re-submit 7711565 - does positive rumination predict resilience

DOES POSITIVE RUMINATION PREDICT RESILIENCE? 1

Does positive rumination predict psychological resilience?

Molly Tuck

PSYC 30920

The University of Manchester

April 2014

Word Count: 5,863


Table of Contents

Abstract

Research suggests that high levels of psychological resilience are associated with positive

mental health outcomes. Therefore enhancing resilience could have substantial benefits to

Abstract………………………………………………………………………………….

3

Introduction……………………………………………………………………………...

4

Method…………………………………………………………………………………

10

Results………………………………………………………………………………….

16

Discussion……………………………………………………………………………...

22

Acknowledgements………………………………………………………………….....

27

References……………………………………………………………………………...

28

Appendices……………………………………………………………………………..

39


individuals. Research examining the relationship between positive rumination and resilience

is sparse, while the links between resilience and factors such as problem solving, emotional

regulation and social support are well understood. For the present study, a total of 30

undergraduate students were recruited and completed eight questionnaire measures, although

one outlying participant was removed for analysis purposes. A multiple regression model was

used in order to determine, firstly, whether positive rumination predicted psychological

resilience and, secondly, to explore which variable, amongst positive rumination, problem

solving, social support and emotional regulation, was the stronger predictor of psychological

resilience. Positive rumination was found to not predict psychological resilience, while

problem solving was observed as the strongest predictor of resilience. Nevertheless, given the

potential clinical relevance of positive rumination and the pioneering nature of this area

research, further analysis using wider and more generalizable samples along with additional

assessment of the underpinning mechanisms of positive rumination should be pursued.

Introduction

Mental health issues affect an estimated 450 million people worldwide and current

figures in Britain indicate that each year one in four adults will experience at least one


diagnosable mental health problem (Counselling Directory, n.d.). The high prevalence and

debilitating impact of mental health consequently makes prevention a priority for health

practitioners (HM Government/DH, 2011). Conventionally, negative mental health outcomes

have been explored from a risk-based approach, centered on analyzing the negative and

maladaptive processes associated with the pathway to mental illness (Seligman &

Csikszentmihalyi, 2000). However, risk factors are not the only predictor of outcomes for

individuals. Therefore, this approach may not be the most effective way to study mental

health (Seligman, Steen, Park & Peterson, 2005). The observation that not all individuals in

conditions of high risk succumb to negative mental health (Hawton & Van Heeringen, 2009),

suggests variable levels of protective factors that influence outcomes. It may therefore be

more constructive to emphasize optimism by identifying and developing positive

psychological processes that could act as protective mechanisms and ameliorate an

individual's response to adversity (Tarrier, 2010).

Central to this protective approach is the concept of psychological resilience (Masten,

2001). Psychological resilience has been identified as a pivotal protective mechanism that

operates in conditions of high risk in order to prevent the occurrence of psychiatric disorders

(Bryant, West &Windle, 1997). It has been well documented that resilience has a moderating

effect on the associations between negative life events and mental health problems (Peng,

1994; Pinquart, 2009; Armstrong, Galligan & Critchley, 2011). Ong, Bergeman, Bisconti, and

Wallace (2006) as well as Patel and Goodman (2007) associated the concept of resilience with

promotion of good mental health. Additionally, both Rew and Horner (2003) and Roy and

Sarchiapone (2011) found that children and adolescents with high resilience could generally

maintain high quality mental health in the face of negative events. Therefore, enhancing

resilience can have wide reaching benefits for individuals in relation to mental health


outcomes.

The concept of psychological resilience has been used to describe a plethora of

characteristics (Masten & Reed, 2002) including, inter alia, the capacity to effectively adapt to

change (Cicchetti & Cohen, 1995), the characteristics of hardiness and invulnerability

(Kobasa, 1979), and the capability to thrive in the face of adversity (Connor, 2006). Although

resilience is a widely used concept, it has faced definitional challenges (Manyena, 2006).

Resilience studies vary considerably in their definition and measurement, with these

shortcomings consequently casting doubt on the utility of resilience as a theoretical construct

(Davydov, Stewart, Ritchie, & Chaudieu, 2010). For the purposes of this study the definition

set out by the Johnson, Gooding, Wood & Tarrier, (2010) will be utilised. This is specifically,

the ability or perceived ability of an individual to overcome problems, or a set of positive

beliefs or resources, which can act as a buffer in the face of adversity.

The mechanics underlying resilience have been grounded in psychological theory.

The Buffering Hypothesis (Cohen and Wills, 1985; Johnson, Wood, Gooding, Taylor &

Tarrier, 2011) offers one explanation, suggesting that resilience acts to moderate or buffer

individuals against risk factors, which in turn prevents detrimental mental health outcomes.

Therefore, resilience is not just the absence of such risk factors, but also a buffering

mechanism against these factors. Increasing resilience could, as a result, have substantial

benefits to individuals when in conditions of high risk.

Research has attempted to breakdown psychological resilience into its specific

components. Johnson, Gooding & Tarrier, (2008) established a theoretical model called the

Schematic Appraisals Model of Suicide (SAMS). This provided a valuable framework under


which resilience could be understood and explored empirically, with particular reference to

suicide. The model postulated that positive self-appraisals were a key source of resilience.

Enhancing these appraisals may therefore contribute to buffering individuals against suicide.

The SAMS further suggests three types of positive self-appraisals may be key in buffering

individuals from suicidal thoughts. Specifically, these are the individual's ability to: regulate

emotions, solve problems, and gain social support. Johnson et al., (2010) developed the

Resilience Appraisal Scale (RAS), which is an objective measure based on the predictions set

out by the SAMS. Using a student sample they tested whether positive self-appraisals

comprising of problem solving appraisal, perceived social support and perceived emotional

regulation buffered the impact of negative life events in the development of suicidality. They

found individuals who self-reported moderate or high levels of positive self- appraisal, did not

experience heightened suicidality. This supports the predictions set out by the SAMS. Connor

& Davidson’s (2003) self-report instrument, the Connor–Davidson Resilience Scale (CD-

RISC), designed to establish the norms in resilience, also supported these components of

resilience by reference to, inter alia, tolerance of negative affect, positive acceptance of

change, sense of social support, and an action-oriented approach to problem solving.

