STUDENTS’ ADVERSITY QUOTIENT AND RELATED FACTORS AS PREDICTORS
OF ACADEMIC ACHIEVEMENT IN THE WEST AFRICAN SENIOR SCHOOL
CERTIFICATE EXAMINATION IN SOUTHWESTERN NIGERIA
______________________________
BAKARE, BABAJIDE MIKE (JNR)
MATRIC NO: 75197
MARCH, 2015 STUDENTS’ ADVERSITY QUOTIENT AND RELATED FACTORS AS PREDICTORS
OF ACADEMIC ACHIEVEMENT IN THE WEST AFRICAN SENIOR SCHOOL
CERTIFICATE EXAMINATION IN SOUTHWESTERN NIGERIA
BY
BAKARE, BABAJIDE MIKE (JNR)
MATRIC NO: 75197
B. SC. (STATISTICS), U. I., (IBADAN)
M. SC (STATISTICS), U. I., (IBADAN)
M. ED (EDUCATIONAL EVALUATION), U. I., (IBADAN)
A THESIS SUBMITTED TO THE INTERNATIONAL CENTRE FOR EDUCATIONAL
EVALUATION (ICEE),
INSTITUTE OF EDUCATION,
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
OF THE
UNIVERSITY OF IBADAN, IBADAN
NIGERIA
MARCH 2015
2 ABSTRACT
Adversity Quotient (AQ) is an innate ability that enables people turn their adverse situations into lifechanging advantage. Determining students’AQ and its influence in relation to other factors that affect
achievement is likely to provide greater understanding and better prediction of academic achievement.
Many researches have been carried out on this concept without focussing on its combined impacts with
student-teacher psychological factors on academic achievement among secondary school students.
The study, therefore, examined the prediction effects of AQ and student-teacher psychological
constructs (Students’ attribution; students’ school connectedness; teachers’ self-efficacy; school type;
gender; school location and age) on students’ academic achievement in Mathematics and English
Language in the West African Senior School Certificate Examination (WASSCE) in Southwestern
Nigeria.
The study was a survey research that adopted a multi-stage sampling technique. Lagos and Oyo states
were randomly selected from the six states in Southwestern Nigeria. Thirty schools each were
randomly selected from the two states. In all the selected schools, the most senior secondary school III
Mathematics and English Language teachers were purposively selected along with their intact classes
that sat for the May/June 2013 WASSCE. The total number of respondents involved were 120 teachers
and 3,712 students. Student’s Adversity Quotient Profile (r=0.79), Students Attribution Questionnaire
(r=0.82), School Connectedness Scale (r=0.85) and Teachers’ Self-efficacy Scale (r=0.89) were used to
collect data. Data were analysed using descriptive statistics and multiple regressions at p=0.05.
The AQ of the students ranged from 40 to 200. The distribution pattern of the AQ scores showed that
21.0% were low; 62.0% moderate while 17.0% were high. The candidates’ scores in Mathematics and
English Language were normally distributed and ranged from 6.0% to 86.0% and 11.0% to 76.0%,
respectively. Majority of the students were of moderate adversity quotient and the higher the quotient,
the higher the students’ academic achievement in the two states. The intercorrelation matrix showed
that there is no multicollinearity among the predictor variables. The eight predictor variables explained
achievement in Mathematics (R2=.392, F(8,3703)= 298.866) and English language (R2=.405, F(8,3703)=
315.206). The coefficient of determination showed that all the predictor variables explained 39.2% and
40.5% of the variability of students’ achievement in Mathematics and English Language, respectively.
The most significant predictors of students’ academic achievement in the examination were AQ
(βM=0.032; βE=0.032); teacher self-efficacy (βM=0.096;βE=0.094); school type (βM=-0.461;βE=-0.488);
gender (βM=0.057;βE=0.056); age (βM=-0.031;βE=-0.034) and school location (βM=-0.422;βE=-0.444);
while students’ attribution and school connectedness were not significant in predicting academic
achievement. The beta weights showed that for every increase by one standard deviation in the
independent variables, academic achievement increased by the associated β-value.
Students’ adversity quotient and teacher self-efficacy positively predicted students’ academic
achievement in Mathematics and English Language in the West African Senior School Certificate
Examination in Lagos and Oyo States. There is the need for stakeholders in the education sector in
Nigeria to consider cognitive and psychological characteristics of students in seeking answers to the
decline of academic achievement in secondary schools.
Key words:
Students’ adversity quotient, Teacher self-efficacy, Achievement in mathematics and
English language, Nigerian secondary school
Word count: 485
3 DEDICATION
This study is dedicated to MY LORD JESUS CHRIST, the immortal, the invisible and
the only wise God. The one who restored me back to life when it seemed all hope was lost, and
placed my feet on the solid rock. To Him be all glory and honour - Amen.
4 ACKNOWLEDGEMENTS
To the Triune in Council (God the Father, God the Son and God the Holy Spirit) be all
glory and honour for the gift of life and the grace, ability and unction to see this programme to a
logical conclusion. The first few months of my life after birth was so challenging to the extent that
men had written off my case as one of those that can pass off as ordinary statistics of births and
deaths; but God intervened and brought me back to life and crowned my academic pursuits with
this glorious achievement which is beyond my comprehension. May His name be praised for this
wondrous act.
I wish to express my profound gratitude to my supervisor Dr. Ifeoma Mercy IsiugoAbanihe who is both a mother and an academic mentor for her priceless academic and professional
guidance in ensuring that this work was completed on time. Her timely attention in and out of
office; even at home and at odd hours is highly commendable. Your ability to drive your students
towards excellence is buttressed by the statistics of the numbers of Ph.D students you have
produced in the last few years; this has impacted on me and further strengthened me for my
sojourn in the academic world. Truly, you are a tool in the hands of God in moulding me into the
status I have attained today. I am deeply grateful to you for everything ma.
My gratitude goes to Prof. Uche Isiugo-Abanihe, whom, I am short of words to describe.
A father figure with uncommon grace for relating positively with everyone that comes across his
path, irrespective of age or tribe. A mentor, whose door is always open for consultation. The
golden opportunity he gave to me to serve as a research assistant on the team that conducted an
Impact Assessment Programme of national importance will forever be ingrained in my memory.
The various training sessions, meetings and workshops with the attendant discussions and views
which were generated have contributed to the attainment of this lofty height. Prof., I am grateful to
you for your fatherly role and your words of counsel. May God in His infinite mercies continue to
sustain you and your home in Jesus, Name – Amen.
I want to appreciate the West African Examinations Council (WAEC) for approving and
sponsoring this programme. I am indeed grateful for the stress free environment which has
contributed to the completion of this programme in record time. I want to thank Alhaja (Mrs).
M.A. Bello for approving this study for me as the then Registrar of the West African Examinations
5 Council (WAEC). I thank God for your belief in my ability, even when so many people were of
contrary opinion. You stood your ground for me and Mr. A. A. Adelakun (of blessed memory) for
us to be able to undertake this programme; without this singular action, this accomplishment would
have been a mirage. Thank you for allowing God to use you to fulfil this aspiration of mine. I want
to also thank the current Registrar to the West African Examinations Council (WAEC), Dr Uyi
Iwadeai, the current Head of the National Office, Nigeria - Mr. Charles Eguridu, the Head of the
West African Examinations Council International Office and Research Division, Dr M.G. Oke, as
well as all the Heads, and Staff of all the Departments in WIO in Lagos coupled the whole WAEC
family for their encouragement, and prayers.
I wish to thank Prof. E. Adenike Emeke, the current Director of the Institute of Education
for her contribution to this work right from inception. I also appreciate her exceptional leadership
style which has contributed into making ICEE the envy of others. I will forever continue to cherish
those ideologies of hardwork, diligence, truthfulness, foresight and commitment to the Almighty
God that she stands for.
I wish to thank Dr. A.O.U. Onuka, Dr. J. G. Adewale, Dr. I. Junaid, Dr. M. N. Odinko,
Prof. A. Gbadegesin, Prof. A. Gboyega, Prof. Ayoade, Prof. C.B.U. Uwakwe, Prof. A. Bandura,
Prof. M. Seligman & Andy Field, Prof. Weiner & John Cox, Prof. Tabachnik & Fidell, Prof. M. A.
Araromi, Prof. Onocha, Prof. T. W. Yoloye, Dr. M. Osokoya, Dr. J.A. Adeleke, Dr. B. A.
Adegoke, Dr. F.O. Ibode, Dr. S.F. Akorede, Dr. C. V. Abe, Dr. F.V. Falaye, Dr. J. A. Adegbile,
Dr. G.N. Obaitan, Dr. E. Okwilagwe, Dr. J. A. Abijo, Dr. O. A. Otunla and Dr. S. Babatunde for
their wise counsel and unquantifiable inputs of time and efforts towards the successful completion
of this work
I am grateful to the whole team at The Peak Learning Centre, under whose purview is the
Global Resilience Institute in the USA (of which I am a FELLOW, by the reason of this research
study) for their support right from the conception stage of this work. To the proponent of the AQ –
Prof. Paul Stoltz, who is always ready to give explanations even at odd times due to the time
difference between the two continents; Dr. Martin Katie and others too numerous to mention at the
Peak Learning Centre – I am saying a big thank you. To all the authors that I made reference to in
the course of this research work - I am grateful to you for charting the way forward for the younger
6 generation, of which I am one. Also to all the schools, principals, teachers and students in Oyo and
Lagos states who participated in this study – I want to say a big thank you to you all.
To the greatest Icon of Education of our time, popularly referred to as the Grand Sage of
Education in Africa - Emeritus Prof. PAI Obanya and ‘mummy’ as his wife is fondly called for
their contributions towards the speedy completion of this work. Prof. I will always be guided by all
those lessons learnt during my discussions with you. You are a rare gem and one of a kind. Thank
you for allowing me to learn at your feet, it is a rare privilege. I want to thank all my fellow Ph.D
students at the Institute of Education, University of Ibadan, for allowing me to learn through our
interactions (e.g. Dr. Alieme, Dr. Durowoju, Dr. Onabamiro, Dr. Akinyemi, Dr. Daberechukwu,
Dr Odeniyi, Messers Olaoye, Nicholas, Sola, Oyekanmi, Peter Onuka, Ofem, Mrs. Apah, Uchechi
coupled with others too numerous to mention. I also wish to specially appreciate all the supervisee
under my supervisor for their contributions in various ways to the success of this work (e.g. Dr
Babatunde, Dr. M. Egbe, Christine, Elizabeth, Mrs. Adeosun and Uba).
I want to appreciate all my uncles, Aunts and family friends coupled with their families,
who have been there for me all the while: the Babatunde’s, the Akintunde’s, the Sali’s, the
Adebisi’s, the Okunade’s, the Ademakinwa’s, Sister Rose, The Owokade’s, The Ogunlewe’s, The
Osoba’s, The Akano’s, The Ariyibi’s, Mummy Ibeji (Ibafo), The Dairo’s, The Adelodun’s, The
Afolabi’s and the Akande’s. I am also grateful to the General Overseer of the Redeemed Christian
Church of God (RCCG), Pastor E.A. Adeboye; and the wife, Pastor Folu Adeboye for the approval
for exemption in some church activities which has contributed to the speedy completion of this
programme. I am also grateful to Mr and Mrs Falaju and family coupled with all their staff
members: Debbie, Esther and Mercy.
This acknowledgement would not be complete without expressing my deepest gratitude
to the following: The bone of my bone and the flesh of my flesh, my one and only true friend, my
one and only love, my jewel of inestimable value, and a true mother – Victoria Olabosede Bakare
(Jnr). You embody the description given by the Bible in the book of Proverbs 31: 10-end, and that
is why I am not surprised at the way God has been dealing with us. Truly, you are a virtuous
woman because you have been there for me all the way, and I deeply appreciate your labour of
love. You made me realize that failure is not an option and that with God by my side, success was
7 sure. You drum it into my ears always that the sky is not just the limit, but the beginning for me in
this race – I LOVE YOU SO MUCH and thank you for loving me.
To my two angels and the only Heritage bequeathed to us by the Almighty, the tapping
foot that have been keeping us on our foot: Enitan Oluwadunbarin Michael and Inioluwa
Oluwadarasimi Deborah Bakare. For the cry for their dad at any time of the day, the countless” I
will follow you” at the motor-parks followed by various tantrum displays, and the painful
expressions on their faces when parting for school, I want to let you know that I LOVE YOU. I
also appreciate you for “understanding” in your own way, and for your sacrifices throughout the
length of the entire programme. I want you to also know that all I have gleaned from you have
contributed to this work and some of my published works during this period in question.
To my wonderful parents, Elder Mike Bolanle Morenikeji and Mrs. Omolola Atinuke
Bakare (Snr) for deliberately releasing yourselves to God into making you veritable-potent tools
in His hands in moulding me into what I am today. Babaagba and Mamaagba as you are fondly
called by your Granchildren, I am grateful for all your support right from birth. Truly, you are an
epitome of what good parents should be. You are my first mentors and I am grateful for your
mentoring. If I am given the opportunity to come back to this world after my sojourn; I WILL
CHOOSE YOU over and over again as my parents. I am happy for the two of you and I love you
so much.
To my siblings, Olukunle, Oluyemi, Olubunmi and Oluwatofunmi Bakare; what can I do
without you all? You have all been wonderful; you made me realize that we are all in it together
and that UNITED WE STAND. To all your spouses: Esther and Bisi, I say a big thank you for all
your words of encouragements and care. To all the grandchildren born into the BAKARE
DYNASTY (THE OBA ‘NLA NI JESU FAMILY); Iyanuoluwa, Enitan, Oluwatobiloba, Inioluwa,
Oluwaferanmi, Araoluwa, Oluwadarasimi (Iyanuoluwa), and Oba’nla ni Jesu tamilore – I love you
all for all the lessons I have tapped from you all which has formed part of this work. You will all
fulfil destinies and purposes in Jesus Name - Amen.
And to the entire Tugbobo’s Family,
especially Daddy and Mummy – I say thank you for showing how much you care.
Finally, I would like to thank the International Centre for Educational Evaluation (ICEE),
Institute of Education, University of Ibadan, for counting me worthy of a doctoral programme.
8 9 ABBREVIATION AND ACRONYMS
Page
WAEC
- West African Examinations Council.
WASSCE - West African Senior School Certificate Examination.
NERDC
- Nigerian Educational Research and Development Council.
NECO
- National Examination Council.
CO2RE
- Control; Ownership; Reach; and Endurance.
10 i
TABLE OF CONTENTS
PAGES
Title
i
Abstract
ii
Dedication
iii
Acknowledgements
iv
Certification
viii
Abbreviation and acronyms
ix
Table of contents
x
List of Appendices
xiii
List of Tables
xiv
List of Figures
xv
CHAPTER ONE: INTRODUCTION
1.1
Background to the study
1
1.2
Statement of the Problem
12
1.3
Research Questions
13
1.4
Scope of the Study
15
1.5
Significance of the study
15
1.6
Operational Definition of Terms
16
CHAPTER TWO: REVIEW OF RELATED LITERATURE
2.1. Theoretical Background - The Learned Helplessness Theory,
Social Cognitive or Learning Theory and Attribution Theory.
18
2.2. AQ’s Scientific Building Blocks
28
2.3. Adversity, Resilience and Hardiness
30
2.4. Concept of Adversity and Academic Achievement
32
2.5. Concept of Attribution and Academic Achievement
37
2.6. a. Concept of Self Efficacy
43
11 2.6. b. Concept of School Connectedness
50
2.7. Achievement in WAEC and WASSCE
56
2.8. Teacher’s Self Efficacy and Academic Achievement
59
2.9. Gender and Academic Achievement
61
2.10. Age and Academic Achievement
63
2.11. Factors affecting Academic Achievement
65
2.12. Conceptual Framework
68
2.13. Appraisal of Literature and Gaps in the existing Literatures
72
CHAPTER THREE: METHODOLOGY
3.1
Research Design
73
3.2
Variables of the Study
73
3.3
Population of Study
74
3.4
Sampling Procedure and Sample
74
3.5
Instrumentation
76
3.6.
