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Running head: 1
Louis Oberdiear, Taylor Herrick, Stephanie Persaud, Maryann Stassen, & Stephen Steiner
Missouri State University
2
Abstract
The popularization of emotional intelligence has sparked controversy over the definition,
dimensionality, and operationalization of the construct. Traditionally, EI is measured through
explicit measures including performance-based tests and self-report questionnaires. The use of
such measures has been highly scrutinized; the susceptibility to faking, need for individual
introspection, and the subjective nature of EI demonstrates threats to validity. The objective of
this study is to develop and validate an Implicit Association Test (IAT) for EI. IAT’s reveal
associations that are introspectively assessed and reported verbally. Through the measurement of
associations between concepts and attributes, an EI-IAT will yield a true evaluation of one’s EI.
The EI-IAT demonstrated convergent and discriminate validity and showed good internal
consistency. The EI-IAT was significantly positively correlated with the TEIQue global trait EI
factor. The EI-IAT was also significantly negatively correlated with the TAS total factor and the
TAS subfactor, difficulty identifying feelings.
3
Table of ContentsAbstract................................................................................................................................2
Table of Contents.................................................................................................................3
Introduction..........................................................................................................................4
Emotional Intelligence.....................................................................................................4
IAT Measures...................................................................................................................7
Method...............................................................................................................................10
Construction of the IAT.................................................................................................10
Participants....................................................................................................................12
Procedure.......................................................................................................................12
Measures........................................................................................................................12
Trait Emotional Intelligence Questionnaire (TEIQue-SF)........................................12
Toronto Alexithymia Scale (TAS -20).......................................................................12
Data Analysis.................................................................................................................13
Results................................................................................................................................15
Discussion..........................................................................................................................20
Summary of Findings....................................................................................................20
Study Implications.........................................................................................................20
Future Research.............................................................................................................20
References..........................................................................................................................21
Appendix A: TAS-20.........................................................................................................23
Appendix B: TEIQue-SF...................................................................................................25
4
Introduction
From Forbes to elite academic journals, emotional intelligence (EI) has received
substantial interest in both the academic and practitioner arena (Lievens & Chan, 2010).
Simultaneously, this construct has stimulated criticism and controversy. Described as an “elusive
term”, researchers have debated its definition, dimensionality, and operationalization (Lievens &
Chan, 2010; Mayer, Salovey, & Caruso, 2004).
In modern society, being emotionally intelligent has been attributed to personal and
professional success (Matthews, Zeidner, & Roberts Need date). Some have argued that
emotional intelligence is a better predictor than general intellectual ability of “academic
achievement, occupational success and satisfaction, and emotional health and adjustment” (Elias,
Zins, Weissberg, Frey et al., 1997; Goleman, 1995). Furthermore, EI has been conceived as
trainable; many workplaces and educational settings are developing EI interventions.
The public exposure and media attention EI has received, has fostered misrepresentations
of the construct (Mayer, Salovey, and Caruso, 2004). Given the popularization of EI, it is
imperative for researchers to promote a consistent conceptualization and valid measure of EI that
is rooted in theory and supported through empirical evidence.
Emotional Intelligence
Ability versus Trait. Generally, EI is conceptualized as either an ability or trait (Lievens
& Chan, 2010). Mayer, Caruso, and Salovey (2000) describe intelligence as an umbrella term
encompassing various types of mental abilities. Ability models of EI conceptualize EI as true
intelligence; thus, EI is referred to “as emotional cognitive ability or information processing
emotional intelligence” (Lievens & Chan, 2010; Mayer, Caruso, & Salovey, 2000).
