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EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 1 of 98
Emotional Intelligence Predicts Academic Performance: A Meta-Analysis
Carolyn MacCanna, Yixin Jiang, Luke E R Brown
The University of Sydney
Kit S. Double
University of Oxford
Micaela Bucich
The University of Sydney
Amirali Minbashian
University of New South Wales, Sydney
a Carolyn MacCann, Yixin Jiang, and Luke E. R. Brown, School of Psychology, The
University of Sydney; Kit S. Double, Department of Education, University of Oxford;
Micaela Bucich, School of Psychology, The University of Sydney; Amirali Minbashian,
School of Management, UNSW Business School, University of New South Wales Sydney.
Correspondence concerning this article should be addressed to Carolyn MacCann, School of
Psychology, The University of Sydney, Paramatta Road, Sydney, NSW 2037, Australia. E-
mail: carolyn.maccann@sydney.edu.au
©American Psychological Association, 2019. This paper is not the copy of record and
may not exactly replicate the authoritative document published in the APA journal.
Please do not copy or cite without author's permission. The final article is available,
upon publication, at: http://dx.doi.org/10.1037/bul0000219 (supplementary materials
are available at http://dx.doi.org/10.1037/bul0000219.supp)
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 2 of 98
Emotional Intelligence Predicts Academic Performance: A Meta-Analysis
Keywords: emotional intelligence, personality, intelligence, academic performance, meta-
analysis
Public Significance Statement: This meta-analysis shows that emotional intelligence has a
small to moderate association with academic performance, such that students with higher
emotional intelligence tend to gain higher grades and achievement test scores. The
association is stronger for skill-based emotional intelligence tasks than rating scales of
emotional intelligence. It is strongest for skill-based tasks measuring understanding emotions
and managing emotions.
Funding statement. This research was funded in part by an Australian Research Council
Discovery Grant (DP150101158) awarded to the Carolyn MacCann and Amirali
Minbashian.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 3 of 98
Abstract
Schools and universities devote considerable time and resources to developing students’
social and emotional skills such as emotional intelligence (EI). The goals of such programs
are partly for personal development but partly to increase academic performance. The current
meta-analysis examines the degree to which student EI is associated with academic
performance. We found an overall effect of ρ = .20 using robust variance estimation (N =
42,529, k = 1,246 from 158 citations). The association is significantly stronger for ability EI
(ρ = .24, k = 50) compared to self-rated (ρ = .12, k = 33) or mixed EI (ρ = .19, k = 90).
Ability, self-rated and mixed EI explained an additional 1.7%, 0.7% and 2.3% of the variance
respectively, after controlling for intelligence and big five personality. Understanding and
management branches of ability EI explained an additional 3.9% and 3.6% respectively.
Relative importance analysis suggests that EI is the third most important predictor for all
three streams, after intelligence and conscientiousness. Moderators of the effect differed
across the three EI streams. Ability EI was a stronger predictor of performance in humanities
than science. Self-rated EI was a stronger predictor of grades than standardized test scores.
We propose that three mechanisms underlie the EI/academic performance link: (a) regulating
academic emotions, (b) building social relationships at school, and (c) academic content
overlap with EI. Different streams of EI may affect performance through different
mechanisms. We note some limitations, including the lack of evidence for a causal direction.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 4 of 98
Introduction
Emotional intelligence (EI) has captured the public imagination, and rightly so.
Recent meta-analyses clearly demonstrate that emotionally intelligent people perform better
in their jobs (Joseph, Jin, Newman, & O'Boyle, 2015; Joseph & Newman, 2010; O'Boyle,
Humphrey, Pollack, Hawver, & Story, 2011), and have better health and wellbeing outcomes
(Martins, Ramalho, & Morin, 2010; Schutte, Malouff, Thorsteinsson, Bhullar, & Rooke,
2007). In education, there is a growing consensus among educators, researchers, and policy-
makers that EI is an important skill for students to develop, both for their future wellbeing as
well as their future workplace success. While there is evidence that social and emotional
learning programs in school are effective (Durlak, Weissberg, Dymnicki, Taylor, &
Schellinger, 2011), and that non-cognitive constructs are powerful predictors of academic
performance (Poropat, 2009; Richardson, Abraham, & Bond, 2012), there is not yet a large-
scale meta-analysis examining the extent to which EI correlates with academic performance.
The current manuscript provides the first comprehensive large-scale meta-analyses estimating
the extent to which EI predicts academic performance. We consider all major
conceptualizations of EI, all stages of education (from elementary school through to
university), and the different facets of EI. We also examine the incremental validity of EI
above and beyond the traditional psychological characteristics known to predict academic
performance (intelligence and the five major personality domains).
Emotional Intelligence
Emotional intelligence (EI) is a relatively new construct compared to intelligence or
personality, with the first academic article appearing in 1990 (Salovey & Mayer, 1990). The
concept was relatively unknown until it was popularized by science journalist Daniel
Goleman in his 1995 book Emotional Intelligence: Why it Can Matter More than IQ. This
book sparked massive interest from researchers and the general public in the late 1990s. One
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 5 of 98
effect of this sudden widespread popularity was that research teams commenced their work in
parallel, creating their own theories and assessments rather than building on existing research.
For much of the 1990s there was little agreement on how to define or measure EI, leading to
many different theories and measures that were often quite dissimilar from each other
(Davies, Stankov, & Roberts, 1998). To bring some clarity to the field, researchers suggested
that a distinction should be made between two kinds of measurement models—ability scales
and rating scales (Mayer, Caruso, & Salovey, 2000). Ability scales require test-takers to
demonstrate knowledge or to process emotion-related information to provide a response.
Rating scales require test-takers to rate their agreement with a series of statements about
themselves (e.g., “I am able to handle most upsetting problems”; Brackett, Rivers, Shiffman,
Lerner, & Salovey, 2006). Evidence to date suggests that rating scales and ability scales of EI
capture different constructs and are only weakly related to each other (Brackett & Mayer,
2003; Brackett et al., 2006).
Paralleling the distinction between two measurement models is a similar distinction of
two theoretical models—mixed model and ability model theories of EI. Mixed model
conceptualizations of EI include a broad mix of constructs that lead to emotionally intelligent
behaviour, including emotion-related abilities, character traits, and motivational elements
(Bar-On, 2006; Petrides, Pita, & Kokkinaki, 2007). In contrast, ability models of EI
conceptualize EI as a cognitive ability of a similar type to verbal ability or quantitative
ability, with the content domain as emotions rather than words or numbers (MacCann,
Joseph, Newman, & Roberts, 2014). Ashkanasy and Daus (2005) distinguished between
rating scales based on ability theories and those based on mixed-model theories. They refer to
three ‘streams’ of EI measures: (1) ability scales, (2) ratings of EI abilities (self-perceptions
of EI, sometimes referred to as emotional self-efficacy; Qualter, Gardner, Pope, Hutchinson,
& Whiteley, 2012); and (3) ratings of mixed model EI (often referred to as trait EI, after the
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major mixed model conceptualization; Furnham & Petrides, 2003; Petrides & Furnham,
2000, 2003). In the current meta-analysis, we separately consider results for these three
different types of assessments, based on theoretical and empirical evidence that these are
three separate constructs (Joseph & Newman, 2010; O'Boyle et al., 2011). We refer to these
as ability EI, self-rated EI, and mixed EI. The paragraphs below describe the major ability
model of EI (and the ability and self-rated EI assessments based on this model), and the
major mixed models of EI (and the mixed model assessments based on these).
Ability EI: A four-branch hierarchical model of emotional skills
There is general agreement on a single theoretical model that describes the component
abilities of EI. The hierarchical four-branch model was first described by Mayer and Salovey
in 1997. This model outlines four key branches of emotion-related abilities that range in
complexity from low-level information processing to strategic and deliberative use of
emotional information to meet personal goals. These four branches are: (1) perceiving
emotions accurately, (2) using emotions to facilitate decision-making, (3) understanding
emotions, and (4) managing emotions to up-regulate positive emotions and down-regulate
negative emotions. We describe these in detail below. The best-known assessment of these
four branches is the ability-based Mayer-Salovey-Caruso Emotional Intelligence Test
(MSCEIT, Mayer, Salovey, Caruso, & Sitarenios, 2003), which has two subtests for each of
the four branches. The MSCEIT is the only commercially-available ability EI measure and is
the most commonly-used ability measure in research. Although there are several non-
commercial alternative ability EI assessments, these tend to measure only one or two of the
four branches (e.g., Freudenthaler & Neubauer, 2007; MacCann & Roberts, 2008; Matsumoto
et al., 2000). Earlier research on the four-branch model also used the Multi-factor Emotional
Intelligence Scale (MEIS), the precursor to the MSCEIT (Mayer, Caruso, & Salovey, 1999).
Youth versions of the MSCEIT and MEIS have often been used for research in schools
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(Rivers et al., 2012).
In addition to the ability-based assessments, there are several assessments that use
rating-scales to assess self-rated ability EI. One of the earliest measures of this kind was the
33-item Assessing Emotions Scale (AES; Schutte et al., 1998). The AES was based on an
earlier definition of EI that pre-dated the four-branch hierarchical model. The early definition
included perceiving, using, and managing emotions, but did not include understanding
emotions (Salovey & Mayer, 1990). Because the AES was available early in the public
domain, it was frequently used in EI research. Another EI measure that used this early
definition is Wong’s Emotional Intelligence Scale (WEIS), which contains four subscales that
assess perceiving one’s own emotions, perceiving others’ emotions, using emotions, and
managing emotions (i.e., it does not include emotion understanding, in line with the earlier
definition of EI; Law, Wong, & Song, 2004). A rating-scale instrument designed specifically
after the four-branch model is the Self-Rated Emotional Intelligence Scale (SREIS; Brackett
et al., 2006), which contains 19 items that assess five subscales (perceiving emotions, using
emotions, understanding emotions, managing one’s own emotions, and managing others’
emotions). The four branches of EI are described in detail below.
Emotion perception is the ability to “identify emotional content in faces, voices, and
designs and ability to accurately express emotions” (Mayer, Caruso, & Salovey, 2016).
Theoretically, this branch includes several related abilities, including: (a) the ability to
identify emotions in external stimuli (b) the ability to identify one’s own emotions (i.e.,
internal stimuli); (c) the ability to express one’s own emotions accurately; (d) the ability to
distinguish between genuine emotion expressions and deceptive or forced expressions; and
(e) knowledge of display rules for emotion expression in different cultures and contexts
(Mayer et al., 2016; Mayer & Salovey, 1997b). However, this branch has been
operationalized solely as the first of these—tests assess the capacity to identify the type and
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extent of emotion present in external stimuli such as facial expressions, micro-expressions,
tone-of-voice, body postures, landscapes and evocative art (Matsumoto et al., 2000; Mayer et
al., 2003; Nowicki & Duke, 1994; Schlegel, Grandjean, & Scherer, 2014). As such, the
empirical basis for what is known about emotion perception is solely defined as individual
differences in identifying emotions in others (and not the wider array of abilities that may
theoretically be included). Once emotions are perceived, this emotion information acts as
input for the cognitive system (Mayer, Salovey, Caruso, & Sitarenios, 2001).
Emotion facilitation of thought involves the use of emotions and emotional
information as input or guidance in cognitive tasks or decisions. It has been defined as the
ability to “facilitate thinking by drawing on emotions as motivational and substantive inputs”
(Mayer et al., 2016, p. 296). Both the theory and measurement of this branch involve two key
elements: (1) using existing emotions to guide task selection or approaches to tasks, and (2)
generating new emotions to aid performance on a specific task. When using existing
emotions, a person uses their current emotional state as a critical task parameter to guide the
strategies or processes used in problem solving in two ways. First, emotions can direct
attention to critical information through the action tendencies associated with each emotion.
For example, positive affect relates to a broad rather than narrow outlook and may lead to
creative exploration whereas anxiety is associated with hypervigilance to threat (Bar-Haim,
Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007; Fredrickson, 2001).
Second, tasks can be selected to take advantage of a mood state that might help performance.
For example, one could choose to write an enthusiastic welcome email when in a happy
mood but wait to counsel a disgruntled employee until one is feeling more serious (i.e., the
emotion regulation strategy of situation selection). The MSCEIT contains two subtests
assessing facilitation. The Sensations test assesses the generation of emotions—test-takers
must generate an emotion and rate the similarity of their sensory experience to sensations
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such as hot, red, or quiet. The Facilitation test assesses knowledge of which mood states will
be most helpful in different types of tasks.
The facilitation branch has been criticized on both empirical and theoretical grounds.
Empirically, factor analyses have generally not supported the inclusion of a clear and distinct
facilitation branch, possibly due to the dual nature of facilitation as both emotion generation
and situation selection (Fan, Jackson, Yang, Tang, & Zhang, 2010; Palmer, Gignac, Manocha,
& Stough, 2005; Rossen, Kranzler, & Algina, 2008). Theoretically, emotion facilitation seems
like a subset of emotion management (the fourth branch). Emotion generation is a core
concept for emotion management, as managing emotions involves the ability to generate the
desired emotion to match the task at hand (mostly, but not always, the up-regulation of
positive emotions and down-regulation of negative emotions) (Joseph & Newman, 2010;
MacCann et al., 2014; Mestre, MacCann, Guil, & Roberts, 2016). The other element of
emotion facilitation—situation selection—is a well-known emotion regulation strategy
(Gross & Thompson, 2007; Werner & Gross, 2010), and thus might also be considered a key
element of emotion management (the emotion management branch is sometimes also referred
to as ‘emotion regulation’; e.g., Joseph & Newman, 2010; Mayer & Salovey, 1997a).
Emotion understanding encompasses one’s knowledge base regarding emotions and
emotion-related phenomena. It is the “central locus of abstract processing and reasoning
about emotions and emotional information” (Mayer et al., 2001, p. 235). It includes the
following types of emotion knowledge: the vocabulary of emotion terms; the antecedents and
consequences of emotions; the way emotions may combine or change over time; and the
likely effect of a specific situation on one’s emotions now or in the future (Mayer, Caruso, &
Salovey, 2016, in press; Mayer & Salovey, 1997a). Emotion understanding may be
considered as domain-specific knowledge for the content domain of emotions. Emotion
understanding shows the strongest links to conventional cognitive abilities of the four EI
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 10 of 98
branches, with meta-analytic estimates ranging from ρ = .39 to .42 (Joseph & Newman, 2010;
MacCann, 2010; Olderbak, Semmler, & Doebler, 2019; Roberts, Schulze, & MacCann,
2008).
Emotion management is the ability to manage emotions in oneself and others by up-
regulating positive emotions and down-regulating negative emotions in order to achieve a
desired outcome such as personal growth (Mayer et al., 2016; Mayer & Salovey, 1997a;
Mayer et al., 2001). There are four key elements to emotion management. First, this branch
involves managing both one’s own and others’ emotions (termed intrinsic and extrinsic
emotion regulation in the process model of emotion regulation; Gross, 2008; Gross &
Thompson, 2007). Second, this branch includes both: (a) knowledge of emotion management,
and (b) meta-cognitive strategies pertaining to emotion management, such as the ability to
“monitor emotional reactions” and to “evaluate strategies to maintain, reduce, or intensify an
emotional response” (Mayer et al., 2016, p. 294). Third, emotions are managed with respect
to personal goals. That is, up- or down-regulation of emotion is undertaken strategically to
achieve goals such as personal growth. As such, emotion management represents not only the
knowledge of how emotions are managed, but the motivational elements that determine when
and why emotions are managed. That is, emotion management “interface(s) with personality
and personal goals” (Mayer et al., 2001, p. 235). Of the four EI branches, emotion
management shows the strongest relationships to personality traits, particularly agreeableness
(ρ = .29; Joseph & Newman, 2010).
