Emotional Intelligence: Toward a Consensus of Modelsand Measures

Richard D. Roberts1*, Carolyn MacCann2, Gerald Matthews3, and MosheZeidner4

1 Educational Testing Service2 University of Sydney3 University of Cincinnati4 University of Haifa

Abstract

This article outlines the many different ways that emotional intelligence (EI) has been conceptual-ized, measured, and used from the early antecedents of emotional abilities such as facial expressionresearch to the recent multimedia assessment paradigms. The divide between models describing EIas a character trait versus modeling EI as a form of information processing or knowledge isdescribed, with both kinds of models evaluated based on theory and empirical evidence. It is con-cluded that the latter type of model, exemplified by the four-branch hierarchical model of EI, isthe only logical construct to bear the label ‘EI’. Potential emendations to the way EI is currentlyconceptualized and measured are discussed, with this review covering emotion recognition assess-ments, situational judgment tests, and multimedia assessments such as the empathic agent para-digm. The article concludes with a suggested agenda for future research in the EI field.

‘‘Reason is, and ought only to be the slave of the passions, and can never pretend to any otheroffice than to serve and obey them.’’ (Hume, 2003 ⁄ 1739, A Treatise of Human Nature).

Introduction and Brief History of Emotional Intelligence

What happens when a new construct emerges after nearly a century of modern psycho-logical science? Emotional intelligence (EI), the topic of the current article, provides anexemplary case study. On the one hand, it garnishes widespread media hype, beingoffered up as a panacea for many of contemporary society’s problems, including unethicalbehavior and people’s general sense of malaise (e.g., Goleman, 1995). Scientists flock tostudy it, with numerous different measures touted as the latest and greatest in psychomet-ric assessments. On the other hand, it is treated scornfully as a passing fad that the erst-while researcher views as a debilitating distraction from the work of science (e.g.,Murphy & Sideman, 2006). A cadre of journalists emerges to take passing shots on thetopic based on limited (sometimes even unpublished!) selective reading (e.g., Bronson &Merryman, 2009). Numerous studies are conducted without a clear (and restrictive) defi-nitional framework for determining what EI is and is not (Zeidner, Roberts, & Mat-thews, 2008). And there are some that hold it up to high-handed criticisms that over acentury of research on topics such as motivation and cognitive abilities has still failed ade-quately to address (e.g., Locke, 2005).

A more even-handed treatment of EI is needed. In this article, we will attempt to pro-vide a balanced account, or at the very least make known our biases and assumptions.We will begin by covering some of the historical antecedents around which the concept

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ª 2010 The AuthorsSocial and Personality Psychology Compass ª 2010 Blackwell Publishing Ltd

has emerged, before providing a review of various conceptual frameworks. We also coverattempts to measure EI, pointing out some of the problems with self-report techniquesthat have been used that do not always pass scientific muster. Next, we discuss perfor-mance assessments of EI, including recent attempts to make use of multimedia technolo-gies and other innovative approaches. We then review some of the recent data thatspeaks to the issue of the societal value of the construct, especially whether there is evi-dence suggesting that EI predicts valuable outcomes. We conclude with what we see as aviable future research agenda that might place the study of EI on more solid scientificfoundations.

The idea that emotions and rationality are interconnected, as the concept of EI implies,dates back to Aristotle’s writings on passions, with passions conceived as the guidingmotivators of all human behavior. The early proposal that emotions motivate beneficialbehaviors, including both approach and avoidance behaviors, has been adopted by manymodern emotion theories that stress the action tendencies associated with different emo-tions (e.g., Frijda, 1988). According to modern appraisal theories of emotion, emotionsare generated in response to automatic evaluation of the self-relevant aspects of the sur-rounding environment and subsequently produce action impulses that influence behavior(Ellsworth & Scherer, 2003; Lazarus, 1991; Parkinson & Manstead, 1992). Other moderntheorists view emotions as a source of information that can be processed to predict one’sown and others’ reactions and to help successfully engage in social life (Keltner & Kring,1998; Parkinson, Fischer, & Manstead, 2005). As such, individuals who can work fluentlywith emotional information have advantages over the emotionally illiterate – by accu-rately recognizing the indications of emotions in others, they can more accurately predictwhat others will do and approach or avoid others in particular ways accordingly.

Different areas of psychology have studied different aspects of this fluency with emo-tional information under different guises and names and with different methods at differ-ent time periods. Early in the history of intelligence research, Thorndike (1920, p. 228)proposed a concept of social intelligence that encompassed the idea of understanding themotivations and emotions of others (‘the ability to understand and manage men andwomen, boys and girls’). However, difficulty in empirically distinguishing social fromcognitive intelligence resulted in the dismissal of the idea of a separate socio-emotionalability for much of the history of intelligence testing, with two notable exceptions. First,Guilford (1967) posited a behavioral category of intelligence within the content facet ofhis structure of intellect model that parallels the idea of working with emotional informa-tion (e.g., Landy, 2006; Matthews, Roberts, & Zeidner, 2004). Second, Gardner (1983)proposed intrapersonal and interpersonal abilities as two components in his theory ofmultiple intelligences. The meaning of facial expressions and the cognitive, evolutionary,and neuropsychological processes involved in interpreting them also have a long historyin psychological research (e.g., Ekman, 1999; Panksepp, 1998). Likewise, concepts suchas temperament, emotional competency, and ‘emotion perception and labeling’ fromdevelopmental psychology identified what may be further aspects of these emotion-related abilities (Zeidner, Matthews, Roberts, & MacCann, 2003). Work on implicit the-ories of intelligence suggests that people do intuitively view social and emotional skills asa distinct ability from analytic intelligence (Sternberg, Conway, Ketron, & Bernstein,1981).