5
Emotional cognitive ability is defined as mental processes that include “appraising and
expressing emotions in the self and others, regulating emotion in the self and others, and using
emotions in adaptive ways” (Salovey & Mayer, 1990). Utilizing this conceptualization of EI,
abilities and skills of EI can be divided into four branches: (a) perceive emotion, (b) use emotion
to facilitate thought, (c) understand emotions, and (d) manage emotion (Mayer, Salovey, and
Caruso, 2004). The first branch describes one’s aptitude to identify emotion in others’ non-verbal
expressions (Mayer, Salovey, and Caruso, 2004). Branch two includes the capacity of emotions
to assist thinking; hence, the ability to link and utilize emotions to drive future behavior (Mayer,
Salovey, and Caruso, 2004). The third branch includes the ability to reflect, analyze, and
understand the outcomes of emotions (Mayer, Salovey, and Caruso, 2004). Lastly, the fourth
branch reflects the capacity to manage emotions to achieve one’s goals (Mayer, Salovey, and
Caruso, 2004).
The trait EI model views “emotional intelligence as akin to personality” (Lievens &
Chan, 2010). The EQ-i is the most commonly used trait EI measure within the literature (Perez,
Petrides, & Furnham, 2005). The EQ-i is based on the premise that trait EI is “an array of non-
cognitive capabilities, competencies, and skills that influence one’s ability to succeed in coping
with environmental demands and pressures” (Bar-On, 1997). The EQ-i contains 15 subscales
with 5 higher-order factors including: “intrapersonal”, “interpersonal”, “adaptation”, “stress
management”, and “general mood” (Perez, Petrides, & Furnham, 2005).
Measuring Emotional Intelligence. Trait EI and ability EI do not measure the same
constructs (Lievens & Chan, 2010). The key distinction between trait EI and ability EI is the type
of measurement employed. Ability EI utilizes performance-based tests and trait EI measures it
via self-report questionnaires (Lievens & Chan, 2010). Some research studies employ mixed
6
models; mixed models incorporate cognitive abilities and personality traits (Perez, Petrides, &
Furnham, 2005).
The use of performance-based and self-report measures to capture EI has been
scrutinized. Van Rooy, Viswesvaran, & Pluta’s (2005) meta-analytic research demonstrated that
the models (ability and trait measures) only correlated .14 (Lievens & Chan, 2010). Further,
research has demonstrated that the two constructs (ability EI and trait EI) hold different
correlates (Lievens & Chan, 2010). EI models based on ability are more correlated with
cognitive ability than personality (Lievens & Chan, 2010). Mixed models of EI corresponded
with personality scores but not with cognitive ability scores (Lievens & Chan, 2010). Thus, the
use of performance-based and self-report tests to capture EI yields construct validity concerns.
Problems concerning the use of performance-based and self-report measures have also
been conveyed. Self-report measures are cost effective and easily distributed; however, self-
report measures are susceptible to “various validity problems such as faking and inflation of
correlations because of common method variance” (Lievens & Chan, 2010). Trait EI measured
through performance-based tests presents participants with problems or situations thought to
have a correct response. The main critique of EI performance-based tests is (unlike performance-
based tests to measure general mental abilities) emotional intelligence tests “do not have
objectively correct answers” (Lievens & Chan, 2010).
Given the wide controversy with the existing explicit measures of EI, it can be argued
that a new instrument to measure EI is warranted. Lievens and Chan (2010) state “in comparing
the findings from the ability and the trait models, a major methodological problem exists because
of a method-construct confound resulting from the fact that ability model is often measured using
performance-based tests whereas the trait model is often measured using self-reports.”
7
IAT Measures
The susceptibility to faking and need for individual introspection demonstrate a major
threat to the validity of the explicit measures that are currently being used to measure EI. These
threats introduce a great need for a more accurate measure of EI. We can focus on one such
measure, The Implicit Association Test (IAT). Lane, Banaji, Nosek, & Greenwald (2007)
provide an overview of the basic concepts behind the IAT.
