Self-other report and ability EI 1
Running Head: SELF-OTHER REPORT AND ABILITY EI
Self-other Reports of Emotional Intelligence:
Using trait and ability measures to aggregate and moderate
Jessica Palladina
Masters of Science in the Psychology of Individual Differences
The University of Edinburgh
2008
Self-other report and ability EI 2
Abstract
Despite over a decade of recognition in popular psychology, the construct known as emotional
intelligence (EI) remains elusive in many aspects of empirical research. Self-other reporting as
well as multiple measurement methods were used to contribute towards the construct validity of
EI. Self- and other-ratings of EI were expected to positively correlate. In addition, this
correlation was expected to be moderated by the level of each partner’s EI. Four different
measures of ability EI were also expected to positively correlate with one another, and the
aggregation of these measures into one ability score was expected to enhance the correlation
between the performance-based and self-report measures. A sample of adults (N=83) completed
the self-report Trait Emotional Intelligence Questionnaire (TEIQue) as well as four measures of
ability EI: the Ekman-60 faces test (Faces), the Situational Test of Emotion Management (STEM),
the Situational Test of Emotional Understanding (STEU), and the Reading the Mind in the Eyes
test (Eyes). A subset of (M=28) couples also rated their partner on the TEIQue. Evidence was
found for strong associations between self- and partner-rated trait EI indicating construct validity
for the TEIQue. The four ability measures yielded strong interrelationships among them showing
convergent validity with one another. Levels of EI as well as multiple demographic variables
were found to moderate the ability of partners to rate one another, again supporting the construct
validity of EI. Lastly, even when all four ability measures were aggregated to form one score, no
correlations emerged between trait and ability EI measures, suggesting a need for further research
into the conceptualization of EI. Implications of these results for future work on trait and ability
EI are discussed.
Self-other report and ability EI 3
Acknowledgements
This work was made possible by the endless support, guidance and information provided by my
MSc dissertation supervisor Dr. Elizabeth Austin, without whom my inspiration to embark on the
bourgeoning field of EI may have never been sparked.
Statement
I have read and understood The University of Edinburgh guidelines on plagiarism and declare
that this dissertation is all my own work except where I indicate otherwise by proper use of
quotes and references.
Name: Jessica Palladina
Signed:
Date:
Self-other report and ability EI 4
Contents
Introduction
6
Trait EI
7
Ability EI
9
Bridging the gap
10
The question of measurement
11
Self-other rating methodology
16
Research questions and hypotheses
20
Method
20
Participants
20
Materials
20
Procedure
22
Results
22
Discussion
29
Limitations
37
Future directions
37
Conclusion
39
References
41
Appendix A: Hierarchical structure of ability EI as explained by the MSCEIT
48
Appendix B: Hierarchical structure of trait EI as explained by the TEIQue
49
Appendix C: Gender and age differences for the 15 facets of the TEIQue
50
Appendix D: Self and partner correlations for TEIQue facets
52
Self-other report and ability EI 5
Table of Tables
Table 1
Descriptive Statistics for All Participants
23
Table 2
Gender Differences on the TEIQue and its Four Higher Order Factors
24
Table 3
Correlations Amongst the Measures for All Participants
26
Table 4
Self and Partner Correlations for Global TEIQue and Factor Scores
27
Table 5
Correlations between Self- and Partner-Rated EI for High, Average,
and Low EI Judges
Table 6
Correlations between Self- and Partner-Rated EI for High, Average,
and Low EI Targets
Figure 1
28
28
Relationships between Status and Age Group with Self-Report TEIQue
Scores
24-25
Self-other report and ability EI 6
Self-other reports of emotional intelligence:
Using trait and ability measures to aggregate and moderate
The idea that there may be differences between people in their emotional skills has been
present in psychological research for several decades. As early as the 1920s, E.I. Thorndike
described something called social intelligence, as one of several intellectual abilities related to an
individual’s ability to understand and relate with other people. In more recent years, Gardner
(1993) also referred to multiple intelligences in which person-related intelligences are described
as referring to interpersonal and intrapersonal intelligence. This definition also includes the
ability to understand and relate to others, and in addition, refers to one’s self perception and how
it affects their life. Another precursor to what is now referred to as ‘emotional intelligence’ was
introduced by Sternberg and colleagues, termed practical intelligence (Sternberg & Grigorenko,
2000). Practical intelligence refers to a capability to deal with real-life problems that are not
related to abilities that can be assessed with measurements such as IQ tests, arguing that some
problem solving demanded by real-world events is not facilitated by academic type intelligences
alone. While the term ‘emotional intelligence’ (EI) was first introduced in 1990 by Salovey and
Mayer, the construct was launched into popular psychology by Goleman’s (1995) bestseller,
Emotional Intelligence: Why it can matter more than IQ, followed by the lead article in TIME
magazine entitled, The EQ Factor (Gibbs, 1995). With these developments, the interest of the
public as to the potential implications of this alleged construct prompted the emergence of a
wealth of popular and experimental psychology to assess its nature and validity. EI refers to the
capability to identify, express and understand emotions, to assimilate emotions in thought, and to
regulate the emotions of the self and others, whether positive or negative (Matthews, Zeidner, &
Roberts, 2002). The potential differences between people and their emotional skills may be able
to determine real life consequences, even more than general intelligence or features of ones
personality. For example EI has been claimed to be related to academic and occupational success,
emotional health and well-being, and adaptive coping (Elias, et al., 1997, Salovey, et al., 1999).
In Gibbs’ TIME magazine article he even went so far as to claim that “IQ gets you hired but EQ
gets you promoted” (Gibbs, 1995, p.59). Despite such practical effects of EI, it is still debated as
to how this construct should be measured, and indeed, whether it is a construct at all. While
intelligence researchers feel it should be measured as ability (e.g. Mayer, Caruso, & Salovey,
2000), personality researchers consider it a trait, measurable by self-report (e.g. Bar-On, 2000).
The present study utilizes both methods of measurement, in combination with self and other
reporting methodology. This study will aggregate ability and self-report results, and examine if
Self-other report and ability EI 7
self- and other-ratings moderate observed EI levels and validate self-report measures of EI. In
doing so, it is hoped to contribute to the growing body of empirical research contributing to the
construct validation of EI.
Trait EI
With the inception of a new psychological construct, its precise conceptualization is
essential before it can be measured as something reliable, relevant and valid. Following its
recognition in popular psychology, researchers began trying to operationally define EI and
identify a theoretical framework for the new construct. Petrides and Furnham (2001) were the
first to distinguish between ability and trait EI, such that ability EI is measured by maximum
performance tasks testing actual performance, and trait EI is measured by self-report
questionnaires. Ability EI therefore, is a cognitive aptitude which should be considered within
the same framework as psychometric intelligence. Trait EI, contrarily, refers to behaviours,
dispositions and one’s perception of their abilities, which should be considered within personality
frameworks. Based on these differences, Petrides and Furnham (2001) argue that while trait EI
should be related to personality measures, it should not be related to ability measures. Likewise,
ability EI should be related to intelligence, however it should also correlate with affectively
loaded personality traits (e.g. extraversion and neuroticism). The researchers assert that trait EI
includes dispositions within established personality dimensions, such as empathy, as well as
components of social and practical intelligence, and ability EI (Petrides, & Furnham, 2001).
Studies comparing trait and ability measures have yielded fairly small correlations (Matthews, et
al., 2006, Zeidner, et al., 2005), however while this modest relationship suggests that selfreported EI may not be accurate, trait EI by definition does not claim to be measuring an
intelligence through self-report (Austin, et al., 2007). As a result, it has become imperative that
when discussing EI, trait and ability measures are distinguished and properly conceptualized.
Trait EI refers to general emotion-related perceptions of the self and behavioural
dispositions that fit within established personality trait hierarchies and assess the affective
components of ones personality (Austin, et al., 2007). Trait EI theory derived its sampling
domain from content analysis of early EI models (e.g. Salovey and Mayer. 1990) by including
core elements that reappear amongst different models, and excluding the marginal elements that
are inconsistent (Petrides, Furnham, & Mavroveli, 2007). Such a method is consistent with other
scale development procedures in which core elements are used to create an internally consistent
score, meanwhile, random variance that affects the score is cancelled out. The operationalization
of trait EI as a construct measured by self-report and made up of emotion-related dispositions and
self-perceived abilities seems intuitively linked with the subjectivity of emotional experience,
Self-other report and ability EI 8
unlike veridically scored ability tests. Petrides, Furnham and Frederickson (2004) argue that trait
EI must not be identified as a traditional intelligence, and that early work in the field mistook the
trait of EI to be a cognitive ability which led to the criticisms faced by ability EI. Trait EI
however, also faces challenges. Accusations of the jangle fallacy, whereby a new construct is
simply a renamed variable that already exists, are often suggested with regard to trait EI’s strong
correlations with existing personality traits (Matthews, et al., 2002). Findings have indicated that
trait EI is in fact a distinct construct that exists at the lower levels of personality hierarchies
(Furnham & Petrides, 2003, Petrides, et al., 2004). This distinct construct must retain
significance even after the effects of established trait taxonomies are partialled out of the equation;
providing incremental validity. Past research has found that trait EI is able to account for
variance over and above such hierarchies (Austin, et al., 2007, Furnham & Petrides, 2003).
While findings of incremental and predictive validity are important, they are not sufficient to
create a theoretical framework with the breadth necessary to indisputably conceptualize a single
EI construct.
Another important concern that has been studied in previous work is the criterion validity
of trait EI. As trait EI is conceptualized as a collection of emotion-related perceptions and
dispositions, it should therefore have an effect on a variety of contextually different variables. A
study by Petrides, Perez-Gonzalez, and Furnham (2007) found that trait EI was a reliable
predictor of rumination (high EI individuals are less likely to ruminate), life satisfaction
(positively correlated with high EI), and coping (high EI related to adaptive coping styles), and
most of the relationships were incrementally valid over and above the Big Five personality
dimensions (Petrides, et al., 2007). In a similar study, it was hypothesized that EI would be
distinct, yet correlated with the Big Five; positively associated with life satisfaction, negatively
related to feelings of powerlessness, and positively related to employees’ job performance (Law,
Wong, & Song, 2004). Their results indicated that the EI dimensions as measured by a selfreport scale were distinct from the Big Five. Neuroticism and conscientiousness were moderately
correlated with some of the EI facets, however still conceptually different (Law, et al., 2004).
The researchers also used the EI ratings of parents to predict student’s self-ratings of life
satisfaction and feelings of powerlessness. The parent ratings indicated criterion and discriminant
validity after the student’s self-ratings were controlled for the Big Five. Similarly, after
controlling for the Big Five, employees who were also rated by their peers and supervisors
indicated that EI levels accounted for more than 10% of the variance in job performance ratings
(Law, et al., 2004). In light of the challenges faced by trait EI, findings such as these suggest the
Self-other report and ability EI 9
utility of applied EI research, such as the development of workplace EI training programs. In turn,
further research lending to the construct validation of EI is thus warranted.
Ability EI
The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, et al., 2003),
is currently the most prominent measure of ability EI. This test was created in accord with other
intelligence measures using a hierarchical model (see Appendix A). Generally, the MSCEIT
conceptualizes ability EI as a construct that exists within both cognitive and emotional systems,
composed of four main branches. Emotional perception is the branch that deals with the
perception and encoding of emotional information. Emotion facilitation refers to the ability to
process emotional information to improve cognitive problem solving. Thirdly, emotional
understanding refers to the cognitive processing of emotion (somewhat the obverse of facilitation).
Lastly, emotion management refers to the ability to regulate emotion in the self and in others
(Mayer, Salovey, & Caruso, 2000). While the MSCEIT has been found to be relatively reliable,
the proposed factor structure has met with mixed findings indicating problems with the way this
measure conceptualizes EI. The MSCEIT operates under the assumption that the concepts,
assessment methods, developmental course, and interrelationships with other variables and EI
should be similar to that of other abilities. Mayer and Mitchell (1998) explain that an ability
system has capacities both to input and process information, and as well, the ability to understand
symbols instantaneously, as well as reference previous knowledge. In order for EI to be
conceptualized within an intelligence framework, it must be validated as a system with these
capacities, and it must fit into a hierarchical model, as does general intelligence (Austin, et al.,
2007). Ability EI as measured by the MSCEIT has been found to moderately correlate with
general intelligence measures; the stronger correlations are with crystallized rather than with fluid
intelligence (Austin, et al., 2007, Ciarrochi, et al., 2000, Roberts, at al., 2001, Roberts, et al.,
2006). Fluid intelligence refers to each person’s processing capacity, as limited biologically,
whereas crystallized intelligence refers to abilities resulting from things such as learning,
acculturation, and socialization. The subjective and contextual nature of emotional experience,
intuitively, seems to be more of a social than inherent ability, lending some support to the
MSCEIT’s conceptualization of ability EI.
