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Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O = University of Nigeria, Nsukka
OU = Innovation Centre
Agboeze Irene E.
EDUCATION
SCIENCE EDUCATION
INFLUENCE OF EMOTIONAL INTELLIGENCE
ON SECONDARY SCHOOL STUDENTS’
ACHIEVEMENT
CHUKWUKA, CONSTANCE UJU REG NO: PG /M.ED/12/62700
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INFLUENCE OF EMOTIONAL INTELLIGENCE ON
SECONDARY SCHOOL STUDENTS’ ACHIEVEMENT
IN
CHEMICAL QUANTITATIVE PROBLEM SOLVING
BY
CHUKWUKA, CONSTANCE UJU
REG NO: PG /M.ED/12/62700
DEPARTMENT OF SCIENCE EDUCATION
FACULTY OF EDUCATION
UNIVERSITY OFNIGERIA, NSUKKA
SUPERVISOR
DR F.O. EZEUDU
NOVEMBER, 2014
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CERTIFICATION
This is to certify that, this study on: “Influence of Emotional Intelligence on
Secondary School Students’ Achievement in Chemical Quantitative Problem Solving”
was carried out by Chukwuka Constance Uju, a postgraduate student in the
Department of Education, with Registration Number PG/MED/12/62700 to complete
the requirements of research work for the award of the degree of Master in Science
Education. The work incorporated in this thesis is original and has not been submitted
in part or full, for any other degree in this or any other university.
CHUKWUKA, CONSTANCE UJU
STUDENT
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APPROVAL PAGE
This thesis, submitted in partial fulfillment of the requirements for the award of
Master degree in Education (M.Ed) to the Department of Science Education,
University of Nigeria, Nsukka has been approved.
BY
……………………….. ……………………..
DR. F.O.EZEUDU DR.N.J. ANYAEGBUNAMSupervisor Internal
External
............................... … …………………………
External Examiner PROF. Z. C. NJOKU
Head of Department
…………………………
PROF. U. UMOH
Dean, Faculty of Education
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DEDICATION
This work is dedicated to the Almighty God who by His infinite mercy made this
research work, a reality.
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ACKNOWLEDGEMENTS
I am fortunate to benefit from the insight, advice and encouragement of mentors,
colleagues and many dear friends. Without them, I am sure it would have been too difficult
for me to complete this thesis alone.
First, I thank my honorable formersupervisor, Dr. J.C. Adigwe for abundant patience
and generosity. I am very fortunate to have worked with a supervisor who was so involved
with my research. His commitment, encouragement and cooperation helped me to achieve the
most challenging goal of M.Edprogramme. My immense appreciation also goes to my present
supervisor, Dr. (Mrs) F.O Ezeudu, the immediate pastHead of Department, Prof. D. N. Ezeh,
the present Head of Department, Prof. Z. C.Njoku and my lecturers; Dr. (Mrs) N.J.
Anyaegbunam , Dr.D. U. Ngwoke, Dr. J.J. Agah, Dr. E.U. Okorie for their contributions to
the success of this research work.
I am obliged to the source of inspiration Emmanuel Orji,whosespecialization in data
analysis (SPSS software)made this work a reality. I also express my appreciation to my
Pastor Borngreat Clement, IzuchukwuAkamigbo, RachealOnuigbo andEuchariaOgbonna for
their advice, prayers, encouragement and moral support.
Last but not the least, I acknowledge the financial and moral support of my parents
Mr&Mrs E. Chukwuka and my siblings; Ifeanyi, Chiwuzie, Benjamin, Udoka, Onyeka,
Ogechukwu, Precious and ChinenyeChukwuka. I am grateful as well for their regular
prayers. Also to all others who have contributed in one way or the other in the execution of
this work whose names are not mentioned, I express my overwhelming gratitude.
Above all I give God thanks for His unmerited favor, guidance, protection, and
provision throughout my academic pursuit. He is worthy to be praised.
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TABLE OF CONTENTS
COVER PAGE i
CERTIFICATION ii
APPROVAL PAGE iii
DEDICATION iv
ACKNOWLEDGEMENTS v
TABLE OF CONTENTS vi
LIST OF TABLES xi
LIST OF FIGURES xii
ABSTRACT xiv
CHAPTER ONE: INTRODUCTION
Background of the study 1
Statement of the Problem 11
Purpose of the study 13
Significance of the study 14
Scope of the study 15
Research Questions 16
Hypotheses 16
CHAPTER TWO: LITERATURE REVIEW
Conceptual Framework
Chemical Quantitative Problem-solving 19
Emotional Intelligence 26
Gender 31
School Location 33
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Theoretical Framework
Mayer and Salovey Theory of Emotional Intelligence 37
Goleman Theory of Emotional Intelligence 39
Bar-on Theory of Emotional Intelligence 41
Empirical Studies
Emotional Intelligence and Quantitative Problem-solving Achievement in
Chemistry 43
Gender and QuantitativeProblem-solving Achievement in Chemistry 50
School Location and QuantitativeProblem-solving Achievement in Chemistry 56
Summary of Literature Review 61
CHAPTER THREE:RESEARCH METHOD
Research Design 63
Area of the Study 63
Population of the Study 64
Sample and Sampling Techniques 64
Instrument for Data Collection 65
Validation of the instrument 65
Reliability of the instrument 66
Method of Data Collection 67
Method of Data Analysis 68
CHAPTER FOUR: RESULTS
Research Question One 69
Hypothesis One 70
Research Question Two 70
Hypothesis Two 71
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Research Question Three 72
Hypothesis Three 73
Research Question Four 73
Hypothesis Four 75
Research Question Five 76
Hypothesis Five 78
Research Question Six 79
Hypothesis Six 80
Research Question Seven 81
Hypothesis Seven 83
Summary of Findings 84
CHAPTER FIVE: DISCUSSION, CONCLUSION AND SUMMARY
Discussion of the findings of the study 86
Conclusion Reached from the Findings of the Study 92
Educational Implications 94
Limitations of the Study 94
Recommendations 95
Suggestions for Further Studies 95
Summary of the Study 96
REFERENCES 98
APPENDICES
A. Chemistry Achievement Test 109
B. Emotional Quotient Inventory 112
C. Table of Specification for Chemistry Achievement Test 115
D. Marking Guide for Chemistry Achievement Test 116
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E. Validity (Factor Analysis) 117
F.Reliability Coefficient for EQI and CAT 121
G.Population of S.S 2 Chemistry Students in Nnewi Education Zone 123
H.Sample Size of S.S 2 Chemistry Students 126
I. Letter of Validation of Instrument 127
J. Output for Analysis of Data 28
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LIST OF TABLES
Table
Pages
1 Mean and Standard deviation of students on CAT on basis of Emotional
Intelligence levels 69
2 Summary of one-way ANOVA comparing mean achievement of students
on CAT based on their Emotional Intelligence levels 70
3 Mean and standard deviation of students on CAT and Gender 70
4 Summary of one-way ANOVA comparing mean achievement of students
On CAT and Gender 71
5 Mean and standard deviation of rural and urban students on CAT 72
6 Summary of one-way ANOVA comparing mean achievement of urban
and rural students on CAT 73
7 Summary of Mean and standard deviation of interaction effect of gender
and emotional intelligence on students’ achievement in chemical quantitative
problem-solving 74
8 Results of two-way analysis of variance of interaction effect of gender and
emotional intelligence on students’ achievement in chemical quantitative
problem-solving 75
9 Mean and standard deviation of interaction effect of school location and
emotional intelligence on students’ achievement in chemical quantitative
problem solving 76
10 Results of two-way analysis of variance on the interaction effect of school
location and emotional intelligence on students’ achievement in chemical
quantitative problem-solving 78
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11 Mean and Standard deviation scores of interactive effect of school location and
gender on students’ achievement in chemical quantitative problem- solving 79
12 Results of two-way analysis of variance between school location and gender
on students’ achievement in chemical quantitative problem-solving 80
13 Mean and standard deviation of interaction effect of emotional intelligence,
gender and school location on students’ problem-solving in chemistry 81
14 Results of the two-way analysis of variance between school location and
gender on students’ achievement in problem-solving in chemistry 83
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LIST OF FIGURE
Pages Figure
1 A Schema showing the relationship between the variables, dependent and
independent 36
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ABSTRACT
This study focused on the investigation of influence of emotional intelligence on
students’ achievement in chemical quantitative problem solving in Nnewi Education
zone. The main purpose was to find out the influence of emotional intelligence, gender
and school location on students’ achievement in chemical problem solving. Seven
research questions and their corresponding hypotheses were formulated to guide the
study. The study adopted an expost-facto design. The population of the study was 757
SS2 students from 49 government secondary schools in Nnewi Education zone. The
sample for the study was 304 SS2 chemistry students comprising of 135 males and 169
females. Two- stage sampling consisting of simple random and stratified sampling
techniques were used for selection of the schools for the study. An emotional quotient
inventory (EQi) and a chemistry achievement test (CAT) were used for data
collection. Data were subjected to descriptive and inferential statistical analysis using
means, standard deviation and Analysis of variance (ANOVA). Means and standard
deviation of scores were used to answer the research questions, and the null
hypotheses were tested using one-way ANOVA and two-way ANOVA. The results
revealed that there was a significant difference among the problem-solving mean
achievement scores of students of high, medium and low emotional intelligence
students in chemistry. Results also revealed that there was a significant difference
between chemistry achievements mean scores of male and female students and a
significant difference between chemistry achievements mean scores of students in
rural and urban areas. It was concluded that emotional intelligence influences
students’ achievement in chemical quantitative problem solving, male students
achieved relatively better than female students and that students from rural schools
achieved relatively better than students from urban schools. Based on the findings, it
was recommended that government should organize seminar and workshop for
teachers to enable them understand the concept of emotional intelligence in order to
build their own emotions and that of their students. Also, teaching of chemistry should
encourage gender equality and this should encourage the use of techniques that build
confidence in both males and females alike. Finally, teachers should be gender
sensitive to encourage both male and female students alike in developing scientific
aptitude.
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CHAPTER ONE
INTRODUCTION
Background of the Study
Problem solving is an application of previously acquired knowledge and skills
to achieve certain goals. Various definitions of problem-solving abound in the
literature. Every researcher/author defines it in terms of his/her own psychological
orientations. Krulik and Rudnick (as cited in Carson, 2007) defined problem solving
as the means by which a person uses previously acquired knowledge, skills, and
understanding to satisfy the demands of an unfamiliar situation. The student must
synthesize what he or she has learned, and apply it to a new and different situation.
Behaviorists (Pavlov, 1955; Thorndike, 1932) view it in terms of association between
the problem situations and ideas/objects that may have the greatest potentials for
providing the correct solutions. The solution to a problem is seen as a matter of
scanning and association; connecting chains of conditioned responses; turning up the
right association or searching for the responses that can be associated with the
problematic situation. It is described as reproductive thinking such as drill and
practice; trial and error.
Gestaltists (Duncker 1945; Wertheimer, 1959) view problem solving as an
insightful or intuitive process involving the perceptual processes of the solver.
Cognitivists (Piaget, 1970; Ausubel, 1969;Gagné, 1965) view problem-solving in
terms of information processing involving internal mediating factors and refer it to the
mental process that people go through to discover, analyze and solve problems. This
involves the entire discovery of the problems, the decision to tackle the issue, and
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understanding the problem itself. If the understanding of the problem is faulty, the
attempts to resolve it will also be incorrect or flawed. It is a type of discovery
learning, whose emergence depends on the structure of the task, and may be
independent of the solver’s prior knowledge. It largelydepends on the solver’s ability
to discover general procedures for solving problems of particular kinds through
certain manipulations at times involving a period of fumbling and search, and of the
emergence of correct hypotheses. In this case, the problem-solving is reflective of a
process of progressive clarification of means-ends relationships in which
formulations, testing and rejection of alternative hypotheses plays a leading role.
Carson (2007) stated three characteristics of problem solving. Problem-solving
connects theory and practice. Problem-solving teaches creativity. It also teaches
transfer and application of conceptual knowledge. Villegas, Castro andGuterrez (as
cited in Mushtaq, 2010) also stated two characteristics of problem-solving as
providing opportunity for practicing heuristics as a valuable procedure producing
added motivation due to their potential for application and creativity required in using
multiple mental representations. Problem solving is also very important to students.
It can also be said that problem-solving is like a fun game. It stimulates the
students and makes them enthusiastic. It makes the process of teaching and learning
lively (Wilson, Fernandez andHadaway, 1993). Problem solving provides students
with the chance to solidify and extend their knowledge and also stimulate new
learning (Akinsola, 2008). Problem-solving can be concluded to be part of classroom
instruction.
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Chemical problems are of two types: qualitative and quantitative problems.
Qualitative problems refer to the determination of what given component is present in
a sample of a substance e.g spectroscopy, while quantitative problems refer to
determination of the amounts of each element in the substance e.g stoichiometry.
Quantitative problems are those problems that involve calculations. Many students
have difficulties in understanding and applying concepts in stoichiometry (Krishnan
and Howe, 1994). These difficulties are reflected in the process of solving quantitative
problems in chemistry by students (Gabel andBunce, 1994; Schmidt, 1990).
Quantitative chemistry problems include mathematical manipulations such as
proportional reasoning, changing orders of problem magnitude and converting units.
Students tend to adopt a variety of algorithmic techniques for solving quantitative
problems rather than understanding the meaning of the concept and constructing their
own solution path ways. The combination of lack of mathematical reasoning and lack
of understanding of chemical concepts are major causes of difficulties chemistry
students experience while solving quantitative problems (Gabel, 1999). Previous
studies show that students’ ability to solve problems falls far below their capability to
compute because they do not know how to choose correct operations to apply to solve
problem (Goldberg, 2003). Under the context of cognitive learning theories, problem
solving represents a complex mental activity consisting of a variety of cognitive skills
and actions.
Problem solving in chemistry is a very complex process. It involves an
understanding of the language in which the problem is stated, interpretation of what is
sought, an understanding of chemistry concepts involved in the problem and ability to
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perform mathematical operations that are inherent in the problem (Gabel, 1994). In
solving chemical problems effectively, students are required to identify, define and
carryout executive operations using logic and creative thinking. In the process,
students arrive at a deep understanding of the topic area and construct new knowledge
and understanding on which they are able to make decisions. Acquisition of problem
solving capability is an important component of chemistry because of its practical
value in solving individual and societal problems (BandhanaandDarshana, 2012).
Solving problems in Chemistry therefore aims at preparing students for adult life and
it must therefore develop in them skills and capabilities which make them competent
enough to deal with various challenges in life. Problem solving is one such capability
which is a process of overcoming difficulties that appear to interfere with the
attainment of a goal.
Simple problems can be solved by instructive and habitual behaviors. Complex
problems require certain degree of understanding and perception of relationship
between significant factors of the problem. Problem solving occurs in novel or
difficult situations in which a solution is not attainable by habitual methods of
applying concepts and principles derived from the past experience (Woodworth
andMarquies, 1948). With respect to problem solving in science education
(chemistry), researches had shown that in certain cases, working memory capacity,
mental capacity, and other cognitive (psychometric) variables can be predictive of the
students’ achievement in problem solving (Tsaparlisand Angelopoulos, 2000;
Tsaparlis, KousathanaandNiaz, 1998; NiazandLogie, 1993; Johnstone, Hogg
andZiane, 1993; Niaz, 1988; JohnstoneandKellet, 1980). Cognitive variables include
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dis-embedding ability (degree of field dependence and independence), developmental
level and the Mobility–fixity dimension. High school and college students across the
world have considerable difficulty in solving stoichiometric problems
(BouJaoudeandBarakat, 2000; Schmidt, 1990, 1992; Gabel, Sherwood andEnochs,
1984). The ability to solve stoichiometric problems is of high relevance in chemistry
research and practice, medical fields and branches of the industry. Researchers found
that deficiencies in conceptual knowledge (knowledge of the theories, principles and
chemical processes underlying the problems) account for most of the difficulties
students have in solving stoichiometric problems (BouJaoudeandBarakat, 2003;
Schmidt, 1990). These findings give rise to the question of what can be done to help
students acquire deeper understanding of stoichiometric concepts.
Problem solving involve higher order thinking skills such as visualization,
association, abstraction, comprehension, manipulation, reasoning, analysis, synthesis,
generalization-each needing to be managed and coordinated (Garofaloand Lester,
1985). Problem solving is the highest type of learning within Gagne’s hierarchy as it
is the ability to encounter a novel situation and resolve it with a new rule by the
combination of two or more rules. To resolve a novel situation by combination of new
rules, one needs high level of emotional stability. Hence, solving problems by making
wise decision using both thoughts and feelings or logic and intuition is a part of what
it is referred to as emotional intelligence (Mayer andSalovey, 1997; Mayer
andSalovey, 1990). Linking emotions and intellectual capacity is relatively novel in
academic field, it started about a decade ago (Mayer andSalovey, 1990).
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Emotional intelligence is a construct that has to do with the evaluation and
expression of emotions experienced by oneself and others as well as the ability to
understand and regulate such emotions. Mayer andSalovey (1993) have formally
defined it as “a type of social intelligence that involves the ability to monitor one’s
own and others’ emotions, to discriminate among them and to use that information to
guide one’s thinking and actions.” Mayer, Saloveyand Caruso (2000) also defined
emotional intelligence as the ability to perceive, express and assimilate emotion in
thought, understand and reason with emotion, and regulate emotion in self and others.
Emotional intelligence could be utilized to enhance the necessary problem–solving
procedural knowledge and skills of students.
Emotional intelligence is a combination of skills and capabilities that students
need to survive in life. These skills are intertwined with their intellectual, emotional
and social development (Peterson, 2012). These skills could help students to recognize
and manage their emotions and intellect, and acquire problem solving strategies/skills
and effectively handle interpersonal relationship. Studies have demonstrated that
students with high emotional intelligence have better academic achievement in the
area of health science, social work, psychological well-being, peer relations,
psychopathology and social competence in both higher schools and colleges (Parker,
Hogan, Eastabrook, Okeand Wood, 2006; Parker, Duffy, Wood, Bond and Hogan,
2005; Petrides, Frederickson, andFurnham, 2004; Van der Zee, Thisjs, andSchakel,
2002).
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Developing emotional intelligence is a key to success in school and in life. A
review of research on emotional intelligence demonstrates that emotional intelligence
has positive effects on the well-being of the students (Marroveli, Petrides, Rieffeand
Bakker, 2007; Spencer, OadesandCaputi, 2004), and life satisfaction (Kluemper,
2008; Gannon andRanzijin, 2005; Saklofske, Austin andMinski, 2003; Ciarochi, Chan
andCaputi, 2000). Having higher emotional intelligence is also linked to better health
condition (Schutte, Mahouff, Thorsteinson, BhullarandRooke, 2007), reduced risk of
depression and anxiety (Fernandes-Berrocal, Alcaide, Extremeraand Pizarro, 2006),
and substance abuse (Trinidad and Johnson, 2002). The level of Intelligence quotient
(IQ) has been shown to be a predictor of the level of academic achievement of
students in all subjects including chemistry but in this century, intelligence and
success are not achieved the same way they were before.
New theories of intelligence (emotional intelligence) have been introduced and
they are gradually replacing the traditional intelligence theory (intelligent quotient).
The whole student has become the center of concern, not only his/her reasoning
capacities but also his/her creativity, emotions and interpersonal skills. The multiple
intelligence has been introduced by Gardner (1983) and emotional intelligence theory
by Mayer and Salovey (1990) and then Goleman (1995). Intelligent quotient (IQ)
alone is no more the only measure of success. Emotional intelligence, social
intelligence and luck also play a big role in a person’s cognitive success (Goleman,
1995). According to Goleman (1995), intelligent quotient accounts for only 20% of
the total success and the rest is accounted for by emotional and social intelligence.