Extensive research has reinforced the relationship between resilience and problem

solving, emotional regulation and social support. Problem solving is defined as the capability

to plan, resourcefully seek help from external sources and to think creatively, critically, and

reflectively (Benard, 1995) where the relevant appraisal refers to an individual’s self-appraisal

of his/her problem-solving abilities (Heppner, He, Tsai, and Lin, 2008). Research has

consistently reported problem-solving ability as a component of resilience (Neiger, 1991;

Bernard, 1994; Rutter, 1999). For example, when studying resilient children the presence of

well-developed problem-solving skills has been found to be commonplace (Rutter, 1984;


Brookes & Goldstein, 2001). Holahan, Moos, Holahan & Brennan (1995) defined social

support as the social means that individuals feel to be available to them or that are actually

offered to them by external sources. Parental warmth, cohesion and care within the family, or

a close relationship with a caring adult are commonly associated with resilient young people

(Bond, Burns, Vella-Brodrick & Sawyer 2003). Moreover, Ozbay et al., (2007) found positive

social support of high quality can enhance resilience considerably. Finally, emotional

regulation is defined as the process that individuals consciously or unconsciously alter their

emotions (Gross & Thompson, 2007). Peng et al., (2014) explored emotion regulation and

found it played an important role in the resilience of students. Additionally, Min, Yu, Lee &

Chae (2013) also investigated cognitive emotion regulation strategies in a sample of patients

suffering from depression and/or anxiety disorders and their contribution to resilience. Their

findings suggested that the cognitive emotion regulation strategies of positive re-appraisal, a

focus on planning, and less rumination contributed positively to resilience.

The benefits of positive emotions have been examined in the “broaden-and-build

theory” (Fredrickson, 1998; Fredrickson, 2003; Fredrickson, 2004). This hypothesizes that

positive emotions broaden individuals' thought–action repertoires, assisting individuals in

accessing a range of perceptions, actions and ideas. This broadened cognition consequently

allows behavioral flexibility that builds mindfulness, resilience and mental health (Cohn,

Fredrickson, Brown, Mikels & Conway, 2009). Psychologically resilient individuals have

been shown to experience these increased positive emotions (Ong, Zautra & Reid, 2010). This

supports their role as an active ingredient within resilience, which in turn has been found to

reduce the risk of depression. (Fredrickson, Tugade, Waugh & Larkin, 2003).

Negative emotions are now understood to constrict the scope of an individual’s


attention and thinking (Schmitz, De Rosa & Anderson, 2009; Talarico, LaBar & Ruben

2004). Less is however understood about the positive processes, which have the potential to

serve as pivotal factors in preventing negative mental health outcomes (Feldman, Joormann,

& Johnson, 2008). In particular, positive rumination is an under-researched process that has

been defined as ‘repetitive positive self- and symptom-focused responses to positive mood’

(Feldman et al., 2008). Negative rumination, in contrast, is characterized by recurring,

pervasive thoughts about the symptoms, causes, and future repercussions of one's depression

(Nolen-Hoeksema, 1991; Nolen-Hoeksema, Wisco & Lyubomirsky, 2008; Smith & Alloy,

2009). Negative rumination has been identified as a significant predictor of depressive

symptoms (Nolen-Hoeksema et al., 2008) episodes (Abela & Hankin, 2011) and also a core

process in the maintenance of depression, notably in studies of adolescents and children (Riso

et al, 2003).

Brain imaging studies have also supported the role of negative rumination in the

maintenance of depression. For example, Hamilton, Furman, Chang, Thomason, Dennis &

Gotlib (2011) observed increased activation of the default mode network, a network

associated with maladaptive, depressive rumination with lower levels of adaptive and

reflective thinking in depressed individuals. The inability of such studies to claim cause and

effect however must be noted.

Nolen-Hoeksema et al., (2008) in their Response Styles Theory (RST), postulate that

this negative rumination preserves and increases depression by enhancing negative thinking,

damaging problem solving, and eroding social support. . Lyubomirsky & Nolen-Hoeksema

(1995) evidenced the damaging effect of rumination on problem solving, finding dysphoric

ruminating individuals generated ineffective solutions to problems. Lyubomirsky, Tucker,


Caldwell & Berg (1999), additionally found rumination leads people to see obstacles to the

implementation of problem solving solutions. Sustained negative rumination has been

associated with difficulties in regulating emotions (Joormann & Quinn, 2014) and gaining

social support (Nolen-Hoeksema & Davis, 1999). Negative rumination therefore damages the

key components identified as having central importance in promoting resilience by the SAMS

(Johnson et al., 2008), the RAS (Johnson et al., 2010) and Connor & Davidson (2003).

Conversely, therefore, it can be argued that if individuals ruminate positively, thus

enhancing rather than damaging these components, resilience could consequently be

increased. Cross-sectional research has suggested that, just as depressive rumination has an

important impact on depression, positive rumination may also be relevant to promoting high-

quality mental health (Feldman et al., 2008; Hughes, Smith & Alloy 2006) however, one

cannot draw any inferences about cause and effect from such research design. Exploration of

positive rumination, using a similar approach to that illustrated by Nolen-Hoeksema and

colleagues with negative rumination, is essential in order to provide empirical support for its

impact on promoting resilience. With adolescence being characterized by dramatic rises in

depression (Hankin, Abramson, Moffitt, Silva, McGee & Angell) and increased rumination

(Jose & Brown, 2008; Rood, Roelofs, Bégels, Nolen Hoeksema, & Schouten 2009), assessing

the impact of positive rumination on psychological resilience in a student population could

have preventative benefits.

Based on this rationale, the main aim of the present study was to determine whether

positive rumination predicts psychological resilience in a student population. An additional

exploratory hypothesis was to examine which out of key variables namely, positive

rumination, social support, emotional regulation or problem solving, was a stronger predictor

of psychological resilience.


Method

Design

A cross-sectional questionnaire design was utilized for the present study. The

predictor variables were positive rumination, divided into self-focused, emotion focused and

dampening, alongside social support, emotional regulation and problem solving. The outcome

variable was the level of psychological resilience.