Modest correlations between the MSCEIT and intelligence measures are promising but
not sufficient to deem ability EI a psychometric intelligence. Other studies of ability EI use
highly specific tasks in order to establish where EI may exist in a cognitive framework. Austin,
(2005) used inspection time (IT) tasks which are commonly used in intelligence research to test
performance on emotionally-laden tasks. These tasks uncovered an emotion-processing factor
Self-other report and ability EI 10
that was correlated but distinct from general information-processing speed as seen with word and
symbol ITs (Austin, 2005). This correlation suggests that such tasks can be related to emotion
perception, providing a means to relate EI to psychometric intelligence. When looking at
psychometric intelligence, a better performance is associated with the ability to process
information more quickly. With the emotionally-laden IT tasks, if individuals are quicker at
making emotional judgments, they would be higher in EI and score higher on tasks which
measure the speed of emotion processing (Austin, 2005). Findings such as these conceptualize EI
within intelligence frameworks and suggest that individual differences in EI may be related to
emotion processing abilities.
Validation studies have also been conducted using ability EI frameworks, suggesting that
in order for ability EI to meet intelligence standards it must be based on conceptual, correlational
and developmental criterion (Law, et al., 2004, Mayer, Caruso, & Salovey, 1999). In other words,
EI must reflect an ability, not a preferred behaviour, it should correlate with other intelligence
factors, but not too high or too lowly, and lastly it should increase with age (Law, et al., 2004).
However, even though these three criteria can be argued as met, in order to establish construct
validity, ability EI must be shown as at least partially independent from other concepts (Davies,
Stankov, & Roberts, 1998). Ability EI meets some, but far from all standards to deem it a
psychometric intelligence. Further research is needed using ability measures other than the
MSCEIT in order to flesh out the preliminary findings relating EI components to psychometric
intelligence.
Bridging the gap
There is a common debate in psychological research whereby broad and specific notions
are regarded as being opposed to one another. Cronbach (1956) suggested that psychologists tend
to either split concepts into components, or aggregate several concepts into one broad construct.
Studies in individual differences attempt to blend these two tendencies by creating hierarchies
such as those seen in personality and intelligence research. As EI lies somewhere between these
two camps, validation must consider both sides of the debate. All of the trait components likely
do not indicate a single broad construct, as overinclusive definitions of EI would suggest,
however developing new measures must be assessed with the possibility that they share some
core commonalities. Bar-On (2000), famous for his EQ-i self-report measure of EI claims that
the construct is a multifactorial collection of interrelated social, personal and emotional
capabilities that affect the overall ability to effectively cope with everyday demands and stressors.
This conceptualization suggests that the personality traits relevant to EI have aspects of ability
Self-other report and ability EI 11
within them, but as of yet, differential psychology cannot access this relationship with personality
and intelligence separated into two domains.
Based on the aforementioned research, this study will attempt to contribute to the
construct validity of EI by examining correlations between performance tasks and self-reported
levels of EI. As the construct develops further different links and commonalities are expected to
emerge, and although trait EI, by definition, exists independently of cognitive ability, research
such as the present study could bridge the gap between individual differences in personality and
intelligence. A study assessing the correlations between self-report EI, psychometric intelligence,
emotional and non-emotional intelligence, indicated that both emotional and non-emotional
processing speeds affected performance on emotion-related tasks (Austin, 2005). Schutte’s EI
questionnaire, which assessed the appraisal of the emotions of others, was significantly correlated
with performance on emotional processing speed tasks (Austin, 2005). This study was therefore
successful in indicating that there is indeed a potential relationship between self-report and
performance measures. Similarly, Petrides and Furnham (2003) sought to examine whether there
was a link between self-perceived abilities to recognize, process and use emotion-related
information and their actual ability to identify emotions displayed in facial expressions. They
argued that self-perceived abilities need not necessarily be accurate to have an influence on
behaviour, however finding out the extent to which self-perceptions are accurate, could assist in
understanding how valid the construct is itself (Petrides & Furnham, 2003). These researchers
found that those scoring high on global trait EI performed better on the emotion recognition task
then their low EI counterparts; this finding supported the validity of the trait EI sampling domain
(Petrides & Furnham, 2003). By using trait and ability EI measures, these findings were able to
link high trait EI with an increased ability to interpret the facial expressions of others.
Based on the promising findings described above, the present study will attempt to
contribute towards linking task performance and self-report measures of EI in order to validate
the construct currently being conceptualized as two separate and imperfect entities, ability and
trait EI.
The question of measurement
Evidently, in order to contribute to the validation of EI as a construct, the means by
which it is measured are crucial. To operationally define intelligence, for example, measurement
often precedes understanding, Matthews, Zeidner, and Roberts (2002) argue that to reliably
measure a human quality, defining that quality’s relationship to other variables is an integral part
of the process. Arguably one of the main obstacles in EI’s recognition as a construct is a result of
all of its measurement issues. In the attempt to conceptualize EI, a basic idea of EI as a set of
Self-other report and ability EI 12
related emotion information processing methods suggests that multiple processes may be
included in this conceptualization, and therefore multiple measurement methods. For example,
the process of appraising and expressing one’s own emotions has been measured by the Toronto
Alexithymia Scale (Bagby, Parker, & Taylor, 1994), the Trait Meta-Mood Scale (Salovey, et al.,
1995), and the Affective Communication Test (Friedman, et al., 1980), all of which attempt to
asses whether or not individuals are aware of their mood. Similarly, assessing the appraisal and
recognition of the emotions of others is integral to EI, specifically via measures of empathy, a
trait which is included in several existing scales (Mills & Hogan, 1978, John & Srivastava, 1999).
Measures of EI must also however include assessments of regulating mood, and using emotion to
facilitate performance. So, while a variety of self-report measures, traditional intelligence tests,
measures of social intelligence and personality can all be used to flesh out the component parts of
EI, precision is needed to decipher which measures will provide the most reliable and valid
conceptualization of the construct. Indeed, to prove its worth, an EI test must satisfy validity
criteria. For one, it must possess content validity, meaning the qualities that are covered by a
predefined sampling domain should all be assessed by the test. The usefulness or predictive
validity of the construct must also be accessed by the test, if practical outcomes cannot be
predicted by the test, than its worth is questionable. Reliability is also integral in deeming a test
meaningful. For EI to be a defensible construct in individual differences research, people must be
reliably different across EI’s dimensions. A final necessity that an ideal EI test must satisfy, is
the process of construct validation, which will be the main focus of this study. Construct
validation is a long and complex process, whereby extensive empirical research must be
conducted in order to ensure that tests are actually measuring a theoretical construct (Matthews,
et al., 2002). In order for this complex process to be a success, a variety of evidence from
multiple sources must accumulate; this study attempts to act as another source on the path to EI’s
construct validation.
A wealth of self-report scales have been developed, some better than others in attempts to
access the elusive sampling domain of EI (Schutte, et al, 1998, Bailie & Ekermans, 2006, Petrides
& Furnham, 2000a, Bar-On, 2000). Schutte and colleagues (1998) were among the first to
attempt a self-report measure for EI. They were attempting to create a model that could reliably
measure an individuals’ current level of emotional intelligence. To do so they drew a pool of 62
items from Mayer and Salovey’s (1990) initial model, and attempted to draw out a factor
structure from their findings (Schutte, et al., 1998). However, later researchers found that Schutte,
et al.’s (1998) suggested factor structure was forced by statistical manipulations, which lead
Petrides and Furnham (2000a), to create their own self-report scale, the Trait Emotional
Self-other report and ability EI 13
Intelligence Questionnaire (TEIQue). The TEIQue comprehensively addresses the components
of personality that are emotion-laden using a particular factor structure and way of interpreting
variance based on trait EI theory as derived from the content analysis of early EI models (see
Appendix B). Operationalizing EI in this manner, whereby all affect-related personality traits are
gathered under one umbrella provides several advantages. In theory, it enhances explanatory
power such that variance can be accounted for by the single trait EI framework rather than a
complex combination of Big Five factors, for example. In a practical sense, a construct that
umbrellas all affect-related personality traits is advantageous in achieving predictive validity over
affect-related outcomes, over and above that which established trait taxonomies are capable of
(Mikolajczak, et al., 2007). Furthermore, in their validation study of the TEIQue, Mikolajczak
and colleagues (2007) indicate that operationalizing EI via self-report methodology is consistent
both with the subjectivity of emotion itself, and the concept that EI is made up of a set of typical
dispositions, rather than a maximal performance, elucidated through ability measures. Another
important finding justifying usage of the TEIQue is its cross-cultural stability. A highly similar
factor structure has been found in French, English, Spanish, New Zealand and Greek cultures
(Mikolajczak, et al., 2007). While cultures may have varied in how they expressed a given trait,
cultural differences did not affect the structure of traits across individuals. Thus, it can be argued
that the TEIQue is effective at depicting the structure of affect-related traits. Mikolajczak and
colleagues (2007) go on to point out that the TEIQue is the first EI test that has been able to
demonstrate this kind of cross-cultural stability. As a result of this validation, the present study
will utilize the TEIQue and also attempt to further its validity through self-other methodology.
As aforementioned, the MSCEIT is the most popular ability measure of EI. It aims to
measure perceiving, using, understanding and managing emotions as the four main branches of EI,
and is based on the theory that EI requires the ability to problem solve with and about emotions
(Mayer, et al., 2003). One of the main issues with the MSCEIT is its use of consensus and expert
scoring. The consensus scoring method compares each participant’s answers against the
proportion of the sample that answered in the same way, leading to questions of generalizability
and validity. One argument is that consensus scoring will lead to problems in separating the very
high scorers on EI, from the average. The contention is that if a very difficult emotional situation
is presented, only a small number of ‘emotional geniuses’ will correctly answer, and therefore the
answer derived by consensus will be incorrect (Matthews, et al., 2002). The authors refuted such
criticisms of consensus scoring by arguing that within their model of EI, emotional knowledge
exists within a general social context of interaction, and therefore, correct answers can in fact be
ascertained by consensus responses (Mayer, et al., 2003). Consensus scoring can also be
Self-other report and ability EI 14
defended by an accepted truism in sociology – while felt emotions cannot be deemed right or
wrong, there can be correct or incorrect perceptions of emotions (Matthews, et al., 2002). As
such, the usefulness of EI will be reflected by the perception of emotion by the majority of
individuals. A related scoring method, expert scoring, works similarly, except it uses responses
from experts in the field, but again, the question of what makes someone an expert on emotions
leads to concerns as to the validity of this method. While the scoring methods are problematic,
the theory behind the MSCEIT is useful to determine branches of EI that can be measured by
ability performance. The test has been found reliable as each of its branches manages to access
different tasks using different item types, and the test-retest reliability of the MSCEIT has been
reported as high as .86 (Mayer, et al., 2003). By varying response formats across tasks, results
can be generalized while reducing measurement error. It is also argued that the MSCEIT is able
to access the multidimensional nature of EI (Mayer, et al., 2003).
While the MSCEIT is presently the leading broad bandwidth ability EI measure, it faces
several criticisms, and settling for the MSCEIT risks confounding problems that may exist in the
theoretical framework of the test. The conceptualization of EI as presented by the MSCEIT is
useful, however in order to examine convergent validity issues, this study sought to use a range of
ability measures that assess specific components of EI and are gaining in usage, and establishing
reliability and validity. Baron-Cohen and colleagues (1999) used a Theory of Mind test entitled
the “Reading the Mind in the Eyes” test to assess normal participants and patients with autism or
Asperger’s Syndrome, who possess deficits in social intelligence. In this task, participants are
presented with photos of the eye region of the face and are required to choose from four emotion
words, which is most likely to be the one expressed in the photo. This idea continued from an
earlier conception that there may be a “language of the face” that is able to express mental states
(Numenmaa, 1964). The forced choice methodology used to interpret the facial region in terms
of mental states, has been successful in previous studies using the test (Baron-Cohen, et al., 1996,
Baron-Cohen, et al., 1997). The correct answers are based on testing of a gender equivalent panel
of judges, who were independent raters, unaware of the hypothesis of the study (Baron-Cohen, et
al, 1997). In its use within autism research, the researchers used fMRI to determine which brain
regions were being used for this task of social intelligence. Findings indicated that the normal
functioning social brain areas were not activated in autism patients (Baron-Cohen, et al., 1999).
Originally developed as a Theory of Mind test, this study demonstrates that social intelligence is
dissociable from general intelligence, and that valid and reliable individual differences are
observable via this methodology.
