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AN EXPLORATION OF ADOLESCENT EMOTIONAL
INTELLIGENCE IN RELATION
TO DEMOGRAPfflC CHARACTERISTICS
Nicholas R. Harrod and Scott D. Scheer
ABSTRACT
Emotional intelligence (EI) was measured in 200 youth ages 16-19. EI scores
were compared to demographic characteristics of the individuals (age, sex,
household income, parents' level of education, and location of residence). Find-
ings indicate that EI levels were positively related to females, parents' educa-
tion, and household income. The study did not show significant relationships
between adolescent EI and location of residence or
Etge.
EI scores were signifi-
cantly different between females and males, with females reporting higher EI
levels.
A one-way ANOVA showed no significant differences between EI scores
and age, location of residence, and household income. Significant differences
were found based upon EI scores for parents' education; as they increased, so
did EI levels. In a linear regression model, with demograpics as the indepen-
dent variables and EI as the dependent variable, father's education and sex
were both predictors. The results will guide future studies to determine the
factors behind adolescent EI formation and development.
Emotional intelligence (EI), a concept rooted in the theory of social
intelligence (Rehfield, 2002) is defined in a number of
ways.
One defi-
nition denotes EI as the combination of factors that allow a person to
feel, be motivated, regulate mood, control impulse, persist in the face
of frustration, and thereby succeed in day-to-day living (Goleman,
1995).
EI is a "different way of being smart" (Goleman, 1995). EI has
also been identified as the ability to monitor one's own and others'
feelings and emotions, to discriminate among them, and to use this
information to guide one's thinking and actions (Salovey & Mayer,
1990).
In a concise definition, EI is the collection of
a
person's success-
oriented traits.
Emotional intelligence has not traditionally seen the amount of
re-
search or exploration that has been given to topics such as cognitive
intelUgence, mental health, and mental capabilities. Since emotions
Nicholas R. Harrod, George Washington University.
Reprint requests to Dr. Scott
D.
Scheer, Department of Human and Commu-
nity Resource Development, The Ohio State University, 2120 Fyffe Rd. Room
204A, Columbus, OH 43210. E-mail: scheer.9@osu.edu
ADOLESCENCE, Vol. 40, No. 159, Fall 2005
Libra Publishers, Inc., 3089C Clalremont Dr., PMB 383, San Diego, CA 92117
play a vital part in the ways humans interact with each other and
perform in home, school, and work settings, the need to understand
emotions and EI is ohvious. Emotional intelligence is the driving force
behind the factors that affect personal success and everyday interac-
tions with others. Studies of EI have shown its relevance to many
aspects of life and the role it plays in the interactions and decisions of
any given day. EI predicts as much as
80%
of a person's success in hfe,
whereas
IQ
predicts about
20%,
according
to
Goleman
(1995).
Research
indicates that there is a relationship hetween EI and leadership (Ber-
tges,
2002), achievement test scores (Fannin, 2002), and problem solv-
ing (Schutte et al., 2000).
Because of research in these areas, recent publications, and contin-
ued progressive thinking in regard to the
topic,
EI and its implications
have been brought
to
the attention of educators and researchers across
the nation. However, as almost all EI research targets adults, a need
exists for the exploration of adolescent EI. This study begins to reveal
the demographic characteristics of adolescent EI through an explora-
tion of the relationship of specific demographic variables with adoles-
cent EI.
METHOD
Participants
Participants in this study
were
200 students 16-19 years of age from
three Midwestern high schools.
Two
ofthe schools were in rural public
districts and the third was a private high school in a suburb of a
Midwestern state capital
city.
All
three ofthe schools had diverse socio-
economic compositions. Of a possible 275 respondents, 200 (73%) re-
turned the consent forms and participated in the study by completing
the assessment.
The sample consisted of
91
females and 109 males, and the mean
age ofthe respondents was
17.24.
A
slight majority ofthe respondents
(50.8%) lived in urban areas and the rest in rural areas (49.3%). Most
of the respondents' mothers and fathers had a high school diploma
(respectively,
44.3%
and
44.0%)
or a college degree (respectively, 28.4%
and 26.9%). Household income was reported most frequently in the
ranges of $80,000+ (32.9%), $40,000-$59,999 (29.3%), and $60,000-
$79,999
(19.8%).
Though these figures may seem infiated, a confirma-
tion ofthe average family income for each municipality was conducted
according
to
U.S.
Census data, revealing that the responses were simi-
lar to the reported Census values.