However, the first known reference to the term ‘EI’ was in 1960s Germany, wherewomen who rejected their social roles were diagnosed with low EI and subsequentlytreated with the hallucinogenic drug, LSD (Leuner, 1966). The field has come a longway toward credibility since then, with the first scientific publication on EI in 1990

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(Salovey & Mayer, 1990). However, the exponential growth in public interest in intelli-gence began with Daniel Goleman’s (1995) book, Emotional Intelligence, and subsequentmedia saturation. A time magazine cover, an episode of Oprah Winfrey devoted to EI,and a proposal by the American Dialect Society that ‘EI’ was one of the most usefulphrases of the year (1995), both echoed and snowballed the public’s interest in this ‘new’topic. Scientists (being members of the public) also became part of this zeitgeist ofrenewed interest in emotional abilities, with the number of scientific journal articlesincreasing rapidly from the late 1990s onwards.

With the popularization of EI occurring so rapidly, multiple different research tradi-tions and methods for studying EI sprang up in parallel. This flurry of activity has had thepotential for great confusion among scientist and practitioner, student and expert, alike:Different models and measures of EI frequently emerge as entirely dissimilar. Among themultitude of models, there are two distinct traditions for defining and measuring EI(Mayer, Roberts, & Barsade, 2008). The first tradition measures EI as a mixed bag of‘noncognitive’ traits (e.g., assertiveness, optimism, reality testing, conflict resolution, etc.)measurable by self- and other-ratings and will be referred to as ‘self-report EI’. Some the-orists in this tradition now view EI as a personality trait, such that EI is conceptually andempirically subsumed within the domain of personality. The second tradition positions EIas a cognitive ability measurable by tasks involving cognitive processing of emotionalinformation, and is commonly referred to as ‘ability EI’. In the passages that follow, webriefly evaluate some of the major psychological instruments emerging from these tradi-tions, concluding with suggestions for the model we believe has the potential to movethe field forward, albeit with important modifications.

The Trait Approach to Emotional Intelligence

The two models of EI with the highest public visibility are both self-report models, pro-posed by Bar-On and Goleman, respectively. Both of these models define EI primarily interms of behavioral outcomes without specifying links to the research fields of emotionintelligence. This seems conceptually problematic for a concept labeled ‘EI’, which shouldreasonably be defined with reference to both emotion and cognitive abilities. For exam-ple, Bar-On (1997) characterizes EI as ‘an array of noncognitive (italics added by authors)capabilities, competencies, and skills that influence one’s ability to succeed in coping withenvironmental demands and pressures’ (p. 14). Goleman (1998), by contrast, links EI tocompetencies associated with self-awareness, self-monitoring, social awareness, and rela-tionship management. Other researchers have suggested that EI is actually a previouslyunder-investigated component of personality, writ large, developing a model specificallylabeled as ‘trait EI’ (e.g., Petrides, Furnham, & Mavroveli, 2007). Unfortunately, each ofthese models has received telling criticisms in the scientific literature (e.g., Burns, Bastian,& Nettelbeck, 2007; De Raad, 2005; Grubb & McDaniel, 2007; Landy, 2005, 2006;Locke, 2005; O’Sullivan, 2007; Schulze, Wilhelm, & Kyllonen, 2007; Zeidner, Mat-thews, & Roberts, 2009). One reason these models are considered problematic is thatthey tend to embrace so many different aspects of human performance that they losemeaning: They are essentially protean (Landy, 2005) and likely not falsifiable (Locke,2005). In addition, they share too much in common with already established constructs,especially those describing human personality (Matthews et al., 2004).

The measurement of these theoretical models through self-ratings has several ramifica-tions for their operational use. The Bar-On, Goleman, and trait EI models have eachgenerated various self-report assessments of the proposed constructs constituting their

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respective frameworks. The procedure is simple enough: Ask questions to respondents ofthe following form: ‘I am good at managing relationships’, have respondents rate this ona Likert-type scale (e.g., from ‘Strongly Disagree’ to ‘Strongly Agree’), and include a suf-ficient number of these types of items to ensure reliability. But scientific studies suggest anumber of characteristics that these assessments share. These characteristics and theirimplications for applications and construct validity are described in the following sections.

Near complete overlap with personality

The extent that self-report measures correlate with personality and especially assessmentsof the Big Five personality factors is very high. In other words, the potential conceptualproblem of over-inclusive definitions for these models actually ends up being a demon-strable empirical shortcoming of the related assessments. For example, De Raad (2005)showed that 66% of items drawn from self-report inventories of EI could be classifiedunder the Big Five framework (Agreeableness and Emotional Stability were especiallyover-represented). The main reason for items not being classifiable was that they wereambiguous or difficult for respondents to understand. In numerous other studies, correla-tions between the Big Five and self-report measures has been found to be around 0.50–0.70 for at least one of the five superfactors, with multiple correlations approaching 0.80,and near unity if corrected for attenuation (Austin, Saklofske, & Egan, 2005; Brackett &Mayer, 2003; Brackett, Rivers, Shiffman, Lerner, & Salovey, 2006; Davies, Stankov, &Roberts, 1998; Dawda & Hart, 2000; Grubb & McDaniel, 2007; O’Connor & Little,2003; Petrides & Furnham, 2003; Roberts, Schulze, & MacCann, 2008). There are somestudies that claim impressive correlations between self-report measures of the Bar-On,Goleman, and trait EI models and outcomes valued in both educational and workplacesettings (e.g., Bar-On, 2000; Mavroveli, Petrides, Sangareau, & Furnham, 2009; Petrides,Fredrickson, & Furnham, 2004; Sala, 2002). However, these studies generally do notcontrol first for the likely source of these correlations: Self-report measures of ‘EI’ servingas a proxy for the Big Five personality factors. Although the particular permutations ofpersonality traits measured by EI may prove practically useful, such strong overlap withpersonality suggests that ‘EI’ is not an accurate label for trait EI, as trait EI is clearly partof the personality domain.