When people see a stimulus in the environment an automatic thought is presented
reflecting the individual’s attitudes and beliefs toward the stimulus. For example, when an
individual sees a woman they may be quick to think she is a mother or caretaker; seeing a doctor
may elicit thoughts of intelligence or skill associated with that doctor; a spider may produce a
reaction of immediate fright or disgust; upon hearing the word elder, words such as gray, slow,
and perhaps ill might come to mind. Although you may think these invisible thoughts and have
these implicit attitudes, you may make statements contradicting these thoughts, saying that you
like spiders or that you don’t actually think of women as caretakers. When using explicit
measures, one can see how an individual may mark answers that are not accurately capturing
their thoughts, attitudes, and beliefs.
People experience countless invisible thought processes every day. These implicit
cognitive notions lead to many explicit behaviors, which inherently reflect those implicit
attitudes. Lane et al. (2007), as well as Greenwald, McGhee, and Schwarz (2008), describe the
ways in which an IAT captures those implicit attitudes using reaction time, or the time that
elapses between a stimulus presentation and a response production. The idea behind an IAT is
that “the easier a mental task, the quicker the decision point is reached and the fewer the errors
that result” (Lane et al, 2007).
8
An IAT “provides an estimate of the strength of association between concepts and
attributes” (Lane et al., 2007). IAT’s are a computer based test in which two categories of words
(e.g., flower and good, or insect and good) are assigned to each of two response keys. If your
implicit attitudes more closely associates flower with good, it should be easier to respond to them
as a pair and your reaction time will therefore be faster for this pair over insect and good.
Participants are first given directions on which words go into which category and then are given
the chance to practice. When taking the IAT, participants are to use the response keys to sort the
words into the correct categories as quickly as possible.
We gain a measure of the implicit attitude when responding is faster with good/flower
than with good/insect pairing. The faster response time indicates greater positivity of the implicit
attitude associated with flower. The use of IAT’s provides an indirect measurement of individual
differences in implicit social cognition.
For example, in Stage 1 of the IAT, participants quickly organize words into the
categories flower (by pressing the left computer key) and insect (by pressing the right computer
key). In Stage 2, the task is repeated for the categories good and bad. In Stage 3, the categories
are combined and participants press either the left computer key when items referring to flower
or good appear on the screen, and press the right computer key when items in the insect or bad
category appear on the screen. A further set of trials are conducted in Stage 4. In Stages 5-7, the
earlier combined pairings are reversed, now pairing flower with bad and insect with good.
Flower and bad now share the left computer key, and insect and good share the right response
key. The structure of this IAT can be seen in Table 1. Typically attitudes toward flowers are
more positive than attitudes toward insects and so it can be predicted that participants will
9
respond more rapidly, on average, when the categories flower and good are combined, and insect
and bad are combined, as opposed to flower and bad, and insect and good. (Lane et al., 2007)
Table 1. Outline Structure of the IAT.
Block Number of Trials Left key response Right key response
1* 20 Flower Insect
2* 20 Good Bad
3* 20 Flower + Good Insect + Bad
4 40 Flower + Good Insect + Bad
5* 40 Insect Flower
6* 20 Insect + Good Flower + Bad
7 40 Insect + Good Flower + Bad
*Practice blocks
In addition to effectively discriminating between groups of people who perform a
behavior and those who do not (such as smoking, or avoidance of spiders), the IAT successfully
predicts behavior (Greenwald, McGhee, & Schwarz, 2008). Poehlman, Uhlmann, Greenwald,
and Banaji (2005) produced a meta-analysis using 86 independent samples. Results for that
meta-analysis found that the IAT predicted multiple criterion variables, including social
judgments, physiological responses, and social action. Most research using IATs focus attention
on stereotyping and prejudice, and health-related behaviors, such as food choices, alcohol use,
and smoking. A review of the IAT literature suggests there is substantial evidence that the IAT
predicts behaviors and judgments in these domains (Spruyt, Lemaigre, Salhi, Van Gucht,
Tibboel, Van Bockstaele, & Nackaerts, 2015; Bongers, Jansen, Houben, & Roefs, 2013; Houben,
& Wiers, 2006).