Participants

The study aimed to recruit 110 first and second year undergraduate psychology

students from the University of Manchester. The target sample size was based on the guidance

provided by Tabachnick & Fidell, (2007) on what constitutes a sufficient sample size to

produce significant statistical power. They suggest that when observing the impact of

combined and individual variables the number of participants should be more than 108.

Recruitment took place online via SONA (“Sona Systems Ltd.”, 1997) - a recruitment

platform that enables universities to recruit participants and manage research. The study

however only recruited 30 participants, specifically 28 females and 2 males.

Measures

Participants were required to complete the following nine questionnaires online via

SONA (“Sona Systems Ltd.”, 1997).


The Resilience Appraisals Scale (RAS; Johnson, Gooding, Wood & Tarrier, 2010) is a 12-

item self-report measure of psychological resilience. This scale constitutes three sub-scales

reflecting social support: ‘my family or friends are very supportive of me’, problem solving

ability: ‘I can generally solve problems that occur’ and emotional regulation skills: ‘I can

control my emotions.’ Participants indicate to what extent statements apply to them on a five-

point likert scale ranging from 1 ‘strongly disagree’ to 5 ‘strongly agree’. Overall reliability

for the scale has been reported as (α = .88) and for the three individual sub-scales: social

support (α = .93), emotional regulation (α = .92) and problem solving ability (α = .92)

(Johnson et al., 2010). For this study overall reliability of the RAS was calculated as α= .86

and for three subscales social support α= .83; problem solving α = .76; emotional regulation α

= .85.

The Emotion Regulation Questionnaire (ERQ; Gross & John, 2003) is a 10-item self-report

measure assessing individuals’ habitual use of two emotional regulation strategies. These

strategies are the sub-scales for this questionnaire, namely cognitive reappraisal e.g., ‘When I

want to feel more positive emotion, I change what I’m thinking about’ and expressive

suppression e.g., ‘I keep my emotions to myself’. Participants respond using a seven point

likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. Overall reliability for the

scale has been reported as α=.69, with reliabilities for the reappraisal subscale reported as

α=.79 and expressive suppression sub-scale reported as α= .73 (Gross & John, 2003). In this

study overall reliability for the ERQ was calculated as α= .70.

Social Support Behaviours Scale (SSB-S; Vaux, Riedel & Stewart, 1987) is a 45-item self-

report measure of perceived accessibility of social support from family and friends. The scale

comprises five sub-scales: emotional support e.g., “Would comfort me if I was upset”,


socializing e.g., “Would chat with me”, practical assistance e.g., “Would give me a ride if I

needed one”, financial assistance e.g., “Would pay for my lunch if I was broke” and

advice/guidance e.g., “Would give me advice about what to do”. Participants were required to

respond to statements on a 5-point likert scale ranging from “no one would do this” to “most

family members/friends would certainly do this”. In a number of student samples strong

reliability was found with the lowest reliability for the sub-scales being α=.82 and the highest

being α=.9 (Vaux et al., 1987). Overall reliability for the scale in this study was calculated as

α=.96

The Responses to Positive Affect Scale (RPA; Feldman, Joorman & Johnson, 2008) is a 17-

item self-report measure containing three sub-scales. The sub scales comprise self-focused

rumination (e.g., “think about how proud you are of yourself”), emotion-focused rumination,

(e.g., “think about how happy you feel”) and finally, dampening (e.g., “remind yourself that

these feelings won’t last”). Participants respond by rating rate each item on a lickert scale

ranging from 1 (almost never respond in this way) to 4 (almost always respond in this way).

Reliability for the dampening sub scale has been reported as α= 0.72, emotional focused

rumination α= 0.69 and self-focused rumination α= 0.71 (Feldman et al., 2008). Reliabilities

for the RPA subscales in this study were calculated as: emotion-focused α=.51, self-focused α

= .71 and dampening α = .85.

The Cassidy-Long Problem-Solving Questionnaire (PS; Cassidy & Long, 1996) is a 24-

item self-report measure of six problem solving styles. The questionnaire consists of six sub-

scales, namely, helplessness: ‘I think only of myself when faced with problems’, problem

solving control: ‘I blame myself for the problems’, creative style: ‘I make a plan of action and

follow it’, problem solving confidence: ‘I make decisions and I am happy with them later’,


avoidance style: ‘I just wish that things might go away’ and finally approach style: ‘I take

some positive action’. Participants respond according to how much each statement applies to

them using ‘true/false’ options. Reliability of the overall measure has been reported as α=0.8

(Baker 2003). For this study overall reliability for this questionnaire was calculated as α=.85.

The following questionnaires were administered to participants, however not further analyzed

for the purposes of the present study:

The Beck Hopelessness Scale (Beck, Weissman, Lester & Trexler, 1974) is a 20-item self-

report measure of hopelessness. It taps into three major aspects of hopelessness: feelings

about the future, loss of motivation and expectations. Participants respond to statements e.g.,

‘My future seems dark to me’ using ‘true or false’. Each item is subsequently given a score of

0 or 1 accordingly, with some scores are reversed. The sum of all scores is the hopelessness

score, the higher the score the more hopeless they are. Overall reliability for the scale has

been reported as high α= .93 (Beck et al., 1974)

The Centre for Epidemiological Studies Depression scale (CES-D scale; Radloff, 1977) is a

20-item questionnaire measuring depression. Respondents are required to indicate how often

they had felt a certain way in the past week e.g., ‘I was bothered by things that usually don't

bother me.’ They respond on a 4-point lickert scale ranging from ‘rarely or none of the time

(<1 day)’ to ‘most or all of the time (5-7 days)’. Taylor, Doane & Eisenberg, (2013) using a

student sample found high reliabilities ranging from α= .86 to α=.91.

The Hypomanic Personality Scale (HYP; Eckbald & Chapman, 1986) is a 48-item measure

of hypomanic personality designed to identify those at risk for hypomania and mania. It taps


into 3 aspects of hypomania which are emotion, behaviour, and energy. Respondents indicate

whether statements are ‘true or false’. Reliability for the questionnaire has been reported to be

α=.87 and test-retest over 15 weeks was α= .81 (Eckbald & Chapman, 1986).

