students' adversity quotient and related factors as predictors of ...

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

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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

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ABSTRACT Adversity Quotient (AQ) is an innate ability that enables people turn their adverse situations into life-changing 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

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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.

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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 Isiugo-

Abanihe 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

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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

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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

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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.

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ABBREVIATION AND ACRONYMS

Page

WAEC - West African Examinations Council. i

WASSCE - West African Senior School Certificate Examination.

NERDC - Nigerian Educational Research and Development Council.

NECO - National Examination Council.

CO2RE - Control; Ownership; Reach; and Endurance.

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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

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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

3.8 Methodological Challenges in this work 86

CHAPTER FOUR: RESULTS AND DISCUSSION

4.1 Research Questions, Results, Interpretation and Discussion 88

CHAPTER FIVE: SUMMARY OF FINDINGS, 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

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5.4. Limitation and Suggestion for further studies 128

5.5. Recommendations 129

5.6. Contribution to knowledge 130

REFERENCES 131

APPENDICES 147

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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 157

Appendix VIB Achievement Profile Sheet (PPS) - List B1 – Oyo State &

List B2 – Lagos State 159

Appendix VII Frequency Distribution of the (AQ®) of the Student Respondents 161

Appendix VIII CO2RE Dimensions of all the Student Respondents 164

Appendix IX Descriptive Statistics of the CO2RE Dimensions Of AQ® and Student

Academic Achievement in 2013 May/June WASSCE in Mathematics

and English Language 175

Appendix X Selected School 181

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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 75

Table 3.3 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) 100

Table 4.2 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

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LIST OF FIGURES

Pages

Figure 1.1 Students’ Achievement in Mathematics in WASSCE

between 2000 and 2012 7

Figure 1.2 Students’ Achievement in English Language in WASSCE

between 2000 and 2012 8

Figure 2.1 Triadic Reciprocal Determinism Model 22



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 88

Figure 4.1(b) Distribution of Candidates’ Achievement in Mathematics

in 2013 May/June WASSCE and the Corresponding Adversity Quotient (AQ®)’s. 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

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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 109

Figure 4.4(b) The Scatterplot of the Standardized Predicted Value - Mathematics in 2013 May/June WASSCE 109

Figure 4.5(a) The Normal P-P Plot of Regression Standardized Residual

– English Language in 2013 May/June WASSCE 116

Figure 4.5(b) The Scatterplot of the Standardized Predicted Value –

English Language in 20132 May/June WASSCE 117

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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)

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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

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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.

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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

22

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’

23

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.

24

Figure 1.1: Students’ Achievement in Mathematics in WASSCE between 2000 and 2013; Source: WAEC TDD, Ogba

25

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

26

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.

27

Table 1.1: Statistics of Achievement WASSCE in Mathematics

Source: The West African Examinations Council (WAEC), TDD, Ogba Table 1.2: Statistics of Achievement in WASSCE in English Language

Source: The West African Examinations Council (WAEC), TDD, Ogba

SUBJECT YEAR SEX TOTAL ENTRY

TOTAL SAT

TOTAL CREDIT

1-6

TOTAL PASS

7-8

FAIL

MATHEMATICS 2008 MALE 702956 688884 381258 (55.34)

171617 (24.91)

124888 (18.13)

FEMALE 589934 579329 345140 (59.58)

130649 (22.55)

93730 (16.18)

2009 MALE 755955 741577 330397 (44.55)

195026 (26.30)

186981 (25.21)

FEMALE 617054 606951 303985 (50.08)

149609 (24.65)

128757 (21.21)

2010 MALE 727165 713240 283609 (39.76)

200412 (28.10)

207682 (29.12)

FEMALE 604209 593295 264456 (44.57)

163508 (27.56)

147700 (24.89)

2011 MALE 844546 825971 315563 (38.21)

263352 (31.88)

244844 (29.64)

FEMALE 695595 682994 293303 (42.94)

211312 (30.94)

176568 (25.85)

2012 MALE 937102 915637 438468 (47.88)

270508 (29.54)

183244 (20.01)

FEMALE 758776 742720 400411 (53.91)

208011 (28.00)

115498 (15.55)

SUBJECT YEAR SEX TOTAL ENTRY

TOTAL SAT

TOTAL CREDIT

1-6

TOTAL PASS

7-8

FAIL

ENGLISH LANGUAGE

2008 MALE 702971 692132 224970 (32.50)

216488 (31.27)

239254 (34.56)

FEMALE 589939 582034 221315 (38.02)

189454 (32.55)

160868 (27.63)

2009 MALE 755955 745952 286453 (38.40)

219851 (29.47)