Given the known relationships of ability EI with both intelligence and personality,
some researchers have argued that ability EI shows little incremental prediction of key
outcomes above and beyond the effects of personality (e.g., Schulte, Ree, & Carretta, 2004).
For this reason, it is essential to provide evidence of incremental validity when considering
whether EI predicts academic performance. We will therefore control for personality and
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intelligence in the prediction of academic performance from EI.
Mixed Models of EI
While there are many mixed models of EI, the three major conceptualizations are
Goleman’s emotional competence (Goleman, 1998), Bar-On’s emotional and social
competence (Bar-On, 2006) and Petrides and Furnham’s trait emotional intelligence
(Petrides, Perez-Gonzalez, & Furnham, 2007; Petrides, Pita, et al., 2007). We describe these
below.
Emotional competence. Goleman’s model, first outlined in his 1998 book Working
with Emotional Intelligence, consists of four major competencies: (1) self-awareness (being
aware of one’s emotions, accurate in one’s self-assessments, and self-confident); (2) self-
management (being conscientious, trust-worthy, adaptable, achievement-oriented, and able to
control one’s emotions and behaviours); (3) social awareness (showing empathy to others,
and having a service orientation and organizational awareness); and (4) social skills (being
skilled in leadership, communication, influence, conflict management, building relationships,
showing good teamwork and collaboration skills, as well as the ability to mentor others)
(Boyatzis, Goleman, & Rhee, 2000; Goleman, 1998). That is, the model distinguishes
between awareness (the tendencies and abilities to detect essential emotional information in
oneself and one’s environment) and management (being able to change or regulate the social
and emotional content of oneself and one’s surroundings) as they are applied to the self and to
others. This emotional competence model is the basis for the emotional competence
inventory (ECI), a rating-scale inventory commercially available to assess EI. There is
relatively little peer-reviewed empirical evidence evaluating the ECI, and it has been
criticized based on a lack of content validity and predictive validity evidence (Landy, 2005;
Matthews, Zeidner, & Roberts, 2002). Nevertheless, the theoretical model has been very
influential in both business and education settings. For instance, this model formed the basis
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for the social and emotional learning (SEL) competency model used by the Collaborative for
Academic, Social, and Emotional Learning (CASEL, 2003; Durlak et al., 2011). CASEL’s
SEL model includes the four Goleman competencies as well as a fifth competency of
responsible decision making (being able to make constructive choices about personal
behaviour and social interactions). This model is widely used internationally to guide
educational interventions designed to increase student, class and school-level socio-emotional
competencies.
Social and emotional competence. Bar-On’s model of social and emotional
competence was developed to represent “key components of effective emotional and social
functioning that lead to psychological well-being” (Bar-On, 2000, p. 364). There are five
major domains of emotional and social competence—interpersonal competence, intrapersonal
competence, stress management, adaptability, and general mood (Bar-On, 2006). The
Emotional Quotient Inventory (EQ-i) instrument is based on this model, and contains 15
subscales that are unevenly distributed across these five domains: (1) intrapersonal
competence (self-awareness and self-expression) contains five subscales—self-regard,
emotional self-awareness, assertiveness, independence, and self-actualization; (2)
interpersonal competence (social awareness and interpersonal relationships), contains three
subscales—empathy, social responsibility, and interpersonal relationship; (3) stress
management (emotion management and regulation) contains two subscales—stress tolerance
and impulse control; (4) adaptability (change management) contains three subscales—reality
testing, flexibility, and problem solving; and (5) general mood (self-motivation) contains two
subscales—optimism and happiness (Bar-On, 2006). Exploratory and confirmatory factor
analysis of the items provided support for only 10 of the 15 subscales. Both of the general
mood subscales, two of the five intrapersonal competence subscales and the social
responsibility subscale of interpersonal competence were not supported, but were retained in
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the instrument as ‘facilitators’ of social and emotional competence (Bar-On, 2000, 2006). A
youth version of the EQ-i is also available, and the EQ-i has frequently been used in peer-
reviewed research linking EI to academic performance. EQ-i scores correlate very highly
with personality traits. For example, EQ-i total scores correlate at -.62 to -.72 with
neuroticism (Dawda & Hart, 2000), .52 to .56 with extraversion and -.76 with anxiety (a facet
of neuroticism) (Dawda & Hart, 2000; O’Connor & Little, 2003). Correlations of this
magnitude have led some researchers to claim that EI (especially mixed-model
conceptualizations) represents ‘old wine in a new bottle’—that is, a re-branding of
personality rather than a new and distinct construct (Matthews et al., 2002).
Trait Emotional Intelligence. Trait EI is the most comprehensive mixed model of EI,
consisting of 15 facets taken from both the ability model of emotion as well as the two
models of emotional competence described above (Petrides, Pita, et al., 2007; Petrides, 2009).
The four ability facets in this model are: (1) accurately perceiving emotions in oneself and
others; (2) expressing and communicating emotions clearly; (3) managing others’ emotions;
and (4) regulating one’s own emotions. The non-ability facets include adaptability,
assertiveness, low impulsivity, fulfilling personal relationships, self-esteem, self-motivation,
social awareness, stress management, trait empathy, trait happiness, and trait optimism. The
trait EI model is assessed with the Trait Emotional Intelligence Questionnaire (TEIQue;
Petrides, Pita, et al., 2007), which also has a short form, adolescent form, child form, and
adolescent and child short forms. The TEIQue is very frequently used in EI peer-reviewed
research. TEIQue scores show very high correlations with the five major domains of
personality (Matthews et al., 2002; Mayer, Roberts, & Barsade, 2008). Some researchers
have argued that this is a problem for the discriminant validity of trait EI—that trait EI is in
fact indistinguishable from personality. A recent meta-analysis provides empirical
justification for this idea, showing that trait EI correlates at .85 with a general personality
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factor derived from the five major domains of personality, suggesting that these two
constructs are “very similar, perhaps even synonymous” (p. 36, van der Linden et al., 2017).
Given that personality is known to predict academic performance, the question of whether
trait EI can add anything to this prediction is therefore important. In the current manuscript,
we thus examine whether trait EI predicts academic performance over and above the effects
of personality.
Emotional Intelligence and Academic Performance
There is ample evidence to suggest that EI has a positive association with academic
performance. Social and emotional learning programs (which are broadly based on
Goleman’s model of EI) are known to increase academic performance, with Durlak et al.’s
(2011) meta-analysis showing that such programs result in an 11-percentile improvement in
academic performance. Social and emotional learning focuses on developing five key
competencies that overlap substantially with Goleman’s emotional competencies (self-
awareness, social awareness, self-management, relationship skills and responsible decision
making; CASEL, 2003). Programs were more effective if they followed a sequenced, step-by-
step approach, used active forms of learning, allowed adequate time for skill development,
and had explicit learning goals (Durlak et al., 2011). The effect of SEL programs on academic
performance was stronger when teachers ran the programs (d = .34) compared to non-school
personnel (d = .12).
There is also some direct evidence that EI is positively associated with academic
performance. Three meta-analyses to date have examined this question, and all have found a
positive association. First, Van Rooy and Viswesvaran (2004) estimated a corrected
correlation of .10 between EI and academic performance (k = 10). This analysis did not
distinguish between the different streams of EI. Second, Perera and DiGiacomo (2013)
examined rating scales of EI, finding a corrected correlation of .20 with academic
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performance (k = 48). Effects were stronger for younger students and at earlier levels of
education. Other moderators were examined were not significant. These included: (a) the
gender composition of the sample; (b) the instrument used; and (c) whether the sample was in
a transition year (i.e., first year of high school or university). Perera and DiGiacomo did not
include ability scales in their analysis nor distinguish between mixed EI and self-rated EI.
They also did not assess the incremental prediction of EI above the known effects of
personality and intelligence. Third, Richardson et al. (2012) examined the relationship
between EI and academic performance as part of a wide-sweeping meta-analytic review of 42
non-cognitive correlates of academic performance. They reported a slightly smaller
relationship between EI and academic performance (ρ = .17) but included only 14 studies and
did not differentiate between ability scales and rating scales.
The current comprehensive meta-analysis expands on previous work in five ways.
First, we cover all relevant research whereas previous studies included only a small subset
(i.e., we located 162 relevant citations, such that Richardson’s k of 14 studies represents less
than 10% of the available data). Second, we include ability-based EI assessments as well as
rating scales. The relation between ability EI and academic performance has never previously
been reported in meta-analyses, despite being the most objective and arguably most valid
assessments of EI (Matthews et al., 2002; Mayer et al., 2008). Third, we use the now-standard
categorization of EI scales into ability EI, self-rated EI, and mixed EI to separately examine
the effects of EI on academic performance across these three different constructs (cf.
Ashkanasy & Daus, 2005; Joseph & Newman, 2010; O'Boyle et al., 2011). Fourth, we
examine a range of moderators of the effects (described in more detail in the sections below),
including EI stream, EI facet, sample age, gender composition, and publication type. Fifth,
and perhaps most importantly, we examine the incremental validity of EI above and beyond
the effects of personality and intelligence by constructing a correlation matrix of EI,
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 16 of 98
intelligence, personality and academic performance. The correlations in this matrix are drawn
from: (a) our original meta-analyses (EI/academic performance and intelligence/academic
performance correlations); and (b) previously published meta-analyses (personality/EI,
personality/performance, intelligence/performance, intelligence/peraonlity, and the relations
among personality domains; Joseph & Newman, 2010; van der Linden, Pekaar, Bakker,
Schermer, Vernon, Dunkel, & Petrides, 2016; Poropat, 2009; Judge, Jackson, Shaw, Scott, &
Rich, 2007; van der Linden, te Nijenhuis, & Bakker, 2010). Moreover, we use relative
importance analysis to compare the relative contribution of intelligence, personality, and EI to
explaining differences in academic performance (Johnson, 2000; LeBreton & Tonidandel,
2008). That is, we are building a more comprehensive picture of the extent to which EI
predicts academic performance than ever before, including a consideration of the relative
importance of EI compared to other well-known predictors of academic performance. Our
major hypothesis is that EI will be positively associated with academic performance
(Hypothesis 1).
Moderators of the EI/Academic Performance Relationship
EI Stream. While evidence suggests a positive association of EI with academic
performance, it is not clear whether this relationship differs for ability versus self-rated versus
mixed EI. Meta-analyses predicting workplace performance and wellbeing outcomes have
found different effects for these three streams (Harms & Credé, 2010; Joseph & Newman,
2010; Martins et al., 2010; Miao, Humphrey, & Qian, 2017; O’Boyle et al., 2011; Sanchez-
Alvarez, Extremera, & Fernandez-Berrocal, 2016; Schutte et al., 2007). These findings are
summarized in Table 1, and cut across multiple outcomes: workplace performance,
organizational citizenship behaviours, counter-productive workplace behaviours, leadership,
subjective wellbeing and health outcomes. Across all major meta-analyses linking EI to
positive life outcomes, there are two consistent findings. First, the relationship of EI to
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 17 of 98
positive outcomes is uniformly positive, showing that high EI confers benefits to those who
possess it. Second, ability EI consistently shows the lowest relationships with criteria of the
three streams of EI. This difference among streams may relate to method effects. To date, the
criteria examined in meta-analyses are dominated by rating scales. With little objective data
for criteria such as job performance, health outcomes, or wellbeing, higher criterion-
correlation with rating scales of EI (both mixed models and self-rated EI) compared to ability
scales is consistent with a mono-method bias in measurement. That is, rating scales correlate
more highly with rating scales, irrespective of content. There is some support for this idea of
a method-effect component to the EI/outcome associations in Harms and Credé’s (2010)
meta-analysis of EI and leadership. They found that effects were vastly stronger when EI and
leadership were both rated by the same source (self, subordinate, peer or supervisor)
compared to different sources—ρ = .59 versus ρ = .12. The pattern of correlations in Table 1
also supports this argument. Self-rated EI measures have much higher association with
criteria as compared to ability EI measures. As both types of assessments are meant to
measure the same underlying constructs, the difference must be due to method effects rather
than construct effects.
Unlike the other outcomes in previous meta-analyses, academic performance is rarely
assessed with rating scales (either self- or observer-rated) but rather is an objective composite
composed of course-work, examination, and participation results (in the case of course
grades) or objective performance on standardized assessments (in the case of standardized
test results). While neither course grades nor standardized test scores are problem-free as
valid measures of academic performance, their issues are qualitatively different to rating-
scale measures of critical criteria. If some of the predictive superiority of EI rating scales over
EI ability scales is due to method bias (rather than substantive differences), then ability EI
would show a stronger prediction of an objective criteria such as course grades, as compared
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to self-rated EI or mixed EI. We thus propose a directional hypothesis that ability EI will
show stronger prediction of performance as compared to self-rated or mixed EI
(Hypothesis 2).
Facet of EI. There are three possible mechanisms that may account for the
relationship between EI and academic performance. First, emotionally intelligent students
may be able to deal more easily with negative emotions elicited by academic settings. The
prototypical academic emotion is test anxiety, but there are a variety of other emotions
specific to academic settings (Pekrun, Elliot, & Maier, 2009; Pekrun, Goetz, Titz, & Perry,
2002). For example, students need to regulate the disappointment of lower-than-expected test
scores or negative feedback, or the boredom involved in learning concepts and subject matter
that are of instrumental rather than intrinsic interest (e.g., learning a tax code to pass an
accountancy exam) (Pekrun, Goetz, Daniels, Stupnisky, & Perry, 2010). Students with the
emotion management skills to down-regulate their anxiety, disappointment or boredom will
be able to achieve better exam results, and to learn more from negative feedback or boring
subject matter. If this mechanism accounts for the EI/academic performance association, the
emotion management branch should show the strongest association with academic
performance.
Second, social demands are present at all stages of education, from sharing crayons in
kindergarten, resisting peer pressure and managing group projects in high school, to adjusting
to moving out of home when starting university. For learning and achievement to take place,
students must first be able to manage their lives so that they show up to class in a fit state to
concentrate on the subject matter. Students with higher EI may be better able to manage the
social world around them, forming better relationships with teachers, peers, and family. This
may directly influence grades (e.g., in subjective criteria such as participation marks, teachers
may award better marks to students who have formed better relationships with them). This
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may also indirectly influence grades through providing the student with a social support
network that protects them in times of stress. Developing social relationships would require
emotion management (particularly managing social relationships—a subscale of the
MSCEIT; Mayer et al., 2016). If this mechanism accounts for the EI/academic performance
relationship, then we would again expect emotion management branch to show stronger
effects than the other branches.
Third, there may be an overlap between emotional competencies and intellectual
competencies. For example, knowing a lot of emotion words and being able to communicate
one’s feelings could be conceptualized as a subset of vocabulary/verbal ability. Teaching
emotional competencies may in fact result in teaching academic competencies—learning the
language of emotions increases vocabulary and spelling more generally, and interventions
involving written text may also improve reading comprehension. That is, one possibility is
that there is nothing particularly special about emotional skills—it is the overlap between
emotional skills and other academic skills that mean students who are emotionally intelligent
are also likely to be intelligent in other ways (e.g., good with language). MacCann et al.
(2014) proposed that EI may in fact be one element of intelligence—a student can be smart
with emotions in the same way that they can be good with numbers or good with words.
Under this conceptualization, the overlap between EI and academic skills is the purported
mechanism by which EI would affect academic performance. If this mechanism accounts for
the relationship between EI and academic performance, we would expect the EI/academic
performance association to: (a) be strongest for ability EI and weakest for mixed EI
(providing further justification for Hypothesis 2); (b) decrease in size after controlling for
intelligence; and (c) be strongest for the knowledge-based branch of EI (understanding
emotions). In combination with the previously described mechanisms regarding regulating
academic emotions and building social relationships, we thus hypothesize that effects will be
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strongest for the understanding and management branches of EI than the perception and
facilitation branches (Hypothesis 3).