The Frequency of Positive Thinking Scale (Sit, 2013) is a nine- item questionnaire that

measures the frequency of positive thinking in one month. It was developed for the purpose

of this study. It comprises the same sub-scales as the RAS (Johnson et al., 2010), namely,

social support: (e.g., ‘I have people who will stick by me in a crunch’), problem solving

ability: (e.g., ‘There are lots of ways to get out of any problem’), and emotional regulation

strategies: (e.g., ‘I control my emotions by changing the way I think about the situation I'm

in’.) However, the individual questions are sampled on the Social Support Behaviour Scale

(Vaux et al., 1987), Emotion Regulation Questionnaire (Gross & John, 2003) and Trait Hope

Scale (Snyder et al., 1991). The phrasing of the questions was modified for the present study

(see Appendix A). Participants responded on a 7-point scale from “No Times” to “More than

Ten Times”, and were given a score ranging from 0 to 6, in respective to the responses given.

A higher score reflects higher frequency of positive thinking. The scale's overall alpha

reliability for this study was calculated as α=.92, and for the three subscales: social support

α=.94 emotion coping α=.84 and problem solving α=.90.

Procedure

Participants signed up online via SONA (“Sona Systems Ltd.”, 1997). Participants

were required to read a participant information sheet that outlined the study. After 24 hours

participants provided informed consent, after which they were able to proceed to the nine

questionnaire measures. The order which the questionnaires were presented to each


participant was randomized. Students were incentivized to participate and complete all 9

questionnaires within 60 minutes in exchange for four course credits.

Analysis Strategy

Firstly, the normality of all variables will be examined, by observing histograms and

as well numerical methods, specifically, the Shapiro-Wilk test and tests of Skewness and

Kurtosis (Field, 2013). If the outcome variable is not normally distributed, the data will be

transformed via one of the common methods: log transformation, square root transformation,

and reciprocal transformation or reverse score transformation (Field, 2013). Descriptive

statistics will then be generated in order to summarize the data set. Following this,

Correlations will be conducted to determine the relationship between the key predictor

variables and the outcome variable of psychological resilience, using Pearson’s for the

predictors that are parametric and Spearman’s rho for non-parametric variables (Field, 2013).

A multiple regression will then be conducted using the enter method. This will firstly,

examine how much of the total variance in resilience is accounted for by the predictor

variables (combined) and secondly, how much each predictor individually accounts for the

variance in resilience (unique variance). The analyses will be conducted utilizing SPSS

version 20 (SPSS Inc., Chicago, IL)

Ethics

Ethical approval was granted by ethics committee 3 at the University of Manchester.

Results

Participant characteristics


A total of 30 participants were recruited for the study, including 27 first year students

and three second year undergraduates. However, Skewness and Kurtosis scores indicated that

the outcome variable was not normally distributed. Skewness reported as -2.16 (SE=.43) and

Kurtosis as 8.18 (SE=.83). This violates the assumptions of multiple regression, as the

outcome variable is required to be normal. Using a histogram, one participant’s scores

(participant 22) was observed to be an outlier on each questionnaire measure (Appendix B1).

Although Field (2013) advocates data transformation, alternatively we removed this

participant and the outcome variable became normal (Appendix B2). Renewed Skewness and

Kurtosis scores for the outcome variable RAS as well as all other variables are illustrated in

Table 1. The final sample was therefore 29 participants, (Mage = 19.17, SD = 1.04). The

sample included twenty-seven females (Mage = 19.15, SD = 1.06) and two males (Mage = 19.5,

SD = .71). The time participants took to complete the questionnaires was varied, ranging from

12-61 minutes (MDuration = 26.03, SD = 13.01).

Descriptive Statistics, Tests of Normality and Zero-order correlations

Descriptive statistics of the key variables analysed in this study, including means,

ranges, standard deviations, 95% confidence intervals and normality tests of Skewness and

Kurtosis are illustrated in Table 1. Convergent results from tests of normality including the

Shapiro-Wilk test and scores of Skewness and Kurtosis indicated that all the variables were

normally distributed apart from social support however importantly the outcome variable is

normally distributed (Field, 2013).


Table 1. Means, Ranges, Standard deviations, 95% confidence intervals and normality tests of Skewness and Kurtosis for the key variables in this study.

M Range SD 95% CI(Lower)

95% CI(Upper)

Skewness

(SE)

Kurtosis

(SE)Resilience Appraisal Scale

Emotion Focus 2.92 1.8 .45 2.75 3.09 -.54(.43) .45 (.85)

Self- Focus 2.59 2 .62 2.35 2.82 .26(.43) -1.01(.85)

Dampening 1.82 1.88 .58 1.60 2.04 .54(.43) -1.16(.85)

Emotional Regulation Questionnaire (ERQ) 43.79 35 6.9 41.17 46.42 -.34 (.43) 1.57 (.85)

Problem Solving (PS) 15.62 16 4.81 13.79 17.45 -.15 (.43) -1.17 (.85)

Social Support Behaviour Scale (SSBS) 271.86 145 34.58 258.71 285.02 -1.73(.43) 2.96 (.85)

Resilience Appraisal Scale (RAS) 12.15 5.5 1.36 11.63 12.66 .02 (.43) -.07(.85)

Note. M=mean, SD=standard deviation, 95% CI= 95% confidence interval and SE= Standard Error.u


Spearman's rho correlation was run for the non-parametric variable of social support to

determine its relationship with resilience. For parametric variables, emotion focus, self- focus,

dampening, emotional regulation and problem solving Pearson’s r correlation was conducted.

For zero-order correlations see Table 2.

Scores obtained on the emotion focused subscale and self- focus subscale of the RPA

as well as the ERQ and SSBS were not significantly correlated with psychological resilience.

A positive relationship was found between scores on the PS questionnaire and psychological

resilience. The relationship was strong; 56% of the variance in psychological resilience was

accounted for by variations of scores on the PS scale. Additionally, a negative correlation was

found between the dampening subscale of the RPA and psychological resilience. The

relationship was moderate; 24% of the variance in psychological resilience was accounted for

by the variations in scores of dampening.


Table 2. Results of the Zero-order correlations for the key variables in this study.

2. 3. 4. 5. 6. 7.