Near complete divergence from intelligence

These self-report measures of ‘EI’ (or variants thereof) show near zero, or sometimes evennegative correlations with established measures of intelligence (e.g., Barchard & Hakstian,2004; Bastian, Burns, & Nettelbeck, 2005; Brackett et al., 2006; Derksen, Kramer, &Katzko, 2002; Mavroveli et al., 2009; Newsome, Day, & Catano, 2000; Sala, 2002; Zeid-ner, Shani-Zinovich, Matthews, & Roberts, 2005). There are very few laws in psychol-ogy, but one that stands out as highly replicable in literally thousands of studies is the‘law of positive manifold’ (e.g., Carroll, 1993; Roberts et al., 2008), or the ‘first law ofintelligence’ (Guttman, 1992; Guttman & Levy, 1991). Basically, the law suggests that inall empirical instantiations intelligence measures will correlate positively with other stan-dard forms of intelligence. A simple corollary of this law is that whatever measures havebeen derived to assess the constructs proposed by the Goleman, Bar-On, or trait EI mod-els, they do not assess a form of intelligence. Thus, the label ‘EI’ is not accurate terminol-ogy to describe these models.

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Weak and inconsistent correlations with emotions measures

Attempts to link self-report measures of EI to established emotional phenomena have metwith limited success. In particular, several studies failed to confirm that EI is associatedwith speed and ⁄or accuracy of emotional stimulus processing. For example, Fellner et al.(2007) showed that several trait EI scales (including the TEIQue) were unrelated to speedand accuracy of visual search for facial emotion target stimuli. Trait EI was also uncorre-lated with accuracy of a task requiring identification of brief ‘microexpressions’ of facialemotion. In addition, Fellner, Matthews, Warm, Zeidner, and Roberts (2006) found thattrait EI was unrelated to performance on a task requiring discrimination learning that usedfacial emotions as cues. Such findings question whether the emotional part of various self-report models also warrants the label ‘EI’.

Problems of insight, faking, and the like

Self-report methods are open to various biases and distortions that make them suspect.Typically, these questionnaires ask for self-ratings of skills and competencies. In an influ-ential article, Dunning, Heath, and Suls (2004) showed that people may not be particu-larly good at making such self-assessments. In addition, rating one’s own EI depends oninsight and self-understanding; qualities that are themselves central to EI. It is paradoxicalto assume that an emotionally unintelligent person may be able to meaningfully rate theirown levels of this proclivity. In addition, individuals asked to rate their level of positivecharacteristics may engage in response distortion of the deliberate ‘faking’ of high scores,particularly for high-stakes scenarios. Grubb and McDaniel (2007) demonstrated that theBar-On measure could be faked by nearly a full standard deviation if respondents wereinstructed to do so. Similar studies have yet to be conducted with the Goleman or traitEI models but there is nothing in their technical make-up to suggest that a different resultwould obtain.

For these reasons, we conclude that the self-report or ‘trait’ models of EI, while poten-tially informative for the personality field, cannot reasonably be interpreted by a laypersonor scientist as measuring a concept called ‘EI’. The utility of research on these types ofmeasures might instead highlight the importance of personality traits in predicting sociallyvalued outcomes. The concept that personality (couched as ‘EQ’) as well as intelligenceis important for life success can in fact be taken as a primary message of Goleman’s(1995) book that launched public interest in ‘EQ’. The book’s sub-title and initial defini-tion of EI as personality make this clear: ‘there is an old fashioned word for the body ofskills that EI represents – character’ (p. 285) that ‘can matter more than IQ’ (sub-title).Nevertheless, it is equally clear that measures of personality with no relationship to eitherintelligence or emotion should not accurately be referred to as ‘EI’. For this reason, wenow turn our attention to ability measures of EI, as generally measured under the four-branch hierarchical model of EI.

The Ability Approach and the Four-branch Hierarchical Model of EmotionalIntelligence

The most commonly accepted conceptual model for EI in the scientific community isthe four-branch hierarchical model (Mayer et al., 2008). The four branches represent:(1) perception and expression of emotion (emotion perception); (2) using emotions tofacilitate task performance (emotion facilitation); (3) understanding relationships

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between emotions, situations, and time courses (emotional understanding); and (4) theregulation of management of one’s own and others’ emotions (emotion management;Mayer, Salovey, Caruso, & Sitarenios, 2001). Notably, this model defines EI as anability resembling other standard forms of intelligence. That is, high EI persons areobjectively superior to those of lower EI in performing certain activities associated withemotions.

There are three different forms of tests that arise from the four-branch hierarchicalmodel. First, rating scales obtain self- or observer reports of the target’s emotion percep-tion, facilitation, understanding, and management. These rating scales assess trait EI(described previously), with scores on these scales relating to personality but not intelli-gence. Second, omnibus assessment batteries that contain subtests for all four branchesand use a maximum performance measurement approach (i.e., where the goal is to mea-sure a person at their maximum level of effort; see Roberts et al., 2008). This is currentlythe most common form of ability assessment of EI. Third, assessments that may measureone or more of the four sub-components (e.g., emotion regulation only), or constructsclosely related to the four-branch model (e.g., empathic ability or emotion recognition).This third approach can be considered as an alternative to the dominant paradigm ofomnibus assessment measures. In the passages that follow, we first cover existing omnibusmeasures, before moving to consideration of alternative approaches.

Existing approaches to EI: Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT)

Until very recently, one testing paradigm has been developed to capture EI as a form ofintelligence – the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) and anearlier incarnation, the Multi-factor Emotional Intelligence Test (MEIS) (Mayer, Caruso,& Salovey, 1999; Mayer, Salovey, Caruso, & Sitarenios, 2003). Table 1, which links andfurther defines each of the constructs in the four-branch hierarchical model, also providesa brief capsule description of each of the measures comprising the MSCEIT. An evalua-tion of this EI assessment follows based on standards for evaluating psychological tests,which require that the underlying test scores are reliable and have demonstrable validityevidence (American Educational Research Association, American Psychological Associa-tion, & National Council on Measurement in Education, 1999).

