What Is the Relationship Between Emotional
Intelligence and Dental Student Clinical
Performance?
Kristin Zakariasen Victoroff, D.D.S., Ph.D.; Richard E. Boyatzis, Ph.D.
Abstract: Emotional intelligence has emerged as a key factor in differentiating average from outstanding performers in managerial and leadership positions across multiple business settings, but relatively few studies have examined the role of emotional
intelligence in the health care professions. The purpose of this study was to examine the relationship between emotional intelligence (EI) and dental student clinical performance. All third- and fourth-year students at a single U.S. dental school were invited
to participate. Participation rate was 74 percent (100/136). Dental students’ EI was assessed using the Emotional Competence
Inventory-University version (ECI-U), a seventy-two-item, 360-degree questionnaire completed by both self and other raters. The
ECI-U measured twenty-two EI competencies grouped into four clusters (Self-Awareness, Self-Management, Social Awareness,
and Relationship Management). Clinical performance was assessed using the mean grade assigned by clinical preceptors. This
grade represents an overall assessment of a student’s clinical performance including diagnostic and treatment planning skills,
time utilization, preparation and organization, fundamental knowledge, technical skills, self-evaluation, professionalism, and
patient management. Additional variables were didactic grade point average (GPA) in Years 1 and 2, preclinical GPA in Years
1 and 2, Dental Admission Test academic average and Perceptual Ability Test scores, year of study, age, and gender. Multiple
linear regression analyses were conducted. The Self-Management cluster of competencies (b=0.448, p<0.05) and preclinical GPA
(b=0.317, p<0.01) were significantly correlated with mean clinical grade. The Self-Management competencies were emotional
self-control, achievement orientation, initiative, trustworthiness, conscientiousness, adaptability, and optimism. In this sample,
dental students’ EI competencies related to Self-Management were significant predictors of mean clinical grade assigned by
preceptors. Emotional intelligence may be an important predictor of clinical performance, which has important implications for
students’ development during dental school.
Dr. Victoroff is Associate Dean for Education and Association Professor of Community Dentistry, School of Dental Medicine,
Case Western Reserve University; Dr. Boyatzis is Distinguished University Professor and Professor in the Departments of
Organizational Behavior, Psychology, and Cognitive Science, Case Western Reserve University. Direct correspondence
and requests for reprints to Dr. Kristin Zakariasen Victoroff, School of Dental Medicine, Case Western Reserve University,
10900 Euclid Avenue, Cleveland, OH 44106-4905; 216-368-6616 phone; 216-368-3204 fax; [email protected].
Keywords: dental education, dental students, clinical education, emotional intelligence
Submitted for publication 12/29/11; accepted 6/17/12
T
he work professionals do is of great importance to society because it impacts the health,
security, and well-being of those served.1,2
Educators in the professions are faced with the
challenge of selecting individuals for admission to
professional education programs who are most likely
to perform in a superior manner. Cognitive ability
has traditionally been the primary measure upon
which such decisions are based. Although educators
agree that cognitive ability is necessary in professional work, they have expressed concern that it is
not a sufficient condition to guarantee professional
excellence. Emmerling and Goleman have noted
that “traditional measures of intelligence, although
providing some degree of predictive validity, have
not been able to account for a large portion of the
variance in work performance.”3
Research in the area of emotional intelligence
has provided important insights regarding what else
416
is required for superior professional performance.
Emotional intelligence, as defined by Goleman,
refers to “the capacity for recognizing our own feelings and those of others, for motivating ourselves,
and for managing emotions well in ourselves and
in our relationships. It describes abilities distinct
from, but complementary to, academic intelligence, the purely cognitive capacities measured
by IQ.”4 Emotional intelligence forms the basis for
an individual to develop and use emotional intelligence competencies.5 These competencies enable
an individual to, for example, accurately read and
respond to the moods of others, remain calm in
stressful situations, remain optimistic in the face
of setbacks, adapt to changing circumstances, seek
out opportunities, and work effectively in groups.4
Emotional intelligence has been linked to performance in a variety of managerial and executive
positions across multiple business settings and has
Journal of Dental Education ■ Volume 77, Number 4
emerged as a key factor in differentiating average
from outstanding performers.6
Although there is a great deal of evidence linking emotional intelligence to superior performance
in managerial and leadership roles, there have been
fewer studies to date examining emotional intelligence in the health care professions. Many behaviors
that characterize exemplary professional practice in
health care may reflect underlying abilities related
to emotional intelligence. For example, exemplary
professionals are expected to put patient needs ahead
of their own interests, strive to perform to a high
standard at all times, continually self-assess their
knowledge and skills, know their limitations, and act
with integrity.1 Educators and researchers in medicine, dentistry, and nursing have expressed interest
in emotional intelligence as a potentially important
factor in student selection and in student, resident,
and practitioner training, as well as a predictor of
clinician performance and patient outcomes.7-22 There
is a need for additional empirical studies.