Peer-reviewed journal article status. The recent ‘replication crisis’ in psychology and
other sciences based on inferential statistics demonstrates that published articles may
represent selective reporting, where effects reported in peer-reviewed publications are
systematically higher than studies that remained unpublished (Collaboration for Open
Science, 2015). Along with other questionable research practices (such as selective analysis),
selective reporting may over-estimate effect sizes to such an extent that published estimates
are up to twice as large as the real effects (Collaboration for Open Science, 2015). To assess
whether there is a file-drawer problem where non-significant results remain unpublished, we
will test for any differences between peer-reviewed publications and unpublished data (e.g.,
unpublished data sets, dissertation abstracts, conference proceedings). If published findings
are biased towards significant results, we would expect a stronger association for peer-
reviewed journal articles than other sources of data. The fourth hypothesis is thus a test of the
file-drawer problem, where we hypothesize that the EI/academic performance association
will be significantly larger in published than unpublished sources (Hypothesis 4).
Student age. Perera and DiGiacomo (2013) reported a stronger effect of EI on
academic performance in elementary school students than in university students (r = .28
versus r = .18) but did not examine ability-based EI. Similarly, Poropat (2008) found
significantly stronger associations between conscientiousness and academic performance at
earlier stages of education. This implies that self-regulatory processes (such as EI) may be a
more critical determinant of educational outcomes at earlier ages and stages of education. We
will therefore test whether the EI/academic performance relationship differs across different
across age groups. Our expectation is that EI may prove more important at earlier ages. This
is because when the average level of emotional skills is relatively low (as for 5 to 7-year-
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olds), being below average constitutes a lower absolute level of skill that might prove more
detrimental to performance overall. For example, a 5-year-old with lower-than-average
emotion management skills may spend the day crying, hitting other children, and be
completely unable to focus on the tasks at hand. A university student with lower-than-average
emotion management skills may have a less satisfying a time at university (Brackett, Mayer,
& Warner, 2004; Brackett et al., 2006) but is nevertheless likely be able to self-regulate
sufficiently well so as not to spend the day crying and hitting others. Moreover, there is likely
to be selective attrition based on emotional skills, resulting in a restriction of range at
university compared to elementary schools and high schools. Students with very low
emotional skills are more likely to have anxiety-based school refusal, conduct problems
leading to suspensions or expulsion, and/or more difficulties resisting peer pressure (resulting
in early drug and alcohol use or unintended pregnancy), and are therefore less likely to obtain
entrance to university. Thus, we hypothesize that the EI/academic performance association
will be stronger at younger ages (Hypothesis 5).
Type of academic performance: Grades versus standardized test scores. Academic
performance is commonly measured in two ways; as standardized test scores or as end-of-
semester grades. Standardized tests are usually developed and administered by state or
national bodies (or even international bodies, as in the case of the OECD’s Program for
International Student Assessment [PISA]). They are often considered high-stakes either for
the individual student or for their institution. School funding, reputation, or position within
publicly-available league tables may depend on test score results, and students’ entrance into
selective secondary schools or university programs may depend on their individual test
scores. Standardized tests would normally be administered in large groups with a set time
limit for completion, and classroom teachers and school administrators have no direct input
on the content of these tests. In contrast, end-of-semester grades are composite measures of
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multiple forms of assessment, which may include formal examinations, written assignments,
regular home-work, contribution to class discussion, group projects or other assessment tasks.
Classroom teachers and school or university administrators have much more control over the
content of these assessments. That is, one of the primary ways in which EI could contribute to
high scores on standardized tests is through the regulation of test anxiety. Grades (being a
more diffuse criterion) would also be influenced via the ability to maintain relationships with
one’s instructors and other students, to manage group projects, and to manage the emotions
that motivate procrastination as well as the ability to manage test anxiety. We therefore
hypothesize that EI will show a stronger relationship to grades than to standardized test
scores (Hypothesis 6).
Gender composition of the sample. While Perera and DiGiacomo (2013) found that
gender composition of the sample did not moderate the effect of EI on academic
performance, we revisit this moderator for two reasons. First, we examine ability EI (which
Perera and DiGiacomo did not). Second, we have a much larger sample and therefore greater
power to detect an effect.
There are two main ways in which the gender composition of the sample could
moderate the EI/academic performance association. First, moderation could occur due to the
gender effects at the individual level of analysis. To the extent that say males are more likely
to benefit academically from higher levels of EI than females, having a greater proportion of
males in the sample will result in a stronger relationship between EI and academic
performance. In this regard, there is some evidence that the relationship of EI to social and
emotional outcomes is stronger for males than females, suggesting that EI may confer greater
benefit to males (Brackett et al., 2006; Brackett, Warner, & Bosco, 2005). There is evidence
that males and females experience different kinds of emotions—females experience greater
internalising emotions such as anxiety whereas males experience greater externalising
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emotions such as anger (Chaplin, & Aldao, 2013; Fischer, Rodriguez Mosquera, Van Vianen,
& Manstead, 2004). To the extent that uncontrolled anger may be more damaging to school
engagement and mastery goals as compared to anxiety (Pekrun, Elliot, & Maier, 2009;
Pekrun et al., 2002), this may mean that higher EI is more beneficial to males than females,
as the consequences of failing to regulate are different for the different kinds of emotions.
Second, moderation could occur due to contextual effects. The gender composition of
the sample defines the broader context in which academic performance takes place, which in
turn could influence the beneficial value of high EI, regardless of whether an individual is
male or female. As described above, the beneficial value of EI may be increased by contexts
that place greater demands on dealing with social relationships, managing negative emotions,
or learning academic material with emotional or social content. Co-educational schools place
greater emphasis on affiliation and social relationships than same-sex schools (e.g., Schneider
& Coutts, 1982). Furthermore, academic contexts with a greater proportion of males can
create an intellectually threatening environment and heightened text anxiety among females,
thus hampering their performance (see Inzlicht & Ben-Zeev, 2000).
Taken together, the above arguments suggest that samples that are comprised of
largely female participants will benefit less from the effects of EI than samples that are more
mixed or male dominated. We thus hypothesize that samples with a higher proportion of
females will show weaker effects of EI on academic performance (Hypothesis 7).
Student minority status. SEL programs are of interest to educators and policy-makers
as a pathway to address issues of equity for students from traditionally disadvantaged
backgrounds or at disadvantaged schools. For SEL programs to reduce achievement gaps
between groups: (a) programs must be at least as effective for minority/disadvantaged
populations, and (b) the EI/achievement association at least as large for
minority/disadvantaged groups (because increasing EI will increase achievement only in so
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much as EI and achievement are related). Evidence is mixed for point (a). Taylor, Oberle,
Durlak & Weissberg’s (2017) recent meta-analysis demonstrates that there are no significant
differences in the effectiveness of SEL programs for different levels of SES or for majority
versus minority groups. However, there is research to suggest that SEL programs are less
effective in disadvantaged schools (Bierman et al., 2010). Such differences may be due to the
challenges of implementing SEL programs in schools serving disadvantaged students (who
are disproportionately ethnic minorities).
Our moderation analysis will test point (b). We hypothesize that samples with larger
proportions of minorities may have a stronger EI/achievement association. Our logic is that
low EI constitutes a greater barrier in a disadvantaged environment (where EI is required to
deal with daily obstacles) than in more supportive environment. Ethnic minority status is bot
associated with social and educational disadvantage. In addition, ethnic minority students
face additional daily obstacles in the form of micro-aggressions and racism which produce
negative emotions (such as anger and anxiety) that require emotion regulation.
As our arguments are partly based on advantaged versus disadvantaged environments,
they would also hold for comparisons of high versus low socio-economic status (irrespective
of ethnic minority status). We do not examine this directly because sample socio-economic
status is rarely if ever reported in correlational research on EI. We use the sample percentage
who are of a minority ethnicity (i.e., non-White) as a moderator of the EI/academic
performance association. Because of the difficulty of defining ‘minority’ across different
countries, we have restricted this analysis to samples from the USA (where sample ethnicity
is commonly reported using consistent categories of White, Black, Asian, and/or Hispanic in
most educational and psychological research). We hypothesize that the EI/academic
performance association will be larger for samples with higher proportions of minority
students (Hypothesis 8).
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Subject area: Mathematics/Science versus Humanities. Many of the arguments
mentioned above assume that EI affects the learning process, where high EI students learn
more and perform better by regulating their anxiety or boredom, or managing the social
relationships of the classroom, playground or campus. However, it is also possible that the
EI/academic performance association is due to the content of what is learned. Emotional
content is self-evidently more relevant to a performing arts grade (where accurately
portraying and evoking emotion form part of the assessable content) as compared to
mathematics (where the content is unrelated to emotions). Similarly, analyzing the universal
themes of a poem or canonical play requires an understanding of the emotions and
motivations of the characters, such that emotions may be a required knowledge base for
humanities-based but not science/mathematics-based content domains. Understanding human
motivations and emotions may be a required skill for interpreting the meaning of some texts.
This is true from the earliest levels of education (e.g., the dramatic tension of The Cat in the
Hat derives from understanding both the protagonist’s initial boredom and the anxiety caused
by the cat’s chaos) through to the higher ones (e.g., analyzing the role of charismatic leaders
in creating a totalitarian state when studying modern history). To test whether the
EI/performance association derives from process versus content, we will test whether the
effect is stronger for humanities-based subjects versus mathematics and science-based
subjects. We hypothesize that EI will show a stronger relationship to humanities
performance as compared to mathematics/science performance (Hypothesis 9).
The Incremental Validity and Relative Importance of EI for Academic Performance
Other Predictors of Academic Performance: Intelligence
The dominant view for much of the twentieth century was that intelligence is the
critical ingredient for success at school. While different theorists have defined intelligence in
slightly different ways, the American Psychological Association Taskforce defined it as the
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“ability to understand complex ideas, to adapt effectively to the environment, to learn from
experience, [and] to engage in various forms of reasoning” (Neisser et al., 1996, p. 77).
Since the dawn of intelligence testing, one of the key tenets of intelligence tests is that
scores should correspond to student’s achievement at school. In fact, intelligence tests were
originally devised by Alfred Binet to assess students’ ability to succeed at school. Prominent
economists and psychologists have tried to gauge the size of the intelligence/achievement
association, with estimates ranging from around r = .40 to .70 (e.g., Deary, Strand, Smith, &
Fernandes, 2007; Jencks, 1979; Kaufman & Lichtenberger, 2005). Early meta-analyses
estimated this relationship as .34, .43, and .48 respectively, but were focused only on
achievement in science in primary and secondary school (Boulanger, 1981; Fleming &
Malone, 1983; Steinkamp & Maehr, 1983). Three recent meta-analyses have estimated the
size of the intelligence/academic performance correlation more generally but have come to
different conclusions on the size of this effect. Roth et al. (2015) found a corrected correlation
of .54 across 240 studies of primary and secondary students. Poropat (2009) found a
corrected correlation of .25 across 47 studies of primary, secondary and university students.
Richardson (2012) found a corrected correlation of .21 across 35 studies of university
students.
There are two possible reasons for this large discrepancy—stage of education and the
time-period covered in the meta-analysis. Roth et al.’s (2015) samples were predominantly
pre-adolescent children (no university students were included, and only 1/3 of the samples
were from secondary school). In contrast, Poropat (2009) included primary, secondary, and
tertiary students with 55% of studies from university samples, and less than 10% from
primary school samples. Richardson et al. (2012) included university students only and had
the lowest estimate of the three meta-analyses. Although no single meta-analysis has tested
for moderation across tertiary versus earlier levels of education on the intelligence/academic
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performance relationship, this comparison of different meta-analyses suggests that effects
may be smaller in tertiary education than they are in primary or secondary education. Jensen
(1998) also believed that the importance of intelligence for academic achievement dropped
from primary to secondary to tertiary to post-graduate education.
Second, more recent studies show smaller effects. Roth et al. (2015) reported that
older studies (pre-1983) showed a significantly stronger relationship than newer studies. All
studies included in Poropat’s (2009) meta-analysis were published after 1990, all but four
after 1995, and the majority after 2000. As such, discrepancies between Roth et al. and
Poropat may be due to the time-periods examined. Roth et al. propose that the reduced effect
of intelligence on academic performance over time is due to increasing grade inflation (which
reduces the variance in academic performance in more recent studies, depressing
correlations). There is abundant evidence of continued grade inflation since 1983 (Bachan,
2017; Nata, Pereira, & Neves, 2014; Rojstaczer & Healy, 2012). Given that
conceptualizations and study of EI did not commence until the 1990s, and much of the
research occurred only in the last 10 years, we believe it is important to control for
intelligence/academic performance estimates obtained in the current era, ideally from
similarly constituted samples (i.e., from tertiary samples as well as primary and secondary).
For this reason, we conduct a secondary meta-analysis of studies in our EI/academic
performance citations, examining the intelligence/academic performance relationship
reported in these citations. This effectively controls for both the time period of the study, and
the stage of education (i.e., estimates of the intelligence/academic performance association
are then drawn from the same time period and same samples as the estimates of EI/academic
performance). We will then use our intelligence/academic performance estimate (along with
other meta-analytic correlations) to construct a correlation matrix to test for the incremental
validity of EI on academic performance, over and above the effect of intelligence and the five
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major domains of personality.
Other Predictors of Academic Performance: Non-cognitive constructs and
Personality
A recent zeitgeist lead by behavioural economist James Heckman has focused on
‘non-cognitive’ factors such as personality traits as critical predictors of education and
workplace outcomes (Heckman & Rubinstein, 2001; Heckman, Stixrud, & Urzua, 2006).
While earlier canonical writings on the impact of intelligence stressed that non-cognitive,
motivational, or conative factors were also likely to be impactful for life success (Jencks,
1979; Wechsler, 1943), these lacked an organizing framework.
One possible organizing framework for non-cognitive factors is the Big Five model of
personality, which gained acceptance among psychologists from the 1990s onwards (Digman,
1986; Goldberg, 1981; John, 1989; Tupes & Christal, 1961). Factor analysis of trait
adjectives produced a five-factor solution (the Big Five) that was largely similar to the Five
Factor Model (FFM) of personality that emerged from factor analysis of questionnaire data.
These five personality factors are extraversion (positive affect, high energy levels, and
sociability), agreeableness (sympathy, kindness, and submissiveness), conscientiousness
(hard-working, detail-minded, and organized), neuroticism (easily and frequently feeling
stress and negative affect) and openness to experience (open to new ideas, enjoyment of arts,
culture, and aesthetics; referred to as ‘Intellect’ in the Big-Five and ‘Openness’ in the Five-
Factor Model). These five major domains of personality provide a conceptual framework for
grouping the multiple different non-cognitive constructs that have emerged from different
sub-disciplines of psychology and education. For example, conscientiousness provides an
overarching framework for the conceptually similar psychological constructs of delay of
gratification, ego control, effortful control, self-control, self-regulation and grit (Heckman &
Kautz, 2012; Roberts, Lejuez, Krueger, Richards, & Hill, 2014).
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A series of meta-analyses demonstrates that these five personality domains, and
particularly conscientiousness, predict workplace performance independently from the effects
of intelligence (Barrick & Mount, 1991; Salgado, 1998; Schmidt & Hunter, 1998). However,
research on the importance of these personality traits for academic performance did not
follow for another 20 years. Poropat (2009) provided the first large scale meta-analysis
linking five domains of personality to academic performance. Across all levels of education,
he found a conscientiousness/academic performance relationship of ρ =.22, which was of
similar magnitude to his estimate of intelligence/performance relationship (ρ = .25).