Abisamra (2000) argued that if this is found to be so, why do teachers not begin to
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teach its components (i.e. emotional intelligence) to students at schools? It was
concluded that if emotional intelligence (E.I) affects students’ cognitive achievement,
then it is imperative for schools to integrate it in their curricula and thereby raise the
level of students’ cognitive success. According to Azuka (2012), much attention has
not been focused on exploring EI in the school system and in the teaching of science
in Nigeria. Many teachers, educationists, schools and students in Nigeria have little or
no idea of emotional intelligence and its effects on learning. The consideration of
factors affecting academic achievement in secondary schools in Nigeria often neglects
the role of crucial non-cognitive variables including emotional intelligence (EI). It is
therefore imperative to determine whether emotional intelligence influences students’
achievement in chemical quantitative problem-solving. Gender is one of the factors
that might affect students’ achievement in chemical quantitative problem solving.
Gender refers to effects or influences that relate to one being a male or female.
The term gender is often used to indicate the distinction between human beings on the
basis of masculinity and feminity in relation to their expected roles. Okeke (2008)
refers to gender as the social or cultural construct, characteristics, behaviors and roles
which society ascribes to females and males. Previous studies reported that there are
no distinguishing differences in the cognitive, affective and psychomotor skills
achievement of students in respect of gender in the area of chemistry, mathematics
and science (Bilesanmi-Awoderu, 2006; ArigbabuandMji, 2004; Din, Ming andEsther,
2004; SungurandTekkaya, 2003; Freedman, 2002; David and Stanley, 2000).
However, Kolawole (2007), Cornford (2002), Billings (2000), Eccles, Lord, Roeser,
Barber andJozefowwicz (1997) and Hyde andMckinley (1997) in their respective
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studies found that male students performed better than female students in cognitive,
affective and psychomotor skills achievements.
According to Umoh (2003), more difficult tasks are usually reserved for males
while less difficult ones are considered feminine in social settings. Examples of these
is breaking of firewood, which is often seen as manly task and washing of plates seen
as a female task at home. Thus at school, males are more likely to take difficult
subject areas and challenging problem-solving situations while females on the other
hand prefer simple subjects and often shy away from difficult tasks and problem-
solving situations. Ekeh (2003) discovered that male secondary school students
performed better than females in science and mathematics subjects. These differences
in achievements may be attributed to gender stereotyping which encourages male and
female students to show interest in subjects relevant and related to the roles expected
of them in the society. National Assessment of Educational Progress (1994) showed
that males had higher average scores than girls between the ages of 9, 13 and 17 in
science, mathematics and reading assessments. Oludipe (2012) maintained that gender
has no significant influence on students’ achievement in science. These researches are
inconclusive in their reports as to whether females and males differ in the ways they
solve chemistry problem. As there is controversy on gender difference on chemical
problem solving, it is imperative to find out if there are any gender differences in
chemical problem solving among secondary school chemistry students and more
importantly any interaction effect between students’ emotional intelligence and
students’ gender.
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Another variable of interest is school location.The location of schools has a lot
to do with how a child learns in schools. The location of schools simply refers to
where the school is located, whether in urban or rural area. Differentiation between
urban and rural areas are demographically done by the government offices of Regional
planning and development. Urban areas are those with social facilities while rural
areas lack social facilities like electricity, pipe borne water supply, tarred roads etc.
However, the specific problems of teaching chemistry in urban and rural environments
and whether urban students achieve significantly better than their rural counterparts in
chemical problem- solving have not been adequately investigated. According to
NbinaandObomanu (2011), Federal and State Governments in Nigeria had been
making concerted efforts to improve the educational system in the rural areas using
certain education management commissions to ensure that qualified specialist teachers
and facilities are sent to rural schools. These efforts by the Government
notwithstanding, secondary schools in rural areas appear to be disadvantaged in areas
of infrastructure (NbinaandObomanu, 2011). The fact is that most rural secondary
schools are comparatively new and they are not as well equipped as most urban
secondary schools. Furthermore, teachers are known to prefer postings to urban than
in rural schools.
Literatures on students’ achievement in chemical problem solving with respect
to urban and rural schools’ locations had been scanty. Akpan (as cited in Ezeuduand
Obi 2013) indicated that schools in urban areas have electricity, water supply, more
teachers more learning facilities and infrastructure. Onah (2011) and Owoeye (2002)
also indicated that schools in the urban areas achieved more than schools in the rural
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areas in science subjects. Specifically, OwoeyeandYara (2011) showed in their studies
that schools in urban locations had better academic achievement than their rural
counterpart in chemistry. On the contrary, Ezeudu (2003) and Bosede (2010) showed
that location has no effect on students’ academic achievement. Against these
contradictory findings, it is also imperative to investigate whether school location
influences students’ achievement in chemical problem solving and any interaction
effect among the students’ emotional intelligence and their school location on
chemical quantitative problem solving. This study therefore, ascertained the level of
chemical quantitative problem-solving achievement of students with respect to
emotional intelligence, gender and location of schools and the interaction effect
among these variables.
Statement of the Problem
Since our society is becoming increasingly more technical, problem-solving
capability also increasingly becomes a highly valuable skill. The capability to solve
stoichiometric problems is of high relevance in chemistry research and practice,
medical fields and in industry. Students should therefore not be constrained in career
choice and restricted from a full and rewarding participation in society by a failure to
develop problem solving capabilities and skills. Evidences abound that researches
have focused more actively on identifying ways and means to improve the problem
solving capabilities of chemistry students. Research studies on chemical problem
solving capabilities have dealt with different cognitive factors such as reading ability,
reasoning ability, memory, spatial ability; affective factors such as stress, pressure,
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tolerance for ambiguity, anxiety to perform, perseverance, resistance to premature
closure, interest, motivation and experience factors such as age, previous experience,
familiarity with solution strategies, familiarity with problem context and content that
interact to affect problem solving capabilities. The consideration of factors
influencing chemistry students’ problem-solving capabilities in Nigerian secondary
schools has often neglected the influence of a crucial non-cognitive variable,
emotional intelligence. Most studies on problem-solving in chemistry have therefore
not focused on the influence of emotional intelligence on problem-solving capabilities
of students, and its interaction effect with school location and gender on problem-
solving achievement of the students. This therefore becomes an important area of
study and a gap for this study to fill. Most studies on problem-solving in chemistry
have tried to improve on the students problem-solving capabilities. Despite all their
efforts, deficiencies still abound. Students still encounter difficulties in solving
problems in chemistry. Could this be attributed to the influence of emotional
intelligence on their capability to solve chemical problems?
Emotional intelligence has been thought to be an important variable in
cognitive achievement of students. In Nigeria, not much attention has been focused on
exploring EI in the school system and in the teaching of chemistry precisely chemical
problems. Many teachers, educationists, schools and students in Nigeria have little or
no idea of emotional intelligence and its effects on problem solving. This is evident in
the lack of literature on this subject in Nigeria. It is therefore of utmost significance to
investigate influence of emotional intelligence on problem solving capabilities of
students in the Nigerian context. Thus, this research focused on finding out the
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influence of emotional intelligence, school location and gender on problem-solving
achievement of chemistry students in secondary schools.
Purpose of the Study
The purpose of this study is to investigate the influence of emotional
intelligence, gender and school location on chemistry students’ achievement in
chemical quantitative problem solving. Specifically, this study seeks to determine the:
1. Influence of emotional intelligence on the students’ achievement in chemical
quantitative problem-solving.
2. Influence of gender on the students’ achievement in chemical quantitative
problem-solving.
3. Influence of school location on the students’ achievement in chemical
quantitative problem-solving.
4. Interaction effect of gender and emotional intelligence on the students’
achievement in chemical quantitative problem solving.
5. Interaction effect of school location and emotional intelligence on the
students’ achievement in chemical quantitative problem-solving.
6. Interaction effect of school location and gender on the students’ achievement
in chemical quantitative problem-solving.
7. Interaction effect of emotional intelligence, gender and school location on the
students’ achievement in chemical quantitative problem-solving.
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Significance of the Study
The findings of the present study have both practical and theoretical
significance. This study is deemed theoretically significant because it provides
insight into the currently existing theories of emotional intelligence, which could
influence one’s problem solving capability. In particular, Mayer andSalovey (1990),
Bar-On (1997)and Goleman (1995) theories of emotional intelligence, held that
positive emotions influence one’s problem solving capability by altering memory
organization in such a way that cognitive material is better integrated and diverse
ideas that are more related are investigated, and the findings of the present study is
considered to be theoretically significant because it will contribute additional
empirically-derived theoretical body of knowledge on the emotional intelligence
theories. The results of this study will strengthen the tenets of these theories and shall
help to expand the body of knowledge in the area of students’ achievement in
chemical problem solving.
The results of this study will be of use to teachers and students. It will be of
benefit to teachers because it will help them to develop their emotional intelligence. If
emotional intelligence is found to relate to achievement in problem solving, the
teachers will take emotional intelligence in to consideration in planning problem-
solving instructions.
It will be of benefit to students because it will help the students to build their
emotional intelligence in order to enhance achievement in chemical problem solving.
Through the development of their emotional intelligence, students will be able to use
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their emotional intelligence in solving problems in both school setting and outside
school settings thereby increasing their problem solving capability.
Scope of the Study
This study was carried out in Nnewi Education Zone of Anambra State. It took
into consideration urban and rural locations of schools. Nnewi Education zone of
Anambra state was used on the fact that the present study has not been carried out in
it. According to Azuka (2012), much attention has not been focused on exploring EI in
the school system and in the teaching of science in Nigeria. Many teachers,
educationists, schools and students in Nigeria have little or no idea of emotional
intelligence and its effects on learning. Extensive literature search revealed that no
such study has been done in Nnewi Education Zone of Anambra State. Senior
secondary school class two students (SS 2) was used in the study since the content
scope of chemical stoichiometry is in S.S 1 and S.S 11 syllabus and they had covered
one year of study in stoichiometry and on their second year study of stoichiometry, so
they are judged appropriate for this study. The content scope of the study is chemical
stoichiometry in SS I and SS II. It consists of three topics namely, mass, volume and
electrolysis. These topics are selected in order to measure students’ achievement in
chemical quantitative problem solving.
Research Questions
The following research questions were posed to guide the study:
30
1. What is the influence of emotional intelligence on students’ achievement in
chemical quantitative problem-solving?
2. What is the influence of gender on students’ achievement in chemical
quantitative problem-solving?
3. What is the influence of school location on students’ achievement in chemical
quantitative problem-solving?
4. What is the interaction influence of gender and emotional intelligence on
students’ achievement in chemical quantitative problem-solving?
5. What is the interaction influence of school location and emotional intelligence
on students’ achievement in chemical quantitative problem-solving?
6. What is the interaction influence of school location and gender on students’
achievement in chemical quantitative problem-solving?
7. What is the interaction influence of emotional intelligence, gender and school
location on students’ achievement in chemical quantitative problem-solving?
Hypotheses
The following null hypotheses were formulated and tested at 0.05 probability level:
H01: There are no significant differences among the problem-solving mean
achievement scores of students of high, medium and low level emotional
intelligence.
31
H02: There is no significant difference between chemistry achievement mean scores of
male and female students.
H03: There is no significant difference between chemistry achievement mean scores of
students of rural and urban areas.
H04: There is no significant interaction influence of gender and emotional intelligence
on students’ achievement in chemical quantitative problem- solving.
H05: There is no significant interaction influence of school location and emotional
intelligence on students’ achievement in chemical quantitative problem-
solving.
H06: There is no significant interaction influence of school location and gender on
students’ achievement in chemical quantitative problem-solving.
H07: There is no significant interaction influence of emotional intelligence, gender and
school location on students’ achievement in chemical quantitative problem-
solving.
32
CHAPTER TWO
LITERATURE REVIEW
In this chapter,review of related literature is presented under the following
headings; Conceptual framework, theoretical framework, the review of empirical
studies and summary of literature review.
Conceptual Framework
• Chemical Quantitative Problem-solving
• Emotional Intelligence
• Gender
• School Location
Theoretical Framework
• Mayer and Salovey Theory of Emotional Intelligence (1990)
• Goleman Theory of Emotional Intelligence (1995)
• Bar-on Theory of Emotional Intelligence (1997)
Empirical Studies
• Emotional Intelligence and Quantitative Problem-solving Achievement in
chemistry
• Gender and Quantitative Problem-solving Achievement in chemistry
• School location and Quantitative Problem-solving Achievement in chemistry.
Summary of Literature Review
Conceptual Framework
Chemical Quantitative Problem Solving
33
A problem is a difficult situation. We face problems whenever we want to
attain a goal but could not do it. Educationists see problem as a novel situation in
which one is motivated to attain a goal where some obstacles do not allow him to
attain such goal and there are no readymade responses or direct means to attain the
said goal (Medin, Ross andMarkman, 2005; ZimbardoandRuch, 1980). A chemical
problem is a task to undertake or question to answer in chemistry.
Problems in chemistry may be in the form of questions to answer or task to
undertake. According to Blum andNiss (1991), a problem is a challenging situation
which has certain open questions for one who is not in immediate possession of direct
methods or procedural knowledge and algorithms sufficient to answer the question or
undertake the task. Some problems may not be simple and may have many stages.
Wallace and Goldstein (1994) explained that a problem in chemistry contains three
parts; a given state – a state where one is currently on, a goal state - the place where
one wants to be and a path state- the route from the current state to the goal state.
According to Orlich, Harder, Callahan and Gibson (1998), a problem must have two
characteristics; it should be important and relevant to the culture and to the students.
Also, Wolfolk (2004) said that a problem has an initial state or the current situation, a
goal or the desired outcome and a path for reaching the goal including operations or
activities that move the subject toward the final solution. From the whole definitions,
it can be concluded that problem occurs when some obstacles hinders someone from
achieving his/her goals and there is no available means of achieving such goals. The
person may be able to overcome the obstacles through many capabilities developed
from education and training. Problems activate thought processes of the solver.
34
Chemical problems are of two types: qualitative and quantitative problems.
Qualitative problems refer to the determination of what given component is present in
a sample of a substance e.g qualitative analytic problem, while quantitative problems
refer to determination of the amounts of each component in the substance e.g.
stoichiometric problems. Quantitative problems are those problems that involve
calculations. Krishnan and Howe (1994) reported that many students have difficulties
in understanding and applying concepts in stoichiometry. These difficulties are
reflected in the process of solving quantitative problems in chemistry by students
(Schmidt, 1990 and Gabel andBunce, 1994). Chemical quantitative problems require
mathematical manipulations such as proportional reasoning, changing orders of
problem magnitude and converting units. Therefore, students tend to adopt a variety of
algorithmic techniques for solving quantitative problems rather than understanding the
meaning of the concept and constructing their own solution path ways using first
principles of derivation of relationships among concepts and skills. Gabel (1999)
reported that lack of mathematical reasoning, skills and understanding of chemical
concepts and skills have been found to be major causes of the difficulties chemistry
students experience while solving chemical quantitative problems. In solving
qualitative problems, students’ solutions require an explanation drawn from their
conceptual knowledge base, while chemical quantitative problems require students to
integrate their conceptual knowledge/skills and mathematical skills. Such problems
may be written or hands on investigation problem solving tasks. In Chemistry, mole is
a rudimentary concept that poses considerable challenge to chemistry students (Uce,
2009; Chandrasegaran, Treagust, WaldripandChandrasegaran, 2008;
35
BauJaoudeandBarakat, 2003; Cohen, Kennedy-Justice, Pai, Torres,
ToomeyandDePiero, 2000; Case and Fraser, 1999; Schmidt, 1990).
Stoichiometry problems are a fundamental type of chemistry problems in
which the amounts of reactants and products for a chemical reaction are compared
using ratios obtained from a balanced chemical equation. The problems are grounded
in the concept of mole, ratio and proportion. Students encounter these stoichiometric
relationships across many units in the senior secondary chemistry courses, including
mass balance, solution concentration, gas laws, energetics, and rate of reaction,
equilibrium and electrochemistry. Sternberg (2003) described two types of chemical
problems, well-structured problem and ill-structured problem. According to Wallace
and Goldstein (1994) well-structured problem or well-defined chemical problem
provides necessary information directly or indirectly for a single possible correct
solution. They specify conditions, goals, and means of transforming conditions i.e.
starting points, end points (Medin, Ross andMarkman, 2005). For well-structured
problems, Feldman (2002) explained that forward judgment can be made whether a
potential solution is appropriate or not. Application of few chemical concepts, rules
and principles is needed to solve well-structured problem (Jonassen, 2003). Examples
of well-structured problems are chemical problems that are straight forward and
demand direct logical thinking or simple formal reasoning skills. Ormerod (2003)
gave the following as properties of well-structured problem; a clearly defined
statement, limited set of operators or rules one may apply and a clear goal state of the
problem.
36
An ill-structured or ill-defined chemical problem is that in which all the needed
or specified information are not given or less obvious and for which there may be
more than one solution. Nevertheless, some solution may be better than other
solutions. Most of our real life problems are ill-defined (Adams, 2007; Medin, Ross
andMarkman, 2005; Jonassen, 2003; Feldman, 2002; Wallace and Goldstein, 1994).
Ill-structured problems cannot be solved by the application of concepts and principles
from a single discipline or domain. Ill-structured problems can be solved from many
disciplines and to evaluate their solutions, multiple criteria are needed, but sometimes
no criterion is known. According to Jonassen (2003) learners often make personal
judgments or give opinions about this in solving ill-structured problems. There may be
well-defined sub-problems within an ill-structured problem. Conversely, everyday
well-defined problem may have a great deal of creativity and ambiguities (Ball
andKnobloch, 2004). Nevertheless, Ferguson-HesslerandBroekkam (2001) explained
that the complexity of a particular problem depends on the number of variables
involved, such as the number of sub-problems that have to be solved to arrive at the
final answer and the number of formulae, laws and principles from which the subject
has to make a choice from when planning the solution. The skills required to solve
well-structured problems are different from the one needed for ill-structured problems.
Therefore, methods used in teaching well-structured problem to the students cannot be
used effectively to teach ill-structured problems. According to Jonassen (2003) the
problem solver must be experienced and have general intelligence and procedural
knowledge in dealing with ill-structured problems.
37
The ill-structured problems are also complex problems. A complex problem
has many numbers of issues, variables or functions inherent in it. Moreover, there are
relationships among the properties and dependence of the variables considered on the
problem (Funke, 1991). According to Kluwe (1995), the complex problems involve
more cognitive operations than simpler ones. The number of variable components,
their clarity and reliability are part of the complexity of the problem. The complexity
and structural overlap of ill-structured problems tend to be more complex especially
those emerging from everyday practice while many well-structured problems tend to
be less complex. However, some well-structured problems can be extremely complex
and ill-structured problems can be fairly simple (Parham, ChinnandStevenson, 2009).
The dynamics of a problem which is the task environment and timely changing factors
also determines its complexity. Jonassen (2003) explained further that the problem-
solver should use new ways in solving a complex problem with its changing
conditions because with the changing scenario, old methods may not work out. It was
further explained that in static problems, the factors are stable over time that is why
ill-structured problems tend to be more dynamic, and well-structured problems tend to
be fairly stable.
Edmund (2006) described another two other types of chemical problems by
focusing on the nature of solutions: close-ended and open-ended problems. Problems
with only one correct answer are classified as close-ended problems. Examples
include puzzles, textbook, test, exercise, game, and theory problems. They could be
both simple and difficult problems. Open-ended problems on the other hand involve
more than one correct answer or open answers. The open-ended problems are often
38
referred to as real-world problems in contrast to textbook-type problems. According to
Reif (2002) open-ended problem involves the complex problem-solving situation
characterized by the presence of not one but several goals or endings. Solving any
problem demands understanding of the nature of the problem (Fergusson-
HesslerandBroekkamp, 2001).