In dental education, the literature reveals
long-standing interest in predictors of dental student performance. Traditional cognitive measures,
such as undergraduate grade point average (GPA)
and standardized Dental Admission Test (DAT)
scores, are useful in predicting performance in the
didactic components of the curriculum. However,
these measures have been less helpful in predicting
how a student will perform in the clinical setting.23-26
There is, therefore, significant interest in identifying
noncognitive factors that may be associated with
clinical performance.
There are good reasons to investigate emotional
intelligence as a possible noncognitive predictor of
dental student clinical performance. In a significant
portion of the curriculum, students spend their time
providing patient care in the clinical setting. Many
of the skills and abilities now considered important
for clinical effectiveness appear to be related to the
emotional intelligence competencies articulated by
Goleman4 and Goleman et al.6 These include communication skills, patient management skills, ability
to work with a multicultural patient population, and
ability to collaborate with others and lead teams.27
In the clinical environment, students must engage
in complex management of both themselves and
others (patients, clinical instructors, support staff,
and peers) as they apply the knowledge and skills
gained during the didactic and preclinical portion
of the curriculum. The purpose of this study was
to investigate the relationship between emotional
April 2013 ■ Journal of Dental Education
intelligence and dental student clinical performance.
The two main hypotheses tested were the following:
1) there is a relationship between students’ cognitive
ability and didactic academic performance, and 2)
there is a relationship between students’ emotional
intelligence and clinical performance.
Models of Emotional
Intelligence
The prevailing models of emotional intelligence have been articulated by Goleman,4 Goleman et al.,6 Boyatzis,28 Mayer et al.,29 and Bar-On.30
Although the models differ in the specific ways in
which emotional intelligence is conceptualized, all
focus on the recognition and management of emotions in self and others.3 There is agreement among
these theorists that emotional intelligence is distinct
from traditional intelligence (IQ).
The Goleman and Boyatzis model has its roots
in the work of Boyatzis, who developed a theory of
managerial competencies that differentiate average
from superior performers.31 Emmerling and Goleman
noted that “though not originally a theory of social
and emotional competence, as research on ‘star’
performers began to accumulate, it became apparent
that the vast majority of competencies that distinguish
average performers from ‘star performers’ could be
classified as falling into the domain of social and
emotional competencies.”3 The Goleman and Boyatzis model is unique among the prevailing theories
of emotional intelligence in its focus on developing a
“theory of work performance based on . . . emotional
competencies.”3,28 In this model, emotional intelligence is defined as “the capacity for recognizing
our own feelings and those of others, for motivating
ourselves, and for managing emotions effectively in
ourselves and others,” and an emotional competence
is “a learned capacity based on emotional intelligence
that contributes to effective performance at work.”32
The Goleman and Boyatzis model consists of
emotional competencies grouped into four clusters.6
The model continues to be refined (the current model
can be found at www.eiconsortium.org). The twentytwo competencies included in the model at the time
of this study, grouped into four clusters, are shown in
Table 1. The instrument associated with the Goleman
and Boyatzis model is the Emotional Competence
Inventory-University version (ECI-U). The ECI-U
is a seventy-two-item, 360-degree assessment ques-
417
Table 1. Goleman and Boyatzis model of emotional intelligence: clusters and competencies
Self-Awareness
Knowing one’s internal states, preferences, resources, and intuitions
Emotional Awareness: Recognizing one’s emotions and their effects.
Accurate Self-Assessment: Knowing one’s strengths and limits.
Self-Confidence: A strong sense of one’s self-worth and capabilities.
Self-Management
Managing one’s internal states, impulses, and resources
Emotional Self-Control: Keeping disruptive emotions and impulses in check.
Adaptability: Flexibility in handling change.
Achievement: Striving to improve or meeting a standard of excellence.
Initiative: Readiness to act on opportunities.
Optimism: Persistence in pursuing goals despite obstacles and setbacks.
Trustworthiness: Maintaining integrity.
Conscientiousness: Taking responsibility for personal performance.
Social Awareness
How people handle relationships and awareness of others’ feelings, needs, and concerns
Empathy: Sensing others’ feelings and perspectives, and taking an active interest in their concerns.
Organizational Awareness: Reading a group’s emotional currents and power relationships.
Service Orientation: Anticipating, recognizing, and meeting customers’ needs.
Cultural Awareness: Respecting and relating well to people from varied backgrounds.
Relationship Management
Skill or adeptness at inducing desirable responses in others
Developing Others: Sensing others’ development needs and bolstering their abilities.
Inspirational Leadership: Inspiring and guiding individuals and groups.