By measuring associations between concepts and attributes, the IAT can reveal
associations that often differ from those that are introspectively accessed and reported verbally
10
(Lane et al., 2007). The IAT may prevent against self-presentational forces that may hide
personally or socially undesirable evaluative associations, such as the ethnic and racial attitudes
an individual may have. Further, the IAT is an adaptable tool that provides the opportunity for
assessment of a wide variety of associations (Greenwald, McGhee, & Schwarz, 2008).
Method
Construction of the IAT
When constructing the IAT, guidelines from Lane et al (2007) were used. The structure of
the IAT was kept the same as outlined in Table 1. Using 40 trials in blocks 4 and 7 has shown to
reduce order effects when the categories are switched. The design chosen was the balanced-
valance IAT meaning category names and attribute stimuli have similar valances. Valance can be
described as the emotion that is evoked and can be positive or negative to different degrees.
When choosing the comparison construct, the researchers conducted a literature review in
order to find a construct that was negatively correlated with trait emotional intelligence. The
intent here was to exaggerate the IAT effect as most people who are high in trait EI will be low in
the comparison construct and vice-versa. The researchers found narcissism to be negatively
correlated with empathy (Watson, Grisham, Trotter, & Biderman, 1984). In Thomas and
Walker’s (2014) article on the development of an environmental IAT, they used Egoistic Values
as a comparison construct which is in close relation to narcissism. The researchers used words
such as ambition, money, and prestige. It was in the opinion of the researchers that this
comparison construct would work well in contrast to trait EI. The researchers were polled to rate
the valence of “Emotional Intelligence” and “Egoistic Values.” The researchers concluded that
“Emotional Intelligence” had a slight positive valence and “Egoistic Values” had a slight
11
negative valence. Since a balanced-valance IAT design was being used, the name “Egoistic
Values” was changed to “Individualism.”
The researchers then developed stimuli for each category construct. Nosek, Greenwald,
and Banaji (2005) advise on using a minimum of four items per category and this guideline was
followed. EI stimuli were chosen to represent each facet of Daniel Goleman’s model of EI
(1995); self-awareness, social awareness, self-management, and relationship management. One
word for each facet was chosen and can be seen in Table 2. Three words were borrowed from
Thomas and Walker’s article: prestige, reputation, and money. The researchers added legacy to
more accurately represent narcissism.
Table 2. Word Stimuli for categories (Emotional Intelligence and Comparison construct)Emotional Intelligence Individualism
Relationships Legacy Empathy Prestige
Poise ReputationAdaptability Money
Attribute categories were chosen to be “Good/Bad” (Table 3) on the evidence produced
from the Nosek and Hansen (2008) article suggesting using other attribute categories can elicit
more conscious processing thus reducing the effectiveness of the IAT.
Table 3. Word Stimuli for AttributesGood Bad
Marvelous Tragic Superb Horrible
Glorious TerribleWonderful Awful
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Participants
A total of 181 psychology students were recruited from Missouri State University and
received course credit for participating in the study. No demographic information was collected.
Procedure
Subjects were recruited using the university’s SONA systems, which allowed students to
select and sign-up for studies online. Participants were then sent a link to the study using their
school email. Upon clicking the link, participants viewed an informed consent statement and
agreed by clicking continue. After clicking continue, the three different measures would be
launched on the participants computer screen.
Measures
Trait Emotional Intelligence Questionnaire (TEIQue-SF). Petrides and Furnham
(2006) created a shorter EI questionnaire aimed to capture the global trait of EI. The TEIQue-SF
includes 30 items on a 7-point Likert scale (1 = “Completely Disagree” to 7 = “Completely
Agree”). There are 2 items from each of the 15 subscales of the original TEIQue included in this
shorter form (e.g. adaptability, self-esteem, and social awareness). Items were selected based on
their high correlations with total subscale scores. These items ensure adequate internal
consistency and a broad coverage of the EI domain. Internal consistencies were satisfactory for
both males and females (αmale = .84, αfemale = .89) (Petrides & Furnham, 2006).