Data Collection and Scoring Procedure
80
3.7
Methods of Data Analysis
82
3.7.1. Building the Regression Equation Model
84
3.7.2. The Regression Equation Models for the study
85
Methodological Challenges in this work
86
3.8
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1
Research Questions, Results, Interpretation and Discussion
CHAPTER
FIVE:
SUMMARY
OF
FINDINGS,
88
IMPLICATIONS
AND
RECOMMENDATIONS, LIMITATIONS AND SUGGESTIONS FOR FURTHER
STUDIES, AND CONCLUSION
5.1 Summary of the findings
123
5.2. Conclusion
125
5.3. Implications of the findings for the study
126
12 5.4. Limitation and Suggestion for further studies
128
5.5. Recommendations
129
5.6. Contribution to knowledge
130
REFERENCES
131
APPENDICES
147
13 LIST OF APPENDICES
Pages
Appendix I
Student’s Adversity Quotient Profile (SAQP)
147
Appendix II
Teachers’ Self-Efficacy Scale (TSES)
149
Appendix III
Students’ Attribution Questionnaire (SAQ)
151
Appendix IV
The School Connection Scale (SCS)
153
Appendix V
Letter of Request for Statistics of Achievement from WAEC
154
Appendix VA
Letter of Permission
155
Appendix VB
Letter of Agreement
156
Appendix VIA
Achievement Profile Sheet (PPS) - List A1 - Oyo State &
List A2 – Lagos State
Appendix VIB
Appendix VII
157
Achievement Profile Sheet (PPS) - List B1 – Oyo State &
List B2 – Lagos State
159
Frequency Distribution of the (AQ®) of the Student Respondents
161
Appendix VIII CO2RE Dimensions of all the Student Respondents
Appendix IX
164
Descriptive Statistics of the CO2RE Dimensions Of AQ® and Student
Academic Achievement in 2013 May/June WASSCE in Mathematics
and English Language
Appendix X
175
181
Selected School
14 LIST OF TABLES
Pages
Table 1.1
Statistics of Achievement in WASSCE in Mathematics
10
Table 1.2
Statistics of Achievement in WASSCE in English Language
10
Table 2.1
Dimensions of Adversity Quotient (AQ®)
35
Table 3.1
Educational districts and local government areas in Lagos State
75
Table 3.2:
Sampling Frame for the study according to educational districts
and local government areas in Lagos State
Table 3.3
75
Sampling frame for the study according to educational zones and
local government areas in Oyo State
76
Table 3.4
Summary of the psychometric properties of the instruments
79
Table 3.5
Summary of the coding pattern of the instruments
82
®
®
Table 3.6
Scoring Procedure for the Student’s Adversity Quotient Profile (SAQP )
82
Table 3.7
Interpretation of the SAQP® score
82
Table 4.1
Inter-Correlation Matrix of the Predictor Variables and the
Criterion Variable (Mathematics)
Table 4.2
100
Inter-Correlation Matrix of the Predictor Variables and the
Criterion Variable (English Language)
102
Table 4.3
Regression ANOVA in relation to Mathematics
105
Table 4.4
Model summary in relation to Mathematics
106
Table 4.5
Coefficients in relation to Mathematics
111
Table 4.6
Regression ANOVA in relation to English Language
113
Table 4.7
Model summary in relation to English Language
114
Table 4.8
Coefficients in relation to English Language
118
Table 4.9
Zero order and semi-partial correlations in Mathematics and English Language
120
15 LIST OF FIGURES
Pages
Figure 1.1
Students’ Achievement in Mathematics in WASSCE
between 2000 and 2012
Figure 1.2
7
Students’ Achievement in English Language in WASSCE
between 2000 and 2012
8
Figure 2.1
Triadic Reciprocal Determinism Model
22
Figure 2.2
Triadic Reciprocal Determinism Model
22
Figure 2.3
Triadic Reciprocal Determinism Model
23
Figure 2.4
Processes of Goal Realization
25
Figure 2.5
Causes of success and failure
27
Figure 2.6
Building Blocks of AQ®
29
Figure 2.7
Human Capacity Structure Model
34
Figure 2.8
Bell-shaped curve of AQ score distribution
36
Figure 2.9
SE Sources of Information
48
Figure 2.10
Strategies for Promoting School Connectedness
55
Figure 2.11
Assessment Procedures of the WAEC
58
Figure 2.12
The schematic diagram of the variables in this study
(i.e. the conceptual framework); showing the possible links
between them
71
Figure 4.1(a) Bell-shaped curve of AQ® score distribution
Figure 4.1(b)
Distribution of Candidates’ Achievement in Mathematics
in 2013 May/June WASSCE and the Corresponding
Adversity Quotient (AQ®)’s.
88
90
Figure 4.1(c) Distribution of Candidates’ Achievement in Mathematics
in 2013 May/June WASSCE
90
Figure 4.2(a) Distribution of Candidates’ Achievement in English Language
in 2013 May/June WASSCE and the Corresponding
Adversity Quotient (AQ®)’s
91
16 Figure 4.2(b) Distribution of Candidates’ Achievement in
English Language in 2013 May/June WASSCE.
92
Figure 4.2(i)
Candidate 1
94
Figure 4.2(ii)
Candidate 2
94
Figure 4.2(iii)
Candidate 3
94
Figure 4.2(iv)
Candidate 4
95
Figure 4.2(v)
Candidate 5
95
Figure 4.2(vi)
Candidate 6
95
Figure 4.2(vii)
Candidate 7
96
Figure 4.2(viii)
Candidate 8
96
Figure 4.2(ix)
Candidate 9
96
Figure 4.2(x)
Candidate 10
97
Figure 4.2(xi)
Candidate 11
97
Figure 4.2(xii)
Candidate 12
97
Figure 4.2(xiii)
Candidate 13
98
Figure 4.2(xiv)
Candidate 14
98
Figure 4.2(xv)
Candidate 15
98
Figure 4.4(a)
The Normal P-P Plot of Regression Standardized Residual
- Mathematics in 2013 May/June WASSCE
Figure 4.4(b)
Figure 4.5(a)
Figure 4.5(b)
109
The Scatterplot of the Standardized Predicted Value
- Mathematics in 2013 May/June WASSCE
109
The Normal P-P Plot of Regression Standardized Residual
– English Language in 2013 May/June WASSCE
116
The Scatterplot of the Standardized Predicted Value –
English Language in 20132 May/June WASSCE
17 117 CHAPTER ONE
INTRODUCTION
1.1.
Background to the problem
Stakeholders in education, including researchers have long been interested in exploring
variables that are associated with the quality of achievement of learners. The variables may be
grouped as either within or outside the school. Literature has also classified studies on student
achievement in terms of student factors, family factors, school factors and peer factors (Crosnoe,
Johnson & Elder, 2004). Generally, the factors include age, gender, geographical belongingness,
ethnicity, marital status, socioeconomic status (SES), parents’ educational level, parental
profession, language and religious affiliations which are usually discussed under the umbrella of
demography or “demographic variables” (Ballatine, 1993). Unfortunately, defining and
measuring quality of education is not a simple issue and its complexity tends to increase due to
the divergent views expressed by the stakeholders on the meaning of the word quality. (Blevins,
2009; Parri, 2006).
Other related studies have been carried out to identify and analyze numerous factors that
affect academic achievement. Findings identified student-teacher and other related factors like
school environment (Isiugo-Abanihe & Labo-Popoola, 2004); numerical ability (Falaye, 2006);
socio-psychological factors (Osokoya, 1998.); cognitive ability (Rohde & Thompson, 2007);
home and school factors (Odinko, 2002); facilities and class size (Owoeye, 2002); teachers
attitude and vocational variables (Falaye & Okwilagwe, 2008); intelligence and creativity
(Aitken, 2004); test anxiety (Osiki & Busari, 2002); educational standard (Adeyegbe, 2005);
and; educational policies and institutionalization (Obemeata, 1995; Obanya, 2003).
Despite all these studies, the quality of educational achievement still remains low
(WAEC, 2012). Some researchers have looked beyond the aforementioned factors to other
related areas within the teaching-learning grid including cognitive structures and psychological
or psychosomatic constructs. Under such psychological domain are constructs like self-efficacy,
self-esteem, self-concept, Intelligence quotient, emotional quotient amongst others, which are
concerned with different stimuli that drive the attainment of high quality educational
achievement. An emerging variable within this realm of psychological construct is Adversity
Quotient (AQ®). It has been stated that this variable is capable of bridging the gap in the
attainment of high quality educational achievement (Stoltz, 1997, 2010)
18 Human existence, from time immemorial has always been permeated by adversities. Life
itself is full of paradoxes. For instance, though in recent times there has been knowledge
explosion and technological revolution on the one hand, on the other hand, there is poverty,
scarcity of food and resources, increase in crime, social and political problems and other kinds of
societal upheaval. All these have created situations of stress and anxiety that tend to demand
tougher psychological skills both for adults and students in order to survive.
Adversity, as defined by the American Heritage Dictionary of the English Language,
Fourth Edition (2010), means “a state of hardship and affliction; misfortune; a calamitous event;
distress or an unfortunate event or incident”. Stress, conflict, hardship, misfortune, danger, and
challenge are but a few synonyms of adversity. Adversity can be both a general condition and a
specific situation. Stoltz (1997, 2010) attested to the fact that students of every age group face
different adversities unique to them with respect to time and place. This struggle against
adversities according to Stoltz, continues even after school into adult life. He affirmed in one of
his studies that the number of adversities an individual faces each day on an average has
increased from seven (7) to twenty three (23) in the last 10 years, with the students population
not being exempted.
The education of young people in the past years has been lopsided, with major emphasis
being placed on the cognitive domain at the detriment of the affective and psychomotor domains.
However, there is a growing awareness of educators, curriculum experts, school counsellors and
parents about other levels of measurements that could provide a better index of students’ ability
in terms of both emotional and cognitive components of the students’ ability structure. The need
for this can be illustrated by the fact that students with the same Intelligence Quotient do not
always respond in the same way to identical situations. In the context of the teaching-learning
process, it has been observed over time that some students, in spite of seemingly insurmountable
odds; somehow keep going, while others are weighed down by the avalanche of changes within
their environment. This implies that there are other underlying factors that are responsible for
forging ahead despite the changes. Researchers like Stoltz (1997, 2010) identified the Adversity
Quotient (AQ®) as one of such psychological variables.
Stoltz (1997) perceives the variable of adversity as one which has the capacity to change
a learner’s expected performance despite intelligence ability. He thus defines Adversity
19 Quotient® as “an indicator of how one withstands adversity, the ability to overcome it or the
capacity of a person to deal with the adversities of his life” (Stoltz, 1997, 2010). As such, it is the
science of human resilience. It entails remaining stable and maintaining healthy levels of
physical and psychological functions, even in the face of chaos. It can also be seen as a
successful adaptation response to high risk. Conceptually, this has been described as the outcome
of both individual attributes and environmental effects (Markman, 2000; Peak Learning, 2010).
More recently, other researchers have shown through the results of their studies that
measurement of AQ® might be a better index of achieving success than IQ, not just for academic
achievement in education, but in other related social skills (Zhou, 2009; Williams, 2008; Tantor,
2007).
Adversity is a part of the educational life of students and teachers in Nigeria. An
individual’s response to adversity may be determined by personal characteristics and
environmental factors. Studies conducted by Stoltz (2010) showed that one’s response to
adversity is formed through the influence of parents, teachers, peers, and other key people.
Furthermore, people’s response to adversity can be interrupted and permanently changed, due
to some factors. Studies, particularly the study by Dweck (2012) have shown that responses to
adversity are learned. Thus discovering and measuring AQ® and factors that influence it allows
one to understand how and why some people consistently exceed the predictions and
expectations of their natural intellectual ability.
There are four sub-components of the AQ®. The sum of the four scores is the person's
Adversity Quotient (AQ®). The four sub-components are Control, Ownership, Reach, and
Endurance, represented by CO2RE. Although they may be intercorrelated, they measure very
different aspects of AQ®. Where C is the amount of perceived control one has over an adverse
event or situation. O is the degree to which an individual is willing to own the outcome of
adversity, the extent to which the person owns, or takes responsibility for the outcomes of
adversity or the extent to which the person holds himself or herself accountable for improving
the situation. R is a reflection of how far the adversity reaches into other aspects of an
individual’s life or the degree to which impacts other areas of life positively or negatively. E is
the measure of endurance, which assesses how long the adversity and its causes and effects will
last in one’s life or the perception of time over which good or bad events and their consequences
will last or endure.
20 Five major reasons why according to Stoltz (2010), there is the need to improve one’s
ability to deal with adversity are: (1) AQ® can be validly and reliably measured as well as
tracked against performance or other critical variables. (2) AQ® can be permanently rewired and
strengthened. (3) AQ® is a natural enhancer, not an add-on for current learning, performance,
assessment and change initiatives. (4) AQ® is an adaptable technology and not a program,
lending it to a wide array of applications. (5) AQ® is deeply grounded in 37 years of research and
10 years of organizational/institutional application and industries. Few people are aware of their
AQ®, yet, we all have it. Unlike IQ (Intelligent Quotient) or EQ (Emotional Intelligent Quotient),
which are only identifiable, our AQ® (Adversity Quotient) is identifiable and most significantly,
can be improved upon.
In view of the aforementioned, it is pertinent to study the role of Adversity Quotient
(AQ®) in learning among Nigerian children. In view of the fact that AQ® can be influenced by
environment, it is important to look at the inter-play of some psychological constructs (e.g.
students’ attribution, school connectedness and teachers’ efficacy) and some demographic
variables (e.g. school ownership type, state location, age and gender) that are likely contributors
to students’ academic achievement.
Teacher factor is a major contributory component of students’ sound academic
achievement; hence its importance in the interplay of the teaching-learning process cannot be
overemphasized. In the same vein, teacher’s self-efficacy is an important variable that requires
investigation. Efficacy beliefs according to Bandura (2006 and 2008) refer to judgments
regarding the ability to perform actions required to achieve desired outcome. Research studies
have shown a positive correlation between teachers’ perceived self-efficacy and student
achievement. Teachers often need to reflect on teaching practices as well as knowledge and
pedagogy in an effort to better meet the needs of students.
Teacher efficacy has long been identified as a crucial construct in the research on
teachers and teaching; therefore, it has been considered as integral to the practice of education.
Teacher efficacy refers to “the teacher’s belief in his or her capability to organize and execute
courses of action required to successfully accomplish a specific teaching task in a particular
context”. Based on the works of Bandura, it was concluded that individuals’ inherent beliefs are
the best indicators of the decisions individuals make throughout their lives. Imperatively, it
follows that teachers’ beliefs about their personal teaching abilities are a key indicator of teacher
21 behavior, decisions, and classroom organization. Therefore, in the teaching context, teacher
efficacy is expected to affect the goals teachers identify for the learning context as well as to
guide the amounts of effort and persistence given to the task.
Apart from the teacher and students’ personal factors, other psychological factors have
been found to influence students’ learning and achievement. Bandura (2010) and others (Schunk,
1991; 1993; and Weiner, 2010) have shown from their studies that people’s beliefs about their
abilities to exercise personal control over important events in their lives are thought to play a
major role in motivating the self-regulation of cognitive performance and learning. These beliefs
are thought to play a foundational role in motivating behavior for tasks that require high levels of
personal self-initiation and active self-regulation, typical of that which is involved in the
teaching-learning process. However, the direction of this study will be limited to students’
attribution and self-efficacy of the teacher.
Attributions are the justifications provided by people for the events taking place in their
lives, especially when outcomes are unsatisfactory to what was hoped for. People tend to give
self-justifications to massage their ego and maintain their esteem levels by providing these
explanations. In the field of education where successes and failures are two possible outcomes
for learners, the conceptualization of causes of success and failure becomes most important as
they have to attribute it to some possible factors like ability, effort or environment (Weiner,
2005; Abodunrin, 1988, 1989). In the educational context, two influential theories of attribution
are Weiner’s (1986) theory of motivation and Abramson, Seligman and Teasdale’s (1978) theory
of learned helplessness. Each theory proposes that the attributions students make for their
successes and failures can significantly affect their future achievement of academic tasks.
Some studies on effect of school ownership type on student achievement show that the
type of ownership of schools does matter in the achievement of students at the secondary school
setting. In particular, it is specially believed that students in private-owned schools have better
academic achievements than their counterparts in public owned schools. Although agreement has
not been reached on the reasons for the differences in academic achievement, some of the
reasons advanced include differences in resource levels, academic organization and school
environments (Adomako, 2005 and WAEC, 2011).
In Nigeria, it is the general opinion of people that private-owned schools are better in
terms of the availability of human capital and physical facilities and consequently, students’
22 achievement in such schools are considered better than those in the public-owned schools. This
situation has made many parents to enroll their children in private secondary schools. Experience
has also shown that more students from private-owned schools secured admission into tertiary
institutions such as Colleges of Education, Polytechnics and Universities. Despite this, research
findings on the influences of school type on students’ academic achievement remains
controversial. These imply that the purported effect of school ownership type is far from
conclusive.
The standardized measurement of achievement in Nigeria has been carried out for several
decades by The West African Examinations Council (WAEC) and later, The National
Examination Council (NECO) came on board. The West African Examinations Council (WAEC)
is West Africa’s foremost examining board established by law to determine the examinations
required in the public interest in the West African Anglo-phone countries, the board is to conduct
examinations and award certificates comparable to those of equivalent examining authorities
internationally.
WAEC examines forty (40) subjects that are taught at the senior secondary school level
out of which Mathematics and English language are considered the basic foundation and
languages of other subjects and the foundation for studying at tertiary institutions. The benefits
of Mathematics and English Language are further exemplified by the recognition given to them
in official policies on examination, admissions to tertiary institutions and even employment.
23 Figure 1.1: Students’ Achievement in Mathematics in WASSCE between 2000 and 2013;
Source: WAEC TDD, Ogba
24 Figure 1.2: Students’ Achievement in English Language in WASSCE between 2000 and 2013;
Source: WAEC TDD, Ogba
An examination of candidates’ achievement in Mathematics and English Language shown in
Figures 1.1 and 1.2 indicates a sinusoidal trend. The poor achievement of students in the two
critical subjects (Mathematics and English Language) is arguably and indication of a danger
signal in the educational system. The poor results in these subjects have continued to be
stumbling-blocks in the realization of the educational and employment desire of many
candidates. The situation is worse in the Nigeria of today, where job opportunities and number of
places available for further education at various entry points are few compared to the demands. It
25 is a known fact that many candidates are denied admissions because of poor result in
mathematics and English Language at the WASSCE (Adeyegbe, 1991; Uwadiae, 2006).