Change Catalyst: Initiating or managing change.
Influence: Wielding effective tactics for persuasion.
Conflict Management: Negotiating and resolving disagreements.
Communication: Listening openly and sending convincing messages.
Building Bonds: Nurturing instrumental relationships.
Teamwork and Collaboration: Working with others toward shared goals and creating group synergy in pursuing
collective goals.
Source: Sala F. Emotional competence inventory (ECI) technical manual. Boston: Hay Group, 2002. At: www.eiconsortium.org/measures/eci_360.htm. Accessed: June 26, 2006.
tionnaire completed by both self and other raters.32
This instrument has been used extensively for both
developmental and research purposes in work and
academic settings. The ECI-U was selected for use
in this study because of its focus on the work setting,
which is consistent with the study’s focus on dental
students’ performance in the professional setting. In
addition, the ECI-U utilizes a 360-degree assessment
based on observations of the individual’s behavior by
others, instead of relying solely on self-reported data.
Methods
All Year 3 (sixty-nine) and Year 4 (sixty-seven)
students enrolled in the four-year D.M.D. program
418
at the School of Dental Medicine at Case Western
Reserve University were invited to participate in
the study. All third- and fourth-year students were
actively engaged in the clinical aspect of the curriculum, spending an average of thirty hours per week in
the clinical setting. Each student received a letter of
invitation to participate. In addition, the investigator
described the study in person during a class session.
Participation was voluntary, and the study protocol
was approved by the university’s Institutional Review
Board. Each student participant signed a consent
form and completed an ECI-U Self-Assessment
Questionnaire. Individuals identified as other raters
(patient care coordinators, clinical preceptors, and
peers) also received a letter describing the study
and inviting their participation, a consent form, and
Journal of Dental Education ■ Volume 77, Number 4
an ECI-U Other Assessment Questionnaire for each
student they were asked to assess. Year of study,
age, gender, DAT academic average and Perceptual
Ability Test (PAT) scores, didactic GPA, preclinical
GPA, and clinical grades assigned by clinical preceptors are on file with the dental school registrar and
were released for those students who had agreed to
participate in the study. Data were analyzed using
SPSS 14.0.
The study constructs were cognitive ability,
perceptual ability, didactic performance, preclinical
performance, clinical performance, and emotional
intelligence. The corresponding study variables were
DAT academic average and PAT scores, didactic GPA
in Years 1 and 2, preclinical GPA in Years 1 and 2,
clinical grades assigned by clinical preceptors, and
ECI-U ratings.
Cognitive ability, the first construct, was measured using the DAT academic average score, which
is the arithmetic mean of the biology, general chemistry, organic chemistry, quantitative reasoning, and
reading comprehension subtest scores.33 This score
is accepted as a measure of general cognitive ability.
Smithers et al. noted that, “In particular, the DAT
academic average is a measure of general cognitive
ability. . . . Individuals with high cognitive ability
should have more success in academic components
of their programs.”26
Perceptual ability, the second construct, was
measured using the DAT PAT score. Didactic performance, the third construct, was measured using the
weighted GPA from all didactic courses in Years 1
and 2. Preclinical performance, the fourth construct,
was measured using the weighted GPA from all preclinical laboratory courses in Years 1 and 2.
Clinical performance, the fifth construct, was
measured using clinical grades assigned by clinical preceptors. Students are assigned two types of
grades in the clinical component of the curriculum.
One set of grades is derived from individual clinical
competency examinations designed to determine a
student’s proficiency in performing specific clinical
procedures. These grades are almost exclusively
a measure of technical skill, so an association between emotional intelligence and these grades was
not predicted. These grades were not included in
the analyses. The second set of grades is assigned
by clinical preceptors based on their overall rating
of a student’s clinical performance. In assigning
these grades, the preceptors take a more holistic
view of the student’s performance based on multiple
April 2013 ■ Journal of Dental Education
observations over many weeks. Assessment criteria
focus on such aspects of clinical performance as
diagnosis and treatment planning skills, work ethic
and time utilization, preparation and organization,
fundamental knowledge, technical skills, self-evaluation, professionalism, and patient management.
An association between these grades and emotional
intelligence was predicted. These grades are the dependent variable in this study. In the clinical setting,
each student works closely with two clinical preceptors who supervise the student’s patient care activities
and a patient care coordinator who is responsible for
patient scheduling. Students are assigned two grades
(one from each clinical preceptor) in December and
May of the third and fourth years. The average of the
preceptor grades was used.