Problem-solving has been defined in different ways by different
researchers/authors. Polya (1957) defined problem-solving as search for some action
appropriate to attain a clearly conceived, but not immediately attainable aim. It was
said that where there is no difficulty, there is no problem. Getzels (1964) described it
in a more general way that all learning is problem-solving, whereas Davis (1973)
argues that problem-solving is a complex task which has not already been identified
by another name. Gagné (1977) further defined problem-solving as a process of
applying a combination of previously learned rules to achieve a solution of a novel
problem, and also yields new learning. Strengthening this thought, Krulik and
Rudnick (1980) stated that problem-solving is a means by which an individual uses
previously acquired knowledge, skills, and understanding to satisfy the demands of an
unfamiliar situation. The students must synthesize what he or she has learned and
applies it to a new and different situation. In a comprehensive way, Goldstein and
Levin (1987) described problem-solving as higher order cognitive processes that
require control or use of fundamental skills beyond the context or routines.
The fundamental skills needed for the solution of any problem could be easily
acquired through teaching and learning processes. McCormick (1990) related
39
problem-solving to classroom situation and explained it depending on the context, that
problem-solving is a teaching method that encourages active learning, a generic ability
to deal with problem situations, a method used in such subjects as mathematics or
science, or an empirical investigation. Adigwe (1993) viewed problem-solving as a
process involving procedural strategies, psychological processes and knowledge of the
domain. Also, Mayer (1992) described problem-solving as cognitive processes
directed to achieve a goal when there is no clear solution and method in the mind in
the subject involved in solving the problem. Hetherington and Parke (1999) added that
problem-solving involves a higher level of information processing than other
functions and mobilizes perception, attention and memory in a connected effort to
reach a higher goal.
Problem solving in any area of science especially in chemistry is a very
complex process. According to Gabel (1999) chemical problem-solving involves an
understanding of the language in which the problem is stated, the interpretation of
what is given in the problem and what is sought, and an understanding of the science
concepts involved in the problem. Problem solving in chemistry according to Gabel
(1999) is a process by which a student arrives at a solution to a chemical problem.
Integral to this are students thinking, planning, reasoning and executing of their
solution plan as they progress from the initial problem state of the problem to
fulfillment of their goal. Gabel (1999) explained that lack of mathematical reasoning
skills and understanding of chemical concepts and skills have been found to be major
causes of difficulties chemistry students experience while solving quantitative
problems. In order to solve chemistry problems in an acceptable manner, the problem
40
solver must have both conceptual scientific and procedural knowledge (Gabel
andBunce 1994;Ekpete, 2002). According to Festus andEkpete (2012),many studies
showed that students frequently do not use conceptual understanding in solving
chemistry problems. These studies also provided evidence that students were limited
in their ability to solve distant transfer problems without an in-depth understanding of
relevant chemistry concepts. Nakhleh (1993) opined that chemical educators and
teachers have often assumed that success in solving chemistry problems should
indicate mastery of the chemistry concepts. The best way for students to learn science
is to experience challenging problems and the thoughts and actions associated with
solving them. Chemical problem solving according to the researcher is a process of
applying previously acquired knowledge, skills and understanding of chemical
concepts to find solution to problems in chemistry.
Emotional Intelligence
According to Mayer and Salovey (1990) emotional intelligence is a type of
social intelligence that involves the ability to monitor one’s own and others feelings
and emotions, to discriminate among them, and to use that information to guide one’s
thinking and actions. They stated that emotional intelligence was formed on three
adaptive abilities: regulatory emotion; reading and expressing emotion; and using
emotions to solve problems. Regulatory emotion means to simply control your own
emotion and the ability to respond to others emotion. Ability of reading and
expressing emotions consists of understanding others emotion through verbal and non-
41
verbal cues and one’s ability to empathize. Finally, using emotions to solve problems
include motivation, creative thinking, flexible planning and redirected attention.
The use of one’s feelings to motivate, plan and achieve in one’s life involves
the tendency to harness one’s own emotions in order to solve problems. According to
Mayer andSalovey (1990), emotion and mood affect problem solving in the following
ways. First, mood swings may promote flexible planning or the ability to generate
multiple future plans. This concept is based on the assumptions that people who are
consistently in a good mood will perceive positive events as more likely to occur and
negative events are less likely to occur. Such a mood may assist people in considering
a wider variety of options and in generating a larger number of future plans. Secondly,
they claimed that positive emotion may influence one’s problem solving capabilities
by altering memory organization in such a way that cognitive material is better
integrated and diverse ideas are seen as more related. This principle has been
demonstrated through research involving traditional creativity tasks where subjects
presented with positive moods generated more creative responses (Mayer andSalovey,
1990; Isen, Johnson, Mertz and Robinson 1985). Third, powerful emotions have the
potential to affect problem-solving capabilities. For instance, Mayer andSalovey
(1990) purported that attention is directed to new problems when powerful emotions
occur. At such time, one’s attention may be focused on solving problems of greater
immediate importance, thus leading to increased adaptability. Finally, mood may be
used to motivate persistence at challenging tasks, which can assist in the performance
of complex intellectual endeavors such as problem-solving.
42
Mayer andSalovey (1997) gave a revised definition of emotional intelligence
that emphasizes one’s ability to think through emotions in order to adapt to a given
environment. Emotional intelligence therefore, is defined as the ability to perceive
emotions, to access and generate emotions to assist thoughts, to understand emotions
and emotional knowledge, and to reflectively regulate emotions to promote emotional
and intellectual growth (Mayer andSalovey, 1997). Their new model has four pillars:
perception, appraisal and expression of emotions; emotions facilitation of thinking;
understanding and analyzing emotions and employing emotional knowledge; and
reflective regulation of emotions to promote emotional and intellectual growth.
Bar-on (1997) also defined emotional intelligence as an array of non-cognitive
capabilities, competencies and skills that influences one’s ability to succeed in coping
with environmental demands and pressures. This array includes: the ability to be
aware of, to understand, and to express oneself; the ability to be aware of, to
understand, and to relate with others; the ability to deal with strong emotions and
control one’s impulses; the ability to adapt to changes; and to solve problems of a
personal or a social nature. Goleman (1995) defined emotional intelligence as the
capacity for recognizing our own feelings and those of others for motivating ourselves
and for managing emotions well in us and in our relationships. Goleman (1995)
supported this theory by conceptualizing five basic emotional and social
competencies: self-awareness, self-regulation, motivation, empathy and social skill.
Self-awareness is the ability of knowing what we are feeling in the moment, and using
those preferences to guide our decision making, having a realistic assessment of our
own abilities and a well-grounded sense of self-confidence. Self-regulation is handling
43
our emotions so that they facilitate rather than interfere with the task at hand, being
conscientious and delaying gratification to pursue goals, recovering well from
emotional distress. Motivation is using our deepest preferences to move and guide us
towards our goals, to help us take initiative and strive to improve, and to preserve in
the face of set back and frustrations. Empathy is sensing what people are feeling,
being able to take their perspectives, and cultivating rapport and attainment with a
broad diversity of people. Social skill is handling emotions in relationships well and
accurately reading social situations and networks, interacting smoothly, using these
skills to persuade and lead, negotiate and settle dispute for cooperation and team
work. Goleman (1995) suggested that emotional intelligence can predict academic
achievement better than traditional measures of intelligence.
By enhancing emotional intelligence, Mayer andSalovey (1990) said that
several effects could improve students’ educational performance. First, with enhanced
emotional intelligence, negative factors such as discouragement and low self-esteem
may be decreased. A decrease in such negative factors may result from emotionally
intelligent student’s ability to approach life problem more adaptively due to ability to
solve problem effectively. Second, enhanced ability to harness one’s emotions and
moods has been theorized to assist in the performance of complex intellectual task, a
skill vital to academic success. Finally improved emotional intelligence may allow
students to use their feelings to increase motivation, achievements and flexibility.
With increased problem-solving capabilities and peer relationships, academic
achievement may increase. According to researcher, emotional intelligence is an
ability to monitor one’s own and others’ emotions, to discriminate among them and to
44
use that information to guide one’s thinking and actions. Measures of emotional
intelligence have been identified.
Today, there are two scientific approaches to emotional intelligence. They can
be characterized as the ability model and mixed models (Mayer, Caruso andSalovey,
2000). The ability model views emotional intelligence as a standard intelligence and
argues that the construct meets traditional criteria for intelligence, (Mayer, Saloveyand
Caruso, 2008; Mayer, Robert andBarsade, 2008; Mayer andSalovey, 1997).
Proponents of the ability model measure emotional intelligence as a mental ability
with performance assessments that has a criterion of correctness (i. e, there are better
and worse answers, which are determined using complex scoring algorithms). Mixed
models are so called because they mix the ability conception with personality traits
and competencies such as optimism, self-esteem, and emotional self-efficacy
(Cherniss, 2010). Proponents of this approach use self-report instruments as opposed
to performance assessments to measure emotional intelligence (i. e, instead of asking
people to demonstrate how they perceive an emotional expression accurately, self-
report measures ask people to judge and report how good they are at perceiving others
emotions accurately). There has been a debate about the ideal method to measure
emotional intelligence. On the surface, self-report (or self-judgment) scales are
desirable: they are less costly, easier to administer, and take considerably less time to
complete than performance tests (Brackett, Rivers, Shiffman, Learner andSalovey,
2006). On the other hand, constructing ability scale is challenging because there are
no clear right or wrong answers so, items are not objectively scored and it is
expensive. Based on these reasons, self-report measure will be used in the study.
45
Gender
Gender refers to the social attributes and opportunities associated with being
male and female and the relationships between girls and boys, as well as the
relationship between women and men. These attributes, opportunities and relationships
are socially constructed and are learned through socialization processes. They are
context/time-specific and changeable. The concept of gender also includes the
expectations held about the characteristics, aptitudes and likely behaviors of both
women and men (femininity and masculinity). Gender determines what is expected,
allowed and valued in a woman or a man in a given context. In most societies there are
differences and inequalities between women and men in responsibilities assigned,
activities undertaken, access to and control over resources, as well as decision-making
opportunities. Keller (1991) sees gender as a cultural construct which distinguishes the
roles, behavior, mental and emotional characteristics between the male and the female.
To Shettima (1996), gender is a socially defined status as roles and actions ascribed to
women and men so as to distinguish who they are, what is expected of them by the
society and how they relate to each other for meaningful coexistence. Okeke (2008)
defined gender as the socio-cultural constructed characteristics and roles which are
ascribed to males and females in any society. Okeke cited in Ezeuduand Obi (2013)
described the males’ attributes as bold, aggressive, tactful, economical use of words
while the females are fearful, timid, gentle, dull, submissive and talkative.
According to Umoh (2003), more difficult tasks are usually reserved for males
while less difficult ones are considered feminine in social settings. Examples of these
46
are breaking of firewood, which is often seen as manly task and washing of plates seen
as a female task at home. Females across cultures have been found to have a stronger
ethic of care and to be more interdependent, compassionate, nurturing, cooperative and
in care giving roles (Gilligan, 1982). On the other hand males have been found to be
more independent and competitive (Gilligan, 1982; Keller, 1985). These characteristics
of males and females might affect the way they solve problem and their achievement in
schools. Thus at school, males are more likely to take difficult subject areas and
challenging problem-solving situations while females on the other hand prefer simple
subjects and often shy away from difficult tasks and problem-solving situations. Ekeh
(2003) discovered that male secondary school students performed better than females in
science and mathematics subjects. These differences in achievements may be attributed
to gender stereotyping which encourages male and female students to show interest in
subjects relevant and related to the roles expected of them in the society.
National Assessment of Educational Progress (1994) showed that males had
higher average scores than girls between the ages of 9, 13 and 17 in science,
mathematics and reading assessments. Some people even believed that males perform
better than females in any course that deal with calculation as observed by Awoniyi
(2000) who stated that male candidates performed better, relative to female in subjects
requiring quantitative ability. He said that male show superiority in science, statistics
and accounting. RaimiandAdeoye (2006) in their research on gender differences among
college students as determinants of performance in integrated science revealed a
significant difference between males and females in terms of their attitude towards
Integrated science in favor of male, and this may result into the better performance of
47
the male in Integrated science cognitive achievement. Studies on influence/effect of
gender on achievement on chemical problem solving are inconclusive as to whether
females and males differ in the ways they solve chemistry problems. As there is
inconclusive result on gender difference on chemical problem solving, it is imperative
to find out if there are any gender differences in chemical quantitative problem solving
among secondary school chemistry students and more importantly any interaction effect
of students’ emotional intelligence and gender.
School Location
School location simply refers to where the school is located, whether in urban
or rural areas. Differentiation between urban and rural area are demographically done
by the government offices of Regional planning and development. Urban areas are
those with social facilities while rural areas lack social facilities like electricity, pipe
borne water supply, tarred roads, etc. Ojoawo (1990) stated that school location has
been found as one of the potent factors that influence the distribution of educational
resources. According to Ezike (1997) urban environment are those environment which
have high population density containing a high variety and beauty and common place
views while the rural environment are those environment which have low population
density containing a low variety and isolated place views. Lipton cited in
OwoeyeandYara (2011) corroborated that rural community is characterized by low
population, subsistence mode of life, monotonous and burdensome while city hotels,
recreational centers, markets, banks and good road network are present in urban
environment. According to OwoeyeandYara (2011), Owolabi (1990) accentuated that
our highly qualified teachers prefer to serve in urban areas rather than rural areas.
48
NbinaandObomanu (2011) also stressed that teachers are known to prefer postings in
urban than in rural schools. Accordingly, Kuliman, Weather andBatterworth (1977)
observed that teachers do not accept postings to rural areas because their conditions
are not up to the expected standard as their social life in the areas is virtually restricted
as a result of inadequate amenities; facilities are deficient, playground are without
equipment, libraries are without books while laboratories are glorified ones.
Making a critical analysis of locational factors, Hallak (1977) also stated that
provision of education in rural areas is normally fraught with the following difficulties
and problems; qualified teachers refuse appointment in isolated villages; villagers
refuse to send their children to schools because they are dependent on them for help;
parents hesitate to entrust their daughters to male teachers; some villagers have few
children for an ordinary primary school; lack of roads or satisfactory means of
communication makes it difficult to get books and teaching materials to the school
which place difficulties in the way of organizing school transport among others.
According to NbinaandObomanu (2011), Federal and State Governments in Nigeria
have been making concerted efforts to improve the educational system in the rural
areas using certain education management commissions to ensure that qualified
specialist teachers and facilities are sent to rural schools. NbinaandObomanu (2011)
noted that notwithstanding all the efforts made by the Government, secondary schools
in rural areas appear to be disadvantaged in areas of infrastructure. The fact is that
most rural secondary schools are comparatively new and they are not as well equipped
as most urban secondary schools. Akpan cited in Ezeuduand Obi (2013) indicated
49
that schools in urban areas have electricity, water supply, more teachers more learning
facilities and infrastructure. Ezike (2001) supported this by stating that urban areas are
those with high population density, high variety and beauty while rural areas are those
with low population, subsistence mode of life, monotonous and burden. Onah (2011),
and Owoeye (2002) indicated that schools in the urban areas achieved more than
schools in the rural areas in science subjects. Specifically Owoeye and Yara (2011)
showed in their studies that schools in urban locations had better academic
achievement than their rural counterpart in chemistry. On contrary, Ezeudu (2003) and
Bosede (2010) showed that location has no effect on students’ academic achievement.
Gana (as cited in Owoeye, 2011) on the effect of using designed visual teaching
models on the learning of Mathematics at Junior Secondary level of Niger State, found
that there was no significant difference in Mathematics achievement scores of students
in urban and rural locations. Alokan (2010) found out that students’ problems are
strongly associated with poor performance and that sex and location do not affect the
negative relationship between student problems and academic performance. In another
development, ConsidineandZappala (2002) studied students in Australia and found out
that geographical locations do not significantly predict outcomes in school
performance. Shield andDockrell (2008) while looking at the effects of classroom and
environmental noise on children’s academic performance found out that both chronic
and acute exposure to environmental and classroom noise have a detrimental effect
upon children’s learning and performance. Against these contradictory findings, it is
also imperative to investigate whether school location influences students’
achievement in chemical quantitative problem solving.
50
Fig.1: A schema showing the relationship between the variables (dependent and
independent)
The conceptual diagram above shows the relationship between the variables of
this study. It shows the relationship between the dependent variable (achievement in
chemical quantitative problem solving) and independent variables (Emotional
Intelligence, Gender and School location). Achievement in chemical quantitative
problem solving could be influenced by Emotional Intelligence (high, medium and
low), Gender (male and female) and School location (urban and rural).
DEPENDENT VARABLES:
ACHIEVEMENT IN CHEMICAL
QUANTITATIVE PROBLEM SOLVING
INDEPENDENT
VARIABLES
EMOTIONAL INTELLIGENCE GENDER
SCHOOL LOCATION
51
Theoretical Framework
Mayer and Salovey (1990) Theory of Emotional Intelligence
Mayer and Salovey (1990) introduced the term emotional intelligence and
defined it as a set of skills hypothesized to contribute to the accurate appraisal,
expression and regulation of emotions in oneself and in others, and use of feelings to
motivate, plan, and achieve in one’s life. This initial theory of intelligence grew out of
work on social intelligence as well as personal intelligence (Sullivan, 1999).
According to Mayer and Salovey (1990), both emotional intelligence and social
intelligence are subsets of Howard Gardners’ (1983) personal intelligence which
consist of two factors; intra- and interpersonal intelligence.
Accurate appraisal of emotions in oneself and others, the emotional first
domain of Mayer and Salovey’s theory of emotional intelligence, includes both verbal
(language) and non-verbal (facial expressions, body language) communication as the
key medium through which emotions are appraised and expressed. The appraisal and
expression of emotions in oneself involves the ability to learn about one’s emotion,
introspect on these emotions, form coherent prepositions based on those emotions, and
express the content of those emotions (Mayer and Salovey, 1990). On the other hand,
appraisal and expression of emotions in the other include the ability to perceive the
emotions of others so as to ensure smoother interpersonal cooperation (Mayer and
Salovey, 1990) or interaction. A key component of this area is empathy, which is the
ability to comprehend another’s feeling and re-experience them oneself.
52
The second domain of Mayer and Salovey’s theory, regulation of emotions,
involves the degree to which individuals have access to knowledge regarding their
own and others mood. According to them, the extent to which one reflects on personal
knowledge regarding the moods of self and others represents a willingness and ability
to monitor, evaluate, and regulate emotions. Regulation of emotion in the self includes
the ability to alter and regulate one’s own affective reactions, while regulation of
emotion in others involves the ability to regulate and alter the affective reactions of
others.
The final domain, the use of one’s feelings to motivate, plan and achieve in
one’s life involves the tendency to harness one’s own emotions in order to solve
problems. According to Mayer and Salovey (1990), emotion and mood affect problem
solving in the following ways. First, mood swings may promote flexible planning or
the ability to generate multiple future plans. This concept is based on the assumptions
that people who are consistently in a good mood will perceive positive events as more
likely to occur and negative events as less likely to occur. Such a mood may assist
people in considering a wider variety of options and in generating a larger number of
future plans.
Secondly, they claimed that positive emotion may influence one’s problem
solving capabilities by altering memory organization in such a way that cognitive
material is better integrated and diverse ideas are seen as more related. This principle
has been demonstrated through research involving traditional creativity tasks where
subjects presented with positive moods generated more creative responses (Mayer
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andSalovey, 1990; Isen, Johnson, Mertz andRobinson 1985). Third, powerful
emotions have potential effect on problem-solving capabilities. For instance, Mayer
and Salovey (1990) purported that attention is directed to new problems when
powerful emotions occur. At such time, one’s attention may be focused on solving
problems of greater immediate importance, thus leading to increased adaptability.