Greaney and Kellaghan cited in Bakare & Osoba (2008) asserted that poor achievement
in examinations has been found to have negative effects on the candidates. Students who do not
do well in public examinations, especially in Mathematics and English Language are in most
cases stigmatized as failures and therefore become dropout from schools. Acknowledging the
poor achievement of candidates in English Language, Faloye cited in Uwadiae, (2006), asserted
that:
Judging by the annual list of failures, the subject seems to strike terror in
candidates while their achievement in turn embarrasses the Council. Most
candidates who fail the paper seem to have resigned themselves to fate.
They believe that it is impossible to obtain a pass in the paper. Others on
the other hand cynically assume that the Council is the architect of their
failure. They believe that they are good enough to pass the paper but that
the Council deliberately fails them so as to make them enroll for
subsequent examinations, thereby making money out of their misfortune
The fact that poor achievement contributes significantly to examination malpractice cases
now prevalent in public examinations cannot be overemphasized; as fear of failure lures
candidates into adopting mal-adaptive strategies in examinations. Government, educational
administrators, educators, parents and even students themselves are not unaware of the
importance of Mathematics and English Language, and therefore are concerned about the poor
results in these subjects especially at the WASSCE (Uwadiae, 2007).
Gender is one variable that has been examined in several studies. It has been found to
have an effect on academic achievement (Onuka, 2004 & 2007; Adeleke, 1994, 2007 & 2008;
Okwilagwe, 2012; Yoloye, 1995; Isiugo-Abanihe, 1997; Emeke, 2012; Bakare & Osoba, 2008;
Bello & Bakare, 2012). Gender is one of the personal variables that have been related to
differences found in academic achievement.
26 Table 1.1: Statistics of Achievement WASSCE in Mathematics
SUBJECT
YEAR
MATHEMATICS
2008
2009
2010
2011
2012
SEX
TOTAL
ENTRY
TOTAL
SAT
MALE
702956
688884
FEMALE
589934
579329
MALE
755955
741577
FEMALE
617054
606951
MALE
727165
713240
FEMALE
604209
593295
MALE
844546
825971
FEMALE
695595
682994
MALE
937102
915637
FEMALE
758776
742720
TOTAL
CREDIT
1-6
381258
(55.34)
345140
(59.58)
330397
(44.55)
303985
(50.08)
283609
(39.76)
264456
(44.57)
315563
(38.21)
293303
(42.94)
438468
(47.88)
400411
(53.91)
TOTAL
PASS
7-8
171617
(24.91)
130649
(22.55)
195026
(26.30)
149609
(24.65)
200412
(28.10)
163508
(27.56)
263352
(31.88)
211312
(30.94)
270508
(29.54)
208011
(28.00)
FAIL
124888
(18.13)
93730
(16.18)
186981
(25.21)
128757
(21.21)
207682
(29.12)
147700
(24.89)
244844
(29.64)
176568
(25.85)
183244
(20.01)
115498
(15.55)
Source: The West African Examinations Council (WAEC), TDD, Ogba
Table 1.2: Statistics of Achievement in WASSCE in English Language
SUBJECT
YEAR
ENGLISH
LANGUAGE
2008
2009
2010
2011
2012
SEX
TOTAL
ENTRY
TOTAL
SAT
MALE
702971
692132
FEMALE
589939
582034
MALE
755955
745952
FEMALE
617054
609773
MALE
727172
713886
FEMALE
604209
593859
MALE
844546
829719
FEMALE
695595
684445
MALE
937102
916071
FEMALE
758776
742816
TOTAL
CREDIT
1-6
224970
(32.50)
221315
(38.02)
286453
(38.40)
276891
(45.40)
229088
(32.09)
230316
(38.78)
444544
(53.57)
422148
(61.67)
499509
(54.52)
471169
(63.43)
Source: The West African Examinations Council (WAEC), TDD, Ogba
27 TOTAL
PASS
7-8
216488
(31.27)
189454
(32.55)
219851
(29.47)
180573
(29.61)
217819
(30.51)
189903
(31.97)
207853
(25.05)
158523
(23.16)
218279
(23.82)
158721
(21.36)
FAIL
239254
(34.56)
160868
(27.63)
197652
(26.49)
117312
(19.23)
247407
(34.65)
158270
(26.65)
174354
(21.01)
101569
(14.83)
176780
(19.29)
96015
(12.29)
Tables 1.1 and 1.2 further highlighted the existence of a consistent difference in
performance in WASSCE between female and male candidates. Despite previous studies, this
consistent gender-gap makes gender an important variable of this study.
Age is another factor to consider in relation to how personal variables affect achievement.
A considerable amount of research (e.g., McMillen, 2004 and Bakare, 2009) has examined the
relationship between age and academic performance in the areas of reading and mathematics.
The results show that age has a way of predicting educational achievement. Though age is not
the only factor that influences academic achievement, others such as gender and socioeconomic
status (SES) also affects academic achievement. It is popularly assumed and believed that age is
synonymous (all things being equal – i.e. ceteris paribus) with wisdom and knowledge. The
assumption is that the more the growth, the better the person, due to accumulation of
experiences, which in no small way always guides the decision making of such individual.
Another factor under consideration that is a likely predictor of students’ educational
achievement is students’ school connectedness. “School connectedness was defined as the belief
by students that adults in the school care about their learning as well as about them as
individuals”. (Wingspread Declaration on School Connections, 2004). Students are more likely
to succeed when they feel connected to the school. Critical requirements for feeling connected
include high academic rigor and expectations coupled with support for learning, positive adultstudent relationships, and physical and emotional safety. Strong scientific evidence demonstrates
that increased student connection to school decreases absenteeism, fighting, bullying and
vandalism while promoting educational motivation, classroom engagement, academic
performance, school attendance and completion rates (Blum, 2004).
For any form of success to be recorded by students, there must be that strong need or
attachment that makes them feel they “belong” in their school. These have been referred to as
sense of belonging, school engagement, school attachment, school bonding or other related terms
and have prompted researches in virtually all fields of human disciplines, such as education,
health, psychology and sociology. According to Blum, (2004), while each discipline may
organize data and terms differently, conduct analyses in different ways, and even use different
descriptive words, consistent themes emerge. These seven qualities that seem to influence
students’ positive attachment to their schools include: (i). Having a sense of belonging and being
part of the school; (ii). Liking school; (iii). Perceiving that teachers are supportive and caring;
28 (iv). Having good friends within school; (v). Being engaged in their own current and future
academic progress; (vi). Believing that discipline is fair and effective and (vii). Participating in
extracurricular activities
These factors, measured in different ways, are highly predictive of success in schools;
this is because each of these seven factors bring with it, a sense of connection to the individual
student, one’s community, or one’s friends. Research studies have showed a strong relationship
between school connectedness and educational outcomes (McNeely, 2003; and Klem and
Connell, 2004). However, there are still divergent views as to its predictive effect on educational
outcomes, hence its inclusion in this study. It is clear that school connectedness can make a
difference in the lives of students
In summary, an individual’s response to adversity is likely to be determined by several
factors which may include personal characteristics and environmental factors. Studies have
shown that Adversity Quotient (AQ®) is an inner ability that enables people to turn their
adverse situations into life-changing experiences. Thus, determining students’ AQ® and other
related factors that influence achievement is likely to provide necessary knowledge that would
allow greater understanding and better prediction of achievement beyond the individual’s natural
intellectual ability. Several studies reviewed on the effect of the Adversity Quotient (AQ®)
have been mainly studies outside Nigeria and have focused primarily on organizational
achievement, job output with proven results in terms of improved performance levels,
leadership styles and commitment to change. However, none has been at the secondary level of
education and more importantly; there is dearth of research that aligns the Adversity Quotient
(AQ®) and these other related variables with academic achievement in Nigeria. This reveals a
knowledge gap in the study of this unique psychological construct among Nigerian students and
its probable effect on achievement in public examinations.
1.2
Statement of the problem
Possible predictors of academic achievement that could apply to both Mathematics and
English Language achievement have been identified yet the results remain inconclusive.
Majority of the studies reviewed on the effect of the Adversity Quotient (AQ®) have been
outside the confines of Nigeria, where the primary focus has been on organizational
performance, job output and related issues rather than achievement at the secondary level of
29 education. More importantly, there is dearth of research that estimated the composite relationship
between Adversity Quotient (AQ®) and related variables with academic achievement in Nigeria.
The foregoing reveals a considerable gap in the present knowledge about the pattern of
relationship of AQ® and other variables among Nigerian students and how it affects their
achievement in public examinations.
This study, therefore, seeks to determine (i) the profile of Adversity Quotient (AQ®) of
SS III students in the Southwestern States of Nigeria;
and (ii) whether student-teacher
psychological constructs (i.e. students’ Adversity Quotient (AQ®); students’ attribution;
students’ school connectedness; teachers’ self-efficacy; school ownership type; gender;
geographical location (i.e. state where school is located) and age) are predictive of students’
academic achievement in WASSCE in a school-based sample of Senior Secondary Students in
Southwestern, Nigeria.
1.3
Research questions
Based on the problem of this study the following research questions were answered:
1. (a) What is the profile of Adversity Quotient (AQ®) of the students in this study?
(b) Is the observed distribution of the Students’ Adversity Quotient (AQ®) and achievement
in Mathematics and English Language in 2013 May/June WASSCE in Southwestern, Nigeria
consistent with the normal distribution curve?
2. What is the distribution pattern of the CO2RE dimensions of the Students’ Adversity
Quotient (AQ®) in this study?
3. What type of correlation exists among the predictors (students’ Adversity Quotient (AQ®);
students’ attribution; students’ school connectedness; teachers’ self-efficacy; school
ownership type; gender; geographical location and age) and the criterion variables (students’
academic achievement) in Mathematics in WASSCE in Southwestern, Nigeria?
4. What type of correlation exists among the predictors (students’ Adversity Quotient (AQ®);
students’ attribution; students’ school connectedness; teachers’ self-efficacy; school
30 ownership type; gender; geographical location and age) and the criterion variables (students’
academic achievement) in English Language in WASSCE in Southwestern, Nigeria?
5. (a) Does the obtained regression equation resulting from a set of eight (8) predictor variables
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
teachers’ self-efficacy; school ownership type; gender; geographical location (i.e. state where
school is located) and age) allow reliable prediction of students’ academic achievement in
Mathematics in WASSCE in Southwestern, Nigeria?
(b) How much of the total variance in students’ academic achievement in Mathematics in
WASSCE in Southwestern, Nigeria is accounted for by student-teacher psychological
constructs (students’ Adversity Quotient (AQ®); students’ attribution; students’ school
connectedness; teachers’ self-efficacy) and demographic factors (school ownership type;
gender; geographical location and age)?
(c) How well can the full model predict scores of a different sample of data from the same
population or generalize to other samples?
(d) Which of the eight (8) predictor variables (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) are most influential in predicting students’ academic
achievement in Mathematics in WASSCE in Southwestern, Nigeria?
(e) Are there any predictor variables (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) that do not contribute significantly to the prediction
models?
6. (a) Does the obtained regression equation resulting from a set of eight (8) predictor variables
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
teachers’ self-efficacy; school ownership type; gender; geographical location age) allow
reliable prediction of students’ academic achievement in English Language in WASSCE in
Southwestern, Nigeria?
(b) How much of the total variance in students’ academic achievement in English Language
in WASSCE in Southwestern, Nigeria is accounted for by student-teacher psychological
constructs (students’ Adversity Quotient (AQ®); students’ attribution; students’ school
31 connectedness; teachers’ self-efficacy) and demographic factors (school ownership type;
gender; geographical location and age) differences?
(c) How well can the full model predict scores of a different sample of data from the same
population or generalize to other samples?
(d)Which of the eight (8) predictor variables (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) is most influential in predicting students’ academic
achievement in English Language in WASSCE in Southwestern, Nigeria?
(e) Are there any predictor variables (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) that do not contribute significantly to the prediction
models?
7.
How important are students’ Adversity Quotient (AQ®); students’ attribution; students’
school connectedness and teachers’ self-efficacy when each is used as the sole predictor of
students’ academic achievement in Mathematics and English Language in WASSCE in
Southwestern, Nigeria?
1.4
Scope of the study
The study was limited to randomly-selected Senior Secondary Schools located in
Southwestern, Nigeria. The study did not go beyond the interplay of the key variables of the
study and responses were restricted to randomly selected SS III students who participated in the
May/June 2013 diet of the WASSCE; known as the School Candidate Examination.
1.5
Significance of the study
This study will help in providing some evidence and information on the historical
antecedent of the key constructs referred to as the Adversity Quotient (AQ®), attribution, school
connectedness and teachers’ self-efficacy. It will also help in understanding the relationships, the
directions of the relationships and the possible causal links between these constructs and
academic achievement in WASSCE. Also, based on the results of the findings of this study,
stakeholders in the educational sector in Nigeria will have to look beyond cognitive structures for
answers to the decline in the academic performance of students. In addition, the findings of this
32 study will serve as a veritable means of increasing educators’ knowledge and understanding of
Adversity Quotient (AQ®), attribution, school connectedness and students’ achievement in public
examinations.
1.6
Operational definition of terms
Adversity Quotient (AQ®) – The total score obtained on the AQ® Profile: an indicator of how
respondents withstand adversity; the ability to overcome adversities; or “the capacity of a
respondent to deal with the adversities of his or her life.
AQ® Profile - The instrument used to measure an individual’s style or profile of responding to
adverse situations.
Attribution – the causal explanations that respondents assign to the events that happen to and
around them or their perceptions about the causes of success and failure in relation to
achievement in Mathematics and English Language in the 2013 May/June WASSCE.
Teacher efficacy – the respondent’s belief in his or her capability to organize and execute
courses of action required to successfully accomplish specific teaching tasks in a particular
context.
Control Score (C) – a measure of the degree of control a respondent perceives that he or she has
over adverse events. It is a sub-measurement scale on the AQ® Profile and a component of the
Adversity Quotient (AQ®).
Ownership
Score (O) - measure of the extent to which a respondent owns, or takes
responsibility for, the outcomes of adversity or the extent to which a respondent holds himself or
herself accountable for improving the situation. It is a sub-measurement scale on the AQ® Profile
and a component of the Adversity Quotient (AQ®).
Reach Score (R) - measure of the degree to which a respondent perceives good or bad events
affecting other areas of his or her life. It is a sub-measurement scale on the AQ® Profile and a
component of the Adversity Quotient (AQ®).
Endurance score (E) – measure of the respondent’s perception of length of time over which
good or bad events and their consequences will last or endure. It is a sub-measurement scale on
the AQ® Profile and a component of the Adversity Quotient (AQ®).
Resilience - successful adaptation-response of the respondents to high risk or adversity.
33 Risk Factors - individual or environmental characteristics, conditions, or behaviors that increase
the likelihood that a negative outcome will occur.
Protective Factors - individual or environmental characteristics, conditions, or behaviors that
reduce the effects of stressful life events; increase an individual’s ability to avoid risks or
hazards; and promote social and emotional competence to thrive in all aspects of life now and in
the future.
School Connectedness - the belief by students that adults and peers in the school care about
their learning as well as about them as individuals.
Giftedness – Synthesis of Knowledge and ability.
Ability – The innate component of a person, which can be dormant, passive or active.
Effort – An entity (e.g. an action, event, occurrence) that triggers the innate component of a
person.
34 CHAPTER TWO
REVIEW OF RELATED LITERATURE
This chapter deals with the presentation of the review of related literatures. Relevant literatures
were reviewed in the following order:
2.1. Theoretical Background - The Learned Helplessness Theory, Social Cognitive or Learning
Theory and Attribution Theory.
2.2. AQ’s Scientific Building Blocks
2.3. Adversity, Resilience and Hardiness
2.4. Concept of Adversity and Academic Achievement
2.5. Concept of Attribution and Academic Achievement
2.6. a. Concept of Self Efficacy
2.6. b. Concept of School Connectedness
2.7. Achievement in WAEC and WASSCE
2.8. Teacher’s Self Efficacy and Academic Achievement
2.9. Students’ Gender and Academic Achievement
2.10. Students’ Age and Academic Achievement
2.11. Factors affecting Academic Achievement
2.12. Conceptual Framework
2.13. Appraisal of Literature and Gaps in the existing Literatures
Note:
(Contact
the
author
for
the
full
[email protected])
35 review
of
related
Literature
at
CHAPTER THREE
METHODOLOGY
This chapter presents the procedures that were employed in carrying out the study. It
features the: research design, population of study, sample and sampling technique, research
instruments, data collection and Data analysis
3.1
Research design
This study adopted the survey design which is a non-experimental research type. This is a type
of research in which data is collected after the fact i.e. after the occurrence of the noticeable
change and where the variables of interest are not manipulable (Kerlinger & Lee, 2000). This
research design allows the researcher to examine how specific independent variables (students’
Adversity Quotient®, attributions, school connectedness, teachers’ efficacy, gender, age, School
Ownership type and Geographical Location) affect the dependent variable (student academic
achievement in Mathematics and English Language in the May/June 2013 WASSCE) and this
allows generalization to be made from the sample to the larger population..