Response to Positive Affect (RPA)

1.Emotion focus .55** .09 .21 .19 -.06 -.09

2.Self-focus -.12 .01 .36 -.06 .13

3.Dampening .01 -.27 -.001 -.40*

4.Emotion Regulation Questionnaire (ERQ) .08 .27 .24

5.Problem Solving Questionnaire (PS) -.13 .75**

6. Social Support Behaviour Scale (SSBS) .01

7.Resilience Appraisal Scale (RAS)

Note. ***Correlation significant at the 0.001 level (2-tailed). **Correlation significant at the 0.01 level (2-tailed). *Correlation significant at the 0.05 (2-tailed). RPA= Response to positive affect, ERQ=Emotion regulation Questionnaire, PS = Problem Solving, RAS=Resilience Appraisal Scale, SSBS = Social Support Behaviour Scale. Figures for the Spearman’s rho correlation are bolded and italicised.


Multiple Regression analysis

Multiple regression was run in order to determine the relationship the key predictor

variables: positive rumination divided into self-focused, emotion focused, dampening along

with problem solving, emotional regulation and social support and the outcome variable of

psychological resilience. Although some variables were found to not be correlated with

psychological resilience, in order align with the initial analysis strategy all variables were

included in the regression analysis. The results of the multiple regression analysis are

illustrated in table 3.

The regression model was significant, F(6,22) = 8.99, p =.000. Positive rumination as

measured by the three subscales of the RPA; emotion focus, self-focus and dampening along

with problem solving, emotional regulation and social support accounted for 63% of the total

variance in psychological resilience (adjusted R2). Problem solving predicted psychological

resilience however, emotion focus (RPA) dampening (RPA) self-focus (RPA) emotion

regulation and social support did not.


Table 3. Results of the multiple regression analysis for the key variables in the study.

B SE B 95% [CI] for B β t Sig.

Constant 9.32 1.89 [5.39, 13.24] 4.92 .000

Response to Positive Affect (RPA)

Emotion focus -.68 .44 [-1.59, .23] -.23 -1.54 .138

Self-focus -.05 .32 [-.73, .62] -.02 -.16 .873

Dampening -.43 .29 [-1.01, 1.7] -.18 -1.5 .149

Emotional Regulation Questionnaire (ERQ) .04 .03 [-.21, .1] .19 1.31 .203

Problem Solving Questionnaire (PS) .21 .04 [.14, .28] .74 5.82 .000

Social Support Behaviour Scale (SSB-S) .003 .01 [-.01, 0.2] .08 .54 .59


Discussion

The overarching aim of this study was to examine whether positive rumination would

predict psychological resilience. Research findings illustrated that it did not. However,

correlations did reveal that of the three sub-scales of the RPA, emotion focus and self-focus

were found to have no relationship with resilience, while dampening was found to have a

moderately negative relationship with resilience. The study also addressed an additional aim,

exploring which of the components, namely: positive rumination, problem solving, emotional

regulation and social support, were a stronger predictor of psychological resilience. The

multiple regression analysis illustrated that problem solving was the only predictor of

psychological resilience.

The finding that positive rumination did not predict psychological resilience does not

necessarily support or contradict a recognized body of research, given that positive rumination

is a comparatively under-developed research area. It does, however, contradict the logical

expectation of the effects of positive rumination when set against the known effects of

negative rumination, as identified by Nolen-Hoeksema (1991) in their RST. This confirmed

that negative rumination enhances negative thinking, damages problem solving and erodes

social support (Nolen-Hoeksema, 2008). Logically, it was anticipated that positive rumination

would, conversely, enhance such components and consequently increase resilience. This,

however, was not the case. This finding further undermines cross-sectional research, which

has suggested that, as negative rumination contributes to depression, positive rumination may

have the inverse effect by promoting psychological resilience (Feldman et al., 2008; Hughes

et al., 2006).


One explanation for this finding could be that, although negative rumination is a

damaging mechanism, positive rumination may not simply have a reverse enhancement effect.

In practice, the underlying mechanisms of these processes may be fundamentally different.

Feldman et al., (2008) state that positive rumination is a more voluntary and active process,

associated with high arousal and internal sensations. By contrast, negative rumination is an

involuntary process (Rime et al., 1994 as cited in; Luminet, Zech, Rime & Wagner, 2000)

implying different underlying functionalities. Future research could investigate the

psychological mechanisms underpinning positive rumination and its relationship with

resilience, additionally with the suggestion that positive rumination is involuntary, research

could induce positive rumination and then assess its mechanisms and impact.

Research has shown that variations exist in individuals’ propensity and ability to

ruminate (Nolen-Hoeksema, Morrow & Fredrickson, 1991; Conway, Csank, Holm & Blake,

2000; Ray et al., 2005). Cross-sectional findings focusing specifically on positive rumination

suggest that it occurs; not only with variable frequency, but that there are also variations

between individual capacity to do so (Luminet et al., 2000). Therefore, our findings could be

accounted for by a lower propensity to engage in positive rumination amongst our student

sample. Future research may benefit from developing a baseline assessment measure to

determine a sample’s capacity to positively ruminate prior to assessing its predictive

relationship with resilience. A similar instrument to that of the Ruminative Responses Scale

of the Response Styles Questionnaire (Nolen-Hoeksema & Morrow, 1991), which assesses

the tendency to negatively ruminate, could be valuable.

The use of the RPA as the only measure of positive rumination must be

acknowledged. With the area being so novel, limited instruments are available to measure


positive rumination. Although the RPA has been reported to have acceptable reliability and

validity (Feldman et al., 2008), this study would have benefited from using more than one

measure. A supplementary method could have been The Euphoric Rumination Questionnaire

(ERQ; Hughes, Smith & Alloy, 2006; as cited in Hughes, 2009) which has also been

demonstrated to have reliability and validity within a student sample (Hughes et al., 2006).

This is a 25-item, positively modified version of a commonly used measure of negative

rumination, the Ruminative Responses Scale (RRS; Nolen-Hoeksema & Morrow, 1991).

Although the ERQ has been shown to have psychometric weaknesses, it would have allowed

for the convergence of results in relation to positive rumination in this study. Certainly, this

area of research generally would benefit from the development of additional measures

through which positive rumination can be assessed.

