VOCATIONAL INTEREST AND OTHER NON-COGNITIVE
FACTORS AS PREDICTORS OF ACADEMIC
PERFORMANCE IN HIGH SCHOOL
by
Elton Jeremy Bloye
A minor-dissertation submitted in partial fulfilment of the
requirements for the degree of
Master in Science in Psychology
at the
University of Johannesburg
2007
Supervisor: Dr K de Bruin
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ACKNOWLEDGEMENTS
• I would firstly like to acknowledge my Creator, Saviour and Friend, Jesus Christ. To Him be
all the honour and glory.
• Thank you to the Grade 10 pupils as well as Mr. Anton Dempsey, Mrs. Linda Blore, the
Department of Life Orientation and the Grade 10 Grade Tutors at Randpark High School.
This study would not have been possible without your input and effort.
• A heartfelt thank you to Dr. Karina De Bruin, a supervisor better “then” the rest. Thank you
for your experience, solid advice, patience and encouragement throughout the process.
• Prof. Gideon P. De Bruin (University of Johannesburg) and Prof. Herman Aguinis
(University of Colorado) for their technical input and great ideas that made the study
workable.
• Rene Van Eeden (University of South Africa) for her initial ideas about the research topic.
• The National Research Foundation (NRF) for granting a bursary that was used to fund the
project.
• The University of Johannesburg for awarding a student bursary that assisted with funding the
degree.
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• The staff of Statistical Consultation Services (STATCON) that assisted with the data
capturing and analysis.
• My wife Ilse and Morgan for their constant love, support, patience and encouragement.
• Pastor Wayne Gordon for his consistent encouragement and support and Mr. Craig Aitchison
for his help with the data analysis during the initial stages of the project.
• The Psychology Masters group of 2006 as well the Psychology Department at the University
of Johannesburg for consistently supporting me in the study.
The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF.
INDEX
Summary
Opsomming
CHAPTER 1 OVERVIEW OF STUDY
1.1 INTRODUCTION 14
1.2 PROBLEM STATEMENT 15
1.3 PURPOSE OF THE STUDY 17
1.4 DEFINITIONS OF CONSTRUCTS 18
1.4.1 Academic ability 18
1.4.2 Cognitive factors 18
1.4.3 Non cognitive factors 18
1.4.4 Vocational interest 18
1.4.5 Self-efficacy 19
1.4.6 Person-environment fit 19
1.4.7 Achievement motivation 19
1.4.8 Coping strategies 19
1.4.9 Self-directedness in learning 19
1.4.10 Avoidance of procrastination 20
1.4.11 Academic performance 20
1.4.12 Social Cognitive Career theory 20
1.5 OVERVIEW OF THE STUDY 20
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CHAPTER 2 LITERATURE REVIEW
2.1 INTRODUCTION 22
2.2 COGNITIVE PREDICTORS OF ACADEMIC PERFORMANCE 24
2.2.1 Academic ability and intelligence 24
2.2.1.1 The General Scholastic Aptitude Test as a measure of academic ability 26
2.2.1.2 Academic ability as a predictor of academic performance 27
2.3 NON-COGNITIVE FACTORS AFFECTING ACADEMIC PERFORMANCE 28
2.3.1 Vocational interest 28
2.3.1.1 Vocational interest, personality and cognitive ability 29
2.3.1.2 Holland’s theory of vocational personalities and work environments 31
2.3.1.3 Measurement of vocational interest 33
2.3.1.4 Gender and culture differences with respect to vocational interest 34
2.3.1.5 Vocational interest as a predictor of academic performance 36
2.3.1.6 Influence of vocational interest on educational and occupational pathways 36
2.3.2 Person-environment fit 38
2.3.2.1 Holland’s congruence theory 38
2.3.2.2 Person-environment fit and academic performance 39
2.3.3 Self-efficacy 40
2.3.4 Achievement motivation 44
2.3.5 Coping strategies 46
2.3.6 Self-directedness in learning 48
2.3.7 Avoidance of procrastination 49
2.3.8 Conclusion 51
2.4 CONTEXTUAL FACTORS AFFECTING ACADEMIC PERFORMANCE 51
2.4.1 The influence of significant others 52
2.4.2 The influence of socio-economic factors 52
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2.5 SOCIAL-COGNITIVE CAREER THEORY EXPLAINING DIFFERENCES IN
ACADEMIC PERFORMANCE 53
2.6 CHAPTER SUMMARY 54
CHAPTER 3 RESEARCH METHOD
3.1 INTRODUCTION 56
3.2 RESEARCH PROBLEM 56
3.3 PURPOSE OF THE STUDY 58
3.4 PARTICIPANTS 59
3.5 MEASUREMENT INSTRUMENTS 59
3.5.1 General Scholastic Aptitude Test (GSAT) 60
3.5.1.1 Uses of the GSAT 61
3.5.1.2 Description of the subtests 61
3.5.1.3 Reliability and validity of the GSAT 62
3.5.2 Self-Directed Search (SDS) 63
3.5.2.1 Purpose of the SDS 63
3.5.2.2 Uses of the SDS 64
3.5.2.3 Description of the subtests 64
3.5.2.4 Reliability and validity of the SDS 65
3.5.3 Academic Behaviours and Attitudes Questionnaire (ABAQ) 67
3.5.3.1 Purpose of the ABAQ 67
3.5.3.2 Uses of the ABAQ 68
3.5.3.3 Description of the subtests 68
3.5.3.4 Reliability and validity of the ABAQ 68
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3.6 PROCEDURE 70
3.7 RESEARCH HYPOTHESES 72
3.7.1 Research hypothesis 1 72
3.7.2 Research hypothesis 2 72
3.7.3 Research hypothesis 3 72
3.8 STATISTICAL ANALYSIS 73
3.8.1 Descriptive statistics 74
3.8.2 Inferential statistics pertaining to Hypothesis 1 74
3.8.2 Inferential statistics pertaining to Hypotheses 2 and 3 75
3.9 CHAPTER SUMMARY 76
CHAPTER 4 RESULTS
4.1 INTRODUCTION 77
4.2 DESCRIPTIVE STATISTICS 77
4.3 RESULTS PERTAINING TO HYPOTHESIS 1 79
4.4 RESULTS PERTAINING TO HYPOTHESIS 2 80
4.4.1 Results pertaining to Accounting 82
4.4.2 Results pertaining to Business Economics 83
4.4.3 Results pertaining to English 85
4.4.4 Results pertaining to Life Orientation 88
4.4.5 Results pertaining to Life Science 90
4.4.6 Results pertaining to Mathematics 91
4.5 RESULTS PERTAINING TO HYPOTHESIS 3 94
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4.6 CHAPTER SUMMARY 95
CHAPTER 5 DISCUSSION OF FINDINGS
5.1 INTRODUCTION 97
5.2 VARIABLES AFFECTING ACADEMIC PERFORMANCE 98
5.2.1 Academic ability 98
5.2.2 Vocational interest 100
5.2.3 Academic attitudes and study behaviours 104
5.2.3.1 Self-efficacy and academic performance 105
5.2.3.2 Person-environment fit and academic performance 106
5.2.3.3 Achievement motivation and academic performance 106
5.2.3.4 Self-directedness learning, Coping and academic performance 107
5.2.3.5 Avoidance of procrastination and academic performance 108
5.3 A NEW EXPLANATORY MODEL FOR HIGH SCHOOL STUDENTS 109
5.4 IMPLICATIONS OF THE RESEARCH FINDINGS 113
5.5 LIMITATIONS OF THE STUDY AND IMPLICATIONS FOR FUTURE
RESEARCH 114
5.6 CONCLUSION 117
REFERENCES 118
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INDEX OF FIGURES
Figure 2.1 Spearman’s two factor theory of abilities 25
Figure 2.2 Vernon’s hierarchical model of the organisation of the ability factors 26
Figure 2.3 Hexagonal model illustrating relative distances among personality types 32
Figure 2.4 Model of person, contextual and experiential factors affecting career related
choice behaviour 37
Figure 2.5 Social Cognitive Interest Model 42
Figure 2.6 Social Cognitive Performance Model 43
Figure 2.7 Model of Task Performance 54
Figure 5.1 Social Cognitive Interest Model 109
Figure 5.2 Social Cognitive Performance Model 110
Figure 5.3 Model of person, contextual and experiential factors affecting career related
choice behaviour 111
Figure 5.4 Explanatory model for academic performance in high school students 112
INDEX OF TABLES
Table 3.1 Correlation coefficients for shortened GSAT and examination marks 63
Table 3.2 Reliability coefficients for SDS adaptation study (Sichel formula) 66
Table 3.3 Intercorrelations of the SDS fields (1987 study) 66
Table 3.4 SDS reliability statistics for a sample of high school students 67
Table 3.5 Reliability coefficients for the ABAQ across language and gender groups 69
Table 4.1 Age statistics for sample of 285 Grade 10 students 77
Table 4.2 Gender statistics for sample of 285 Grade 10 students 78
Table 4.3 Racial designation statistics for sample of 285 Grade 10 students 78
Table 4.4 Home language statistics for sample of 285 Grade 10 students 79
Table 4.5 Predictive effect of academic ability on overall academic performance 80
Table 4.6 Subjects considered in study with corresponding vocational interests 81
Table 4.7 Predictive effect of vocational interests on academic performance in
Accounting 82
Table 4.8 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Accounting 83
Table 4.9 Predictive effects of vocational interests on academic performance in Business
Economics 84
Table 4.10 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Business Economics 85
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Table 4.11 Predictive effects of vocational interests on academic performance in
English 86
Table 4.12 Regression weights, t-tests and effect sizes in the prediction of academic
performance in English 87
Table 4.13 Predictive effects of vocational interests on academic performance in Life
Orientation 88
Table 4.14 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Life Orientation 89
Table 4.15 Predictive effects of vocational interests on academic performance in Life
Sciences 90
Table 4.16 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Life Sciences 91
Table 4.17 Predictive effects of vocational interests on academic performance in
Mathematics 92
Table 4.18 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Mathematics 93
Table 4.19 Predictive effects of ABAQ factors on overall academic performance 94
Table 4.20 Regression weights, t-tests and effect sizes pertaining to the predictive effects of
ABAQ factors on overall academic performance 95
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SUMMARY
Research has indicated that there are many factors affecting academic performance of high school
students, which in turn can have a significant effect on their future educational and occupational
opportunities. While much international research has been done on cognitive and non-cognitive
factors affecting academic performance, there seems to be a lack of empirical studies within the
South African context, especially with regard to the effect of vocational interests, academic
attitudes and study behaviours. The study investigated three hypotheses. Firstly, academic ability
has a significant influence on school students’ academic performance; secondly, school students
who show vocational interest patterns that correspond with specific subject content, perform
academically better than school students who do not have interests that are in line with the subject
content; and thirdly, school students with positive academic attitudes and study behaviours perform
academically better than students with negative academic attitudes and study behaviours. The study
included 285 Grade 10 students who completed the General Scholastic Aptitude Test, the Self-
Directed Search and the Academic Behaviours and Attitudes Questionnaire. The results of multiple
regression analyses revealed that academic ability, vocational interests, self-efficacy, achievement
motivation, self-directedness in learning and avoidance of procrastination all contributed toward
predicting academic performance. With regard to the role of vocational interests, the results also
revealed that Investigative and Realistic interests had a significant effect on academic performance
even when subject content did not match vocational interest patterns. An adjusted model, based on
Social Cognitive Career Theory was formulated in order to conceptualise the study.