197652 (26.49)

FEMALE 617054 609773 276891 (45.40)

180573 (29.61)

117312 (19.23)

2010 MALE 727172 713886 229088 (32.09)

217819 (30.51)

247407 (34.65)

FEMALE 604209 593859 230316 (38.78)

189903 (31.97)

158270 (26.65)

2011 MALE 844546 829719

444544 (53.57)

207853 (25.05)

174354 (21.01)

FEMALE 695595 684445 422148 (61.67)

158523 (23.16)

101569 (14.83)

2012 MALE 937102 916071 499509 (54.52)

218279 (23.82)

176780 (19.29)

FEMALE 758776 742816 471169 (63.43)

158721 (21.36)

96015 (12.29)

28

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 adult-

student 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;

29

(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

30

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

31

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’


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


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

32

connectedness; teachers’ self-efficacy) and demographic factors (school ownership type;

gender; geographical location and age) differences?



(d)Which of the eight (8) predictor variables (students’ Adversity Quotient (AQ®); students’


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’


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

33

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.

34

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.

35

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 review of related Literature at

[email protected])

36

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).

37

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

38

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 DISTRICTs (EDs)

District One

District Two

District Three

District Four

District Five

District Six

LOCAL GOVERNMENT AREAs (LGAs)

Agege Ikorodu Epe Apapa Ajeromi-Ifelodun

Ikeja

Alimosho Kosofe Eti-Osa Lags Mainland

Amuwo-Odofin

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 TNSS NPS NTS

1 Agege; Alimosho; Ifako-Ijaye. 38 10

2258

10 20

2 Ikorodu; Kosofe; Somolu. 61 10 10 20

5 Ajeromi-ifelodun; Amuwo-odofin; Badagry; Ojo. 70 10 10 20

Total 9 169 30 2258 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.

39

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

(Urban)

Ibadan North; Ibadan

South- West; Ibadan

South-East; Ibadan

North-East; Ibadan

North-West.

Ibadan North;

Ibadan North-East;

Ibadan North-West.

Lagelu;

60

19

9

22

17

5

2

6

1454

30

60

2 - Ibadan Less

City (Rural)

Akinyele; Ido;

Oluyole; Lagelu

Egbeda; Ona-ara.

TOTAL 4 110 30 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

40

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

41

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

42

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 self-

efficacy, 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 Reliability Coefficient

(Cronbach Alpa α) Content Validity Coefficient

(Lawshe Content Validity Ratio) 1 Student’s Adversity Quotient® Profile

(SAQP®) 0.79 0.70

2 Students Attribution Questionnaire (SAQ) 0.82 0.70 3 School Connectedness Scale (SCC) 0.85 0.75 4 Teachers’ Self-efficacy Scale (TSES) 0.89 0.72

43

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

44

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 Section B

1 Student’s Adversity

Quotient® Profile

(SAQP®)

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. Not responsible at all-1; Rarely responsible-2; Sometimes responsible-3; Often responsible-4; Completely responsible-5.

2 Students Attribution

Questionnaire (SAQ)


E.g. Very much like me-4; Like me-3; Somewhat like me -2; and Not like me-1.

3 School Connectedness

Scale (SCS)


E.g. Strongly Agree-4; Agree-3; Disagree-2; and Strongly Disagree-1.

4 Teachers’ Self-efficacy

Scale (TSES)


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.

45

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.

• Tendency to be a Quitter.

• Underutilization of potential. • Problems take a significant and unnecessary toll, making climbing difficult. • A sense of helplessness and despair arises from time to time.

• Tendency to be a Camper.

• Able to withstand significant adversity and continue forward and upward progress. • Maintains appropriate perspective on events and responses to them.

• Tendency to be a Climber.

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

46

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

47

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, x1, x

2, … , x

k ; A multiple linear regression model with p independent variables has the

equation

Y= βo+β1X1+ β2X2+β3X3+ ….. +βpXp+ε……………….. (1) Where:

Β

0 is the intercept and β

i determines the contribution of the independent variable x

i and ε is the

error term or random variable with mean 0 and variance σ2.

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

48

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.

49

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

50

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.

51

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.

Figure 4.1(a): Bell-shaped curve of AQ® score distribution

Low AQ Moderate AQ High AQ 0–59 95–134 166–200

52

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?

53

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

54


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 mid-

point 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®).

55

Figure 4.2(b): Distribution of Candidates’ Achievement in English Language in 2013 May/June WASSCE.


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

56

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.


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).


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.