Self-other report and ability EI 15
The empirical link between facial expressions and emotions has been an area of interest
for over a decade within psychological research. While early studies suggested that the face was
not sufficient to decipher emotional information from, Ekman (1993) conducted multiple studies
of nonverbal behaviour, body movements and facial expressions. He found cross-cultural
evidence (ranging from Western and Eastern literate cultures to New Guinea tribes) that
individuals can select emotion terms to fit facial expressions universally (Ekman, 1993). With
these and other similar results, there was an increasing interest in the study of emotion and the
need to develop multiple measures to increase the validity and reliability of this concept of
individual differences in emotional responses. As Ekman continued his research, he proposed the
idea of a family of emotions, such that a variety of related expressions share core properties that
can be distinguished from other core properties (Ekman, 1993). For example, there are a family
of expressions that suggest fear, but these faces are all configured differently than the family of
expressions that suggest anger. Ekman (1993) explains that the variation within a given family of
emotions result from how intense the emotion is that is being felt, and the contextual features that
have caused the emotion to occur. Based on these findings, Ekman and colleagues developed a
series of tests using images of facial expressions to interpret individual differences in emotion
perception (Young, et al., 2002).
While the faces and eyes tests are able to assess one’s ability to perceive emotions, two
other ability tests will be utilized in this study to address emotional understanding and
management. The Situational Test of Emotion Management (STEM; MacCann, 2006) is a
multiple choice task whereby each item describes a situation in which emotion management is
required, the participant then must choose which provided option would be the most effective
choice for the person described in each situation. Similarly, the Situational Test of Emotional
Understanding (STEU; MacCann, 2006), is also a multiple choice task which presents
participants with situations in which they must choose which emotion would likely be felt from a
list of given options. Both the STEM and the STEU have been found to be positively and
significantly correlated with performance on tests of emotion perception, such as the Eyes task
(Austin, submitted). Furthermore, the STEM and the STEU were found to be correlated with one
another, as well as with MSCEIT components other than the Perceiving branch (Austin, 2005).
These findings support convergent validity for the STEM and the STEU. In addition, both the
Eyes and the Faces tests have been found to significantly correlate with the Understanding branch
of the MSCEIT, and the Eyes test also correlated with the Perceiving branch of the MSCEIT
(Austin, submitted). Austin (submitted) also performed a factor analysis and found that the
Self-other report and ability EI 16
Understanding branch of the MSCEIT and the STEU load on the same factor. This suggests that
understanding emotions is related to the emotion task performance used.
Two prominent issues with using the MSCEIT as the sole measure of ability EI are the
problems with the test itself (i.e. aspects of the theoretical model by Mayer, et al. (2000) are
arguable), and the lack of transparency in scoring, as the scoring key is not made available to
researchers. As a result of these and other aforementioned risks associated with using the
MSCEIT, the present study will use the Faces, Eyes, STEM and STEU tests as ability EI
measures. Previous work verifies that the STEM and the STEU have convergent validity with the
MSCEIT, and adequate reliability that is equivalent to the internal reliability of the corresponding
MSCEIT branches (Austin, submitted). In addition, the correlations found between the Eyes test
and the other ability EI measures, suggests that this test could be a promising addition to current
EI research. Matthews, Zeidner, and Roberts (2002) provide guidelines for achieving success
when trying to establish a new test. First, distinct human qualities must be identified and
conceptualized in such a way that their potential to combine as a meaningful construct that can be
scientifically studied is realized. Second, this combination must not be a result of an artifact such
as measurement error or response bias. Third, the combination of qualities must create something
entirely new to the science. Lastly, the construct emerging from the test should have predictive
validity, i.e. it should relate to other variables or be relevant in real world applications. Each of
the measures utilized in this study, and any study, should be considered for their ability to address
these guidelines for a ‘good test’.
Self-other rating methodology
In addition to multiple ability measures being used, this study will also use multiple trait
EI measures in the form of self- and other-reports. Among the first to utilize the self- and otherrating methodology were Kenrick and Stringfield (1980) in an attempt to combine idiographic
and nomothetic measurement. In their study, each participant chose what they considered their
most consistent personality trait, stated how observable they considered it to be, and parents and
peers were then asked the same question. Results yielded high correlations between self, parent,
and peer ratings, especially on highly observable and consistent characteristics (Kenrick &
Stringfield, 1980). Pervin (1976) had found that participants tended to consider themselves to be
consistent across situations on certain characteristics; indeed Kenrick and Stringfield’s (1980)
results indicate that correlations were strengthened by participants choosing their most consistent
traits. It is therefore suggested that such characteristics can be confirmed by others who know the
participant well. Before this development, studies using expert ratings of personality had yielded
poor inter-rater reliability, but through these and related findings, the intuitive ability of
Self-other report and ability EI 17
acquaintances to make judgements of behaviour were verified for effective use in psychological
research. In 1975, Lancaster argued that perhaps it is an adaptive and intelligent process for
humans to be able to make intuitive judgements of others as a result of their social nature. In this
sense then, assessing EI with self-other ratings seems logical since EI is so relevant to social
interactions.
Self- and other-rating methods have been extensively used within personality research.
In fact, one of the most robust arguments for the validity of the well-known five-factor model of
trait personality has been its self and other agreement. As early as Norman (1963) and Goldberg
(1980), trait factor structures have been reported in both self- and peer-reports. Costa and
McCrae, creators of the five-factor model, and their colleagues have used this method as proof of
the validity of their model (McCrae & Costa, 1987; McCrae, 1982; Piedmont, et al., 2000;
McCrae, 1993; McCrae, et al., 1998; Piedmont, 1994). Correlations between self- and otherratings have ranged as high as .4 to .6, across all five dimensions (McCrae & Costa, 1987), and
this method avoids the artifacts of social desirability, acquiescence, shared stereotypes, or
extreme responding, as these common self-report issues, are not found to be common between
self- and peer-ratings (McCrae, 1982). In addition to simply validating the use of self-report data,
peer-ratings can be informative in and of themselves as indicators of how people are perceived by
others. Peer-ratings can be aggregated with self-reports, adding precision to trait personality
variables, and they can also elucidate areas of disagreement, warranting further research
(Piedmont, et al., 2000). Despite the extensive and successful use of self- and other-ratings in
personality, EI research has only included informant reports tangentially, and not usually with full
item sets (Petrides, Furnham, & Martine, 2004; Petrides, Furnham, & Frederickson, 2006;
Mavroveli, et al., 2007; Lopes, et al., 2005). There are several benefits to using informant reports,
and despite some concerns, they are just as easy to run as are self-reports (Vazire, 2006).
Informant reports are a helpful addition to self-reports because while self-reports indicate one’s
identity, informant reports are able to indicate one’s reputation, or how they are perceived. In
addition, reports from others can be aggregated with self-reports and other measures to produce a
more reliable assessment.
The acquaintanceship effect is an important consideration when using self- and otherreports. In other words, when choosing the type of relationship desired between the participants,
it is important to decide what you want the study to assess. Letzrig, Wells, and Funder (2006)
compared information quality with information quantity to determine which factor provides a
greater likelihood of self and other agreement in responses. Realistic accuracy refers to a
hypothetical construct that indicates the amount of agreement between an informant’s personality
Self-other report and ability EI 18
rating, and the target’s actual personality. This can be addressed on the basis of multiple methods
of measurement and combined to create a reliable accuracy criterion for a given target (Letzrig, et
al., 2006). With regard to self and other agreement, a self-report can be taken as actual reality,
and the informant rating can then be used to denote the degree to which informant ratings are
related to self-reports (Letzrig, et al., 2006). Previous studies have found in between-subjects
designs, quantity of information has a positive linear relationship with self and other agreement,
regardless of acquaintanceship. In within-subjects designs however, where participants are
compared with multiple informants of different acquaintance levels, as acquaintance levels
increase, consensus remains constant, while self-other agreement increases (Letzrig, et al., 2006).
Based on these findings the current study uses couples who have been in their relationship for at
least one year to ensure that there exists a common baseline of acquaintanceship between
participants. Furthermore, the relationships will be more reliably similar in quality than would
friendships for example, even of equal durations.
In addition to using self- and other-reporting, this study will also use a multi-method
approach using both trait and ability measures of EI so that the two measures can be aggregated
to optimize validity (Campbell & Fiske, 1959; Sjoberg, 2001). Early research that used self- and
other-reports found varied results, with some self-other correlations being as low as .20 (Jackson,
1967; Shrauger, & Schoeneman, 1979). The use of aggregator variables was introduced by
Cheek (1982) who wished to increase self-other correlation beyond .30 by paying close attention
to methodology. Cheek (1982) asserted that peer-ratings were only valid and reliable if the test
items were valid and reliable; the participants were well-acquainted; and they were in a noncompetitive environment. Results yielded a mean self-other correlation of .44 suggesting that .30
was likely a floor, rather than a ceiling effect of validity (Cheek, 1982). In a similar vein,
previous research has used multiple raters to aggregate responses with the targets’ self-report.
This type of research is useful in occupational psychology to initiate and assess human resource
and organizational change (Law, et al., 2004). For example, reports of an individual’s behaviour
can be compared with their own self-perceptions, to encourage self-awareness and behavioural
change. A study by Church (2000) assessed if higher performing managers would receive better
behaviour ratings from their coworkers, and if higher performing managers were more self-aware
of their own behaviour in the workplace. Results indicated that managers who were higher
performers were also rated higher by their peers, and were also more aware of their own specific
strengths and weaknesses, as compared to lower performing managers (Church, 2000). These
findings suggest that the use of multiple raters and methods, when based on meaningful criteria,
have the potential to enhance the validity of a construct.
Self-other report and ability EI 19
Previous research has tried to account for discrepancies between self- and other-ratings
by looking for systematic ways of increasing validity coefficients. Perhaps some people are more
consistent in their characteristics, or their expression of said characteristics, or perhaps some
people are simply easier to rate than others. Mills and Hogan (1978) found that high scorers on
Hogan’s Empathy Scale yielded smaller self-other discrepancies than did low scorers. From this
it can be inferred that more empathetic people are better at role-taking and have better social
communication skills. If in fact, social skills lead to better communication of one’s self, than
individuals with stronger social skills should also yield stronger correlations in self and other
responses. This study will therefore assess whether individuals higher in EI will yield higher selfother correlations, and whether this relationship is a result of their ability to better rate others, or
as a result of their tendency to be easier to accurately rate. Wymer and Penner (1985), sought to
understand this concept in looking at how moderators would affect types of predictability.
Previous research showed that moderator variables could improve self- and other-rating
correlations of traits with the best correlations being found among individuals with good social
communication skills (Cheek, 1982). Researchers posited that with self-other rating, validity
relies on the ability to communicate one’s self image to another person’s perspective. Wymer
and Penner (1985) hypothesized that people with high social skills would yield higher self-other
congruence, as these individuals would be able to see themselves as others see them. Their
findings indicated that those people who had strong self and other correlations tended to have a
better knowledge of their own traits and were better at communicating their traits to others
(Wymer & Penner, 1985). Overall, the validity of self- and other-reporting can be optimized if
the correlational relationship is moderated by individuals who know themselves well, express
their traits clearly, and thus can be known well by others. Lopes, et al. (2005) sought to associate
emotion regulation abilities with self- and other-reports of interpersonal sensitivity and prosocial
tendencies, the proportion of positive to negative peer nominations and reciprocal friendship
nominations. Integral to EI is the ability to regulate emotions by adjusting emotions to achieve
desired affect, which in turn has a large effect on social relationships. This effect is based on the
ability of an emotional tone to permeate a social interaction. Individuals who were better able to
regulate their emotions, self-reported as being more interpersonally sensitive and prosocial,
likewise, their peers also rated them more favourably (Lopes, et al., 2005). Based on the
relevance of EI in social interactions and its commonalities with personality, which has
successfully been measured with self- and other-rating methodologies, this study seeks to
compare self and other EI ratings of romantic partners to contribute to the construct’s validation.
Self-other report and ability EI 20
Research questions and hypotheses
Finding correlations in trait and ability EI measures, and congruence in self- and partnerratings would provide further support for the construct validity of EI. The present study
hypothesizes that:
1. Self and other EI ratings will be positively and significantly correlated.
2. The four ability EI measures will be positively and significantly correlated.
3. The correlations between self-reported EI and partner-rated EI will be moderated by the
self- and partner-ratings (i.e. higher EI makes a better judge and makes a target easier to
rate).
4. Self-report and ability measures will act as aggregator variables to optimize the validity
of accuracy ratings.