504
Procedure
A two-part assessment designed to collect EI and demographic infor-
mation was administered to the students in an introductory or home-
room type class to ensure a more widespread sampling of the student
body. Consent was obtained from all individuals prior to participation.
Participants
18
years and older were allowed to sign their own consent
forms and return them to the school. Respondents under the age of 18
could verbally assent to participate, but they were also required to
return a written consent fonn signed hy a parent or legal guardian.
The demographic information was immediately recorded, and the EI
section of the assessment was analyzed based on the methods and
procedures set forth by Bar-On (2000). After all of the information
was collected and data entered, a total EI score was computed for
each participant.
Instrument
The instrument consisted of
two
sections: (1) demographics and (2)
EI assessment. The demographic section obtained information regard-
ing the respondent's age, sex, household
income,
parents' levels of edu-
cation, and location of
residence.
Guidelines for income brackets and
classification of residence were ohtained from the United States Cen-
sus Bureau. These models were used in order to gather information in
line with current standards of measurement from a recognized and
valid demographic information collection tool.
Adolescent EI was measured with the Bar-On Emotional Quotient
Inventory Youth Short Version (Bar-On
EQ-i:YV(S)),
a specific EI test-
ing device designed hy Bar-On (2000) and purchased from Mental
Health Systems (MHS). The Bar-On EQ-i:YV(S) is an inventory of 30
items,
and each item has a choice of four responses ranging from "Not
True of
Me
(Never, Seldom)"; to "Very Much True of
Me
(Very Often)"
(Bar-On & Parker, 2000). The Bar-On EQ-i:YV(S) Technical Manual
(2000) provided the information and instructions concerning interpre-
ting and grouping the results of the EI survey. The Youth Version of
this assessment includes a correction factor that accounts for the posi-
tive response hias that may he present with adolescents, and several
reverse score questions are used to help the respondents read each
question carefully. This assessment tool has heen tested for validity,
and the responses from this form of the Bar-On assessment have heen
highly correlated with responses from the long form ofthe assessment
(Bar-On & Parker, 2000). The results of Bar-On's assessments have
identified concurrent validity with personality assessments such as
the Connors-Wells Self Report
Scale,
the Connors Parent Rating Scale-
505
Revised, and the Children's Depression Inventory (CDI) (Bar-On &
Parker, 2000). The intemal consistency and reliability of the instru-
ment was measured using Cronbach's alpha (r =
.83);
therefore the EI
instrument displayed a high level of intemal consistency.
RESIILTS
The study examined the relationships between the total EI scores
and the demographic characteristics. Correlations were first conducted
in order to determine if these relationships existed.
A
Pearson's corre-
lation (ratio by ratio) was used for age with mean EI scores. Kendall's
tau-b correlations (ordinal by ratio) were used to determine relation-
ships of EI with sex, residence, father's and mother's education, and
hiausehold income. In addition, Hopkins' descriptors were employed to
use words to describe the correlation coefficients according to their
numeric values (Hopkins,
2002).
Next, difference tests were performed
to examine possible differences between EI and each demographic vari-
able.
Finally, the variables of sex, age, household income, location of
residence, and parents' level of education were used as independent
variables in a regression model with EI as the dependent variable.
Bivariate
Correlation
Analysis
Correlation tests showed no significant relationship between EI and
age or location of residence. A Kendall's tau-b correlation test illus-
trated a negative correlation coefficient (tau-b =
—.128),
with a signifi-
cance of
.031,
hnking EI and
sex.
Therefore, EI levels were significantly
higher for females (coded "1") than for males (coded
"2").
A significant
positive relationship was found for EI with mother's education (tau-b
= .205) and father's education (tau-b =
.296);
therefore the higher the
level of mother's and father's education, the higher the reported EI. A
Kendall's tau-b correlation test revealed a positive correlation coeffi-
cient (tau-b = .242) between EI and household income. The results
imply that as household income increases, so do EI scores. See Table
1 for correlation data.
Difference Tests
Difference tests were utihzed to further illustrate the relationships
between the demographic variables and adolescent EI. For age, a one-
way ANOVA was performed. No differences were found between EI
scores and age. A means test was used to compare the EI scores for
506
sex. An independent samples
t-test
indicated female EI scores (M =
69.73,
n = 91) were significantly higher than the male scores (Af =
67.08,
n = 109). See Table 2. Emotional intelligence levels for location
of residence subcategories were within four units of each other (ramg-
ing from 66.62 to 70.17) and showed little variation by subcategory. A
one-way
ANOVA
revealed no significant differences for location of
resi-
dence.