Notably, each of the MSCEIT branches has acceptable internal consistency reliability(Mayer et al., 2003). Super-ordinate constructs such as general EI, Experiential EI, andStrategic EI have especially high reliability coefficients (i.e., 0.90 or above). However,several subtests (e.g., Blends) of the MSCEIT have marginal (i.e., less than 0.60) reliabili-ties (Mayer et al., 2003). This result also holds true for the MEIS (Roberts, Zeidner, &Matthews, 2001). With regard to structural validity, although some studies suggest thatthe MSCEIT has four recoverable factors as theory dictates (Mayer et al., 2003), this find-ing has proven difficult to replicate. The problem appears largely with Emotional Facilita-tion, which often fails to emerge as an independent construct (e.g., Roberts et al., 2006).Nevertheless, meta-analysis demonstrates that MSCEIT sub-tests show little relationshipwith personality, aside from a small relationship between Agreeableness and EmotionManagement (Roberts et al., 2008). This is important from the standpoint of providingdivergent validity evidence for these test scores. In demonstrating convergent validity evi-dence, MSCEIT scores also relate to cognitive ability, with the driving force of this rela-tionship being the link between understanding emotions (the third branch of EI) andcrystallized intelligence (verbal facts and knowledge explicitly gained from formal school-ing) (Roberts et al., 2008).

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Tab

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ayer

–Sal

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Inte

lligen

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st(M

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T)(M

ayer

etal

.,2003)

Level

Ab

ilit

ies

an

din

dic

ato

rs

Gen

eral

Emotional

inte

lligen

ce(E

I)A

reas

‘Exp

erie

ntial

EI’

isth

eab

ility

toper

ceiv

e,re

spond,

and

man

ipula

teem

otional

info

rmat

ion

without

nec

essa

rily

under

stan

din

gits

mea

nin

g

‘Str

ateg

icEI

’as

sess

esab

ility

tounder

stan

dan

dm

anag

eem

otions

without

nec

essa

rily

exper

ienci

ng

the

feel

ings

of

emotion

Bra

nch

esTh

eab

ility

to‘p

erce

ive

emotions’

infa

ces

and

pic

ture

s

‘Fac

ilita

ting

thought’

by

cognitiv

epro

cess

ing

of

emotions

‘Under

stan

din

gem

otions’

and

how

they

ble

nd

and

chan

ge

ove

rtim

e

‘Man

agin

gem

otions’

by

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ng

feel

ings

tocr

eate

bet

ter

outc

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esTa

sks

Face

s:Fo

rea

chof

four

photo

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phs

of

face

s,par

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tsm

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rate

the

pre

sence

of

five

emotions

from

1(n

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to5

(ext

rem

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otion)

Pict

ure

s:Fo

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six

pic

ture

spar

tici

pan

tsm

ust

rate

the

pre

sence

of

five

emotions

from

1to

5

Sensa

tions:

For

each

of

five

stat

emen

ts,

par

tici

pan

tsar

eas

ked

tom

ake

thre

eju

dgm

ents

about

the

sim

ilarity

of

anem

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toa

phys

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tion

from

1(n

ot

alik

e)to

5(a

like)

Faci

litat

ion:

For

each

of

five

scen

ario

s,par

tici

pan

tsm

ust

rate

the

hel

pfu

lnes

sof

thre

ediffe

rent

moods

from

1(n

ot

use

ful)

to5

(use

ful)

Ble

nds:

Part

icip

ants

answ

er12

multip

le-c

hoic

eques

tions

asse

ssin

gw

hic

hco

mbin

atio

ns

of

emotions

form

com

ple

xem

otions

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ges

:Pa

rtic

ipan

tsan

swer

20

multip

le-c

hoic

eques

tions

asse

ssin

gw

hic

hem

otions

are

rela

ted

topar

ticu

lar

situ

atio

ns

Man

agem

ent:

For

each

of

5sc

enar

ios,

par

tici

pan

tsra

teth

em

ood-m

anag

emen

tof

actions

from

1(v

ery

inef

fect

ive)

to5

(ver

yef

fect

ive)

Rel

atio

ns:

For

each

of

five

scen

ario

s,par

tici

pan

tsm

ust

rate

the

effe

ctiv

enes

sof

thre

ere

sponse

sfr

om

1(v

ery

inef

fect

ive)

to5

(ver

yef

fect

ive)

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There is also some evidence for test-criterion relations (where MSCEIT scores predictoutcomes of societal worth; Mayer et al., 2008). MSCEIT scores predict job performanceover and above personality and ability, although the effect size is small [r = 0.10–0.20,when personality and intelligence are controlled (Cote & Miners, 2006)]. Meta-analyticevidence suggests that MSCEIT scores only predict job performance for high-emotionallabor jobs, which require positive emotional displays [e.g., wait-staff, counselors (Joseph &Newman, 2010)]. This limitation makes conceptual sense, as EI might be expected to con-tribute to performance only when the related outcome requires an emotional component.Results are similar for the educational domain, where MSCEIT scores show a small incre-mental relationship to grade point average (GPA) (about r = 0.10–0.20). Psychologistshave argued that EI may link to educational success by protecting students from salientbarriers to classroom learning such as debilitating anxiety, mental distress, teen pregnancy,substance abuse, truancy, delinquency, and violence (Hawkins, Smith, & Catalano, 2004).In the clinical domain, MSCEIT branch scores for understanding and managing emotionsare lower for people diagnosed with major depressive disorder, substance abuse disorder,borderline personality disorder, schizotypy and schizophrenia than nonclinical controls(Aguirre, Sergi, & Levy, 2008; Hertel, Schutz, & Lammers, 2009; Kee et al., 2009).

Given that the scales depicted in Table 1 can legitimately be labeled ‘EI’ and that vir-tually all research to date has used the MEIS or MSCEIT, many of the findings on EI areinstrument specific. This may be consequential, as the MEIS ⁄MSCEIT paradigm has bothan unusual scoring rubric (consensus-scoring) and an unusual answer format (a rate-the-extent answer format is used in three-quarters of MSCEIT tests).