3.2
Variables of the study
3.3.1 Dependent Variables. These are:
(i) Academic achievement in Mathematics in the May/June 2013 WASSCE.
(ii) Academic achievement in English Language in the May/June 2013 WASSCE.
3.3.2
Independent Variables:
(i) Students’ Adversity Quotient (AQ®)
(ii) Students’ Attribution;
(iii) School Connectedness;
(iv) Teachers’ Self-efficacy;
(v) Students’ Gender;
(vi) Students’ Age;
(vii) School Ownership type and;
(viii) Geographical Location (State where school is located).
36 3.3
Population of study
The target population for the study comprised all Senior Secondary School III students,
SS III Mathematics and English Language teachers and school principals in Southwestern,
Nigeria. The choice of SS III was premised on the fact that this group of respondents (i) have
been exposed to the teaching-learning of Mathematics and English Language in the last three
years of secondary school covering SS I to SS III, and are thus expected assumed to have
attained mastery of the subjects; and (ii) to have participated in the May/June 2013 WASSCE in
Nigeria.
3.4
Sampling procedure and sample
A multistage sampling technique was used in the selection of the target samples. The first
stage involved the selection of two states based on the following procedure: The South-western
part of Nigeria was clustered along the existing six states (Lagos, Ogun, Ondo, Oyo, Osun and
Ekiti states). The six states were further clustered into two; i.e. Coastal and Inland states with
two states (Lagos and Oyo states) randomly selected to represent Coastal and Inland states
respectively. The second stage which was carried out in two phases was conducted as follows:
In Lagos State, three (3) educational districts were randomly selected from a total of six (6)
educational districts, followed by a random selection of ten (10) schools and their principals
from each of the districts. In all the randomly selected schools, most senior Mathematics and
English Language teachers teaching SS III were purposively sampled coupled with the available
SS III students who sat the May/June 2013 WASSCE.
In Oyo State; 2 educational zones were purposefully selected from the 8 existing
educational zones to represent both urban and rural areas respectively; from which four (4)
LGAs were randomly selected from a total of eleven (11) LGAs. This was followed by a random
selection of thirty (30) schools from the four (4) LGAs. Also in all the randomly selected
schools,
Mathematics and English Language teachers who teach SS III students were
purposively sampled, coupled with the intact class of Senior Secondary School (SS III) students
that sat the May/June 2013 WASSCE, making a total of three thousand, seven hundred and
twelve (3,712) Senior Secondary School (SS III) students in both states. Altogether the total
number of respondents involved in the study was Three thousand, eight hundred and ninety-two
(3,892) respondents. The sampling distributions are presented in tables 3.1 to 3.3, while the
37 sampled schools and the respective number of respondents per school and from each state are as
shown on Appendix X.
Table 3. 1: Educational districts and local government areas in Lagos State
EDUCATIONAL
District
District
District
District
District
Two
Three
Four
Five
DISTRICTs
One
(EDs)
LOCAL
GOVERNMENT
AREAs (LGAs)
District
Six
Agege
Ikorodu
Epe
Apapa
AjeromiIfelodun
Ikeja
Alimosho
Kosofe
Eti-Osa
Lags
Mainland
AmuwoOdofin
Mushin
Ifako-Ijaye
Somolu
Ibeju-Lekki
Surulere
Badagry
Oshodi
Lagos
Island
Ojo
Source: Policy Planning Research & Statistics Department, Ministry of Education Lagos (2012)
Table 3.2: Sampling Frame for the study according to educational districts and local government
areas in Lagos State
EDs
LGAs
TNS
TNSS
1
Agege; Alimosho; Ifako-Ijaye.
38
10
2
Ikorodu; Kosofe; Somolu.
61
10
5
Ajeromi-ifelodun; Amuwo-odofin; Badagry; Ojo.
70
10
9
169
30
Total
TNSS
2258
2258
NPS
NTS
10
20
10
20
10
20
30
60
Source: Policy Planning Research and Statistics Department, Ministry of Education, Lagos & Field Trip
(Lagos, 2013).
Key: TNS - Total Number of Schools; TNSS – Total Number of Schools Selected; TNss – Total Number
of Students Selected; NPS – Number of Principals Selected; and NTS – Number of Teachers Selected.
38 Table 3.3: Sampling frame for the study according to educational zones and local government
areas in Oyo State
EZs
LGAs
SLGAs
TNS TNSS TNss NPS NTS
1 - Ibadan City Ibadan North; Ibadan
(Urban)
South- West; Ibadan
Ibadan North;
60
17
South-East;
Ibadan
Ibadan North-East;
19
5
North-East;
Ibadan
Ibadan North-West.
9
2
Lagelu;
22
6
4
110
30
North-West.
2 - Ibadan Less Akinyele;
City (Rural)
1454
30
60
Ido;
Oluyole; Lagelu
Egbeda; Ona-ara.
TOTAL
1454
30
60
Source: Research and Statistics Dept., Ministry of Education, Secretariat, Ibadan & Field Trip (Oyo, 2013).
Key: EZs – Educational Zones; SLGAs – Selected Local Government Areas; TNS - Total Number of
Schools; TNSS – Total Number of Schools Selected; TNss – Total Number of Students Selected; NPS –
Number of Principals Selected; and NTS – Number of Teachers Selected.
3.5 Instrumentation
The following four (4) research instruments were used for the collection of data for the study:
(a)
Student’s Adversity Quotient® Profile (SAQP®)
(b)
Students Attribution Questionnaire (SAQ)
(c)
School Connectedness Scale (SCS)
(d)
Teachers’ Self-efficacy Scale (TSES)
(a)
Student’s Adversity Quotient® Profile (SAQP®)
An instrument tagged the “Student’s Adversity Quotient® Profile” (SAQP®) was used to
collect relevant data from the target audience (i.e the students). The instrument was adapted from
the standardized paper-and-pencil form of the “Adversity Quotient Profile” (AQP®) designed by
the proponent of the Adversity Theory, Paul Stoltz in 1997 (PEAK Learning Inc., 2008) and
reflects the ongoing improvements and evolution gained over preceding versions, since 1993.
The Adversity Quotient Profile (AQP®) is the only scientifically-grounded tool in existence for
measuring how effectively one deals with adversity (Stoltz, 1997). The questionnaire was
developed, tested, and validated by Peak Learning with over 7,500 participants from diverse
39 organizations and institutions all over the world. The adapted instrument (which was re-validated
here in Nigeria, after due pemision was given by PEAK Learning Inc.) is a self-report
questionnaire five point Likert- type scale response. Through the instrument, information on the
adversity profile of the respondents were elicited.
Section A of the instrument, contains questions/items soliciting Demographic Information of the
respondent, while Section B, is made up of questions/items on a continuum where the
respondent is to state the degree of agreement or disagreement by circling a point on the
continuum. Typical response format on the instrument are: 1 = Not responsible at all; 2 = Rarely
responsible; 3 = Sometimes responsible; 4 = Often responsible; 5 = Completely responsible; while
®
examples of items on the sub dimensions of the SAQP are Control – “I missed an important
subject period”. The consequences of this situation will”; Ownership – “My parents ignore my
attempt to discuss an important issue. To what extent do you feel that you are responsible for
improving the situation?” Reach – “I lost something that is important to me. The consequence
of this situation is that” and Endurance – “I am not doing well in some of my subjects. To what
extent do you feel that you are responsible for improving the situation?”
The psychometric properties were determined using the Cronbach Alpha which is a
measure of the internal consistency and reliability of the instrument with a value of 0.79, while
the content validity of the instrument was established using the Lawshe Content Validity Index
(CVI), which gives a value of 0.70 respectively.
**NOTE: This instrument has not been validated by PEAK Learning, Inc. It has been
adapted, and therefore, not tested as reliable by the PEAK Learning, Inc. standards.
(b)
Students Attribution Questionnaire (SAQ)
A self-reporting instrument tagged the “Students Attribution Questionnaire” (SAQ)
which is an adapted form of the Self-confidence Attitude Attribute Scale developed by
Campbell, (1996) was used for this study. The instrument used a five-point Likert scale, ranging
from strongly disagree to strongly agree. The instrument’s final version included twenty items
measuring the students' ability, effort, luck and level of giftedness attributions, based on
Weiner’s attribution theory (1974). It was adapted by Petri, Kirsi, Hanna, and Välimäki, (2006)
to measure the four aspects of effort, ability, luck and task difficulty with the aspect of task
difficulty replaced by level of giftedness. The four dimensions are as follows: (1) "Success due
40 to Ability or Failure due to a lack of Ability"; (2) Success due to Effort or Failure due to a lack
of Effort"; (3) " Success due to Luck or Failure due to a lack of Luck"; and (4) " Success due to
level of giftedness or Failure due to lack of giftedness".
Section A of the instrument, contains questions/items soliciting Demographic
Information from the respondents, while Section B is made up of questions/items on a continuum
where the respondent is to respond to the degree of agreement or disagreement by circling a
point on the continuum. Examples of some of the items on the sub dimensions of the SAQ are
Ability – “I did poorly when I did not work hard enough”; Luck – “Luck plays an important
part in everyone’s life”; Effort – “When I performed well, it was because I was particularly well
prepared” and Level of Giftedness – “I believe in my gift to pass my exams rather than
studying”.
The two psychometric properties of the instrument was determined using the Cronbach
Alpha which is a measure of the internal consistency and reliability of the instrument with a
value of 0.82, while the content validity of the instrument was established using the Lawshe
Content Validity Index (CVI), which gives a value of 0.70 respectively.
(c)
School Connectedness Scale (SCS)
The School Connectedness Scale (SCS) is a self-reporting instrument adapted from the School
Connectedness Scale developed by Brown and Evans, (2002); and it has been used for numerous
studies to measure school connection e.g. Dixon, (2007). The School Connectedness Scale (SCS)
was to measure students’ school connection in relation to their schools in all ramifications.
Section A of the instrument, contains questions/items soliciting Demographic Information of the
respondent, while Section B is made up of questions/items on a continuum to which the
respondent is to respond on the degree of agreement or disagreement by circling a point on the
continuum. The response format ranges from: SA - Strongly Agree; A - Agree; D - Disagree;
SD -
Strongly Disagree. Some examples of the items on the SCS are; “I have many opportunities to
make decisions in my school; I am comfortable talking with adults in this school about my
problems; the rules in my school are fair; I can reach my goals through this school and Students
of all ethnic groups are respected in this school”.
The two psychometric properties of the instrument was determined using the Cronbach
Alpha which is a measure of the internal consistency and reliability of the instrument with a
41 value of 0.85, while the content validity of the instrument was established using the Lawshe
Content Validity Index (CVI), which gives a value of 0.75 respectively.
(d)
Teachers’ Self-efficacy Scale (TSES)
A self-reporting instrument tagged the “Teachers’ Self-efficacy Scale (TSES)” was
adapted to measure Teachers’ Self-efficacy. This is an adapted instrument from Bandura, one of
the major proponents of the concept of Self-efficacy. Bandura developed this instrument several
years ago (1986) and it has been used for various studies all over the globe and the same
instrument will be used in this study. However, the instrument was re-validated. The instrument
consists of 30-items and the index have seven (7) dimensions, which was reduced to (5)
dimensions: efficacy to influence decision making, , instructional self-efficacy, disciplinary selfefficacy, efficacy to enlist parental involvement, , and efficacy to create a positive school
climate. Each item is measured on a 5-point Llikert scale anchored by the following: “1 - None
at all, 2 - Very little, 3 - Some extent, 4 - A good extent, to 5 - A great extent” (Bandura, 2006).
Section A of the instrument, contains questions/items soliciting for Demographic
Information of the respondents, while Section B, is made up of questions/items on a continuum
which the respondent is to respond to in terms of the degree of agreement or disagreement by
ticking the appropriate column or box on the continuum.
The psychometric properties were determined using the Cronbach Alpha which is a
measure of the internal consistency and reliability of the instrument with a value of 0.89, while
the content validity of the instrument was established using the Lawshe Content Validity Index
(CVI), which gives a value of 0.72 respectively.
Table 3.4 Summary of the psychometric properties of the instruments S/N
Instrument
1
Student’s
Adversity
Quotient®
Profile
®
(SAQP ) Students Attribution Questionnaire (SAQ) School Connectedness Scale (SCC) Teachers’ Self-efficacy Scale (TSES) 2
3
4
Reliability Coefficient
(Cronbach Alpa α)
42 Content Validity Coefficient
(Lawshe Content Validity Ratio)
0.79
0.70
0.82
0.85
0.89
0.70
0.75
0.72
The following formula, proposed by Lawshe (1975) was used to calculate the CVI which is a
quantitative indicator of the content validity of an instrument:
Content Validity Index (CVI) = [(E - (N / 2)) / (N / 2)]
Where: (a) N is the total number of judges or experts; (b) E is the number of judges or experts
who rated the item/instrument as essential or content valid.
The CVI is between the continuum -1.0 and 1.0. The closer to 1.0 the CVI is, the more essential
or content valid the instrument is considered to be and conversely, the closer to -1.0 the CVI is,
the more non-essential or non-content valid it is.
(e)
Achievement Profile Sheet (APS)
The Achievement Profile Sheet (APS) was designed by the researcher to obtain students’
May/June 2013 WASSCE numbers and to extract scores in Mathematics and English Language.
WAEC graded scores in Mathematics and English Language in the May/June 2013 WASSCE
were used as the index of Academic Achievement in both subjects.
3.6 Data collection and scoring procedure
The instruments were administered by six research assistants in collaboration with
the SS III year tutors in all the randomly selected schools. The capacity of the six research
assistants were strengthened in questionnaire administration in order for them to be abreast of
current trends and thereafter, the content of each instrument was explained to them. An abridged
version of what the questionnaires entail and the mode of administration were also shared with
all the SS III year tutors of all the schools involved in the study. . A letter of introduction from
the Institute of Education was collected for all the selected schools in order to enlist their consent
and cooperation; after which respondents were asked to complete the questionnaires under the
supervision of the research assistants and the SS III class tutors. The research assistants were
present to ensure proper and valid completion of all the questionnaires.
Once completed, the questionnaires were coded as shown in Table 3.6; to ensure
anonymity in publication of results. Only completed questionnaires were used. About 4,000
43 questionnaires were administered to students in the SS III intact classes. However, only 3,712
were found to be usable for the analysis.
The field administration and retrieval lasted for 12 weeks. Graded scores in Mathematics
and English Language for all the participating respondents in the May/June 2013 WASSCE were
extracted from the WAEC Computer Service Division (CSD) with official permission from the
Head of National Office of WAEC in Nigeria.
Table 3.5: Summary of the coding pattern of the instruments S/N
Instrument
Coding Pattern
Section A – Demographics
1
Student’s
Adversity Type of School: Private Sch-1; Public Sch-2
Gender: Male-1; Female-2
Profile Age: Below 15 years-1; 16 years and above-2
State: Lagos-1; Oyo-2
Quotient®
(SAQP®) 2
Students
Attribution Type of School: Private Sch-1; Public Sch-2
Questionnaire (SAQ) 3
School Connectedness
Scale (SCS) 4
Teachers’ Self-efficacy
Scale (TSES) Gender: Male-1; Female-2
Age: Below 15 years-1; 16 years and above-2
State: Lagos-1; Oyo-2
Section B
E.g. Not responsible at all1; Rarely responsible-2;
Sometimes responsible-3;
Often
responsible-4;
Completely responsible-5.
E.g. Very much like me-
4; Like me-3; Somewhat
like me -2; and Not like
me-1.
Type of School: Private Sch-1; Public Sch-2
Gender: Male-1; Female-2
Age: Below 15 years-1; 16 years and above-2
State: Lagos-1; Oyo-2
E.g. Strongly Agree-4;
Agree-3; Disagree-2; and
Strongly Disagree-1.
Type of School: Private Sch-1; Public Sch-2
Gender: Male-1; Female-2
Age: Below 15 years-1; 16 years and above-2
State: Lagos-1; Oyo-2
E.g. None at all-1; Very
Little-2; Some extent-3; A
good extent-4; and A Great
extent-5.
Scoring procedure for the students’ adversity quotient® profile (SAQP®)
The method of scoring the SAQP® and the interpretation of the calculated AQ® according to
Stoltz (1997, 2010) is as follows:
(i) Insert each of the 20 numbers circled on the Student’s Adversity Quotient Response
Profile (SAQP®) in the corresponding boxes that appear on Table 3.6.
(ii) Insert the total for each column in the corresponding box.
(iii) Add the four totals and then multiply that number by two for your final score.
44 Table 3.6: Scoring Procedure for the Student’s Adversity Quotient® Profile (SAQP®)
AQ scores fall into 3 broad bands, with an expected normal distribution.
Table 3.7: Interpretation of the SAQP® score
®
®
®
Low (0-59) AQ characteristics
Moderate (95-134) AQ characteristics
High (166-200) AQ characteristics
• Low levels of motivation,
energy, performance, and
persistence.
• Tendency to ‘catastrophise’
events.
• Underutilization of potential.
• Problems take a significant and
unnecessary toll, making climbing
difficult.
• A sense of helplessness and despair
arises from time to time.
• Able to withstand significant
adversity and continue forward and
upward progress.