Emotional intelligence (EI), the final construct,
was measured with the ECI-U, which is designed to
assess “how a person expresses handling of emotions in life and work settings.”32,34 In addition to
the twenty-two EI competencies, the instrument
measures two cognitive competencies, with three
items per competency. Each item describes a specific
behavior, and the rater is asked to indicate how often
the individual demonstrates, shows, or uses the behavior. Possible ratings range from 1 (never shown)
to 5 (consistently shown). If a rater has not had the
opportunity to observe a behavior, it is recorded as
“n” (have not had the opportunity or don’t know).
The two cognitive competencies were not included
in the analyses for this study.
Administration of the ECI-U results in two
sets of ratings: self-ratings and others’ ratings. In the
ECI-U technical manual, Sala notes that ECI selfratings may be used for developmental purposes, but
“they do not provide valid and reliable measures of
emotional intelligence for research purposes.”32 The
analyses conducted for our study included only the
ratings of others: patient care coordinators, clinical
preceptors, and dental student peers. The mean other
ratings for each of the twenty-two competencies and
each of the four clusters were calculated as specified
in the ECI-U technical manual. The four “other rater”
cluster ratings (Self-Awareness, Self-Management,
Social Awareness, and Relationship Management
clusters) were included in the study analyses. Since
the sample consisted of two cohorts of students (one
in the third year and one in the fourth year), year
of study was included in the analyses as a control
variable. Age and gender were also included in the
analyses as control variables.
419
*p<0.05, **p<0.01, two-tailed test
Note: Spearman’s rho was calculated for correlations between year of study or gender and other study variables. Pearson’s r was calculated for correlations between all other study variables.
Year of study is dummy coded as Year 3=0 and Year 4=1. Gender is dummy coded as female=0 and male=1.
0.058-0.041 -0.246*
-0.112-0.019 0.1110.183 0.380**
0.427**0.434**
1.00
0.1070.061-0.043
-0.0800.0260.0050.261**
0.377**
0.416**
0.436**
0.839**
1.00
0.045-0.011 -0.090-0.130-0.033 0.0330.189 0.255*0.146 0.289**
0.763**0.831**
1.00
0.062 -0.141 -0.129 -0.120 -0.028 0.200* 0.216* 0.329** 0.329** 0.370** 0.827** 0.770** 0.800**1.00
0.44
0.41
0.44
0.51
3.96
3.95
4.05
3.77
—
—
1.00
—
—
-0.013
1.00
27.67
3.00
0.205*
0.305**
1.00
18.48
1.70
-0.076 0.136
0.092 1.00
18.62
1.81
0.051
0.235*
0.014
0.266**
1.00
3.21
0.42
-0.037
-0.057
0.024
0.419**
0.119
1.00
3.41 0.33 -0.0710.153 0.056
-0.0510.364**
0.276**
1.00
3.25
0.48
-0.068 -0.055 -0.198* 0.005 -0.046 0.192 0.292** 1.00
3.74
0.33
—
0.000 -0.190 0.102 0.033 0.138 0.409* 0.618** 1.00
3.36
0.43
0.210* -0.042 -0.178 -0.059 -0.027 0.142 0.271** 0.921** 0.840** 1.00
Year of study
Gender
Age (years)
DAT academic average
PAT
Didactic GPA
Preclinical GPA
Clinical GPA Year 3
Clinical GPA Year 4
Clinical GPA Years 3 and 4
EI Self-Awareness
EI Self-Management
EI Social Awareness
EI Relationship Management
Year DAT
Clin
Clin
of
Acad Didactic
Preclin
GPA
GPA
Clin
EIEIEI
EI
Mean
SD
Study Gender
Age Average PAT
GPA
GPA
Year 3 Year 4
GPA
SA
SM
SocA RM
Table 2. Descriptive statistics and bivariate correlations (N=100)
420
Results
The participation rate was 74 percent (100/136).
Of the participating students, 62 percent (62/100) were
third-year students, and 38 percent (38/100) were
fourth-year students. These numbers reflect a participation rate of 90 percent (62/69) for third-year students
and 57 percent (38/67) for fourth-year students. Data
collection began in March, and the variation in rate
of participation can be attributed primarily to senior
students’ proximity to graduation in the month of
May. As graduation approached, seniors focused on
completing mandatory tasks and were less inclined to
complete optional tasks, such as participation in the
study. Participating students were 77 percent (N=77)
male and 23 percent (N=23) female. The gender distribution of the student body at the time of the study
was 73 percent male and 27 percent female. Mean age
of participating students was 27.7±3.0 years, with a
minimum of 22.5 years and a maximum of 38.5 years.
A total of 453 ECI-U Other Assessment questionnaires
were completed by other raters (clinical preceptors,
peers, and patient care coordinators).
Descriptive Statistics and Bivariate
Correlations
Descriptive statistics and bivariate correlations
for the study variables are shown in Table 2. Bivariate
correlations were calculated using Pearson’s r, with
the exception of correlations involving year of study
or gender, for which Spearman’s rho was calculated.