A prototypical item of emotion perception, depicted in Figure 1, exemplifies bothconsensus-scoring and rate-the-extent characteristics. In this item, the test-taker rates theextent of happiness, fear, and surprise in the facial expression, rather than judgingwhether the expression is happy, angry, or scared. For each of these three ratings, thescore awarded would be equivalent to the number of people in a screening sample whochose that rating point (e.g., if 50% of people said the face was ‘extremely happy’ thescore awarded for choosing a ‘5’ for happiness would be 0.50). The possibility that thesetwo measurement methods constitute a source of method variance that might affectempirical results is of concern when the MEIS ⁄MSCEIT paradigm is the only EI scaleavailable. Because of this limitation, and the fact that alternative assessments from other

No Happiness

Indicate how much of each emotion is expressed by this face:

1 2 3 4 5 Extreme Happiness

No Anger 1 2 3 4 5 Extreme Anger

No Fear 1 2 3 4 5 Extreme Fear

Figure 1 Example of the Multi-factor Emotional Intelligence Test (MEIS) ⁄ Mayer–Salovey–Caruso Emotional Intelli-gence Test (MSCEIT) item format.

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fields may be applied to emotional phenomena, researchers have begun exploring newmethods for assessing EI; several of which are discussed later in some detail.

Alternative approaches to measuring the branch-level abilities of emotional intelligence

Emotion recognition assessments. The study of emotions in facial expressions is a far olderfield than EI. Scientific research on facial expressions stretches back more than a century,to Darwin’s (1872) The Expression of the Emotions in Man and Animals. In psychology, Sil-van Tomkins’ (e.g., 1962) research inspired both Paul Ekman and Carroll Izard, whomade seminal contributions to this field. Ekman’s corpus of work, in particular, includedspecification of lawful rules linking muscular changes to facial expressions in the form ofthe facial action coding system (FACS; Ekman & Friesen, 1978; Ekman & Rosenberg,1997). The FACS was later used as the basis for several research tools assessing the recog-nition of facial expressions, including the Japanese and Caucasian Brief Affect Recogni-tion Test (JACBART) and Diagnostic Analysis of Nonverbal Accuracy in Adult FacialExpressions (DANVA2-AF) listed in Table 2. The vast research corpus in this domaincould clearly be valuably applied to EI research. However, because early research focusedon these tasks as experimental paradigms or measures of group processes rather than indi-vidual differences, only in the last few years have some researchers begun to use these asputative indicators of EI (e.g., Roberts et al., 2006).

In addition to these tests that use faces as stimuli, there are currently assessments ofemotion recognition ability for tone-of-voice such as the Vocal Expression RecognitionIndex (Vocal-I) and the prosody measures from the Diagnostic Analysis of NonverbalAccuracy (DANVA2-AP). The DANVA ‘suite’ of assessments also includes tests thatassess the recognition of emotions from postures or gestures. Recently, Banziger, Grand-jean, and Scherer (2009) have developed an assessment that combines all of these features(i.e., face, voice, and body) in a single test known as the Multimodal Emotion Recogni-tion Test (MERT). Table 2 also includes a capsule description of each of these tests ofemotion recognition.

These assessments appear to correlate highly with each other when in the same modal-ity, providing some evidence of convergent validity. For example, the DANVA2-AF andthe JACBART correlate at r = 0.80 (Mayer et al., 2008). However, Roberts et al. (2006)found that measures from different modalities (i.e., vocal and facial tests) were onlyweakly correlated. Specifically, the JACBART and Vocal-I correlated at r = 0.17. Inaddition, these measures do not seem to correlate with the Perceiving Emotions testsfrom the MSCEIT: Roberts et al. (2006) report a correlation of r = 0.07 betweenMSCEIT Faces and the JACBART. These findings suggest two testable hypotheses. First,the MSCEIT method of rating-the-extent of emotion in a still photo of a face seems tocapture a different skill from multiple-choice assessment of facial expression presented foronly a fraction of a second (as in the JACBART). Second, the ability to perceive emo-tions correctly may not be a single unitary ability, but may be specific to different modal-ities [a finding that is actually supported by Banziger et al.’s (2009) research using theMERT]. In fact, a stronger version of this modality-specific argument is that the emo-tional content of the stimuli may be irrelevant, with perceptual abilities differentiatedbased on sensory modalities alone, irrespective of emotional content. In the abilities liter-ature, cognitive tasks involving different sensory modalities (visual, auditory, kinesthetic,and olfactory abilities) form factors distinct from the memory or reasoning processesinvolved in the tasks (see e.g., Danthiir, Roberts, Pallier, & Stankov, 2001; Horn & Stan-kov, 1982; Roberts, Stankov, Pallier, & Dolph, 1997).

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Situational judgment tests (SJTs) of emotional understanding and management. SJTs haveindividuals encounter a situation (vignette) and then select either the most appropriateresponse or their typical response out of a list of possible choices. The method for con-structing such a test is fairly straightforward: (1) items are generated, most often usingcritical incident interviews with subject-matter-experts; (2) responses are generated, usually

Table 2 Sample measures of emotion recognition ability

Test and source Description

Japanese and Caucasian Brief Affect RecognitionTest (JACBART; Matsumoto et al., 2000)

An instrument consisting of 56 stimuli,presented in video format. Stimuli consist ofJapanese or Caucasian faces portraying one ofseven emotions: happiness, contempt, disgust,sadness, anger, surprise, and fear. Eachstimulus is briefly presented (1 ⁄ 5 s) inside abackward and forward mask, which shows aneutral face

Diagnostic Analysis of Nonverbal Accuracy inAdult Facial Expressions (DANVA2-AF; Nowicki& Carton, 1993)

A total of 24 photographs of an equal numberof happy, sad, angry and fearful facialexpressions of high and low intensities,balanced also by gender. The participants’ taskis to indicate which of the four emotions ispresent in the faces. A youth form is alsoavailable

Diagnostic Analysis of Nonverbal Accuracy inPosture (DANVA2-POS; Pitterman & Nowicki,2004)

Measures an individual’s ability to identifyemotion in human standing and sittingpostures. The stimuli are two men and twowomen portrayed standing and sitting, yielding32 high- and low-intensity standing and sittingpostures representing happiness, sadness,anger, and fear

Diagnostic Analysis of Nonverbal Accuracy inAdult Paralanguage (DANVA2-AP; Baum& Nowicki, 1998)