57

Figure 4.3(i): Candidate 1 Figure 4.3(ii): Candidate 2 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

co2re 50 50 50 50 48 48 48 48 46 46 46 46 44 44 44 44 42 42 42 42 40 40 40 40 38 38 38 38 36 36 36 36 34 34 34 34 32 32 32 32 30 30 30 30 28 28 28 28 26 26 26 26 24 24 24 24 22 22 22 22 20 20 20 20 18 18 18 18 16 16 16 16 14 14 14 14 12 12 12 12 10 10 10 10

15

25 13 11

46 18

Co2re 50 50 50 50 48 48 48 48 46 46 46 46 44 44 44 44 42 42 42 42 40 40 40 40 38 38 38 38 36 36 36 36 34 34 34 34 32 32 32 32 30 30 30 30 28 28 28 28 26 26 26 26 24 24 24 24 22 22 22 22 20 20 20 20 18 18 18 18 16 16 16 16 14 14 14 14 12 12 12 12 10 10 10 10

49

33 29 13 18

cO2re 50 50 50 50 48 48 48 48 46 46 46 46 44 44 44 44 42 42 42 42 40 40 40 40 38 38 38 38 36 36 36 36 34 34 34 34 32 32 32 32 30 30 30 30 28 28 28 28 26 26 26 26 24 24 24 24 22 22 22 22 20 20 20 20 18 18 18 18 16 16 16 16 14 14 14 14 12 12 12 12 10 10 10 10

25 45

47

19 25

58

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.


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).

59

Table 4.1: Inter-correlation matrix of the predictor variables and the criterion variable

VARIABLE CPM TS GR AR SSL SA SSC MTSE SAQ

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’ Self-

efficacy. 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’

60

academic achievement in Mathematics in WASSCE has the strongest relationship with

Mathematics Teachers’ Self-efficacy.


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


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 TS GR AR SSL SA SSC ELTSE SAQ

CPEL 1.000

TS -0.558* 1.000

61

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 1.000

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

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.


62

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’


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

63

(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


teachers’ self-efficacy; school ownership type; gender; geographical location (i.e. state where

64

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

Model Sum of Squares df Mean Square F Sig.

1 Regression 1478.206 1 1478.206 10.488 .001a

Residual 522879.307 3710 140.938 Total 524357.512 3711

2 Regression 5871.068 3 1957.023 13.996 .000b Residual 518486.444 3708 139.829 Total 524357.512 3711

3 Regression 23371.642 4 5842.910 43.234 .000c Residual 500985.871 3707 135.146 Total 524357.512 3711

4 Regression 205729.343 8 25716.168 298.866 .000d Residual 318628.170 3703 86.046 Total 524357.512 3711

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

65

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


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

Model R R Square

Adjusted

R Square

Std. Error of the

Estimate

Change Statistics

R

Square

Change

F

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

66

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 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).



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 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 4.4). 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).

67

......... (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.

68

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.

69

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’



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.

70

Table 4.5: Coefficients in relation to Mathematics

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

Correlations Collinearity

Statistics

B Std.

Error Beta Zero-order

Partial Part

Tolerance VIF

1 (Constant) 37.902 .886 42.767 .000

ADVERSITY QUOTIENT .025 .008 .053 3.239 .001 .053 .053 .053 1.000 1.000 2 (Constant) 46.798 1.967 23.789 .000

ADVERSITY QUOTIENT .026 .008 .056 3.426 .001 .053 .056 .056 .996 1.004 SCHOOL CONNECTEDNESS -2.267 .472 -.083 -4.798 .000 -.087 -.079 -.078 .900 1.111

ATTRIBUTION -.647 .519 -.021 -1.245 .213 -.049 -.020 -.020 .901 1.110 3 (Constant) 25.539 2.689 9.498 .000


ATTRIBUTION -.497 .511 -.016 -.972 .331 -.049 -.016 -.016 .900 1.111 TEACHER SELF-EFFICACY 1 - MATHEMATICS

5.350 .470 .185 11.380 .000 .183 .184 .183 .980 1.020

4 (Constant) 70.318 2.767 25.416 .000 ADVERSITY QUOTIENT .015 .006 .032 2.458 .014 .053 .040 .031 .976 1.025 SCHOOL CONNECTEDNESS -.196 .374 -.007 -.524 .601 -.087 -.009 -.007 .883 1.133

ATTRIBUTION -.344 .410 -.011 -.838 .402 -.049 -.014 -.011 .890 1.123 TEACHER SELF-EFFICACY 1 - MATHEMATICS

2.789 .383 .096 7.288 .000 .183 .119 .093 .942 1.062

TYPE OF SCHOOL -13.356 .382 -.461 -34.945 .000 -.442 -.498 -.448 .945 1.059 GENDER OF RESPONDENT 1.362 .308 .057 4.426 .000 .052 .073 .057 .983 1.018

AGE OF RESPONDENT -1.678 .707 -.031 -2.375 .018 -.101 -.039 -.030 .970 1.031 STATE WHERE SCHOOL IS LOCATED -10.275 .316 -.422 -32.486 .000 -.384 -.471 -.416 .972 1.028

a. Dependent Variable: PERFORMANCE

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 = -

71

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;


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.