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OPSOMMING
Navorsing het aangetoon dat verskeie faktore die akademiese prestasie van hoërskoolleerlinge
beïnvloed. Akademiese prestasie speel op sy beurt ‘n betekenisvolle rol in die toekomstige
opvoedkundige en loopbaangeleenthede van leerlinge. Op internasionale vlak is baie navorsing
gedoen rakende kognitiewe en nie-kognitiewe faktore wat akademiese prestasie beïnvloed. Dit blyk
egter asof daar ‘n gebrek aan empiriese studies binne die Suid-Afrikaanse konteks is, veral met
verwysing na die invloed van beroepsbelangstellings, akademiese houdings en studiegedrag.
Hierdie studie het drie navorsingshipoteses ondersoek. Eerstens, akademiese vermoë het ‘n
betekenisvolle invloed op leerlinge se akademiese prestasie; tweedens, leerlinge wat
beroepsbelangstellingspatrone toon wat ooreenstem met spesifieke vakinhoude, vaar akademies
beter as leerlinge wie se belangstellings nie ooreenstem met die vakinhoude nie; en derdens,
leerlinge met positiewe akademiese houdings en studiegedrag vaar akademies beter as leerlinge met
negatiewe akademiese houdings en studiegedrag. Tweehonderd-vyf-en-tagtig Graad 10 leerlinge
het die Algemene Skolastiese Aanlegtoets, die Selfondersoekvraelys en die Akademiese Gedrag en
Houdingsvraelys voltooi. Die resultate van meervoudige regressie-ontledings het aangedui dat
akademiese vermoë, beroepsbelangstelling, self-effektiwiteit, prestasiemotivering, self-
rigtinggewendheid ten opsigte van leer en vermyding van uitstel betekenisvolle bydraes tot die
voorspelling van akademiese prestasie gelewer het. Met verwysing na die rol van
beroepsbelangstellings het die resultate ook aangedui dat Ondersoekende en Realistiese
belangstellings ‘n betekenisvolle bydrae gelewer het tot akademiese prestasie, selfs in gevalle waar
die vakinhoud nie met die beroepsbelangstelling ooreengestem het nie. ‘n Gewysigde model,
gebaseer op Sosiaal-Kognitiewe Loopbaanteorie, is geformuleer om die studie te konseptualiseer.
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CHAPTER ONE
OVERVIEW
1.1 INTRODUCTION
When considering the world of education and work, academic performance amongst school
students is a significant factor that affects the opportunity for entry into various educational and
vocational settings. High academic performers find it easier to gain entry into further education and
employment opportunities while low academic performers find it more difficult. In this light,
academic performance may be regarded as an important factor that shapes a particular educational
and vocational pathway for an individual at the expense of other educational and vocational
pathways.
There are many factors which may influence academic performance at a physiological,
psychological, sociological and metaphysical level. Much research has been done on the role of
cognitive factors, namely intelligence and academic ability, in predicting academic performance at
a school level. This literature proposes that academic ability is the predominant factor in
determining academic success in adolescents (Furnham & Chamorro-Premuzic, 2004; Grobler,
Grobler & Esterhuyse, 2001; Lau & Roser, 2002; Masqud, 1983; Midkiff, Burke & Helmstadter,
1989). Despite the fact that research generally shows a significant positive correlation between
academic ability and academic performance, there has been a recent shift in emphasis from studying
cognitive predictors to examining the role of non-cognitive factors such as personality constructs
(Lounsbury, Sundstrom, Loveland & Gibson, 2003). Many non-cognitive factors such as interests,
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academic attitudes and study behaviours also seem to play a role in predicting academic
performance. For example, Lent, Lopez and Bieschke (1991) found that self-efficacy had a
significant influence on Mathematics grades. Significant positive correlations between expectations,
motivation, self-confidence and academic performance were also reported (Tavani & Losh, 2003).
In a recent study, Sparfeldt (2007) showed that gifted individuals with high academic performance
displayed more interest in scientific/investigative activities than average achievers. Research into
non-cognitive factors has also fostered the development of new theories of task performance such
as the Social Cognitive Career Theory proposed by Lent, Hackett and Brown (1996). They propose
that self-efficacy has a positive relationship with vocational interests and this in turn affects
academic performance. Despite the relative influence of cognitive and non-cognitive factors on
academic performance, how these factors relate to one another, remains unclear (Furnham &
Chammorro-Premuzic, 2004). They should however be the object of further research as the various
factors affecting academic performance at a school level may directly and indirectly determine the
level of educational and occupational opportunity available to a school student.
1.2 PROBLEM STATEMENT
School students often believe that cognitive factors or “intelligence” is the predominant influence in
determining academic success at school. A lack of knowledge or a general disregard amongst
students on the importance of the various non-cognitive factors influencing academic performance
may influence choices, attitudes and behaviours that hinder optimal academic performance rather
than promoting it. Vocational interest has been reported as one of the important non-cognitive
factors that may influence academic performance (Brown, 1994; Holland, 1968; Schneider &
Overton, 1983). School students who choose subjects for which they hold little interest or for which
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they fail to see the connection between subject content and its relevance toward their educational
and vocational goals, may perform less well academically than school students who see this
connection and are interested in the subjects they take. Holland (1997) states that level of
accomplishment in a particular field is positively associated with the level of congruency between
interest type and the field’s environment. Other important non-cognitive factors affecting academic
performance include academic attitudes and study behaviours. Attitudes such as self-efficacy
(Siegel, Galassi & Ware, 1985) and achievement motivation (Tavani & Losh, 2003) have been
found to have a strong positive association with performance at school. Study behaviours such as
the use of coping strategies (Zuckerman, Kieffer & Knee, 1998), self-directedness in learning (Long
& Morris, 1996) and avoidance of procrastination (Chu & Choi, 2005) also have been reported to
influence academic performance. Person-environment fit is an additional factor that has been linked
to academic success at school (Feldman, Smart & Ethington, 1999).