A total of 24 audio stimuli where twoprofessional actors (one man, the otherwoman) say a neutral sentence, ‘I am goingout of the room now but I’ll be back later’ inone of four emotional states (happy, sad, angryor fearful) at high and low intensities. Theparticipants’ task is to indicate which of thefour emotions is present in the voices. A youthform is also available

Vocal Expression Recognition Index(Vocal-I; Scherer, 2007)

A 30 item computer-administered, multiple-choice task that requires participants to makejudgments about the emotion heard in a voicespoken in a foreign language. The phrases areuttered by actors so as to portray joy, sadness,fear, anger, and neutral

Multimodal Emotion Recognition Test(MERT; Banziger et al., 2009)

An instrument that objectively measures emotionrecognition ability on the basis of actorportrayals of dynamic expressions of 10emotions, operationalized as recognitionaccuracy in four presentation modes (i.e.,audio ⁄ video, audio only, video only, stillpicture) combining the visual and auditorysense modalities

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by a second group of subject-matter-experts; and then (3) a scoring key is developed by afurther group of experts (McDaniel & Nguyen, 2001). Several variations on this classicthree-step procedure exist, such that virtually any test with situations as the item stimuliis commonly described as an SJT. Existing measures of EI resembling SJTs include eachof the tests given in Table 3.

The various measures depicted in Table 3 point to the application of emotional situationsas useful stimuli when assessing EI. In the case of the final two measures in Table 3, thesituations were based on real emotional experiences of an appropriate sample (i.e., theyconformed to the classic SJT approach). This approach has two potential advantages. First,test items have greater ecological validity as the test situation more closely mimics reality.Second, the test scores may also be more legally defensible. If EI measures are used tomake high-stakes decisions, the possibility of disgruntled job applicants questioning thescoring process in a court of law may make transparent, easily-explainable scoring rubricsof great practical importance (i.e., if the correct answer is what really worked in the life ofa real person, this is a relatively easy scoring rubric to defend). The SJT measures describedin Table 3 have also been used to address measurement issues associated with EI tests. Forexample, MacCann and Roberts (2008) showed that multiple-choice and rate-the-extentversions of the STEM show different patterns of correlations: Multiple-choice scores cor-relate more highly with vocabulary and less highly with personality dimensions, and rate-the-extent scores correlate more highly with personality and less highly with vocabulary.

Indeed, there is an impressive array of validity data accruing from the scores derivedfrom these types of tests that cuts across a number of different sub-disciplines. Lane(2000), for example, has shown how LEAS scores are related to neuropsychological sub-strata. MacCann and Roberts (2008), by contrast, provide data showing meaningful rela-tions between the STEM and other emotions measures (such as measure of alexithymia,where correlations are around r = 0.40) and valued outcomes such as marks in a psychol-ogy course (where having high EI might confer real advantages; r = 0.31, after intelli-gence is controlled). Positive findings notwithstanding, scoring problems have not allbeen resolved for the SJTs listed in Table 3. It is difficult to be certain, in particular, thatany SJT is going to have a response set that can be unambiguously scored by experts.Given that the field of emotions has not yet settled unequivocally on a universal modelof emotion generation, it is quite possible that experts may use different theoretical mod-els and come to different conclusions as to the correct answer. Only if universallyaccepted laws of emotions are discovered might it be possible for tests of emotionalunderstanding to be unambiguously scored.

There are some other concerns for text-based SJTs. In particular, reading comprehen-sion may constitute a source of construct irrelevant variance. In addition, the ability toverbalize strategies for the management of emotion may be distinct from the implicitknowledge of emotion management. Brody (2004) has pointed out that emotion manage-ment tests tend to assess broad knowledge of how to manage emotional situations, butnot the performance skills themselves. For example, a psychology undergraduate coulddescribe how to provide psychotherapy to an anxious client, but, with good reason,undergraduates do not provide actual treatment. Conversely, an individual may be quitegood at managing emotional situations in an automatic and nonconscious way, but maybe unable to explicitly verbalize this knowledge. Thus, there may be a need for moredirect performance measures that do not depend on the conscious verbalization of emo-tion management strategies.

The use of video footage rather than text addresses the issue of reading comprehensionability and may also make the assessments less dependent on referential processing (as the

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test is less dependent on verbal ability and closer to the real life situations where auto-matic management strategies are used). With limitations of text-based, exclusively verbalstimuli in mind, we have developed a video-based SJT to assess emotional understandingusing the classic three-step SJT approach as a methodology, as well as a close derivativeof the SJT paradigm known as the empathic agent paradigm (EAP). The EAP requiresparticipants to perceive and memorize event-emotion contingencies in others and theninfer emotion-behavior contingencies from this observed behavior to predict futurebehavior (see Orchard et al., 2009; for a comprehensive description). In other words,test-takers observe a protagonist’s behavior in one emotional situation, and must theninfer how the protagonist might behave in another similar emotional situation. In boththese sets of tasks, scripts were written by professional writers and developed on the basisof interview data, with professional actors and film crew acting out the scenarios. In thisway, we appear to have developed content (and ecologically) valid measures that are lar-gely independent from reading ability. Assessment paradigms for both the traditional SJTand the EAP-type SJT essentially rely on short films that need to be watched in their

Table 3 Emotional understanding and management measures using the situational judgment testapproach (or close derivatives thereof)

Test and source Description Sample item

Levels of Emotional AwarenessScale (Lane et al., 1990)

Participants are required todescribe their anticipatedfeelings to 20 scenes. Anger,fear, happiness, and sadnessare elicited. Each scene isfollowed by: ‘How would youfeel?’ and ‘How would theother person feel?’ Eachperson’s answer receives twoscores for each emotiondescribed: self and other

You and your best friend are inthe same line of work. There isa prize given annually to thebest performance of the year.The two of you work hard towin the prize. One night thewinner is announced: yourfriend. How would you feel?How would your friend feel?