Finally, mood may be used to motivate persistence at challenging tasks, which can
assist in the performance of complex intellectual endeavors such as problem-solving.
Enhanced emotional intelligence may help alleviate negative factors such as
discouragement and low self-esteem. For example, an emotionally intelligent child
should approach life tasks more adaptively, as the harnessing of one’s emotions and
mood has been theorized by Mayer andSalovey (1990) to enhance one’s ability to
solve problems effectively as well as to assist in the performance of complex
intellectual tasks. Based on this assertion that emotional intelligence enhances one’s
ability to solve problems effectively, the present study ascertained whether emotional
intelligence will positively influence quantitative problem-solving achievement of
students in chemistry.
Goleman (1995) Theory of Emotional Intelligence
Goleman (1995) described emotional intelligence as consisting of five basic
domains. His model is described as developmental because each succeeding domain
builds upon the skills learned in a preceding domain. The first domain, knowing one’s
own emotions is central to his concept of emotional intelligence. Goleman (1995)
described this domain as the “keystone” of emotional intelligence, characterized by
54
self-awareness or the ability to manage one’s own emotions. The key ability of this
domain is showing the skill to handle various feelings, such as anxiety, gloom, or
irritability in appropriate ways (Goleman, 1995). Moreover, Goleman (1995) asserts
that people who have poor skills in this area are always battling with feeling of
distress while those who have superior abilities can bounce back quickly from
stressful like events. The second domain is self-regulation. This is the ability to
control or redirect disruptive impulses and moods; and propensity to suspend
judgment and to think before acting. Characteristics of self-regulation include
trustworthiness and integrity; comfort with ambiguity; and openness to change.
A third domain of Goleman (1995) theory is the ability to motivate oneself.
According to Richardson (2000), this implies that one is able to focus their
enthusiasm, confidence and concentration on achieving an acceptable goal.
Furthermore, emotional self-control defined by Goleman (1995) as delaying
gratification and skipping impulsiveness is viewed as underlying accomplishment in
every area. The forth component is the ability to recognize emotions in others. The
crucial developmental component of this domain is empathy. Goleman (1995)
described empathy as the fundamental “people skills”, which helps one be more
attuned to the social signals that indicate what others want or need. Finally, the last
domain calls for the ability to handle relationships. With the mastery of this domain,
one is able to manage emotions in others or to become “socially competent”. Goleman
(1995) noted that social competency includes the skills needed for popularity,
leadership, and interpersonal effectiveness. Recently, Goleman (1998) asserted that
the ability to manage emotions is of particular importance for achieving success in
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schools. According to Goleman (1995), IQ alone is no more a measure of success, it
accounts for 20% and the rest (80%) goes for emotional, social intelligence and luck.
As we look at our own students, we can see a parallel. Those who did well spent long
time in their homework with a high level of concentration. They have developed a
high level of problem solving ability, so they are efficient in learning new material
and processes. They can apply these skills to a wide variety of subjects. By contrast,
the marginal students learn less efficiently and, typically, minimally. They learn
enough to pass a test, but without the satisfaction of real mastery. Goleman (1995)
suggested that emotional intelligence can predict academic achievement better than
traditional measures of intelligence. Thus emotional intelligence may improve
students’ academic achievement scores and school performance. Based on this
Goleman’s (1995) assertion, this study ascertained whether emotional intelligence
influence students’ achievement in chemical quantitative problem solving.
Bar-on (1997) Theory of Emotional Intelligence
Bar-on (1997) advances a theory of emotional intelligence that seeks to
understand why some individuals are better able to succeed than others. Bar-on (1997)
defined emotional intelligence as an array of non-cognitive capabilities, competencies
and skills that influences one’s ability to succeed in coping with environmental
demands and pressures. This array includes: the ability to be aware of, to understand,
and to express oneself; the ability to be aware of, to understand, and to relate with
others; the ability to deal with strong emotions and control one’s impulses; the ability
to adapt to changes; and to solve problems of a personal or a social nature.
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Bar-on (1997) identified five areas relevant to such success. These five areas
compose his theory of emotional intelligence and correspond to the five areas
measured by his instruments. The first area, intrapersonal skills involves emotional
self-awareness, self-regard, self-actualization and independence. The second area,
interpersonal skills involves interpersonal relationships, social responsibility and
empathy. The third area, his adaptability scales measures problem-solving, reality
testing and flexibility. The forth area, stress management includes stress tolerance, and
impulse control. Finally, general mood is the fifth domain. This area includes
happiness, and optimism, two traits commonly viewed as personality characteristics.
Bar-On (2006) claims that his model is a better predictor of academic performance.
Studies have shown that emotional intelligence is a potent predictor mildly associated
to academic achievement (Maliha, 2011; OgundokunandAdeyemo, 2010; Aremu,
TellaandTella, 2006; Petrides, Frederickson andFurnham 2004). Recent studies also
showed that high emotional intelligence increases problem solving capabilities of
students (BandhanaandDarshama, 2012; Sharei, Kazemi andJafari, 2012). Based on
Bar-On’s (1997) claim that emotional intelligence is a better predictor of academic
performance/achievement and the findings of the recent studies, this study was carried
out to determine the influence of emotional intelligence on students’ achievement on
chemical quantitative problem solving. Self-report instrument developed by Bar-On
(1997) was used for this study; Self-report instruments as opposed to performance
assessments to measure emotional intelligence. Instead of asking people to
demonstrate how they perceive an emotional expression accurately, self-report
measures ask people to judge and report how good they are at perceiving others
57
emotions accurately. Self-report (or self-judgment) scales are desirable: they are less
costly, easier to administer, and take considerably less time to complete than
performance tests (Brackett, Rivers, Shiffman, Learner andSalovey, 2006).
Constructing ability scale is challenging because there are no clear right or wrong
answers so, items are not objectively scored and it is expensive. Based on these
reasons, the researcher decided to use self-report measure in this study.
Empirical Studies
Emotional Intelligence and Quantitative Problem Solving Achievement in
Chemistry
OgundokunandAdeyemo (2010) examined emotional intelligence and
academic achievement and the moderating influence of age and intrinsic and extrinsic
motivation. The purpose of the study was to investigate the relationship between
emotional intelligence and academic achievement among secondary school students.
The sample size of 1563 students (826 males and 737 females) from Oyo State,
Nigeria was used for the study. Their age ranged between 12 and 17 years with mean
age of 15.96 years. Five hypotheses were formulated and tested. The study adopted
survey research design. Two valid and reliable instruments were used to assess
emotional intelligence and academic motivation while achievement tests on English
language and mathematics were used as a measure of academic achievement.
Descriptive statistics, Pearson product moment correlation and hierarchical regression
were used to analyze the data. The results revealed that emotional intelligence, age
and academic motivation were potent predictors mildly associated to academic
58
achievement. The above study reviewed, investigated the relationship between
emotional intelligence and academic achievement on English and Mathematics using
survey research design while the present study investigated the influence of emotional
intelligence on achievement in chemistry quantitative problem solving and expo-facto
design was used.
Maliha (2011) investigated the correlation of Emotional Intelligence with
demographic characteristics, academic achievement and cultural adjustment of the
students of International Islamic University Islamabad (IIUI). The purpose of the
study was to explore the relationship of emotional Intelligence, cultural adjustment
and academic achievement as well as the relationship between emotional intelligence
and cultural adjustment to demographic characteristics of students of IIUI. Nine
hypotheses were formulated to guide the study and were tested at 0.05 level of
significant. Correlational survey method was adopted for the study. The sample for the
study was 615 students that were enrolled in their first semester in year 2008 while
stratified sampling technique were used for the selection of the sample. Three
instruments were used for data collection namely: demographical characteristics, Bar-
On Emotional Quotient Inventory and Cultural Adjustment Scale. Correlation,
regression, ANOVA and t-test statistics were used to analyze the data. Product
moment correlation coefficient was calculated to determine the correlation among the
variables and multivariate regression was accounted for by the independent variables
(EI and cultural adjustment) in academic achievement. The results of the study
revealed significant relationships among the major variables of the study. Emotional
Intelligence was found to be a significant predictor of academic achievement as well
59
as cultural adjustment was found to be a significant predictor of academic
achievement. From the above study reviewed, the relationship of emotional
Intelligence, cultural adjustment and academic achievement as well as the relationship
between emotional intelligence and cultural adjustment to demographic characteristics
of students were explored. The present study explored the influence of emotional
intelligence on students’ achievement in chemistry problem solving.
Aremu, TellaandTella (2006) investigated the relationship among emotional
intelligence, parental involvement and academic achievement of secondary school
students in Ibadan, Nigeria. The purpose of the study was to investigate the significant
impact of emotional intelligence and parental involvement on academic achievement
of in-school adolescents. Three hypotheses were formulated and tested at 0.05 margin
of error. 500 students (250 males and 250 females) were sampled and used for the
study. One instrument titled student emotional intelligence and parental involvement
rating scale was used for the study which was developed by the researcher. Pearson
product moment correlation coefficient statistics and multiple regression statistics
were used to analyze the data. Results showed that both emotional intelligence and
parental involvement could predict academic achievement. Similarly, there were
significant positive relationship between emotional intelligence and academic
achievement; and between parental involvement and academic achievement of
students. The above study reviewed investigated the significant impact of emotional
intelligence and parental involvement on academic achievement of in-school
adolescents in Ibadan, Nigeria while the present study investigated the influence of
60
emotional intelligence on students’ achievement on problem solving in chemistry
using S.S 2 chemistry students in Nnewi Education Zone of Anambra State.
Petrides, Frederickson andFurnham (2004) also investigated the relationships
between trait emotional intelligence, academic performance and cognitive ability on
British secondary school students. Sample of 650 students (Grade II) was used for the
study. The findings revealed that emotional intelligence moderated the relationship
between academic performance and cognitive ability. The above study examined the
relationships between trait emotional intelligence, academic performance and
cognitive ability on British secondary school students while the present study
examined the influence of emotional intelligence on students’ achievement in
chemistry problem solving on Nigerian secondary school students.
O’Connor and Little (2003) carried out a study to assess the relationship
between emotional intelligence and academic achievement on college students. Both
self-report and ability based measures of emotional intelligence were used for the
study. The results showed that emotional intelligence was not a strong predictor of
academic achievement regardless of instrument used to measure it. The above study
used both self-report and ability based measures of emotional intelligence to assess the
relationship between emotional intelligence and academic achievement on college
students while the present study used only self-report measure of emotional
intelligence to assess the influence of emotional intelligence on students’ achievement
on chemistry problem solving.
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Bastain, Burns andNettelbeck (2005) examined the relationship between
emotional intelligence and academic achievement of first year tertiary students in
Australia. Sample of 246 students was used for the study. The findings revealed that
there was no significant correlation between emotional intelligence and academic
achievement. The above study examined the relationship between emotional
intelligence and academic achievement using 246 first year tertiary students in
Australia while the present study used 321 S.S2 students in Nigeria to investigate the
influence of emotional intelligence on their problem solving achievement.
Maryam (2011) investigated the relationship between emotional intelligence
and academic success. The purpose of the study was to explore the relationship
between emotional intelligence and academic success in Iran and to investigate
whether there is a difference between the emotional intelligence of student studying in
different fields (physical education, chemistry, mathematics, English and medicine).
Two research questions were raised to guide the study. Correlational research design
was adopted for the study. The sample for the study was 187 University students
which had undergone instruction for at least two years at State University. The
instrument used for the study was the Persian version of the Bar-On Emotional
Quotient Inventory (EQ-i), Grade Point Average (GPA) was used to find the academic
success of the students which was derived from their files by the researcher. A
Pearson correlation was run to find if there was any relation between the GPA and
emotional intelligence of the participants. Also ANOVA was used to find whether
students majoring in different fields had any differences in their emotional
intelligence. The results showed that there is no significant relationship between
62
emotional intelligence and academic success and that the major one is studying shows
no relationship with one’s emotional intelligence. The above study explored the
relationship between emotional intelligence and academic success of State University
students in Iran and to investigate whether there is a difference between the emotional
intelligence of student studying in different fields while the present study will explore
the influence of emotional intelligence on students’ achievement in chemistry problem
solving in Nigeria secondary schools of Anambra State.
BandhanaandDarshama (2012) investigated emotional intelligence, home
environment and problem solving ability of adolescents. The purpose of the study was
to ascertain the main and interaction effect of Emotional Intelligence, home
environment and sex on the problem solving ability of adolescents. A random sample
of 1007 adolescents (502 male & 505 females) was selected from government and
private higher secondary schools of Jammu City. Seven hypotheses were formulated
and were tested. Mangal Emotional Intelligence Inventory by S. K. Mangaland Shubra
Mangal, Home Environment Inventory by KarunaMisraand Problem Solving Ability
Test by L. N. Dubey was used to collect the data. The data was analyzed using three-
way analysis of variance technique (ANOVA). The results revealed that Emotional
Intelligence & Home Environment has a significant impact on problem solving
technique. The adolescents having higher Emotional Intelligence & good home
environment were better in problem solving ability. However, no interaction effect of
Emotional Intelligence and sex, home environment and sex, and no triple interaction
effects of emotional intelligence, Home environment and sex were found on the
problem solving ability of adolescents. The above study investigated main and
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interaction effect of Emotional Intelligence, home environment and sex on the
problem solving ability of adolescents while the present study will investigate
theinfluence and interaction effect of emotional intelligence, gender and school
location on students’ achievement in chemical problem solving.
Sharei, Kazemi andJafari, (2012)carried out study on the effect of emotional
intelligence skills and meta-cognitive capabilities on student's mathematical problem
solving. The purpose of the study was to investigate relationship between emotional
intelligence skills and meta-cognitive capabilities with the ability of mathematical
problem solving in the high school students. Four hypotheses were formulated. The
sample of 114 students (54 females and 60 males) randomly chosen from Iran high
school was used in the study. The instruments, Bar-On (1997) and Panaoura etal.
(2003) scales were used to assess the emotional intelligence and meta-cognitive
capabilities of the students. Pearson correlation, t-test and multiple regression statistics
were used to analyze the data. The results showed that there is a significant
relationship between the general scores of meta-cognitive capabilities and emotional
intelligence skills, and some of their components with mathematical problem solving
ability. Regarding gender of the students, the findings showed meaningful difference
between males and females in three variables; the performance of male students was
better than females in meta-cognitive capabilities and problem solving, but the score
of female students was higher than males in emotional intelligence skills. Also the
results of a multiple regression analysis showed that meta-cognition and emotional
intelligence contribute significantly to the prediction of problem-solving ability.
However, meta-cognition is a stronger predictor than emotional intelligence.
64
Recommendation was made that national education system of any country must
consider a specific and noticeable position to develop learners’ non-cognitive
variables, such as meta-cognitive capabilities and emotional intelligence skills at all
educational levels. The above study investigated relationship between emotional
intelligence skills and meta-cognitive capabilities with the ability of mathematical
problem solving in the high school students while the present study investigated the
influence of emotional intelligence on secondary school students achievement in
chemical quantitative problem solving.
From the literature reviewed, emotional intelligence was found to have a
significant impact on academic achievement. Emotional intelligence was related to
academic achievement and was found to be a significant predictor of academic
achievement. Studies on emotional intelligence and chemical problem-solving were
insufficient in Nigeria; therefore this study was carried out to find out the influence of
emotional intelligence on secondary school students’ achievement in chemistry
problem-solving.
Gender and Quantitative Problem-Solving Achievement in Chemistry
AbubakarandOguguo (2011) investigatedage and gender as predictors of
academic achievement of college Mathematics and Science students.The purpose of
this study was to determine if there were significant relationship and contributory
effect of gender and age on the academic achievement of Mathematics students. Also,
the effect of gender on academic achievement in Mathematics was ascertained. Three
research questions and one hypothesis guided the study. The design for the study was
65
ex-post factor research design. A sample of three hundred and thirty-two (332)
students; two hundred and twenty-three (223) females and one hundred and nine (109)
males was used. Scatter-plot, mean and Standard deviation were used for the
descriptive statistics while univariate analysis of variance (ANOVA) and multiple
regressions were used for the inferential statistics. Z-test was used to test the null
hypothesis formulated at0.05 level of significant. Result revealed a linear relationship
between age and gender. A low positive correlation coefficients was obtained for age.
The null hypothesis tested was accepted implying no significant gender difference in
academic achievement of the students. It was suggested that some more variables be
included so as to determine significant effect of academic achievement of
Mathematics students.Result revealed no significant gender difference in academic
achievement of the students. This study reviewed was carried out in technical college
of education using mathematics college students in Rivers State while the present
study was carried out in secondary schools using chemistry students in Nnewi Zone of
Anambra State.
AlaoandAbubakar (2010) carried out study on gender and academic
performance of college physics students: a case study of department of
physics/computer science education, Federal College of Education (Technical)
Omoku, Nigeria. The purpose of the study was to assess the gender difference in
academic performance of Physics students. Four hypotheses were formulated and
tested using t-test. A sample of thirty-six (36) students comprising of eighteen (18)
female and eighteen (18) male students was used for the study. The results revealed
that there was no statistical significant difference in academic performance between
66
female and male students. Based on this, it was recommended that teachers in the
department of Physics/Computer should still improve on their pedagogy skills so as to
be able to impact on the students, stressing the importance of Physics in Technology,
giving them the necessary motivation to learn especially in their introductory courses.
The above study was carried out using college physics students: a case study of
department of physics/computer science education while the present study used
chemistry secondary school students in Nnewi Education Zone of Anambra State.
Ayodele (2009) examined gender differences in mathematics and integrated
science achievement among Junior Secondary School Students. The purpose of the
study was to examine gender differences in Mathematics and Integrated Science
achievement among the Junior Secondary School students with particular interest on
the interaction effect of gender and school type on students’ achievement. Three
hypotheses were formulated and tested using Analysis of co-variance (ANCOVA). A
sample of 840 students of both sexes drawn from 2006 and 2007 Junior Secondary
School Certificate Examinations (JSSCE) in Ekiti state, Nigeria was used for the
study. The study adopted ex-post factor research design. The findings from the study
revealed that female students outperformed male students in science and that the
achievement of male students did not differ from female students in mathematics. The
above reviewed study examined gender differences on both mathematics and
Integrated Science achievement among the Junior Secondary School students with
particular interest on the interaction effect of gender and school type on students’
achievement while the present study examined the gender difference on problem
67
solving achievement and interaction effect of gender and school location on students’
achievement in chemistry.
Salami (2013) investigated gender and academic achievement in Delta state
University, Asaba. The purpose of the study was to determine the effect of gender on
academic achievement in selected final year students in the Faculty of Management
Sciences. Three research questions and three hypotheses were used in the study. A
sample of hundred (100) students comprising of sixty (60) males and forty (40)
females was used in the study. Analysis of variance (ANOVA) was used to analyze
the hypothesis and the result indicated that gender affect both academic ethics and
academic achievement. Attributes of academic ethic are critical to good academic
achievement. One recommendation is for male students to be assisted in seeking a
balance between imbibing good academic ethic and other non-academic interests in
order to achieve more. The above study investigated the effect of gender on both
academic ethics and academic achievementusing university students in Delta state
while the present study found out the influence of gender on problem solving
achievement using secondary school chemistry students in Nnewi Education Zone in
Anambra state.