Moreover, conscientiousness predicted academic performance independently of the effects of
intelligence. Other recent meta-analyses found similar estimates of .23 and .24 for the
conscientiousness/academic performance corrected correlation in tertiary academic
performance (O’Connor & Paunonen, 2007; Richardson et al., 2012). Across these three
meta-analyses, agreeableness also showed a significant (but small) positive relationship with
academic performance. As the five factors of personality are known to predict academic
performance, we will examine whether EI provides incremental prediction above and beyond
the effects of personality as well as intelligence.
The combined effect of EI, intelligence and personality on academic performance
Given that intelligence and personality (particularly conscientiousness) are well-
established predictors of academic performance, it is important to examine the incremental
validity of EI alongside the contribution of these traditional predictors. As reviewed in earlier
sections, EI has known relationships with both intelligence and personality. This has led some
researchers to argue that EI is redundant and has little incremental validity above and beyond
the effects of intelligence and personality (Schulte et al., 2004). Indeed, in a few studies
where EI has been related to positive outcomes, the positive effect of EI disappears after
controlling for intelligence and personality (see Walter, Cole, & Humphrey, 2011, for a
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review). Therefore, researchers have argued for the need to establish not only the main effect
of EI, but also the incremental validity of EI relative to intelligence and personality
(Antonakis, Ashkanasy, & Dasborough, 2009; Miao et al., 2017). Moreover, as meta-analyses
arguably provide the best estimates for effect sizes of the relationship between different
variables, this manuscript is well-placed to determine the incremental validity of EI (ability
EI, self-rated EI, and mixed EI) for academic performance. Given the high correlations
between mixed EI and personality (e.g., van der Linden et al., 2017) and the high correlations
between ability EI and conventional non-emotional measures of intelligence (e.g, MacCann
et al., 2014), it is reasonable to expect that correlations of EI with academic performance will
shrink after accounting for these variables. However, we hypothesize that overlap with
personality and intelligence will not be entirely responsible for the effect of EI on academic
performance, such that all three streams of EI will predict academic performance above and
beyond the contributions of intelligence and personality (Hypothesis 10).
While incremental validity examines the prediction of a variable to a criterion above
and beyond the contribution of other variables, relative importance refers to the contribution
each predictor makes to the total criterion variance in combination with other predictors
(Johnson & LeBreton, 2004). The goal of relative weights analysis is to “partition explained
variance among multiple predictors to better understand the role played by each predictor in a
regression equation” (Tonidandel & LeBreton, 2011, p. 1). This is a separate question to
incremental prediction, as the order in which variables are entered into the analysis does not
affect the relative weights. Relative weights can be expressed as a percentage of the R-
squared value that each variable independently contributes. We will examine not only the
incremental prediction of EI but also the relative importance of EI as a predictor when
considered in conjunction with intelligence and personality. Previous meta-analyses have
used relative weights analysis to compare the relative contribution of EI to the prediction of
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workplace outcomes (Miao, Humphrey, & Qian 2017; O’Boyle et al., 2011). Many of the
original claims in the 1990s popular science books on emotional intelligence proposed that EI
was more important than intelligence— that “it can matter more than IQ” (Goleman, 1995, p.
1). A comparison of the relative contribution of EI and intelligence to the prediction of
academic performance would thus test whether there is any merit to these original claims.
Given the ubiquitous influence of intelligence and conscientiousness on academic
performance, we hypothesised that EI will be among the top three predictors of academic
performance, along with intelligence and conscientiousness (Hypothesis 11).
Method
Search Strategy
First, we searched the following nine databases for relevant studies on EI and
academic performance: ERIC, Google Scholar, ISI Web of Science, Medline, ProQuest
Dissertations and Theses, PSYCInfo, PubMed, ScienceDirect, and Scopus. For academic
performance we used the search string “(academic OR education OR university OR school)
AND (grade OR GPA OR performance OR achievement)” in line with Poropat (2009). For
emotional intelligence we used the search string “(emotional intelligence) OR EI OR
(emotion perception) OR (emotion understanding) OR (emotion facilitation) OR (emotion
recognition) OR (emotion management) OR MSCEIT OR MEIS OR TEIQue OR SREIS OR
WLEIS OR (Mayer-Salovey-Caruso Emotional Intelligence Test) OR (Multifactor Emotional
Intelligence Scale) OR (Trait Emotional Intelligence Questionnaire) OR (Schutte Self Report
Emotional Intelligence Test) OR (Wong and Law Emotional Intelligence Scale)”. Combining
these yielded an initial search result of 6139 citations. All citations up until 1 November 2016
were considered, with no lower limit to the publication year (unpublished data or studies
were labelled with the year they were located). After removing duplicates, the title, abstract,
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and, where necessary, full-text, of the remaining citations were reviewed. Ancestry searches
of the reference lists yielded an additional 14 citations. After reviewing the reference section
of relevant existing meta-analytic reviews (Perera & DiGiacomo, 2013; Richardson et al.,
2012) an additional 10 relevant citations were added. We then conducted a search for
unpublished data on this question by: (a) searching the American Education Research
Association database (which resulted in no additional relevant citations); and (b) emailing 23
prolific emotional intelligence and putting out a call on the EMONET listserve to request
unpublished data, and (c) contacting major testing companies and educational research
companies which resulted an additional 7 citations. This search strategy is outlined in the
PRISMA chart in Figure 1.
Inclusion and Exclusion Criteria
Citations were included in the meta-analyses if they met the following inclusion
criteria: (1) written in English; (2) the measure of EI was published in either a test manual or
journal article; (3) included academic performance indices appraised either directly (e.g.,
GPA, SAT results) or self-reported (self-reported GPA has been found to correlate at r = .90
with actual GPA for college students and r = .82 for high school students) (Kuncel, Crede, &
Thomas, 2005); (4) referred to original data not reported in any other citation; (5) reported an
effect size between EI and academic performance, or reported data from which an unbiased
estimate of effect size could be calculated; and (6) reported the sample size. Conversely,
studies were excluded from the meta-analyses based on the following exclusion criteria: (1)
measured clinical or work-related training performance; (2) used degree attainment as an
index of academic performance (e.g., Kapp, 2000; Kashani, Azimi, & Vaziri, 2012); (3) used
an in vivo laboratory achievement test (e.g., Shao, Yu, & Ji, 2013); (4) were publications
based on the same data as another citation in the meta-analysis (e.g., Ahmad, 2010); or (5) the
EI constructs were not based on established ability, trait, or mixed EI theory (e.g., social
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intelligence).
Most citations included multiple correlations estimating the EI/academic performance
association (e.g., correlations for several different EI tests with both university and high
school grade-point-average, for several different subscales of EI, or several different subject
areas of achievement). We recorded all possible combinations of EI/achievement as within-
citation effects. After exclusion criteria, there were n = 162 different citations (188 samples),
with k = 1,276 correlations available for analysis. The final file for analysis is included as an
excel sheet in the supplementary material, and a summary of citations is provided in
Appendix A.
Citations represented 27 different countries, with most data from English-speaking
countries (76.5%, k = 974) such as the USA (43.9%, k = 560), UK (8.2%, k = 105), or
Australia (7.8%, k = 99). The largest number of observations from non-English speaking
countries were from Iran (k = 74), Portugal (k = 54) and Spain (k = 35).
Coding
Each of the 1,276 effects was coded on the criteria described below. The first set of
coding was conducted by the second and third authors, who both hold post-graduate degrees
in psychology. A second coder (the fourth author, who holds an undergraduate psychology
degree) coded 73 randomly-selected citations to test the reliability of coding. Errors were
resolved by the first author going back to the original paper.
Effect size. Pearson’s correlation (r) was used as the metric of effect size. In the few
samples where r was not reported, the available statistic (e.g., chi-square, t value, F value)
was transformed into r using effect size calculators (Lenhard & Lenhard, 2016; Lyons &
Morris, 2017). Coder agreement was 93%.
Sample size. Sample size was ranged from 18 to 2,195 with a median of 180 and a
mean of 256 (25% of observations were based on 99 or fewer participants and 25% of
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observations were based on 291 or more participants). Coder agreement was 90%.
Reliabilities of measures. The reliability of both EI and GPA were coded. For
measures of EI, if a study did not provide reliability information, the average reliability from
all studies that used the same measure in the current meta-analysis was imputed. If no other
studies used the same measure, the reliability estimate was obtained from the test manual or
original psychometric validation study. If either of these were not available, then a reliability
estimate was obtained from a published, large sample study. Coder agreement for EI was
80%. For academic performance indexes, very few studies reported reliabilities. Therefore,
the reliability estimates were obtained from published studies on the reliability of different
academic performance measures, as follows: GPA obtained from academic records (Westrick,
2017); self-reported GPA (Kuncel et al., 2005); self-reported SAT or other equivalent
standardized tests scores (Kuncel et al., 2005); SAT (Ewing, Huff, Andrews, & King, 2005);
and American College Test (ACT, 2014).
Standard deviation of EI. Standard deviations were recorded for EI measures to
correct for range restriction. Coding agreement was 80%.
Gender. Gender was coded as a continuous variable using percentage (%) of female
participants in each citation. This value ranged from 0% to 100%, with a median of 61%.
Coder agreement was 95%.
Ethnicity. Ethnicity was coded as a continuous variable using percentage (%) of
White participants in each citation. This was only coded for samples from the USA, where
ethnicity was commonly reported using the same categories. Most observations (k = 520 of
558) reported this information. Percentage White ranged from 0% to 100% with a median of
52% and a mean of 51%.
Age. Mean sample age of participants was coded as a continuous variable, and ranged
from 7.68 to 40 years, with a median of 19.52. Coder agreement (to the nearest whole number
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of age in years) was 98%. If the study did not report mean age, but gave an age range, the
median age in the range was used. For studies that did not report an age mean or range, the
mean age was imputed based on an average of the mean ages in other studies that included
participants at a similar academic level (Poropat, 2009).
EI stream. EI stream was coded into three categories: Ability EI (31%, k = 399), Self-
rated EI (13%, k = 161) and Mixed EI (56%, k = 716). Coder agreement was 100%. The most
commonly used tests were the EQ-i (31.6%, k = 403) and the MSCEIT (24.1%, k = 307).
EI facet. Effects were coded as representing one of the four ability facets (Perception,
Facilitation, Understanding, and Management) five Bar-On-s EQ-i facets (Intrapersonal,
Interpersonal, Stress Management, Adaptability, General Mood), or Overall EI. Other facets
of EI were not coded. Coder agreement was 95%.
Educational level. Educational level was coded as a categorical moderator with three
levels: primary (7.6%, k = 97), secondary (32.3%, k = 412), or tertiary (59.6%, k = 761). One
study (Alumran & Punamaki, 2008) used a mixed sample of secondary and tertiary students,
and so was excluded from the relevant moderator analyses. Coder agreement was 99%.
Type of academic performance measure. This variable was coded as a categorical
moderator with two levels: course grade (e.g., GPA, semester course grade, and school
subject mark; 83.9%, k = 1071) and standardized test score (e.g., SAT and GRE; 16.1%, k =
205). Coder agreement was 96%.
Publication format. This variable was coded as a categorical moderator with 4 levels:
peer-reviewed articles (54.5%, k = 696), conference proceedings (3.7%, k = 47), dissertations
(38.1%; k = 486), and unpublished data (3.7%, k = 47). Coder agreement was 100%.
Subject area. The subject area was coded as general (for overall GPA or standardized
test covering both numeracy and literacy areas) (54.3%, k = 694), as mathematics/science
(21.1%, k = 267), or as humanities/arts (23.4%, k = 299).
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Statistical analyses
Prior to calculating mean sample-weighted correlations, effect sizes were corrected
for: (a) unreliability in academic performance, (b) range restriction in EI, and (c) unreliability
in EI. We used the formulae and processes outlined in Hunter, Schmidt, and Le (2006) for
direct range restriction. This equation uses mu (the ratio of the sample to population standard
deviation). For citations where mu could not be calculated (because sample or population
standard deviation was not available), we imputed a value of mu equal to the mean mu for
other studies in the same stream and stage of education.
Outliers for each meta-analysis were detected and removed using Hoaglin and
Iglewicz’s (1987) outlier labeling rule, using the more conservative multiplier of g = 2.2.
When interpreting the effect size, we used Cohen’s (1962, 1988) benchmarks of .10, .30
and .50 for small, medium, and large.
We used Robust Variance Estimation (RVE) to control for dependencies between
effect sizes (Hedges, Tipton, & Johnson, 2010). Most citations we located report more than
one effect size. Including multiple effect sizes from the one citation can be problematic due to
the potential for dependencies caused by theoretically irrelevant variables, such as lab
specific procedures, similar participants etc. RVE is a multilevel approach that calculates
standard errors that are adjusted for clustering of effect sizes (i.e. citation level clustering).
We use RVE with a correlated effects structure, which stipulates that effect sizes from the
same citation are likely to be correlated with each other due to erroneous study
characteristics. RVE has been shown to accurately estimate effect sizes even when the precise
nature of the correlation between effects is unknown (Moeyaert et al., 2017). We specified a
correlation rho of .80 as is typical when the correlation between effect sizes unknown
(Hedges et al., 2010). However, we also performed a sensitivity analysis which suggested
none of our findings different substantially as a function of the selected rho. The multi-level
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random effect meta-analysis with RVE estimation were performed using the ‘robumeta’
package (Fisher, Tipton & Hou, 2017) in R version 3.4.3 (R Core team, 2017). The R-code is
provided as supplementary material. To test for the effect of publication bias we looked at
funnel plots and Egger’s test.
To test hypotheses 2 to 9 (testing for moderators) we conducted meta-regressions
using RVE. We conducted multiple regressions simultaneously controlling for publication
type, stream, sample mean age, gender composition, subject area, and performance type
(Table 3). Because there was a lot of missing data for EI facet (only available for a subset of
Stream 1 studies) and ethnicity (only available for USA studies), these were conducted as
separate simple meta-regressions (otherwise we would have a substantially reduced k and
reduced power for multiple regressions). For categorical moderators (EI Stream, EI facet,
publication status, education stage, and subject area of achievement), we also conducted sub-
group analyses so that the relative magnitude of the difference could be easily communicated.
For categorical moderators, we used contrast coding in the meta-regressions (see table note,
Table 3 for details). Because age and stage of education are dependent, we only entered age
into the multiple regressions, to avoid collinearity.
To test hypothesis 10 (the incremental validity of EI in predicting academic
performance controlling for cognitive ability and personality), we created a correlation matrix
of meta-analytic correlations (Table 4). These were drawn from: (a) the current meta-analysis
(EI/academic performance and intelligence/academic performance cells) and (b) other
published meta-analyses. We used this matrix to test hierarchical regression models where
intelligence was entered in Step 1, the big-five personality domains in Step 2, and EI in Step
3. The harmonic mean of the sample size across all cells in the matrix was the input sample
size. Regression models were run separately for the three streams of EI, and for the four
branches of ability EI. It was not possible to conduct a meta-analysis of beta-weights, as only
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4 citations had conducted regressions where academic performance was regressed on all three
of intelligence, big five personality, and EI. SPSS syntax for inputting these matrices and
running these analyses is provided as supplementary material.
To test hypothesis 11 (the relative importance of EI in predicting academic
performance as compared to intelligence and personality), we conducted relative importance
analysis using the R code provided by Tonidandel and LeBreton (2011). This was conducted
for each regression described above.