Notably, one sub-scale of the RPA, specifically dampening, which is defined as the

tendency to respond to positive moods states with strategies to reduce the intensity and

duration of those states (Feldman et al., 2008), was negatively correlated with psychological

resilience. This negative relationship was expected, as higher scores on the dampening

subscale have been noted in research to be associated with depressive rumination, greater

symptoms of depression, and mania vulnerability (Feldman et al.,2008) and thus, would not

positively correlate with resilience.

The drivers that motivated participants to take part in the study also require

examination. Students were incentivized by the reward of four credits in exchange for their

participation. This may have impacted on their responses, in that they may have been

motivated by extrinsic factors rather than intrinsically motivated by a genuine interest in the

topic area. This is reflected in the duration that participants took to complete the measures


with some students taking as little as 12 minutes to complete all questionnaire measures. This

could have affected the accuracy of responses, in turn reducing the reliability of the findings

in this study. Future research may benefit from exploring incentives that evoke intrinsic

motivation.

The multiple regression analysis revealed that problem solving was found to be the

only significant predictor of psychological resilience. These findings were in accordance with

Johnson at al., (2008), Johnson et al., (2010) and Connor & Davidson (2003), which all

postulated that problem solving was a key component of resilience. Additionally, it supported

Neiger, (1991), Bernard, (1994) & Rutter (1999) whose research identified the presence of

problem solving as a component of resilience. However, findings that emotional regulation

and social support did not predict psychological resilience are inconsistent with additional,

well- established findings by Johnson et al., (2008), Johnson et al., (2010) and Connor &

Davidson (2003).

A feasible explanation for the lack of relationship found between emotion regulation

and resilience could be that our sample was composed of individuals who were still

developing the ability and propensity to regulate emotions. Garnefski & Kraaij, (2006) found

adolescents reported significantly less cognitive emotion regulation strategies than adults,

suggesting that, although all cognitive strategies used by adults are to some extent also used

by adolescents, the extent of application develops during the transition from adolescence to

adulthood, at which point the abilities become mastered (Aldwin, 1994). With our sample

being composed of first and second year students in the transition into adulthood, arguably

participants could still developing the ability to regulate their emotions.


However, with emotional regulation, social support and problem solving being so well

documented in psychological literature to all contribute to resilience, the inconsistency of the

findings in this study could most probably be attributed to the limited sample of only 29

participants. This low sample size could consequently result in a lack of statistical power.

Therefore, the results of this study may mask a relationship that does actually exist in the

population; this is specifically a type II error.

Beyond the reliance on a small sample size, which may have impacted on results, a

number of additional limitations of this study warrant discussion. The sample was mainly

composed of females (i.e. females comprised 90% of the total sample). This gender imbalance

raises concerns as to whether the present findings can be applicable to the male population.

Secondly, the use of self-report for all eight measures may be considered a limitation, as

individuals may have responded in a socially desirable manner, negating the accuracy of

responses. Despite this, self-report was considered to be the most appropriate method. This

was because the study was concerned with exploring individuals' personal appraisals of their

problem solving strategies, social support, emotional regulation and positive rumination.

Finally, the cross-sectional design of the study is an additional limitation, which confines the

extent to which findings can be interpreted. This is because this study only tested participants’

appraisals on one occasion, limiting the ability to claim causality.

Despite such limitations it must be acknowledged that this is a pioneering area of

research yet to develop and that this study presents a step towards understanding the role of

positive rumination and the components of resilience. Future longitudinal studies on a larger

and more diverse clinical sample, including a balanced number of males and females should

be used to test the findings of the study in order to produce more reliable and generalizable


conclusions with the potential to inform clinical implications. Moreover, further examination

of the key variables analyzed in this research would add value to this research field generally.

Future research should specifically focus on the development of additional measurements of

positive rumination while also assessing underlying mechanisms involved in the process.

Acknowledgements

I would like to gratefully acknowledge Trish Gooding for her constant support throughout the

duration of the project. Additionally, I would like to thank my family who been a continuous

source of love, care and support throughout my university career.

References


Abela, J. R., & Hankin, B. L. (2011). Rumination as a vulnerability factor to depression during the

transition from early to middle adolescence: a multiwave longitudinal study. Journal of

abnormal psychology, 120(2), 259.

Armstrong, A. R., Galligan, R. F., & Critchley, C. R. (2011). Emotional intelligence and

psychological resilience to negative life events. Personality and Individual Differences, 51(3),

331-336.

Banyard, V. L., & Cross, C. (2008). Consequences of teen dating violence understanding

intervening variables in ecological context. Violence Against Women, 14(9), 998-1013.

Beck, A. T., Weissman, A., Lester, D., & Trexler, L. (1974). The measurement of pessimism: The

hopelessness scale. Journal of Consulting and Clinical Psychology, 42, 861-865

Benard, B. (1995). Fostering Resilience in Children. ERIC Digest

Bond, C. A., L.,Burns, J. M., Vella-Brodrick, D. A., & Sawyer, S. M. (2003). Adolescent

resilience: A concept analysis. Journal of adolescence, 26(1), 1-11.

Brooks, R., & Goldstein, S. (2001). Raising Resilient Children: Fostering Strength, Hope, and

Optimism in Your Child. Contemporary Books, 4255 West Touhy Avenue, Lincolnwood, IL

60712-1975.

Bryant, K. J., Windle, M. E., & West, S. G. (1997). The science of prevention: Methodological

advances from alcohol and substance abuse research. American Psychological Association.


Cassidy, T., & Long, C. (1996). Problem‐solving style, stress and psychological illness:

Development of a multifactorial measure. British Journal of Clinical Psychology, 35(2), 265-

277.

Cicchetti, D., & Cohen, D.J. (1995). Perspectives on developmental psychopathology. In D.

Cicchetti & D.J. Cohen (Eds.), Developmental psychopathology, Vol 1: Theory and methods

(pp. 3-17). New York: Wiley., 56(3), 227.

Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis.

Psychological bulletin, 98(2), 310.

Connor, K. M. (2006). Assessment of resilience in the aftermath of trauma. Journal of Clinical

Psychiatry, 67(2), 46-49.