It is clear that differences do exist in EI scores between the subcate-
gories of
psirents'
education (see Table 3). The EI scores rose steadily
with increasing education levels. The majority of parents fell into the
upper four subcategories (high school graduate, some college, college
graduate, and graduate school), so further comparisons of means were
performed on these groups. A one-way ANOVA showed a significant
difference (F
=
3.26,
p = .01) between the mean EI scores based upon
mother's education. However, the Scheffe post hoc comparison did not
show any significant differences between the means of the subgroups
that would help to explain differences between each category. The AN-
OVA also showed a significant difference (F =
7.99;
p < .00) between
the mean EI scores ofthe respondents in the four categories for father's
education. The post hoc analysis showed that the significant differ-
ences in the means were between "High School Graduate" and "Gradu-
ate
School."
Finally, the majority ofthe respondents were in the upper
three levels for household income ($40,000-$59,999; $60,000-$79,999;
and $80,000 or more), and a one-way ANOVA showed no significant
differences between the mean EI scores of the respondents of these
three sublevels.
Multivariate Regression
Analysis
Linear regression was used to determine which independent vari-
ables were predictive of adolescent
EI.
Knowing that the variables are
intercorrelated, part of the analysis examined the relation between
the independent variables (sex, age, location of residence, mother's
education, father's education, and household income) within the con-
text of each ofthe other variables and used EI as the dependent vari-
able.
An analysis of the multicoUinearity showed that none of the
variables were correlated greater than 0.70. "Mother's Education" and
"Father's Education" were the most related (r =
.58)
ofthe independent
variables in the study. The regression model explained a minimal
amount of variance (.148 adjusted R square), and two independent
variables (sex and father's education) remained significantly predictive
of adolescent EI after colUnearity and regression analysis were per-
formed. See Table 4.
507
Table 1
Correlations between Demographics and El
VariablesHopkins' descriptors
Sex
Age
Residence
Mother's education
Father's education
Household income
-.128
.072
-.116
.205
.296
.242
200
200
197
194
193
167
.031
.314
.106
.000
.000
.000
Small
Insignificant
Small
Small
Small
Small
Table 2
Mean El
Sex
Female
Male
Total
Table 3
Mean El
and Sex f-Test
Mean El
69.72
67.08
68.28
Scores According
n
91
109
200
to Parents'
SD
7.59
8.21
8.02
Education
Parent education level Mother's mean El score
df t
198 2.35
Levels
Father's mean
P
<.O5
El score
Grade school
Some high school
High school graduate
Some college
College graduate
Graduate school
Total
59.00
(n=1)
64.00
(n = 3; SD
=
6.08)
66.66
(A7
= 86; SD = 8.11)
68.19
(n
=
31;
SD
=
7.56)
70.02
(n = 55; SD
=
7.05)
72.44
(n=18;
SD = 7.82)
68.31
(n = 194; SD
=
7.87)
5900
(n=1)
60.67
(n = 9; SD = 5.72)
66.49
(n = 85; SD = 7.12)
69.68
(n = 22;
SD =
9.86)
70.06
(n = 52; SD
=
6.98)
73.67
(n = 24; SD
=
6.09)
68.40
(n = 200; SD
=
7.80)
508
Table 4
Summary of Regression Analysis for Variables Predicting El
Model
(Constant)
Sex
Age
Residence
Mother's education
Father's education
Household income
Unstandardized
coefficients
Beta
54.05
-3.46
.36
.01
.61
1.55
.84
SE
16.15
1.28
.94
.58
.66
.67
.58
Standardized
coefficients
Beta
-.220
.031
.002
.087
.234
.129
t
3.35
-2.71
.39
.03
.92
2.34
1.46
P
.001
.007
.700
.980
.361
.021
.148
Note. R = .424, R^ = .180, Adjusted R^ = .148, SE= 7.28.
DISCUSSION
This investigation examined demographic characteristics of adoles-
cents in relation to emotional intelligence. The primary objectives of
this study
were:
to determine if relationships exist between EI and the
demographic characteristics, to examine if differences were present
between EI based upon demographic variables, and to find predictors
of adolescent EI based upon the demographic information gathered
from the participants.
No
significant relationships were found between
EI scores and age or location of residence. A significant relationship
did appear between EI and sex. The coefficient denotes that EI scores
tend to go down for males in comparison to females. Significant rela-
tionships were evident between adolescent EI and mother's education,
father's education, and household income; as each increases, so does
adolescent
EI.
However, it is important to note that for each character-
istic,
the correlation coefficient was measured as "small" based upon
Hopkins' descriptors.