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Results
Hypothesis 1: Overall correlation between EI and Academic Performance
An initial multi-level random effects meta-analysis with RVE was conducted on the
measurement and range corrected correlations to estimate the mean true correlation between
overall EI (across all EI streams) and academic performance. Prior to conducting the analysis,
30 effect sizes were removed using the outlier labeling rule (Hoaglin & Iglewicz, 1987). All
30 of these effect sizes had corrected correlations in excess of ρ = .73 (such that their
inclusion would have increased the overall size of the effect). There was a significant positive
correlation between overall EI and academic performance, of small to moderate effect size
(ρ = .20, 95% CI [.17, .22]), supporting hypothesis 1. The test for heterogeneity of effect sizes
was statistically significant, Q (1245) = 18050.20, p < .001, indicating true differences in
effect sizes across samples. Moreover, the very large I2 value of 91.41% suggests that an
overwhelming amount of observed variation between samples was due to systematic
between-samples variability. As such, the planned subgroup analyses and meta-regressions
using RVE were conducted, testing hypotheses 2 to 9 (possible moderators of the effect).
Tables 2 and 3 present the results of the moderator analyses.
Hypothesis 2: Moderating Effects of EI Stream
Subgroup analyses and meta-regressions with RVE were used to examine the
moderating effect of EI stream on the association between EI and academic performance (see
Tables 2 and 3). Studies using ability EI measures obtained the largest average effect size
(ρ = .24; CI: .18, .30), followed by studies using mixed EI measures (ρ = .19; CI: .15, .22),
and studies using self-rated EI measures (ρ = .12; CI: .07, .18). Meta-regressions showed that
the ability EI showed a stronger effect than the other two streams (b = .07, p = .006),
supporting hypothesis 2. However, the significant heterogeneity Q statistics and high I2 index
(ranging from 85.28% to 92.39%) suggest heterogeneity within each of the subgroups. Given
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the systematic variability between samples within each of the EI streams, and the differing
theoretical meaning of the three streams, the remaining moderators were examined separately
for each EI stream, as well as overall.
Hypothesis 3: Moderating Effects of EI Facet
For ability EI, there were moderate effect sizes for understanding emotions (ρ =.35;
CI: [.28, .43]) and emotion management (ρ =.26; CI: [.16, .35]), a small to moderate effect
size for emotion facilitation (ρ = .18; CI: [.09, .27]), and a small effect size for emotion
perception (ρ =.09; CI: [.01, .18]) (see Table 2). Follow-up tests of moderation used multi-
level meta-regressions with contrasts coded as (-.5 -.5 .5 .5) (0 0 -1 1) and (-1 1 0 0) for
Perception, Facilitation, Understanding, and Management respectively. Because only a subset
of studies reported branch-level effects, this analysis was run separately to the regression
shown in Table 3 (so as not to reduce power). Results showed that the effect size was
significantly larger for strategic EI (understanding and management), compared with
experiential EI (perceiving and facilitation) (b = .16, SE = .04, 95% CI: .08 to .24, p < .001).
This regression also controlled for publication type, age, gender composition, subject area,
and performance type. This supports hypothesis 3. The effect was not significantly different
for understanding versus management branches (b = -.03, SE = .04, 95% CI: -.07
to .01, p = .157), nor for perception versus facilitation branches (b = .03, SE = .04, 95% CI:
-.05 to .12, p = .408).
Hypothesis 4: Moderating Effect of Publication Status
As can be seen in Tables 2 and 3, publication format (published versus unpublished)
did not significantly moderate the effect of EI on academic performance. The effect size was
larger for published than unpublished research in the case of ability and self-rated EI, the
differences were mostly very small (Δρ = .01, .07, .03, and .01 for overall EI, ability EI, self-
rated EI and mixed EI respectively). The meta-regressions indicated no significant difference
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between published versus unpublished citations for all streams and for overall EI. That is,
there is no evidence for the file drawer effect (selective publication) for included studies. In
addition, we further explored the possibility of publication bias by inspecting funnel plot
asymmetry (see Figure 2). No visual signs of asymmetry were noted, and an Eggers test
suggested that there was no significant asymmetry (z = -1.5788, p = 0.11)
1
. Collectively, the
results do not support Hypothesis 4 (that there would be selective publication such that
published articles showed a higher effect size than unpublished studies).
Hypothesis 5: Moderating Effect of Age
As shown in Table 2, effect sizes tended to be smaller for tertiary samples as
compared to primary or secondary samples. This was true for overall EI (ρ = .16, .23 and .22
for tertiary, secondary, and primary respectively), ability EI (ρ = .18, .30 and .29) and mixed
EI (ρ = .17, .20 and .20) but not self-rated EI (ρ = .10, .14 and .07), although confidence
intervals overlapped in all cases. When we tested the effect of sample mean age using meta-
regressions, the effect of age was not significant for overall EI or for any of the three streams.
Taken together, results do not support Hypothesis 5, showing no moderating effect of age.
Hypothesis 6: Moderating Effect of Type of Achievement
As shown in Table 3, the meta-regressions revealed showed that achievement type
(course grade versus standardized test) was a significant moderator for overall EI (b = -.07, p
= .045) and for self-rated EI (b = -.22, p = .02), but not for ability EI or mixed EI. The effect
was in the same direction mixed EI, but the difference was not significant. There were
stronger effects for grades than standardized tests for overall EI (ρ = .20 versus .17), self-
rated EI (ρ = .13 versus -.03), and mixed EI (ρ = .20 versus .10) but not ability EI (ρ = .24
versus .24). Taken together, hypothesis 6 received partial support.
1
As Egger’s test has not been implemented with robust variance estimation, we therefore calculated Egger’s test
using a meta-regression with the standard error of the effect size as a predictor variable.
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Hypothesis 7: Moderating Effect of Gender Composition
Table 3 shows the results of moderator analyses using meta-regressions for gender
composition. In interpreting results, we note that gender composition was not distributed
evenly across the studies—studies tended to have more females than males. The median
proportion of females was 0.61, and only 17.6% of studies had more males than females. For
overall EI, the simple meta-regression (b = -.002, p = .026) showed a significant effect of
gender, with a higher percentage of females associated with lower effect size. Gender
composition was not significant moderator for any of the streams individually. This result
provides partial support for hypothesis 7, showing a small effect such that samples with larger
proportions of females showed weaker effects than more gender-diverse samples when all
streams were considered together.
Hypothesis 8: Moderating Effect of Ethnic Composition
A simple meta-regression (k = 522) suggested that the ethnic composition of the
sample (percentage White) was not statistically significant, either for overall EI (b = .001, p
=.219) ability EI (b = -.001, p = .289); self-rated EI (b = .002, p = .258) or mixed EI (b
= .000; p = .863). Therefore, hypothesis 8 was not supported. The EI/academic performance
association does not appear to be affected by the proportion of minority students in the
sample.
Hypothesis 9: Moderating Effect of Subject Area
As shown in Tables 2 and 3, subject area (math/science versus humanities) is a
significant moderator of the EI/performance relationship for ability EI only (b = .08, p = .04),
where effects are stronger for humanities (ρ = .38) than for math/science (ρ = .21). The effect
was not significantly different for math/science versus humanities for overall EI, self-rated
EI, or mixed EI. Results therefore provide partial support for Hypothesis 9.
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Hypothesis 10: Incremental prediction of EI to academic performance
The Relationship between IQ and EI
In addition to the above meta-regressions, we examined the relationship between
intelligence and academic performance from the studies included under the current search
criteria, in order to draw comparisons with EI. For the intelligence/academic performance
effect, there were 23 studies with 84 effect sizes and 4,801 participants. Intelligence showed a
moderate to large association with academic performance (ρ = .39, CI [.31, .46], p < .001, I2
=89.98%). This value is larger than that reported by Poropat (2008) where there was no
correction for range restriction, but smaller than that reported by Roth et al. (2015) where
only primary and secondary education samples were included. We will use this value of ρ
= .39 to assess the incremental validity of EI over-and-above intelligence and big five
personality in the analyses below.
Incremental prediction of EI to academic performance
Table 4 shows the meta-analytic correlation matrix we derived from our original
meta-analyses and previously published estimates. We used this matrix to conduct multiple
regressions predicting academic performance from intelligence, personality and EI for each
of the three EI streams. We checked multi-collinearity using tolerance values in each case.
Tolerance values were acceptable for all three streams, ranging from .70 to .91 for ability
EI, .70 to .92 for self-rated EI, and .58 to .92 for mixed EI. Intelligence, personality and EI
predicted 24.0% of the variation in academic performance for the ability EI analysis, 22.9%
of the variance in academic performance for the self-rated EI analysis, and 24.6% of the
variance in academic performance for the mixed EI analysis (see Table 5). Ability EI and
mixed EI showed modest incremental prediction (ΔR2 = .017 and .023 respectively) and self-
rated EI showed negligible incremental prediction (ΔR2 = .007). Partial correlations of EI
with academic performance were significant at p < .001 in all cases, and were .148 for ability
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EI, .092 for self-rated EI, and .248 for mixed EI. These results provide support for hypothesis
10, although the size of the incremental prediction was very small.
We conducted these regressions separately for each of the four branches of ability EI.
Results of these analyses are shown in Table 6. The understanding emotions branch showed
the strongest incremental prediction (ΔR2 = .039), followed by emotion management (ΔR2
= .036). Both emotion perception and emotion facilitation showed negligible incremental
prediction of academic performance (ΔR2 = .000 for emotion perception and .009 for emotion
facilitation). Partial correlations of the ability EI branches with academic performance were
significant for facilitation (r = .108, p < .001), understanding (r = .223, p < .001), and
management (r = .216, p < .001), but not for perception (r = -.017, p = .074). These results
demonstrate that the active ingredients of EI for academic performance are emotion
understanding and emotion management. They also demonstrate that the strong effect of
emotion understanding on academic performance cannot be explained solely by its overlap
with intelligence.
Hypothesis 11: The most important predictors of academic performance will be
intelligence, conscientiousness, and EI
Tables 5 and 6 show the relative weights and the corresponding percentage of R2
explained by each variable in the regressions, as determined by relative weights analysis. For
all three streams: (a) intelligence was the most important variable (accounting for between
58% and 69% of the explained variance); (b) conscientiousness the second-most important
(accounting for between 20% and 21% of the explained variance); and (c) EI the third-most
important variable. The relative importance of EI was greater for mixed EI (RW% = 15%)
than self-rated (RW% = 10%) than ability EI (RW% = 4%).
However, results were very different across the four branches of ability EI. While
intelligence remained the most important predictor in all cases, EI was the second most
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important predictor for both emotion understanding (RW% = 31% for EI versus 19% for
conscientiousness) and emotion management (RW% = 20% for EI versus 19% for
conscientiousness). Relative importance was lower for emotion facilitation (RW% = 9%) and
negligible for emotion perception (RW% = 1%). It is clear that the strategic branches of EI
(emotion understanding and management) are more important than the experiential branches
(perception and facilitation).
Taken together, these results provide support for hypothesis 11, showing that EI is
among the top 3 most important predictors of academic performance. However, this analysis
also highlights that results are quite different for different streams and different branches of
EI.
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Discussion
Results from these meta-analyses demonstrate that EI shows a small to moderate
relationship with academic performance, of similar effect size to well-known non-cognitive
predictors (e.g., ρ = .20 for EI versus ρ = .22 for conscientiousness, based on the current
meta-analysis and Poropat [2009]). Ability EI was a significantly stronger predictor than self-
report or mixed EI, as hypothesized. Within ability EI, understanding and management
branches had a stronger effect than perception or facilitation branches. There is no evidence
for selective publication of larger effects, for stronger effects in younger students, nor that
effects differ depending on the proportion of ethnic minority students in the sample. For the
other moderators, effects were mixed or limited. There is limited evidence that the effect is
stronger: (a) for less female-dominated samples (this effect was significant for total EI, but
not for any of the three streams); (b) for grades than standardized test scores (this was
significant for total EI and Stream 2 only); and (c) for humanities versus mathematics/science
performance (this was significant for ability EI only).
There was evidence of incremental validity of EI over intelligence and personality,
but this was largely restricted to mixed EI (which explained an additional 2.3% of the
variance) and the understanding and management branches of ability EI (which explained an
additional 3.9% and 3.6% of the variance respectively). That is, self-rated EI, total ability EI,
and the lower two branches of ability EI (emotion perception and facilitation) provide little to
no explanatory power for academic performance over intelligence and personality. These
differences across the three streams suggest that the underlying mechanisms accounting for
the EI/performance relationship may differ for ability EI, self-rated EI, and mixed EI.
Why does EI predict academic performance? Insights based on moderators
In our introduction, we suggested there were three reasons why EI may predict
academic performance. First, students with higher EI may be more able to regulate the
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negative emotions such as anxiety, boredom and disappointment involved in academic
performance. If this is true, emotion management would be responsible for the effects.
Second, students with higher EI may be better able to manage the social world around them,
forming better relationships with teachers, peers, and family. If this was the case, emotion
management would again be responsible for the effect, and the effect would be stronger for
grades than for standardized tests. Third, EI competencies may overlap with the academic
competencies required for humanities subjects like history and language arts (e.g.,
understanding human motivations and emotions). In this case: (a) understanding—the
knowledge base of EI—would show the strongest effect and (b) the effect would be bigger
for humanities than sciences. Based on the significant moderations, there is some support for
each of these effects, with slightly different results for different streams of EI. We discuss the
significance moderations below, with respect to these three proposed mechanisms.
Evidence for Mechanism 1: Is ‘emotion management’ the key ingredient in EI?
Joseph and Newman (2010) proposed a “key conceptual role” of emotion
management for predicting job performance (p. 69), proposing emotion management as the
proximal predictor of performance. The facet-level moderation for ability EI provides partial
support for this assumption, finding that management and understanding are jointly the
strongest predictors of academic performance. Both these branches (management and
understanding) showed significantly stronger effects than the two other branches. The effect
was larger for understanding than management, but not significantly so (ρ = .35 versus ρ
= .26) and was equal after accounting for the effects of intelligence and personality (partial ρ
= .22 in both cases). That is, both emotion understanding and emotion management are active
ingredients in the prediction of academic performance. We believe this is consistent with an
interpretation that EI affects academic performance through the regulation of academic
emotions, but also due to the relevance of emotion content knowledge for academic
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performance in the humanities.
The critical role of emotion understanding for academic performance has implications
for comparing ability EI with self-rated EI. For self-rated EI, many of the effects in our meta-
analysis used instruments that did not include emotion understanding content (because they
were based on an older definition of EI that did not have emotion understanding in the
definition). Specifically, 50% of the Stream 2 citations used the Schutte Self-Report Scale,
Trait Meta-Mood Scale or the Wong-Law Emotional Intelligence Scale, which do not include
a subscale assessing emotion understanding. Given that ability EI shows the strongest
relationship for emotion understanding, the difference in effect size between ability EI and
self-rated EI measures may in fact represent a difference in content (i.e., prediction is greater
for tests that include emotion understanding content) rather than a difference in method
(ability scales versus rating-scales). Many of the more recent self-rated EI tests do include an
emotion understanding component (e.g., Anguiano-Carrasco, MacCann, Geiger, Seybert, &
Roberts, 2015; Brackett et al., 2006).
Evidence for Mechanism 2: Are EI competencies required for academic content?
Moderation analyses largely support the idea that performance on academic tasks
require some EI competencies. First, academic performance related significantly more
strongly to ability EI than to the other two streams. This finding differs from meta-analyses
predicting job performance, where ability EI is consistently the weakest predictor of the three
streams (Joseph & Newman, 2010; Miao et al., 2017; O'Boyle et al., 2011). This difference
may relate to assessment methods. Academic performance is mainly assessed with objective
tasks (i.e., evaluations of a product, such as an essay, lab report, speech, worksheet or test),
whereas job performance is most often assessed via supervisor ratings. Similarly, ability EI is
assessed with objective tasks and self-rated and mixed EI are assessed with rating scales. We
would expect stronger predictor-criterion relationships when predictor and criterion have the
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same method. As such, a higher relationship for ability EI (as compared to the other streams)
may represent method bias rather than content overlap of academic and emotional
knowledge.