• Maintains appropriate perspective
on events and responses to them.
• Tendency to be a Quitter.
• Tendency to be a Climber.
• Tendency to be a Camper.
Note: The two other categories of 60-94 and 135-165 are still interpreted as either under low
and high; because falling into these categories means the AQ® Score is either moderately low or
moderately high
3.7. Methods of data analysis
The data was analyzed using the Statistical Package for Social Sciences (SPSS) version
17.0, GENSTAT and MEDCAL version 12. The following statistical procedures were used:
CO2RE Dimension Display; Descriptive Statistics; Correlation and Hierarchical Multiple
Regression. Correlation analyses were used to determine the strength and nature of the
relationship between and among the variables, while Hierarchical Multiple Regression analysis
was used to explain variance components in all the models under consideration.
Hierarchical Multiple Regression is a variant of the basic multiple regression procedure
and a form of Multilevel Analysis that allows the researcher to specify a fixed order of entry for
variables in other to control for the effects of covariates or to test the effects of certain predictors
independent of the influence of others. In other words Hierarchical Multiple Regression is used
to evaluate the relationship between a set of independent variables (predictors) and the dependent
45 variable, controlling for or taking into account, the impact of a different set of independent
variables (control variables) on the dependent variable. In this study, the demographic variables
(i.e. age, sex, school ownership and state where school is located or geographical location) were
entered in the first block. Some student psychological constructs, including student attribution
and school connectedness were entered into the second block; followed by the only teacher
psychological construct (i.e. teacher self-efficacy) which was entered into the third block and
finally, the major variable of this study - Adversity Quotient was entered into the fourth and last
block. The interest in this study is in the R² change, i.e. the increase when the predictor variables
are added to the analysis and the overall R² for the model that includes both controls and
predictors.
The following are notable observations in running a Hierarchical Multiple Regression
analysis:
(1) The null hypothesis for the addition of each block of variables to the analysis is that the
change in R² (contribution to the explanation of the variance in the dependent variable)
is zero.
(2) If the null hypothesis is rejected, then our interpretation indicates that the variables in
block 2 have a relationship with the dependent variable, after controlling for the
relationship of the block 1 variables to the dependent variable, i.e. the variables in block
2 explain something about the dependent variables that were not explained in block 1.
(3) The key statistic in hierarchical regression is R² change (the increase in R² when the
predictor variables are added to the model that included only the control variables). If R²
change is significant, the R² for the overall model that includes both controls and
predictors will usually be significant as well since R² change is part of overall R².
In order to ensure sufficient power, an appropriately large sample size was required
though authorities differ in what they consider to be the minimum number of respondents
needed for a multiple regression analysis. Some recommend 15 times as many respondents
as independent variables. Howell (2002) notes that others have suggested that N should be at
least, 40 + k (where k is the number of independent variables). Tabachnick and Fidell (2007)
citing other authorities and Field (2000; 2005 and 2009) suggested a rule of the thumb using the
formula N ≥ 50 + 8m (where m is the number of IVs), to determine the minimum number of
46 participants necessary to run any variant of multiple regression. It was recommended that N
should be at least 50 + 8m for testing multiple correlation and at least 104 + m for testing
individual predictors. When applied to this research study, the minimum number of participants
required was N ≥ 114.
3.7.1. Building the regression equation model
The study involves a regression application in which there are several independent
variables, x , x , … , x ; A multiple linear regression model with p independent variables has the
1
2
k
equation
Y= βo+β1X1+ β2X2+β3X3+ ….. +βpXp+ε……………….. (1)
Where:
Β is the intercept and β determines the contribution of the independent variable x and ε is the
0
i
2
i
error term or random variable with mean 0 and variance σ .
However, since the interest in this study is to evaluate the relationship between a set of
independent variables (predictors) and the dependent variable, thereby controlling for or taking
into account the impact of a different set of independent variables (control variables) on the
dependent variable; there was need for the adoption of Hierarchical Multiple Regression which is
a variant of the basic multiple regression. The procedure is based on the following findings by
Tabachnick and Fidell (2007) and Field (2000; 2005 and 2009):
1.
Previous research has found that variables a, b, and c have statistically significant
relationships to the dependent variable z, both collectively and individually. It is believed
that variables d and e should also be included and, in fact, would substantially increase
the proportion of variability in z that can be explained. That is, treat a, b, and c as controls
and d and e as predictors in a hierarchical regression.
2.
It was found that variables a and b accounted for a substantial proportion of the
differences in the dependent variable z. In presenting the findings, someone asserted that
while a and b may have a relationship to z, differences in z are better understood by the
relationship that variables d and e have with z. In other words, treat d and e as controls to
47 show that predictors a and b have a significant relationship with z that is not explicable
by d and e.
3.
It was discovered that there are significant differences in the demographic characteristics
(a, b, and c) of groups of subjects in the control and treatment groups (d). To isolate the
relationship between group membership (d) and the treatment effect (z), do a hierarchical
regression with a, b, and c as controls and d as the predictor.
3.7.2. The regression equation models for the study
In this study, the demographic variables (i.e. age, sex, school ownership and state where
school is located or geographical location) were entered in the fourth and the last block. The only
teacher psychological construct (i.e. teacher self-efficacy) was entered into the third block;
followed by some student psychological constructs i.e. student attribution and school
connectedness which was entered into the second block; and finally, the major variable of this
study; Adversity Quotient was entered into the first block. The models are as stated:
YM1= βo+β1X1+ε ……………….. (2)
Where:
M1 - Model 1; X1 – Adversity Quotient (AQ®) and ε is the error term.
YM2= βo+β1X1+ β2X2+β3X3+ε……………….. (3)
Where:
M2 - Model 2; X1 – Adversity Quotient (AQ®); X2 – Attribution; X3 - School Connectedness and
ε is the error term.
YM3= βo+β1X1+ β2X2+β3X3+β4X4+ε……………….. (4)
Where:
M3 - Model 3; X1 – Adversity Quotient (AQ®); X2 – Attribution; X3 - School Connectedness; X4 Mathematics Teacher Self-Efficacy and ε is the error term.
48 YM4= βo+β1X1+ β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+ε……………….. (5)
Where:
M4 - Model 4; X1 – Adversity Quotient (AQ®); X2 – Attribution; X3 - School Connectedness; X4 Mathematics Teacher Self-Efficacy; X5 - State Where School Is Located; X6 - Gender of
Respondent; X7 - Age of Respondent; X8 - Type of School Ownership and ε is the error term.
Tolerance
According to Field (2000; 2005 and 2009), Tabachnick and Fidell, (1996; 2001 and 2007),
Myers (1990) and Bowerman and O’Connell, ( 1990); a tolerance level of less than 0.1 and a
VIF of greater than 10; is a cause for concern. Although Menard (1995); suggested that the
concern becomes grave when the value is below 0.2.
3.8. Methodological challenges
The researcher was faced with the following methodological challenges:
3.8.1: Negative stereotyped attitude towards filling of questionnaires
There is the general apathy to filling of questionnaires which was encountered in the schools
visited, and cutting across all the respondents in the study. However, because of the relative
newness of one of the major variables of the study – Adversity Quotient (AQ®) and the brief
overview given by the researcher in all the schools visited on this concept; there was a massive
zest to participate in the study.
3.8.2: Non-availability/Dearth of literature
A literature search in combination with desk review of one of the major variables of the study –
Adversity Quotient (AQ®) shows that there is a dearth of empirical studies as it relates to studies
carried out in Nigeria. The researcher was forced to rely mainly on literature by foreign scholars
in order to minimize this challenge.
3.8.3: Permission on instrument re-validation
It was a daunting task getting the necessary permission to use and re-validate the Adversity
Quotient (AQ®) Profile from PEAK Learning Inc., in the US. The organization was not ready for
49 any modification of any the items on the instrument; but through the insistence of my supervisor
and I on the need for the re-validation of the Adversity Quotient (AQ®) Profile in Nigeria, it was
eventually agreed that the re-validation can be done with some attached conditions. A
revalidation of the instrument was necessary in order to remove ambiguity in meanings
associated with some items on the instrument, due to the environmental and cultural differences
of the respondents for this study.
50 CHAPTER FOUR
RESULTS AND DISCUSSION
This chapter presents the results and discussions of the findings. The results are presented
and discussed with respect to the research questions highlighted in chapter one.
4.1
Research question 1
(a) What is the pattern of Adversity Quotient (AQ®) of the students in this study?
4.1a
Result
The AQ® for this study ranges from 40 to 200 i.e. from the lowest (40) to the highest
(200). A cursory look at Appendix 1 and Figure 4.1(a) also shows that the distribution of the
AQ® scores of the student respondents in this study followed the bell shaped normal distribution.
Low AQ
0–59
Moderate AQ
95–134
High AQ
166–200
Figure 4.1(a): Bell-shaped curve of AQ® score distribution
51 Interpretation and discussion
The distribution pattern of the AQ® scores showed that 21% (790) were of low AQ®; 62% (2306)
were of moderate AQ® while 17% (616) were of high AQ®. This implies that majority of the
SSS III students from the randomly selected schools that sat the May/June 2013 WASSCE in
Southwestern States in Nigeria were of moderate Adversity Quotient (AQ®); which ranges from
(95 - 134) – (See Appendix VI to VIII). The results further shows that the distribution of the
AQ® scores for the respondents in this study follows the bell shaped normal distribution as
postulated theoretically. This invariably shows that the observed distribution is consistent with
the theoretical distribution which is normally distributed. The above findings is congruent with
the assertion of Stoltz (1997), in which a theoretical normal distribution was postulated and
confirmed with the results from series of research studies across the globe. He further reiterated
the fact that something must be fundamentally amiss if the AQ®’s distribution did not follow a
normal distribution. This, according to him, is because the normal distribution in theory is a
reflection of the journey of life likened to an ascent. Some individuals will quit at the beginning,
whom he refers to as the quitters, majority will camp towards the mid-point in their ascent, these
are referred to as the campers and few, referred to as the climbers, will make the ascent to the
top. This ascension pattern always gives rise to a bell-like shape which represents the normal
distribution pattern.
(b) Is the observed distribution of the Students’ Adversity Quotient (AQ®) and achievement in
Mathematics and English Language in 2013 May/June WASSCE in Southwestern, Nigeria
consistent with the normal distribution curve?
52 4.1b
Result
Figure 4.1(b): Distribution of Candidates’ Achievement in Mathematics in 2013 May/June
WASSCE and the Corresponding Adversity Quotient (AQ®).
Figure 4.1 (c): Distribution of Candidates’ Achievement in Mathematics in 2013 May/June WASSCE
53 Interpretation and discussion
Figure 4.1(b) & (c) shows that the candidates’ achievement in Mathematics in May/June 2013
WASSCE was normally distributed and ranged from 6% to 86% with corresponding AQ® range
of 40 to 200. This implies that majority of the candidates’ achievement in Mathematics in
May/June 2013 WASSCE were on the average with the clustering of the bars towards the midpoint of the distribution chart. A cursory look at Figure 4.1(c) shows that the observed
distribution is consistent with the theoretical distribution which is normally distributed.
Figure 4.2(a): Distribution of Candidates’ Achievement in English Language in 2013
May/June WASSCE and the Corresponding Adversity Quotient (AQ®).
54 Figure 4.2(b): Distribution of Candidates’ Achievement in English Language in 2013 May/June
WASSCE.
Interpretation and discussion
Figure 4.2 (a) & (b) also shows that the candidates’ achievement in English Language in
May/June 2013 WASSCE was also normally distributed and ranged from 11% to 76% with
corresponding AQ® range of 40 to 186. It is observed also that most of the candidates’
achievement in English Language in May/June 2013 WASSCE was also on the average and the
observed distribution is in conformity with the theoretical distribution.
A cursory look at Appendix VI to VIII shows that a majority of the candidates whose
academic achievement in Mathematics and English Language in May/June 2013 WASSCE were
average and above are those with moderate AQ® and above; this means the higher the AQ® the
higher the candidates academic achievement in Mathematics and English Language in the
55 May/June 2013 WASSCE and vice-versa. This finding is in agreement with the findings of Zhou
(2009); where the study investigated the adversity quotient and academic achievement of
selected students in St. Joseph’s College, University Kebangsaan, Malaysia during the school
year 2008-2009 and discovered that the higher the respondents AQ® the better the GPA (grade
point average) during the first semester of the school year. The deduction from these findings is
that there is a link between the Adversity Quotient (AQ®) and academic achievement.
4.2
Research question 2
What is the distribution pattern of the CO2RE dimensions of the Students’ Adversity
Quotient (AQ®) in this study?
4.2
Result
The Adversity Quotient (AQ®) comprised four CO2RE dimensions. CO2RE is the
acronym for the four dimensions of the Adversity Quotient (AQ®) i.e. (C – Control; O – Origin;
R – Reach; and E – Endurance), these dimensions determine the overall Adversity Quotient
(AQ®). The Adversity Quotient (AQ®) reveals little about why the AQ® is in the upper (high),
middle (moderate) or lower (low) ranges, whereas a closer look at the CO2RE dimensions of the
candidates give an indication of where the issue of challenge is, as far as the Adversity Quotient
(AQ®) is concerned. Appendix VII is the display of the CO2RE scores of all the 3712 student
respondents in this study. The CO2RE Dimension Display or Graphing shows the dimensions
that have contributed to why the AQ® was low, moderate or high. For ease of interpretation and
presentation, fifteen (15) reproducible CO2RE dimension pattern to which the respondents in the
study can fall into according to Stoltz (1997; 2010) are as shown in Figures 4.2 (i to xv).
Interpretation and discussion
The upper and lower case letters in CO2RE of the selected student respondents under
consideration are adjusted to reflect their relative score range on each dimension; e.g. Co2Re
would reflect a student respondent profile which is higher on the C and R dimensions and lower
on O2 and E. The aggregate description of the CO2RE profile provides more in-depth
information than just the AQ®. This is because there is an interplay among the dimensions which
influences the overall profile.
56 15
25 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 co2re 13 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 11
18 46 50 50 48 48 46 46 44 44 42 42 40 40 38 38 36 36 34 34 32 32 30 30 28 28 26 26 24 24 22 22 20 20 18 18 16 16 14 14 12 12 10 10 Figure 4.3(i): Candidate 1
49
33 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 Co2re 29 13 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 25 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 18 50 50 48 48 46 46 44 44 42 42 40 40 38 38 36 36 34 34 32 32 30 30 28 28 26 26 24 24 22 22 20 20 18 18 16 16 14 14 12 12 10 10 Figure 4.3(ii): Candidate 2
cO2re 45
47 50 48 46 44 42 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 19 50 50 48 48 46 46 44 44 42 42 40 40 38 38 36 36 34 34 32 32 30 30 28 28 26 26 24 24 22 22 20 20 18 18 16 16 14 14 12 12 10 10 Figure 4.3(iii): Candidate 3
Figure 4.3(i): Candidate 1 – A candidate with this AQ® profile (i.e. with low scores in all the
dimensions) is likely going to suffer unnecessarily great difficulties when faced with adversity.
There is the accumulated effect of blaming self for bad events which is always perceived as far
reaching and long lasting.
Figure 4.3(ii): Candidate 2 – A candidate with this AQ® profile (i.e. with a high score on the
control dimension alone) have a feeling of some sense of control over adversity and its causes;
though it is perceived as far reaching, long-lasting and due to his/her fault.
Figure 4.3(iii): Candidate 3– A candidate with this AQ® profile (i.e. with a high score on the
ownership/origin dimension alone) have a feeling of some sense not blaming himself/herself
over adversity and its causes; though it is perceived as far reaching, long-lasting and due to
57 25 his/her fault. However, there is the ability of being accountable for doing something about the
results.
Generally, the results of the in-depth analysis of the Adversity Quotient (AQ®) through a
critical appraisal of the distribution pattern of the CO2RE dimensions of the scores of all the
3712 student respondents in this study shows that majority of the students have their area of
strengths in this order: C-first; O-second; R-third and E-last. This trend shows that majority of
the candidates have a feeling of some sense of control over adversity and its causes; the adversity
is perceived as far reaching, long-lasting and due to their fault; though there is a feeling
indicating a strong tendency to keep the adversity in its place; i.e. bad events are
compartmentalized and are prevented from leaking into other areas of life. However, there is a
strong sense of having the ability to be accountable for doing something about the results.
4.3
Research question 3
What type of correlation exists among the predictors (students’ Adversity Quotient (AQ®);
students’ attribution; students’ school connectedness; teachers’ self-efficacy; school ownership
type; gender; geographical location and age) and the criterion variables (students’ academic
achievement) in Mathematics in WASSCE in Southwestern, Nigeria?
4.3
Result
In order to answer this question, the original correlations among the eleven variables
were produced. Table 4.1 presents the correlation matrix of the bivariate relationships among the
variables.
Table 4.1 presents the intercorrelation matrix of the correlation coefficients of the
predictors (students’ Adversity Quotient (AQ®); students’ attribution; students’ school
connectedness; teachers’ self-efficacy; school ownership type; gender; geographical location and
age) and the criterion variables (students’ academic performance in Mathematics).
