
5,1
5,2
94,8
31
1
1,7
1,7
96,6
32
1
1,7
1,7
98,3
45
1
1,7
1,7
100,0
Total
58
98,3
100,0
Missing
999
1
1,7
Total
59
100,0
Note: Interpretation of anxiety scores took place according to the BAI Manual
(Margraf & Ehlers, 2012). BAI-II scores in this study range from 0-45. Scores
between 0-7 classify as minimal anxiety, between 8-15 as mild anxiety, between 16-
15 as moderate anxiety, and scores between 26-63, as severe anxiety (Beck & Steer,
1993). Missing values indicate participants with an anxiety diagnosis.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
65
Table 4
Emotion Regulation Questionnaire (ERQ) Total Score
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
16
1
1,7
1,9
1,9
19
1
1,7
1,9
3,8
25
1
1,7
1,9
5,7
28
2
3,4
3,8
9,4
31
3
5,1
5,7
15,1
33
1
1,7
1,9
17,0
34
5
8,5
9,4
26,4
37
7
11,9
13,2
39,6
39
1
1,7
1,9
41,5
40
9
15,3
17,0
58,5
43
3
5,1
5,7
64,2
46
4
6,8
7,5
71,7
49
7
11,9
13,2
84,9
52
1
1,7
1,9
86,8
55
2
3,4
3,8
90,6
58
1
1,7
1,9
92,5
61
3
5,1
5,7
98,1
67
1
1,7
1,9
100,0
Total
53
89,8
100,0
Missing
999
6
10,2
Total
59
100,0
Note: Interpretation of ERQ scores took place according to Gross and John (2003).
ERQ scores in this study range from 16-67. Scores between 10-40 classify as low-
medium use of strategies, between 41-70 as medium-high use of strategies. Missing
values indicate participants who have received instruction in the application of
emotion regulation strategies.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
66
Table 5
Emotion Regulation Expressive Suppression (ERES)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
4
4
6,8
7,5
7,5
7
7
11,9
13,2
20,8
10
5
8,5
9,4
30,2
13
16
27,1
30,2
60,4
16
12
20,3
22,6
83,0
19
4
6,8
7,5
90,6
22
3
5,1
5,7
96,2
25
1
1,7
1,9
98,1
28
1
1,7
1,9
100,0
Total
53
89,8
100,0
Missing
999
6
10,2
Total
59
100,0
Note: Interpretation of ERES scores took place according to Gross and John (2003).
ERES scores in this study range from 4-28. Scores between 4-16 classify as low-
medium use of strategies, between 17-28 as medium-high use of strategies. Missing
values indicate participants who have received instruction in the application of
emotion regulation strategies.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
67
Table 6
Emotion Regulation Cognitive Reappraisal (ERCR)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
12
2
3,4
3,8
3,8
15
1
1,7
1,9
5,7
18
3
5,1
5,7
11,3
21
2
3,4
3,8
15,1
24
19
32,2
35,8
50,9
26
1
1,7
1,9
52,8
27
5
8,5
9,4
62,3
29
1
1,7
1,9
64,2
30
2
3,4
3,8
67,9
33
1
1,7
1,9
69,8
36
8
13,6
15,1
84,9
39
4
6,8
7,5
92,5
42
4
6,8
7,5
100,0
Total
53
89,8
100,0
Missing
999
6
10,2
Total
59
100,0
Note: Interpretation of ERCR scores took place according to Gross and John (2003).