Connor, K. M., & Davidson, J. R. (2003). Development of a new resilience scale: The Connor‐Davidson resilience scale (CD‐RISC). Depression and anxiety, 18(2), 76-82. Olsson,

Conway, M., Csank, P. A., Holm, S. L., & Blake, C. K. (2000). On assessing individual differences

in rumination on sadness. Journal of personality assessment, 75(3), 404-425.

Counselling Directory. (n.d.). Facts and Figures. Retrieved April 25, 2014 from,

http://www.counselling-directory.org.uk/stats.html


Davydov, D. M., Stewart, R., Ritchie, K., & Chaudieu, I. (2010). Resilience and mental health.

Clinical Psychology Review, 30(5), 479-495.

Eckblad, M., & Chapman, L. J. (1986). Development and validation of a scale for hypomanic

personality. Journal of Abnormal Psychology, 95(3), 214.

Feldman, G. C., Joormann, J., & Johnson, S. L. (2008). Responses to positive affect: A self-report

measure of rumination and dampening. Cognitive Therapy and Research, 32(4), 507-525.

Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.

Fredrickson, B. L. (1998). Cultivated emotions: parental socialization of positive emotions and

self-conscious emotions. Psychological Inquiry, 9(4), 279-281.

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-

build theory of positive emotions. American psychologist, 56(3), 218.

Fredrickson, B. L. (2003). The value of positive emotions. American scientist, 91(4), 330-335.

Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotions. Philosophical

Transactions-Royal Society of London Series B Biological Sciences, 1367-1378.

Fredrickson, B. L., Tugade, M. M., Waugh, C. E., & Larkin, G. R. (2003). What good are positive

emotions in crisis? A prospective study of resilience and emotions following the terrorist


attacks on the United States on September 11th, 2001. Journal of personality and social

psychology, 84(2), 365.

Garnefski & Kraaij, V. (2006). Relationships between cognitive emotion regulation strategies and

depressive symptoms: A comparative study of five specific samples. Personality and

Individual differences, 40(8), 1659-1669.

Gross, J. J., & John, O, P. (2003). Individual differences in two emotion regulation processes:

Implications for affect, relationships, and well-being. Journal of Personality and Social

Psychology, 85, 348-362

Gross, J. J., & Thompson, R. A. (2007). Emotion regulation: Conceptual foundations. Handbook of

emotion regulation, 3, 24.

Hamilton, J. P., Furman, D. J., Chang, C., Thomason, M. E., Dennis, E., & Gotlib, I. H. (2011).

Default-mode and task-positive network activity in major depressive disorder: implications

for adaptive and maladaptive rumination. Biological psychiatry, 70(4), 327-333.

Hankin, B. L., Abramson, L. Y., Moffitt, T. E., Silva, P. A., McGee, R., & Angell, K. E. (1998).

Development of depression from preadolescence to young adulthood: emerging gender

differences in a 10-year longitudinal study. Journal of abnormal psychology, 107(1), 128.

Heppner, P.P., He, Y., Tsai, C.L., and Lin Y.J., 2008. Problem-solving Appraisal. Encyclopedia of

Counseling, 787-790.


HM Government/DH. (2011). No Health Without Mental Health: A Cross-Government Mental

Health Outcomes Strategy for People of All Ages. Retrieved April 11 2014 from

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/213761/

dh_124058.pdf

Holahan, C. J., Moos, R. H., Holahan, C. K., & Brennan, P. L. (1995). Social support, coping, and

depressive symptoms in a late-middle-aged sample of patients reporting cardiac illness.

Health Psychology, 14(2), 152.

Hughes, M. E., Smith, J. S., & Alloy, L. B. (2006, November). The Euphoric Rumination

Questionnaire: A measure of repetitive thinking in positive mood. Poster session presented at

the meeting of the Association for the Advancement of Behaviour Therapy, Chicago, IL.

Hughes, M. E. (2009). Responses to positive affect: An examination of positive rumination and

dampening. Retrieved April 22, 2013, from

http://digital.library.temple.edu/cdm/ref/collection/p245801coll10/id/16993.

.

Johnson, J., Gooding, P., & Tarrier, N. (2008). Suicide risk in schizophrenia: explanatory models

and clinical implications, the Schematic Appraisal Model of Suicide (SAMS). Psychology

and Psychotherapy: Theory, Research and Practice, 81, pp. 55–77

Johnson, J., Gooding, P. A., Wood, A. M., & Tarrier, N. (2010). Resilience as positive coping

appraisals: Testing the schematic appraisals model of suicide (SAMS). Behaviour Research

and Therapy, 48(3), 179-186.


Johnson, J., Wood, A.M., Gooding, P., Taylor, P.J., Tarrier, N. (2011). Resilience to suicidality:

The buffering hypothesis. Clinical Psychology Review 31(4), 563-591.

Joormann, J., & Quinn, M. E. (2014). Cognitive processes and emotion regulation in depression.

Depression and anxiety, 31(4), 308-315.

Jose, P. E., & Brown, I. (2008). When does the gender difference in rumination begin? Gender and

age differences in the use of rumination by adolescents. Journal of Youth and Adolescence,

37(2), 180-192.

Kobasa, S. C. (1979). Stressful life events, personality, and health: an inquiry into hardiness.

Journal of personality and social psychology, 37(1), 1.

Lyubomirsky, S., & Nolen-Hoeksema, S. (1995). Effects of self-focused rumination on negative

thinking and interpersonal problem solving. Journal of personality and social psychology,

69(1), 176.

Lyubomirsky, S., Tucker, K. L., Caldwell, N. D., & Berg, K. (1999). Why ruminators are poor

problem solvers: clues from the phenomenology of dysphoric rumination. Journal of

personality and social psychology, 77(5), 1041.

Luminet, O., Zech, E., Rimé, B., & Wagner, H. (2000). Predicting cognitive and social

consequences of emotional episodes: The contribution of emotional intensity, the five factor

model, and alexithymia. Journal of Research in Personality, 34(4), 471-497.


Manyena, S. B. (2006). The concept of resilience revisited. Disasters, 30(4), 434-450.

Masten, A. S. (2001). Ordinary magic: Resilience processes in development. American

psychologist

Masten, A. S., & Reed, M. G. J. (2002). Resilience in development. Handbook of positive

psychology, 74-88.