However, ability EI (but not self-rated or mixed EI) relates more strongly to
performance in humanities than sciences. This is one of the larger differences we found,
where the effect was nearly twice as large for humanities as sciences (ρ = .38 versus .21).
Objective measurement of performance is similar across humanities and sciences. The
academic processes (social context and the student’s emotions and emotion regulation in the
classroom) are also similar for different sub-disciplines. While sub-disciplines differ in the
degree of social interaction involved, the degree of social interaction does not align with the
humanities versus science categorization (e.g., science frequently involves lab partners or
group work, while this is rare for mathematics). As such, we interpret this difference in
subject areas to be largely due to a difference in academic content, and specifically the
relevance of emotion knowledge to subjects requiring an understanding of people and their
interactions, motivations and emotions (i.e., literature, history, geography, drama and other
humanities subjects). The first standard of The Standards for the English Language Arts
(1996), as put forward by the National Council of Teachers of English, states that the purpose
of reading texts is to “build an understanding of … themselves and the cultures of the United
States and the world” and the second standard states that the purpose is “to build an
understanding of the many dimensions (e.g., philosophical, ethical, aesthetic) of human
experience” (p. 19). That is, broad statements of content for achievement in language arts
inherently involve an understanding of oneself and of others in terms of the intangible nature
of being human—which we would argue is essentially emotions and social interactions. That
is, understanding human emotions and the social and situational causes appear to be an
underlying component of achievement in language arts.
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In addition, the fact that the emotion understanding showed the strongest relationship
to academic performance (as compared to the other four branches) supports the interpretation
mentioned above, where understanding emotional content is a key part of the content of
language arts education. It is possible to view emotion understanding as a kind of domain-
specific knowledge, where the content domain is emotions. Content knowledge of emotion
words, as well as the causes and consequences of emotions, appear highly relevant for
understanding character motivations in literature as well as other academic subject matter
relating to people and how they shape societies, countries and history (i.e., history,
geography, psychology, sociology).
One possible interpretation is that the ability EI/academic performance association
may be due to a third variable—reading comprehension. Because ability EI tests involve
interpreting written text, reading comprehension ability may constitute construct-irrelevant
variance on such tests (AERA, APA, & NCME, 2014) that may partially explain the
relationship between EI and academic performance. This particularly affects understanding
and management tests, which involve more and more complex text (e.g., most management
tests involve a paragraph of text in each item stem). However, the fact that emotion
understanding and management predicted academic performance over-and-above the effect of
intelligence suggests that this confound does not account for the entirety of the relationship
between ability EI and academic performance. Nevertheless, the relationship was greatly
reduced, particularly for emotion understanding. Because the partial correlations remained of
small to moderate size after accounting for intelligence, our interpretation is that the bulk of
the content overlap represents more than a reading comprehension method effect, particularly
for emotion management. Taken together, results support the suggested mechanism whereby
EI predicts academic performance due to the emotional content required in academic
subjects.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 51 of 98
Evidence for Mechanism 3: Does EI affect academic performance through
interpersonal processes?
If EI exerts an influence on academic performance via the ability to develop social
relationships in the educational context, then EI should have a stronger effect on grades than
standardized tests (as the social networking and relationship building with other students and
teachers should have a stronger effect on grades than on standardized tests). This difference
was significant only for self-rated EI and the three streams combined (not for ability or mixed
EI). Self-rated EI did not relate to standardized test scores at all (ρ = -.03). In contrast, ability
EI and mixed EI related to both grades and standardized tests. This suggests that academic
performance relates to self-rated EI through relationship building only. In contrast, academic
performance relates to ability EI and mixed EI through both relationship building and
mechanisms related to regulating academic emotions.
For all three streams of EI, there is evidence that higher EI relates to building social
relationships in a school environment. Ability EI relates to peer-nominations of reciprocal
friendship in college students and to higher-quality of social interactions with others (Lopes,
Brackett, Nezlek, Schütz, Sellin, & Salovey, 2004; Lopes, Salovey, Côté, Beers, & Pett,
2005). Self-rated EI predicts greater social support in both high school and university
students (Ciarrochi, Chan, & Bajgar, 2001; Kong, Zhao, & You, 2012). Mixed EI is
associated with peer reports of cooperative behavior (Mavroveli, Petrides, Rieffe, & Bakker,
2007; Petrides, Sangareau, Furnham, & Frederickson, 2006). There is also evidence that both
ability EI and mixed EI relate to using more effective strategies to regulate negative emotions
(Peña-Sarrionandia, Mikolajczak, & Gross, 2015).
Taken together with these findings, we propose that differences between the three
streams of EI relate to the number of mechanisms that underlie the EI/performance
relationship. Specifically: (a) self-rated EI predicts academic performance only through a
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 52 of 98
relationship building pathway (students with higher emotional self-efficacy can build better
relationships with teachers and peers); (b) mixed EI predicts academic performance through
both relationship building and the regulation of academic emotions; and (c) ability EI predicts
academic performance through relationship building, regulation of academic emotions, and
also through emotion content knowledge requirements of some academic areas. This
explanation accounts for the relatively greater prediction of academic performance by ability
EI than mixed EI than self-rated EI and is consistent with the pattern of moderators we found.
The Relative Importance of EI to Academic Achievement
One of the critical drivers of EI's early popularity was the idea that emotional skills
are more important than intelligence in predicting life success. Indeed, the title of Daniel
Goleman's first book, the catalyst for EI's snowballing popularity, was “Emotional
intelligence: Why it can matter more than IQ”. The 1995 cover story of TIME magazine
made similar claims, stating that “emotions, not IQ, may be the true measure of human
intelligence” (Gibbs, 1995, p. 60). These early claims were generally not borne out by
research on job performance. Although EI predicts better job performance (Joseph &
Newman, 2010; O’Boyle et al., 2011), a critical mass of research indicates that intelligence is
a much stronger predictor and is in fact the single best predictor of job performance (Ree &
Earles, 1992; Salgado et al., 2003; Schmidt & Hunter, 1998). We found largely similar results
for academic performance. Although EI predicts academic performance, intelligence was a
much stronger predictor, with relative importance analysis indicating that cognitive ability
was the single most important predictor of academic performance.
While the popular press hype about EI was not substantiated, we nevertheless believe
that demonstrating a small to moderate effect size is informative for research and practice.
Moreover, some of the recent changes occuring in education and assessment practices may
increase the importance of noncognitive qualities, including EI.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 53 of 98
The first such change to modern assessment and learning practice is the increasing use
of group activities, including collaborative group assessments (Ahles & Bosworth, 2004).
Managing the social relationships and interpersonal conflicts of the group may thus become
more and more reflected in students’ end of semester grades. A second change to education
practices is the extent to which graduate attributes (also referred to as ‘twenty-first century
skills’ or ‘non-cognitive constructs’) are emphasized by schools and universities (e.g., Clarke,
Double, & MacCann, 2017). Graduate attributes often include social-emotional skills such as
leadership, communication, teamwork and inter-cultural competencies, with some institutions
explicitly including EI as a graduate attribute. For example, Australia uses Goleman’s model
of EI as the basis for its national K-10 curriculum of personal and social competencies that
students should be developing as they progress through primary and secondary education
(ACARA, 2017). Schools and universities are increasingly attempting to embed these
graduate qualities within the content that is taught and assessed. As such, high grades might
increasingly reflect skill development in these areas.
A third change to the classroom is the extent to which computers and technology are
now an integral part of education. In tertiary education, there are a large and increasing
number of online only courses or courses that have at least some online-only content (most
famously, the Massive Open Online Courses [MOOCs]). There are two main differences
between traditional face-to-face learning and online learning. First, in a traditional face-to-
face university course, the schedule of learning is set by the schedule of the face-to-face
lectures. In contrast, an online only course requires the learner to manage their own schedule
of accessing online content, such that students with poor time management will not succeed
(MacCann, Fogarty, & Roberts, 2012). Second, in a traditional face-to-face course,
communication with teachers and other students occurs through in-person conversation, with
access to multiple channels of information (e.g., facial and vocal expression, body language,
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 54 of 98
and real-time clarification of misunderstandings). Online communication is more often based
on text (e.g., discussion boards, emails, or computer chat). Most neurotypical people find it
more difficult to detect another person’s emotions and social needs from text rather than face-
to-face contact. As such, greater emotional skills are required to build relationships with the
instructor or other students in an online environment. Thus, social and emotional skills (both
self-regulation and interpersonal skills) may become increasingly important as tertiary
education involves a greater amount of online content.
Practical Implications
One of the major findings of this meta-analysis is that different parts of EI are
differentially important for academic performance. Any applied uses of EI in education seem
limited to the three parts of EI with non-trivial incremental validity: mixed EI, emotion
management ability, and emotion understanding ability. There are three broad applications
that might be considered: (a) identifying students at risk for failure, attrition, or under-
performance; (b) selection decisions for high-stakes educational opportunities; and (c) policy
decisions about the relative cost versus benefit of implementing SEL or EI training programs
in schools.
The first two applications (identifying at risk students, and high-stakes selection)
require careful consideration of response distortion issues. Particularly in a high-stakes
selection context, test-takers are motivated to gain high scores and will distort their responses
on rating scales to ‘fake high’ (Birkeland, Manson, Kisamore, Brannick, & Smith, 2006;
Viswesvaran & Ones, 1999). Faking is a consequential issue with personality scales, which
use self-report or observer-report ratings that test takers can fake. Observer-reports do not
necessarily solve this problem, as the observers are often not impartial, but may be school
staff with a vested interest in their students gaining entrance to prestigious colleges or
programs. Faking is a problem for rating-scale measures of EI, but not for ability scales (Day
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 55 of 98
& Carroll, 2008; Grubb & McDaniel, 2007; Tett, Freund, Christiansen, Fox, & Coaster,
2012). The current meta-analysis is the first to demonstrate that the relationship between EI
and academic performance holds for ability-based tests as well as rating scales (in fact, the
relationship is actually higher for ability-based EI tests as compared to rating scales). As
such, we demonstrate a pathway that might provide modest increments in high-stakes
education selection decisions—using ability-based EI assessments of understanding and
managing emotions (based on current results, other parts of ability EI are not important).
Ability-based EI tests are already used for selection into medical schools in several countries,
and evidence supports their use for selecting better candidates (Libbrecht, Lievens, Carette, &
Cote, 2014; Lievens & Sackett, 2006). However, such tests are rarely used in other broader
education selection contexts. If EI is considered as a selection procedure (perhaps as an add-
on to intelligence and personality assessment), we suggest that ability tests of understanding
and managing emotions be preferred over rating scales (due to response distortion) or tests of
facilitation or management (due to low incremental prediction over intelligence and
personality).
A national and international focus on standardized tests to measure academic
performance and milestones has lead schools, districts, states and countries to focus on
achievement in the narrow range of academic content that such tests focus on. Alongside this,
classroom teachers face increasing challenges to their workload, including adapting the
curriculum to individual students’ needs, the mainstreaming of students with special
educational requirements, and adapting to rapidly changing curriculum and policy (Skaalvik
& Skaalvik, 2007). Against this background, devoting resources to teaching children EI skills
can be seen as taking teacher resources and classroom time away from more critical activities
that will increase test scores and achievement. What our meta-analysis shows is that EI skills
are in fact associated with higher academic performance. This implies that time spent
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 56 of 98
teaching EI skills may not necessarily detract from student achievement, given that higher EI
students also show higher achievement. Again, we highlight different importance of the four
EI abilities as a guide for where to focus skills training—a focus on perceiving emotions is
likely to be less useful than a focus on understanding and managing emotions.
Our meta-analysis also has implications for the effects of the such training programs
(or a focus on EI more generally) on the known achievement gaps between ethnic groups and
between males and females. While there is evidence that the Black-White achievement gap is
slowly closing, differences in the achievement for minority students compared to White
students remain substantial, at around 0.75 standard deviations for Black students and around
0.60 standard deviations for Hispanic students (Hansen, Mann Levesque, Quintero, & Valant,
2018). There is also increasing evidence that males are falling behind females in terms of the
grades they receive and their participation in higher education (Fortin, Oreopoulos, & Phipps,
2015). Against this background, it is important to note that the effect of EI on academic
performance does not appear to differ for minority students versus White students, and that
gender differences are negligible and when significant favor males (who currently show
lower achievement). These results imply, at the very least, that efforts to improve EI are
unlikely to widen the achievement gaps.
The key role of emotion understanding and management is also important to consider
in terms of EI training programs. Three recent meta-analyses on the effectiveness of EI
training have reported significant increases in EI, with effect sizes of .45, .46, .51, and .61
(Hodzic, Scharfen, Ripoll, Holling, & Zenasni, 2018; Mattingly & Kraiger, 2018; Schutte,
Malouff, & Thorsteinsson, 2013). Hodzic et al. found that programs based on the ability
model were significantly more effective than those based on mixed models (g = .60
versus .31), and that emotion understanding showed the largest increase of all the ability EI
branches—significantly more than emotion facilitation (g = .69 versus .42). That is, it seems
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 57 of 98
that programs are effective for increasing ability EI, and particularly its emotion
understanding facet. This is highly relevant for our own meta-analysis, where ability EI (and
specifically emotion understanding) showed the highest association with academic
performance. That is, EI training seems to produce the strongest increases in exactly those
competencies that are most relevant for academic performance.
Although Hodzic et al. did not distinguish between EI training programs for
workplace applications and EI training programs for schools and universities, several studies
conducted in schools and universities report similar findings regarding the largest increases
for emotion understanding. For example, Pool and Qualter (2012) conducted a training study
in university students and found the largest increase in ability EI was for emotion
understanding (and the second-largest for emotion management). Moreover, evidence from
the RULER Feeling Words curriculum (an EI development program for secondary school
students) shows that EI training programs increase grades as well as social and emotional
competencies. Specifically, students completing the RULER showed improved school grades
as well as improved teacher ratings of social and emotional competencies compared to
control groups (Brackett, Rivers, Reyes, & Salovey, 2012). In fact, the relative increase in
school grades was a larger effect than the relative increase in social and emotional
competencies. That is, EI training programs are likely to increase academic performance as
well as social and emotional outcomes, such that education decision-makers and policy-
makers are not faced with a decision of whether to invest in social/emotional wellbeing at the
expense of student achievement—evidence suggests that these programs likely do both. This
is a critical piece of information for schools deciding where to best allocate their resources.
Limitations
Our results demonstrated only that EI and academic performance are significantly
associated, but not that higher EI causes higher achievement. Only three of the citations
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 58 of 98
reported a longitudinal design, such that the empirical evidence for EI causing later
achievement is very weak (Costa & Faria, 2015; Qualter et al., 2012; Stewart & Chrisholm,
2012). This association could occur because: (a) higher EI causes increased academic
performance; (b) higher achievement causes increased EI; or (c) there are one or more
variables that influence both EI and academic performance. In the introduction, we outlined
the reasons we believe theoretically that EI could cause later achievement. However, there are
also feasible pathways by which greater academic performance could cause higher EI.
Greater academic performance could feasibly result in increased self-esteem, greater
opportunities for social and emotional development, and higher expectations for social skills
and emotion regulation. High academic performance may act as a gateway for gifted and
talented programs, streaming into enrichment activities, and a culture of high expectations
from teachers, parents, and communities that permeate social and emotional behaviors as well
as academic ones via the halo effect (Nisbett, & Wilson, 1977). Conversely, low academic
performance may act as a barrier to opportunities to develop social and emotional skills
through loss of privileges for academic failures (e.g., losing recess playtime or evening
socialization to complete work or denied extra-curricular activity participation due to course
failure), the development of strong negative emotions surrounding school and schoolwork,
and the correspondingly low expectations for social and emotional behaviors. It seems likely
that the reality is complex, with bidirectional effects of academic and emotional development,
particularly in the earlier years of school.