Clinical grades in Year 3 and Year 4 were
highly correlated (r=0.618, p<0.01). Therefore, for
fourth-year students the average of Year 3 and Year 4
clinical grades was calculated and used in all subsequent analyses. For third-year students, average Year
3 clinical grade was used in all subsequent analyses.
Year of study was entered as a control variable in all
subsequent analyses.
A t-test was performed to determine if the
two cohorts of students differed in their clinical
performance in Year 3. There was no significant
difference between mean Year 3 grade of third-year
students (M=3.29, SD=0.43) and mean Year 3 grade of
fourth-year students (M=3.20, SD=0.55, t[98]=0.942,
p=0.348).
Multivariate Analyses
Didactic performance. Multiple linear regression analyses were conducted to determine the contri-
Journal of Dental Education ■ Volume 77, Number 4
bution of the DAT scores (academic average and PAT)
and the four ECI-U cluster ratings (Self-Awareness,
Self-Management, Social Awareness, and Relationship
Management) in predicting didactic GPA in Years 1
and 2. Control variables were year of study, age, and
gender. In all multiple linear regression analyses, year
was dummy coded as Year 3=0 and Year 4=1. Gender
was dummy coded as female=0 and male=1.
The results of these analyses are shown in Table
3. Model 1 is the regression of didactic grades in
Years 1 and 2 on year of study, gender, age, DAT academic average score, and PAT score. DAT academic
average score was positively correlated with didactic
GPA in Years 1 and 2 (ß=0.424, p≤0.001). Model 2
is the regression of didactic GPA in Years 1 and 2 on
year of study, gender, age, DAT academic average
score, PAT score, and the four ECI-U cluster ratings.
DAT academic average score (ß=0.442, p≤0.001)
and the Relationship Management cluster rating
(ß=0.507, p≤0.01) were positively correlated with
didactic GPA in Years 1 and 2. The Self-Management
cluster rating was negatively correlated with didactic
GPA in Years 1 and 2 (ß=-0.398, p≤0.05).
Preclinical performance. Multiple linear regression analyses were conducted to determine the
contribution of the DAT scores (academic average
and PAT), didactic GPA in Years 1 and 2, and the four
ECI-U cluster ratings in predicting preclinical GPA in
Years 1 and 2. Control variables were year of study,
age, and gender. The results of the analyses are shown
in Table 4. Model 1 is the regression of preclinical
GPA in Years 1 and 2 on year of study, gender, age,
Table 3. Regression of didactic GPA on DAT scores, ECI-U cluster scores, and control variables (N=100)
Independent Variable
Standardized Regression Coefficients
Model 1
Model 2
Year of study
-0.009
-0.011
Gender
-0.138-0.070
Age
0.017
0.083
DAT academic average 0.424***
0.442***
PAT
0.038
0.041
EI Self-Awareness
0.186
EI Self-Management -0.398*
EI Social Awareness -0.123
EI Relationship Management 0.507**
R2
Change in R2
0.193
—
0.314
0.121**
*p≤0.05, **p≤0.01, ***p≤0.001
Table 4. Regression of preclinical GPA on DAT scores, didactic GPA, ECI-U cluster scores, and control variables
(N=100)
Independent Variable
Standardized Regression Coefficients
Model 1
Model 2
Year of study
-0.077
-0.074
Gender
0.114
0.167
Age
0.054
0.048
DAT academic average -0.180
-0.342***
PAT
0.388***
0.373***
Didactic GPA
0.383***
EI Self-Awareness
EI Self-Management
EI Social Awareness
EI Relationship Management
R2
Change in R2
0.179
—
0.297
0.118***
Model 3
-0.089
0.139
0.019
-0.336***
0.360***
0.402***
-0.204
0.430*
-0.045
0.002
0.357
0.061
*p≤0.05, **p≤0.01, ***p≤0.001
April 2013 ■ Journal of Dental Education
421
DAT academic average score, and PAT score. PAT
score was positively correlated with preclinical GPA
in Years 1 and 2 (ß=0.388, p≤0.001). Model 2 is the
regression of preclinical GPA in Years 1 and 2 on
year of study, gender, age, DAT academic average
score, PAT score, and didactic GPA in Years 1 and 2.
DAT academic average score was negatively correlated with preclinical GPA (ß=-0.342, p≤0.001). PAT
score was positively correlated with preclinical GPA
(ß=0.373, p≤0.001). Didactic GPA was positively
correlated with preclinical GPA (ß=0.383, p≤0.001).
