Emotional intelligence and psychological resilience to negative life events

Abstract

This study investigated the relative importance of six emotional intelligence (EI) dimensions in the prediction of psychological resilience to multiple negative life events. The strength of relations between life events and distress varied markedly across three latent classes of participants, reflecting vulnerable, average and resilient profiles. Discriminant function analysis indicated that class membership varied as a function of four EI dimensions, with higher scores predicting membership to the resilient class. Across the 414 participants, Emotional Self-Awareness, Emotional Expression, Emotional Self-Control and particularly Emotional Self-Management appeared central to psychological resilience in the aftermath of multiple negative life events.

Full text

Emotional intelligence and psychological resilience to negative life events
Andrew R. Armstrong
a,
⇑
, Roslyn F. Galligan
b
, Christine R. Critchley
b
a
AP-HP, Saint-Louis Hospital, Dept. of Clinical Research (PRO Unit), Paris, France
b
Swinburne University of Technology, Faculty of Life and Social Sciences, Melbourne, Australia
article info
Article history:
Received 8 June 2010
Received in revised form 15 March 2011
Accepted 17 March 2011
Available online 6 May 2011
Keywords:
Emotional intelligence
Psychological resilience
Stress
Coping
Life events
abstract
This study investigated the relative importance of six emotional intelligence (EI) dimensions in the pre-
diction of psychological resilience to multiple negative life events. The strength of relations between life
events and distress varied markedly across three latent classes of participants, reflecting vulnerable, aver-
age and resilient profiles. Discriminant function analysis indicated that class membership varied as a
function of four EI dimensions, with higher scores predicting membership to the resilient class. Across
the 414 participants, Emotional Self-Awareness, Emotional Expression, Emotional Self-Control and par-
ticularly Emotional Self-Management appeared central to psychological resilience in the aftermath of
multiple negative life events.
Ó2011 Elsevier Ltd. All rights reserved.
1. Introduction
Major life events, including the death of loved-ones, serious ill-
ness, or job loss, precede almost all types of mood disorder (Stueve,
Dohrenwend, & Skodol, 1998). Emotional intelligence (EI), or the
ability to intelligently utilise emotional information, may temper
their impact on mental health (Ciarrochi, Forgas, & Mayer, 2001).
How EI might buffer the effect of aversive events is the focus of
the present study.
Stressful or negative life events have typically been construed
as change events that precipitate movement from one set of living
conditions to another. The life transitions resulting from such
events pose significant adaptational challenges that can strain peo-
ple’s ability to cope to the point of clinical distress, manifest for in-
stance in symptoms of depression, anxiety, and stress. Moreover,
the experience of multiple such events can compound distress
(Monroe & Simons, 1991). Indeed, one stressful event can impede
coping efficacy for additional events, increasing vulnerability to
and even the likelihood of further negative events (Kessler,
1997). As well, transitional recovery periods are typically quite
long. Research has shown that significant life events often retain
their impact over a two-year period (Monroe & Simons, 1991).
While such events are potentially traumatic, people are im-
pacted differently. Some people experience long-term trauma.
Others suffer significant short-term impairment. Then there are
those who experience only mild, transient perturbations. Such per-
sons are considered resilient (Bonanno, 2004).
Emotional intelligence may well be directly connected to resil-
ience, such that emotionally intelligent behaviour in stressful cir-
cumstances is adaptive. Salovey, Bedell, Detweiler, and Mayer
(1999) theorize that persons with higher EI cope better with the
emotional demands of stressful encounters because they are able
to ‘‘accurately perceive and appraise their emotions, know how
and when to express their feelings, and can effectively regulate
their mood states’’ (p. 161). EI is thus postulated to buffer the ef-
fects of aversive events through emotional self-awareness, expres-
sion and management.