Emotional Accuracy ResearchScale (Geher et al., 2001)

This test consists of threevignettes followed by 12 pairsof mood items (e.g., mad –delighted; stomping feet –happy for another), where test-takers must select one of thepair as representative of theperson in the vignettes

My best friend’s father died thisweekend. He had diabetes fora long time and as he gotolder his health grew worseand worse. I went to hisfuneral on Monday. Many ofmy friends from high schoolwere also there …

Emotional Management Test(Freudenthaler & Neubauer,2007)

This vignette-based test assessesthe management of one’s ownothers’ emotions

Your father is very scared of acomplicated operation, whichis absolutely necessary. Option(1 of 4): To calm him down, Iadvise him to talk to thedoctor once more

Situational Judgment Test forManagement (MacCann& Roberts, 2008)

This 44 item situationaljudgment test was developedfrom critical incidents, whererespondents chooseappropriate responses toemotionally charged scenariosfrom multiple-choice options

Dan has been overseas for along time and returns to visithis family. So much haschanged that Dan feels leftout. What action would be themost effective for Dan? Option(1 of 4): Nothing, it will sortitself out soon enough

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entirety to fully understand the items. To give the reader a very rough sense of these par-adigms, two screenshots from the SJT is presented in Figure 2.

So far, we have conducted two studies where these assessments were administered tosamples of community college and university students across the United States (Roberts,2009). These studies demonstrated that the SJT and EAP assessments: (1) had adequateinternal consistency (0.70 and 0.80 respectively) and test-retest reliability (0.54 and 0.74

Task

Erin Phelps is a software engineer at a large software design firm. She is talking to Wait, another engineer, about being passed over for acompany-wide award for best software design.

Use the PLAY button to view their dialogue. Then click Next in order to see four potential responses form Wait, and rate the quality of eachresponse as a way of helping Erin cope with her disappointment.

Use the PLAY button to see a potential response form Wait, and rate the quality of his response. After you have indicated your rating for aresponse, click Next. On the next page, you will then be asked to give your reason(s) for the rating.

Situational JudgmentSection 1 1 of 8

unaemnea

Question Number

Task

Situational JudgmentSection 1

ExtremelyBad

ExtremelyGood

SomewhatBad

SomewhatGood Good

Neither Badnor GoodBad

1 of 8

Question Number

Testing Tools

Back Next

Testing Tools

Back Next

Figure 2 Screenshots of an item of the video-based situational judgment tests (SJT) of emotional understanding.

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respectively): (2) related meaningfully to other EI assessments, (c) related meaningfully tocognitive abilities, (4) did not show strong relationships with personality, but (5) relatedto well-being. Specifically, SJT and EAP scores correlated with each other (0.50), withthe MSCEIT branches (from 0.25 with Perception to 0.52 with Understanding), withthe Diagnostic Analysis of Nonverbal Accuracy Scale in Adult Faces (0.31), and withcognitive ability indicators (from 0.58 between the EAP and crystallized ability, to 0.27between the SJT and quantitative ability). For both SJT and EAP, the strongest relation-ship with personality occurred for the personality trait Agreeableness (�0.30). In addition,regression analyses suggested that cognitive ability, Conscientiousness, and the multimediaassessments consistently added to the prediction of GPA. Perhaps most importantly, bothSJT and EAP correlated moderately with psychological well-being indicators (�0.30) andindicators of net positive affect taken over a period of time using the Day ReconstructionMethod (DRM; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). These correla-tions exceeded r = 0.40 for reported net positive affect while socializing and working.Correlations were noticeably lower for reported affect while in class, and lowest when,for example, spending time on the internet or eating food (though always still positive insign).

These findings indicate we have seemingly been successful in developing multimediaassessments that are face valid and have acceptable measurement properties. Relationshipswith personality, intelligence, other emotions measures, and valued outcomes are all asmight be expected in the light of our aforementioned review of measures and indicatethat the scores have demonstrable construct validity. The DRM results are highly sugges-tive for the practitioner; this approach is used in economic models and indicates thatemotional abilities may play a role in worker satisfaction and productivity, for example.Clearly, there is a need to continue collecting data with these new assessments, but thusfar they have shown some promise. One research direction that we plan to pursueinvolves experimental manipulations of stress and decision making under uncertainty andcomparing individual’s performance under these conditions with their performance onthe multimedia assessments of EI.

Summary and Future Directions

Along with cumulative basic research on EI, there is an emerging consensus amongexperts that, properly defined, a hierarchical model of EI resembling the four-branchmodel is scientifically plausible and practically meaningful. Theory development will beenhanced by better measures and better research on the adaptive functions of EI. Mostespecially research is needed that systematically explores the correlates of EI beyond per-sonality and intelligence factors to a wide range of theoretically relevant constructs fromemotions, coping, positive psychology, and other fields.

Cronbach (1990) suggested that intelligence testing constitutes one of the most impor-tant practical contributions that psychology had made to society. A similar status is doubt-less hoped of measures of EI. By contrast with intelligence testing, the science of EI isrelatively new. Currently, there appear a sufficient number of instruments available todevelop a taxonomic model, and over time, also to ascertain how EI fits in relation tocomprehensive models of intelligence. Such work, ultimately, will feed back to improve-ments in the measurement instruments themselves, as has been the case in the realm ofintelligence testing, where iterations of the Wechsler and Stanford-Binet scales reflectadvances in the knowledge acquired by basic researchers. One of the current difficulties isthat the importance and scale of EI in the landscape of psychological constructs is hard to

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gauge. Are we looking at a major new construct or a minor construct with a fancy label?Fitting EI into multi-level models of the kind developed by Carroll (1993) is essential todetermine whether EI is no more than a novel primary mental ability (e.g., Davies et al.,1998), or, in line with Gardner’s (1983) multiple-intelligence theory, a major, high-levelfactor of similar scope and importance to general cognitive ability.