ERCR scores in this study range from 12-42. Scores between 6-24 classify as low-
medium use of strategies, between 24-42 as medium-high use of strategies. Missing
values indicate participants who have received instruction in the application of
emotion regulation strategies.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
68
Table 7
Resilience (RS) Total Score
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
70
1
1,7
1,7
1,7
78
1
1,7
1,7
3,4
96
2
3,4
3,4
6,8
101
1
1,7
1,7
8,5
104
1
1,7
1,7
10,2
113
1
1,7
1,7
11,9
114
1
1,7
1,7
13,6
115
1
1,7
1,7
15,3
119
1
1,7
1,7
16,9
122
1
1,7
1,7
18,6
130
1
1,7
1,7
20,3
133
1
1,7
1,7
22,0
135
1
1,7
1,7
23,7
136
1
1,7
1,7
25,4
137
2
3,4
3,4
28,8
139
2
3,4
3,4
32,2
142
1
1,7
1,7
33,9
143
1
1,7
1,7
35,6
144
3
5,1
5,1
40,7
145
3
5,1
5,1
45,8
146
4
6,8
6,8
52,5
150
2
3,4
3,4
55,9
151
3
5,1
5,1
61,0
152
1
1,7
1,7
62,7
154
1
1,7
1,7
64,4
155
1
1,7
1,7
66,1
157
3
5,1
5,1
71,2
158
2
3,4
3,4
74,6
159
2
3,4
3,4
78,0
160
1
1,7
1,7
79,7
161
2
3,4
3,4
83,1
164
2
3,4
3,4
86,4
165
1
1,7
1,7
88,1
167
2
3,4
3,4
91,5
168
1
1,7
1,7
93,2
169
1
1,7
1,7
94,9
170
1
1,7
1,7
96,6
172
1
1,7
1,7
98,3
175
1
1,7
1,7
100,0
Total
59
100,0
100,0
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
69
Note: Interpretation of RS scores took place according to Wagnild (2009). RS scores
in this study range from 70-175. Scores between 25-120, classify as low resilience,
between 125-145, as moderately low-moderate resilience, between 145-175 as
moderately high-high resilience.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
70
Table 8
Resilience Personal Competence (RSPC)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
55
1
1,7
1,7
1,7
56
1
1,7
1,7
3,4
64
1
1,7
1,7
5,1
68
1
1,7
1,7
6,8
71
1
1,7
1,7
8,5
80
1
1,7
1,7
10,2
81
1
1,7
1,7
11,9
82
1
1,7
1,7
13,6
85
1
1,7
1,7
15,3
86
1
1,7
1,7
16,9
90
1
1,7
1,7
18,6
91
1
1,7
1,7
20,3
92
2
3,4
3,4
23,7
93
1
1,7
1,7
25,4
95
1
1,7
1,7
27,1
97
1
1,7
1,7
28,8
99
1
1,7
1,7
30,5
100
1
1,7
1,7
32,2
101
6
10,2
10,2
42,4
102
2
3,4
3,4
45,8
103
4
6,8
6,8
52,5
104
2
3,4
3,4
55,9
105
3
5,1
5,1
61,0
106
2
3,4
3,4
64,4
107
1
1,7
1,7
66,1
108
4
6,8
6,8
72,9
109
2
3,4
3,4
76,3
110
2
3,4
3,4
79,7
111
1
1,7
1,7
81,4
112
2
3,4
3,4
84,7
114
1
1,7
1,7
86,4
115
2
3,4
3,4
89,8
116
3
5,1
5,1
94,9
117
1
1,7
1,7
96,6
118
1
1,7
1,7
98,3
119
1
1,7
1,7
100,0
Total
59
100,0
100,0
Note: Interpretation of RSPC scores took place according to Wagnild (2009). RSPC
scores in this study range from 55-119. Scores between 17-82, classify as low RSPC,
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
71
between 85-99 moderately low-moderate RSPC, between 100-119 as moderately
high-high RSPC.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
72
Table 9
Resilience Acceptance of Self and Life (RSASL)
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
14
1
1,7
1,7
1,7
20
1
1,7
1,7
3,4
23
1
1,7
1,7
5,1
25
1
1,7
1,7
6,8
29
1
1,7
1,7
8,5
30
2
3,4
3,4
11,9
32
2
3,4
3,4
15,3
33
1
1,7
1,7
16,9
34
1
1,7
1,7
18,6
36
1
1,7
1,7
20,3
37
1
1,7
1,7
22,0
38
1
1,7
1,7
23,7
39
1
1,7
1,7
25,4
40
4
6,8
6,8
32,2
41
1
1,7
1,7
33,9
42
5
8,5
8,5
42,4
43
3
5,1
5,1
47,5
44
1
1,7
1,7
49,2
45
3
5,1
5,1
54,2
46
2
3,4
3,4
57,6
47
3
5,1
5,1
62,7
48
1
1,7
1,7
64,4
49
3
5,1
5,1
69,5
50
5
8,5
8,5
78,0
51
3
5,1
5,1
83,1
52
1
1,7
1,7
84,7
53
3
5,1
5,1
89,8
54
4
6,8
6,8
96,6
55
1
1,7
1,7
98,3
56
1
1,7
1,7
100,0
Total
59
100,0
100,0
Note: Interpretation of RSASL scores took place according to Wagnild (2009).
RSASL scores in this study range from 14-56. Scores between 8-38, classify as low
RSASL, between 40-46 moderately low-moderate RSASL, between 47-56 as
moderately high-high RSASL.