Generally, the difference tests support the correlational
findings;
the
only relationship that did not register as significant in the difference
test was that between household income and EI. Emotional intelli-
gence scores for females were slightly higher than for males, and the
t-test
showed a significant difference between EI for each sex. No sig-
nificant differences were found between EI based upon location of
resi-
dence or age. Regarding parents' education and household income,
there were differences in the mean EI scores. As mother's education.
509
father's education, and household income increased, so did
EI.
Finally,
determining which demographic variables were predictive of EI was
conducted through regression analysis. Overall, the model was not
effective at predicting EI with only
14.8%
ofthe variance explained by
the model. However, father's education and sex illustrated predictive
properties. Sex in the direction of females and increasing father's edu-
cation were predictor variables for EI. Though only a small amount
of variance was explained in the model, these results are a steirt for
understanding possible factors involved in EI formation and devel-
opment.
Directions
for
Further Research
and Limitations ofthe Study
The importance of understanding the factors that play a part in EI
formation and composition is critical to EI research. In light ofthe lack
of research in the area of adolescent
EI,
the importance of this study is
evident. This investigation has shown various statistically significant
relationships, differences, and predictive variables that may
help
guide
future EI research in the area of adolescence and beyond. By studying
EI in individuals who are in transition from childhood to adulthood,
it is possible to capture a glimpse of the formative elements of EI
development. Because EI plays an integral role in interactions with
others and success in day-to-day living (Goleman, 1995), examining
adolescent EI may help in understanding the areas of life that are
infiuential to EI formation, just as one begins to interact and play an
active role in the adult world.
In studies such as Conger and Elder's (1994, 2000) research into
youth during the farm crisis in Iowa, relationships have been deter-
mined between demographic characteristics such as family hardship
and adolescent emotional development. Similar studies have linked
sex (Ge, Conger,
&
Elder; 2001), age (Fernandez & Rodriguez, 2003),
and location of residence (Conger & Elder; 1994, 2000) to emotional
processes and development during adolescence. These relationships
and the present findings demonstrate that further examination of EI
in relation to demographic factors may prove useful to those searching
for insight into adolescent emotional, social, and psychological devel-
opment.
The results of this study also call for further examination of the
variables identified as predictive of
EI.
It is necessary to examine why
youth
who
are female and from families of higher socioeconomic status
(including household income and parents' education) show higher lev-
els of
EI.
Future studies may explore the relationships between sex,
SES characteristics, and EI more closely in order to reveal additional
510
information about possible predictive characteristics and the relation-
ships that exist concerning adolescent El development. Furthermore,
examining adolescent El in relation to race and ethnicity will help
to better understand another critical demographic element. Race and
ethnicity are extremely important issues to consider, but the complexi-
ties of measuring and defining the suhcategories within each of these
two areas were beyond the scope of this study. The sample (ra = 200)
ofthe study
was
homogenous (at least
93%
of each ofthe three commu-
nities were White/Non-Hispanic according to the 2000 U.S. Census)
and would not have allowed appropriate data analysis or generalizable
results for the issues of race smd ethnicity. Race, ethnicity, and each
ofthe other demographic characteristics influential in El development
need to he explored further.
Several limitations were encountered during this study. They in-
clude: a relatively homogenous sample in terms of race/ethnicity £uid
the lack of inner-city respondents, the use of consent forms may have
reduced the number of actual respondents in comparison to the num-
her of potential respondents, and the lack of response to some of the
assessment items. These limitations may have played a part in influ-
encing its outcomes; therefore, additional research is needed in order
to duplicate and verify the results.
CONCLUSIONS
Relationships were observed between El and demographic charac-
teristics (sex, mother's education, father's education, and household
income).
There were differences in mean El outcomes for sex, mother's
education, and father's education, and two ofthe characteristics dem-
onstrated predictive ability for El (father's education and sex). Since
most El research has focused on adult
populations,
this study of adoles-
cent El begins the importeuit process of understanding the develop-
ment and formation of El in non-adult age groups.
Multiple areas of life success and characteristics have been linked
to El, and this study has demonstrated that demographic characteris-
tics are also linked to El. Emotional intelligence is related to life suc-
cess and everyday social interactions; therefore, any factor or
environmental variable that has an effect upon El formation is im-
portant to take into consideration for personality and individual devel-
opment. The implications for future research are evident, and the
potential possibilities of connecting demographic characteristics of an
individual to emotions and Ufe success are significant for social sci-
ence research.
511
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