Method
Participants
The participants were N=83 adults (32 males, 51 females), including a subset of M=28
heterosexual couples recruited via University websites which advertise for research participation
and part-time jobs. The participants’ ages ranged from 18-55+, with 37.3% of participants
between the ages of 18 and 24, 22.9% between 25 and 34, 3.6% between 35 and 44, 13.3%
between 45 and 54, and the remaining 22.9% of participants were over the age of 55.
Participation in the couples subset, was conditional upon having been in their current
relationship for at least a year. The mean length of relationship was 15.41 years, standard
deviation 17.13 years. 10.7% of the participants were dating, 30.4% were dating and living with
their partner, 12.5% were engaged, and 46.4% of the participants were married.
Materials
Trait Emotional Intelligence Questionnaire (TEIQue; Petrides & Furnham, 2003). The
TEIQue is made up of 153 items which use a seven-point Likert scale ranging from ‘disagree
completely’ to ‘agree completely’. The TEIQue items were created to comprehensively cover the
EI sampling domain. Previous studies have found full-scale internal consistency to be as high
as .86 (Petrides & Furnham, 2003). The full test measures 15 distinct facets: adaptability,
assertiveness, emotion perception, emotion expression, emotion management, emotion regulation,
impulsiveness, relationships, self-esteem, self-motivation, social awareness, stress management,
trait empathy, trait happiness, and trait optimism. These facets can be grouped into 4 higher order
factors; Well-being, Self-control, Emotionality and Sociability, creating a hierarchical structure of
trait EI (see Appendix B).
Self-other report and ability EI 21
Situational Test of Emotional Understanding (STEU; MacCann, 2006). The items that
make up the STEU are based on Roseman’s (2001) appraisal-based emotion model. In this model,
felt emotions are believed to stem from one’s appraisal of the emotion-provoking situation. For
example surprise is felt because a situation occurred which was not expected. The STEU is made
up of 42 multiple choice questions, each with 5 options of which only one is correct. Veridical
scoring is possible for the STEU as the scoring method is based on emotions theory. For example
if a participant chooses surprise when asked what a person would feel if they found results that
were unexpected, their item response would be scored correct. Within the 42 items, 14 emotions
are addressed whereby each emotion is covered in 3 items: surprise, hope, fear, joy, relief, liking,
pride, fear, sadness, distress, frustration, dislike, anger, contempt, and regret. The STEU has
previously been found to have reasonable internal and test-retest reliability, and has been found to
be correlated with intelligence test scores and with branches of the MSCEIT (MacCann, 2006;
Austin, submitted).
Situational Test of Emotion Management (STEM; MacCann, 2006). The STEM was
based on the situational judgment test method (McDaniel, et al., 2001). Each of the 44 multiple
choice items describe a situation which demands the managing of emotions. Out of 4 options, the
participant must choose which action is the best for the person in the situation to take. The
original generation of test items was done by a group of undergraduates and volunteers; a second
sample then generated the response options. Expert scoring methods were then used with ratings
from EI researchers, counsellors, life coaches and postgraduate clinical psychology students
(MacCann, 2006). As with the STEU, internal and test-retest internal reliabilities for the STEM
have been found reasonable in previous research, and correlations between STEM scores and
branches of the MSCEIT have also been found (MacCann, 2006; Austin, submitted).
Reading the Mind in the Eyes (Eyes; Baron-Cohen, et al., 2001). This test presents 36
photographs of the eye region of people’s faces to the participant one by one. Each image is
accompanied by four emotions words from which the participant must choose which the person in
the photo is most likely thinking or feeling. There are an equal number of male and female faces
used. The Eyes task was created from a selection of magazine photos, four judges then generated
the target words (i.e. the correct answers). To increase validity, the photos were then shown to a
second panel of eight judges who were blind to the fact that a ‘right’ or ‘wrong’ answer had been
decided upon; 100% agreement was found between the two panels of judges (Baron-Cohen, et al.,
1997).
Ekman-60 Faces (Faces; Ekman & Friesen, 1976). This test presents faces to the
participant one by one on the computer screen and the participant must choose if the facial
Self-other report and ability EI 22
expression is one of anger, sadness or fear. The first six faces presented are ‘pure’ expressions of
the three emotions, one male and one female of each. The following 54 stimuli are a blend of two
pictures of the same person expressing different emotions, each emotion (ie. anger, sadness and
fear) are presented with either 10%, 20%, 30%, 40%, 60%, 70%, 80%, or 90% in proportion to
another one of the emotions. All eight blends were used for the three emotion pairings. The
blends were taken from the Ekman images of facial affect (Ekman, & Friesen, 1976) and were
taken from the stimulus set from the Facial Expressions of Emotion – Stimuli and Tests (FEEST;
Young, et al., 2002). The correct answer for each item is the majority emotion shown in each
image.
Procedure
Participants followed the link in an invitation sent via email to get to the web survey.
Each test was completed via the website; if in a relationship, the participant was prompted to
decide on a keyword in order for researchers to link the couples’ responses. The chosen keyword
was then included in an email template that they were prompted to send to their partner. Upon
receipt of the email, the partner followed the link provided and were immediately asked to enter
their keyword, so to avoid them forgetting. Both partners completed the exact same
questionnaires and all data was submitted anonymously.
Results
Descriptive statistics for all participants are shown in Table 1 as mean scores per item.
The internal reliabilities of the ability tests were STEM, α=.54, STEU, α=.67, Faces, α=.42, and
Eyes, α=.66. Global TEIQue self-report scores yielded an internal reliability of, α=.96.
Reliabilities of the 15 subscales range from, α=.51 to .92, and the internal reliabilities of the four
higher order factors were: Well-being, α=.83; Self-control, α=.77; Emotionality, α=.83, and;
Sociability, α=.75. Internal reliabilities for partner ratings on the TEIQue were very similar, with
the global score yielding, α=.96 as well, the 15 subscales ranging from, α=.59 to .87, and the
higher order factors yielding: Well-being, α=.86; Self-control, α=.79; Emotionality, α=.85, and;
Sociability, α=.77.
Self-other report and ability EI 23
Table 1
Descriptive Statistics for All Participants
Scale
Mean
Standard deviation
TEIQue (self)
4.85
.59
Well-being factor
5.35
.88
Self-control factor
4.53
.84
Emotionality factor
5.16
.82
Sociability factor
4.63
.68
Faces
.91
.04
STEM
.75
.02
STEU
.67
.11
Eyes
.71
.02
Examination of gender differences showed that there were no significant differences
between scores on the global TEIQue, however three of the four factors were significantly
different between the sexes (see Table 2). Results of the Eyes, Faces, and STEU tests were not
significantly different between males and females. There were however, significant gender
differences on the STEM, F(82)=6.35, p=.01 with females scoring higher than males. The
sample was composed of a wide age range, and for analyses was divided into age group bands of
18-24, 24-34, 34-44, 45-54, and 55+. Based on the wide age range of the sample, potential age
differences were examined yielding no significant differences between age groups on any of the
tests or higher order TEIQue factors. Each of the 15 facets of the TEIQue was also examined for
gender and age differences. Significant gender differences emerged for the emotion regulation,
stress management, emotion perception, relationship skills, empathy, and emotion management
facets. Significant age differences emerged for the optimism, emotion regulation and adaptability
facets (see Appendix C).
Self-other report and ability EI 24
Table 2
Gender Differences on the TEIQue and its Four Higher Order Factors
M
F
p
male
female
Global TEIQue
4.85
4.86
.01
.93
Well-being factor
5.29
5.38
.21
.65
Self-control factor
4.82
4.35
6.68
.01
Emotionality factor
4.86
5.35
7.61
.01
Sociability factor
4.85
4.49
5.74
.02
In anticipation of forthcoming couples analyses, differences in relationship status were
examined. There were no significant differences between relationship status groups on any of the
ability measures; however TEIQue scores did differ between status groups, F(82)=2.86, p=0.29.
Tukey’s HSD post-hoc test indicated that the TEIQue scores differed significantly between
people who were dating and those who were married, with a mean difference of -.62, p=.01.
Interestingly, a similar age effect was still not observed in the subset of couples (see Figure 1).
Figure 1
Relationships Between Relationship Status and Age Group with Self-report TEIQue Scores
Self-report TEIQue score
5.00
4.75
4.50
Dating
Dating and living
together
Status
Engaged
Married
Self-other report and ability EI 25
Self-report TEIQue score
5.40
5.20
5.00
4.80
18-24
25-34
35-44
45-54
55+
Age group
Correlations between all of the measures are shown in Table 3. It can be seen that there
were no significant correlations between the TEIQue and any of the four ability measures. In an
attempt to find a potential relationship, the four ability measures were transformed into z-scores
to equate their means and standard deviations so that they could be aggregated. With these ztransformed scores a mean ability score was created from the average of the Faces, STEM, STEU
and Eyes scores. This aggregate ability EI z-score was then assessed based on its potential
correlational relationship with self-reported measures, which were also transformed into z-scores;
still, no significant correlation was found.
All of the ability measures however, correlated with at least one of the others. The
correlation between Faces and STEU scores were positive and significant, r=.34, p=.03; as was
that between STEU and Eyes scores, r=.46, p=.000; STEU and STEM scores, r=.49, p=.000; and
Eyes and STEM scores, r=.29, p=.01. In order to flesh out the relationships between the ability
measures, each measure was entered as the outcome variable in a regression model, with the other
three ability measures entered as predictors. 56% of the variance in Faces scores was found to be
accounted for by the three other ability measures, though there were no significant individual
relationships. 24% of the variance in STEM scores was accounted for by the other ability
measures, with STEU scores emerging as the best predictor, t=3.95, p=.000. 37% of the variance
in STEU scores can be accounted for by the other ability measures, with STEM and Eyes scores
Self-other report and ability EI 26
emerging as the strongest predictors, t=3.95, p=.000; t=3.62, p=.001, respectively. Lastly, 22%
of the variance in Eyes scores was accounted for by the other ability measures, with STEU scores
being the best predictor, t=3.62, p=.001.
Table 3
Correlations Amongst the Measures for All Participants
TEIQue
Faces
STEM
STEU
TEIQue
-
Faces
.07
-
STEM
.11
.14
-
STEU
-.04
.23*
.49**
-
Eyes
.09
.12
.29**
.46**
*. Correlation is significant at the 0.05 level
**. Correlation is significant at the 0.01 level
In an attempt to draw out a potential relationship between trait and ability measures, selfand partner-ratings from the subset of couples were transformed into z-scores and aggregated to
see if this aggregate measure would correlate with ability measures. The aggregate trait EI zscore was created by the mean of one’s self-report and the estimate made by their partner. Ability
EI z-scores were aggregated in the same manner as described above for the subset of couples only.
The correlation between the aggregate trait and aggregate ability EI scores still yielded no
significance. The aggregate trait EI score was also assessed in relation to each of the ability
measures individually. Of the four measures, the Eyes test was the only to yield a significant
correlation with the aggregate trait EI score, r=.27, p=.05
Multiple correlational analyses were conducted to examine the strength and nature of the
self and partner agreement on trait EI as measured by the TEIQue. As seen in Table 4,
correlations between the self-reported global trait EI levels made by partners were found to be
marginally significant, r=.35, p=.07. Perhaps individuals with similar EI levels are attracted to
one another. Self-reports are seen to significantly correlate with partner-reported estimates, and
one’s self-report also significantly correlated with their rating of their partner. Lastly, the
estimates made by each partner correlated with one another. In other words, one’s partner sees
them similar to the way they see themselves, and as well, they estimate one another similarly.
Table 4 also includes the above mentioned relationships for each of the higher order factors of the
TEIQue. It can be seen that the majority of the self and partner scores correlated relatively
equally as strongly across the factors. Appendix D gives these correlational relationships for each
of the 15 facets of the TEIQue and indicates that the trends remain for each facet, though the
Self-other report and ability EI 27
strength and consistency of the correlations decrease as the TEIQue score is extrapolated into
more subcomponents.
Table 4
Self and Partner Correlations for Global TEIQue and Factor Scores
Partner 1 (female)
Female Self-report
Female Rating of Male
Partner
Partner 2 (male)
Male Self-report
Male Rating of Female
Partner
Female Rating of Male
Partner
Global
.35
Global
.59**
Well-Being
-.00
Well-Being
.57**
Self-Control
.31
Self-Control
.69**
Emotionality
.22
Emotionality
.49**
Sociability
-.13
Sociability
.69**
Global
.66**
Global
.52**
Well-Being
.66**
Well-Being
.07
Self-Control
.72**
Self-Control
.26
Emotionality
.56**
Emotionality
.51**
Sociability
.57**
Sociability
-.18
Global
.67**
Well-Being
.31
Self-Control
.41*
Emotionality
.59**
Sociability
-.40
*. Correlation is significant at the 0.05 level
**. Correlation is significant at the 0.01 level
To examine whether EI levels influence one’s ability to rate other people, participants in
the subset of couples were divided into low (N=15), average (N=29), and high (N=12) EI groups.