Model 3 is the regression of preclinical GPA in Years
1 and 2 on year of study, gender, age, DAT academic
average score, PAT score, didactic GPA in Years 1 and
2, and the four ECI-U cluster ratings. DAT academic
average score was negatively correlated with preclinical GPA (ß=-0.336, p≤0.001). PAT score (ß=0.360,
p≤0.001), didactic GPA (ß=0.402, p≤0.001), and
Self-Management cluster rating (ß=0.430, p≤0.05)
were positively correlated with preclinical GPA in
Years 1 and 2.
Clinical performance. Multiple linear regression analyses were conducted to determine the
contribution of the DAT academic average score,
PAT score, didactic GPA in Years 1 and 2, preclinical GPA in Years 1 and 2, and the four ECI-U cluster
ratings in predicting clinical grade. Control variables
were year of study, age, and gender. The results of
the analyses are shown in Table 5. Model 1 is the
regression of clinical grade on DAT academic average score, PAT score, and the control variables. Year
of study (ß=0.236, p≤0.05) was positively correlated
with clinical grade. Age (ß=-0.212, p≤0.05) was
negatively correlated with clinical grade. Model 2
is the regression of clinical grade on DAT academic
average score, PAT score, didactic GPA in Years 1 and
2, preclinical GPA in Years 1 and 2, and the control
variables. Year of study (ß=0.264, p≤0.01) and preclinical GPA (ß=0.341, p≤0.01) were positively correlated with clinical grade. Age (ß=-0.232, p≤0.05)
was negatively correlated with clinical grade. Model
3 is the regression of clinical grade on DAT academic
average score, PAT score, didactic GPA, preclinical
GPA, the four ECI-U cluster ratings, and the control
variables. Year of study (ß=0.221, p≤0.01), preclinical GPA (ß=0.229, p≤0.05), and Self-Management
cluster rating (ß=0.490, p≤0.05) were positively
correlated with clinical grade.
Discussion
This study explored the relationship between
emotional intelligence and dental students’ clinical
performance by examining the empirical association
between students’ ECI-U cluster ratings and clinical
grades. The results provide empirical support for a
relationship between cognitive ability and didactic
performance and between emotional intelligence and
clinical performance.
An empirical association between DAT academic average score and didactic GPA in Years 1 and
2 was found, providing support for the hypothesized
relationship between cognitive ability and academic
Table 5. Regression of clinical GPA (Years 3 and 4) on DAT scores, didactic GPA, preclinical GPA, ECI-U cluster scores,
and control variables (N=100)
Independent Variable
Standardized Regression Coefficients
Model 1
Model 2
Model 3
Year of study
0.236*
0.264**
0.221**
Gender
0.001
-0.028
-0.058
Age
-0.212*
-0.232*-0.184
DAT academic average
-0.012
0.018
0.012
PAT
-0.028
-0.163-0.134
Didactic GPA
0.074
0.087
Preclinical GPA
0.341**
0.229*
EI Self-Awareness
0.133
EI Self-Management
0.490*
EI Social Awareness
-0.313
EI Relationship Management
0.027
R2
Change in R2
*p≤0.05, **p≤0.01
422
0.088
—
0.203
0.116**
0.349
0.146**
Journal of Dental Education ■ Volume 77, Number 4
performance in the first two years of dental school.
Neither DAT academic average score nor didactic
GPA was correlated with clinical grade. These findings are consistent with prior research.24-26,33
An empirical association between ECI-U SelfManagement cluster rating and clinical grade was
found, providing support for the hypothesized relationship between emotional intelligence and clinical
performance. Students who were rated higher by others on the Self-Management cluster of competencies
(which includes emotional self-control, achievement
orientation, initiative, trustworthiness, conscientiousness, adaptability, and optimism) were more likely
to perform well in the clinical setting.
There was not a significant correlation between
the Relationship Management cluster rating and
clinical grade. In the clinical and preclinical settings,
students primarily work on their own tasks (e.g., seeing their own patients, completing their own laboratory projects), rather than working on a group task.
It seems reasonable to conclude that management of
self (Self-Management cluster) would be more important than management of relationships with others
(Relationship Management) in this particular context.
Associations between Self-Awareness cluster
rating and Social Awareness cluster rating and clinical grade were not found. Although the correlation
between Social Awareness cluster rating and clinical
grade was not statistically significant, it approached
significance and therefore merits discussion. The
near-significant negative correlation between Social
Awareness cluster and clinical grade was unexpected
but not inexplicable. The nature of the providerpatient relationship in the educational setting is
unique, and this may influence the extent to which
the competencies in the Social Awareness cluster
are demonstrated. Christakis and Feudtner discuss
the transient nature of the social relationships experienced by medical students and residents in training
and the problems this creates.35 Student relationships
with patients are generally of short duration in the
educational setting, and, given multiple demands on
the student’s time, the goal becomes the provision
of treatment in the most efficient manner possible.
This typically means that a patient’s immediate
health problems are addressed (task orientation), but
students do not spend a significant amount of time
“getting to know” the patient (process orientation).