A lack of knowledge or general disregard of the importance of factors such as vocational interests,
academic attitudes and study behaviours amongst school students may be related to lower academic
performance. Social Cognitive Career Theory (Lent et al., 1996) suggests that workplace
performance is positively associated with vocational interests, therefore one may hypothesise that a
disregard for this factor at a school level may also lead to poor academic performance, decreasing
the opportunity for entry into further education and training as well as limiting valuable career
options. Also, if school students realise on the basis of objective research that maintaining positive
academic attitudes and study behaviours will increase their academic performance, they may adopt
these attitudes and behaviours. With this in mind, it seems important that research be conducted to
establish the influence of these non-cognitive factors on academic performance so that measures
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can be put into place to make school students and their parents aware of the significant impact
thereof on academic results. While the influence of certain non-cognitive factors such as self-
efficacy and personality factors have been well researched (cf. Ackerman & Heggestad, 1997;
Andrew, 1998; Barrick, Mount & Gupta, 2003; Bong, 2002; De Fruyt & Mervielde, 1999; Mount,
Barrick, Scullen & Rounds, 2005; Sullivan & Hansen, 2004), the relationship between vocational
interest and academic performance is in need of more research, especially within the South African
context. In addition, most research on the relationship between academic attitudes and study
behaviours has been conducted in university settings and not at a high school level. Therefore, it
seems appropriate that more empirical data be gathered in high school settings within a South
African context.
1.3 PURPOSE OF THE STUDY
The purpose of this study is to investigate the relationship between academic performance and non-
cognitive factors. These factors include vocational interest, person-environment fit, academic
attitudes such as self-efficacy and achievement motivation, and study behaviours such as the use of
coping skills, self-directedness in learning and avoidance of procrastination. The results of the study
may lead to valuable information about factors, other than cognitive factors, that influence
academic performance. In addition to this, the research may assist in developing and implementing
interventions in both a home and school setting that will assist school students in improving their
academic performance.
Research which shows a relationship between vocational interest and academic performance may
encourage learners to choose subjects on the basis of what they are interested in with regard to their
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future career, thereby increasing their chances of better academic performance. The purpose of this
study therefore is to provide empirical data regarding the relationship between vocational interests,
academic attitudes, study behaviours and academic performance.
1.4 DEFINITIONS OF CONSTRUCTS
1.4.1 Academic ability
Academic ability is defined according to Vernon’s (1950) definition of the verbal educational
aptitude (v.ed.) cited in Jensen (1980). Vernon describes v.ed. as a characteristic of numerical,
verbal and logical reasoning. Claassen, De Beer, Hugo and Meyer (1998) state that the purpose of
the General Scholastic Aptitude Test (GSAT), which is used to operationalise academic ability in
this study, is largely to determine the verbal educational factor.
1.4.2 Cognitive factors
In this study, cognitive factors relate to the definition of academic ability in paragraph 1.4.1.
1.4.3 Non-cognitive factors
For purposes of this study, non-cognitive factors refer to vocational interest, self-efficacy, person-
environment fit, achievement motivation, coping strategies, self-directedness in learning and
avoidance of procrastination.
1.4.4 Vocational interest
Vocational interest, as defined by Holland (1997) is an expression of an individual’s personality in
work, school subjects, hobbies, recreational activities and preferences.
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1.4.5 Self-efficacy
Self-efficacy is defined as a person’s beliefs about his or her ability or confidence to bring about
intended results (Colman, 2006).
1.4.6 Person-environment fit
Person-environment fit is defined as the degree to which the personality and the environment match
(Nel, 2006).
1.4.7 Achievement motivation
Achievement motivation is described as the striving tendency towards success with the associated
positive effects and towards the avoidance of failure and the associated negative effects (Busato,
Prins, Elshout & Hamaker, 2000).
1.4.8 Coping strategies
Coping strategies are defined as the cognitive and behavioural tactics that individuals utilise to
control their environmental surroundings and to alleviate any stress which may occur when
environmental demands surpass individuals’ resources (Collins & Onwuegbuzie, 2003).
1.4.9 Self-directedness in learning
Self-directedness in learning is defined according to Knowles (1975) as the process in which
individuals take the initiative to identify their learning needs, formulate learning goals, identify
resources for learning, choose and implement learning strategies and evaluate learning outcomes.
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1.4.10 Avoidance of procrastination
Procrastination is defined as the lack or absence of self-regulated performance and the behavioural
tendency to postpone what is necessary to reach a goal (Ellis & Knaus, 1977). An avoidance of
procrastination can be defined as an avoidance of this kind of behaviour.
1.4.11 Academic performance
Academic performance is defined in line with Kobal and Musek’s (2001) definition, which refers to
the numerical scores of a student’s knowledge, representing the degree of a student’s adaptation to
schoolwork and the educational system.
1.4.12 Social Cognitive Career theory
Social Cognitive Career Theory, according to Lent, Brown and Hackett (2002) attempts to
consolidate constructs such as self-concept, self-efficacy, interests and abilities and composes a
comprehensive explanatory system of the complex connections between persons and their career-
related contexts
1.5 OVERVIEW OF THE STUDY
In Chapter Two, the relevant literature pertaining to the study of cognitive and non-cognitive factors
affecting academic performance is reviewed. Theoretical orientations and empirical data relating to
academic ability, vocational interest, person-environment fit, self-efficacy, achievement motivation,
coping strategies, self-directedness in learning and avoidance of procrastination are discussed. This
is followed by a review of Social Cognitive Career Theory as an explanatory model. Chapter Three
provides an overview of the research problem as well as the main aims and purposes of the study.
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The participants, research instruments, research hypotheses, research procedures and statistical
methods used to analyse the data, are also described in this chapter.
Chapter Four provides a summary of the results of the study. More specifically, it presents a
summary of the descriptive data, including factors such as gender, age, racial designation and
language groups. The chapter also presents a summary of the simple and multiple regression
analyses pertaining to the hypotheses. Chapter Five presents a discussion of the results. The results
of the study are reviewed followed by a discussion of the implications of the results in light of the
literature review and implications for future research.
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CHAPTER TWO
LITERATURE REVIEW
2.1 INTRODUCTION
Chapter Two reviews the current literature pertaining to the study of cognitive and non-cognitive
academic factors affecting academic performance. Definitions of the constructs are followed by a
review of the theoretical orientations and empirical data relating to the influence of academic
ability, vocational interest, person-environment fit, self-efficacy, achievement motivation, coping,
self-directedness in learning, avoidance of procrastination and contextual factors on academic
achievement. The chapter concludes with an overview of Social-Cognitive Career Theory as an
explanatory model for academic achievement.
According to Paa and McWhirter (2000), the adolescent years are important in laying a foundation
for future career and educational pursuits. An individual’s academic performance and subject
choices can affect entry into further education and training opportunities which may shape
particular educational and vocational pathways for an individual at the expense of other educational
and vocational pathways. This factor is especially relevant in the South African context in which a
large proportion of the population comes from disadvantaged backgrounds. Botha, Brand, Cilliers,
Davidow, de Jager and Smith (2005) highlight that students who are academically unprepared and
enter into higher education settings are more likely to experience adjustment problems to university
life. It is therefore not surprising that researchers have conducted empirical studies to investigate
factors affecting academic performance. For example, Aitken (1994) conducted research among
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6500 first degree graduates in the United Kingdom who had just entered the labour market. He
concludes that success in the career marketplace depended on past academic performance, as well
as subject-choices and socio-economic factors. In a similar study, Athanasou (2001) investigated
factors affecting Australian school leavers’ educational-vocational achievement and found that the
most powerful influences on ultimate educational-vocational achievement were academic
achievement in literacy and numeracy, the completion of Grade 12 (final year of schooling) and
vocational interests.
With regard to subject choice and academic performance, Ainley, Jones and Navratnam (1990) state
that subjects studied in senior secondary years are a major influence on the educational and career
options available to young people upon leaving school. Research into the subject selection process
shows that academic performance in a particular subject is an influential factor affecting the choice
of that subject for senior schooling. Early work by Ball (1981) and Woods (1976, 1979), as
described in Stables (1997), concludes that selection of certain subjects is constrained by factors
operating within and beyond the school, one of these factors being academic ability. Research with
school students in the United Kingdom conducted by Garrat (1985) and Kelly (1988) shows that
previous performance of school students in O-Level subjects was a significant factor in determining
the choice and continuation of certain A-level subjects until the end of formal schooling. In
Australia, Ainley et al. (1990) and Dellar (1994) report strong relationships between subject
enrolment and school students’ level of achievement. Dellar (1994) also found that lower ability
school students tended to select subjects in which they had gained prior success and eliminated
subjects which they perceived as difficult.
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It is important to note the difference between academic ability and academic performance. While
academic ability refers to a cognitive phenomenon that forms part of the concept of intelligence, or
the ability to succeed in academically related activities, academic performance is a measure of
success in the academic task undertaken. Jensen (1980) states that many formal definitions suggest
some kind of distinction between intelligence and performance and it is recognised that a person of
high academic ability may be affected by various non-cognitive and contextual factors that may
increase or decrease his or her academic performance. A review of some these non-cognitive and
contextual factors appears later in the present chapter, however it is first appropriate to review the
cognitive factors affecting academic performance.