These distinctions were based on the normally distributed mean self-report TEIQue score
(M=4.95, s.d.=0.57). The small sample sizes used in the following analyses did not justify doing
detailed analyses on all of the subcomponents of the TEIQue, and so only global scores were used.
Each group was assessed as to how well their rating of their partner correlated with their partners’
self-reports, to assess if high EI made people ‘good judges’. Both the low, average and high EI
Self-other report and ability EI 28
group yielded positive and significant correlations between self- and partner-rated trait EI (see
Table 5). Notably, it was the high EI group that yielded the strongest correlation. In a similar
analysis, the idea that individuals with high EI may be easier to rate was examined by again
dividing the subset of couples into the three groups. Each group was assessed as to how easy it
was for their partner to rate them (i.e. making them ‘good targets’), by correlating the target’s
self-reported EI with that estimated by their partner. Both the low, average and high EI groups
yielded insignificant results (see Table 6). Notably here, the high EI group was marginally
significant with a moderate correlation between estimated and self-reported TEIQue scores.
Table 5
Correlations Between Self- and Partner-Rated EI for High, Average and Low EI Judges
Low EI Judge
Self-partner correlation
Average EI Judge
High EI Judge
r
p
r
p
r
p
.66
.01
.47
.01
.84
.00
Table 6
Correlations Between Self- and Partner-Rated EI for High, Average and Low EI Targets
Low EI Target
Self-partner correlation
Average EI Target
High EI Target
r
p
r
p
r
p
.39
.15
.14
.47
.51
.08
Individual differences were also examined as to how they may influence one’s ability to
rate or be rated on EI. Estimates of partner EI were compared with partner’s self-reported EI
separately for male and female raters. Female estimates of their partner’s EI significantly and
positively correlated with their partner’s self-reported EI, r=.59, p=.001. Similarly, male ratings
of their partner’s EI, also positively and significantly correlated with their partner’s self-report,
r=.66, p=.000. Male’s estimates appear to correlate slightly higher with females self-reports than
do female estimates with males self-reports. This was further examined using a moderated
regression analysis whereby partner’s estimated EI, gender, and the interaction of these two
variables were assessed based on their ability to predict self-reported EI. The regression model
indicated that partner’s estimates and gender were able to account for 38% of the difference in
self-reported EI, F=10.90, p=.000. In agreement with the above mentioned correlations, while
partner’s estimate alone was a significant predictor of self-reported EI, t=3.85, p=.000; neither
gender nor the interaction between partner estimates and gender were significant predictors of
self-reported EI.
Self-other report and ability EI 29
Age differences were also considered, in one’s ability to rate and be rated. As a result of
the smaller sample size, some of the age group bands were merged for this analysis. In the 18-24
age group (N=26), self-report and partner estimates only nearly correlated, r=.35, p=.08. In the
25-34 age group, 35-44 age group, and 45-54 age group (N=13), the correlations between selfreport and partner’s estimates were all insignificant. Lastly, in the 55+ age group (N=17), selfreports and partner estimates correlated positively and significantly, r=.81, p=.000. Apparently,
older people are better raters of one another’s EI levels. Relationship status was also considered
as an influence on rating and rateability differences. Couples who were dating (N=6), dating and
living together (N=17), or engaged (N=7), yielded insignificant correlations between self-report
and partner’s estimates. It was only the married group (N=26) that yielded significant and
positive correlations between self-report and partner estimated EI, r=.79, p=.000, suggesting that
married people are superior at rating one another. Lastly, the length of relationship was
considered in a similar analysis. Couples were grouped by their relationship length with N=12
participants in 1year relationships, N=20 in the 2-5 years group, N=8 in the 6-20 years group, and
N=16 having been in a relationship for 30 or more years. It was only the final group whose selfreport and partner estimates correlated positively and significantly, r=.79, p=.000. The other
groups yielded no significant correlations, indicating that the longer people have been together,
the better they are at rating one another.
Discussion
The present study’s primary goal was to provide further support for the construct validity
of EI. Achieving construct validation was attempted in a variety of ways, each of which will be
discussed in terms of the four primary hypotheses. In addition, related information derived from
the various analyses will be discussed with regard to its potential role in the burgeoning field of
EI research. Preliminary support is lent to the validity of the TEIQue as a measure of trait EI
based on its excellent internal reliabilities across all factors and global scores. Similarly, all of
the ability measures used also achieved satisfactory internal reliabilities, lending to their validity.
Some interesting individual differences based on demographic distinctions emerged prior
to the in depth analyses conducted for the specific hypotheses. First, while no significant gender
differences emerged on global TEIQue scores, three of the four higher order factors did differ
based on gender. Namely, males were higher in Self-control and Sociability, and females were
higher in Emotionality. Previous findings indicated these same gender differences, however
unlike the present study, males have also been found to score higher on global trait EI than
females (Mikolajczak, et al., 2007; Petrides & Furnham, 2000b). Perhaps if a larger sample was
obtained the present study would have replicated the gender difference on global trait EI. The
Self-other report and ability EI 30
replication of factor level gender differences however is indicative of the reliability of the
TEIQue. The gender differences retained their significance at the facet level as well, indicated
through males scoring significantly higher than females on emotion regulation and stress
management (facets included in the Self-control factor) and emotion management (included in the
Sociability factor); and females scoring significantly higher than males on emotion perception,
relationship skills, and empathy (three of the four facets composing the Emotionality factor). The
finding that females scored higher on Emotionality is consistent with the dominant Western view
that females are more overtly emotional than are males, and indeed that males are often
discouraged to express their emotions too fervently (Brody, 2000). Similarly, the finding that
males scored higher on Self-control than females is also coherent with Western social norms
which suggest that men should be stronger (emotionally) and more logical than females (Brody,
2000). Contrarily, the male superiority on the Sociability factor does not appear consistent with
dominant Western views of women being more socially skilled than men. Here however, the
facets that make up the Sociability factor are important to consider. Facets related to social skills
such as empathy and relationship skills perceived as dominantly feminine qualities, fall under the
Emotionality factor, which females do indeed score higher on. The Sociability factor, refers to
such skills as networking, influencing the feelings of others, and being assertive; traits that are
indeed commonly attributed to males. Again, the inconsistent findings between the present and
previous studies on gender differences on global trait EI scores warrants further research to
examine whether differences are because of test bias engrained in the content of the TEIQue, or
actual gender differences.
Of the four ability measures, the STEM was the only test on which females scored
significantly better than males. Eyes, Faces and STEU scores did not differ between males and
females. Potential differences between the STEM and the other ability measures were considered
as to why a gender difference may have emerged. The emotional scenarios that make up each
item of the STEM were derived from a collection of interviews about times when the people had
felt some sort of emotion. The strategies to deal with these emotions were then collected from a
different set of people and judged by experts based on how they adhered with coping strategies
that past empirical research had deemed effective means of dealing with similar emotional
scenarios. The ‘correct’ answers on the STEM were therefore deemed as so based on effective
means of coping. While problem-focused coping is often considered the more effective strategy
to alleviating stress (e.g. Folkman, & Lazarus, 1988), both problem-focused and emotion-focused
coping strategies are well represented in the answer key of the STEM. Specifically, on many of
the items the correct answer has to do with seeking social support; nearly as many items whose
Self-other report and ability EI 31
answer deals with active problem solving. It is also important to note that unlike other tests of
coping, all of the stressors within scenarios on the STEM are emotion-laden. Coping research
indicates that women are more likely than men to utilize emotion-focused coping strategies, and
that women turn to social support as a coping strategy far more than do men (Eaton & Bradley,
2008; Day & Livingstone, 2003). In a study of occupational stressors, females were found to use
more emotion-focused coping strategies in an interpersonal work-overload context than did men
(Krajewski, & Goffin, 2005). If then, females are more inclined to utilize emotion-focused
coping strategies such as social support, perhaps the STEM, with its basis in coping research and
focus on emotion-laden stressors, is more geared toward a female perspective of emotion
management.
Previous work has not been extensive in its consideration of gender differences on ability
EI measures. A study by Brackett and colleagues (2006) utilized the MSCEIT to assess how EI
relates to social functioning. Findings indicated that while females scored higher on the MSCEIT
than males, the MSCEIT was predictive of social competence in males, but not in females. The
authors argued that perhaps since women are generally higher in EI, the gender differences they
observed were a result of a threshold effect whereby the proportion of men that fell below the
minimum level of EI required to function in social situations is higher than the proportion of
women (Brackett, et al., 2006). If this is the case, than women (as a group) may be able to attain
this threshold and thus differences in female MSCEIT scores would fail to explain variance in
social competence. Contrarily, the authors also posit that the MSCEIT may not be accessing EI
the same way in males and females; perhaps the MSCEIT is better at assessing the emotional
abilities of males and not adequately covering the emotional abilities of females (Brackett, et al.,
2006). If the MSCEIT is biased towards male EI, it would therefore make sense that it would be
better at predicting outcomes for males. The lack of gender differences on the ability EI
measures used in the present study (with the exception of the STEM) suggest that perhaps these
tests have been able to avoid the issues faced by the MSCEIT in the study by Brackett and
colleagues (2006). However, more research into gender differences on ability EI measures are
needed to flesh out the potential issues faced by the MSCEIT to verify that they can indeed be
avoided by the tests used in the present study.
The sample examined ranged a great deal in age (from 18-55+) and so potential
differences based on age were considered. Previous findings found global trait EI to weakly
correlate with age, with only the Self-control factor significantly correlating with age
(Mikolajczak, et al., 2007). Similarly, there were no significant age differences on the ability
measures used in the present study, nor the global TEIQue score or any of the higher order
Self-other report and ability EI 32
TEIQue factors. The only age differences that emerged were within the facet level of the TEIQue,
with the 35-44 age group scoring significantly higher than the 25-34 group in optimism.
Similarly, the 25-34 age group scored much lower in emotion regulation than did the 35-44 group.
Lastly, the 25-34 age group scored much lower than the 45-54 group on adaptability. In
agreement with previous research age differences do not appear to be prevalent in EI and the facet
level differences can perhaps be attributed to the uneven and relatively small number of
participants in each age band.
Relationship status was also considered in its potential to effect differences in EI levels
across the sample. There were no significant differences between relationship status groups on
any of the four ability measures, however global TEIQue scores did differ between status groups
suggesting a role for interpersonal maturity in the enhancement of EI. Specifically, married
people had much higher EI scores than people who were dating, however as mentioned above
(and seen in Figure 1), older people did not necessarily have higher EI than younger people.
Indeed, marriage status was represented across all of the age groups, and so, marriage must not be
equated with a developmental trajectory of EI. Matthews and colleagues (2002) argued that even
though previous researchers indicated that a developmental increase in EI is a necessary criterion
for viewing EI within an intelligence framework (Law, et al., 2004, Mayer, Caruso, & Salovey,
1999), other intelligence research has indicated that while some abilities (like fluid intelligence)
decline with age, others increase (like crystallized intelligence). Defining a specific
developmental trajectory as part of an intelligence-related concept is therefore debateable. As
such, discovering how EI develops is an important consideration, and if factors of EI do reliably
change with age, testing will have to be adjusted accordingly and implications in ageing research
are pertinent.
Hypothesis 1 was that self- and partner-rated EI would positively and significantly
correlate. A variety of potential relationships between self- and partner-reports on the TEIQue
were considered. First, the self-report of each partner were correlated with one another yielding a
marginally significant moderate correlation of .35. To some extent it would appear that
individuals with similar EI levels tend to be in relationships together. In line with the basic
hypothesis, self-reported EI significantly correlated with partner-rated EI. This indicates that
similar to the reliability and validity of self-other methodology demonstrated in a wealth of
personality research, this method also has the potential to play an important role in EI research.
Interestingly, self-reported EI also significantly correlated with what one estimates their partner’s
EI to be. In other words, people tend to consider their partner to have a similar level of EI as their
own. The estimates that partners make of one another, also significantly correlate. This finding
Self-other report and ability EI 33
is interesting because although self-reports of partners are only marginally significant in their
correlation, the estimates that each partner makes of the other are highly correlated. If selfreports are meant to be an accurate depiction then it would appear that partners do consider
themselves to be relatively different from one another, however when the estimates made about
one another are correlated among partners, EI ratings have a tendency to be more similar. This
suggests a potential commonality among respondents when people are rating the EI levels of
others. The ramifications of this potential will be discussed in more detail shortly.