58 Table 4.1: Inter-correlation matrix of the predictor variables and the criterion variable
VARIABLE
CPM
TS
GR
AR
SSL
SA
SSC
MTSE
CPM
1.000
TS
-0.284*
1.000
GR
0.0422
-0.046*
1.000
AR
-0.088*
0.063*
0.056*
1.000
SSL
-0.435*
-0.095*
0.043*
0.071*
1.000
SA
-0.035*
0.101*
0.059*
0.068*
-0.037
1.000
SSC
-0.087*
0.116*
0.071*
0.051*
0.043
0.312*
1.000
MTSE
0.156*
0.126*
-0.050*
-0.053*
0.159*
-0.015*
0.016* 1.000
SAQ
0.056*
-0.013
0.021
-0.045
-0.055
-0.031
0.044
-0.130*
1.000
MEAN
40.41
1.79
1.53
1.95
1.39
2.92
3.16
113.72
3.45
SD
15.502
0.410
0.499
0.219
0.488
0.394
0.433
25.634
0.495
Key: CPM - Candidate Performance in Mathematics: TS – Type of School: GR – Gender of
Respondent; AR - Age of Respondent; SSL - State where school is located; SA – Students’
Attribution; SSC – Students’ School Connectedness; SAQ – Students’ Adversity Quotient;
MTSE – Mathematics Teachers’ Self-Efficacy; SD - Standard Deviation.
* Significant @ p < .05; n =3712
It is noted from Table 4.1 that at p < .05; the intercorrelation matrix of the correlation
coefficients of the predictors and the criterion variable (students’ academic achievement in
Mathematics) are mostly significant; though some are positive while others are negative. The
table shows that there is no multicollinearity between or among the variables of study. Table 4.1
also shows that there is a positive relationship between students’ academic achievement in
Mathematics in WASSCE and both students Adversity Quotient (AQ®) and Teachers’ Selfefficacy. The implication of this is that as the students’ Adversity Quotient (AQ®) increases, the
students’ academic achievement in Mathematics in WASSCE also increases. Those that are
negatively correlated with academic achievement are students’ school connectedness, attribution,
state where school is located and type of school. A general overview shows that students’
59 SAQ
academic achievement in Mathematics in WASSCE has the strongest relationship with
Mathematics Teachers’ Self-efficacy.
4.4
Research question 4
What type of correlation exists among the predictors (students’ Adversity Quotient (AQ®);
students’ attribution; students’ school connectedness; teachers’ self-efficacy; school ownership
type; gender; geographical location and age) and the criterion variables (students’ academic
achievement) in English Language in WASSCE in Southwestern, Nigeria?
4.4
Result
In order to answer this question, the original correlations among the eleven variables
were produced. Table 4.2 presents the correlation matrix of the bivariate relationships among the
variables.
Table 4.2 presents the intercorrelation matrix of the correlation coefficients of the predictors
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
teachers’ self-efficacy; school ownership type; gender; geographical location and age) and the
criterion variables (students’ academic achievement in English Language).
Table 4.2: Inter-correlation matrix of the predictor variables and the criterion variable
VARIABLE
CPEL
CPEL
1.000
TS
-0.558*
TS
GR
AR
SSL
1.000
60 SA
SSC
ELTSE
SAQ
GR
0.113*
-0.046*
1.000
AR
-0.094*
0.063*
0.056*
1.000
SSL
-0.218*
-0.095*
0.043*
0.071*
1.000
SA
-0.057*
0.101*
0.059*
0.068*
-0.037*
1.000
SSC
-0.066*
0.116*
0.071*
0.051*
0.043*
0.312*
1.000
ELTSE
0.036*
-0.013*
0.021*
-0.045*
-0.055*
-0.031
0.044
SAQ
0.057
0.126
-0.050
-0.053*
0.159*
-0.015
0.016* -0.130*
1.000
MEAN
40.99
1.79
1.53
1.95
1.39
2.92
3.16
3.45
113.72
SD
10.944
0.410
0.499
0.219
0.488
0.394
0.433
0.495
25.634
1.000
Key: CPEL - Candidate Performance in English Language: TS – Type of School: GR – Gender
of Respondent; AR - Age of Respondent; SSL - State where school is located; SA – Students’
Attribution; SSC – Students’ School Connectedness; SAQ – Students’ Adversity Quotient;
ELTSE - English Language Teachers’ Self-Efficacy; SD - Standard Deviation.
* Significant @ p < .05; n =3712
In relation to students’ academic achievement in English Language in WASSCE in
Southwestern, Nigeria, it is observed from Table 4.2 that at p < .05, there is no multicollinearity
between or among the variables of study. Also the intercorrelation matrix of the correlation
coefficients of the predictors and the criterion variable (students’ academic achievement in
English Language in WASSCE) are mostly significant though some are positive while others are
negative. The table shows that there is a positive relationship between students’ academic
achievement in English Language in WASSCE and three predictors, namely students’ Adversity
Quotient (AQ®), Teachers' Self-efficacy and Gender of students,.
An inference that can be drawn from this is that as the students’ Adversity Quotient
®
(AQ ) increases, their academic achievement in English Language in WASSCE in
Southwestern, Nigeria also increases. A general overview shows that students’ academic
achievement in English Language in WASSCE in Southwestern, Nigeria has the strongest
relationship with Gender of students.
Interpretation and discussion
61 Multicollinearity is detected by examining the tolerance for each independent variable.
Tolerance is the amount of variability in one independent variable that is not explained by the
other independent variables. Tolerance values less than 0.10 indicate collinearity. The detection
of collinearity in the regression output means the rejection of the interpretation of the
relationships as false. A critical inspection of tables 4.1 and 4.2, shows that there is no
multicollinearity between the predictors (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) and the criterion variables (students’ academic
performance in Mathematics and English Language). This is because none of the values of the
correlation coefficients are highly correlated with each other (i.e r>0.85). The implication of this
is that all the predictor variables in the study are good enough to be part of the models in
predicting achievement in either Mathematics or English Language. This is a clear indication of
non-violation of one of the major assumptions required for running a regression analysis. This is
in agreement with Tabachnick and Fidell’ (2007) views that multicollinearity amongst the
variables of interest must be resolved before proceeding with regression analysis. ThoughAgresti
and Finlay, (2013), were of the view that multicollinearity can be resolved by combining the
highly correlated variables through principal component analysis, or omitting any of the highly
correlated variables from the analysis.
The results also revealed that the intercorrelation matrix of the correlation coefficients of
the predictors and the criterion variables (students’ academic achievement in Mathematics and
English Language) are mostly significant; though some are positive while others are negative.
This view is supported by the results of the studies carried out by Jonas and Gozum, (2011) and
Rochelle, (2006); where there were significant correlations of academic achievement with
student related variables. Furthermore it was discovered from the results of this study that
students’ Adversity Quotient (AQ®) has a positive-significant relationship with students’
academic achievement in Mathematics and English Language in WASSCE. This finding
supports the findings of Williams, 2008, who have shown through the results of the study, that
measurement of AQ® is likely to be a better index of achieving success, not just for academic
achievement in education, but in other related social skills. Tantor, (2007) also attested to the fact
that the Adversity Quotient is a major component in the determination of academic achievement;
while Zhou (2009) in the same vain, supported the above assertion that the Adversity Quotient
62 (AQ®) is one of the core fibers of the foundational structure of the eventual outcome of the
teaching-learning process.
Another predictor, which has a positive relationship with students’ academic
performance, is teachers’ self-efficacy. This finding is in consonant with that of Bandura,
Skaalvik and Skaalvik, (2004) where they concluded that individual inherent beliefs are the best
indicators of the decisions individuals make throughout their lives. Imperatively, it follows that
teachers’ beliefs about their personal teaching abilities are a key indicator of teacher behavior,
decisions, and classroom organization. Therefore, in the teaching context, teacher efficacy is
expected to affect the goals teachers identify for the learning context as well as to guide the
amounts of effort and persistence given to the task. Some of the negative statistically significant
correlations like students’ school connectedness, attribution etc. and students’ academic
performance in Mathematics and English Language in WASSCE showed that irrespective of the
level of connection to the school or attribution for failure and success, both had negative
relationship with academic achievement in Mathematics and English Language in WASSCE. In
addition, the results also showed that gender has a positive-significant relationship with students’
academic achievement, but only in English Language. This result is supported by the assertions
of Onuka, (2007); Isiugo-Abanihe, (1997); and Emeke, (2012); from the results of their research
studies that gender is one of the personal variables that have been related to differences found in
academic achievement. The above views which corroborate the findings of this study show that
gender is significantly related to academic achievement, not just in Mathematics and English
Language only, but in all sphere of academic achievements in general.
Generally, the significance of the values of the correlation coefficients points to the fact
that irrespective of the numerical values; there is a degree of relationship that is not due to
chance between the predictor and criterion variables.
4.5
Research Question 5
(a) Does the obtained regression equation resulting from a set of eight (8) predictor variables
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
teachers’ self-efficacy; school ownership type; gender; geographical location (i.e. state where
63 school is located) and age) allow reliable prediction of students’ academic achievement in
Mathematics in WASSCE in Southwestern, Nigeria?
4.5(a) Result
The F-ratio in the ANOVA table as depicted in Table 4.3; tests whether the overall regression
model is a good fit for the data i.e. does it examines the degree to which the relationship between
the Dependent Variable and the Independent Variables are linear?. The table shows that the
independent variables (i.e. students’ Adversity Quotient (AQ®); students’ attribution; students’
school connectedness; teachers’ self-efficacy; school ownership type; gender; geographical
location and age) statistically and significantly predict the dependent variable (i.e. students’
academic achievement in Mathematics in WASSCE).
Table 4.3: Regression ANOVA in relation to Mathematics
Sum of
Squares
Model
1
2
3
4
Regression
df
Mean Square
1478.206
1
1478.206
Residual
522879.307
3710
140.938
Total
524357.512
3711
5871.068
3
1957.023
Residual
518486.444
3708
139.829
Total
524357.512
3711
23371.642
4
5842.910
Residual
500985.871
3707
135.146
Total
524357.512
3711
Regression
205729.343
8
25716.168
Residual
318628.170
3703
86.046
Total
524357.512
3711
Regression
Regression
F
Sig.
10.488
.001a
13.996
.000b
43.234
.000c
298.866
.000d
From Table 4.3; all the four specified models; [Model – 1: F (1, 3710) = 10.488, p < .05];
[Model – 2: F (3, 3708) = 13.996, p < .05.]; [Model – 3: F (4, 3707) = 43.234, p < .05.] and
[Model – 4: F (8, 3703) = 298.866, p < .05.] show that the regression models are good fits for the
data). This means that the relationship is linear and therefore all the four specified models
significantly predict the Dependent Variable (i.e. students’ academic achievement in
64 Mathematics in WASSCE in Southwestern, Nigeria). This is an indication that the test of
significance of the model using an ANOVA is not by chance but due to the predictor variables.
There are 3711 (N-1) total degrees of freedom. With eight predictors, the Regression effect has
8 degrees of freedom. The Regression effect is statistically significant, indicating that prediction
of the dependent variable is not by chance.
(b) How much of the total variance in students’ academic achievement in Mathematics in
WASSCE in Southwestern Nigeria is accounted for by student-teacher psychological constructs
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
teachers’ self-efficacy) and demographic factors (school ownership type; gender; geographical
location and age)?
4.5b
Result
Table 4.4: Model summary in relation to Mathematics
Change Statistics
R
Adjusted Std. Error of the Square
Model
R
R Square R Square
Estimate
F
Change Change
df1
df2
Sig. F Change
1
.053a
.003
.003
11.87172
.003 10.488
1
3710
.001
2
.106b
.011
.010
11.82494
.008 15.708
2
3708
.000
3
.211c
.045
.044
11.62523
.033 129.494
1
3707
.000
4
.626d
.392
.391
9.27610
.348 529.826
4
3703
.000
Table 4.4 shows the Model Summary of the regression analysis in relation to
Mathematics as the criterion variable. The "R" column represents the value of R, the Multiple
Correlation Coefficient. R is considered to be one measure of the quality of the prediction of the
dependent variable; which in this case, is students’ academic achievement in Mathematics in
WASSCE. A value of 0.626, from this research study indicates a good level of prediction.
The "R Square" column represents the R2 value (also called the Coefficient of
Determination), which is the proportion of variance in the dependent variable that can be
explained by the independent variables (technically, it is the proportion of variation accounted
65 for by the regression model above and beyond the mean model). The value of 0.392; shows that
all the independent or predictor variables in this study explained 39.2% of the variability of the
dependent variable. Which means that 39.2% of the total variance in students’ academic
achievement in Mathematics in WASSCE in Southwestern Nigeria is accounted for by studentteacher psychological constructs – X1-X4 ( i.e. students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy) and demographic factors –
X5-X8 ( i.e. school ownership type; gender; geographical location and age).
(c) How well can the full model predict scores of a different sample of data from the same
population or generalize to other samples?
4.5c
Result
To answer this question, a cross-validation was carried out on the model. This is the
assessment of the accuracy of a model across different samples. If a model can be generalized,
then it is capable of accurately predicting the same outcome variable from the same set of
predictors in a different group of people. If the model is applied to a different sample and
there is a severe drop in its predictive power, then the model clearly does not generalize.
Once there is a regression model, there are two main methods of cross-validation: (i)
adjusted R2 and
(ii) Data Splitting. Using the adjusted R2 Method; in SPSS, we have the
calculations for the values of R and R 2 , but also an adjusted R2. This adjusted value indicates
the loss of predictive power or shrinkage. Whereas R2 tells how much of the variance in Y is
accounted for by the regression model from the sample, the adjusted value tells h ow much
variance in Y would be accounted for if the model had been derived from the population
from which the sample was taken (see Table 4.4). SPSS derives the adjusted R2 using
Wherry’s equation. However, this equation has been criticized because it tells nothing about
how well the regression model would predict an entirely different set of data (i.e. how well
can the model predict scores of a different sample of data from the same population?). One
version of R2 that does tell us how well the models cross-validates uses Stein’s formula
which is shown in equation (4.1) (Stevens, 2002): In Stein’s equation, R2 is the unadjusted
value, n is the number of participants (3712) and k (8) is the number of predictors in the
model. For this research study; the value is as calculated using equation (4.1).
66 ......... (4.1)
adjusted R2 = 1- [(1.00216)(1.00216)(1.00026)(0.608)]
= 1-0.6108
= 0.389
This Stein’s value (0.389) is very similar to the observed value of R2 (0.392) indicating that
the cross-validity of this model is very good. Also the adjusted R2 gives us some idea of how
well our model generalizes and ideally, we would like its value to be the same, or very close to,
the value of R2. In this study, the difference for the final model is minute (in fact it is the
difference between the values 0.392 − 0.391= 0.001 (about 0.1%). This shrinkage means that
if the model were derived from the population rather than a sample, it would account for
approximately 0.1% less variance in the outcome. This means that the full model is
capable of predicting scores of a different sample of data from the same population
or the full model accurately represent the entire population.
67 Figure 4.4(a): The Normal P-P Plot of Regression Standardized Residual - Mathematics in 2013
May/June WASSCE.
In addition, the Normal Probability Plot (P-P) of the Regression Standardized Residual for this
study in relation to Mathematics as the criterion variable shows that all the points laid in a
reasonably straight diagonal line from bottom left to top right. The non-deviation of the plotted
points from the straight line shows the predictive nature of the models under consideration.
68 Figure 4.4(b): The Scatterplot of the Standardized Predicted Value - Mathematics in 2013 May/June
WASSCE.
Also, the rectangular distribution of the points in the scatter plot of the residuals, with
most of the scores concentrated in the center (along the point 0) with Standardized residuals that
ranges from -2 to +3 shows that there are no outliers. This supports Tabachnick and Fidell’s
(1996; 2007) assertion that Standardized residuals of more than 3.3 or less than -3.3 indicate
outliers.
(d) Which of the eight (8) predictor variables (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) are most influential in predicting students’ academic
achievement in Mathematics in WASSCE in Southwestern, Nigeria?
4.5d
Result
For each predictor, the regression weight, β, is the amount of change in the dependent
variable resulting from a one-unit change in the independent variable when all other
independent variables are held constant. However, the size of β is related to the scale used to
measure the independent variable; this is achieved by looking at the standardized coefficients
or beta values. These can vary from −1 to +1.
69 Table 4.5: Coefficients in relation to Mathematics
Standar
dized
Unstandardized Coeffici
Coefficients
ents
Model
1
B
(Constant)
.025
.008
46.798
1.967
.026
.008
-2.267
Sig.