Abdu-Raheem (2012) investigated the influence of gender on secondary
schools students’ academic performance in South-West, Nigeria. The purpose of this
study was to investigate the problem of influence of gender on secondary school
students’ performance in South-West, Nigeria. The study aimed at investigating the
performance of male and female students in English language, Mathematics, Science,
Social Science and Art subjects. Five hypotheses were formulated for the study. The
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results of 2003/2004 to 2007/2008 West African School Certificate Examinations
(WASCE) were collected on English Language, Mathematics, Biology, Chemistry,
Physics, Economics, Geography, Government, Yoruba, Christian Religious Studies
and French from 10 Secondary Schools selected from 5 States in Nigeria. The samples
consisted of 2,305 students. The study adopted an ex-post factor design. Chi-square
was used to analyze the data collected. The study revealed that male and female
students performed equally in English language. Males performed better than females
in Mathematics, Science and Social Science while females also did better than males
in Arts except in Yoruba. Recommendation was made that necessary
materials/equipment should be provided to make Mathematics, Science and Social
Science interesting to girls, French and Christian Religious Studies encouraging to
boys. The above reviewed study investigated the influence of gender on secondary
schools students’ academic performance in South-West, Nigeria using the results of
2003/2004 to 2007/2008 West African School Certificate Examinations (WASCE) on
English Language, Mathematics, Biology, Chemistry, Physics, Economics,
Geography, Government, Yoruba, Christian Religious Studies and French from 10
Secondary Schools selected from 5 States in Nigeria. While the present study
investigated the influence of gender on secondary school chemistry problem solving
achievement using scores from chemistry problem solving achievement test.
Oludipe (2012) investigatedgender difference in Nigerian Junior Secondary
students’ academic achievement in basic science.The main purpose of this study was
to investigate the influence of gender on Junior Secondary students’ academic
achievement in basic science using cooperative learning teaching strategy. Three
69
hypotheses wee formulated for the study. Sample of one hundred and twenty (120)
students obtained from the intact classes of the three selected Junior Secondary
Schools in the three selected Local Government Areas of Ogun State, South-west
Nigeria was used for the study. The study employed a quasi-experimental design. The
instrument used for data collection was Achievement Test for Basic Science (ATBS).
The data collected was analyzed using mean, standard deviation and t-test. Findings of
the study revealed that there was no significant difference in academic achievement of
male and female students at the pretest, posttest, and delayed posttest levels
respectively. The above study investigated gender difference in junior secondary
school achievement in basic science using quasi-experimental design while the present
study used ex-post factor to investigate influence of gender on students’ achievement
in chemical problem solving.
Salman (2010) investigated the analysis of gender influence on performance
and techniques of Nigerian JSS students in solving simultaneous linear equations by
graphical method. The purpose of the study was to analyze the techniques used by
male and female Junior Secondary school students in Kwara State to solve
simultaneous linear equations by graphical methods. It examines the performance of
the students and their techniques in solving these problems on the basis of gender.
Four research questions and one hypothesis were used in the study. Sixty (60) female
and sixty (60) male JSS III students totaling one hundred and twenty (120), were
sampled from four (4) government owned secondary schools in Ilorin west local
government area of Kwara State, Nigeria. Fifteen (15) males and fifteen (15) females
were randomly selected from each of the four schools. The researcher ascertained that
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the students in the study sample had been taught simultaneous linear equations by
graphical method by their respective mathematics teachers. Data were gathered
through paper and pencil tests designed by the researcher and administered by the
subject teachers. Four (4) research questions and one hypothesis were raised or
formulated. The research questions were answered using frequency counts while the
hypothesis was tested using t- test. Findings from the study revealed that both male
and female JSS III students had difficulty with graphical method of solving
simultaneous linear equations. Mathematics teachers are therefore, advised to make
sure that students have graph booklets when treating the topic, expose them to the
stages involved and actively engage them in plotting graphs of given linear equations.
The above study analyzes the techniques used by male and female Junior Secondary
school students in Kwara State to solve simultaneous linear equations by graphical
methods with respect to gender while the present study investigated the influence of
gender on secondary school students’ achievement in chemical quantitative problem
solving in Anambra State.
School Location and Quantitative Problem Solving Achievement in
Chemistry
Ezeuduand Obi (2013) investigated the effect of gender and location on
students’ achievement in chemistry in secondary schools in Nsukka Local
Government Area of Enugu State, Nigeria. The study was guided by three research
questions and three hypotheses. The sample of the study was 827 students comprising
473 males and 354 females. Ex-post factor design was adopted for the study. The
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school past records was the instrument used to collect data for the study. Means and
standard deviations were used to answer the research questions and t-test statistics
were used to analyze the hypotheses. The findings showed that male students achieved
significantly better than the female students in both urban and rural schools. Also
there was no significant difference in the academic achievement of student in urban
and rural schools. It was recommended among others that adequate incentives from
federal Government, parents and stake holders of education should be provided to
female students. From the above study, past academic records was used to collect data
while the present study used achievement test on stoichiometry to collect data.
OwoeyeandYara (2011) investigated school location and academic
achievement of secondary school in Ekiti State, Nigeria. The purpose of the study was
to look at the location of schools as it relates to academic performance of students in
Ekiti state of Nigeria between 1990 and 1997. The study population was results of the
West African School Certificate Examinations (WASCE) conducted between 1990
and 1997 in 50 secondary schools in both rural and urban areas of the state. One
validated instrument "Student Location Questionnaire (SLQ)" was used for data
collection. One hypothesis was formulated and answered. Data were analyzed using
mean and t - test. The results showed that there was a significant difference between
students' academic achievement of rural and urban secondary schools in senior school
certificate examinations. It was recommended that Government should bridge the gap
between the rural and urban locations by providing the rural dwellers the social
amenities which will enhance better academic performance of students in their final
examinations like the SSCE. The community should assist the government by
72
providing taxis and buses to facilitate movement of teachers and students to their
school. Adequate incentives should be provided to rural area teachers to encourage
them to put in their best to remain in their duty stations. The study above used results
of the West African School Certificate Examinations (WASCE) conducted between
1990 and 1997 while the present study used chemistry achievement test.
Igboegwu andOkonkwo (2012)conducted a study on the influence of gender
and school location on students’ achievement in chemistry. The study was carried out
in Anambra State Secondary Schools. The study was guided by three research
questions and three hypotheses. Descriptive survey research design was used for the
study. The sample for the study was 1889 subjects comprising 844 males and 1045
females. The sample was drawn through multistage proportionate random sampling
technique. The study was based on student’s achievement in Chemistry Achievement
Test (CAT) for senior secondary school class two (SS II). The validity and reliability
of the instrument were established. CAT has a reliability coefficient of 0.82. Mean and
standard deviation were used to answer the research questions while ANOVA
statistics was used to test the null hypotheses. A scheffe test was also done for a step-
wise comparison of the means. The findings of the study showed a higher but not
statistically significant difference in the achievement of male and female students in
favor of females. Location and education zone exerted some significant influence on
students’ achievement in chemistry.The study reviewed above used descriptive survey
research design while the present study used expo-facto research design.
Okoi (2003) in Ondo state conducted a study to find out problem-solving
ability of chemistry students in secondary school. A sample of 100 students, 50
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students each was drawn from urban and rural schools respectively. The instrument
used to collect data was teacher made test. The result revealed that urban school
students solved problems better than rural school students.
Ubong (2004) investigated problem-solving among biology students in rural
and urban schools in AkwaIbom state. A sample of 500(250 males and 250 females)
was used. No significant difference was found in the problem-solving ability of male
and female students in both rural and urban schools. Okoro (2006) in another related
study examined the problem-solving ability of students in biology using a sample of
206 students. The problem-solving involved the identification and classification of
organisms obtained from a pond. The findings obtained indicated that male students in
the rural schools out performed those in urban schools. However, the study indicated
that the males in the urban schools performed better than the females in the rural
schools.
Umanah (2008) conducted a study to test problem-solving skills of secondary
school students in River state using a sample size of 380 students. 190 students each
from urban and rural schools respectively were used. His findings indicated that urban
school students were better problem-solver than their rural counterparts.
In a related study, Olu (2005) tested the skills of biology students in secondary
school on problem-solving by applying grid mapping in identification of plants
species. Sample size of 500 students was used, 250 students each from rural and urban
schools respectively. The findings obtained revealed a better performance in favor of
rural school students.
74
NbinaandObomanu (2011) carried out research on assessment of the effects of
problem solving instructional strategies on students’ achievement and retention in
chemistry with respect to location in Rivers State. The study focused on how problem
solving instructional strategies would affect students’ achievement and retention in
Chemistry with particular reference to River State. A pre-test, post-test, non-
equivalent control group design was adopted. Two research questions and two
hypotheses were answered and tested. Purposive and stratified random sampling was
used to select 428 SS II students from two rural and two urban local government areas
of Rivers State. These students were randomly assigned to the two treatment groups.
Problem solving with Model and Feedback – Correctives (PF), Problem solving with
Model Only (PM), and the control Problem Solving by the Conventional Method
(PC). The model used is a Generic Problem Solving Inquiry Model developed by
Hungerford (1975). A researcher developed and modified instrument, Chemistry
Achievement Test (CAT) and lesson plans were used for the study. Data collected
were analyzed using Mean, Standard Deviation (SD) and some gains of achievement
and retention and that the hierarchical order of achievement is PF, PM and PC. No
significant differences were observed in the post-test mean scores of urban and rural
subjects in the achievement is PF, PM and PC. No significant differences were
observed in the post-test mean scores of urban and rural subjects in the achievement
and retention tests administered in the course of the study. Based on the findings, it
was recommended that both rural and urban Chemistry teachers use problem solving
instructional strategies, particularly that in which use of a model is supplemented with
feedback-correctives in teaching. The above study focused on how problem solving
75
instructional strategies would affect students’ achievement and retention in Chemistry
with particular reference to River State while the present study focused on how school
location would influence students’ achievement in quantitative problem solving in
Chemistry.
Koloand Ibrahim (2003) investigated the effect of problem-solving of
secondary school physics students in urban and rural areas of Benue state using a
sample of 58 students. 29 students were drawn from rural and urban schools
respectively. The results indicated that there was no significant difference in the
performance of both male and female students in urban and rural schools. The above
studies on school location and problem solving produced a lot of contradictions. The
present study was carried to add to the existing knowledge.
Summary of Literature Review
Literature was reviewed under conceptual framework, theoretical framework
and related empirical studies. Concepts of chemical problem solving, emotional
intelligence, gender and school location were discussed from different perspectives.
Problem solving in chemistry was seen as a very complex process. It involves an
understanding of the language in which the problem is stated, the interpretation of
what is given in the problem and what is sought, an understanding of the science
concepts involved in the problem. Emotional intelligence was seen as a type of social
intelligence that involves the ability to monitor one’s own and others feelings and
emotions, to discriminate among them, and to use that information to guide one’s
thinking and actions. On the other hand, gender refers to the social attributes and
76
opportunities associated with being male and female and the relationships between
girls and boys, as well as the relationship between women and men. School location
simply refers to where the school is located, whether in urban or rural areas. From the
literature reviewed, Emotional intelligence theorists claimed that positive emotion
may influence one’s problem solving capabilities by altering memory organization in
such a way that cognitive material is better integrated and diverse ideas are seen as
more related. Based on this assertion that emotional intelligence enhances one’s
ability to solve problems effectively, the present study ascertained whether emotional
intelligence positively influenced problem-solving capabilities of students in
chemistry.
From the related empirical studies, it was seen that studies done under
emotional intelligence and problem solving were insufficient. It was also found that
emotional intelligence, gender and school location could affect problem-solving
achievement in schools. Studies on gender and school location on problem-solving are
posed with many contradictions. These insufficient studies on emotional intelligence
and problem solving and contradictory findings on gender and school location on
problem solving generated the present study to find the influence of emotional
intelligence, gender and school location on secondary school students’ achievement in
chemical problem-solving in Nnewi Education Zone of Anambra State.
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CHAPTER THREE
RESEARCH METHOD
This chapter describes the research design, area of the study, population of the
study, sample and sampling techniques, instrument for data collection, validation of
instrument, reliability of instrument, method of data collection and method of data
analysis.
Research Design
The design for this study was ex-post facto design. Ex-post facto design
according to Kerlinger (1979) is a systematic empirical inquiry in which the
researcher does not have direct control of independent variables because their
manifestations have already occurred or because they are inherently not manipulable.
Ex-post facto design is appropriate because the variables for the study will not be
manipulated.
Area of the Study
Area of the study was Nnewi Education Zone in Anambra state. Nnewi
Education zone of Anambra state was used on the fact that the present study has not
been carried out in it. According to Azuka (2012), much attention has not been
focused on exploring EI in the school system and in the teaching of science in Nigeria.
Many teachers, educationists, schools and students in Nigeria have little or no idea of
emotional intelligence and its effects on learning. Extensive literature search revealed
63
78
that no such study has been done on Nnewi Education Zone of Anambra State. Nnewi
education zone comprised four Local Government Areas: Nnewi North, Nnewi South,
Ekwusigo and Ihiala Local Government Area.
Population of the Study
The population of the study comprised of all the 757 SS2 Chemistry students in
49 Government Secondary schools in Nnewi education zone in Anambra State. The
total numbers of SS2 male and female students are 320 males and 437 females
respectively. (Source: Post Primary School Education Board Zonal Office Nnewi) .
Sample and Sampling Techniques
The sample for the study was 304 S.S 2 chemistry students (169 girls and 135
boys) from the 11 schools to be used for the study, distributed thus, 3 urban schools
and 8 rural schools respectively. The 3 urban schools consist of 98 girls and 96 boys
while the 8 rural schools consist of 71 girls and 39 boys respectively. Senior
Secondary Two students were used because they have completed two years of
chemistry in the area of stoichiometry.The study employed two-stages sampling
technique. There are 49 secondary schools in Nnewi education zone. In the first stage,
half of the total number of schools, 24 schools, was randomly drawn from 49 schools
which is representative of the population.
In the second stage, the 24 schools were then stratified into rural and urban
schools. Half of each, urban and rural schools was randomly taken. Half of the
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number of students in the sampled rural and urban schools was randomly sampled as
subject for the study sample.
Instrument for Data Collection
The instruments used for data collection in this study were: (i) Emotional
Quotient Inventory (EQi) and (ii) Chemistry Achievement Test (CAT). The Emotional
Quotient Inventory was adapted from Bar-On Emotional Quotient Inventory
developed by Maliha (2011). It is a forty nine (49) item 5 point rating scale type with
five category of responses; very seldom, seldom, sometimes, often and very often with
numerical values of 1, 2, 3, 4 and 5 respectively to measure the emotional quotient of
the chemistry students. The modification done to the original version was to adapt EQi
items to the standard of Nigerian students.
Chemistry achievement test was selected from the past West African
Examination Council (WAEC) question papers from 1988-2012 in the area of
stoichiometry. It consists of 25 multiple choice items from stoichiometry. This was
used to assess students’ achievement in solving quantitative problems.
Validation of the Instrument
The types of validity established for the instruments were face, construct and
content validity. The instruments were validated by four experts from University of
Nigeria Nsukka (one in psychology and three in science education). The face validity
was done to ensure that the questions on the CAT are stoichiometric problems as
enshrined in the senior secondary chemistry curriculum. The validation was also to
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ensure that the items on CAT had content validity in cognizance with stoichiometric
problems in secondary school chemistry syllabus. A table of specification (test blue
print) was constructed to ensure content validity (appendix C page 115). The
construction was based on the first three levels of Bloom’s taxonomies of educational
objectives (knowledge, comprehension and application). Three questions on
knowledge level, five questions on comprehension level and seventeen questions on
application level. Ten questions were selected from each of the two topics of mass and
electrolysis and five questions from volume. Emotional Quotient Inventory underwent
construct validation. Construct validity was established using factor analysis. This was
to determine the extent to which EQi measures emotional intelligence. Factor analysis
was used because research instrument EQi will be used to measure a construct. The
factor analysis was carried out using the principal axis factoring method with Varimax
rotation. First, the minimum factor structure for the data set is represented by five
factors (appendix E page 117). A minimum factor loading of 0.30 was set by the
researcher and the items selected under each factor was shown (appendix E page 120).
The total items selected were forty nine (49) (appendix E page 120).The comments
and recommendations of the various experts served as guide for the modification.
Reliability of the Instrument
The type of reliability established for the instruments was internal consistency.
The reliability of the emotional quotient inventory and chemistry achievement test
was done using a group of 20 S.S 2 chemistry students in Nnewi education zone (not
one of the schools to be used in the study). The instruments were administered to the
81
students in order to determine the reliability index of the instruments. The data
collected on emotional quotient inventory were analyzed using SPSS V20.0. The
estimate of internal consistency of EQi was determined using Cronbach alpha and the
value obtained was 0.757 (appendix F page 121). Cronbach alpha was used because
the test items are non-dichotomous; no response is deemed correct or wrong. Kudder-
Richardson (K-R20) method was used to estimate the internal consistence of CAT and
0.75 was obtained (appendix F page 121). It was used because the items were
dichotomously scored.
Method of Data Collection
The instruments were administered to 321 students in the sample by the
researcher and chemistry teachers in the schools. The chemistry achievement test was
administered first to the students and collected before administering the emotional
quotient inventory. This order was to assess their problem solving achievement first
and then the EQi to make them react emotionally to the problem solving situation they
have just gone through. Thus the emotional quotient inventory was administered
immediately after the achievement test. In the inventory, the students were required to
tick a response that best describe their feelings about the emotion statement. After
scoring the test, the scripts were collated based on the variables of emotional
intelligence, gender and school location and achievement. Emotional Quotient
Inventory scores were ranked and used to sort students into high, medium and low
emotional intelligence level groups. The classification of EQi was based on academic
grade levels of failure (0-39%); pass (40-49%); merit (50-59%); credit (60-69%) and
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distinction (70-100%). Failure and pass levels were merged together as low level
groups; merit and credit levels were merged together as medium level group and
distinction was high level group.
Method of Data Analysis
Mean and standard deviation scores were used to answer the research
questions. The null hypotheses were tested using Analysis of variance (ANOVA).
Students’ scores in CAT were used as dependent variable and emotional intelligence,
school location and gender as independent variables. SPSS version 20.0 was used to
analyze the data.
83
CHAPTER FOUR
RESULTS
The results of the study are presented in tables in accordance with the research
questions and hypotheses guiding the study.
Research Question 1:What is the influence of emotional intelligence on students’
achievement in quantitative problem-solving in chemistry?
Table 1: Mean and Standard deviation of students on CAT on basis of E.I levels.
Variable
Emotional intelligence
N
Mean
Standard
deviation
HIGH 19 66.32 5.04
MEDIUM 268 33.51 12.67
LOW 17 28.94 11.36
Total 304 35.30 14.67
The results presented in Table 1 revealed that high emotional intelligent
students had a mean score of 66.34 with a standard deviation of 5.04; medium
emotional intelligent students had a mean score of 33.51 and standard deviation of
12.67 while low emotional intelligent students had a mean score of 28.94 and standard
deviation of 11.36. From the result above, the mean scores of students with high EI
(66.34) is greater than those of their counterparts with medium EI (33.51) and low EI
(28.94) respectively. This is indicative of the fact that emotional intelligence may have
some influence on chemistry students’ problem-solving capabilities. This is further
verified by hypothesis 1.
69
84
Hypothesis 1:There are no significant differences among the quantitative problem-
solving mean achievement scores of students of high, medium and low level
emotional intelligence students.
Table 2: Summary of one-way ANOVA comparing mean achievement of students
on CAT based on their EI levels.
Source of variation Sum of squares Df Mean square F Sig.
Between Groups 19826.13 2 9913.06 65.70 0.000
Within Groups 45418.03 301 150.89
Total 65244.16 303
The results in Table 2 show that with respect to the achievement mean scores
of students and E.I, an F-ratio of 65.70 was obtained with associated probability value
of 0.000. Since the associated probability (0.000) was less than 0.05, the null
hypothesis (H01) was rejected. Thus, there was a significant difference between the
problem-solving mean achievement scores of students of high, medium and low level
emotional intelligence. This implies that emotional intelligence have an influence on
the achievement of chemistry students’ problem-solving capabilities.