(a) Does the obtained regression equation resulting from a set of eight (8) predictor variables


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.].

72

Table 4.6: Regression ANOVA in relation to English Language

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 573.063 1 573.063 4.790 .029a

Residual 443865.607 3710 119.640

Total 444438.670 3711

2 Regression 3195.988 3 1065.329 8.953 .000b

Residual 441242.682 3708 118.997

Total 444438.670 3711

3 Regression 18175.468 4 4543.867 39.516 .000c

Residual 426263.202 3707 114.989

Total 444438.670 3711

4 Regression 180045.274 8 22505.659 315.206 .000d

Residual 264393.396 3703 71.400

Total 444438.670 3711

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



73

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

Model R R

Square Adjusted R Square

Std. Error of the Estimate

Change Statistics R Square Change F Change df1 df2 Sig. F Change

1 .036a .001 .001 10.938 .001 4.790 1 3710 .029 2 .085b .007 .006 10.909 .006 11.021 2 3708 .000 3 .202c .041 .040 10.723 .034 130.269 1 3707 .000 4 .636d .405 .404 8.450 .364 566.773 4 3703 .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


– 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).



74

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

75

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.

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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’



achievement in English Language in WASSCE in Southwestern Nigeria?

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4.6.6.1d Result

Table 4.8: Coefficients in relation to English Language

Model

Unstandardized Coefficients

Standardize

d Coefficients

T Sig.

Correlations Collinearity

Statistics

B Std.

Error Beta Zero-order

Partial Part

Tolerance VIF

1 (Constant) 37.902 .886 42.767 .000

ADVERSITY QUOTIENT .025 .008 .053 3.239 .001 .053 .053 .053 1.000 1.000 2 (Constant) 46.798 1.967 23.789 .000


ATTRIBUTION -.647 .519 -.021 -1.245 .213 -.049 -.020 -.020 .901 1.110 3 (Constant) 49.977 2.460 20.318 .000


ATTRIBUTION -.678 .519 -.022 -1.306 .192 -.049 -.021 -.021 .900 1.111 TEACHER SELF EFFICACY 2 - ENGLISH -.851 .395 -.035 -2.152 .031 -.043 -.035 -.035 .982 1.018

4 (Constant) 75.080 2.413 31.109 .000 ADVERSITY QUOTIENT .015 .006 .032 2.477 .013 .053 .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 .116 .091 .932 1.073

TYPE OF SCHOOL -14.161 .382 -.488 -37.046 .000 -.442 -.520 -.475 .945 1.058 GENDER OF RESPONDENT 1.335 .308 .056 4.339 .000 .052 .071 .056 .984 1.016

AGE OF RESPONDENT -1.824 .705 -.034 -2.587 .010 -.101 -.042 -.033 .975 1.026 STATE WHERE SCHOOL IS LOCATED -10.799 .321 -.444 -33.668 .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 = -

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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;


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.


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.

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Table 4.9: Zero order and semi-partial correlations in Mathematics and English Language

Predictor Variables Zero-Order Correlation in Mathematics

Semi-Partial Correlation

% Zero-Order Correlation in English Language

Semi-Partial Correlation

%

Attribution -0.049 -0.011 0.01 -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).


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

80

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

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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 student-

teacher 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.

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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 E-

last.

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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.

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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’ Self-

efficacy; 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.

85

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’


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.

86

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


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


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. .

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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.

88

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 teaching-

learning 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.

89

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.

90

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APPENDIX V WEST AFRICAN EXAMINATIONS COUNCIL

INTER-OFFICE MEMORANDUM

From: Bakare, B.M. (Jnr) To: The HNO SAR (Research) WAEC, Yaba

RD & WIO, Thro: HOD (TA) Lagos, Nigeria WAEC, Yaba

Lagos, Nigeria Thro: DR/ Ag. Head

RD & WIO Lagos, Nigeria

Thro: DR/ Head LS-RD

Lagos, Nigeria

Ref: MBJ/STATOFPERF/HNO/VOL1/01 27th August, 2013

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)

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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

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APPENDIX VB

LETTER OF AGREEMENT

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