2.2 COGNITIVE PREDICTORS OF ACADEMIC PERFORMANCE
2.2.1 ACADEMIC ABILITY AND INTELLIGENCE
The concept of academic ability is historically derived from the construct of intelligence. There are
many different definitions of intelligence, some confined to more cognitive constructs while others
taking into account non-cognitive factors. The concept of academic ability involves the cognitive or
intellectual part of intelligence rather than the non-cognitive constructs. According to Kail and
Pellegrino (1985), most people understand intelligence to imply at least two specific concepts,
namely exceptional linguistic ability, evident in having a large vocabulary and good reading
comprehension, and problem-solving ability which is evident in having good logical reasoning,
applying knowledge to problems and making sound decisions. Cited in Jensen (1980), David
Wechsler, developer of the Wechsler Adult Intelligence Scale (WAIS), sees intelligence as
involving personality and values as well as cognition. Mwamwenda (1995) takes into account
cultural phenomena and defines intelligence as what enables a person to think, act and behave in a
24
manner that is normally acceptable to their society, thus facilitating his or her adjustment socially,
intellectually and physically.
The concept of academic ability was borne out of intelligence testing which began with Sir Francis
Galton (1822-1911) on the premise that people differ with regard to their sensory, perceptual and
motor processes (Jordaan & Jordaan, 1998). It was however Alfred Binet who developed
intelligence testing in the context of cognitive academic ability. Binet developed a test to
distinguish children who were ready for formal schooling from those who needed a remedial
programme (Mwamwenda, 1995). Since then there have been many different theories as to what
constitutes cognitive ability as an aspect of intelligence. The most notable pioneer in this area was
Charles Spearman who hypothesised a two-factor theory of intelligence. Spearman’s (1927) theory
held that a test of cognitive ability measures a general factor (g), and a specific factor (s) which was
unique to that particular test (Jensen, 1980). His theory is depicted in Figure 2.1.
Figure 2.1 Spearman’s two factor theory of abilities
Source: Copyright © 1980 by A.R. Jensen. Reprinted with permission from Methuen, London.
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Jensen (1980) states that the most reasonable overall picture from factor analytical studies
of mental abilities is provided by Vernon (1950). Vernon acknowledges Spearman’s g factor, but
applies two major group factors stemming from the g factor, namely a factor of verbal educational
aptitude (v:ed) and spatial mechanical aptitude (k:m). V:ed is characteristic of numerical, verbal and
logical reasoning tests while k:m is characteristic of tests involving spatial visualisation and an
understanding of physical and mechanical principles. Stemming from the major group factors are
minor group factors or primary abilities. Finally, from the minor group factors stem small factors
specific to each test. This model is shown diagrammatically in Figure 2.2.
Figure 2.2 Vernon’s hierarchical model of the organisation of the ability factors
Source: Copyright © 1980 by A.R. Jensen. Reprinted with permission from Methuen, London.
2.2.1.1 The General Scholastic Aptitude Test (GSAT) as a measure of academic ability
Although many tests of cognitive ability may be relevant in the South African context, the General
Scholastic Aptitude Test (GSAT) is a group test designed to measure academic intelligence or
scholastic aptitude specifically within South African schools. Claassen et al. (1998) have described
26
the GSAT as an objective aid in determining the reasoning or problem solving ability of school
students. The items in the GSAT provide a good indication of a person’s general intellectual
functioning that is analogous with Spearman’s g factor. Taking into account the theory by Vernon
(1950), Claassen et al. (1998) state that the purpose of the GSAT is largely to determine the verbal-
educational factor (v:ed) rather than the practical mechanical factor (k:m). The GSAT is
consequently classified as a cognitive test of academic ability or scholastic aptitude.
2.1.1.2 Academic ability as a predictor of academic performance
Literature concerning the relation between academic ability or intelligence factors and academic
performance primarily focuses on achievement in specific subjects and not on overall grade
performance. Much of the research has focused on the relationship between academic ability and
performance in Mathematics. Masqud (1983) conducted a study of Mathematics achievement in
Nigerian secondary school students and found a statistically significant positive relationship
between results on the Raven’s Standard Progressive Matrices (RSPM) as a measure of intelligence
and Mathematics achievement scores. In a similar study, Midkiff et al. (1989) investigated the
relationship between general scholastic aptitude and academic performance in a Mathematics
examination and found a significant positive relationship for boys and a moderate positive
relationship for girls. Focusing on black high school students in South Africa, Grobler et al. (2001)
found meaningful positive relations between verbal scholastic aptitude, non-verbal scholastic
aptitude and Mathematics marks for both boys and girls.
With regard to performance in other subjects, Lau and Roeser (2002) conducted research amongst
high school students in the United States of America and found strong correlations (r = 0.67, p <
27
0.01) between scores in a multiple-choice Science test and results of measures of fluid and spatial
abilities. In a university setting, Furnham and Chamorro-Premuzic (2004) correlated students’
grades on a Statistics examination with scores on an intelligence test measuring verbal and visuo-
spatial ability and found meaningful positive relations. Rigdell and Lounsbury (2004) investigated
the influence of cognitive ability on scores in an undergraduate Psychology test as well as students’
grade point averages and found moderate correlations of r = 0.41 (p < 0.01) and r = 0.39 (p < 0.01)
respectively.
While academic ability seems to be an influential factor on academic performance, it is located
within the realm of cognitive functioning. There are other processes and factors affecting academic
performance which can be categorised as non-cognitive functions. One of these non-cognitive
factors is vocational interest, which involves a person’s interest toward participating in activities
and tasks relating to a certain vocation.
2.3 NON-COGNITIVE FACTORS AND ACADEMIC PERFORMANCE
2.3.1 VOCATIONAL INTEREST
The concept of vocational interest has had a profound effect on career development theory and
practice, including high school students who have dreams and aspirations of working in a particular
profession. The most widely recognised linguistic definition of vocational interest is from one of the
pioneers of vocational interest measurement, Edward K. Strong. Crites (1999, pp. 164) cites
Strong’s (1943) definition which compares vocational interests to “tropisms or activities for which
we have liking or disliking and which we go toward and away from, or concerning which we at
28
least continue or discontinue the status quo; furthermore, they may or may not be preferred to other
interests and they may continue over varying intervals of time”.
Super and Crites (1962) suggest four ways in which to operationally define vocational interests: (1)
expressed interests, which is the verbal expression of interest in an object, activity, task or
occupation; (2) manifest interests, which denotes active participation in an activity or occupation;
(3) tested interests, which refers to interests as measured by objective tests, and (4) inventoried
interests, which denotes responses of like, dislike and indifference to verbal presentations of
activities, objects and types of people. In a review of the development of vocational interest theory,
Barak (1981) outlines that theories of interest and interest development were almost all formulated
from the 1930’s to the 1950’s. He classifies the different theories of vocational interest into six
assumptions: (1) Interests are learned; (2) Interests are adjustment modes; (3) Interests are an aspect
of the personality; (4) Interests are an expression of the self-concept; (5) Interests are motives; and
(6) Interests are determined by many factors.
2.3.1.1 Vocational interest, personality and cognitive ability
While Crites (1999) is of the opinion that vocational interests should be separately and uniquely
defined, a number of empirical investigations have provided convincing evidence for some
commonality amongst cognitive ability, vocational interest and personality variables. Rolfhus and
Ackerman (1996) evaluated commonality across verbal and spatial ability, vocational interests and
personality variables and showed a pattern of positive relations between interest in the arts and
humanities, typical intellectual engagement and openness to experience. They also show
correlations between an attainment of Mathematics and Physical Science knowledge and realistic
29
and investigative interests. In a chronological account of his life’s work in vocational interest
measurement, Holland (1999) provides substantial empirical evidence that interest inventories
assess many of the factors entailed in a comprehensive personality inventory. In this regard, De
Fruyt and Mervielde (1999), Barrick et al. (2003), Sullivan and Hansen (2004) and Mount et al.
(2005) have all reported positive relations between vocational interest and personality variables.
In contrast, other studies have revealed little commonality between abilities, interest and personality
constructs. Lowman, Williams and Leeman (1985) measured primary abilities and interest types of
college women and found relatively little common variance between abilities and their
corresponding vocational interests, which suggests that they may be relatively separate domains.
De Bruin (2002) examined correlations between vocational interests as measured by the 19-Field-
Interest Inventory (19FII) and personality factors as measured by the 16 Personality Factor
Questionnaire (16PF) and found that three second–order personality factors, namely Extraversion,
Tough poise and Independence, had weak yet statistically significant relationships with certain
vocational interest fields as measured by the 19FII. His findings suggest that although there may be
some commonality between personality variables and vocational interests, they may represent two
different domains.
While research shows conflicting evidence regarding the relationship between vocational interest
and personality variables, one of the most successful and influential theories describing the
relationship between vocational interests and personality has been the theory of vocational
personalities and work environments by John Holland (1973). Arnold (2004) describes Holland’s
30
theory of vocational choice as being a dominant force in vocational psychology and career
guidance.