Min, J.A.,Yu, J. J., Lee, C. U., & Chae J.H. (2013).“Cognitive emotion regulation strategies

contributing to resilience in patients with depression and/or anxiety disorders,”

Comprehensive Psychiatry,vol. 54, no. 8, pp. 1190–1197.

Neiger, B. (1991). Resilient reintegration: Use of structural equations modeling (Doctoral

dissertation, Department of Health Education, University of Utah)

Nolen-Hoeksema, S. (1991). Responses to depression and their effects on the duration of

depressive episodes. Journal of abnormal psychology, 100(4), 569.

Nolen-Hoeksema, S., & Davis, C. G. (1999). " Thanks for sharing that": ruminators and their social

support networks. Journal of Personality and Social Psychology, 77(4), 801.

Nolen-Hoeksema, S., & Morrow, J. (1991). A prospective study of depression and posttraumatic

stress symptoms after a natural disaster: the 1989 Loma Prieta Earthquake. Journal of

personality and social psychology, 61(1), 115.


Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination.

Perspectives on psychological science, 3(5), 400-424.

Ong, A. D., Bergeman, C. S., Bisconti, T. L., & Wallace, K. A. (2006). Psychological resilience,

positive emotions, and successful adaptation to stress in later life. Journal of personality and

social psychology, 91(4), 730.

Ong, A. D., Zautra, A. J., & Reid, M. C. (2010). Psychological resilience predicts decreases in pain

catastrophizing through positive emotions. Psychology and aging, 25(3), 516.

Ozbay, F., Johnson, D. C., Dimoulas, E., Morgan III, C. A., Charney, D., & Southwick, S. (2007).

Social support and resilience to stress: from neurobiology to clinical practice. Psychiatry

(Edgmont), 4(5), 35.

Patel, V., & Goodman, A. (2007). Researching protective and promotive factors in mental health.

International journal of epidemiology, 36(4), 703-707.

Peng, S. S. (1994). Understanding resilient students: The use of national longitudinal databases.

Peng, L., Li, M., Zuo, X., Miao, Y., Chen, L., Yu, Y., ... & Wang, T. (2014). Application of the

Pennsylvania resilience training program on medical students. Personality and Individual

Differences.

Pinquart, M. (2009). Moderating effects of dispositional resilience on associations between hassles

and psychological distress. Journal of applied Developmental psychology, 30(1), 53-60.


Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general

population. Applied Psychological Measurement, 1, 385-401

Radloff, L. S., & Locke, B. Z. (1986). The community mental health assessment survey and the

CES-D scale. Community surveys of psychiatric disorders, 4, 177-88.

Ray, R. D., Ochsner, K. N., Cooper, J. C., Robertson, E. R., Gabrieli, J. D., & Gross, J. J. (2005).

Individual differences in trait rumination and the neural systems supporting cognitive

reappraisal. Cognitive, Affective, & Behavioral Neuroscience, 5(2), 156-168.

Rew, L., & Horner, S. D. (2003). Youth resilience framework for reducing health-risk behaviors in

adolescents. Journal of Pediatric Nursing, 18(6), 379-388.

Rime´, B., Philippot, P., Finkenauer, C., Legast, S., Moorkens, P., & Tornqvist, J. (1994).

Rumination and social sharing in current life emotion. Unpublished manuscript, University of

Louvain at Louvain-la-Neuve, Belgium.

Riso, L. P., Du Toit, P. L., Blandino, J. A., Penna, S., Dacey, S., Duin, J. S., ... & Ulmer, C. S.

(2003). Cognitive aspects of chronic depression. Journal of abnormal Psychology, 112(1), 72.

Rood, L. Roelofs, l., Bégels, S., Nolen—Hoeksema, S., 8: Schouten, E.(2009). The influence of

emotion—focused rumination and distraction on depressive symptoms in non—clinical

youth: A meta—analytic review. Clinical Psychology Review, 29, 607-16.


Roy, A., Carli, V., & Sarchiapone, M. (2011). Resilience mitigates the suicide risk associated with

childhood trauma. Journal of affective disorders, 133(3), 591-594.

Rutter, M. (1984). Resilient children. Psychology today, 18(3), 57-65.

Rutter, M. (1999). Resilience concepts and findings: implications for family therapy. Journal of

family therapy, 21(2), 119-144.

Schmitz, T. W., De Rosa, E., & Anderson, A. K. (2009). Opposing influences of affective state

valence on visual cortical encoding. The Journal of Neuroscience, 29(22), 7199-7207.

Seligman, M. E., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction (Vol. 55,

No. 1, p. 5). American Psychological Association.

Seligman, M. E., Steen, T. A., Park, N., & Peterson, C. (2005). Positive psychology progress:

empirical validation of interventions. American psychologist, 60(5), 410.

Sit. K.F.K (2013) The Frequency of Positive Thinking Scale. (Resilience and positive rumination).

Unpublished raw questionnaire.

Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: A review of the definition,

assessment, and conceptualization of this multifaceted construct. Clinical Psychology Review,

29(2), 116-128.


Sona Systems Ltd. (1997). Retrieved November 20, 2013, from http://manchester-psych.sona-

systems.com.

Tabachnick, B. Fidell (2007). Using multivariate statistics, 5.

Talarico, J. M., LaBar, K. S., & Rubin, D. C. (2004). Emotional intensity predicts autobiographical

memory experience. Memory & Cognition, 32(7), 1118-1132.

Tarrier, N. (2010). Broad minded affective coping (BMAC): A “positive” CBT approach to

facilitating positive emotions. International Journal of Cognitive Therapy, 3(1), 64-76.

Vaux, A., Riedel, S., & Stewart, D. (1987). Modes of social support: The social support behaviors

(SS-B) scale. American Journal of Community Psychology, 15, 209-237

Ystgaard, M., Arensman, E., Hawton, K., Madge, N., van Heeringen, K., Hewitt, A., ... & Fekete,

S. (2009). Deliberate self-harm in adolescents: comparison between those who receive help

following self-harm and those who do not. Journal of adolescence, 32(4), 875-891


Appendix A

The Frequency of Positive Thinking Scale (Sit, 2013)


Appendix B1

Appendix B2

re-submit 7711565 - does positive rumination predict resilience

Documents