Toronto Alexithymia Scale (TAS -20). Parker, Bagby, and Taylor’s (1992) Toronto
Alexithymia Scale (TAS-20) was employed. The construct, alexithymia, has been explained as a
“deficit in the cognitive capacity to process and regulate emotions (Parker, Bagby, Taylor,
Endler, & Schmitz, 1993). The TAS-20 was developed to encompass the following dimensions:
(a) difficulty identifying and describing feelings; (b) difficulty distinguishing between feelings
13
and the bodily sensations of emotional arousal; (c) constricted imaginal processes, as evidenced
by a paucity of fantasies; and (d) a cognitive style that is concrete and externally orientated
(Parker, Bagby, Taylor, Endler, & Schmitz, 1993). The TAS-20 has strong validity with a
Cronbach’s alpha of .91 and Test-Retest reliability of .77. The 20 items were rated on a 5-point
Likert scale (1 = “Strongly Disagree” to 1 = “Strongly Agree”).
Data Analysis
The goal of the data analysis is to show construct validity through the Multi-Trait Multi-
Method as presented in the seminal paper by Campbell and Fiske (1959). Construct validity will
be shown through both convergent validity and discriminant validity. Convergent validity is the
degree in which concepts that should theoretically be related are shown to be related. Evidence
for convergent validity is shown when different methods measure the same trait and show
relatively similar results. Discriminant validity is the degree in which concepts that should
theoretically not be related are shown to be unrelated. Evidence for discriminant validity is
shown when independent but same methods measure different traits and show different results.
Two techniques will be utilized in showing convergent and discriminant validity of the
new measure. The first technique is the more traditional approach using correlations as evidence
(MTMM correlation matrix). The second technique is more complicated and a newer method
that is a little more precise then using correlations. The second technique is using the Multi-Trait
Multi-Method (MTMM) through Structural Equation Modeling (SEM). The reason for MTMM
through SEM being used is the criticism that the original MTMM design has received and
limitations being shown in the basic analytic strategy. MTMM through SEM has also been the
most widely supported alternative method (Byrne, 2010).
14
Convergent and discriminant validity is shown through comparison of model fit statistics
through nested models as first presented by Widaman (1985). The technique and guidelines
suggest using a nested model and comparing the first model to a series of more restricted models.
Figure 1 shows this study’s hypothesized model (will be referred as Model 1 henceforth)
that will be compared against three other models. Model 1 has correlated traits and correlated
methods. Figure 2 shows the second model (Model 2). Model 2, like stated before, is more
restrictive with the traits being removed (Figure 2). Model 3 has perfectly correlated traits and
freely correlated methods (Figure 3). Model 4 has freely correlated traits and uncorrelated
methods (Figure 4).
Model 1-Model 2 comparison should show Model 2 having poorer model fit statistics
providing evidence for convergent validity due to the lack of specified traits. Model 1-Model 3
comparison should show discriminant validity by comparing a model in which traits are freely
correlated (Model 1) to a model in which traits are perfectly correlated (Model 3). The bigger
difference in model fit statistics, the stronger the support for discriminant validity. However, it
should be noted in this study we would not expect a large discrepancy because emotional
intelligence and alexithymia are expected to be highly correlated. We would still expect a
significant difference in model fit statistics, though. Model 1-Model 4 comparison uses the same
logic as used in the Model 1-Model 3 comparison but in reverse. Model 4 removes the
correlation between the methods. Discriminant validity is shown in this comparison through
Model 1 and Model 4 not being significantly different because we would not expect methods to
be highly correlated thus showing no method bias.