2.3.1.2 Holland’s theory of vocational personalities and work environments
Holland’s theory of vocational personalities and work environments was first outlined in 1959 and
has proved to be relatively robust, despite rigorous criticism and subjection to empirical testing. The
theory has been revised and updated many times, however according to the latest (1997) version,
Holland states that people can be characterised into six personality types which resemble their
vocational interests, namely Realistic, Investigative, Artistic, Social, Enterprising and Conventional
(RIASEC). These types can also be descriptive of six model environments, which Holland defines
as the situation or atmosphere created by the people who dominate any given environment.
Holland (1997) describes the personality types according to the RIASEC typologies and outlines
certain preferences and aversions that people may have. Realistic (R) people prefer activities that
entail the explicit, ordered or systematic manipulation of objects, tools, machines and animals and
may have an aversion to educational or therapeutic activities. Typical careers in the realistic
environment would include those in the field of technical or agricultural careers. Investigative (I)
people prefer activities that entail the observational, symbolic, systematic and creative investigation
of physical, biological and cultural phenomena. Investigative people may also have an aversion to
persuasive and repetitive activities. Typical careers in the investigative environment would include
work in the geological or biological sciences as well chemistry and physics. Artistic (A) people
prefer ambiguous, free, unsystematic activities that entail the manipulation of physical, verbal or
human materials to create art forms or products. Artistic people may have an aversion to explicit,
31
systematic or ordered activity. Typical careers in the artistic environment would include a career in
music or photography. Social people (S) prefer activities that entail the manipulation of others to
inform, train, develop, cure or enlighten. They are averse to explicit, ordered, systematic activities
involving materials, tools or machines. Typical careers in the social environment include teaching
or religious ministry. Enterprising people (E) prefer activities that entail the manipulation of others
to attain organisational goals or economic gain. They may be averse to observational, symbolic and
systematic activities. Typical careers in the enterprising environment would include jobs in
marketing or entrepreneurship. Conventional people (C) prefer activities that entail the explicit,
ordered, systematic manipulation of data, for example the keeping of records or filing. They may
have an aversion to ambiguous, free, exploratory or unsystematic activities. Typical careers in the
conventional environment include accounting or secretarial work.
In addition to describing the personality types and work environments, Holland has also organised
them into a working model which he calls a calculus theory. Nel (2006) describes Holland’s
calculus theory whereby the six personality and environment types can be geometrically arranged in
a single hexagonal model so that adjacent types theoretically have more in common than non-
adjacent types. The model is shown in Figure 2.3.
Realistic (R)
Artistic (A)
Investigative (I)
Enterprising (E)
Conventional (C)
Social (S)
Figure 2.3 Hexagonal model illustrating relative distances among personality types
32
2.3.1.3 Measurement of vocational interest
When considering the measurement of vocational interests, Crites (1999) states that the oldest,
continuously used interest inventory currently available is the Strong Interest Inventory (SII). The
SII provides information about a person’s interest in 109 occupations, related to six global types
and 25 basic interests, which represent areas commonly recognised as important for understanding
the organisation and structure of interests as well as the world of work (Hansen, 2000). Another
interest inventory that has had a profound effect on the field of interest measurement is the Kuder
Occupational Interest Survey (KOIS). The purpose of the KOIS is to help young people discover
the occupations they will find most satisfying (Diamond & Zytowski, 2000).
While both the Strong Interest Inventory and the Kuder Occupational Interest Survey are still
currently in use by psychologists specialising in vocational counselling, probably the most
influential and successful instrument for the assessment of vocational interests has been the Self-
Directed Search (SDS) (Holland, 1997). The SDS was developed directly from Holland’s theory
(1973) of personalities and work environments and has become the benchmark for self-guided
career assessment. The SDS and Holland’s theoretical model have provided career assistance to
individuals, groups, and has been utilised in career workshops. The typologies have also been used
to organise and interpret client and occupational information in career centres, libraries and
industrial settings. The questionnaire gauges a person’s resemblance to the six interest or
personality types and work environments. The respondent’s vocational interests are described
according to a three letter code based on the three highest scores on the response sheet. (Spokane &
Catalano, 2000). This code can then be compared to a number of occupational classifications
organised according to the same six categories employed in the assessment questionnaire. In South
33
Africa, the occupational classifications are described in The South African Dictionary of
Occupations (Taljaard & Mollendorf, 1987). Consequently, the respondent can complete the
questionnaire and than search for compatible careers in the dictionary of occupations.
2.3.1.4 Gender and culture differences with respect to vocational interest
Subjective research has been conducted in the field of vocational interest measurement between the
genders, especially regarding interests in Mathematics and Science. For example, O’Brien,
Martinez-Pons and Kopala (1999) analysed the data of 415 eleventh grade school students enrolled
in Mathematics and Science courses which disclosed a direct effect of gender on students’ career
interests. In addition, Kelly (1988) found that boys in secondary schools in the United Kingdom
were more likely than girls to regard studying Physics (Physical Science) as interesting. In a similar
study, Watson, McEwen and Dawson (1994) assessed 1073 secondary students in Northern Ireland
and found that girls rated English, Biology and French as significantly more interesting than boys
did, while boys rated their interest in Physics, Chemistry and Mathematics significantly higher than
girls. With regard to differences in measured interests between the genders, Mullis, Mullis and
Gerwels (1998) compared responses on the Strong-Campbell Interest Inventory (SCII) by male and
female adolescents in the United States of America. They found that males had significantly higher
mean scores on the Realistic category while females had significantly higher scores on the Social
and Conventional categories. Research has also been conducted to investigate gender differences
with regard to Holland’s theory of personalities and work environments. Holland (1972) found that
males and females differ in the arrangement of the six occupational fields organised in the calculus
model and as measured by the Self-Directed Search (SDS). In response to these findings, Feldman
and Meir (1976) conducted research with 322 Israeli female high school students and showed
34
similar results. While males showed the arrangement RIASEC, females were more likely to show
the typological order of IRASEC when considering the occupational fields. In addition, Tuck and
Keeling (1980) conducted research amongst high school students in New Zealand and also found
that the IRASEC arrangement was a slightly better fit for girls.
With regard to cultural differences in high school students’ vocational interests, research has
focused on the validity of Holland’s circular structure of the RIASEC types in different cultures.
Rounds and Tracey (1996) located 96 cross-cultural RIASEC matrices from 19 countries and found
that the cross-cultural equivalence of Holland’s circular order model was not supported. Day and
Rounds (1998) investigated the RIASEC circular structure across ten different racial and ethnic
groups in the United States of America and found a similar underlying structure consistent with
conventional interpretations of vocational interest patterns. Wheeler (1992) conducted a study to
investigate the structural validity of Holland’s circular model amongst black high school students in
South Africa. He found that while the model proved to be valid for black high school students on
the whole, some adjustments needed to be made to the Artistic field as he found the artistic interest
amongst black high school pupils to be more data-oriented and associated more with Conventional
and Enterprising constructs than with Investigative and Social constructs. Also in South Africa, Du
Toit and De Bruin (2002) examined the validity of Holland’s circular order amongst four groups of
Black students. Multidimensional scaling analyses revealed a poor fit for all groups which suggest
that the circular model may not be valid for Black South Africans.
35
2.3.1.5 Vocational interest as a predictor of academic performance
Literature associating measured vocational interests with academic performance in high school
seems to be scarce, however a few empirical studies have investigated this issue. As early as 1968,
Holland conducted a longitudinal study using a sample of college students and found that college
grades for men were not independent of personality types. His theory (1973) speculates that
educational achievement is related to the following personality pattern order: I, S, A, C, E, R and
this speculation is reiterated in the latest version of the theory (1997). This speculation has not been
the object of much empirical investigation, however Schneider and Overton (1983) conducted a
study on college freshman in the United States of America and found that although males and
females with the primary personality types I, S, A and C achieved the highest grade point averages,
the ordering of the groups did not conform with Holland’s prediction. Brown (1994) conducted
research on Engineering students and found that interest variables, when combined with personality
and cognitive factors, accounted for the major portion of variance in predicting first semester grade-
point averages. The most recent and relevant research of the predictive effect of vocational interests
on academic performance appears in Sparfeldt (2007). With a sample of 106 intellectually gifted
adolescents and 98 adolescents of average ability, the researcher concluded that gifted adolescents
displayed higher investigative interests (d = 0.54) and lower social interests (d = 0.38) than non-
gifted adolescents. Differences between both groups regarding their realistic, artistic, enterprising,
and conventional interests were negligible.
2.3.1.6 Influence of vocational interest on educational and occupational pathways
Academic abilities may aid in the development of vocational interests which in turn may shape the
choice of certain educational and vocational pathways. Barak (1981) proposes a model for
36
vocational interests whereby interest in a certain vocation is the result of perceived ability as well as
expected success and anticipated satisfaction. Perceived ability in turn is developed from success in
various activities or experiences relating to the specified career.
According to Social Cognitive Career Theory (SCCT) (Lent et al., 1996), interests are assumed to
be important determinants of career choice. SCCT asserts that self-efficacy expectations and
outcome expectations directly affect the formation of career interests. These emergent interests
promote particular goals for activity involvement and this increases the likelihood that a person will
engage in a particular activity. A diagrammatic representation of the model is presented in Figure
2.4.