Christakis and Feudtner observed that these transient
interactions cause “patients and physicians to feel
emotionally disconnected, and lead to care that too
often is coldly competent.”35
April 2013 ■ Journal of Dental Education
Sherman and Cramer examined empathy in
dental students across the four years of training.36
First-year students’ scores on the Jefferson Scale
of Physician Empathy-Health Professionals version
(JSPE-HP) were significantly higher than scores
of second-, third-, or fourth-year students. These
authors concluded that “the timing in decline in
empathy levels corresponded to increasing patient
exposure.” In the educational setting, patient care
goals may conflict with educational goals. Patients’
emotional needs are seen as an impediment to getting the “real work”—the exam or procedure—done.
Pressures unique to the educational setting may push
for behaviors that crowd out demonstration of Social
Awareness competencies, although these competencies may prove to be valuable to students in their
clinical practices after graduation.
Two interesting correlations were found with
respect to the association between ECI-U clusters
and didactic GPA. The Relationship Management
cluster was a predictor of didactic GPA, and the
Self-Management cluster was negatively correlated
with didactic GPA. Three conditions may explain
the empirical association between the Relationship
Management cluster and didactic GPA. First, the
relationship management cluster is associated with an
individual’s ability to influence others.6 It may be that
those with stronger relationship management competencies were able to influence instructors in some
way, perhaps by having their questions answered
more frequently or by negotiating course logistics
such as the timing of examinations. Second, in the
dental school there is a tradition of more senior students’ helping junior students to succeed in didactic
courses, by, for example, passing along lecture notes
and offering advice about instructor expectations and
how to study for a particular examination. There is
no formal, standardized mechanism for the senior
students to pass this information on to more junior
students, so it can be expected that those who establish broader social networks among fellow students
are likely to have greater access to such informal
curricular resources. Finally, in a qualitative study
of characteristics of effective learning experiences in
dental school, studying in groups and learning from
peers were found to be characteristics of effective
learning experiences for some students.37 Again, those
who display higher levels of more of the competencies in the Relationship Management cluster may be
more likely to successfully form and sustain such
study groups and will benefit from learning from their
peers, perhaps leading to better didactic performance.
423
It is unclear why Self-Management cluster ratings were negatively correlated with didactic GPA.
It may be that the nature of the task in the didactic
setting differs significantly from the nature of the
task in preclinical and clinical settings. In the didactic
setting, in which basic sciences are taught, there is
no end point in terms of the amount of material to be
learned, and the amount of time in which it must be
learned is limited. Therefore, a strategy adopted by
many students for dealing with the massive amounts
of information and limited time is to learn a topic
“well enough” and then move on to the next topic,
instead of aiming for perfect mastery of every topic.
Displaying too much achievement orientation or
conscientiousness may, in fact, work against students
by leading them to try to master everything and become overwhelmed. In the preclinical setting, on the
other hand, the goal is mastery of a well-defined set
of tasks. Those with competencies such as achievement orientation, conscientiousness, and emotional
self-control are likely to persist in trying to complete
the task to a level of excellence, whereas others may
hand in a product that is merely adequate.
Limitations of the study must be considered.
First, the validity and reliability of the outcome
measure—clinical grades assigned by clinical preceptors—are somewhat unclear. Ranney et al. noted
that the “generally unknown reliability of dental
school grades included in studies” is a problem common to nearly all studies examining predictors of
performance in dental school.23 Inclusion of grades
assigned by more than one grader (the average of
grades from two clinical preceptors was used in this
study) helped to address this problem. In addition,
the grading practices of the clinical preceptors were
closely monitored by the assistant dean for clinical
education, who intervened with and provided guidance to preceptors whose grade assignments seemed
to be outliers. Although not a formalized calibration
process, it is likely that this process provided some
degree of calibration between preceptors.
When Epstein and Hundert conducted an evidence-based review of current methods of assessing
competence of medical students and residents, they
found subjective ratings by supervising physicians
was one of the three most frequently used assessment
methods and the major form of assessment in the clinical setting.38 These authors discussed the limitations
of this assessment method, including possible halo
effect, sex/racial bias, and interrater variability. However, they also noted that more controlled outcome
measures, such as ratings of student performance
424
made by standardized (i.e., simulated) patients used in
a study of EI and clinical skills by Stratton et al.,9 did
not provide information about students’ performance
in real clinical settings with real patients. Epstein and
Hundert concluded that subjective evaluations by
clinical instructors “often (include) the tacit elements
of professional competence otherwise overlooked by
objective assessment instruments.” For the purposes
of examining the relationship between EI and students’ overall clinical performance (not just discrete
tests of specific skills but how students put all of their
skills into practice in real clinical settings), clinical
grades assigned by instructors were the best outcome
measure available for use in our study.