Researchers investigating these and other health-related links
have frequently distinguished between ability-based EI models in
which EI is assessed via intelligence-like tests (e.g. the Mayer-Salo-
vey-Caruso Emotional Intelligence Test; Mayer, Salovey, & Caruso,
2000) and trait models in which EI is measured via self-reported
emotion-related dispositions, self-perceptions or motivations (e.g.
the Trait Emotional Intelligence Questionnaire; Petrides, Pita, &
Kokinnaki, 2007). While ability tests purport to measure ‘‘maximal
performance’’, trait-models measure ‘‘typical performance’’
(Petrides et al., 2007). In the current study we focus on typical per-
formance rather than episodes of peak EI performance in coping
with event-related distress. Moreover we take the view that emo-
tional intelligence is antecedent to resilience (Matthews, Zeidner,
& Roberts, 2002) rather than encompassing resilience (Bar-On,
1997), such that EI functions through its composite dimensions
to facilitate resilience.
The evidence linking self-reported EI to health is considerable. A
meta analysis of 80 studies involving 20,000 participants found the
0191-8869/$ - see front matter Ó2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.paid.2011.03.025
⇑
Corresponding author.
E-mail address: andrew74.psy@gmail.com (A.R. Armstrong).
Personality and Individual Differences 51 (2011) 331–336
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average strength of relationship between EI and mental health to
be around r= .36, r= .33 for psychosomatic health, and r= .27 for
physical health criteria (Martins, Ramalho, & Morin, 2010). Other
studies have shown that self-reported EI can moderate the psycho-
logical impact of experimentally induced-stress (Mikolajczak,
Petrides, Coumans, & Luminet, 2009), academic exam stress
(Mikolajczak, Luminet, & Mendil, 2006) and emotional labour in
the workplace (Mikolajczak, Mendil, & Luminet, 2007). Two studies
have also examined the buffering hypothesis in the context of mul-
tiple stressful life events (Ciarrochi, Deane, & Anderson, 2002; Day,
Therrien, & Carroll, 2005), but despite positive associations be-
tween EI and mental health, neither study found substantive
evidence of buffering.
In the current study, we examine the relative value of six EI
dimensions that constitute the Swinburne University Emotional
Intelligence Test (SUEIT; Palmer & Stough, 2002) in moderating
the ongoing psychological impact of multiple negative life events
that occurred in the preceding 2 years. The SUEIT was chosen for
its robust psychometric properties, well-defined scales, rigorous
testing history (Gignac, 2005, 2010) and alignment with Salovey
et al.’s (1999) theoretical postulate. The six EI dimensions concern:
(1) awareness of emotions in self, and (2) others, (3) emotional
expression, (4) emotional self-control, (5) emotional management
of self, and (6) others. Research in related fields highlights the po-
tential buffering effects of the six EI dimensions.
1.1. Emotional self-awareness
Research focused on alexithymia, a condition characterized by
poor emotional self-awareness, indicates that persons afflicted
typically fail to respond to rising stress levels until distress is
fully-blown (Martin & Pihl, 1986). They often experience more se-
vere symptoms and longer periods of recovery in the aftermath of
stressful events than more perceptive persons, who deploy per-
sonal coping resources earlier and more effectively (Naatanen,
Ryynanen, & Keltikangas-Jarvinen, 1999).
1.2. Emotional awareness of others
Perspective taking is an important tool in developing quality so-
cial relationships (Soenens, Duriez, Vansteenkiste, & Goossens,
2007), which are a well established source of psychological support
(Kessler, 1997). Propensity to anticipate and account for the feel-
ings of others may therefore play a role in facilitating greater
personal psychological resilience.
1.3. Emotional expression
Emotional expression through overt channels, such as voice and
musculature, has been found to result in attenuation of physiolog-
ical reactivity and associated psychological symptoms. On the
other hand, inhibition results in retention of physiological arousal
and psychological agitation, which over time manifests in physical
illness (Franz, Schaefer, & Schneider, 2003), and mental health
symptoms (Wastell, 2002).
1.4. Emotional self-control
Persons with poor emotional control are more likely to respond
to personal distress with anti-social behaviours (Roger & Najarian,
1989), driving supportive persons away (Benotsch, Christensen, &
McKelvey, 1997). Moreover, impulsive behaviour often translates
into unhealthy coping behaviours such as substance use (Salovey,
2001) Consequently, higher levels of distress are experienced when
faced with stressful situations (Roger & Najarian, 1989).