3.239
.001
23.789
.000
.056
3.426
.001
.472
-.083
-4.798
-.647
.519
-.021
25.539
2.689
.035
.008
-1.879
ATTRIBUTION
TEACHER SELFEFFICACY 1 MATHEMATICS
Partia
Tolera
l
Part nce
VIF
.053
.053 1.000
1.000
.053
.056
.056
.996
1.004
.000
-.087 -.079 -.078
.900
1.111
-1.245
.213
-.049 -.020 -.020
.901
1.110
9.498
.000
.076
4.696
.000
.075
.984
1.016
.466
-.068
-4.035
.000
-.087 -.066 -.065
.895
1.117
-.497
.511
-.016
-.972
.331
-.049 -.016 -.016
.900
1.111
5.350
.470
.185 11.380
.000
70.318
2.767
25.416
.000
.015
.006
.032
2.458
.014
SCHOOL
CONNECTEDNESS
-.196
.374
-.007
-.524
ATTRIBUTION
-.344
.410
-.011
TEACHER SELFEFFICACY 1 MATHEMATICS
2.789
.383
.096
-13.356
.382
1.362
.308
.057
4.426
.000
-1.678
.707
-.031
-2.375
.316
-.422 -32.486
(Constant)
ATTRIBUTION
(Constant)
ADVERSITY QUOTIENT
SCHOOL
CONNECTEDNESS
(Constant)
ADVERSITY QUOTIENT
TYPE OF SCHOOL
GENDER OF
RESPONDENT
AGE OF RESPONDENT
STATE WHERE SCHOOL
-10.275
IS LOCATED
a. Dependent Variable: PERFORMANCE
.053
Zeroorder
.053
SCHOOL
CONNECTEDNESS
4
.886
t
.000
ADVERSITY QUOTIENT
3
37.902
Beta
Correlations
42.767
ADVERSITY QUOTIENT
2
Std.
Error
Collinearity
Statistics
.053
.077
.183
.184
.183
.980
1.020
.053
.040
.031
.976
1.025
.601
-.087 -.009 -.007
.883
1.133
-.838
.402
-.049 -.014 -.011
.890
1.123
7.288
.000
.093
.942
1.062
-.461 -34.945
.000
-.442 -.498 -.448
.945
1.059
.057
.983
1.018
.018
-.101 -.039 -.030
.970
1.031
.000
-.384 -.471 -.416
.972
1.028
.183
.052
.119
.073
Table 4.5 shows that Students’ Adversity Quotient (AQ®) (β1 = 0.032; t = 2.458, p <
0.05); Mathematics Teachers’ Self-efficacy (β4 = 0.096; t = 7.288, p < 0.05); School Ownership
Type (β5 = -0.461; t = -34.945; p < 0.05); Gender (β6 = 0.057; t = 4.426; p < 0.05); Age (β7 = 70 0.031; t = -2.375; p < 0.05) and State where school is located (β8 = -0.422; t = -32.486, p < 0.05)
are the most influential predictors of students’ academic achievement in Mathematics in
WASSCE in Southwestern, Nigeria.
(e) Are there any predictor variables (students’ Adversity Quotient (AQ®); students’ attribution;
students’ school connectedness; teachers’ self-efficacy; school ownership type; gender;
geographical location and age) that do not contribute significantly to the prediction models?
4.5e Result
Table 4.3(c) shows that only students’ attribution and school connectedness; did not contribute to
the prediction of the final overall full model, as depicted in model-4.
4.6
Research Question 6
(a) Does the obtained regression equation resulting from a set of eight (8) predictor variables
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
teachers’ self-efficacy; school ownership type; gender; geographical location age) allow reliable
prediction of students’ academic achievement in English Language in WASSCE in Southwestern
Nigeria?
4.6a
Result
In relation to English Language as the criterion variable; table 4.6 shows that the independent
variables (i.e. students’ Adversity Quotient (AQ®); students’ attribution; students’ school
connectedness; teachers’ self-efficacy; school ownership type; gender; geographical location and
age) statistically and significantly predict the dependent variable (i.e. students’ academic
achievement in English Language in WASSCE in South-Western States in Nigeria); for all the
four specified models; [Model – 1: F (1, 3710) = 4.790, p < .05]; [Model – 2: F (3, 3708) =
8.953, p < .05.]; [Model – 3: F (4, 3707) = 39.516, p < .05.] and [Model – 4: F (8, 3703) =
315.206, p < .05.].
71 Table 4.6: Regression ANOVA in relation to English Language
Sum of
Model
1
2
3
4
Squares
Regression
df
Mean Square
573.063
1
573.063
Residual
443865.607
3710
119.640
Total
444438.670
3711
3195.988
3
1065.329
Residual
441242.682
3708
118.997
Total
444438.670
3711
18175.468
4
4543.867
Residual
426263.202
3707
114.989
Total
444438.670
3711
Regression
180045.274
8
22505.659
Residual
264393.396
3703
71.400
Total
444438.670
3711
Regression
Regression
F
Sig.
4.790
.029a
8.953
.000b
39.516
.000c
315.206
.000d
The table 4.6 shows that the regression models are good fits of the data; this means that
the relationship is linear and therefore all the four specified models significantly predict the
Dependent Variable (i.e. students’ academic achievement in English Language in WASSCE in
Southwestern, Nigeria). This is also an indication that the test of significance of the model using
an ANOVA is not by chance but due to the predictor variables. There are 3711 (N-1) total
degrees of freedom. With eight predictors, the Regression effect has 8 degrees of freedom. The
Regression effect is statistically significant, indicating that prediction of the dependent variable
is not by chance.
(b) How much of the total variance in students’ academic achievement in English Language in
WASSCE in Southwestern Nigeria is accounted for by student-teacher psychological constructs
(students’ Adversity Quotient (AQ®); students’ attribution; students’ school connectedness;
72 teachers’ self-efficacy) and demographic factors (school ownership type; gender; geographical
location and age) differences?
4.6b
Result
Table 4.7: Model summary in relation to English Language
Change Statistics
Model
1
2
3
4
R
Adjusted Std. Error of R Square
Square R Square the Estimate Change F Change
R
a
.036
.085b
.202c
.636d
.001
.007
.041
.405
.001
.006
.040
.404
10.938
10.909
10.723
8.450
.001
.006
.034
.364
df1
4.790
11.021
130.269
566.773
df2
1
2
1
4
3710
3708
3707
3703
Sig. F Change
.029
.000
.000
.000
Table 4.7 shows the Model Summary of the regression analysis. The "R" column
represents the value of R, the Multiple Correlation Coefficient. R is considered to be one measure
of the quality of the prediction of the dependent variable; in this case, students’ academic
performance in English Language in WASSCE in South-Western States in Nigeria. A value of
0.636, from this research study indicates a good level of prediction.
The "R Square" R2 value (also called the Coefficient of Determination), is the proportion
of variance in the dependent variable that can be explained by the independent variables
(technically, it is the proportion of variation accounted for by the regression model above and
beyond the mean model). The value of 0.405; shows that all the independent or predictor
variables in this study explained 40.5% of the variability of the dependent variable. Which means
that 40.5% of the total variance in students’ academic achievement in English Language in
WASSCE in Southwestern Nigeria is accounted for by student-teacher psychological constructs
– X1-X4 i.e. (students’ Adversity Quotient (AQ®); students’ attribution; students’ school
connectedness; teachers’ self-efficacy) and demographic factors – X5-X8 i.e. (school ownership
type; gender; geographical location and age).
(c) How well can the full model predict scores of a different sample of data from the same
population or generalize to other samples?
73 4.6c
Result
To answer this question, a cross-validation was carried out on the model. This is the
assessment of the accuracy of a model across different samples. If a model can be generalized,
then it is capable of accurately predicting the same outcome variable from the same set of
predictors in a different group of people. If the model is applied to a different sample and there
is a severe drop in its predictive power, then the model clearly does not generalize.
Once there is a regression model there are two main methods of cross-validation: (i)
adjusted R2 and
(ii) Data Splitting. Using the adjusted R2 Method; in SPSS, we have the
calculations for the values of R and R2, but also an adjusted R2. This adjusted value indicates the
loss of predictive power or shrinkage. Whereas R2 tells how much of the variance in Y is
accounted for by the regression model from the sample, the adjusted value tells how much
variance in Y would be accounted for if the model had been derived from the population from
which the sample was taken (see Table 4.7). SPSS derives the adjusted R2 using Wherry’s
equation. However, this equation has been criticized because it tells nothing about how well the
regression model would predict an entirely different set of data (i.e. how well can the model
predict scores of a different sample of data from the same population?). One version of R2 that
does tell us how well the models cross-validates uses Stein’s formula which is shown in equation
(4.2) (Stevens, 2002): In Stein’s equation, R2 is the unadjusted value, n is the number of
participants (3712) and k (8) is the number of predictors in the model. For this study; the value is
calculated using the following equation (4.6.6.1):
................. (4.2)
adjusted R2 = 1- [(1.00216)(1.00216)(1.00026)(0.595)]
= 1-0.5977
= 0.402
This value (0.402) is very similar to the observed value of R2 (0.401) indicating that the
cross-validity of this model is very good. Also the adjusted R2 gives us some idea of how well
our model generalizes and ideally we would like its value to be the same, or very close to, the
value of R2. In this study, the difference for the final model is small (in fact it is the difference
74 between the values 0.405 − 0.404 = 0.001 (about 0.1%). This shrinkage means that if the model
were derived from the population rather than a sample, it would account for approximately 0.1%
less variance in the outcome. Which means either that the full model is capable of predicting
scores of a different sample of data from the same population, or the full model accurately
represents the entire population.
Figure 4.5(a): The Normal P-P Plot of Regression Standardized Residual – English Language in 2013
May/June WASSCE.
The Normal Probability Plot (P-P) of the Regression Standardized Residual for this study in
relation to Mathematics as the criterion variable shows that all the points laid in a reasonably
straight diagonal line from bottom left to top right. The non-deviation of the plotted points from
the straight line shows the predictive nature of the models under consideration.
75 Figure 4.5(b): The Scatterplot of the Standardized Predicted Value - English Language in 2013
May/June WASSCE. The rectangular distribution of the points in the scatter plot of the residuals, with most of
the scores concentrated in the center (along the point 0) with Standardized residuals that range
from -2 to +3 shows that there are no outliers. This supports Tabachnick and Fidell, (1996; 2007)
assertion that Standardized residuals of more than 3.3 or less than -3.3 indicate outliers.
(d)Which of the eight (8) predictor variables (students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) are most influential in predicting students’ academic
achievement in English Language in WASSCE in Southwestern Nigeria?
76 4.6.6.1d
Result
Table 4.8: Coefficients in relation to English Language
Stand
ardize
d
Unstandardized Coeffi
Coefficients cients
Model
1
B
(Constant)
ADVERSITY QUOTIENT
2
.886
.025
.008
46.798
1.967
.026
.008
T
Sig.
42.767
.000
3.239
.001
23.789
.000
.056
3.426
.001
-2.267
.472 -.083
-4.798
-.647
.519 -.021
.053
.053
1.000 1.000
.053
.056
.056
.996 1.004
.000
-.087 -.079 -.078
.900 1.111
-1.245
.213
-.049 -.020 -.020
.901 1.110
20.318
.000
.051
3.114
.002
-2.236
.472 -.082
-4.735
ATTRIBUTION
-.678
.519 -.022
TEACHER SELF
EFFICACY 2 - ENGLISH
-.851
.395 -.035
(Constant)
SCHOOL
CONNECTEDNESS
ATTRIBUTION
(Constant)
ADVERSITY QUOTIENT
SCHOOL
CONNECTEDNESS
4
37.902
Beta
(Constant)
ADVERSITY QUOTIENT
49.977
2.460
.024
.008
75.080
2.413
.015
.006
.053
Zero- Partia
Toleran
order
l
Part
ce
VIF
.053
ADVERSITY QUOTIENT
3
Std.
Error
Collinearity
Statistics
Correlations
.053
.051
.979 1.022
.000
-.087 -.078 -.077
.899 1.112
-1.306
.192
-.049 -.021 -.021
.900 1.111
-2.152
.031
-.043 -.035 -.035
.982 1.018
31.109
.000
.032
2.477
.013
.053
.051
.041
.032
.974 1.026
SCHOOL
CONNECTEDNESS
-.365
.374 -.013
-.976
.329
-.087 -.016 -.013
.885 1.130
ATTRIBUTION
-.311
.410 -.010
-.758
.449
-.049 -.012 -.010
.890 1.124
TEACHER SELF
EFFICACY 2 - ENGLISH
2.267
.319
.094
7.111
.000
-.043
.091
.932 1.073
.382 -.488
-37.046
.000
-.442 -.520 -.475
.945 1.058
.056
4.339
.000
-1.824
.705 -.034
-2.587
STATE WHERE SCHOOL
-10.799
IS LOCATED
.321 -.444
-33.668
TYPE OF SCHOOL
GENDER OF
RESPONDENT
AGE OF RESPONDENT
-14.161
1.335
.308
.052
.116
.071
.056
.984 1.016
.010
-.101 -.042 -.033
.975 1.026
.000
-.384 -.484 -.431
.946 1.057
Table 4.8 shows that Students Adversity Quotient (AQ®) (β1 = 0.032; t = 2.477, p <
0.05); Mathematics Teacher Self-efficacy (β4 = 0.094; t = 7.111, p < 0.05); School Ownership
Type (β5 = -0.488; t = -37.046; p < 0.05); Gender (β6 = 0.056; t = 4.339; p < 0.05); Age (β7 = -
77 0.034; t = -2.587; p < 0.05) and State where school is located (β8 = -0.444; t = -33.668, p < 0.05)
are the most influential predictors of students’ academic performance in English Language in
WASSCE in Southwestern Nigeria.
(e) Are there any predictor variables (students’ Adversity Quotient (AQ®); students’ attribution;
students’ school connectedness; teachers’ self-efficacy; school ownership type; gender;
geographical location and age) that do not contribute significantly to the prediction models?
4.6d Result
Table 4.8 shows that only student’ attribution and school connectedness; did not contribute to the
prediction of the final overall full model, as depicted in model-4.
4.7
Research Question 7
How important are students’ Adversity Quotient (AQ®); students’ attribution; students’ school
connectedness and teachers’ self-efficacy when each is used alone to predict students’ academic
achievement in Mathematics and English Language in WASSCE in Southwestern Nigeria?
4.7.7.1.
Result
The question is answered by looking at the zero-order and the semi-partial correlation
between the criterion variable (i.e. students’ academic achievement in Mathematics and English
Language in WASSCE) and students’ Adversity Quotient (AQ®); students’ attribution; students’
school connectedness and teachers’ self-efficacy. The larger the absolute value of the zero-order
coupled with the semi-partial correlation coefficient, the stronger the linear association and the
higher the level or extent of importance of the predictor variable.
78 Table 4.9: Zero order and semi-partial correlations in Mathematics and English Language
Predictor Variables
Attribution
Zero-Order
Correlation
in
Mathematics
-0.049
Semi%
Partial
Correl
ation
-0.011 0.01
Zero-Order Semi%
Correlation Partial
in English Correlation
Language
-0.049
-0.010 0.01
School connectedness
0.087
-0.013
0.01
0.087
-0.013
1.32
Teacher self-efficacy
0.183
0.093
0.90
-0.043
0.091
0.80
Adversity quotient
0.053
0.031
0.09
0.053
0.032
0.10
For students’ academic achievement in Mathematics in WASSCE in South-Western
States in Nigeria, the predictors are arranged in terms of importance as a predictor of choice:
Teachers Self-efficacy (0.90) > Adversity Quotient (AQ®) (0.09) > Students School
Connectedness (0.01) > Students Attribution (0.01). While for English Language, they are
arranged in terms of importance; the arrangement is Students’ School Connectedness (1.32) >
Teachers Self-efficacy (0.80) > Adversity Quotient (AQ®) (0.10) > Students Attribution (0.01).
Interpretation and discussion
The Multiple R is the correlation between the observed values of Y and the values of Y
predicted by the multiple regression models. Therefore, large values of the multiple R represent a
large correlation between the predicted and observed values of the outcome. A multiple R of 1
represents a situation in which the model perfectly predicts the observed data. As such, multiple
R is a gauge of how well the model predicts the observed data. The result for this study revealed
that the Multiple Correlation Coefficient, R which is a measure of the quality of the prediction of
the dependent variable; in this case, students’ academic performance in Mathematics and English
Language in WASSCE indicated good levels of prediction. This is buttressed by Gibson and
Dembo (1984) in whose view, the quality of prediction is premised on the numerical value
assigned to the multiple correlation coefficients in a study involving many predictor variables.
The results showed that the tests of whether the overall regression model is a good fit for
the data (i.e. examines the degree to which the relationship between the Dependent Variable and
the Independent Variables are linear) testifies to the predictability and linearity of the variables
of study. Since the relationship is linear it means all the four specified models significantly
79 predict the Dependent Variable. This result tells us that there is less than a 0.05% chance that an
F-ratio this large would happen if the null hypothesis were true. Therefore, it can be concluded
that the regression model results is a significantly better prediction of students’ academic
achievement than the mean value of students’ academic achievement. In short, the regression
model overall, predicts students’ academic achievement significantly well. This is in consonance
with Tabachnick and Fidell, (2007) who posit that the regression model results are a better
prediction of the predictor from the outcome or criterion variable. Overall, the inspection of the
structure coefficients suggests that, with the possible exception of one or two of the variables, all
the others were significant predictors. This is a strong indication of the predictiveness of the
underlying (latent) variable described by the model.