Figure 2.4 Model of person, contextual and experiential factors affecting career related
choice behaviour
Source: Copyright © 1994 by Lent, R.W., Brown, S.D. & Hackett, G. Reprinted with permission by Jossey-Bass.
As can be seen from the model, according to SCCT, career choices and performance domains and
attainments relating to those choices are affected by a number of variables including a variety of
personal non-cognitive inputs and background contextual factors. It is therefore reasonable to argue
37
that choices of academic subjects and academic performance will be affected by these factors as
well.
2.3.2 PERSON-ENVIRONMENT FIT
2.3.2.1 Holland’s congruence theory
In addition to describing the structure and organisation of vocational interests, Holland (1997)
describes a theory of congruence in which he maintains that a person’s behaviour is determined by
an interaction between personality and the environment (Spokane, Luchetta & Richwine 2002). In a
review of Holland’s congruence theory, Nel (2006) outlines that the degree to which the personality
and the environment match is known as the person-environment fit. He states that people search for
environments that will let them exercise their skills and abilities, express their attitudes and values,
and take on agreeable problems and roles. This has important implications for school students in
that the academic attitudes and study behaviours that influence their academic performance may be
affected by the way in which their personalities correspond with the environment. For example, a
Realistic person in a Realistic environment will experience a higher degree of congruence than a
Realistic person in a Social environment. Holland (1997) states that while teachers are usually S
types, students who do not achieve academically in school are usually R types. These are
incongruent opposites in the hexagonal model and the implication is that in addition to failure to
achieve the minimal skills, R type students may underachieve because they are in an environment
which suits S type personalities. Those students who experience a high degree of congruence
between personality and environment, for example, an S type personality in an S type environment,
may achieve better academic results.
38
2.3.2.2 Person-environment fit and academic performance
There has been little research done on whether the concept of person-environment fit is related to
performance or achievement (Feldman, Smart & Ethington, 1999), however, some research has
been done in higher education settings. Posthuma and Navran (1970) assessed the personalities of
academic staff members and first year students at a military college and found that the highest
academic achievers reflected the most amount of congruence between personality and environment
while the lowest academic achievers reflected the lowest amount of congruence. Reuterfors,
Schneider and Overton (1979) conducted research with first year college students who were either
decided or undecided on their college majors. They found that students with college major choices
in congruence with their personality obtained higher grade-point averages than students who were
incongruent and also undecided on their choice of major. They also found that there was no
significant difference in grade-point average between students of definite and indefinite personality
types who had both decided on college majors. Bruch and Krieshok (1981) conducted research on
students enrolled for an Engineering degree and found that differences in academic performance
were not consistent with Holland’s (1973) congruence hypothesis. Feldman et al. (1999) analysed
differential patterns of change and stability (over a four year period) in the abilities and interests of
congruent and incongruent first year college students. They found that students in congruent fields
(Investigative, Artistic and Enterprising) showed a stronger increase in their dominant skills and
interests over time than incongruent students who showed less of an increase or a decrease in
abilities and interests.
In addition to the relationship between person-environment fit and academic achievement, limited
but important research has been conducted on the relationship between interest and enrolment in
39
school subjects. This can be seen as a measure of person-environment fit in that, according to
Holland (1997), people will choose environments in which they experience the most amount of
congruence. Garrat (1985) and Ainley et al. (1990) both found strong relationships between interest
in a particular subject and enrolment in that subject at high school. Dellar (1994) found that
Australian school students of both high and low ability considered interest in a subject to be a very
important factor. Athanasou (2001) conducted a study on Australian school leavers and found a
strong relationship between interests and enrolment in a particular course in the first year of higher
education.
Holland’s theory implies that vocational interests are not separate constructs in relation to
personality variables and he was of the opinion that factors of intelligence and cognitive ability
were also linked to vocational interests. In some of his earliest work conducting longitudinal studies
on college graduates, Holland (1968) found that college grades for men were not independent of the
personality types and he maintains that cognitive factors, personality variables and vocational
interests share some commonality.
2.3.3 SELF-EFFICACY
The Oxford Dictionary of Psychology (Colman, 2006) defines self-efficacy as a person’s beliefs
about their ability or confidence to bring about intended results. According to Meyer, Moore and
Viljoen (1997), Albert Bandura, one of the most important representatives of Social Cognitive
Learning Theory, is regarded as a pioneer in the study of self-efficacy and its impact on behaviour.
Bandura’s (1986) theory states that self-efficacy perceptions considerably influence a person’s
choice of situation because they will tend to choose situations in which they will achieve success.
40
Consequently, persons with high self-efficacy will produce more success experiences which further
reinforce their self-efficacy while persons with low self-efficacy will produce less successful
experiences thereby reducing their self-efficacy (Meyer et al., 1997). The concept of self-efficacy
has important implications in the study of factors affecting academic performance in high school in
that a student’s perception of subject difficulty may influence whether they are motivated to study
the material and achieve academically in the first place.
Most of the research on self-efficacy has been conducted in the area of academic performance in
Mathematics and Science related subjects. Siegel et al. (1985) found a moderate relationship
between college students’ scores on a Mathematics self-efficacy scale and performance in an
introductory Mathematics course. Also in a university setting, Lent et al. (1991) measured self-
efficacy for Mathematics in 166 Psychology students and found that effects of past achievement
and self-efficacy in Mathematics were useful predictors of Mathematics grades, with effects of past
achievement being partially mediated by self-efficacy. Andrew (1998) found that scores on the
Self-Efficacy for Science Scale could predict 24% of the variance in academic performance in
Physical Science amongst Australian nursing students.
There is also evidence that suggests that self-efficacy in certain activities promotes an interest to
participate in those activities, and this may produce behaviour which increases positive
performance in the activity. Social Cognitive Learning Theory (Bandura, 1986), suggests that
people develop interests in activities in which they view themselves to be efficacious and for which
they anticipate positive outcomes (Lopez, Lent, Brown & Gore, Jr., 1997). Bandura, Barbaranelli,
Caprara and Pastorelli (2001) state that the higher people’s self-efficacy to fulfil educational
41
requirements and occupational roles, the wider the career options they seriously consider pursuing,
the greater interest they have in them, the better they prepare themselves educationally for different
occupational careers, and the greater their staying power in challenging career pursuits. Betz and
Hackett (1983) found that Mathematics self-efficacy expectations were significantly related to the
extent to which students selected Science-based college majors. Lopez et al. (1997) conducted
research on 296 high school students enrolled in advanced Algebra and Geometry courses. They
showed that high school students’ self-efficacy and outcome expectations predicted their interest in
Mathematics and that self-efficacy partially mediated the effect of ability on grades in Mathematics.
They conducted path analysis for their social-cognitive interest and social cognitive performance
models represented in Figure 2.5 and Figure 2.6.
Figure 2.5 Social Cognitive Interest Model
Source: Copyright © 1997 by Lopez et al. Reprinted with permission.
42
Figure 2.6 Social Cognitive Performance Model
Source: Copyright © 1997 by Lopez et al. Reprinted with permission.
In a study amongst 415 eleventh grade high school students, O’Brien et al. (1999) found that career
interest in Science is predicted solely by Science-Mathematics self-efficacy. Lapan, Adams, Turner
and Hinkelman (2000) used cluster analysis to statistically explore interest and efficacy patterns of
seventh grade high school students across Holland’s RIASEC interest themes. They found that boys
who had a high self-efficacy for Enterprising and Artistic careers were also more interested in
pursuing those careers, and boys who had a moderate self-efficacy for Realistic and Investigative
occupations were more interested pursuing those occupations. Turner and Lapan (2002) conducted
regression analysis on high school students’ gender, gender-career typing, career self-efficacy and
career planning/exploration patterns in order to predict their career interests across Holland’s
RIASEC themes. They found that self-efficacy significantly predicted some of the total variance for
all six of Holland’s themes.
43
2.3.4 ACHIEVEMENT MOTIVATION
Achievement motivation, or a person’s need for achievement is described by the Oxford Dictionary
of Psychology (Colman, 2006) as a social form of motivation involving a competitive drive to meet
standards of excellence. Busato et al. (2000) describe achievement motivation as the striving
tendency towards success with the associated positive effects and towards the avoidance of failure
and the associated negative effects and state that it is an important predictor of cognitive
performance.
Achievement motivation and its relationship with academic performance are well documented in
the literature. Busato et al (2000) conducted research with psychology students in the Netherlands
and found that scores on a measure of achievement motivation (Prestatie-Motivatie Test) were
significantly related to academic success, measured in the form of the amount of study points
gained by students at a first, second and third year level. Tavani and Losh (2003) found a
statistically positive relationship between 4012 students’ levels of motivation and their academic
performance as measured by grade point average. Lounsbury et al. (2003) investigated the effect of
work drive, defined as the enduring motivation to spend time and effort to finish projects, meet
deadlines, be productive and achieve success, on academic performance and found a statistically
significant positive relationship.