1.5. Emotional management of self
Persons able to self-induce positive moods are happier in both
positive and negative circumstances (Ciarrochi, Chan, & Caputi,
2000), and tend to be more physically and mentally healthy
(Extremera & Fernandez-Berrocal, 2002). They engage more fre-
quently in active coping behaviours such as problem-solving,
self-pep talks and physical exercise (Salovey, Stroud, Woolery, &
Epel, 2002).
1.6. Emotional management of others
Persons able to induce positive moods in others often have
greater access to social supports (Ciarrochi, Chan, & Bajgar,
2001). They are more willing to seek help when feeling over-
whelmed, and to benefit (Ciarrochi & Deane, 2001).
Evidently, there are a variety of ways in which EI can potentially
buffer individuals against life event distress. Why then did
Ciarrochi et al. (2002), and Day et al. (2005) fail to find substantive
support for this position? One possible answer stems from the fact
that moderator effects are notoriously difficult to detect in
observational field studies, using traditional moderated multiple
regression procedures. The measurement error typical of non-
experimental variables creates levels of noise that make reliable
effects hard to detect. Compounding this, measurement error is
exacerbated when independent variables are multiplied together
to form moderator variables (McClelland & Judd, 1993). This makes
moderator effects even harder to detect.
In light of this, we used a different approach to explore the
question of whether persons with higher EI scores are more
resilient to the effects of multiple events. We performed a series
of latent class regression analyses to determine whether the rela-
tionship between the frequency of negative events experienced
in the past two years and psychological distress was relatively
homogenous across all participants in the study, or, whether the
strength of this effect differed across participants to the extent that
latent classes of participants better represented the data. (i.e.
whether there were distinct clusters of participants who varied
according to their event-distress regression scores).
In line with previous research, it was expected that there would
be a latent class (i.e. cluster) of participants who would demon-
strate a significantly stronger association between life events and
distress (a vulnerable group). Conversely, it was expected that
there would be at least a second latent class that would demon-
strate a non-significant or weaker life events – distress relationship
(i.e., a resilient group). Moreover it was expected that EI would dis-
criminate between these two classes in that the vulnerable group
would have lower EI scores, whereas the resilient group would
have higher EI scores.
2. Method
2.1. Participants and procedure
Members from 56, life event focused, online discussion forums
(e.g. healingwell.com; widownet.org; joblayoffsupport.com) were
invited to complete an online survey. Of 1156 persons who an-
swered the first survey question, 414 (48.5%) completed the survey
and were of the age of adult consent. Participants were mostly wo-
men (76%), aged between 24 and 58 (M= 36.7, SD = 12.4), who had
completed a university degree (60%). Most were in paid employ-
ment (42% full-time, 17% part-time). A smaller number were full-
time students (19%) or performed home duties (10%). Citizens of
the USA comprised the largest proportion of participants (45%), fol-
332 A.R. Armstrong et al. / Personality and Individual Differences 51 (2011) 331–336

lowed by Australia (24%), the UK (15%) and Canada (9%). The
remainder were citizens of European, Asian or African countries.
2.2. Measures
2.2.1. Emotional intelligence
Emotional intelligence was measured using a revised 44-item
version of the SUEIT. This version was derived from extensive fac-
tor analytic investigation involving data from 1503 participants
(Gignac, 2005). It is the predecessor to the newly published
70-item Genos EI, factorially validated on some 4700 participants
(Gignac, 2010), with which it shares the same dimensional
structure and 44 items (Palmer, Stough, Harmer, & Gignac, 2009).
Participants responded to statements on a five-point Likert scale
from 1 = ‘almost never’ to 5 = ‘almost always’. Scores were calcu-
lated separately for six subscales: (1) Emotional Self-Awareness
and (2) Emotional Awareness of Others concern perceiving and
understanding one’s own and others’ emotions respectively; (3)
Emotional Expression concerns expressing one’s emotions effec-
tively; (4) Emotional Self-Control concerns controlling one’s strong
emotions; (5) Emotional Management of Self and (6) Emotional Man-
agement of Others concern managing one’s own and others’ emo-
tions respectively. A seventh and final subscale concerned with
‘Emotional Reasoning’ in decision-making, was not included in the
current study due to weak factorial validity, an issue common to
all such factors across EI inventories (Gignac, 2010).