Focused research is needed. Studies combining many of the measures reviewed in thisarticle are scant, yet many of the advances in the field of intelligence testing haveoccurred using multivariate designs. In fact, multivariate designs are essential to the devel-opment of taxonomic models of human abilities. Multivariate research in EI could deter-mine the number of EI factors and the status of these factors in relation to other broadfactors of cognitive ability. A second vital area of EI research is the study of test-criterionrelationships that would empirically test the theoretical statements about the utility of EI.There are a number of valued outcomes (e.g., retention, citizenship, alexithymia) thatmight plausibly be related to an ability model. Perhaps because some of these outcomesare difficult to operationalize in a short space of time (i.e., they require complicated lon-gitudinal designs), these studies have been meager. If a science of EI is to develop, thiscan no longer be the case. Experimental studies where some of these outcomes aremanipulated would especially seem requisite. Similarly, psychometric modeling of EImeasures has yet to involve some of the newer techniques available (e.g., polytomousIRT, mixture modeling, or latent class analysis). While these models do not ‘save’ badmeasures from the proverbial scientific scrap-heap, they do offer insights into scoring rub-rics, measures, and ultimately constructs.

In addition, there are several other fields of psychological research where EI might beincluded. One that especially stands out is gerontology. Knowledge of cognitive abilitieshas benefited greatly from this line of research, which has shown how fluid abilitiesdecline over the life span, while crystallized abilities are maintained, at least for the mostpart (e.g., Carroll, 1993). Similarly, we know of no studies that have yet been conductedwith maximum performance measures of EI that aim to understand these constructs usingthe powerful tools of behavioral genetics. Moreover, there is a need to constantly re-eval-uate EI constructs in light of theoretical advances occurring in the emotions field, andespecially what is learned from psychobiological studies.

In conclusion, this article has presented a model of EI that views it as a form of cogni-tive ability. Measures of this ability were reviewed and scores from a good many of theseassessments were shown to be reliable and demonstrably valid. Relationships with valuedoutcomes were typically found to be modest, but so far only the tip of the criterion spaceappears to have been explored. As with any science, there are some unresolved issues.Scoring of EI measures remains problematic, and the status of the various branches of EIin relation to established intelligence constructs remains poorly understood. There is aneed for focused research to resolve these, and still further, issues addressed in the article.It is our hope this modest little piece might encourage the motivated reader to seriouslyentertain such research as a possible topic for a thesis, a step toward tenure, a fundedgrant or contract, or just for good-old knowledge’s sake.

Acknowledgment

This research was supported in part by US Army Research Institute (ARI) ContractW91WAW-07-C-0025 to the Educational Testing Service (ETS). We thank Jeremy Bur-rus, Patrick Kyllonen, Bobby Naemi, Don Powers, John Sabatini, and Matthew Venturafor comments on an earlier draft of this manuscript and Caspian Aicher, Anthony Betan-

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court, Daniel Howard, Teresa Jackson, Mary Lucas, Jennifer Minsky, and Tom Smith forsupporting the preparation of this manuscript. All statements expressed in this article arethe author’s and do not necessarily reflect the official opinions or policies of the US gov-ernment, ARI, or any of the authors respective institutions.

Short Biographies

Richard D. Roberts, PhD is a Principal Research Scientist in the Center for New Con-structs in the Educational Testing Service’s Research & Development Division, Prince-ton, New Jersey and an Adjunct Professor in The Klingenstein Center for IndependentSchool Leadership, Teachers College, Columbia University, New York. His main areasof specialization are assessment and human individual differences, and he has conductedresearch on cognitive abilities, EI, personality, health and well-being, motivation, self-confidence, sensory processes, aging, processing speed, situational judgment tests, andhuman chronotype. Dr Roberts has published around 150 peer-review articles or bookchapters on these topics, with a near equal number of presentations around the world.He has also received significant grants and contracts and a number of honors (includingtwo ETS Presidential Awards). He has co-authored ⁄ co-edited seven books, the latestbeing What We Know About Emotional Intelligence: How it Affects Learning, Work, Relation-ships, and Our Mental Health. A book on the topic of faking in personality assessment issoon to be published with Oxford University Press.

Carolyn MacCann, PhD is a psychology academic at The University of Sydney. Shehas previously held posts at the Accelerated Learning Laboratory (University of NewSouth Wales, Sydney, Australia) and the Center for New Constructs (Educational TestingService, Princeton, NJ, USA). Her primary research interests are the use of innovativeassessment methodology to measure noncognitive constructs for educational and organiza-tional applications. She has published around 20 articles and book chapters on this topic,particularly on EI, and is currently co-editor of a book on faking and personality assess-ment soon to be published with Oxford University Press.

Gerald Matthews is Professor of Psychology at the University of Cincinnati. Heobtained his PhD from Cambridge University in 1984, and he has previously held facultypositions at Aston University and the University of Dundee in the UK. His researchfocuses on human performance, cognitive models of personality, EI, and the assessmentof acute states of stress and emotion. He has published over 200 journal articles and bookchapters on these topics. He has authored and edited several books on the topics of per-sonality, emotion, stress and human performance. He has been elected Secretary-Trea-surer of the International Society for the Study of Individual Differences and President ofthe Traffic and Transportation Division of the International Association for Applied Psy-chology. He is also an associate editor for Personality and Individual Differences, and a con-sulting editor for Journal of Experimental Psychology: Applied.

Moshe Zeidner received his PhD in 1983 in Psychology from the Faculty of SocialSciences of the Hebrew University. He is currently Professor of Educational Psychologyat the University of Haifa and founding Director and Head of the Center for the Interdisci-plinary Research on Emotion. He served as Dean of Research at the University from 2000to 2005. Professor Zeidner has conducted research in the area of personality and individ-ual differences – with particular concern for the interface of personality and intelligence,emotions, EI, and the stress and coping process. He is the author or co-editor of 10books and author or co-author of about 200 scientific articles and chapters. ProfessorZeidner currently serves on the Editorial Board of the Journal of Educational Psychology,

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Emotion, Anxiety, Stress and Coping, and Personality and Individual Differences. He co-editsthe Springer series on Human Exceptionality. He received the lifetime achievement awardfor outstanding contribution to stress and anxiety research at the annual meeting of theSociety for Stress and Anxiety Research in July, 2003, in Lisbon, Portugal.

Endnote

* Correspondence address: Richard D. Roberts, Center for New Constructs, R&D, MS 16-R, EducationalTesting Service, Rosedale Road, Princeton, NJ 08541, USA. Email: rroberts@ets.org

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