The b values (i.e the raw - unstandardized and standardized regression weights) represent
the gradient of the regression line. It is the outcome of the regression of academic achievement
on all the predictor variables in this study. Although this value is the slope of the regression line,
it is more useful to think of this value as representing the change in the outcome associated with
a unit change in the predictor;
for example, for every unit change in percentage of students
adversity quotient (that is, for every increase by a factor of one standard deviation on students’
adversity quotient), Y (student’s academic achievement) will increase by a multiple of 0.032
standard deviations. The results from the coefficients shows that at least only four out of the
eight predictor variables of interest in this study were significant in influencing students’
academic performance in Mathematics and English Language in 2013 May/June WASSCE. This
finding aligns with Field’s (2009) view that it is not all predictor variables in a study that are
capable of influencing the criterion variable. However, it must be noted that the raw regression
coefficients are partial regression coefficients because their values takes into account, the other
predictor variables in the model; they inform us of the predicted change in the dependent
variable for every unit increase in that predictor (Bakare, 2014). For example, adversity quotient
is associated with a partial regression coefficient of 0.015 and signifies that for every additional
point on the adversity quotient measure, one would predict a gain of 0.015 points on the
academic achievement measure, which invariably means that for a variable like school
connectedness associated with a partial regression coefficient of -0.365, it signifies that for
every additional point on the school connectedness measure, one would predict a decrement of
0.365 points on the academic achievement measure
80 The results showed that the R2 value (also called the Coefficient of Determination),
which is the proportion of variance in the dependent variable that can be explained by the
independent variables in this study is 0.392 and 0.405 respectively for Mathematics and English
Language. The means that 39.2% and 40.5% of the total variance in students’ academic
achievement in Mathematics and English Language in WASSCE is accounted for by studentteacher psychological constructs in this study. Generalization is a critical additional step and if it
is discovered that the model is not generalizable, then one must restrict any conclusions based on
the model to the sample used. However, the models in this study are generalizable, which means
the results of this study can be used in making inferences about the larger population of the
study. This supports Field’s (2009) view that the R2 value is a better index of how much
variation in the criterion variable is accounted for by the response variable than the adjusted R2.
The Zero-order Correlations lists the Pearson r-values of the dependent variable
(achievement in Mathematics and English Language in this case) with each of the predictors.
These values are the same as those shown in the correlation matrix. The Partial column under
Correlations lists the partial correlations for each predictor as it was evaluated for its weighting
in the model (the correlation between the predictor and the dependent variable when the other
predictors are treated as covariates). The Part column under Correlations lists the semi-partial
correlations for each predictor once the model is finalized; squaring these values informs us of
the percentage of variance each predictor uniquely explains. For example, type of school
accounts uniquely for about 2% of the variance of students’ academic achievement (- 0.475 * 0.475 = .0225 or approximately 0.02) given the other variables in the model.
81 CHAPTER FIVE
SUMMARY OF THE FINDINGS, CONCLUSION, IMPLICATIONS, LIMITATION
AND SUGGESTION FOR FURTHER STUDIES, AND RECOMMENDATION
This chapter highlighted the summary of findings discussed in chapter four; the conclusion,
educational implications, suggestions for further research and recommendations.
5.1 Summary of the findings
The major findings of this study are summarised below:
1.
The Adversity Quotient (AQ®) of all the student respondents in this study ranges
from 40 to 200 i.e. from the lowest (40) to the highest (200).
2.
The distribution pattern of the AQ® scores shows that 21% (790) were of low AQ®;
62% (2306) were of moderate AQ® while 17% (616) were of high AQ®.
3.
Candidates’ achievement in Mathematics in May/June 2013 WASSCE was normally
distributed and ranges from 6% to 86% with corresponding AQ® range of 40 to 200.
Majority of the candidates’ achievement in Mathematics in May/June 2013 WASSCE
was on the average.
4.
Also, candidates’ achievement in English Language in May/June 2013 WASSCE was
normally distributed and ranged from 11% to 76% with corresponding AQ® range of
40 to 186. It was also observed that majority of the candidates’ achievement in
English Language in May/June 2013 WASSCE was on the average.
5.
®
The observed distribution of randomly selected students’ Adversity Quotient (AQ )
and achievement in Mathematics and English Language in 2013 May/June WASSCE
in Southwestern Nigeria is consistent with the theoretical distribution which is
normal.
6.
Majority of those candidates whose academic achievement in Mathematics and
English Language in May/June 2013 WASSCE were average and above are those
with moderate AQ® and above.
7.
The CORE analysis of the Adversity Quotient (AQ®) shows that majority of the
students have their area of strengths in this order: C-first; O-second; R-third and Elast.
82 8.
The correlation coefficients of the predictors and the criterion variables (students’
academic achievement in Mathematics and English Language) are mostly significant;
though some are positive while others are negative.
9.
Students’ Adversity Quotient (AQ®), Mathematics and English Language Teachers’
Self-efficacy has a positive-significant relationship with students’ academic
achievement in Mathematics and English Language in WASSCE in Southwestern
Nigeria.
10.
Another predictor that has a positive-significant relationship with only students’
academic achievement in English Language in WASSCE in Southwestern, Nigeria is
the gender of the student respondents.
11.
School connectedness, attribution etc. and students’ academic performance in
Mathematics and English Language in WASSCE in Southwestern Nigeria have
negative-significant relationship with both academic achievement in Mathematics and
English Language in WASSCE.
12.
The tests of whether the overall regression model is a good fit for the data i.e.
examines the degree to which the relationship between the Dependent Variable and
the Independent Variables are linear testified to the predictability and linearity of the
variables of study i.e. the relationship is linear and therefore all the four specified
models significantly predict the Dependent Variable (i.e. students’ academic
achievement in Mathematics and English Language in WASSCE in Southwestern
Nigeria).
13.
The Multiple Correlation Coefficient, R, which is a measure of the quality of the
prediction of the dependent variable which in this case, is students’ academic
achievement in Mathematics and English Language in WASSCE in Southwestern,
Nigeria; recorded values of 0.626 and 0.636 respectively.
14.
The Coefficient of Determination, which is the proportion of variance in the
dependent variable that can be explained by the independent variables, gave values of
0.392 and 0.405 respectively for Mathematics and English Language. These values
showed that all the independent or predictor variables in this study explained 39.2%
and 40.5% of the variability of the dependent variable.
83 15.
The assessment of the accuracy of the models across different samples recorded
values of 0.1% for both models. This is an indication that the full models are
capable of predicting scores of a different sample of data from the same
population.
16.
Students Adversity Quotient (AQ®), Mathematics Teacher Self-efficacy, School
Ownership Type, Gender, Age and State where school is located were the most
influential predictors of students’ academic achievement in Mathematics and English
Language in WASSCE in Southwestern Nigeria.
17.
Students’ attribution and school connectedness did not contribute to the prediction of
the final overall full model for both Mathematics and English Language.
18.
The zero-order correlation between the criterion variable (i.e. students’ academic
achievement in Mathematics and English Language in WASSCE) and students’
Adversity Quotient (AQ®); students’ attribution; students’ school connectedness and
teachers’ self-efficacy showed that the predictors were arranged in terms of their
importance as predictors of choice: Students School Connectedness; Teachers’ Selfefficacy; Students’’ Attribution and Adversity Quotient (AQ®).
19.
The predictor variables (i.e. students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership
type; gender; geographical location and age) were well tolerated in the two models.
5.2. Conclusion
Adversity Quotient (AQ®) is an inner ability that enables people to turn their adverse
situations into life-changing advantage. Determining students’ AQ® and other related factors
that influence achievement is likely to provide necessary knowledge that would allow greater
understanding and better prediction of achievement beyond the individual’s natural intellectual
ability. It was in this vein that this study investigated the profile of Adversity Quotient (AQ®) of
selected SS III students in Southwestern Nigeria, and ascertained whether student-teacher
psychological or psychosomatic constructs are predictive of their academic achievement in
WASSCE.
84 The results of the study showed that majority of the SS III students from Southwestern
Nigeria are of moderate AQ®. In addition, 39.2% and 40.5% of the total variance in students’
academic achievement in Mathematics and English Language in WASSCE is accounted for by
student-teacher psychological constructs – X1 - X4 i.e. (students’ Adversity Quotient (AQ®);
students’ attribution; students’ school connectedness; teachers’ self-efficacy) and demographic
factors – X5 - X8 i.e. (school ownership type; gender; geographical location and age).
The import of this is that the data from this study supports the assertion that Adversity
Quotient (AQ®) and some other predictor variables in this study are actual predictors of students’
academic achievement in the 2013 May/June WASSCE in a school-based sample of Senior
Secondary Students in Southwestern Nigeria; and these must be taken into consideration for
effective teaching-learning process and better academic achievement in public examinations like
WASSCE.
5.3. Implications of the findings for the study
The findings of this study which sought to determine (i) the profile of Adversity Quotient
(AQ®) of SS III students in Southwestern Nigeria;
and (ii) whether student-teacher
psychological or psychosomatic constructs (i.e. students’ Adversity Quotient (AQ®); students’
attribution; students’ school connectedness; teachers’ self-efficacy; school ownership type;
gender; geographical location and age) are predictive of students’ academic achievement in
WASSCE. The study, which was conducted with a school-based sample of Senior
Secondary Students in Southwestern Nigeria, has grave implications for stakeholders within the
educational sector.
1.
This critical ability to overcome adversity i.e. the AQ® has long been the fuel for many
inspirational stories, yet it seems to be a quality one either possesses or lack from birth,
but it can be learnt or improved upon and this process can be set in motion within the
school system by highly efficacious teachers who are one of the major components of the
teaching-learning process.
2.
In view of the moderate level of Adversity Quotient (AQ®) identified among students in
this study, the implication of this for academic achievement in WASSCE is that students
are likely to be performing averagely due to the moderate level of their AQ®. Hence the
need for the inclusion of programmes that can enhance AQ.
85 3.
The AQ® should not be viewed as a whole sum but as an inter-play of underlying
dimensions that gives the overall ability of an individual student to surmount educational
and other societal adversities that are capable of affecting academic achievement.
4.
The negative statistically significant correlations of students’ school connectedness,
attribution etc. with students’ academic performance in Mathematics and English
Language observed in the study implies that irrespective of the level of connection to the
school or students’ pattern of attribution for failure or success, they do not correlate
positively with the pattern of students’ achievement in Mathematics and English
Language in WASSCE in Southwestern Nigeria.
5.
The linearity of the relationship between the dependent and the independent variables is
an indication that the predictor variables significantly predict the Dependent Variable, the
import of which is that the combinations of all the predictor variables in this study are
capable of predicting students’ academic achievement in Mathematics and English
Language in WASSCE in Southwestern Nigeria.
6.
The ascribed percentage of 39.2% and 40.5% which is an indication of the total variance
in students’ academic achievement in Mathematics and English Language in WASSCE
respectively is accounted for by student-teacher psychological constructs in this study.
This implies that of all the variables in the whole world (running into thousands), that are
capable of affecting academic achievement; if these eight (8) predictor variables
accounted for the aforementioned high percentage; then there is the need to improve on
these variables in every strata of national educational pursuit.
7.
The assessment of the accuracy of the models across different samples shows that the full
models are capable of predicting scores of different samples of data from the same
population. The implication of this is that the results of this research study can be
generalized to the population. This is an indication of the fact that inferences
can be
made in relation to the whole student/teacher population in southwestern Nigeria, based
on the results from the randomly selected samples from the selected states in
this study. .
86 5.4. Limitation and Suggestion for further studies
(1) This study was limited to the Southwestern part of Nigeria. It was further limited to
randomly selected private and state owned schools in Oyo and Lagos States respectively. It is
therefore of paramount importance for this type of study to be replicated in all geo-political
zones of the country for a sound comparison of the AQ®; school connectedness, attribution and
self-efficacy constructs across the whole country.
(2) The Hierarchical Multiple Regression which is a form of Multilevel Analysis that was
restricted to main effects was used in this study. An extension of this for further research is the
Moderated or interaction aspect that can be included for further study; which is capable of
providing a more robust result and interpretation; especially, when the criterion variable is a
single entity for the study.
(3) The results of this study showed that the AQ® though statistically significant in
predicting students’ academic performance in both Mathematics and English Language in 2013
May/June WASSCE in selected schools in Southwestern Nigeria, yielded only a small level of
prediction. One reason for this; which is a likely limitation for this study and which can be
improved upon for further research study is in the area of the student’s imagination of the
occurrence of the events to be rated as depicted on the Student’s Adversity Quotient Profile
(SAQP). This is a difficult concept for a majority of the students in public schools, despite the
initial detailed explanation given by the researcher on how to go about filling the questionnaires.
Organization of sound counselling sessions to address this apathy in filling of questionnaire both
by the student and the teacher populace will go a long way in getting better data for research
studies.
(4) The results of this study can lead to testable experimental hypotheses; which is a
foundational base for undertaking and supporting subsequent true experimental research capable
of improving on all the variables in this study; especially the AQ® construct. It has been shown
that a majority of the student respondents are of average AQ® with corresponding average result.
Since AQ® can be learned and improved upon; an experimental study needs to be conducted that
will provide an intervention capable of enhancing the AQ® construct in students.
87 5.5. Recommendations
The following recommendations are made based on the findings of the study:
1. School authorities should make available, educational materials on AQ®, which would
include its importance, how it determines one’s success and how it can be improved
upon in the school libraries. This may be through periodic newspaper analyses or
reviews, highlighting the various types of adversities and the expected students’
responses to such.
2. Teachers should be encouraged to organize discussions and debates in order to educate
students on the need for improving their AQ®. This may also be done through
workshops and seminars on positive thinking and personality development
programmes.
3. Assignments on biographies of people within and outside the immediate environment
of students who have distinguished themselves through dint of discipline, hard-work
and sheer bravery should be assigned to the students to enable them learn from their
examples and also encourage the use of discovery learning, role-play, game-play and
similar techniques to encourage students to strive hard in overcoming difficult
situations and strengthening their survival instincts.
4. Teacher-parent fora should be organized for parents to help them realize the increasing
adversities in their children’s lives and equip them with the ability to help their
children to develop better responses to these adversities.
5. Government should provide an enabling environment suitable for the teachinglearning process in all schools, thereby minimizing, if not totally eradicating
adversities from such quarters.
6. The national curriculum should be designed in such a way as to teach social and other
life skills (e.g. active listening, friendship making, decision making, problem solving
and assertiveness) which are basic requirements for life-long learning.
88 5.6. Contribution to knowledge
1. The findings of the study will form a major methodological guide for enhancing psychosocial
supports by teachers and other trained stakeholders within the educational domain in Nigeria.
2. The findings of the study which have shown that Students’ Adversity Quotient (AQ®) and the
other related variables causal links to academic achievement should be taken into cognisance in
improving existing guidelines for effective teaching and classroom management within the
educational sector in Nigeria.
3. The findings of this study will form a major addition to the literary works on the concept of
Adversity Quotient (AQ®) and other related variables since there is a dearth of research that
aligns Adversity Quotient (AQ®) and other related variables with academic achievement in
Africa and Nigeria in particular.
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105 APPENDIX V
WEST AFRICAN EXAMINATIONS COUNCIL
INTER-OFFICE MEMORANDUM
From:
Bakare, B.M. (Jnr)
SAR (Research)
RD & WIO,
Lagos, Nigeria
To:
The HNO
WAEC, Yaba
Thro: HOD (TA)
WAEC, Yaba
Lagos, Nigeria
Thro: DR/ Ag. Head
RD & WIO
Lagos, Nigeria
Thro: DR/ Head
LS-RD
Lagos, Nigeria
27th August, 2013
Ref: MBJ/STATOFPERF/HNO/VOL1/01
REQUEST FOR STATISTICS OF ACHIEVEMENT
I am currently undergoing a Ph.D programme at the International Centre for Educational
Evaluation (ICEE), Institute of Education; University of Ibadan, Ibadan. The programme was
approved and it is being sponsored by Council.
I am to undertake a fieldwork for the purpose of data collection for my eventual Thesis
titled “Students’ Adversity Quotient (AQ®), Attribution, School Connectedness and Teachers’
Self-efficacy as predictors of Academic Performance in the West African Senior School
Certificate Examination (WASSCE) in South-West Nigeria”; where the index of achievement
for the category of respondents that are students, is their achievement scores in Mathematics and
English Language in the May/June 2013 WASSCE.
To this end, it will be highly appreciated if I could be furnished with the following
information.
1. Scores of randomly selected respondents (i.e. students that participated in filling the
Questionnaires during the examination period) in Mathematics and English Language
in the May/June 2013 WASSCE (See attached list A of respondent’s details in the
May/June 2013 WASSCE).
2. School Scores of randomly selected schools in Mathematics and English Language in
the May/June 2013 WASSCE (See attached list B of randomly selected schools in
the May/June 2013 WASSCE).
Thanks immensely sir, for a favourable consideration of my humble request
Thank you
Bakare, B.M. (Jnr)
SAR (Research)
106 APPENDIX VA
LETTER OF PERMISSION
From Jeff Thompson <[email protected]>
To Bakare Babajide Mike Jnr<[email protected]>
Jun 10, 2013
LETTER OF PERMISSION
Thank you for your thoughtful and thorough responses. We appreciate your understanding the
parameters that we all must abide by to protect the integrity of AQ and the associated research.
That said, we grant you permission to proceed with your research and the adapted profile within
the guidelines agreed to below. Keep us posted on your progress. Good luck!
Jeff
Dr. Jeff Thompson
3940 Broad Street, Suite 7-385
San Luis Obispo, CA 93401
p 805.595.7775
c 805.712.2314
f 805.595.7771
e [email protected]
PEAK Learning, Inc.
www.peaklearning.com
www.3gmindset.com
107 APPENDIX VB
LETTER OF AGREEMENT
108 109