2.2.2. Psychological distress
Distress was assessed using the short version of the Depression
Anxiety Stress Scale (DASS-21; Lovibond & Lovibond, 1995), which
contains 21 items distributed across depression, anxiety, and stress
subscales. Participants rate the extent to which they had experi-
enced each of the items over the past month on a 4-point sever-
ity/frequency scale ranging from 0 = ‘did not apply to me at all’
to 3 = ‘applied to me very much’. In the present study, the full scale
score was used.
2.2.3. Negative life events
Life events were sampled from a revised Social Readjustment
Rating Scale (SRRS; 43 items; Scully, Tosi, & Banning, 2000), the
original being devised by Holmes and Rahe (1967) and cited in
over 4000 publications (Hobson et al., 1998). As with the original,
events range from minor (e.g. change in dietary habits) to major
(e.g. death of a loved one). In the present study, events referring
to a change in circumstances were re-itemized to reflect improve-
ment or worsening of the situation (e.g. dietary habits worsened).
The number of events subsequently expanded to 59
1
. Participants
indicated whether they had experienced each event within the past
two years and whether the event continued to have a positive or
negative effect on their wellbeing. The frequency of events contin-
uing to have a negative effect was summed for a total out of 59 for
each participant.
3. Results
Table 1 presents descriptive statistics and correlations among
the variables. On average, the sample remained negatively affected
by five events that had occurred in the past 2 years, and reported
distress symptoms consistent with mild depression, anxiety and
stress. EI levels were comparable to those reported by Gignac
(2005). All six dimensions were positively interrelated, and most
were negatively related to life events and distress.
3.1. Latent class regression analysis: negative life events and distress
A series of latent class regression (LCR) analyses were per-
formed using Latent Gold (Version 4) to determine whether the ef-
fect of negative life events on distress was homogenous across all
participants, or whether the strength of this effect differed across
participants to the extent that latent classes of participants better
represented the data. Unlike traditional regression techniques,
which assume that a similar regression coefficient holds true for
all cases in a given sample, LCR detects and extracts distinct latent
classes of participants who share similar regression coefficients on
a set of predictor-outcome variables (Magidson & Vermunt, 2004).
As recommended by Vermunt and Magidson (2000), a 1-class
model was initially estimated using maximum likelihood (ML), fol-
lowed by additional models which successively incremented the
number of classes by one, until the simplest model with the small-
est Baysian Information Criterion (BIC) value was found. The de-
fault Latent Gold LCR setting of 10 random starts was retained
for each model.
As shown in Table 2, similar BIC values occurred for the 3 and
4-class models. However, classification error was notably larger for
the 4-class model, and parameters were fewer for the 3-class mod-
el. The 3-class model was thus considered better fitting and more
parsimonious. Eighty percent of variation in distress was explained
by negative life events in the 3-class model.
Latent class regression statistics for the 3-class model are
shown in Table 3. The unstandardized beta values indicate that
for each class, higher numbers of negative life events predicted
higher distress levels. However, Wald statistics for events indicated
that the strength of this relationship was significantly different be-
tween classes.
Regression lines depicting the relationship between accumu-
lated events and distress for each latent class are plotted in
Table 1
Intercorrelations among EI dimensions, distress and negative life events.
MSD1234567
1. Emotional self-awareness 7.56 1.42 (.74)
2. Emotional expression 15.87 3.97 0.44
c
(.82)
3. Emotional awareness of others 63.93 8.43 0.43
c
0.31
c
(.89)
4. Emotional self-control 11.87 3.00 0.24
c
0.30
c
0.32
c
(.77)
5. Emotional self-management 29.60 6.52 0.36
c
0.36
c
0.35
c
0.62
c
(.86)
6. Emotional management of others 20.27 3.56 0.38
c
0.39
c
0.58
c
0.31
c
0.42
c
(.74)
7. Distress 36.57 28.59 0.24
c
0.36
c
0.09 0.38
c
0.61
c
0.17
c
(.95)
8. Negative life events 4.83 3.34 0.10
a
0.16
b
0.04 0.13
a
0.36
c
0.08 0.46
c
N= 414.
Cronbach’s alphas are located on the diagonal in parentheses.
a
p< 0.05.
b
p< 0.01.
c
p< 0.001.