Wentzel (1989) states that the concept of goal pursuit has been central to the study of achievement
motivation and performance outcomes. She conducted a study on the relationship between high
school students’ single and multiple goals and their academic achievement as indexed by their
grade point averages and found a significant relationship. Results indicated that students with high
44
grade point averages were primarily concerned with the pursuit of social-responsibility and learning
goals while students with low grade-point averages showed more concern for social interaction
goals. In Australia, McInerney, Hinkley, Dowson and Van Etten (1998) investigated the mastery,
performance and social learning goals of Aboriginal, Anglo and Immigrant Australian high school
students. They found that students who embraced a mastery goal-orientation achieved better success
academically and that profiles between cultures were remarkably similar. In a similar study in the
United States of America, Harackiewicz, Tauer, Barron and Elliot (2002) found that performance
goal-orientations were a significant predictor of academic success in a particular subject while
mastery goal-orientation was a significant predictor of continued interest in that subject. In South
Africa, Bosch, Boshoff and Louw (2003) also found that mastery as a goal-orientation had a
significant influence on the academic performance of undergraduate Business Management
students.
Achievement motivation may be partially mediated by other cognitive and non-cognitive factors
that may affect academic performance. Tavani and Losh (2003) state that levels of students’ internal
characteristics, such as motivation and self-confidence strongly influence their achievements during
high school, however little is known concerning the extent to which each of these factors affects
academic performance and expectations. They found that high school students’ motivation was
significantly linked to a drive to achieve as well as leadership ability. In addition, they found that
when students’ levels of motivation were high, so too were their expectations of academic success.
45
2.3.5 COPING STRATEGIES
Students may face many stressors when attempting to achieve in the academic environment. Nonis,
Hudson, Logan and Ford (1998) report that time constraints, financial strain, academic workload
and interpersonal difficulties with lecturers, peers and significant others contribute to stress for
college students. When considering the school environment, many of these stressors may also be
applicable to high school students. Academic performance may depend on students’ utilisation of
resources to effectively develop strategies of coping with academic pressures. Nounopoulos, Ashby
and Gilman (2006) define coping resources as traits, abilities, and assets, both human and material,
that are used to determine subsequent coping strategies. Collins and Onwuegbuzie (2003) define
coping strategies as the cognitive and behavioural tactics that individuals utilise to control their
environmental surroundings and to alleviate any stress which may occur when environmental
demands surpass individuals’ resources. Chu and Choi (2005) describe the three most frequently
mentioned coping strategies in the literature, namely, task-oriented strategies, emotion-oriented
strategies and avoidance-oriented strategies. Task-oriented coping strategies reduce stress by
focusing on immediate problems. Emotion-oriented coping strategies involve diminishing the
emotional distress that is induced by the stressor. Avoidance-oriented strategies involve ignoring a
problem or distracting oneself from it (Chu & Choi, 2005).
A number of empirical studies have investigated different coping strategies and its impact on
academic performance. Zuckerman et al. (1998) investigated self-handicapping as a coping strategy
and its impact on academic performance. Self-handicapping involves the tendency to erect obstacles
to successful performance such as drug and alcohol consumption or choosing debilitating
performance settings in order to protect self-esteem. They found that students high in self-
46
handicapping performed less well academically than students low in self-handicapping and that this
effect was mediated by poor study habits. Nonis et al. (1998) investigated the influence of perceived
control over time as a stress coping strategy amongst college students. They found that high levels
of academic performance were associated with students that perceived high levels of control over
time compared with students who perceived low levels of control over time. Malefo (2000)
investigated the coping strategies of black women in a predominantly white university in South
Africa and found no statistically significant relationship between scores on a measure describing a
broad range of behavioural and cognitive coping strategies and academic performance. Chu and
Choi (2005) conducted research on the effects of procrastination as a coping strategy and its impact
on grade point averages of Canadian university students. They found that students who used
“active” procrastination (a preference to work under pressure) as a coping strategy had higher
grade-point averages than those students who were “passive” procrastinators. Nonopoulos et al.
(2006) investigated the relationship between the coping resources and academic performance of
high school students. They found that confidence in academic pursuits as a coping resource was
positively associated with higher grade-point average. In addition, academic confidence mediated
the relationship between perfectionist tendencies and grade point average.
Apart from its direct effect on academic performance, coping may also indirectly affect certain
learning styles that may impact academic performance. Collins and Onwuegbuzie (2003) studied
two aspects of coping strategy, namely study coping strategies and examination-taking coping
strategies. They found that both of these constructs were statistically significantly related to certain
learning modalities and seem to be a function of learning styles. Educators’ understanding of
47
learning styles can foster an environment more conducive to learning and this may increase
academic performance.
2.3.6 SELF-DIRECTEDNESS IN LEARNING
The concept of self-directedness in learning involves the capacity for an individual to take
responsibility for his or her own learning. Hoban and Hoban (2004) describe self-directed learning
as an elusive construct that may lend itself to a multitude of definitions but which ultimately places
the responsibility for learning on the individual regardless of the method of instruction. Knowles
(1975) defines self-directed learning as a process in which individuals take the initiative to identify
their learning needs, formulate learning goals, identify resources for learning, choose and
implement learning strategies and evaluate learning outcomes. Another definition related to this is
outlined by Costa and Garmston (2001) and Costa and Kallick (2004) who define self-directed
school students as exhibiting self-managing, self-monitoring and self-modifying dispositions of
mind when confronted with complex and sometimes ambiguous and intellectually demanding tasks.
With regard to the influence of self-directedness in learning on academic performance, significant
research has been conducted, the bulk of which suggests that individuals demonstrating high levels
of self-directed learning are more likely to experience success in various learning contexts (Reio,
Jr., 2004). Wall, Hoban and Sersland (1996) found that higher levels of self-directed learning
readiness predicted classroom mathematical performance while Long and Morris (1996) found self-
directed learning readiness to be a useful single-predictor variable of academic success after
controlling for intelligence (Reio, Jr., 2004). A study of the literature also suggests a link between
self-directedness in learning and the concept of self-efficacy in impacting academic performance.
48
This is highlighted in Hoban and Hoban’s (2004) review of Bandura’s (1995) postulates about the
link between self-directed learning, self-efficacy and academic performance. They mention that
Bandura clearly links self-efficacy with self-directed learning in that he states that efficacy beliefs
play an important role in the development of self-directed lifelong school students and that a
student’s belief in their ability to master academic activities affects their academic
accomplishments. Bandura further proposes that students who have a strong belief that they can
regulate their behaviour will also have a strong belief in their ability to master academic
achievements. Expanding on these postulates, Hoban and Hoban (2004) state that a student’s
mastery experiences contributes to raising self-efficacy which, in turn, influences performance and
competence in an almost circular process.
2.3.7 AVOIDANCE OF PROCRASTINATION
Ellis and Knaus (1977) define procrastination as the lack or absence of self-regulated performance
and the behavioural tendency to postpone what is necessary to reach a goal. This act of needlessly
delaying tasks to the point of subjective discomfort is an all-too-familiar problem amongst students
(Solomon & Rothblum, 1984). There is a general consensus amongst researchers and practitioners
that procrastination is a self-handicapping and dysfunctional behaviour and that it may have an
important negative impact on learning and achievement (Solomon & Rothblum, 1984; Wolters,
2003). Chu and Choi (2005) state that procrastination may have serious consequences for students
whose lives are characterised by frequent deadlines. Despite the negative impact that
procrastination may have on academic performance and activity, it is fairly commonplace among
adults as well as students at the high school and college levels (Wolters, 2003).
49
A number of empirical studies have reported the relationship between procrastination and academic
performance. Semb, Glick and Spencer (1979) found that procrastination resulted in detrimental
academic performance, including poor grades and course withdrawl (Solomon & Rothblum, 1984).
Rothblum, Solomon and Murakami (1986) found that self-reported procrastination was negatively
correlated with grade-point average in first year Psychology students and came to the conclusion
that subjects who reported procrastination performed less well academically than did non-
procrastinators. Tice and Baumeister (1997) report that university students who rated high on
procrastination not only received low grades but also reported a high level of stress along with poor
self-rated health. Chu and Choi (2005) distinguish between “active” procrastinators (who prefer to
work under pressure) and “passive” procrastinators defined in the traditional sense. They found that
“passive” procrastinators scored significantly lower on grade point average than “active”
procrastinators and non-procrastinators.
In addition to research on procrastination and its impact on academic performance, studies have
been conducted in order to explain the etiology of procrastination behaviour. Milgram and
Toubiana (1999) tested the appraisal-anxiety-avoidance (AAA) model of procrastination amongst
Israeli high school students. The AAA model proposes that people characterised by fear of failure
about doing certain tasks become anxious when called to perform them and allay their anxiety by
postponing them as much as possible. The researchers found that the more high school students
were anxious about preparing for examinations and writing papers, the more they procrastinated on
these assignments, confirming the AAA model. In a study involving first-year Psychology students,
Wolters (2003) found that procrastination was related to self-efficacy, work-avoidant goal
orientation and students’ metacognitive strategies.