A second limitation of the study is that the
sample of dental students was limited to students
enrolled in one institution and one curriculum. The
extent to which these findings can be generalized to
dental students in other schools is not clear.
Despite these limitations, this study makes
an important contribution to the dental literature in
that it examines the relationship between emotional
intelligence and dental student clinical performance.
It establishes the importance of noncognitive factors
in students’ clinical performance, providing support
for the role of emotional intelligence in clinical performance. However, additional studies are needed.
For example, future studies could benefit from the
inclusion of additional outcome variables, such as
patient satisfaction.
Patient satisfaction data were collected for our
study but ultimately were not included in the final
analyses due to restriction in the range of responses.
In the data collected, nearly all patients were completely or nearly completely satisfied, with a median
satisfaction score of 24 on a 25-point scale and a
mean score of 23.3±2.7. Seventy-five percent of
responses indicated that the patient was completely
satisfied (5 on a scale of 1 to 5), and 93 percent of
responses were a 4 or 5. Restriction of range has been
a problem in other studies that use patient satisfaction
as an outcome measure.39,40 It will be an important
challenge for future researchers to develop patient
satisfaction measures that yield a range of patient
responses and target specific aspects of satisfaction
that may be expected to be related to provider emotional intelligence.
This study focused on dental students, but
future studies should also focus on dentists in clinical practice. In their literature review focusing on
evaluation of applicants for admission to dental
school, Ranney et al. noted that “we could not find
Journal of Dental Education ■ Volume 77, Number 4
studies that attempted to relate admissions criteria
to success in practice, however that may be defined,
or to serving populations of particular need, leadership positions, academic positions, or other needed
service to society.”23 Such studies could include a
variety of outcome variables, including, for example,
new patient flow and patient retention, patient satisfaction, efficiency and financial viability of the
practice, dentist career satisfaction, productivity
and job satisfaction of the dental team members,
and, ultimately, patient health outcomes. Given the
relatively greater complexity of the clinical practice
environment in comparison to the dental school
clinical environment, we hypothesize that several
of the EI clusters will be associated with superior
performance in clinical practice. This hypothesis,
however, remains to be tested.
Do these findings minimize the role of cognitive ability and acquisition of knowledge in graduate
and professional education? No. Several authors have
concluded that cognitive ability and knowledge are
threshold aspects of professional work, necessary
but not sufficient for outstanding professional performance.3 Given that students are selected for admission based largely on evidence of cognitive ability
(undergraduate GPA, DAT scores) and that admission
is highly competitive, the student body is relatively
homogeneous with respect to cognitive abilities. All
are capable of gaining the requisite knowledge, and
indeed all of the students who participated in our
study passed all of their courses and advanced to the
next year and/or graduated. One can conclude that all
these students had the threshold amount of knowledge necessary for effectiveness in the clinical setting
and that what differentiated them in terms of clinical
performance was in large part related to differences
in their emotional intelligence competencies—specifically, their Self-Management competencies. This
does not minimize the role of knowledge in dental
students’ effectiveness in the clinical setting, but it
does point to the important role that emotional intelligence competencies may play in enabling dental
students to excel in the clinical setting.
The major finding of this study—that emotional intelligence is a predictor of dental students’
clinical performance—has important implications
for health professions education. First, the results
should encourage other educational researchers in the
health professions to examine the role of emotional
intelligence in student performance. Second, as evidence for the role of emotional intelligence in health
professions student and practitioner effectiveness
April 2013 ■ Journal of Dental Education
accumulates, curriculum designers should consider
implementing and evaluating components designed
to help students develop emotional intelligence competencies. Health professions students invest a great
deal of time, effort, and financial resources in their
training. It is the responsibility of educators, then, to
equip them in the best way possible. It is increasingly
clear that this means not only providing knowledge,
but helping students to develop the competencies they
will need to best serve their patients and to prosper
in their chosen careers.
Finally, these findings may have implications
for the health professions admissions process in the
future. Educators have discussed the possibility of
including consideration of ratings of applicants’
emotional intelligence in the admissions process,41-43
but this prospect must be approached with caution.
Emotional intelligence in the health professions is
a relatively new line of inquiry. Additional work
will be required to confirm and elaborate the role of
emotional intelligence in health professions student
and practitioner performance. In addition, an easily
administered, valid, and reliable instrument must
be available to measure applicants’ emotional intelligence. Currently available measures of emotional
intelligence were not developed for use in the admissions process. For example, the ECI-U was developed
for developmental and research purposes and was not
designed for use in administrative decision making.32
Although it is likely that current instruments could be
adapted for use in the admissions process, more study
will be required to establish the predictive validity
of the instruments for this purpose.
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