1
Expanded scale available from first author
A.R. Armstrong et al. / Personality and Individual Differences 51 (2011) 331–336 333

Fig. 1. Anchors for negative life events comprise the mean and one
standard deviation above and below. Anchors for distress comprise
percentile ranks and ranges sourced from Lovibond and Lovibond
(1995).Figure 1 illustrates a pattern of graduated life event distress
class profiles. Class 2 were most distressed by higher numbers of
life events, Class 3 were least distressed, while Class 1 fell in be-
tween. Classes 2, 1 and 3 were thus labelled Vulnerable, Average
and Resilient.
3.2. Discriminant function analysis: class membership and emotional
intelligence
A discriminant function analysis was subsequently modelled to
test whether membership to the three classes varied as a function
of the six EI variables. Summary statistics for the EI variables by
class are presented in Table 4.
The three classes were reliably distinguished by one discrimi-
nant function, which comprised four EI variables and explained
35% of the variation in class membership, Wilks = .65,
v
2
(12) = 174.27, p< .001. The function was very strongly correlated
with Emotional Management of Self, r= .90, p< .001, strongly cor-
related with Emotional Self-Control, r= .51, p< .001, and moder-
ately correlated with Emotional Expression, r= .44, p< .001, and
Emotional Self-Awareness, r= .39, p< .001. Centroids for the dis-
criminant function revealed that the Resilient class (.96) had signif-
icantly higher EI scores than both other classes, that the Vulnerable
class (.94) had the lowest EI scores, and that the Average class
(.04) fell almost precisely in between.
4. Discussion
The current study sought to identify which participants were
more and less successful at adapting to the emotional demands
of stressful events, and to identify the extent to which individual
differences in adaptation could be attributed to respective aspects
of emotional intelligence. The study found that the relationship be-
tween negative life events and distress varied as a function of four
intrapersonal EI dimensions. The life event-distress relationship
was weaker for participants with higher levels of Emotional Self-
Awareness, Emotional Expression, Emotional Self-Control and par-
ticularly, Emotional Self-Management.
4.1. The nature of relations between stressful life events and distress
Ninety-five percent of participants reported that one or more
stressful events continued to exert a negative influence on their
wellbeing up to 2 years later. On average, participants remained
negatively affected by around five events, while event distribution
data indicated that the number of such events typically ranged
from a low one or two, to a high eight events. A greater accumula-
tion of such events predicted heightened symptoms of psycholog-
ical distress in the preceding month.
Yet, while pervasive, the life event-distress relationship was not
uniform across the sample. Instead, three latent classes with dis-
tinct life event-distress profiles were identified. These classes were
subsequently labelled Vulnerable, Average and Resilient. For the
29% of participants classified into the Vulnerable class, the ongoing
negative affect of just one or two past events corresponded with
current distress levels within the moderate clinical symptom
range. Worse were those who continued to be affected by the sam-
ple average of five events who reported clinical symptoms border-
ing on severe. Those who remained affected by a high eight events
reported symptoms well within the severe clinical range.
For the 44% of participants in the Average class, the ongoing
negative affect of 1–2 events in the past 2 years corresponded with
normal present functioning. Five events corresponded with symp-
toms bordering on mild distress. Moderate distress symptoms
emerged for those who remained affected by a high eight events.
Finally, the 27% of participants that comprised the Resilient class
exhibited the weakest relationship between accumulated life
events and distress. Although their reported distress symptoms
were worse when event numbers were greater, their symptoms re-
mained well within the range of normal psychological functioning
even when event numbers were high.
4.2. Emotional intelligence and psychological resilience to life event
distress
EI was negatively associated with events and distress. Most
persons with higher EI scores reported that fewer stressful events
Table 2
Latent class regression model fit statistics: negative life events regressed on distress.
Model LL BIC AIC NP Class. Error R
2
1-Class 1926.53 3871.14 3859.06 3 0 .21
2-Class 1873.62 3789.43 3761.24 7 .16 .71
*
3-Class 1855.97 3778.23 3733.94 11 .24 .80
4-Class 1844.22 3778.84 3718.44 15 .29 .92
Note. N= 414
LL, log-likelihood; AIC, akaike information criterion; NP, number of parameters;
Class. Error, Classification error.