In addition to looking at correlations between self- and partner-ratings on global TEIQue
scores, each of the correlational relationships were considered for the higher order factors and
each of the 15 facets. Not surprisingly based on the only marginal significance of the correlation
between self-reports, there were no significant correlations between self-reports on any of the
sub-factors or facets. Also as would be expected, the relationships between male self-reports and
female ratings of males were significant for all of the higher order factors and 12 of the 15 facets.
Similarly, for the female self-report and male rating of female partners, all of the higher order
factors were highly significant with 10 of the 15 facets retaining significant correlations. This
indicates that to a large extent partners tend to agree on the specific strengths and weaknesses
present in the EI of one another. The facets that correlated were largely the same when males
were self-reporting as when the females were self-reporting, with a few exceptions. When males
self-reported, emotion perception, emotion management, and adaptability yielded correlations
with female ratings of their male partner, whereas these facets did not significantly correlate
when females were self-reporting and males were rating. In other words, both partners agree on
the male’s ability to perceive their own and the emotions of others, to influence the feelings of
others, and on their flexibility to adapt to new conditions; however the agreement is not observed
with the same abilities in females. Also, when females were self-reporting and males were rating,
empathy scores significantly correlated; this relationship did not emerge when males were selfreporting. So, while males and females agree on the female’s ability to take someone else’s
perspective, they do not necessarily agree on the male’s ability to do so. Though beyond the
scope of the present study, it would be of interest to assess where the above mentioned
disagreements stem from; i.e. do males consistently perceive themselves to be higher on certain
facets than do females, or is the inverse relationship true? Previous research in IQ has found that
males tend to have a self-enhancing bias and females a self-derogatory bias when it comes to selfreporting (Petrides & Furnham, 2000b). If these biases also exist in EI self-reporting than some
disagreement may be accounted for by the gender-specific biases.
Self-other report and ability EI 34
When female ratings of male partners and male ratings of female partners were correlated,
in addition to a significant correlation on global scores, only Emotionality factor scores were
significantly correlated. Interestingly, though insignificant, Sociability indicated a negative
relationship between partner ratings. The only significant correlations at the facet level were in
emotion perception, emotion expression, and empathy all of which are included in the
Emotionality factor. It appears that people have a general tendency to rate others in a similar
fashion. Specifically when it comes to facets of Emotionality, as one partner estimates the other’s
higher levels of a given facet, so to does the other partner. Though insignificant in this sample,
the Sociability factor and two of its three facets (emotion management and assertiveness) indicate
negative relationships. In other words, on traits referring to the ability to influence the feelings of
others and the tendency to be forthright, as one partner rates the other highly, the second partner
estimates their partner to have low levels of these qualities. As was alluded to above, the
potential ramifications of a common tendency in ratings require more in depth consideration. As
the present study utilized heterosexual couples for self-other analyses, gender has the potential to
play a role in these findings as the correlational relationships are unavoidably males compared
with females. For instance, aforementioned gender stereotypes or social norms may play a role in
how individuals make their estimates of their partner simply based on preconceived notions of the
opposite sex rather than their specific partner. Therefore, if self-enhancing and self-derogating
biases hold merit, these may play a role in finding commonalities among partner-ratings of EI as
the enhancement and derogation come to a common plateau in rater reports.
Lastly, when female self-reports were correlated with female ratings of male partners,
global, Self-control and Emotionality factor scores were all significant; Sociability again
demonstrated an insignificant but negative relationship. At the facet level, self-esteem, happiness,
emotion regulation, emotion perception, emotion expression, relationship skills, emotion
management, and adaptability all yielded significant correlations between female self-reports and
female ratings of their partner. Females appear to regard themselves similarly as they do their
partners across a range of facets, perhaps relating to their attraction to their partner in the first
place. However, as observed in the correlational relationships between male and female
estimated partner scores, female self-reports of Sociability indicated a trend towards a negative
relationship with female ratings of their partner’s Sociability. This negative trend was retained in
the assertiveness facet of the Sociability factor, which was previously reported as both
empirically and popularly viewed as a male strength (Mikolajczak, et al., 2007). This trend
therefore follows with the aforementioned finding that males tend to be higher in Sociability than
females. It would appear that females are aware of this discrepancy between the genders such
Self-other report and ability EI 35
that when rating themselves and their partners, the strengths and weaknesses that they report
differ on the facets related to Sociability. The present study’s use of self-other methodology has
added to the construct validation of trait EI as measured by the TEIQue by the strong self-other
agreement that emerged. In addition, the use of couples has allowed for a variety of gender
difference analyses which should be considered in future studies that wish to replicate the method
and flesh out the particular moderating effects of gender.
Hypothesis 2 posited that the four ability measures would be positively and significantly
correlated. Previous research has found correlations between the STEM, STEU and Eyes tests; as
well as correlations between the Eyes and Faces tests, with the Perceiving branch of the MSCEIT
(Austin, submitted). In agreement with these past findings, several relationships emerged among
the Faces, STEM, STEU and Eyes tests. Namely, significant and positive correlations emerged
between Faces and STEU scores; STEU and Eyes scores; STEU and STEM scores; and Eyes and
STEM scores. Specifically, STEU scores are strongly predictive of STEM scores; STEM and
Eyes scores are highly predictive of STEU scores; and STEU scores are highly predictive of Eyes
scores. The robust interrelationships among the ability measures used in the present study argue
for their validity and continued examination and usage in EI research. While the MSCEIT
remains an important and in many ways dominant measure of ability EI, these promising findings
offer an alternative to the MSCEIT’s use. By offering an alternative, the risk of possible
problems with the theoretical framework of the MSCEIT does not have to be a roadblock to the
progress in ability EI testing.
Hypothesis 3 stated that the correlations between self-reported and partner-rated EI
would be moderated by the self- and partner-ratings (i.e. EI levels would affect the ability to rate
and be rated amongst partners). When the subset of couples was divided based on self-reported
EI, both low, average, and high EI judges yielded strong correlations between their estimates and
their partner’s self-reports. However, in line with hypothesis 3, it was the judges in the high EI
group that yielded the strongest correlation. When the sample was divided based on targets’ selfreports, both low, average, and high EI targets yielded insignificant correlations with their
partner’s ratings of them. However, again in line with hypothesis 3, the high EI targets indicated
a marginally significant correlation between estimated and self-reported EI. As indicated by
previous research (Mills & Hogan, 1978; Cheek, 1982; Wymer & Penner, 1985), the present
study supports the idea that individuals who are high in EI are both better able to express their
emotional abilities to others, and better able to perceive the EI of others. These moderating
effects of EI on ones rateability and ability to rate enhance the utility of self-other methodology in
EI research by adding depth to the information that can be derived from such analyses. In
Self-other report and ability EI 36
addition, these findings lend to EI’s construct validation in their ability to indicate that EI is not
only something that is reliably observable by others, but also a construct that based on it’s level
can moderate one’s ability to rate and be rated.
Potential demographic influences on individual differences in the ability to rate and be
rated were also examined as possible moderating effects. Both male and female ratings of their
partners correlated with their partner’s self-report. Male estimates were only slightly more highly
correlated with partner reports than were females, and thus gender does not appear to have a
moderating effect on the ability to rate or be more easily rated. Contrarily, the other demographic
factors investigated did appear to moderate the correlations between self- and partner-ratings.
Older people (55+), people who are married, and people who are in longer relationships (30+
years) are better at rating one another than younger, unmarried, and couples in relationships of
shorter durations. The acquaintanceship effect likely plays a large role in these findings. The
stipulation that couples had to have been in their relationship for at least one year created a
baseline of acquaintanceship between couples within the sample. Evidently, and not surprisingly,
the longer and better that people know each other the better they are at rating one another. Indeed
the information quality and quantity described by Letzrig and colleagues (2006) both play an
important role in self-other agreement. This evidence from couples again lends to the usefulness
of self-other methodology in EI research, and suggests that assessing different levels of
acquaintance may be of interest in future studies.
The final hypothesis considered was that self-report and ability measures would act as
aggregator variables to optimize the validity of accuracy ratings. Unfortunately, no significant
correlations between the TEIQue and any of the four ability measures emerged. Even when the
ability measures were aggregated to create one ability score, this ‘super ability EI score’ still
failed to correlate with the TEIQue. Similarly, when self- and partner-rated EI scores were
aggregated to create a ‘super trait EI score’, this aggregate score did not correlate with the ‘super
ability EI score’. The four ability tests were also considered individually, and it was only the
Eyes test that correlated with the ‘super trait EI score’. As the Eyes test is relatively new in its
use within EI research, this significant correlation with trait EI supports the role for the test within
the field of EI. In spite of this study’s attempt to ‘bridge the gap’ between trait and ability
measures, fundamental issues still present themselves despite the construct validity attributed to
the measures independently. While the TEIQue as a measure of trait EI has gained validity by the
success of the self and partner agreement, and the four ability measures used gained validity
through their strong relationships with one another, the issues among these methodologies are still
evident in the present study. Matthews, Zeidner and Roberts (2002) reviewed the differences
Self-other report and ability EI 37
between trait and ability measures which are problematic in comparing the results that emerge
from the tests. While an ability measure has answers that can be compared to responses
predetermined to be ‘correct’, self-reports refer to one’s opinion of their own functioning. So
while ability measures require objective criteria for rating responses, self-report responses have
been pre-specified by the nature of the items included. Matthews and colleagues (2002) therefore
argue that while ability measures are assessing actual EI, self-report is assessing perceived EI. If
however, we refer to the sociology axiom previously mentioned in the defence of consensus
scoring that even though felt emotions cannot be right or wrong, perceptions of emotions can be
deemed correct or incorrect; then are ability and self-report measures of EI both addressing the
perception of emotion? Matthews, and colleagues (2002) also suggest that self-report measures
risk distortion as a result of response bias, such that people report themselves to appear ‘better’
than they actually are; the present study does manage to counter this issue with partner-ratings as
response bias has previously been found to not play a role in peer ratings (McCrae, 1982; Vazire,
2006). The present study also partially avoids the issue of consensus scoring in ability measures
as the different measures utilize different methods of scoring including consensus (Eyes), expert
(STEM) and veridical (STEU and Faces). While the present study was unsuccessful in
seamlessly fusing trait and ability EI, it has been successful in addressing some prominent issues
of measurement, and contributing to the construct validation of trait and ability EI as measured by
the TEIQue, Faces, STEU, STEM, and Eyes tests.
Limitations
The modest sample size used in this study likely limited the results observed. Future
research should aim to use a larger sample and assess if some of the marginally or insignificant
findings might gain in significance. Likewise, based on the strong findings within the subset of
couples, despite their small numbers, future research should increase the number of couples used
and expand the hypotheses examined referring to the self-other agreement present in EI.
The tests utilized in the present study were all derived from preconceived theories, which
may have lent to the lack of agreement between the trait and ability measures. The tests used did
however gain validity through the relationships that emerged among them. As such, they should
be considered for their utility in measuring EI. However, the demand for conceptualizing a single
broad bandwidth test is further justified in the attempt to consolidate trait and ability EI.
Future Directions
Previous research has indicated that global trait EI was moderately associated with social
desirability however the higher order factors were not equal contributors to this relationship. For
example, Emotionality was associated with social desirability in females but not males, and
Self-other report and ability EI 38
Sociability was associated with social desirability in males but not females (Mikolajczak, et al.,
2007). Based on this study’s strong findings of self-other agreement, future research may benefit
from including a social desirability score in self-other TEIQue ratings to see if this method may
alleviate the association between trait EI and social desirability as has been realized in personality
research (McCrae, 1982). The strong relationships between all of the ability measures used in the
present study argues for their further use within EI research. The utility of these ability measures
and their aggregate should be examined for their relationships with the MSCEIT branches. As
previous research found that the ability measures used were found to correlate with branches of
the MSCEIT (Austin, submitted), the present study’s ability to further validate the Faces, STEM,
STEU and Eyes tests, argues for continued analysis of their relation to the MSCEIT branches.