One further limitation of the current manuscript concerns the use of the meta-analytic
correlation matrix (used to test hypotheses 10 and 11). This was composed of estimates taken
from different journal articles from different research teams, and therefore did not use the
same methods for estimation nor the same samples. While we used RVE in the current study,
all other sources for effect sizes were obtained by aggregating multiple effect sizes from the
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 59 of 98
same study. All studies except for Poropat (2009) corrected for unreliability as well as range
restriction. The personality/academic performance estimation was not corrected for range
restriction of measurement in either the predictor or criterion (Poropat, 2009). The possible
effect of this would be to under-estimate the prediction and relative importance of personality
traits such as conscientiousness.
Future research and recommendations
One obvious future direction for further research is to test our three proposed
mechanisms of the EI/performance relationship: (a) social relationship building, (b)
regulation of academic emotions, and (c) content overlap between EI and academic subject
matter. For point a, an analysis of content overlap between the competencies of EI and the
different processes required for success in different disciplines could be undertaken by a
panel of educators. Longitudinal research involving all three streams could test whether all
three mediate ability EI, (a) and (b) mediate mixed EI, and (a) along mediates self-rated EI,
as we proposed. As we mention above, there is a paucity of long-term longitudinal research
on EI and academic performance. As such, examining mediators of the link as well as
conducing lagged panel models to tease apart the direction of causation is important.
While there is ample evidence that training EI works (e.g., Hodzic, Scharfen, Ripoll,
Holling, & Zenasni, 2018; Mattingly & Kraiger, 2018; Schutte, Malouff, & Thorsteinsson,
2013), we are not aware of experimental studies on EI training that examine the effects of
training different branches of EI. Such designs would isolate which facets of EI are most
relevant for the improvement of which types of outcomes and would also provide stronger
evidence for the causal direction from EI to academic performance.
Conclusion
While we know that intelligence and conscientiousness are collectively the most
important psychological characteristics needed for academic performance, this manuscript
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 60 of 98
highlights that there is a third broad psychological characteristic that may help students
succeed—EI. The different varieties of EI most likely predict academic performance via
different pathways. Emotional self-efficacy (the self-beliefs about one’s emotional skills
captured by self-rated EI) is the least important. Knowledge about the causes and
consequences of emotions and a vocabulary of emotions words, along with knowing how to
manage emotional situations are potentially the most important parts of EI for academic
performance. It is not enough to be smart and hard-working—to have the added edge for
success, students must also be able to understand and manage emotions to succeed at school.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 61 of 98
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EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 82 of 98
Table 1.
Meta-analytic correlations of EI with positive life outcomes, by EI stream
Ability
EI
Self-Rated
EI
Mixed
EI
Workplace performance (Joseph & Newman, 2010)
.18
.23
.47
Workplace Performance (O’Boyle et al., 2011)
.24
.30
.28
Organizational Citizenship behaviours (Miao et al., 2017)
.17
.57
.48
Counter-productive workplace behaviours (Miao et al., 2017)
.01
-.38
-.42
Transformational Leadership (Harms & Credé, 2010)
.24
.66a
Subjective well-being (Sanchez-Alvarez, et al., 2016)
.22
.32
.38
Health (Schutte et al., 2007)
.11
.32a
Health (Martins et al., 2010)
.17
.34a
a These analyses did not distinguish between rating scales based on mixed models and rating
scales representing self-ratings of ability EI.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 83 of 98
Table 2.
Meta-analytic Results of the Overall Link between EI and Academic Performance and
Subgroup Analyses—Overall EI, and by Streams 1, 2 and 3
Sub-group
n
k
N
r
rho
95% CI
I2
p
Low
Upp
Overall
158
1,246
42,529
.14
.20
.17
.22
91.41%
<.001
Stream
Stream 1
50
383
8,826
.16
.24
.18
.30
92.39%
<.001
Stream 2
33
160
8,492
.10
.12
.07
.18
85.28%
<.001
Stream 3
90
703
27,939
.13
.19
.15
.22
91.49%
<.001
Education level
Primary
13
91
2,329
.14
.22
.10
.34
91.81%
.001
Secondary
60
507
16,139
.16
.23
.18
.28
91.93%
<.001
Tertiary
103
643
28,596
.11
.16
.13
.20
90.64%
<.001
Achievement measure
Course grade
141
1,050
38,759
.14
.20
.17
.23
91.24%
<.001
Stand. test
37
196
8,178
.12
.17
.10
.23
93.53%
<.001
Publication Type
Journal Article
82
684
23,798
.14
.19
.15
.23
91.35%
<.001
Unpublished
76
562
18,731
.14
.20
.16
.24
91.57%
<.001
Subject Area
General
125
687
33,941
.13
.19
.16
.22
89.60%
<.001
Humanities
35
285
7,231
.17
.24
.18
.29
91.94%
<.001
Math/Science
40
258
9,750
.11
.16
.10
.22
92.27%
<.001
Stream 1 (Ability)
50
383
8,826
.16
.24
.18
.30
92.39%
<.001
Facet
Perception
29
68
3,919
.07
.09
.01
.18
87.86%
.025
Facilitation
25
53
3,234
.12
.18
.09
.27
90.52%
.001
Understanding
29
85
4,815
.22
.35
.28
.43
94.01%
<.001
Management
31
67
4,898
.16
.26
.16
.35
93.54%
<.001
Education level
Primary
3
11
199
.15
.29
-.86
1.43
96.92%
.394
Secondary
25
176
5,177
.20
.30
.22
.38
90.99%
<.001
Tertiary
35
196
5,515
.11
.18
.11
.24
89.13%
<.001
Achievement measure
Course grade
46
291
8,406
.15
.24
.17
.30
92.07%
<.001
Stand. test
19
92
3,135
.17
.24
.12
.36
94.84%
.001
Publication Type
Journal article
25
183
5,443
.17
.28
.20
.36
92.52%
<.001
Unpublished
25
200
3,383
.14
.21
.11
.30
92.45%
<.001
Subject Area
General
40
207
6,608
.14
.22
.16
.28
90.25%
<.001
Humanities
14
71
3,221
.26
.38
.28
.49
93.34%
<.001
Math/Science
15
98
3,205
.13
.21
.05
.38
96.00%
.014
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 84 of 98
Table 2 (continued)
Sub-group
n
k
N
r
rho
95% CI
I2
p
Lower
Upper
Stream 2 (Self-rated)
33
160
8,492
.10
.12
.07
.18
85.28%
<.001
Education level
Primary
2
10
479
.05
.07
.03
.12
90.35%
.033
Secondary
10
92
3,777
.12
.14
.03
.26
88.35%
.022
Tertiary
24
58
4,676
.08
.10
.03
.18
83.32%
.006
Achievement measure
Course grade
31
146
8,013
.11
.13
.07
.19
83.89%
<.001
Stand. test
5
14
1,087
-.03
-.03
-.18
.13
84.29%
.67
Publication Type
Journal article
24
134
6,757
.10
.13
.06
.20
84.50%
<.001
Unpublished
9
26
1,735
.08
.10
-.04
.24
88.27%
.135
Subject Area
General
27
73
5,337
.10
.13
.06
.19
82.01%
<.001
Humanities
7
45
1,683
.07
.10
.00
.20
83.51%
.056
Math/Science
9
37
3,722
.05
.07
-.08
.21
90.92%
.324
Stream 3 (Mixed)
90
703
27,939
.13
.19
.15
.22
91.49%
<.001
Education level
Primary
9
70
1,723
.15
.20
.14
.27
79.50%
<.001
Secondary
33
239
9,035
.15
.20
.13
.27
90.64%
<.001
Tertiary
53
389
19,715
.11
.17
.12
.21
92.67%
<.001
Achievement measure
Course grade
78
613
24,996
.14
.20
.16
.24
91.58%
<.001
Stand. test
19
90
5,088
.08
.10
.04
.16
88.18%
0.004
Publication Type
Journal article
48
336
14,585
.13
.19
.14
.24
91.76%
<.001
Unpublished
42
367
13,354
.13
.18
.13
.23
91.25%
<.001
Subject Area
General
68
407
23,439
.13
.19
.15
.23
90.29%
<.001
Humanities
22
169
4,406
.12
.17
.11
.22
79.62%
<.001
Math/Science
22
123
4,488
.10
.13
.07
.19
89.16%
<.001
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 85 of 98
Table 3.
Meta-Regressions testing Moderators of EI/Academic Performance Link (multiple
regressions with all moderators in same equation)
Moderator
b
SE
95% CI
p
Lower
Upper
Overall EI (k = 1,057)
Publication type
-.013
.031
-.073
.048
.680
Age
-.004
.003
-.009
.002
.191
Gender (% female)
-.002
.001
-.003
.000
.026
Stream a
Ability vs Others
.070
.024
.021
.118
.006
Self-rated vs. mixed
-.017
.019
-.054
.021
.372
Subject area b
General vs others
.016
.019
-.022
.055
.401
Math/science vs. humanities b
.034
.017
-.001
.068
.057
Performance type
-.071
.034
-.140
-.002
.045
Stream 1: Ability (k = 331)
Publication type
.062
.058
-.057
.180
.296
Age
-.006
.008
-.023
.010
.428
Gender (% female)
.000
.002
-.004
.004
.852
Subject area b
General vs others
.090
.036
.013
.167
.025
Math/science vs. humanities
.082
.037
.003
.162
.043
Performance type
-.086
.065
-.221
.050
.202
Stream 2: Self-rated (k = 140)
Publication type
.008
.078
-.165
.181
.918
Age
-.010
.007
-.025
.006
.188
Gender (% female)
-.003
.001
-.006
.001
.088
Subject area b
General vs others
.003
.046
-.104
.110
.950
Math/science vs. humanities
-.005
.038
-.094
.084
.896
Performance type
-.219
.070
-.384
-.053
.017
Stream 3: Mixed (k = 586)
Publication type
-.015
.039
-.094
.064
.702
Age
-.002
.003
-.009
.005
.530
Gender (% female)
-.002
.001
-.004
.001
.161
Subject area b
General vs others
-.015
.024
-.065
.034
.527
Math/science vs. humanities
.020
.016
-.013
.052
.222
Performance type
-.072
.044
-.163
.019
.115
a Contrasts were (1 -.5 -.5) and (0 1 -1) for ability, self-rated, and mixed EI respectively. bCon
trasts were (-1 .5 .5) and (0 -1 0) for general, math/science and humanities respectively.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 86 of 98
Table 4.
Meta-analytic Correlation Matrix of EI (Stream 1 Total and Four Branches, Stream 2, and Stream 3), Academic Performance, Cognitive Ability,
and Big Five Personality (EI/Academic Performance and Intelligence/Academic Performance Cells from Current Meta-analysis)
Stream 1
EI
Stream 2
EI
Stream 3
EI
Perf
g
O
C
E
A
Total
Perc
Fac
Und
Man
Perf
.24 a
50/8,826
.09 a
29/3,919
.18 a
25/3,234
.35 a
29/4,815
.26 a
31/4,898
.12 a
33/8,492
.19 a
90/27,939
g
.25 b
28/5,383
.10 b
21/4,710
.18 b
18/3,971
.39 b
20/4,581
.16 b
19/4,277
.00 b
16/2,158
.11 b
19/2,880
.39 a
27/4,801
O
.14 c
47/10,258
.07 c
23/3,582
.10 c
23/3,582
.18 c
22/3,374
.16 c
22/3,374
.29 b
26/8,479
.29 b
30/5,386
.12 d
113/60,442
.22 e
46/13,182
C
.09 c
47/10,258
.28 c
23/3,582
.11 c
23/3,582
.09 c
22/3,374
.16 c
22/3,374
.38 b
27/8,566
.38 b
31/5,591
.22 d
138/70,926
-.04 e
56/15,429
.24 f
n.r./39,595
E
.05 c
47/10,258
.09 c
24/3,696
.10 c
23/3,582
.07 c
22/3,374
.18 c
22/3,374
.32 b
26/8,479
.46 b
30/5,552
-.01 d
113/59,986
.02 e
61/21,602
.39 f
n.r./39,595
.27 f
n.r./39,595
A
.16 c
47/10,258
.15 c
23/3,582
.17 c
23/3,582
.12 c
22/3,374
.30 c
22/3,374
.31 b
23/8,479
.43 b
30/5,386
.07 d
109/58,552
.00 e
38/11,190
.30 f
n.r./39,595
.46 f
n.r./39,595
.33 f
n.r./39,595
N
-.09 c
47/10,258
-.12 c
24/3,696
-.11 c
23/3,582
-.09 c
22/3,374
-.17 c
22/3,374
-.04 b
26/8479
-.53 b
30/5,386
-.02 d
114/59,554
-.09 e
61/21,404
-.18 f
n.r./39,595
-.36 f
n.r./39,595
-.37 f
n.r./39,595
-.34 f
n.r./39,595
Note. g = intelligence; Perf = academic performance; O = Openness to experience; C = Conscientiousness; E = Extraversion; A = Agreeableness;
N = Neuroticism; Perc = Emotion Perception; Fac = Emotion Facilitation; Und = Understanding Emotions; Man = Managing Emotions.
aOriginal meta-analysis; bJoseph and Newman (2010); cvan der Linden, Pekaar, Bakker, Schermer, Vernon, Dunkel, & Petrides (2016); dPoropat
(2009); eJudge, Jackson, Shaw, Scott, & Rich (2007); fvan der Linden, te Nijenhuis, & Bakker (2010), university student population.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 87 of 98
Table 5.
Regressions Predicting Academic Performance from Intelligence (Step 1), Personality (Step 2), and EI (Step 3) based on the Correlation Matrix
shown in Table 3: Ability EI versus Self-rated EI versus Mixed EI
Stream 1: Ability EI
(Nharmonic = 18,367)
Stream 2: Self-rated of EI
(Nharmonic = 14,647)
Stream 3: Mixed EI
(Nharmonic = 12,939)
β
ΔR2
RW
RW%
β
ΔR2
RW
RW%
β
ΔR2
RW
RW%
Step 1
.152**
.152**
.152**
Intelligence
.38**
.152
69.42%
.42**
.143
62.01%
.40**
.135
58.01%
Step 2
.071**
.071**
.071**
Openness
.00
.005
2.49%
-.01
.006
2.42%
-.01
.005
2.29%
Conscientiousness
.29**
.044
20.20%
.27**
.045
19.52%
.27**
.049
21.05%
Extraversion
-.05**
.004
1.87%
-.07**
.006
2.75%
-.10**
.003
1.32%
Agreeableness
-.03**
.003
1.17%
-.02*
.003
1.36%
-.05**
.003
1.24%
Neuroticism
.10**
.002
0.88%
.12**
.004
1.65%
.16**
.002
0.81%
Step 3
.017**
.007**
.023**
EI
.14**
.009
3.96%
.09**
.024
10.28%
.20**
.036
15.28%
Total R2
.240**
.230**
.248**
* p < .05, ** p < .01
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 88 of 98
Table 6.