Research Question 2: What is the influence of gender on students’ achievement in
problem-solving in chemistry?
Table 3: Mean and standard deviation of students on CAT and gender.
Gender N Mean Standard Deviation
Male 135 39.08 16.56
Female 169 32.28 12.20
Total 304 35.30 14.67
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The results presented in Table 3 above show that male students had a mean score of
39.08 and standard deviation of 16.56, while female students had a mean score of
32.28 and standard deviation of 12.20. Mean score of male students is greater than
mean score of female students. This is indicative that gender appears to have some
influence on chemistry students’ problem-solving capabilities. The significance of this
difference was determined in hypothesis 2.
Hypothesis 2:There is no significant difference between chemistry achievement
mean scores of male and female students.
Table 4: Summary of one-way ANOVA comparing mean achievement of students
on CAT and gender.
Source of Variation Sum of Square df Mean Square F Sig.
Between Groups 3467.687 1 3467.687 16.952 0.000
Within Groups 61776.471 302 204.558
Total 65244.158 303
Results in Table 4 show that with respect to achievement mean scores of male
and female chemistry students, an F-ratio of 0.237 was obtained with associated
probability value of 0.000. Since the associated probability (0.000) was less than
stipulated level of significance which is 0.05, the null hypothesis (H02) was rejected.
Consequently, there is a significant difference between achievement means scores of
male and female chemistry students in favor of males. Therefore we can say that
gender has influence on the achievement of chemistry students on their problem-
solving capabilities.
86
Research Question 3:What is the influence of school location on students’
achievement in quantitative problem-solving in chemistry?
Table 5:Mean and standard deviation of rural and urban students on CAT.
Variable
School location
N
Mean
Standard Deviation
Urban male 96 34.33 14.56
Rural male 39 50.77 15.50
Urban female 98 32.53 11.41
Rural female 71 31.94 13.29
Urban student 194 33.42 13.06
Rural student 110 38.62 16.70
Total 304 35.30 14.67
The result presented in Table 5 showed that urban students had a mean
achievement score of 33.42 with a standard deviation of 13.06 while rural students
had a mean score of 38.62 with a standard deviation of 16.70 respectively. Also, urban
male had a mean achievement score of 34.33 with a standard deviation of 14.56 and
rural male had a mean achievement score of 50.77 with a standard deviation of 15.50.
Urban female had a mean achievement score of 32.53 with a standard deviation of
11.41 and rural female had a mean achievement score of 31.94 with a standard
deviation of 13.29 respectively. From the results of the analysis, rural students
performed better than their urban counterparts in favor of male students. This is
indicative that school location appears to have an influence on chemistry students’
achievement on problem-solving.
87
Hypothesis 3:There is no significant difference between chemistry achievement
mean scores of students in rural and urban areas.
Table 6:Summary of one-way ANOVA comparing mean achievement of urban and
rural students on CAT.
Source of Variation Sum of Square Df Mean Square F Sig.
Between Groups 10973.719 3 3657.906 20.220 0.000
Within Groups 54270.439 300 180.901
Total 65244.158 303
Result in Table 6 shows that with respect to the achievement mean score of chemistry
students in rural and urban areas; an F-ratio of 20.220 was obtained with associated
probability value of 0.000. Since the associated probability (0.000) was less than 0.05,
the null hypothesis (H03) was rejected. Hence there was a significant difference
between chemistry achievement mean scores of students in rural and urban areas in
favor of rural male.
Research Question 4:What are the interaction influences of gender and emotional
intelligence on students’ achievement in quantitative problem-solving in chemistry?
88
Table 7:Mean and standard deviation of interaction effect of gender and emotional
intelligence on students’ achievement in chemical quantitative problem-solving.
Variables
E.I levels Gender
N
Mean
Standard Deviation
High Male
Female
Total
14
5
19
66.86
64.80
66.32
5.7
1.79
5.04
Medium Male
Female
Total
114
154
268
36.21
31.51
33.51
14.50
10.75
12.67
Low Male
Female
Total
7 10
17
30.29
28.00
28.94
6.87
13.98
11.36
Total Male
Female
Total
135
169
304
39.08
32.28
35.30
16.56
12.20
14.67
Results in Table 7 show that for high emotional intelligent male, the mean
achievement score was 66.86 with a standard deviation of 5.75, while high emotional
intelligent female had a mean achievement score of 64.80 with a standard deviation of
1.79. For medium emotional intelligence male, the mean achievement score was
36.21 with a standard deviation of 14.50, while medium emotional intelligence
female had a mean achievement score of 31.51 with a standard deviation of 10.75.
Low emotional intelligence male had a mean achievement score of 30.29 with a
standard deviation of 6.87 while low emotional intelligence female had a mean
achievement score of 28.00 with a standard deviation of 13.98 respectively. For each
of the levels, mean scores of males are greater than that of their female counterparts.
89
This is indicative that gender and emotional intelligence appears to have some
interaction influence on students’ problem-solving achievement in chemistry.
Hypothesis 4: There is no significant interaction influence of gender and
emotional intelligence on students’ achievement in quantitative problem- solving in
chemistry.
Table 8:Results of two-way analysis of variance of interaction effect of gender
and emotional intelligence on students’ achievement in quantitative problem-
solving in chemistry.
Source Type III Sum of
Squares
df Mean Square F Sig.
Corrected Model 21312.774a
5 4262.555 28.914 0.000
Intercept 125367.705 1 125367.705 850.407 0.000
E.I levels 14900.607 2 7450.303 50.538 0.000
Gender 154.558 1 154.558 1.048 0.307
E. I levels* Gender 44.594 2 22.297 .151 0.860
Error 43931.384 298 147.421
Total 444112.000 304
Corrected Total 65244.158 303
a. R2 = 0.327 (Adjusted R
2 = 0.315)
Table 8 reveals that interaction influence between gender and emotional
intelligence on students’ achievement in problem-solving in chemistry was not
significant. An F-ratio of 0.151 was obtained with associated probability value of
0.860. Since the associated probability (0.860) was greater than the stipulated level of
significance 0.5, the null hypothesis (H06) was not rejected. Consequently, the
interaction influence between gender and emotional intelligence on students’
achievement in problem-solving in chemistry was not significant.
90
Research Question 5:What are the interactive influences of school location and
emotional intelligence on students’ achievement in quantitative problem-solving in
chemistry?
Table 9:Mean and standard deviation of interaction effect of school location and
emotional intelligence on students’ achievement in quantitative problem-solving in
chemistry.
Variables
E.I Levels School location N Mean Standard
Deviation
Urban Male 6 66.00 5.51
Rural Male 8 67.50 6.21
High Urban Female 1 64.00 -
Rural Female 4 65.00 2.00
Total 19 66.32 5.04
Urban Male 83 32.39 12.72
Rural Male 31 46.45 14.19
Medium Urban Female 90 32.13 11.06
Rural Female 64 30.63 10.31
Total 268 33.51 12.67
Urban Male 7 30.29 6.87
Low Urban Female 7 33.14 11.00
Rural Female 3 16.00 14.42
Total 17 28.94 11.36
Urban Male 96 34.33 14.56
Rural Male 39 50.77 15.50
Total Urban Female 98 32.53 11.41
Rural Female 71 31.94 13.29
Total 304 35.30 14.67
The result presented in Table 9 above shows that for high emotional
intelligence level, urban male had a mean score of 66.00 with a standard deviation of
5.51, rural male had a mean score of 67.50 with a standard deviation of 6.21, urban
female had a mean score of 64.00 while rural female had a mean score of 65.00 with a
standard deviation of 2.00 respectively. It also revealed that for medium emotional
91
intelligence level, urban male had a mean score of 32.39 with a standard deviation of
12.72, rural male had a mean score of 46.45 with a standard deviation of 14.19, urban
female had a mean score of 32.13 with a standard deviation of 11.06 while rural
female had a mean score of 30.63 with a standard deviation of 10.31 respectively.
Furthermore, the results show that for low emotional intelligence level, urban male
had 30.29 with a standard deviation of 6.87, urban female had a mean score of 33.14
with a standard deviation of 11.01 while rural female had a mean score of 30.63 with a
standard deviation of 14.42 respectively. For each of the levels, rural mean scores are
higher than the corresponding mean scores in favor of rural male. These indicate that
emotional intelligence and schoollocation have an interaction influence on students’
problem-solving achievement in chemistry.
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Hypothesis 5:There is no significant interaction effect of school location and
emotional intelligence on students’ achievement in quantitative problem- solving in
chemistry?
Table 10: Results of two-way analysis of variance on the interaction effect of school
location and emotional intelligence on students’ achievement in quantitative
problem-solving in chemistry.
Source
of Variation
Type III Sum
of Squares
Df Mean Square F Sig.
Corrected Model 26489.132a
10 2648.913 20.027 0.000
Intercept 108300.339 1 108300.339 818.784 0.000
E.I Levels 9348.565 2 4674.282 35.339 0.000
School Location 1565.194 3 521.731 3.944 0.009
E.I. Levels*School
location
1151.556 5 230.311 1.741 0.125
Error 38755.026 293 132.270
Total 444112.000 304
Corrected Total 65244.158 303
a. R2 = 0.406 (Adjusted R
2 = 0.386)
Results on Table 10 reveal that the interaction influence of sc521.731hool location
and emotional intelligence on students’ achievement in problem-solving in chemistry
was not significant. An F-ratio of 1.741 was obtained with associated probability
value of 0.125. Since the associated probability value (0.125) was greater than the
stipulated level of significance 0.05, the null hypothesis (H07) was accepted.
Consequently, the interaction influence between the school location and emotional
intelligence on students’ achievement in problem-solving in chemistry was not
significant.
93
Research Question 6:What is the interaction influence of school location and
gender on students’ achievement in quantitative problem-solving in chemistry?
Table 11:Mean and Standard deviation of interactive effect of school location and
gender on students’ achievement in quantitative problem- solving in chemistry.
Variables
Gender School Location N Mean Standard
Deviation
Male Urban 96 34.33 14.56
Rural 39 50.77 15.50
Total 135 39.08 16.56
Female Urban 98 32.53 11.41
Rural 71 31.94 13.29
Total 169 32.28 12.20
Total 304 35.30 14.67
The results presented in Table 11 show that the male group from urban and
rural schools had a mean score of 34.33 with a standard deviation of 14.56 and 50.77
with a standard deviation of 15.50 respectively. It also revealed that the female group
from urban and rural schools had a mean score of 32.53 with a standard deviation of
11.41 and 31.94 with a standard deviation of 13.29 respectively. The mean score of
rural male is greater than that of their male counterparts and even greater than that of
the female rural and urban groups. This indicates that gender and school location
appears to have some interaction influence on students’ achievement in problem-
solving in chemistry.
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Hypothesis 6: There is no significant interaction of school location and gender on
students’ achievement in problem-solving in chemistry.
Table 12:Results of two-way analysis of variance between school location and
gender on students’ achievement in quantitative problem-solving in chemistry.
Source of
VariationCorrected
Type III Sum of
Squares
df Mean Square F Sig.
Model 10973.719a 3 3657.906 20.220 0.000
Intercept 370747.463 1 370747.463 2049.444 0.000
Gender 4162.462 1 4162.462 23.010 0.000
School Location 7051.410 1 7051.410 38.979 0.000
Gender*School Location 4801.909 1 4801.909 26.544 0.000
Error 54270.439 300 180.901
Total 444112.000 304
Corrected Total 65244.158 303
a. R 2 = 0.168 (Adjusted R
2= 0.1)
Table 16 shows that the interaction influence between school location and gender
on students’ achievement on problem-solving in chemistry was significant.An F-ratio
of 26.544 was obtained with associated probability value of 0.000. Since the associated
probability value (0.000) was less than the stipulated level of significance 0.05, the
null hypothesis (H07) was rejected. Consequently, the interaction influence between
the school location and emotional intelligence on students’ achievement in problem-
solving in chemistry was significant.
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Research Question 7: What is the interaction influence of emotional intelligence,
gender and school location on students’ achievement in quantitative problem-solving
in Chemistry?
Table 13:Mean and standard deviation of interaction effect of emotional
intelligence, gender and school location on students’ achievement in quantitative
problem-solving in chemistry.
Variables
E.I Levels Gender School Location N Mean Standard
Deviation
High Male Urban 6 66.00 5.51
Rural 8 67.50 6.21
Total 14 66.86 5.75
Female Urban 1 64.00 .
Rural 4 65.00 2.00
Total 5 64.80 1.79
Medium Male Urban 83 32.39 12.72
Rural 31 46.45 14.19
Total 114 36.21 14.50
Female Urban 90 32.13 11.06
Rural 64 30.63 10.31
Total 154 31.51 10.75
Low Male Urban 7 30.29 6.87
Rural - - -
Total 7 30.29 6.87
Female Urban 7 33.14 11.00
Rural 3 16.00 14.42
Total 10 28.00 13.98
Grand Total 304 35.30 14.67
The results presented on Table 13 show that the high emotional intelligence
male from urban schools had a mean achievement score of 66.00 with a standard
deviation of 5.51 while the high emotional intelligence rural male had a mean
achievement score of 67.50 with a standard deviation of 6.21 respectively. High
emotional intelligence female from urban schools had a mean achievement score of
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64.00 with no standard deviation while high emotional intelligence female from rural
schools had a mean score of 65.00 with a standard deviation of 2.00 respectively.
High emotional intelligence rural male had a mean score slightly higher than their
male and female counterparts.
The results also revealed that medium emotional intelligence male from urban
schools had a mean achievement score of 32.39 with a standard deviation of 12.72
while that of rural male had a mean score of 46.45 with a standard deviation of 14.19
respectively. Medium emotional intelligence female from urban school had a mean
achievement score of 32.13 with a standard deviation of 11.06 while that of rural
female had a mean score of 30.63. Medium emotional intelligence male also had a
mean score greater than that of their male and female counterparts.
Furthermore, the results also revealed that low emotional intelligent male from
urban schools had a mean achievement score of 30.29 with a standard deviation of
6.87 while there was no low emotional intelligence male mean score from rural
schools. Also low emotional intelligence female from urban schools had a mean score
of 33.14 with a standard deviation of 11.01 while that of rural female had a mean
score of 16.00 with a standard deviation of 14.42 respectively. For each of the groups,
rural male had mean score greater than their male and female counterparts. This is
indicative that emotional intelligence, gender and school location had some interaction
influence on students’ problem-solving achievement in chemistry.
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Hypothesis 7: There is no significant interaction of emotional intelligence, gender
and school location on students’ achievement in quantitative problem-solving in
chemistry.
Table 14:Results of the two-way analysis of variance between school location and
gender on students’ achievement in quantitative problem-solving in chemistry.
Source of Variation Type III Sum
of Squares
Df Mean Square F Sig.
Corrected Model 26489.132a 10 2648.913 20.027 0.000
Intercept 89587.991 1 89587.991 677.313 0.000
E.I Levels 10175.832 2 5087.916 38.466 0.000
Gender 218.455 1 218.455 1.652 0.200
School Location 11.340 1 11.340 0.086 0.770
E.I Levels * Gender 255.080 2 127.540 0.964 0.382
E.I Levels * School
location 823.490 2 411.745 3.113 0.046
Gender* School
location 160.219 1 160.219 1.211 0.272
E.I Levels *
Gender * School
location
140.904 1 140.904 1.065 0.303
Error 38755.026 293 132.270
Total 444112.000 304
Corrected Total 65244.158 303
a. R Squared = 0.406 (Adjusted R Squared = 0.386)
The results presented above reveals that the interaction influence between
emotional intelligence, gender and school location on students’ achievement in
problem-solving in chemistry was not significant. An F-ratio of 1.065 was obtained
98
with associated probability value of 0.303. Since the associated probability value
(0.303) was greater than the stipulated level of significance 0.05, the null hypothesis
(H07) was accepted. Consequently, the interaction influence between the school
location and emotional intelligence on students’ achievement in problem-solving in
chemistry was not significant.
Summary of Findings
The summary of the findings is presented below:
1. There was a significant difference between the quantitative problem-solving
achievement of students of high, medium and low emotional intelligence. This
indicates that emotional intelligence had some influence on students’
achievement in quantitative problem-solving in chemistry. High emotional
intelligence students achieved better than medium and low emotional
intelligence students.
2. There was a significant difference between chemistry achievement of male and
female students. Gender appears to have some influence on students’
achievement in problem-solving with male students having higher achievement
than the female chemistry students.
3. There was a significant difference between chemistry achievement of students
in rural and urban areas. This shows that school location had some influence on
students’ problem-solving achievement in chemistry with the rural males
achieving more than the urban males, females and rural females.
99
4. The interaction influence of gender and emotional intelligence on students’
achievement in problem- solving in chemistry was not significant. Gender and
emotional intelligence appears to have some interaction influence on students’
achievement in problem-solving in chemistry with rural male achieving better
than their male and female counterparts.
5. The interaction influence of the school location and emotional intelligence on
students’ achievement in quantitative problem-solving was not significant.
School location and emotional intelligence appears to have some interaction
influence on students’ achievement in quantitative problem-solving in
chemistry. Rural male have high emotional intelligence than the rest group.
6. The interaction influence of school location and gender on students’
achievement on problem-solving in chemistry was significant.
7. The interaction influence of emotional intelligence, gender and school location
on students’ achievement in problem-solving in chemistry was not significant.
100
CHAPTER FIVE
DISCUSSION, CONCLUSION AND SUMMARY
This chapter presents the discussion of the findings, conclusion based on the
results, implications of the study, limitations of the study; recommendations,
suggestions for further studies and summary of the study.
Discussion of the Findings of the Study
The discussion of the findings is presented under the following sub-headings:
• Influence of Emotional Intelligence on the Students’ Achievement in
Quantitative Problem-Solving Achievement in Chemistry
• Influence of Gender on the Students’ Problem-Solving Achievement in
Chemistry
• Influence of School Location on Students’ Achievement in Chemical
Quantitative Problem Solving
• Interaction Influence of Gender and Emotional Intelligence
on the Students’ Problem-Solving Achievement in Chemistry
• Interaction Influence of School Location and Emotional Intelligence on
the Students’ Problem-Solving Achievement in Chemistry
• Interaction Influence of School Location and Gender on the Students’
Problem-Solving Achievement in Chemistry
• Interaction Influence of Emotional Intelligence, Gender and School
Location on the Students’ Problem-Solving Achievement in Chemistry
86
101
Influence of Emotional Intelligence on the Students’ Achievement in
Quantitative Problem-Solving Achievement in Chemistry
This study sought to investigate the influence of emotional intelligence on the
students’ achievement in quantitative problem-solving in chemistry. The results
revealed that there was a significant difference between the problem-solving mean
achievement scores of high, medium and low emotional intelligence students in
chemistry. This then implies that emotional intelligence has an influence on students’
problem-solving capabilities in chemistry. It signifies that high emotional intelligence
increases students’ problem-solving capabilities.
Evidence from the results show that high emotional intelligence students had a higher
mean score than those of medium and low emotional intelligence students. This
finding confirms the findings of BandhanaandDarshama (2012) that found out in their
study on the main and interactional effect of emotional intelligence, home
environment and sex on the problem solving ability of adolescents that emotional
intelligence and home environment have a significant impact on problem solving
ability of adolescents. Also the finding confirms the finding of Sharei, Kazemi,
andJafari, (2012) in their study on the relationship between emotional intelligence
skills and metacognitive capabilities with the ability of mathematical problem solving
in the high school students that emotional intelligence was related to mathematical
problem-solving abilities of high school students. Furthermore, this finding
corroborates earlier findings of Maliha (2011), OgundokunandAdeyemo (2010),
Aremu, TellaandTella (2006) Petrides and Frederickson andFurnham (2004) that
102
Emotional intelligence was found to be a significant predictor of academic
achievement. Emotional intelligence competences such as ability to regulate one’s
feeling, problem solving, intrapersonal and interpersonal skills are highly relevant to
academic success. For instance a student who is adept in emotional management could
use such skill to ward off stress and anxiety associated with problem-solving, test
taking and examination. Furthermore, ability to display interpersonal skills may assist
students to seek academic help from teachers, peers and resource persons.