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
73
Table 10
Pearson Product-Moment Correlation Analysis
BDI-II Score
BAI Score
ERES
ERCR
BDI-II Score
Pearson Correlation
1
,579**
,409**
,291
Sig. (2-tailed)
,000
,005
,053
N
49
49
45
45
BAI Score
Pearson Correlation
,579**
1
,390**
-,041
Sig. (2-tailed)
,000
,004
,769
N
49
58
53
53
ERES
Pearson Correlation
,409**
,390**
1
,226
Sig. (2-tailed)
,005
,004
,104
N
45
53
53
53
ERCR
Pearson Correlation
,291
-,041
,226
1
Sig. (2-tailed)
,053
,769
,104
N
45
53
53
53
ER Total Score
Pearson Correlation
,420**
,168
,680**
,868**
Sig. (2-tailed)
,004
,229
,000
,000
N
45
53
53
53
RSPC
Pearson Correlation
-,586**
-,422**
-,219
,197
Sig. (2-tailed)
,000
,001
,116
,158
N
49
58
53
53
RSASL
Pearson Correlation
-,518**
-,623**
-,291*
,106
Sig. (2-tailed)
,000
,000
,034
,448
N
49
58
53
53
RS Total Score
Pearson Correlation
-,595**
-,523**
-,258
,169
Sig. (2-tailed)
,000
,000
,062
,225
N
49
58
53
53
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
74
Table 12
Correlation Regression Analysis Depression
BDI-II Score
ERES
ERCR
RSPC
RSASL
Pearson Correlation
BDI-II Score
1,000
,409
,291
-,590
-,526
ERES
,409
1,000
,272
-,173
-,269
ERCR
,291
,272
1,000
,131
,009
RSPC
-,590
-,173
,131
1,000
,757
RSASL
-,526
-,269
,009
,757
1,000
Sig. (1tailed)
BDI-II Score
.
,003
,026
,000
,000
ERES
,003
.
,035
,127
,037
ERCR
,026
,035
.
,196
,477
RSPC
,000
,127
,196
.
,000
RSASL
,000
,037
,477
,000
.
N
BDI-II Score
45
45
45
45
45
ERES
45
45
45
45
45
ERCR
45
45
45
45
45
RSPC
45
45
45
45
45
RSASL
45
45
45
45
45
Note: Pearson correlation shows an effect at > +/- .50. BDI-II scores and ERES
showed a low-moderate positive correlation (r (44) = .409, p = .003), RSPC (r (44) =
-.590, p = .000) and RSASL (r (44) = -.526, P = .000) showed a moderate-high
negative correlation.
* Statistical significance is given at p <.005
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
75
Table 14
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std.
Error
Beta
Tolerance
VIF
1
(Constant)
27,922
6,564
4,254
,000
ERES
,266
,144
,216
1,840
,073
,852
1,174
ERCR
,257
,097
,305
2,653
,011
,887
1,127
RSPC
-,289
,088
-,554
-3,281
,002
,411
2,431
RSASL
-,044
,146
-,051
-,300
,766
,405
2,471
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
76
Table 16
Correlations Regression Analysis Anxiety
BAI
Score
ERES
ERCR
RSPC
RSASL
BAI Score
1,000
,390
-,041
-,420
-,629
ERES
,390
1,000
,226
-,219
-,291
Pearson
Correlation
ERCR
-,041
,226
1,000
,197
,106
RSPC
-,420
-,219
,197
1,000
,817
RSASL
-,629
-,291
,106
,817
1,000
BAI Score
.
,002
,384
,001
,000
ERES
,002
.
,052
,058
,017
Sig. (1-tailed)
ERCR
,384
,052
.
,079
,224
RSPC
,001
,058
,079
.
,000
RSASL
,000
,017
,224
,000
.
BAI Score
53
53
53
53
53
ERES
53
53
53
53
53
N
ERCR
53
53
53
53
53
RSPC
53
53
53
53
53
RSASL
53
53
53
53
53
Note: Pearson correlation shows an effect at > +/- .50. BAI scores and ERES showed
a low-moderate positive correlation (r (52) = .390, p = .002), RSPC (r (52) = -.420, p
= .001) a low-moderate negative correlation, and RSASL (r (52) = -.629, p = .000)
showed a moderate-high negative correlation.
*Statistical significance is given at p <.005
RELATIONSHIP BETWEEN EMOTION REGULATION AND RESILIENCE
77
Table 18
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std.
Error
Beta
Tolerance
VIF
1
(Constant)
19,965
8,297
2,406
,020
ERES
,450
,215
,239
2,095
,042
,848
1,179
ERCR
-,087
,143
-,068
-,611
,544
,884
1,132
RSPC
,190
,122
,289
1,556
,126
,320
3,126
RSASL
-,817
,193
-,788
-4,230
,000
,318
3,141
This Master Dissertation has been developed while studying on an online
programme at the University of Liverpool.
©2016 Tiziana Osel. All Rights Reserved.
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