15
Results
Descriptive statistics and alpha coefficients are shown in Table 4. The EI-IAT was shown
to have good internal consistency with an alpha coefficient of .73. Most of the explicit measures
also showed good internal consistency with alphas .7 or greater. Zero-order correlations of all
study variables are shown in Table 5. The EI-IAT was significantly positively correlated with the
TEIQue global trait EI factor. The EI-IAT was also significantly negatively correlated with the
TAS total factor and the difficulty identifying feelings sub-factor. Both of these correlations are
evidence for convergent validity.
Table 4. Descriptive Statistics for Study VariablesVariables N Min Max Mean SD AlphaImplicit Measurea EI-IAT 181 -1.47 1.20 -0.01 0.47 .73Explicit Measures TEIQue Global Trait EI 181 3.07 6.53 4.99 .65 .88 Well Being 181 2.00 7.00 5.62 .97 .86 Self-Control 181 2.33 6.33 4.51 .83 .62 Emotionality 181 1.88 6.75 4.85 .81 .66 Sociability 181 2.67 6.67 5.00 .83 .68 TAS TAS Total 181 25.00 77.00 49.97 10.35 .83 Diff. ID. Feel 181 7.00 30.00 15.83 5.41 .85 Diff. Desc. Feel 181 5.00 24.00 13.58 4.06 .76 Ext. Orient. Think 181 9.00 33.00 20.56 4.37 .65
a The IAT measure is a D score (standardized mean differences measured in milliseconds); larger (positive) values reflect stronger associations with the global trait EI.
Individual model goodness-of-fit indicies are listed in Table 6. The hypothesized model
(Model 1) showed excellent fit with a RMSEA below .10 with a confidence interval only
containing values below .10 and a CFI above .90. The differential of the goodness-of-fit indicies
of each model comparison are shown in Table 7. Evidence of convergent validity was shown in
the comparison of Model 1 and Model 2 with a significant change in χ2 and CFI. Discriminant
16
validity evidence was shown in the comparison of Model 1 and Model 3 with a significant
change in χ2 and CFI. More evidence for discriminant validity was shown in the comparison of
Model 1 and Model 4 when there was no significant difference or practical difference between
the models.
Running head: 17
Table 5. Zero-order Correlations for Study Variables
Variables 1 2 3 4 5 6 7 8 9 10
Implicit Measure
1. EI-IAT -
Explicit Measures
2. TEIQue Total 0.15* -
3. Well-Being 0.14 .83** -
4. Self-Control 0.13 .72** .48** -
5. Emotionality 0.09 .71** .43** .36** -6. Sociability 0.11 .70** .51** .38** .32** -
7. TAS Total -0.18* -.70** -.52** -.42** -.70** -.40** -
8. Diff. Ident. Feel -0.17* -.64** -.50** -.53** -.47** -.39** .79** -
9. Diff. Desc. Feel -0.13 -.63** -.44** -.34** -.69** -.37** .86** .61** -
10. Ext. Orient. Thghts -0.09 -.28** -.20** -.02 -.44** -.13 .59** .06 .36** -
* p < .05; ** p < .01
Running head: 18
Table 6. Summary of Goodness of Fit Indicies for MTMM Models
Model χ2 df CFI RMSEA 90% C.I.
1. Freely correlated traits; freely correlated methods
20.387 16 0.991 0.039 .000, .084
2. No traits; freely correlated methods
105.653 26 0.845 0.130 .105, .157
3. Perfectly correlated traits; freely correlated methods 38.911 18 0.951 0.080 .045, .115
4. Freely correlated traits; uncorrelated methods
22.514 18 0.991 0.037 .000, .080
Table 7. Differential Goodness-of-Fit Indicies for MTMM Nested Model Comparisons
Difference in
Model Comparisons χ2 df CFI
Test of Convergent Validity
Model 1 vs. Model 2 85.27* 10 0.15*
Test of Discriminant Validity
Model 1 vs. Model 3 18.52* 2 0.04*
Model 1 vs. Model 4 2.13 2 0.00
19
Discussion
Summary of Findings
The EI-IAT demonstrated convergent and discriminate validity and showed good internal
consistency. The EI-IAT was significantly positively correlated with the TEIQue global trait EI
Figure 1. Hypothesized Model (Model 1)Figure 2. Model 2
Figure 3. Model 3 Figure 4. Model 4
20
factor. The EI-IAT was also significantly negatively correlated with the TAS total factor and the
TAS subfactor, difficulty identifying feelings.