This finding did not confirm the findings of O’Connor and Little (2003),
Bastain, Burns and Nettelbeck (2005) and Maryam (2011) on their studies to assess
the relationship between emotional intelligence and academic achievement on
students and found out that emotional intelligence was not a strong predictor of
academic achievement regardless of instrument used to measure it.
Influence of Gender on the Students’ Problem-Solving Achievement in
Chemistry
The study sought to find out the influence of gender on the students’ problem-
solving achievement in chemistry. The results revealed that gender have influence on
students’ problem-solving in chemistry. Male students were found to achieve
significantly higher than the female students.
This finding is in line with the findings of Salami (2013), Ezeuduand Obi
(2013) and Abdu-Raheem (2012) on gender differences in problem-solving. On
contrary, AbubakarandOguguo (2011), Alao andAbubakar (2010) and Oludipe(2012)
103
found no significant difference. The causes of gender differences can be attributed to
some factors like parental influences, motivation, value orientations, interests,
attitudes, stereotyping and school teacher issues that may influence females’
achievement.
Influence of School Location on Students’ Achievement in Chemical
Quantitative Problem Solving
This study also sought to find out the influence of school location on
students’ achievement in chemical quantitative problem solving. The findings shows
that the mean score of rural group is higher than that of the urban group which
indicates that the rural students achieved better than the students from urban group.
From the finding, it was observed that rural male performed far better than their group
counterparts.
This finding is consistent with the findings of the study done by Okoro (2006)
and Olu (2005) to examine the problem-solving ability of students in biology and the
result indicated that male students in the rural schools out performed those in urban
schools. However, their study indicated that the males in the urban schools performed
better than the females in the rural schools.
The finding of this study is contrary to the findings of Ezeuduand Obi (2013),
Okoi (2003) and Umanah (2008) on their study that urban students performed better
than their rural counterparts. The finding is also contrary to the findings of
NbinaandObomanu (2011), Ubong (2004) and,Koloand Ibrahim (2003) on the effect
104
of problem-solving of secondary school students in urban and rural location. Their
studies indicated that there was no significant difference in the performance of both
male and female students in urban and rural schools. Rural schools have made a vast
improvement in problem solving achievement recently. This shows that federal
government has improved on the efforts to improve education system in rural areas.
Interaction Influence of Gender and Emotional Intelligence on the
Students’ Problem-Solving Achievement in Chemistry
Another objective of this study was to find out the interaction influence of
gender and emotional intelligence on the students’ problem-solving achievement in
chemistry. The result revealed that the interaction influence of gender and emotional
intelligence on students’ achievement in problem-solving in chemistry was not
significant. This indicates that when emotional intelligence and gender are considered
together, it does not affect the problem-solving of chemistry students.
Looking at the mean score of male and female and their emotional intelligence
levels, male students achieved slightly higher than females.it can be concluded that
emotional intelligence and gender seems to have an interaction influence on students’
problem-solving capabilities but it is not significant. The finding converged the
finding of BandhanaandDarshana Sharma (2012) 0n their study on influence of
emotional intelligence study to ascertain the main and interactional effect of emotional
intelligence, home environment and sex on the problem solving ability of adolescents.
The findings revealed that the interaction influence of emotional intelligence and sex
was not significant even at 0.05 level of significance. It then means that emotional
105
intelligence and gender is independent of each other with achievement in quantitative
problem-solving.
Interaction Influence of School Location and Emotional Intelligence on the
Students’ Problem-Solving Achievement in Chemistry
Findings of the study have also revealed that there is no significant
interaction influence of school location and emotional intelligence on students’
achievement in problem-solving in chemistry. This is indicative of the fact that
whether school location and emotional intelligence varies or not, students’
problem-solving achievement in chemistry remains the same.
Interaction Influence of School Location and Gender on the Students’
Problem-Solving Achievement in Chemistry
Another objective of this study was to find out the interaction influence of
school location and gender on the students’ problem-solving achievement in
chemistry. The result showed that there was significant interaction effect of school
location and gender on students’ problem-solving in chemistry. This is also indicative
that when school location and gender varies, the students’ problem-solving
achievement in chemistry also varies.
Interaction Influence of Emotional Intelligence, Gender and School
Location on the Students’ Problem-Solving Achievement in Chemistry
This objective was to find out the interaction influence of emotional
intelligence, gender and school location on the students’ problem-solving achievement
106
in chemistry. The findings of the study revealed that the interaction influence of
emotional intelligence, gender and school location was not significant. This signifies
that when emotional intelligence, gender and school location is considered together,
the influence is not significant for students’ problem-solving achievement in
chemistry in Nnewi education zone.
Conclusion Reached from the Findings of the Study
The study set out to investigate the influence of emotional intelligence,
gender and school location on students’ problem-solving achievement in chemistry in
Nnewi education zone, Anambra state. Based on the findings of this study, the
following conclusions have been made:
It can be concluded that apart from the cognitive factors, non –cognitive
factors including emotional intelligence affect students’ problem-solving achievement
in chemistry. Since we cannot manipulate IQ of the students and can improve the
emotional intelligence of students by training, then manipulating the emotional
intelligence of students may be a way of improving the problem-solving capability of
chemistry students.
The results of the study also led to the conclusion that gender influenced
students’ achievement in problem-solving in chemistry with male students achieving
better than the female chemistry students. These differences in achievements may be
attributed to gender stereotyping which encourages male and female students to show
interest in subjects relevant and related to the roles expected of them in the society.
107
The results of this study indicate that chemistry students from rural schools in
Nnewi education zone achieved relatively better in problem-solving in chemistry than
those from urban schools. It was seen from the background of the study that Federal
and State Governments in Nigeria had been making concerted efforts to improve the
educational system in the rural areas using certain education management
commissions to ensure that qualified specialist teachers and facilities are sent to rural
schools.
The interaction effect of gender and emotional intelligence on students’
achievement in problem- solving in chemistry was not significant although gender and
emotional intelligence appears to have some interaction influence on students’
achievement in problem-solving in chemistry with rural male achieving better than
their male and female counterparts.
The interaction effect of the school location and emotional intelligence on
students’ achievement in problem-solving in chemistry was not significant. School
location and emotional intelligence appears to have some interaction influence on
students’ achievement in problem-solving in chemistry. Rural male have high
emotional intelligence than the rest group.
The interaction effect of school location and gender on students’ achievement
on problem-solving in chemistry was significant. This implies that when gender and
school location are considered that students’ achievement in chemical problem
solving increases.
108
The interaction effect of emotional intelligence, gender and school location on
students’ achievement in problem-solving in chemistry was not significant.
Educational Implications
The results of this study have some implications to students and teachers. For
the fact that emotional intelligence is a strong predictor of academic achievement, it is
necessary for the teachers to take emotional intelligence in to consideration in
planning problem-solving instructions. Teachers should try to understand how
students’ emotional intelligence affects problem solving and to make judgment when
emotions are interfering with or supporting their problem solving skills. Teachers
should consider and understand the five aspects of emotional intelligence and to
develop strategies to help themselves and their students become aware of and manage
their emotions. Engaging students in training will help them build their emotional
intelligence and this will improve their achievement in problem solving thereby
increasing their problem solving skills.
To students, the skills acquired could aid them to solve problems they
encounter daily. The skills acquired could enable the students adjust appropriately to
ever dynamic global trend of events and unemployment.
Limitations of the study
The result of the present study was based on senior secondary two chemistry
students (SS2) in Nnewi education zone in Anambra state only. It might not be able to
generalize to whole population of Anambra state chemistry students. This is due to the
109
fact that Nnewi education zone is not a representative of education zones in Anambra
state or the entire country.
Recommendations
Based on the findings, it was recommended that government should organize
seminar and workshop for teachers to enable them understand the concept of
emotional intelligence in order to build their own emotions and that of their students.
Teaching of chemistry should encourage gender equality and this should encourage
the use of techniques that build confidence in both males and females alike. Teachers
should be gender sensitive to encourage both male and female students alike in
developing scientific aptitude.
Suggestions for Further Studies
• This study is limited to the achievement in chemical problem solving in Nnewi
Education zone, a similar study could be carried out in different education zone
of Anambra State or may be to cover the entire state and the results compare to
this results.
• Another investigation could be carried out on the influence of emotional
intelligence on academic achievement in chemistry.
• A comparative study could be carried out on the influence of emotional
intelligence on cooperative learning in chemistry.
110
Summary of the Study
This study focused on the investigation of influence of emotional intelligence
on students’ achievement in chemical problem solving in Nnewi Education zone. The
main purpose was to find out the influence of emotional intelligence, gender and
school location on students’ achievement in chemical quantitative problem solving.
Literature related to the study was reviewed. It was discovered that investigation of
this nature has not been carried out in Nnewi Education zone of Anambra state from
the existing literature and it created a gap that this study seeks to fill. The study
adopted an expost-facto design. Seven research questions and seven hypotheses were
formulated to guide the study. The population of the study was 757 SS2 students
from 49 government secondary schools in Nnewi Education zone. The sample for the
study was 304 SS2 chemistry students made up of 135 males and 169 females) drawn
randomly by stratified sampling techniques from 49 secondary schools in Nnewi
Education zone.
An emotional quotient inventory (EQi) and a chemistry achievement test
(CAT) were used for data collection. The instruments were administered on the
respondents by the researcher herself with the aid of one chemistry teacher in each
school under study. Mean and standard deviation of scores were used to answer the
research questions, and the null hypotheses were analyzed using one way ANOVA
and univariate ANOVA with the help of the Statistical Package for Social Sciences
(SPSS) Version 20.0 (SPSS Inc. 2013). All the null hypotheses were tested at 0.05
level of significant ( .
111
Chapter five presented the discussions according to the ten hypotheses that
guided the study and conclusions were made from the results of the investigation. It
was concluded that emotional intelligence influences students’ achievement in
chemical problem, male students achieved relatively better than female students and
that students from rural schools achieved relatively better than students from urban
schools. Some recommendations were made to Government and teachers, the
education implications that stem from the study were also highlighted by the
researcher. Lastly, the limitations and suggestions for further study based on the
results of the study were proposed.
112
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APPENDICES
APPENDIX A
CHEMISTRY ACHIEVEMENT TEST
NAME:…………………………………………… CLASS:…….…………
GENDER: MALE ( ) FEMALE ( ) AGE: …………….
SCHOOL:………………………………………………………………………..
Answer all the questions.
Each question is followed by five letter options A-E. Find out the correct option for
each questions and circle against the letter option.
1. Avogadro’s number is --------------
A. 6.02 X 1023
B.6 X 1023
C. 602 X 1023
D. 6.0X 1023
E. 6.2 X 1023
2. From ideal gas equation, PV= nRT. The unit of n is ------------------
A. atmdm3
B. atmdm3 K
-1 C. moles D. K
-1mol
-1 E. mol
-1dm
3
3. 120cm3 of hydrogen were sparked with 60cm
3 of oxygen at 110
0C.What was
the volume of steam produced? The equation for the reaction is 2H2(g) +O2(g )
�2H2O(g
A.30cm3 B.60cm
3 C.90cm
3 D.120cm
3 E.180cm
3
4. If the volume of a given mass of gas at 298K and a pressure of 205.2 X 103Nm
-
2 is 2.12dm
3. What is the volume of the gas at s.t.p? (Standard pressure = 101.3
X103Nm
2, standard temperature = 273K).
A.39.3dm3B.22.4dm
3 C.4.93dm
3 D.3.93dm
3 E.0.393dm
3
5. What volume of distilled water should be added to 400cm3 of 0.2moldm
-3
H2SO4 to obtain 0.20moldm-3
of solution?
A.600cm3 B.800cm
3 C.1000cm
3 D.3600cm
3 E.4000cm
3
6. One faraday is equal to …………….
A. 9650 Coulombs B. 9560 Coulombs C. 95600 Coulombs D.96500 Coulombs
E. 9500 Coulombs
7. The quantity of electricity required to discharge 1 mole of univalent ion is……
A. 9600C B. 48250C C. 96500C D. 193000 E. 30000C
8. The quantity of electricity required to discharge 1 mole of divalent ion is …….
A. 96000C B. 482500C C. 965000C D. 193000 E. 30000C
124
9. Calculate the quantity of electricity passed when 0.4A flows for 1 hour 20
minutesthrough an electrolytic cell.
A.4800C B. 3800C C. 1920C D.32C E. 480C
10. What mass of copper would be formed when a current of 10.0A is passed
through a solution of CuSO4for 1hour? (Cu=63.5,1 F=96500C) A.
5.9g B.11.8g C. 23.8g D. 47.3g E.6.5g
11. How many faradays of electricity are required to liberate 9g of aluminum?
(Al=27) A. 0.3 B. 0.1 C. 1.0 D. 2.7 E. 3.0
12. What amount of copper will be deposited if a current of 10A was passed
through a solution of copper (ii) salt for 965 seconds? (1F=96500C)
A. 0.005mole B. 0.025mole C.0.05mole D.1.0mole E.
1.05mole
13. During the electrolysis of dilute tetraoxosulphate (vi) acid solution 0.05 mole
of electrons were passed. What volume of gas was produced at the anode?
(Gaseous molar volume = 22.4dm3 at s.t.p).
A. 0.224dm3 B. 0.280dm
3 C. 0.560dm
3 D. 2.24dm
3 E.22.4dm
3
14. What is the quantity of electricity produced when a current of 0.5A is passed
for 5 hours 45minutes? (1F=96500C)
A. 0.11F B. 0.12F C. 0.22F D. 1.1F
E. 2.2F
15. A current of 4.0A was passed through copper (ii) tetraoxosulphate (vi) solution
for 1 hour using copper electrodes. What was the mass of copper deposited?
(Cu=64, 1F=96500C)
A. 3.2g B. 4.8g C. 6.4g D.48g
E. 96g
16. What is the S.I unit for molar mass?
A.moldm-3
B. mol-1
C. gmol-1
D. mole E. dm-3
17. From this balanced equation, what is the value of x Pb (NO3)2 (aq) + XNaCl(aq)
�PbCl2(s) + 2NaNO3(aq) ?
A. 1 B. 2 C. 3 D. 5 E. 4
18. Determine the value of x from this balanced equation; Mg(s) + XHCl(aq) �
MgCl2(aq) + H2(g)
125
A. 4 B.5 C.3 D.2 E.1
19. 14.8g of a salt (Z) dissolved in 250cm3 of distilled water gives a concentration
of 0.80moldm-3
. Calculate the molar mass of the salt (Z).
A. 13.5gmol-1
B. 18.5gmol-1
C.47.4gmol-1
D.74.0gmol-1
E.14.6gmol-1
20. 9.60g of a gas X occupies the same at 0.30g of hydrogen under the same
conditions. Calculate the molar mass of X (H=1).
A. 8gmol-1
B. 16gmol-1
C. 32gmol-1
D. 64gmol-1
E. 10gmol-1
21. The mass of 800cm3 of a gas X at s.t.p is 1.0g. What is the molar mass of X?
(1mole of a gas at s.t.p occupies 22.4dm3)
A. 18.0gmol-1
B. 22.4gmol-1
C. 28.0gmol-1
D. 36.0gmol-1
E.20.0gmol-1
22. How many mole of AgNO3 are there in 500cm3 of 0.01M AgNO3 solution?
A. 0.005mole B. 0.05mole C. 0.5mole D. 1mole E. 5mole
23. What quantity of electrons (in mole) is lost when one mole of iron (ii) ions is
oxidized to iron (iii) ions?
A. 5mole B. 4mole C. 3mole D. 2mole E. 1mole
24. If 200cm3 of a gas at s.t.p has a mass of 0.26g. What is its molar mass? (Molar
volume of a gas at s.t.p =22.4dm3)
A.300g B.200g C.150g D.30g E.15g
25. What amount of copper is deposited when 13.0g of zinc reacts with excess
copper (ii) tetraoxosulphate (iv) solution according to the following equation?
Zn(s) + CuSO4(aq) �ZnSO4(aq) + Cu(s) (Cu=63.5, Zn=65)
A. 0.1mole B. 0.2mole C.0.3mole D. 0.4mole E. 0.5mole
126
APPENDIX B
EMOTIONAL QUOTIENT INVENTORY (EQI)
NAME:………………………………….. CLASS:…….. GENDER: MALE ( )FEMALE (
)AGE:…… SCHOOL:………………………………………………
This inventory has 5-point self-rating response format according to the following scale:
1- never 2- seldom 3- sometimes 4- often 5- very often
To the number of each item of EQI are given 5 options ranging from 1 to 5. You have to tick
(√√√√) one of the options for each statement according to your degree of agreement. The key of
response format is given with scale.
Example.
Item
No.
Statements Never Seldom Sometimes Often Very
Often
1. Am always happy for being a science student √√√√
Now tick the following items as it appeals to you.
Item
No
Statements Never Seldom Sometimes Often Very
Often
1 It’s hard for me to describe my feelings when
solving chemistry problems.
2 I am aware of the way I feel.
3 It’s hard for me to share my deep feelings
with others.
4 I am unable to express ideas when solving
chemistry problems.
5 When I am angry with classmates I can tell
them about it.
6 It’s difficult for me to stand up for my rights
in learning situation.
7 It’s fairly easy for me to tell people what I
think.
8 I lack self –confidence in chemistry learning
situations.
9 I have good self-respect in chemistry learning
situations.
10 I feel sure of myself in most situations.
11 It’s hard for me to take decisions on my own.
12 When working with classmates, I tend to rely
more on their ideas than my own.
127
13 I am more of a follower than a leader in
chemistry learning situations.
14 I seem to need classmates more than they
need me.
15 I try to make my life as meaningful as I can.
16 I really don’t know what I am good on
chemistry learning situations.
17 I try to continue and develop those things that
I enjoy.
18 I don’t have a good idea of what I want to do
in life.
19 I am unable to understand the way other
people feel.
20 My classmates can tell me intimate things
about themselves.
21 I avoid hurting classmates’ feelings.
22 I think it’s important to be a law abiding
citizen.
23 I don’t get along well with classmates.
24 It’s easy for me to make friends with
classmates.
25 I have good relations with classmates.
26 I don’t keep in touch with classmates.
27 It’s a problem to me controlling anger.
28 Classmates ask me to lower my voice in
discussion.
29 I am impatient in learning situations.
30 I know how to keep calm in difficult
situation.
31 I don’t hold up well under stress.
32 I like to get an overview of a problem before
trying to solve it.
33 It’s hard for me to decide on the best solution
when solving problems.
34 When trying to solve a problem, the first
thing I do is to stop and think.
35 I get carried away with my imaginations and
fantasies.
36 I have had strange experience that can’t be
explained.
37 I tend to exaggerate.
38 It’s hard for me to keep things in the right
perspective.
39 It’s hard for me to begin new things.
40 It’s hard for me to make adjustments in
general.
41 I am able to change old habits.
42 It’s hard for me to change my opinion about
128
things.
43 My classmates feel it is fun to be with me.
44 It’s hard for me to enjoy life.
45 It’s hard for me to smile.
46 I am fairly a cheerful person.
47 Before beginning something new, I usually
feel that I will fail.
48 I am optimistic about most things I do.
49 I believe in my ability to handle most
upsetting problems.