*
Minimum BIC value.
Table 3
Latent class regression statistics for the best fitting 3-class model for negative life
events regressed on distress.
3-Class Model Wald
statistics
Class 1
(n=184)
Class 2
(n=120)
Class 3
(n=110)
Between
class
R
2
.44 .26 .20 –
Distress intercept 15.05
b
(2.28) 37.40
b
(6.72) 6.01
b
(1.54) 24.40
b
Negative life
events B
2.78
b
(0.32) 4.59
b
(0.88) .90
a
(0.29) 26.18
b
Note.N= 414.
SE in parentheses.
a
p< 0.01.
b
p< 0.001.
Low
(1.49)
Average
(4.83)
High
(8.17)
Number of Negative Life Events
Class 1 (Average)
Class 2 (Vulnerable)
Class 3 (Resilient)
Distress
%ile
Range
60
95
87
78
98
Mild
Normal
Moderate
Severe
Extreme
Fig. 1. Distress regressed on negative life events by latent class.
334 A.R. Armstrong et al. / Personality and Individual Differences 51 (2011) 331–336

continued to distress them. Importantly, individual differences in
four EI dimensions were found to distinguish between the Vulner-
able, Average and Resilient latent classes: Emotional Self-Aware-
ness, Emotional Expression, Emotional Self-Control and
particularly, Emotional Self-Management.
The small benefit of Emotional Self-Awareness in predicting
resilience is consistent with alexithymia research (e.g. Naatanen
et al., 1999), in which afflicted persons typically fail to detect stress
or deploy coping mechanisms until a stressor exerts its full impact.
The moderate benefit of Emotional Expression accords with re-
search in which overt expression has been shown to provide a
stress release (e.g., Wastell, 2002). The moderate benefits of Emo-
tional Self-Control concur with research linking this construct to
impulse control in times of stress (Salovey, 2001). The considerable
benefit of Emotional Self-Management is consistent with research
linking emotion regulation to positive mood maintenance
(Ciarrochi et al., 2000) and active rather than passive coping behav-
iours in times of stress (Salovey et al., 2002). Overall, the findings
support Salovey et al.’s (1999) theory that emotional self-aware-
ness, expression and self-management buffer the effects of aver-
sive events, and suggest that emotional self-control plays a role
as well.
It is notable that the two interpersonal EI dimensions, Emo-
tional Awareness of Others and Emotional Management of Others,
did not discriminate between more and less resilient persons in the
presence of the four intrapersonal dimensions. The current findings
suggest that when coping with multiple life events, the benefits of
intrapersonal EI outweigh the benefits of interpersonal EI.
4.2.1. Methodological and future considerations
Ours is the first study to find empirical support for the value of
EI as a psychological buffer in the context of multiple life events,
and to illustrate the relative importance of four intrapersonal EI
dimensions. The findings emphasise the value of examining the
relationship between aggregated life events and psychological
symptoms using latent class regression rather than traditional
regression techniques. Otherwise, the resilience of the majority is
likely to be blurred with the vulnerability of a sizable minority.
Furthermore, the importance of distinguishing between the
respective contributions of EI dimensions in the prediction of psy-
chological resilience, rather than treating EI as unitary construct, is
emphasized.
There were several study limitations. A self-selected rather than
representative sample was used. EI and distress levels were self-re-
ported rather than clinician-rated. The EI, life event, distress asso-
ciations may be an artifact of self-report mono-method bias
whereby mood congruent or dispositional response patterns may
be responsible for observed relations. A single point in time,
cross-sectional design was employed, limiting causal argument.
These are issues that future research may wish to address. Similar
future research would also benefit by identifying and controlling
for pre-event symptoms, and investigating the longitudinal stabil-
ity of latent class membership. Extending such research to samples
undergoing extremely stressful life transitions such as learning to
live with cancer or HIV, and to those who vary more widely in EI
traits, would further clarify the buffering effects of EI.
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Summary statistics and structure coefficients for emotional intelligence in the 3-class model.
Vulnerable Class (n= 120) Average Class (n= 184) Resilient Class (n= 110)
M SD M SD M SD
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Emotional expression 14.20 4.08 16.07 3.74 17.35 3.56
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