Study Implications
The findings in this study have unique implications for organizations, practitioners, and
the field of EI research. As noted, EI is measured through performance-based and self-report
tests. However, these current explicit measures are susceptible to faking and require introspective
ability and therefore, yield validity concerns. The use of a valid implicit measure of EI provides
organizations and I/O practitioners with a tool to capture an individual’s “true” EI. Because EI is
predictive of job performance, it is imperative that it is accurately evaluated. Further, an accurate
assessment of EI can lead to better organizational decisions including promotional and selection
decisions.
Future Research
The development of an implicit measure of EI presents valuable information and
opportunities for the field of EI research. Given the inconsistencies and contradictions in the
measurement of EI, future research of implicit measures is warranted.
We advise that the EI-IAT be utilized in future studies to further assess reliability and
validity. We also recommend the EI-IAT be used with diverse samples to ensure generalizability.
Moreover, future studies should compare explicit measures to EI-IAT to demonstrate incremental
predictive validity over the self-report and performance-based measures.
21
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Watson, P., Grisham, S. O., Trotter, M. V., & Biderman, M. D. (1984). Narcissism and Empathy: Validity Evidence for the Narcissistic Personality Inventory. Journal Of Personality Assessment, 48(3), 301.)
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Appendix A: TAS-20
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Appendix B: TEIQue-SF
Instructions: Please answer each statement below by putting a circle around the number that best reflects your degree of agreement or disagreement with that statement. Do not think too long about the exact meaning of the statements. Work quickly and try to answer as accurately as possible. There are no right or wrong answers. There are seven possible responses to each
statement ranging from ‘Completely Disagree’ (number 1) to ‘Completely Agree’ (number 7).
1 . . . . . . . . . 2 . . . . . . . . . . 3 . . . . . . . . . . 4 . . . . . . . . . . 5 . . . . . . . . . . 6 . . . . . . . . . . 7Completely Completely
Disagree Agree
1. Expressing my emotions with words is not a problem for me. 2. I often find it difficult to see things from another person’s viewpoint. (R) 3. On the whole, I’m a highly motivated person. 4. I usually find it difficult to regulate my emotions. (R) 5. I generally don’t find life enjoyable. (R) 6. I can deal effectively with people. 7. I tend to change my mind frequently. (R) 8. Many times, I can’t figure out what emotion I'm feeling. (R) 9. I feel that I have a number of good qualities. 10. I often find it difficult to stand up for my rights. (R) 11. I’m usually able to influence the way other people feel. 12. On the whole, I have a gloomy perspective on most things. (R) 13. Those close to me often complain that I don’t treat them right. (R) 14. I often find it difficult to adjust my life according to the circumstances. (R) 15. On the whole, I’m able to deal with stress. 16. I often find it difficult to show my affection to those close to me. (R) 17. I’m normally able to “get into someone’s shoes” and experience their emotions.18. I normally find it difficult to keep myself motivated. (R) 19. I’m usually able to find ways to control my emotions when I want to. 20. On the whole, I’m pleased with my life. 21. I would describe myself as a good negotiator. 22. I tend to get involved in things I later wish I could get out of. (R) 23. I often pause and think about my feelings. 24. I believe I’m full of personal strengths. 25. I tend to “back down” even if I know I’m right. (R) 26. I don’t seem to have any power at all over other people’s feelings. (R) 27. I generally believe that things will work out fine in my life. 28. I find it difficult to bond well even with those close to me. (R) 29. Generally, I’m able to adapt to new environments. 30. Others admire me for being relaxed.
Scoring key: Reverse-score (R) items and then sum up all responses