129
APPENDIX C
TABLE OF SPECIFICATION FOR CHEMISTRY ACHIEVEMENT TEST
Content Knowledge Comprehension Application Total
Electrolysis 1 2 7 10
Mass 1 2 7 10
Volume 1 1 3 5
Total Questions 3 5 17 25
130
APENDIX D
MARKING GUIDE FOR CHEMISTRY ACHIEVEMENT TEST
1. A
2. C
3. D
4. D
5. C
6. D
7. C
8. D
9. C
10. B
11. C
12. C
13. C
14. A
15. B
16. C
17. B
18. D
19. D
20. D
21. C
22. A
23. E
24. D
25. B
131
APPENDIX E
VALIDITY
Factor Analysis
Rotated Component Matrixa
Component
1 2 3 4 5
Item 1 .587 -.514 -.126 .196 -.422
Item 2 .201 .719 .020 -.234 -.220
Item 3 .202 .037 -.015 -.253 -.541
132
Item 4 -.034 -.013 .084 .473 .150
Item 5 .372 .470 .067 -.300 .070
Item 6 .296 .265 .027 .365 -.146
Item 7 .655 .212 .223 .211 -.244
Item 8 -.169 .298 .184 .562 -.390
Item 9 .410 .168 .421 -.029 .584
Item 10 -.112 -.042 -.678 .320 -.039
Item 11 -.162 .021 -.573 .062 -.269
Item 12 .035 .716 .108 .271 .226
Item 13 .133 -.275 .000 .718 -.052
Item 14 .627 -.038 .085 .231 .172
Item 15 .050 .006 .319 .408 -.147
Item 16 .072 .159 -.305 .566 .090
Item 17 -.134 .317 -.023 .086 .068
Item 18 .003 .038 .176 .879 -.038
Item 19 -.111 .477 -.325 .302 -.206
Item 20 .521 -.219 .232 -.237 .393
Item 21 .083 -.091 .060 .006 .848
Item 22 .048 .196 -.754 -.108 -.325
Item 23 .209 .014 .433 -.307 -.432
Item 24 -.227 .665 -.045 -.122 -.047
Item 25 -.724 .226 -.105 .100 -.032
133
Item 26 -.607 .266 -.368 -.244 .038
Item 27 -.746 .293 -.209 -.117 -.102
Item 28 -.441 .572 .118 -.100 -.087
Item 29 .548 -.285 .055 -.046 -.188
Item 30 -.720 .109 .465 .116 -.028
Item 31 -.403 .476 .349 -.143 .145
Item 32 .003 .006 .556 .451 -.002
Item 33 .551 -.352 -.244 .214 -.022
Item 34 .421 .083 -.056 .064 .712
Item 35 .159 -.715 .222 -.185 .128
Item 36 -.107 -.313 .230 .046 .464
Item 37 .171 -.417 -.722 .072 .276
Item 38 .011 -.179 .230 .041 -.184
Item 39 -.165 .478 .068 .464 .062
Item 40 .655 .084 .033 -.132 -.266
Item 41 -.086 .076 -.077 .169 -.785
Item 42 0-.030 .261 .014 .486 -.496
Item 43 .373 -.039 -.242 .469 .227
Item 44 .154 .584 -.098 .298 -.220
Item 45 .643 -.039 -.223 .293 .213
Item 46 .339 -.392 .493 -.179 -.450
Item 47 -.039 .031 .338 .173 -.138
134
Item 48 -.221 .170 .591 .239 .425
Item 49 .092 -.022 .220 .693 .251
Item 50 -.094 .059 .442 -.166 .418
Item 51 -.273 -.008 .403 -.074 -.562
Item 52 -.378 .097 .200 .028 .575
Item 53 -.555 -.057 .429 .337 -.067
Item 54 .282 -.034 .659 .137 .103
Item 55 .689 .345 .048 -.076 .238
Item 56 -.055 .506 .034 -.210 -.047
Item 57 -.035 .110 .110 -.717 .153
Item 58 -.058 -.327 .661 .242 .130
Item 59 -.436 .540 -.326 .206 -.119
Item 60 .121 .482 .026 .520 .294
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
Selected Items
Factor 1 1 7 9 14 20 29 33 34 40 43 45 46
Factor 2 2 5 12 17 19 24 28 31 39 44 55 56 59 60
Factor 3 23 30 32 47 48 50 51 53 54 58
Factor 4 4 6 8 10 13 15 16 18 42 49
135
Factor 5 21 36 52
APPENDIX F
RELIABILITY COEFFICIENT FOR EQi AND CAT
SCALE: RELIABILITY COEFFICIENT FOR EQi
Case Processing Summary
N %
Cases
Valid 20 100.0
Excluded
a
0.0 0.0
Total 20 100.0
a. Listwise deletion based on all
variables in the procedure.
Reliability Statistics
Cronbach's
Alpha
N of
Items
.757 49
SCALE: KUDDER-RICHARDSON (K-R)
rt =
Σ
−− 2
1
11 S
qp
k
k ii
136
The symbols are defined as follow:
K = number of items
P = proportion who scored the item correctly (i.e proportion that passed)
q = proportion who scored the item wrongly (i.e proportion that failed)
St2 = variance of the whole instrument
pq = variance of a single item
Σpq = sum of the variances of the individual items
rt =
−−
.54.15
43.41
120
20
rt = { }2851.0119
20−
rt =1.053{ }715.0
rt = 0.75
137
APPENDIX G
POPULATION OF S.S 2 CHEMISTRY STUDENTS IN NNEWI
EDUCATION ZONE
S/NO Names of Schools Number
of Male
Number
of Female
Total
1 Boys’ Secondary School Oraifite 18 - 18
2 Community Secondary School Ichi 6 4 10
3 Community Secondary School Ihembosi 4 2 6
4 Community Secondary school Ozubulu 3 9 12
5 Union Secondary School Ichi 5 4 9
6 Girls’ Secondary School Oraifite - 22 22
7 Girls’ Secondary School Ozubulu - 35 35
8 Zixton Secondary School Ozubulu 5 2 7
9 Abort Girls’ Secondary School Ihiala - 28 28
10 Abort Boys’ Secondary School Ihiala 12 - 12
11 Community High School UmuomaUli 7 11 18
12 Community Secondary School Azia 2 4 6
13 Community Secondary School Isseke 8 4 12
14 Government Technical College Ihiala 6 4 10
15 Community Secondary School Lilu 2 6 8
16 Community Secondary School Mbosi 2 4 6
17 Community Secondary School Orsumolu 4 8 12
18 Community Secondary School Amorka 2 2 4
19 Girls’ Secondary School Uli - 8 8
20 Okija Grammar School Okija 12 4 16
21 Saint Anthony Secondary School Azia 6 6 12
22 Saint Jude Secondary School Ihiala 8 10 18
138
23 Uli High school Uli. 8 - 8
24 Union Secondary School Okija. 9 5 14
25 Akoboezem Community Secondary
School Nnewi.
22 10 32
26 Community Secondary School Nnewichi 4 8 12
27 Girls’ Secondary School Nnewi - 42 42
28 Maria Regina Model Comprehensive
Secondary School Nnewi
- 102 102
29 Nigerian Science and Technology College
Nnewi
21 27 48
30 Nnewi High School Nnewi 46 - 46
31 Okongwu Memorial Grammar School
Nnewi
53 - 53
32 Women Education Center Nnewichi - 10 10
33 Boys’ Secondary School Osumenyi 8 3 5
34 Union Secondary School Amichi 2 4 6
35 Boys’ Secondary school Unubi 6 2 8
36 Community Secondary School Ukpor 2 6 8
37 Community High School Ezinifite 4 2 6
38 Community Secondary School Akwaihedi 2 3 5
39 Community secondary School Azigbo 2 3 5
40 Community secondary School Ebenator 4 6 10
41 Community secondary School Ekwulumili 3 2 5
42 Community secondary School Amichi 1 2 3
43 AworEzimuzo C. S .S Ezinifite 2 2 4
44 Community High School Osumenyi 3 5 8
45 Premier secondary School Unubi 1 - 1
46 Government technical College Utuh 2 - 2
47 Ukpor High School Ukpor 6 12 18
139
48 Utuh High School Utuh 1 3 4
49 CommunitySecondarySchoolOgwuaniocha 2 4 6
140
APPENDIX H
SAMPLE SIZE OF S.S 2 CHEMISTRY STUDENTS
Urban Schools
S/no Names of Schools No. of
males
No. of
females
Total
1 Okongwu Memorial Grammar School
Nnewi
53 - 53
2 Maria Regina Model Comprehensive
Secondary School Nnewi
- 102 102
3 Nnewi High School Nnewi 46 - 46
Rural Schools
S/N
o
Names of Schools
No. of
males
No. of
female
Total
1 Girls’ Secondary School Oraifite - 22 22
2 Uli High School Uli 8 - 8
3 Government Technical College Ihiala 6 4 10
4 Union Secondary School Ichi 9 5 4
5 Community Secondary School Ebenator 4 6 10
6 Boys Secondary School Oraifite 18 - 18
7 Girls Secondary School Ozubulu - 35 35
8 Boys Secondary School Osumenyi 3 5 8
141
APPENDIX I
LETTER FOR VALIDATION OF INSTRUMENTS
Department of Science Education,
Faculty of Education,
University of Nigeria, Nsukka.
Dear Sir/Madam,
VALIDATION OF INSTRUMENT
The researcher is a postgraduate (M.Ed) chemistry student of the above
institution under the supervision of Dr. F.O. Ezeudu. Your assistance is required to
validate the instruments on a proposed study: Influence of Emotional Intelligence on
secondary school students’ problems-solving capabilities in chemistry.
Please critically examine the attached instrument regarding the research title,
research purpose, research questions/hypotheses and make general comments on the
suitability of the instrument for the proposed study.
Thanks for anticipated cooperation.
Chukwuka Constance Uju
PG/M.Ed/12/62700.
142
APPENDIX J
Descriptives
CATSCORES
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
HIGH 19 66.3158 5.04483 1.15736 63.8843 68.7473 60.00 76.00
MEDIUM 268 33.5075 12.67499 .77425 31.9831 35.0319 8.00 68.00
LOW 17 28.9412 11.36041 2.75530 23.1002 34.7822 .00 44.00
Total 304 35.3026 14.67403 .84161 33.6465 36.9588 .00 76.00
One-way ANOVA
CATSCORES
Sum of Squares df Mean Square F Sig.
Between Groups 19826.126 2 9913.063 65.697 .000
Within Groups 45418.032 301 150.890
Total 65244.158 303
Descriptives
CATSCORES
N Mean Std.
Deviation
Std.
Error
95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
MALE 135 39.0815 16.56424 1.42562 36.2618 41.9011 16.00 76.00
FEMALE 169 32.2840 12.20128 .93856 30.4311 34.1369 .00 68.00
Total 304 35.3026 14.67403 .84161 33.6465 36.9588 .00 76.00
One-way ANOVA
CATSCORES
Sum of Squares Df Mean Square F Sig.
Between Groups 3467.687 1 3467.687 16.951 .000
Within Groups 61776.471 302 204.558
Total 65244.158 303
143
Descriptive
CATCATSCORES
N Mean Std. Deviation Std. Error 95% Confidence Interval for
Mean
Minimum Maximum
Lower Bound Upper Bound
urban male 96 34.3333 14.56359 1.48639 31.3825 37.2842 16.00 76.00
rural male 39 50.7692 15.49611 2.48136 45.7460 55.7925 20.00 76.00
urban female 98 32.5306 11.41007 1.15259 30.2430 34.8182 8.00 64.00
rural female 71 31.9437 13.29220 1.57749 28.7975 35.0899 .00 68.00
Total 304 35.3026 14.67403 .84161 33.6465 36.9588 .00 76.00
One-way ANOVA
CATSCORES
Sum of Squares Df Mean Square F Sig.
Between Groups 10973.719 3 3657.906 20.220 .000
Within Groups 54270.439 300 180.901
Total 65244.158 303
144
Univariate Analysis of Variance
Between-Subjects Factors
Value Label N
E.ILEVELS
1.00 HIGH 19
2.00 MEDIUM 268
3.00 LOW 17
GENDER 4.00 MALE 135
5.00 FEMALE 169
Descriptive Statistics
Dependent Variable: CATSCORES
E.ILEVELS GENDER Mean Std. Deviation N
HIGH
MALE 66.8571 5.74934 14
FEMALE 64.8000 1.78885 5
Total 66.3158 5.04483 19
MEDIUM
MALE 36.2105 14.50387 114
FEMALE 31.5065 10.74800 154
Total 33.5075 12.67499 268
LOW
MALE 30.2857 6.87300 7
FEMALE 28.0000 13.98412 10
Total 28.9412 11.36041 17
Total
MALE 39.0815 16.56424 135
FEMALE 32.2840 12.20128 169
Total 35.3026 14.67403 304
Tests of Between-Subjects Effects
145
Tests of Between-Subjects Effects
Dependent Variable: CATSCORES
Source Type III Sum of
Squares
df Mean Square F Sig. Partial Eta
Squared
Corrected Model 21312.774a 5 4262.555 28.914 .000 .327
Intercept 125367.705 1 125367.705 850.407 .000 .741
E.ILEVELS 14900.607 2 7450.303 50.538 .000 .253
GENDER 154.558 1 154.558 1.048 .307 .004
E.ILEVELS *
GENDER 44.594 2 22.297 .151 .860 .001
Error 43931.384 298 147.421
Total 444112.000 304
Corrected Total 65244.158 303
a. R Squared = .327 (Adjusted R Squared = .315)
Univariate Analysis of Variance
Between-Subjects Factors
Value Label N
E.ILEVELS
1.00 HIGH 19
2.00 MEDIUM 268
3.00 LOW 17
SCHOOLLOCATION
6.00 urban male 96
7.00 rural male 39
8.00 urban female 98
9.00 rural female 71
Descriptive Statistics
Dependent Variable: CATSCORES
E.ILEVELS SCHOOLLOCATION Mean Std. Deviation N
HIGH
urban male 66.0000 5.51362 6
rural male 67.5000 6.21059 8
urban female 64.0000 . 1
rural female 65.0000 2.00000 4
Total 66.3158 5.04483 19
MEDIUM urban male 32.3855 12.72009 83
146
rural male 46.4516 14.18647 31
urban female 32.1333 11.06386 90
rural female 30.6250 10.30873 64
Total 33.5075 12.67499 268
LOW
urban male 30.2857 6.87300 7
urban female 33.1429 11.00649 7
rural female 16.0000 14.42221 3
Total 28.9412 11.36041 17
Total
urban male 34.3333 14.56359 96
rural male 50.7692 15.49611 39
urban female 32.5306 11.41007 98
rural female 31.9437 13.29220 71
Total 35.3026 14.67403 304
Tests of Between-Subjects Effects
Dependent Variable: CATSCORES
Source Type III Sum of
Squares
df Mean Square F Sig. Partial Eta
Squared
Corrected Model 26489.132a 10 2648.913 20.027 .000 .406
Intercept 108300.339 1 108300.339 818.784 .000 .736
E.ILEVELS 9348.565 2 4674.282 35.339 .000 .194
SCHOOLLOCATION 1565.194 3 521.731 3.944 .009 .039
E.ILEVELS *
SCHOOLLOCATION 1151.556 5 230.311 1.741 .125 .029
Error 38755.026 293 132.270
Total 444112.000 304
Corrected Total 65244.158 303
a. R Squared = .406 (Adjusted R Squared = .386)
Univariate Analysis of Variance
Between-Subjects Factors
Value Label N
SCHOOLLOCATION 6.00 Urban 194
7.00 Rural 110
147
GENDER 4.00 MALE 135
5.00 FEMALE 169
Descriptive Statistics
Dependent Variable: CATSCORES
SCHOOLLOCATION GENDER Mean Std. Deviation N
urban
MALE 34.3333 14.56359 96
FEMALE 32.5306 11.41007 98
Total 33.4227 13.06329 194
rural
MALE 50.7692 15.49611 39
FEMALE 31.9437 13.29220 71
Total 38.6182 16.70411 110
Total
MALE 39.0815 16.56424 135
FEMALE 32.2840 12.20128 169
Total 35.3026 14.67403 304
Tests of Between-Subjects Effects
Dependent Variable: CATSCORES
Source Type III Sum of
Squares
df Mean Square F Sig.
Corrected Model 10973.719a 3 3657.906 20.220 .000
Intercept 370747.463 1 370747.463 2049.444 .000
SCHOOLLOCATION 4162.462 1 4162.462 23.010 .000
GENDER 7051.410 1 7051.410 38.979 .000
SCHOOLLOCATION *
GENDER 4801.909 1 4801.909 26.544 .000
Error 54270.439 300 180.901
Total 444112.000 304
Corrected Total 65244.158 303
a. R Squared = .168 (Adjusted R Squared = .160)
148
Univariate Analysis of Variance
Between-Subjects Factors
Value Label N
E.ILEVELS
1.00 HIGH 19
2.00 MEDIUM 268
3.00 LOW 17
GENDER 4.00 MALE 135
5.00 FEMALE 169
SCHOOLLOCATION 6.00 Urban 194
7.00 Rural 110
Descriptive Statistics
Dependent Variable: CATSCORES
E.ILEVELS GENDER SCHOOLLOCATION Mean Std. Deviation N
HIGH
MALE
Urban 66.0000 5.51362 6
Rural 67.5000 6.21059 8
Total 66.8571 5.74934 14
FEMALE
Urban 64.0000 . 1
Rural 65.0000 2.00000 4
Total 64.8000 1.78885 5
Total
Urban 65.7143 5.08967 7
Rural 66.6667 5.21071 12
Total 66.3158 5.04483 19
MEDIUM
MALE
Urban 32.3855 12.72009 83
Rural 46.4516 14.18647 31
Total 36.2105 14.50387 114
FEMALE
Urban 32.1333 11.06386 90
Rural 30.6250 10.30873 64
Total 31.5065 10.74800 154
Total
Urban 32.2543 11.85298 173
Rural 35.7895 13.82404 95
Total 33.5075 12.67499 268
LOW
MALE Urban 30.2857 6.87300 7
Total 30.2857 6.87300 7
FEMALE Urban 33.1429 11.00649 7
Rural 16.0000 14.42221 3
149
Total 28.0000 13.98412 10
Total
Urban 31.7143 8.93936 14
Rural 16.0000 14.42221 3
Total 28.9412 11.36041 17
Total
MALE
Urban 34.3333 14.56359 96
Rural 50.7692 15.49611 39
Total 39.0815 16.56424 135
FEMALE
Urban 32.5306 11.41007 98
Rural 31.9437 13.29220 71
Total 32.2840 12.20128 169
Total
Urban 33.4227 13.06329 194
Rural 38.6182 16.70411 110
Total 35.3026 14.67403 304
Tests of Between-Subjects Effects
Dependent Variable: CATSCORES
Source Type III Sum of
Squares
df Mean Square F Sig.
Corrected Model 26489.132a 10 2648.913 20.027 .000
Intercept 89587.991 1 89587.991 677.313 .000
E.ILEVELS 10175.832 2 5087.916 38.466 .000
GENDER 218.455 1 218.455 1.652 .200
SCHOOLLOCATION 11.340 1 11.340 .086 .770
E.ILEVELS * GENDER 255.080 2 127.540 .964 .382
E.ILEVELS *
SCHOOLLOCATION 823.490 2 411.745 3.113 .046
GENDER *
SCHOOLLOCATION 160.219 1 160.219 1.211 .272
E.ILEVELS * GENDER *
SCHOOLLOCATION 140.904 1 140.904 1.065 .303
Error 38755.026 293 132.270
Total 444112.000 304
Corrected Total 65244.158 303
a. R Squared = .406 (Adjusted R Squared = .386)