Regressions Predicting Academic Performance from Intelligence (Step 1), Personality (Step 2), and EI (Step 3) based on the Correlation Matrix
shown in Table 3 for the four branches of Ability EI
Emotion Perception
(Nharmonic = 11,049)
Emotion Facilitation
(Nharmonic = 10,586)
Emotion Understanding
(Nharmonic = 10,786)
Emotion Management
(Nharmonic = 10,736)
β
ΔR2
RW
RW%
β
ΔR2
RW
RW%
β
ΔR2
RW
RW%
β
ΔR2
RW
RW%
Step 1
.152**
.152**
.152**
.152**
Intelligence
.41**
.150
69.62%
.39**
.142
63.28%
.33
.115
45.48%
.38**
.138
54.98%
Step 2
.071**
.071**
.071**
.071**
Openness
.00
.006
2.83%
.00
.006
2.63%
-.01
.005
1.88%
.00
.006
2.23%
Conscientiousness
.30**
.049
22.50%
.29**
.049
21.76%
.28**
.048
18.79%
.29**
.048
19.28%
Extraversion
-.06**
.003
1.53%
-.06**
.003
1.56%
-.06**
.003
1.26%
-.07**
.004
1.73%
Agreeableness
-.01
.003
1.36%
-.03
.003
1.27%
-.03**
.003
1.12%
-.06**
.004
1.41%
Neuroticism
.10**
.002
0.86%
.10**
.002
0.84%
.09**
.002
0.76%
.10**
.002
0.83%
Step 3
.000
.009**
.039**
.036**
EI
-.02
.003
1.30%
.10**
.019
8.67%
.22**
.078
30.72%
.20**
.049
19.54%
Total R2
.222**
.232**
.261**
.258**
* p < .05, ** p < .01
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 89 of 98
Figure 1. PRISMA flow chart for the identification, screening, and inclusion of publications
in the meta-analyses.
Records after duplicates removed
(n = 4,959)
Records identified through
database searching
(n = 6,139)
Records excluded
(n = 4,682)
Additional records identified through
other sources
(n = 29)
Ancestry searches; n = 14
Review of other meta-analyses; n = 10
Requests for unpublished data; n = 7
Full-text excluded
(n = 115)
Records included in meta-
analysis
(n = 162, k = 188)
Records screened
(n = 4,959)
Full-text assessed for
eligibility
(n = 277)
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 90 of 98
Figure 2. Funnel plot for overall analysis of EI with academic achievement.
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 91 of 98
Figure 3. Forest plot for overall analysis of EI with academic achievement, showing separate
effects by education level, performance measure, publication status and subject area
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 92 of 98
Figure 4. Forest plot for analysis of Stream 1 EI with academic achievement, showing
separate effects by facet, education level, achievement measure, publication type and subject
area
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 93 of 98
Figure 5. Forest plot for analysis of Stream 2 EI with academic achievement, showing
separate effects by education level, achievement measure, publication type and subject area
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 94 of 98
Figure 6. Forest plot for analysis of Stream 1 EI with academic achievement, showing
separate effects by education level, achievement measure, publication type and subject area
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 95 of 98
Appendix A. Brief Descriptions of All Data Sources Meeting Inclusion Criteria
Authors
Year
Na
kb
Country
Education
level
Publication
type
Streamc
rd
Abdo
2012
266
1
USA
secondary
dissertation
1
.12
Abdullah et al.
2004
205
1
Kuala Lumpur &
Malaysia
secondary
article
3
.19
Abel
2013
78
1
USA
secondary
dissertation
1
.15
Adeyemo
2007
300
1
Nigeria
tertiary
article
2
.33
Afolabi et al.
2009
110
1
Nigeria
tertiary
article
1
.20
Agnoli et al.
2012
352
2
Italy
primary
article
1
.16
Ahammed et al.
2011
204
7
UAE
tertiary
article
3
-.08
Ahmad
2011
291
6
Pakistan
tertiary
article
1
.17
Ahmad & Rana
2012
538
1
Pakistan
tertiary
article
1
.08
Alhashemi
2014
136
1
Bahrain
tertiary
article
2
.10
Alumran & Punamaki
2008
312
5
Bahrain
mixed
article
1
.08
Amdurer, et al.
2014
266
1
USA
tertiary
article
1
-.02
Anand, et al.
2016
390
1
India
tertiary
article
1
.74
Arbabisarjou et al.
2013
250
12
Iran
tertiary
conference
paper
1
.62
Aremu et al.
2006
300
1
Nigeria
secondary
article
2
.31
Barchard
2003
150
13
USA
tertiary
article
2,3
.05
Barisonek
2005
44
15
USA
primary
dissertation
1
.02
Bastian et al.
2005
185
10
Australia
tertiary
article
2,3
.09
Berenson et al.
2008
82
1
USA
tertiary
article
2
.33
Biggart
2019
89
1
UK
tertiary
unpublished
3
.19
Billings et al.
2014
407
8
Australia
primary
article
2
.05
Bowman
2007
48
27
USA
tertiary
dissertation
3
.15
Boyce
2002
57
5
USA
tertiary
dissertation
3
.16
Brackett & Mayer
2003
207
9
USA
tertiary
article
1,2,3
.10
Brackett et al.
2004
330
6
USA
tertiary
article
3
.23
Bradshaw
2008
60
7
USA
tertiary
dissertation
1,3
.17
Brouzos et al.
2014
103
20
Egypt
primary
article
1
.17
Carrothers et al.
2000
147
2
USA
secondary
article
1
.11
Castro-Johnson & Wang
2003
520
2
USA
tertiary
article
3
.15
Catalina et al.
2012
92
1
Romania
tertiary
article
2
-.08
Cavins
2006
73
1
USA
tertiary
dissertation
1
.33
Chapman & Hayslip
2005
292
1
USA
tertiary
article
2
.08
Cheshire et al.
2015
85
10
USA
tertiary
article
3
.11
Chew et al.
2013
163
14
Malaysia
tertiary
article
3
.17
Clark
2004
161
2
USA
secondary
dissertation
1
.20
Codier & Odell
2014
72
2
USA
tertiary
article
3
.25
Collins
2013
65
5
USA
tertiary
dissertation
3
-.01
Colston
2008
115
1
USA
tertiary
dissertation
1
.17
Costa & Faria
2015
380
54
Portugal
secondary
article
2,3
.21
Cyr, 2007
2007
237
10
USA
tertiary
dissertation
3
.03
Di Fabio & Palazzeschi
2009
124
10
Italy
secondary
article
1,3
.22
Di Fabio & Palazzeschi
2015
133
1
Italy
secondary
article
1
.53
Doring
2006
72
4
USA
primary
dissertation
2,3
-.02
Downey et al.
2014
242
4
Australia
secondary
article
2
.08
Downey et al.
2008
158
45
Australia
secondary
article
2
.10
Drago
2005
32
5
USA
tertiary
dissertation
3
.31
Drati
2010
165
5
USA
secondary
dissertation
1
.11
Edison
2003
61
15
USA
tertiary
dissertation
3
.08
Evenson
2008
100
6
USA
tertiary
dissertation
1
.18
ETS
2019
590
2
USA
Tertiary
Unpublished
3
.15
Fahim & Pishghadam
2007
508
6
Iran
tertiary
conference
paper
1
.18
Fallahzadeh
2011
223
6
Iran
tertiary
conference
paper
1
.09
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 96 of 98
Fannin
2002
115
8
USA
secondary
dissertation
3
.37
Fatum
2008
75
3
USA
primary
dissertation
1
.10
Fayombo
2012
151
7
Barbados
tertiary
article
1
.25
Feldman
2004
79
10
USA
tertiary
dissertation
3
-.12
Garg et al.
2016
299
12
Canada
tertiary
article
1
.08
Gibson
2005
40
6
USA
tertiary
dissertation
2,3
-.01
Gilbert
2003
170
12
UK
tertiary
dissertation
2
.04
Glickman-Rogers
2010
205
8
USA
secondary
dissertation
3
.32
Goodwin
2016
198
4
USA
tertiary
dissertation
1
.32
Hall & West
2011
74
3
USA
tertiary
article
3
.05
Hogan
2003
673
10
Canada
tertiary
dissertation
1
.05
Hogan et al.
2010
44
1
Canada
secondary
dissertation
1
.35
Holt
2007
124
10
USA
tertiary
dissertation
3
.09
Humphrey-Murto et al.
2014
113
12
Canada
tertiary
article
3
.09
Izaguirre
2008
199
6
USA
tertiary
dissertation
1
.06
Jaeger
2003
75
24
USA
tertiary
article
1
.20
Jang et al.
2019
278
27
USA
tertiary
unpublished
1,2,3
-.02
Johnson
2008
111
4
USA
tertiary
dissertation
3
.02
Jones
2014
38
1
USA
secondary
dissertation
3
.28
Jordan et al.
2010
86
20
Ireland
secondary
article
1
.08
Kaliska
2015
169
10
Slovak Republic
secondary
article
1
.19
Khajehpour
2011
300
1
Iran
secondary
conference
paper
2
.32
Killen
2016
47
2
USA
secondary
dissertation
1
.60
Kracher
2009
109
1
USA
tertiary
dissertation
1
.01
Kumar et al.
2016
200
1
India
tertiary
article
1
.24
Kumar et al.
2013
450
1
India
secondary
article
1
.08
Kvapil
2007
237
25
USA
secondary
dissertation
3
.30
Lanciano & Curci
2014
89
5
Italy
tertiary
article
3
.44
Lasser
1997
64
1
USA
tertiary
dissertation
1
.09
Lawrence & Deepa
2013
400
1
India
secondary
article
1
.17
Leddy et al
2011
330
2
USA
tertiary
article
3
-.09
Lewis
2004
93
2
USA
tertiary
dissertation
2,3
-.01
Libbrecht et al.
2014
367
6
Europe
tertiary
article
3
.13
Lochner
2016
119
1
USA
tertiary
dissertation
2
.05
Loera
2013
105
10
USA
tertiary
dissertation
3
.17
Lu
2010
219
5
13
USA
tertiary
dissertation
1
.05
Lui
2009
108
48
USA & Canada
secondary
dissertation
1
.08
MacCann
2019
39
14
Australia
tertiary
unpublished
3
-.03
MacCann & Roberts
2008
175
6
Australia
tertiary
article
3
.30
MacCann & Burrows
2013
120
2
Australia
tertiary
article
2
-.14
MacCann et al.
2011
186
5
USA
secondary
article
3
.34
Malik & Shujja
2013
204
5
Pakistan
primary
article
1
.12
Margavio et al.
2012
409
1
China/USA
tertiary
article
2
.62
Marquez et al.
2006
77
1
Spain
secondary
article
3
.46
Martin
2011
170
12
USA
primary
dissertation
1
.15
Matešić
2015
369
3
Croatia
secondary
article
1
.00
Mavroveli et al.
2009
140
2
UK
primary
article
1
.25
Mavroveli et al.
2008
70
10
UK
secondary
article
1
.21
McClain
2009
85
1
USA
tertiary
dissertation
2
.23
Menzie
2005
55
1
USA
secondary
dissertation
1
.21
MHS
2019
177
1
1
USA
tertiary
unpublished
3
.13
Mikolajczak
2019
74
1
Belgium
tertiary
unpublished
1
.23
Mitrofan & Cioricaru
2014
136
1
Romania
secondary
conference
paper
1
-.06
Naeem et al.
2014
467
1
Saudi Arabia
tertiary
article
2
.14
Nelson
2010
141
1
USA
secondary
dissertation
3
.38
Nesari et al.
2011
120
1
Iran
mixed
article
1
.14
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 97 of 98
Newsome et al.
2000
180
6
Canada
tertiary
article
1
-.02
Nwabuebo
2013
60
1
USA
tertiary
dissertation
1
.19
O'Connor, Little
2003
90
22
USA
tertiary
article
1,3
.15
Ogundokun et al.
2010
156
3
1
Nigeria
secondary
article
1
.74
Olatoye et al.
2010
235
1
Nigeria
tertiary
article
2
-.18
Olson
2008
74
6
USA
tertiary
dissertation
1
.12
Opateye
2014
600
1
Nigeria
tertiary
article
1
.10
Parker et al.
2004
667
5
USA
secondary
article
1
.25
Parker et al.
2005
142
6
5
USA
tertiary
article
1
.12
Parker et al.
2004
372
10
Canada
tertiary
article
1
.10
Petersen
2010
51
6
USA
secondary
dissertation
1
.11
Pishghadam
2009
508
30
Iran
tertiary
article
1
.12
Por et al.
2011
130
1
UK
tertiary
article
2
-.11
Qualter et al.
2012
207
64
UK
secondary
article
1,3
.20
Radford
2011
115
1
USA
tertiary
dissertation
1
.10
Rankin
2013
178
5
UK
tertiary
article
2
.13
Rastegar & Karami
2013
106
1
Iran
tertiary
article
2
.23
Rice
2007
486
13
USA
secondary
dissertation
1
.14
Richardson
2000
98
6
USA
primary
dissertation
1
.20
Rivers et al.
2012
66
3
USA
primary
article
3
.51
Rodeiro, Emery & Bell
2012
874
5
UK
secondary
article
1
.26
Rodrigo-Ruiz
2017
232
19
Spain
secondary
dissertation
1,3
.24
Saklofske et al.
2012
163
5
UK
tertiary
article
1
.05
Saklofske et al.
2019
277
1
Canada
tertiary
unpublished
1
.01
Samples
2010
111
5
USA
tertiary
dissertation
3
.15
Sanchez-Ruiz et al.
2013
323
1
Cyprus
tertiary
article
1
.35
Schutte et al.
1998
64
1
USA
tertiary
article
2
.32
Shipley et al.
2010
193
4
USA
tertiary
article
1
.08
Sierra et al.
2013
129
15
Spain
tertiary
article
3
.06
Singh et al.
2009
389
4
Malaysia
tertiary
conference
paper
2
.18
Skipper & Brandenburg
2013
142
1
USA
tertiary
article
1
.01
Song et al.
2010
173
2
China
tertiary
article
2
.27
Stewart & Chrisholm
2012
154
90
UK or Canada
tertiary
article
1
.11
Stottlemyer
2002
185
26
USA
secondary
dissertation
1
.01
Suliman
2010
98
1
Saudi Arabia
tertiary
article
1
-.15
Sunbul
2007
311
5
Turkey
tertiary
conference
paper
1
.16
Szuberla
2005
61
12
USA
primary
dissertation
3
.30
Tok & Morali
2009
295
3
Turkey
tertiary
article
2
.05
Trapp
2011
118
5
USA
tertiary
dissertation
1
.07
Vargas
2014
402
14
USA
tertiary
dissertation
1
.04
Veitch
2011
251
14
USA
tertiary
dissertation
1
.04
Vela
2004
760
14
USA
tertiary
dissertation
1
.07
Victoroff & Boyatzis
2013
100
12
USA
tertiary
article
1
.06
Walker
2006
120
5
30
USA
tertiary
dissertation
1
.03
Walsh-Portillo
2011
45
1
USA
tertiary
dissertation
1
.18
Willis
2015
591
1
USA
tertiary
dissertation
1
.06
Woitaszewski &
Aalsma
2004
39
1
USA
secondary
article
3
.05
Wraight
2008
243
1
USA
tertiary
dissertation
1
.17
Wurf & Croft-Piggin
2015
81
10
Australia
tertiary
article
2
.14
Xu
2016
179
9
1
China
secondary
article
2
.08
Zandi
2012
239
6
Iran
tertiary
conference
paper
1
.09
EMOTIONAL INTELLIGENCE AND ACADEMIC PERFORMANCE Page 98 of 98
Zarezadeh
2013
330
6
Iran
tertiary
conference
paper
1
.11
Zirak & Ahmadian
2015
337
5
Iran
primary
article
1
.10
Zysberg et al.
2011
102
2
Israel
tertiary
article
3
.28
aN = number of participants (if multiple samples in the paper/source, N is the average)
bk = number of correlation coefficients (e.g., if correlations of the 4 MSCEIT branches with
GPA reported, k = 4)
cStream = 1 (ability EI), 2 (self-rated EI) or 3 (mixed EI)
dr = unweighted, uncorrected average correlation across all observations in the citation
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