Despite the promise observed in the validation of the tests used, this validity was found
for ability and trait measures independent of one another. The potential for bridging the gap
between intelligence and personality research camps was not achieved here; however this is not to
say that this bridge may not exist. As this study used already established tests, the construct’s
conceptualization was already embedded within the tests used. In order for a unified construct to
emerge EI must be conceptualized in such a way that both ability and trait EI are able to be
assessed. Emotion, and in turn EI, are highly complex phenomena, and therefore it makes sense
that differing levels of description may be needed. Theories of cognitive science indicate that
three types of explanation compliment one another to describe cognitive phenomena (Newell,
1982; Pylyshyn, 1984). The biological level refers to actual differences in brain activity that
affects individual differences in emotion processing and functioning. Secondly, the symbolic
level refers to the operations performed in the brain such as memory and communication that in
turn affect the processing and functioning of emotions. The third and final level of explanation
described by cognitive-science theory is the semantic or knowledge level, whereby behaviour is
based on motivations, goals and knowing how to achieve such goals; in other words the personenvironment interaction related to emotions (Pylyshyn, 1984). Within this theoretical framework,
the interrelationship among the three levels is open to interpretation as to whether the levels are
completely independent, or whether they can be reduced to a single level. In between these two
extremes is a position known as explanatory pluralism (Matthews, 1997). This intermediate
position suggests that different levels are important in theories, and interrelationships between the
levels will demonstrate that partial influence and assimilation will develop to bridge these levels.
Matthews and colleagues (2002) suggest that the cognitive-science framework can be applied to
EI such that the three levels of process (neural, information, and knowledge level) may be
indicated by a given emotion and as such, EI must be a quality of processing referring to whether
Self-other report and ability EI 39
the processes behind emotion are adaptive or maladaptive. In this regard then, the
conceptualization of EI must regard it as an individual difference and systematic differences in
processes related to emotions should be assessed with regard to the adaptation (or maladaptation)
that they promote. The underlying problem of how to explicate what felt emotions really are in
objective reality is difficult to rectify, but perhaps if future research can conceptualize EI by
identifying what emotions are for and how well emotions can achieve a given adaptive purpose,
both trait and ability measures will be able to elucidate a single construct that differs between
individuals in its adaptive function.
Another important consideration for future research that the present study has been able
to reveal is the relevance of self-other methodology within EI research. The subset of couples
used were required to have been in their relationship for at least one year. This stipulation
ensured a certain level of acquaintanceship that could not be regulated across relationships such
as friendship or even familial bonds. As suggested by Letzrig and colleagues (2006) both the
quality and the quantity of information provided can have an effect on the self-other agreement
achieved using this methodology. Based on the strong self-other agreement observed in the
present study, future research should use multiple raters with different levels of acquaintanceship
with the target and observe the effect of this on self-other agreement, both individually, and with
the aggregate measure of the raters.
Finally, in addition to lending to the construct validation of EI and in turn warranting its
further inspection, potential applications of the information presented in the present study’s
findings are numerous. Similar to its use in occupational psychology (Law, et al., 2004; Church,
2000), the use of self-other rating could be useful in couples counselling. If individuals are made
to see themselves as others (especially their partner) see them, the groundwork for behavioural
change can be laid. Future research could also utilize the aggregation of self- and peer-ratings
again as a ‘super trait EI score’ to predict variables such as occupational success and adaptive
coping.
Conclusion
Matthews, Zeidner and Roberts (2002) indicated necessary criteria for a test to be deemed
valid. For one, human qualities must be conceptualized so that they may come together as a
meaningful construct for scientific investigation. This construct must contribute something new
to science and have purpose within daily human life. The present study attempted to contribute to
the search for a ‘good’ and inclusive test of EI. To do so, multiple methods were used and though
a single test was not created, the findings for each independent test effectively lend towards the
construct validation of EI. The interrelationships between the ability measures indicate
Self-other report and ability EI 40
convergent validity suggesting that these are ‘good’ tests of ability EI that warrant further
investigation and usage. A variety of differing measures were shown to combine with one
another and allow for a more inclusive assessment of ability EI than just one of the measures
could provide. The self-other agreement found for the TEIQue supports the content and construct
validity of trait EI as something that can be perceived and described both by oneself and by an
acquaintance that knows them well. The wealth of popularity that EI gained when it was
introduced to the general public in the mid 1990s gave it a premature and unfounded sense of
validity. As empirical research catches up to this validation, EI continues to be uncovered as an
important feature of both human intelligence and personality. This complex and distinct
construct requires more studies such as this one to clarify and validate its theoretical framework,
so that its application can be utilized to the best of its ability, with a firm foundation in science
instead of popular psychology.
Self-other report and ability EI 41
References
Austin, E. J. (2005). Emotional intelligence and emotional information-processing. Personality
and Individual Differences, 39, 403-414.
Austin, E. J. (submitted). Looking for the ‘intelligence; in emotional intelligence: Associations of
EI test performance with cognitive test and emotion task performance.
Austin, E. J., Parker, J. D. A., Petrides, K. V., & Saklofske, D. H. (2007). Emotional intelligence,
In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The Sage Handbook of
Personality Theory and Assessment (pp. 572-592). London:SAGE.
Bagby, R. M., Parker, J. D. A., & Taylor, G. J. (1994). The Twenty-Item Toronto Alexithymia
Scale. I: Item selection and cross-validation of the factor structure. Journal of
Psychosomatic Research, 38, 23-32.
Bailie, K., & Ekermans, G. (2006). An exploration of the utility of a self-report emotional
intelligence measure. E-journal of Applied Psychology: Emotional Intelligence, 2, 3-11.
Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Robertson, M. (1997). Another advanced test of
theory of mind: evidence from very high functioning adults with autism or Asperger
Syndrome. Journal of Child Psychology and Psychiatry, 38, 813-822.
Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T., Brammer, M. J., Simmons, A.,
& Williams, S. C. R. (1999). Social intelligence in the normal and autistic brain: an fMRI
study. European Journal of Neuroscience, 11, 1891-1898.
Baron-Cohen, S., Riviere, A., Cross, P., Fukushima, M., Bryant, C., Sotillo, M., Hadwin, J., &
French, D. (1996). Reading the mind in the face: a cross-cultural and developmental
study. Visual Cognition, 3, 39-59.
Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind
in the Eyes” test revised version: A study with normal adults, and adults with Asperger
syndrome or high functioning autism. Journal of Child Psychology and Psychiatry and
Allied Disciplines, 42, 241-251.
Bar-On, R. (2000). Emotional and social intelligence: Insights from the Emotional Quotient
Inventory. In R. Bar-On & J.D.A. Parker (Eds.), The handbook of emotional intelligence
(pp. 363-388). San-Francisco: Jossey-Bass.
Brackett, M. A., Rivers, S. E., Shiffman, S., Lerner, N., & Salovey, P. (2006). Relating emotional
abilities to social functioning: A comparison of self-report and performance measures of
emotional intelligence. Journal of Personality and Social Psychology, 91, 780-795.
Brody, L. R. (2000). The socialization of gender differences in emotional expression: Display
rules, infant temperament, and differentiation. In A. H. Fischer (Ed.), Gender and
Self-other report and ability EI 42
emotion: Social psychological perspectives (pp. 24-47). New York: Cambridge
University Press.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validitation by the
multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105.
Church, A. H. (2000). Do higher performing managers actually receive better ratings? A
validation of multirater assessment methodology. Consulting Psychology Journal:
Practice and Research, 52, 99-116.
Cheek, J. M. (1982). Aggregation, moderator variables, and the validity of personality tests: A
peer-rating study. Journal of Personality and Social Psychology, 43, 1254-1269.
Ciarrochi, J. V., Chan, A. Y. C., & Caputi, P. (2000). A critical evaluation of the emotional
intelligence construct. Personality and Individual Differences, 28, 539-561.
Cronbach, L. J. (1956). Assessment of individual differences. Annual Review of Psychology, 7,
616-628.
Davies, M., Stankov, L., & Roberts, R. D. (1998). Emotional intelligence: Insearch of an elusive
construct. Journal of Personality and Social Psychology, 75, 989-1015.
Day, A. L., & Livingstone, H. A. (2003). Gender differences in perceptions of stressors and
utilization of social support among university students. Canadian Journal of Behavioural
Science, 35, 73-83.
Eaton, R. J., & Bradley, G. (2008). The role of gender and negative affectivity in stressor
appraisal and coping selection. International Journal of Stress Management, 15, 94-115.
Ekman, P. (1993). Facial expression and emotion. American Psychologist, 48, 384-392.
Ekman, P., & Friesen, W. V. (1976). Pictures of facial affect. Palo Alto, CA: Consulting
Psychologists Press.
Elias, M. J., Zins, J. E., Weissberg, R. P., Frey, K. S., Greenberg, M. T., Haynes, N. M., et al.
(1997). Promoting social and emotional learning: Guidelines for educators. Alexandria,
VA: Association for Supervision and Curriculum Development.
Folkman, S., & Lazarus, R. S. (1988). The relationship between coping and emotion: Implications
for theory and research. Social Science Medicine, 26, 309-317.
Friedman, H. S., Prince, L. M., Riggio, R. E., & DiMatteo, M. R. (1980). Understanding and
assessing nonverbal expressiveness: The Affective Communication Test. Journal of
Personality and Social Psychology, 39, 333-351.
Furnham, A., & Petrides, K. V. (2003). Trait emotional intelligence and happiness. Social
Behaviour and Personality, 31, 815-824.
Gardner, H. (1993). Multiple Intelligences. New York: Basic Books.
Self-other report and ability EI 43
Gibbs, N. (1995). (October 2). The EQ Factor. Time, 60-68.
Goldberg, L. R. (1980, May). Some ruminations about the structure of individual differences:
Developing a common lexicon for the major characteristics of human personality. Paper
presented at the meeting of the Western Psychological Association, Honolulu, HI.
Goleman, D. (1995). Emotional Intelligence: why it can matter more than IQ. London:
Bloomsbury.
Jackson, D. N. (1967). Personality research form manual. New York: Research Psychologists
Press.
John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and
theoretical perspectives. In L.A. Pervin & O.P. John (Eds.), Handbook of personality:
Theory and research (Vol. 2, pp.102-138). New York: Guilford Press.
Kenrick, D. T., & Stringfield, D. O. (1980). Personality traits and the eye of the beholder:
Crossing some traditional philosophical boundaries in the search for consistency in all of
the people. Psychological Review, 87, 88-104.
Krajewski, H. T., & Goffin, R. D. (2005). Predicting occupational coping responses: The
interactive effect of gender and work stressor context. Journal of Occupational Health
Psychology, 10, 44-53.
Lancaster, J. B. (1975). Primate behaviour and the emergence of human culture. New York: Holt,
Rinehart & Winston.
Law, K. S., Wong, C. S., & Song, L. J. (2004). The construct and criterion validity of emotional
intelligence and its potential utility for management studies. Journal of Applied
Psychlogy, 89, 483-496.
Letzrig, T. D., Wells, S. M., & Funder, D. C. (2006). Information quantity and quality affect the
realistic accuracy of personality judgment. Journal of Personality and Social Psychology,
91, 111-123.
Lopes, P. N., Salovey, P., Cote, S., & Beers, M. (2005). Emotion regulation abilities and the
quality of social interaction. Emotion, 5, 113-118.
MacCann, C. E. (2006). New Approaches to measuring emotional intelligence: Exploring
methodological issues with two new assessment tasks. Unpublished doctoral dissertation,
University of Sydney, Australia.
Matthews, G. (1997). An introduction to the cognitive science of personality and emotion. In G.
Matthews (ed.), Cognitive science perspectives on personality and emotion (pp. 339-442).
Amsterdam: Elsevier.
Self-other report and ability EI 44
Matthews, G., Zeidner, M., & Roberts, R. D. (2002). Emotional Intelligence: Science and Myth.
London: The MIT Press.
Matthews, G., Emo, A. K., Funke, G., Zeidner, M., Roberts, R. D., Costa, P. T., Jr., et al. (2006).
Emotional intelligence, personality, and task-induced stress. Journal of Experimental
Psychology: Applied, 12, 96-107.
Mavroveli, S., Petrides, K. V., Rieffe, C., & Bakker, F. (2007). Trait emotional intelligence,
psychological well-being and peer-rated social competence in adolescence. British
Journal of Developmental Psychology, 25, 263-275.
Mayer, J. D., Caruso, D., & Salovey, P. (1999). Emotional intelligence meets traditional
standards for an intelligence. Intelligence, 27, 267-298.
Mayer, J. D., Caruso, D. R., & Salovey, P. (2000). Selecting a measure of emotional intelligence.
In R. Bar-On & J. D. A. Parker (Eds.), Handbook of emotional intelligence (pp. 320-342).
San Francisco: Jossey-Bass.
Mayer, J. D., & Mitchell, D. C. (1998). Intelligence as a subsystem of personality: From
Spearman’s g to contemporary models of hot processing. In W. Tomic & J. Kingma
(eds.), Advances in cognition and educational practice (vol. 5, pp. 43-75). Greenwich,
CT: JAI Press.
Mayer, J. D., Salovey, P., & Caruso, D. R. (2000). Emotional intelligence as Zeitgeist, as
personality, and as a mental ability. In R.Bar-On & J. D. A. Parker (eds.), The handbook
of emotional intelligence. San Francisco: Jossey-Bass.
Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2003). Measuring emotional
intelligence with the MSCEIT V2.0. Emotion, 3, 97-105.
McCrae, R. R. (1982). Consensual validation of personality traits: Evidence from self-reports and
ratings. Journal of Personality and Social Psychology, 43, 293-303.
McCrae, R. R. (1993). Agreement of personality profiles across observers. Multivariate
Behavioural Research, 28, 25-40.
McCrae, R. R., & Costa, P. T., Jr. (1987). Validation of the five-factor model of personality
across instruments and observers. Journal of Personality and Social Psychology, 52, 8190.
McCrae, R. R., Stone, S. V., Fagan, P. J., & Costa, P. T., Jr. (1998). Identifying causes of
disagreement between self-reports and spouse ratings of personality. Journal of
Personality, 66, 285-313.
Self-other report and ability EI 45
McDaniel, M. A., Morgeson, F. P., Finnegan, E. B., Campion, M. A., & Braverman, E. P. (2001).
Use of situational judgement tests to predict job performance: A clarification of the
literature. Journal of Applied Psychology, 86, 730-740.
Mikolajczak, M., Luminet, O., Leroy, C., & Roy, E. (2007). Psychometric properties of the Trait
Emotional Intelligence Questionnaire: Factor structure, reliability, construct, and
incremental validity in a French-speaking population. Journal of Personality Assessment,
88, 338-353.
Mills, C., & Hogan, R. (1978). A role theoretical interpretation of personality scale item
responses. Journal of Personality, 46, 778-785.
Newell, A. (1982). The knowledge level. Artificial Intelligence, 18, 87-127.
Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factor
structure in peer nomination personality ratings. Journal of Abnormal and Social
Psychology, 66, 574-583.
Numenmaa, T. (1964). The language of the face. Jyvaskyla studies in education, psychology, and
social research. Jyvaskyla, Finland.
Pervin, L. A. (1976). A free-response description approach to the analysis of personality situation
interaction. Journal of Personality and Social Psychology, 34, 465-474.
Petrides, K. V., & Furnham, A. (2000a). On the dimensional structure of emotional intelligence.
Personality and Individual Differences, 29, 313-320.
Petrides, K. V., & Furnham, A. (2000b). Gender differences in measured and self-estimated trait
emotional intelligence. Sex Roles, 42, 449-461.
Petrides, K. V., & Furnham, A. (2001). Trait emotional intelligence: Psychometric investigation
with reference to established trait taxonomies. European Journal of Personality, 15, 425448.
Petrides, K. V., & Furnham, A. (2003). Trait emotional intelligence: Behavioural validation in
two studies of emotion recognition and reactivity to mood induction. European Journal
of Personality, 17, 39-57.
Petrides, K. V., Furnham, A., & Frederickson, N. (2004). Emotional intelligence. The
Psychologist, 17, 574-577.
Petrides, K. V., Furnham, A., & Frederickson, N. (2006). Trait emotional intelligence and
children`s peer relations at school. Social Development, 15, 537-547.
Petrides, K. V., Furnham, A., & Martine, G. N. (2004). Estimates of emotional and psychometric
intelligence: Evidence for gender-based stereotypes. The Journal of Social Psychology,
144, 149-162.
Self-other report and ability EI 46
Petrides, K. V., Furnham, A., & Mavrovelli, S. (2007). Trait emotional intelligence: Moving
forward in the field of EI. In G. Matthews, M. Zeidner, & R. Roberts. (Eds.). Emotional
Intelligence: Known and unknowns (Series in Affective Science). Oxford: Oxford
University Press.
Petrides, K. V., Perez-Gonzalez, J. C., & Furnham, A. (2007). On the criterion and incremental
validity of trait emotional intelligence. Cognition and Emotion, 21, 26-55.
Piedmont, R. L. (1994). Validation of the rater form of the NEO-PI-R for college students:
Toward a paradigm for measuring personality development. Assessment, 1, 259-265.
Piedmont, R. L., McCrae, R. R., Riemann, R., & Angleitner, A. (2000). On the invalidity of
validity scales: Evidence from self-reports and observer ratings in volunteer samples.
Journal of Personality and Social Psychology, 78, 582-593.
Pylyshyn, Z. W. (1984). Computation and cognition: Toward a foundation for cognitive science.
Cambridge: MIT Press.
Roberts, R. D., Zeidner, M., & Matthews, G. (2001). Does emotional intelligence meet traditional
standards for an intelligence? Some new data and conclusions. Emotions, 1, 196-231.
Roberts, R. D., Schulze, R., O`Brien, K., MacCan, C., Reid, J., & Maul, A. (2006). Exploring the
validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) with
established emotions measures. Emotion, 6, 663-669.
Roseman, I. J. (2001). A model of appraisal in the emotion system: Integrating theory, research,
and applications. In K. R. Scherer & A. Schorr (Eds.), Appraisal processes in emotion:
Theory, methods, research. (pp.68-91). New York: Oxford University Press.
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition, and
Personality, 9, 185-211.
Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C., & Palfai, T. P. (1995). Emotional attention,
clarity, and repair: Exploring emotional intelligence using the Trait Meta-Mood Scale. In
J. W. Pennebaker (Ed.), Emotion, disclosure, and health (pp. 125-154). Washington:
American Psychological Association.
Salovey, P., Bedell, B. T., Detweiler, J. B., & Mayer, J. D. (1999). Coping intelligently:
Emotional intelligence and the coping process. In C. R. Snyder (ed.), Coping: The
psychology of what works (pp. 141-164). New York: Oxford University Press.
Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., et al.
(1998). Development and validation of a measure of emotional intelligence. Personality
and Individual Differences, 25, 167-177.
Self-other report and ability EI 47
Shrauger, J. S., & Schoeneman, T. J. (1979). Symbolic Interactionist view of self-concept:
Through the looking glass darkly. Psychological Bulletin, 86, 549-573.
Sjoberg, L. (2001). Emotional intelligence: A psychometric analysis. European Psychologist, 6,
79-95.
Sternberg, R. J., & Grigorenko, E. L. (2000). Practical intelligence and its development. In R.
Bar-On & J. D. A. Parker (Eds.), The handbook of emotional intelligence (pp. 215-243).
San Francisco: Jossey-Bass.
Thorndike, E. I. (1920). Intelligence and its uses. Harper`s Magazine, 140, 227-235.
Vazire, S. (2006). Informant reports: A cheap, fast, and easy method for personality assessment.
Journal of Research in Personality, 40, 472-481.
Wymer, W. E., & Penner, L. A. (1985). Moderator variables and different types of predictability:
Do you have a match? Journal of Personality and Social Psychology, 49, 1002-1015.
Young, A., Perrett, D., Calder, A., Sprengelmeyer, R., & Ekman, P. (2002). Facial expressions of
emotion – stimuli and tests (FEEST). Bury St. Edmunds: Thames Valley Test Company.
Zeidner, M., Shani-Zinovich, I., Matthews, G., & Roberts, R. D. (2005). Assessing emotional
intelligence in gifted and non-gifted high school students: Outcomes depend on the
measure. Intelligence, 33, 369-391.
Self-other report and ability EI 48
Appendix A
Hierarchical structure of ability EI as explained by the MSCEIT
Faces
Emotional
Identification
Pictures
Emotional
Experiencing
Synesthesia
Emotional
Facilitation
Facilitation
Emotional
Intelligence
Changes
Emotional
Understanding
Blends
Emotional
Reasoning
Emotion
Management
Emotion
Management
Social
Management
Self-other report and ability EI 49
Appendix B
Self-esteem
Hierarchical structure of trait EI as explained by the TEIQue
TTETEIQue
Well-being
Happiness
Optimism
Emotion
Regulation
Self control
Stress
Management
Impulsiveness
Emotional
Intelligence
Emotion
Perception
Emotion
Expression
Emotionality
Relationship
Skills
Empathy
Social
Competence
Sociability
N.B. the two subscales, Adaptability and Motivation do not fit into
one of the four higher order structures and so are left out of the
structure.
Emotion
Management
Assertiveness
Self-other report and ability EI 50
Appendix C
Gender differences for the 15 facets of the TEIQue
M
F
p
male
female
Self-esteem
5.12
4.96
.75
.39
Happiness
5.51
5.75
1.01
.32
Optimism
5.25
5.43
.51
.48
Emotion regulation
4.66
4.08
8.15
.01
Stress management
4.80
4.20
5.81
.02
Impulsiveness
5.01
4.77
1.20
.28
Emotion perception
4.62
5.05
4.23
.04
Emotion expression
4.62
5.10
2.62
.11
Relationship skills
5.38
5.86
8.80
.00
Empathy
4.82
5.39
8.09
.01
Social competence
5.02
4.83
.77
.38
Emotion management
4.84
4.29
15.47
.00
Assertiveness
4.68
4.36
2.92
.09
Adaptability
4.64
4.54
.26
.61
Motivation
4.80
5.04
1.28
.26
Well-being factor
Self-control factor
Emotionality factor
Sociability factor
Self-other report and ability EI 51
Age differences for the 15 facets of the TEIQue
M
F
p
18-24
25-34
35-44
45-54
55+
Self-esteem
4.97
5.00
5.82
4.90
5.09
.89
.48
Happiness
5.66
5.34
6.08
5.85
5.81
.69
.60
Optimism
5.48
4.82
6.58
5.13
5.64
2.57
.04
Emotion regulation
4.15
3.89
4.97
4.55
4.72
2.81
.03
Stress management
4.28
4.10
5.30
4.82
4.67
1.56
.19
Impulsiveness
4.76
4.54
5.15
5.04
5.20
1.40
.24
Emotion perception
4.57
4.79
5.57
5.26
5.15
2.31
.07
Emotion expression
4.76
4.79
5.53
5.35
4.95
.58
.68
Relationship skills
5.51
5.80
6.15
5.76
5.69
.82
.52
Empathy
5.09
4.89
5.81
5.45
5.33
1.29
.28
Social competence
4.93
4.59
5.67
4.66
5.20
1.76
.15
Emotion management
4.63
4.39
4.78
4.15
4.55
1.30
.28
Assertiveness
4.50
4.49
5.48
4.24
4.43
1.32
.27
Adaptability
4.35
4.29
4.78
5.05
4.92
3.16
.02
Motivation
4.91
4.63
5.07
5.26
5.12
1.04
.39
Well-being factor
Self-control factor
Emotionality factor
Sociability factor
Self-other report and ability EI 52
Appendix D
Self and partner correlations for TEIQue facets
Partner 1 (female)
Female Self-report
Female Rating of Male Partner
Self-esteem
.18
Self-esteem
.29
Happiness
.11
Happiness
.47*
Optimism
-.29
Optimism
.59**
Emotion regulation
.32
Emotion regulation
.53**
Stress management
.01
Stress management
.67**
Impulsiveness
.10
Impulsiveness
.47*
Emotion perception
.07
Emotion perception
.55**
Emotion expression
.10
Emotion expression
.51**
Relationship skills
.00
Relationship skills
.20
Empathy
.29
Empathy
.32
Social competence
.07
Social competence
.64**
Emotion management
.04
Emotion management
.51**
Assertiveness
-.10
Assertiveness
.54**
Adaptability
.33
Adaptability
.51**
Motivation
.21
Motivation
.45*
Male Rating of
Self-esteem
.36
Self-esteem
.16
Female Partner
Happiness
.61**
Happiness
.15
Optimism
.64**
Optimism
-.04
Emotion regulation
.68**
Emotion regulation
.14
Stress management
.72**
Stress management
.29
Impulsiveness
.54**
Impulsiveness
.18
Partner 2 (male)
Male Self-report
Emotion perception
.36
Emotion perception
.39*
Emotion expression
.61**
Emotion expression
.58**
Relationship skills
.32
Relationship skills
.07
Empathy
.41*
Empathy
.41*
Social competence
.46*
Social competence
.10
Emotion management
.26
Emotion management
-.26
Assertiveness
.62**
Assertiveness
-.30
Adaptability
.22
Adaptability
.09
Self-other report and ability EI 53
Motivation
.49**
Female Rating of
Self-esteem
.61**
Male Partner
Happiness
.49**
Optimism
-.09
Emotion regulation
.44**
Stress management
.24
Impulsiveness
.15
Emotion perception
.47*
Emotion expression
.53**
Relationship skills
.68**
Empathy
.18
Social competence
.31
Emotion management
.38*
Assertiveness
-.24
Adaptability
.54**
Motivation
.32
*. Correlation is significant at the 0.05 level
**. Correlation is significant at the 0.01 level
Motivation
.07