50
2.3.8 CONCLUSION
From a study of the literature, it seems as if the non-cognitive factors reviewed, namely vocational
interest, person-environment fit, self-efficacy, achievement motivation, coping, self-directedness in
learning and avoidance of procrastination, exert a powerful influence on academic performance. By
reviewing the nature of the relationships, it is apparent that the school students who are interested in
their subject fields, whose personalities fit the academic environment, who believe in their ability to
succeed, who are motivated to achieve, who adopt successful coping mechanisms, who are self-
directed in their learning and who avoid procrastinatory activities, will perform better academically
than those who do not show a tendency toward these attitudes and behaviours. Although research
has shown the above mentioned factors to be important non-cognitive predictors of academic
performance, the list is by no means exhaustive. Factors such as locus of control (Masqud, 1983),
beliefs about learning (Cano & Cardel-Elawar, 2004), work ethic (Hill & Rojewski, 1999) and self-
concept (Burns, 1982) have all shown to affect academic performance. It is important to note that,
with the exception of person-environment fit, the above-mentioned factors are intrapsychic in
nature in that they exist within the boundaries of the individual. However, since individuals do not
live in a vacuum and are relational in a sense that they interact with other human beings, it is
important to briefly mention some of contextual factors existing outside of the realm of the
individual that may have an indirect or mediating influence on academic performance.
2.4 CONTEXTUAL FACTORS AFFECTING ACADEMIC
PERFORMANCE
Although not the main aim of this investigation, a study on factors affecting academic performance
that fails to acknowledge the important influence of significant others and socio-cultural
51
phenomena would be incomplete. While a plethora of literature exists on contextual factors, it is
useful to briefly mention two general areas of research, namely the influence of significant others
and socio-cultural factors.
2.4.1 THE INFLUENCE OF SIGNIFICANT OTHERS
Significant research by Kaplan, Liu and Kaplan (2001) showed that parental academic expectations
of their children were positively related to academic performance and a study by Wong, Wiest and
Cusick (2002) indicated that school students’ attachment to their parents increased their motivation
to succeed academically and this consequently impacted their academic performance. They also
showed that a support for a student’s autonomy by teachers was significantly related to an increase
in academic performance.
2.4.2 THE INFLUENCE OF SOCIO-CULTURAL FACTORS
Extensive empirical research has been conducted showing that many socio-cultural factors
influence academic performance. A study conducted in Nigeria by Masqud (1983) showed
significant relations between socio-economic status and academic achievement. A similar study
conducted in South Africa by Masilela (1988) investigated the effects of socio-economic status on
academic achievement and found that through its effect on both school and home environment, it
has a considerable influence. Also in South Africa, Grobler et al. (2001) conducted research with
high school students enrolled in Mathematics courses and showed a significant positive relationship
between level of training in teachers and Mathematics results. They also reported that class size was
negatively related to Mathematics performance in boys. In the United States of America, Zigarelli
(1996) investigated socio-cultural factors pertaining to the school environment as a catalyst for
52
learning. He found that the most important effective school characteristics that promoted school
students who performed well academically were an achievement-oriented school culture, principal
autonomy in hiring and firing teachers and high levels of teacher morale.
2.5 SOCIAL-COGNITIVE CAREER THEORY EXPLAINING
DIFFERENCES IN ACADEMIC PERFORMANCE
Academic performance and choice of subject and career pathways may be partly explained by the
mediating effect of non-cognitive factors such as self-efficacy, vocational interests, motivation and
self-directedness in learning on academic ability. Current theories which explain the link between
cognitive and non-cognitive variables influencing educational/vocational pathways includes Social-
Cognitive Career Theory (SCCT) (Lent et al., 1996), a theory derived from Albert Bandura’s
General Social Cognitive Theory (Bandura, 1986). SCCT attempts to consolidate constructs such as
self-concept, self-efficacy, interests and abilities and composes a comprehensive explanatory
system of the complex connections between persons and their career related contexts (Lent et al.,
2002).
In an explanation of SCCT, Lent et al. (1996) state that ability, as reflected by achievement directly
and indirectly impacts self-efficacy and outcome expectations. High levels of self-efficacy and the
anticipation of valued outcomes promote higher goals which mobilise and sustain performance
behaviour. Consequently, increased performance behaviour should promote educational and
occupational opportunity. Lent et al. (2002) display a performance model whereby a feedback loop
between performance attainments and subsequent behaviour is shown. Success experiences promote
development of abilities and, in turn, self-efficacy and outcome expectations within a dynamic
53
cycle. They also note the impact of contextual variables (such as teaching quality, socio-economic
status and gender role socialisation) in the refinement of abilities, self efficacy, outcome
expectations and goals. A diagrammatic representation of the model is shown in Figure 2.7:
Outcome expectations
Ability or past performance
Self-efficacy
Performance goals and sub-goals
Performance Attainment levels
Figure 2.7 Model of Task Performance
Source: Copyright © 1994 by Lent et al. Reprinted with permission by Jossey-Bass.
Bandura et al. (2001) also explain the relationship between ability, self-efficacy and career choices.
They state that the higher people’s level of self-efficacy to fulfil educational requirements and
occupational roles, the wider the career options they seriously consider pursuing, the greater the
interest they have in them, the better they prepare themselves educationally for different
occupational careers, the greater their staying power in challenging career pursuits.
2.6 CHAPTER SUMMARY
The preceding chapter summarises the body of literature relating to the study of some of the non-
cognitive factors affecting academic performance. From the literature review, it can be noted that
while there is a large body of evidence which correlate cognitive factors with academic
performance, non-cognitive factors such as vocational interests, person-environment fit, self-
54
efficacy, achievement motivation, coping skills, self-directedness in learning and the avoidance of
procrastination also have a significant impact on academic performance. How these factors relate to
one another is not clear, however Social Cognitive Career Theory has attempted to provide a
theoretical framework in order to understand how self-efficacy and vocational interest affects task
performance. It seems as if research in this area has mainly been conducted in the workplace and
amongst university students and this warrants further research within a school context. In the next
chapter, the research method employed in this study, is discussed.
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CHAPTER THREE
RESEARCH METHOD
3.1 INTRODUCTION
Chapter Three provides an overview of the research problem and the main aims and purposes of the
study. A comprehensive description of the participants, research instruments, research hypotheses
and research procedure is presented. The chapter concludes with an overview of the statistical
analysis of the data obtained in the study.
3.2 RESEARCH PROBLEM
Academic performance in high school may have a significant influence on the opportunity for entry
into various educational and occupational settings. Developmental theorists such as Super (1990),
Ginzberg (1984) and Gottfredson (1981) have placed an importance on an individual’s academic
experiences during school years in preparing for future academic experiences at a higher education
level as well as occupational opportunities in the workplace (Paa & McWhirter, 2000). The
academic results students achieve within their chosen subjects at a high school level may affect
their educational and occupational pathways with respect to the opportunity to enter into higher
education, selection of study field and major subjects at university or technikon, and their eventual
choice of career. Ainley et al. (1990) states that the subjects studied in the senior secondary years
are a major influence upon the educational and career options available to young people when
leaving school.
56
A large body of knowledge exists highlighting the relationship between students’ cognitive ability
and their academic performance (cf. Grobler et al., 2001; Lau & Roser, 2002; Masqud, 1983;
Midkiff et al., 1989; Rigdell & Lounsbury, 2004). In addition, certain non-cognitive factors such as
self-efficacy and achievement motivation, and their relationship with academic performance have
been well researched (cf. Andrew, 1998; Busato et al., 2000; Lent et al., 1994; Siegel et al., 1985;
Tavani & Losh, 2003). However, while evidence suggests that both cognitive and non-cognitive
factors influence academic performance, the relative influence of these factors and their differential
relationships with each other seem to be the subject of much uncertainty (Furnham & Chamorro-
Premuzic, 2004).
One area of research that has received the least amount of attention in this field, especially within a
high school setting in the South African context, is the relationship between students’ vocational
interests and their academic performance. Limited research has shown that school students with
Investigative interests appear to perform better academically and those with Realistic interests do
not perform well academically (cf. Holland, 1973; Schneider & Overton, 1983; Sparfeldt, 2007).
However, there is a possibility that school students who have personalities and interests that match
the content of the subjects they study may perform better academically than students who do not
show a match between personality, interests and subject content. Also, a poor person-environment
fit and a lack of interest in subject material may result in negative academic attitudes and study
behaviours that in turn may hinder academic performance rather than promoting it. This may
consequently influence the opportunity for entry into higher educational settings and various
occupational or vocational fields.
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3.3 PURPOSE OF THE STUDY
The purpose of this study is to provide empirical data on the relationship between vocational
interest and academic performance, when controlling for cognitive academic ability. The results
may supply valuable information to researchers, teachers, parents and high school students on
whether vocational interest should be an important variable to consider when choosing subjects in
high school and whether the relationship between vocational interest and subject choice is an
important predictor of academic success. While Holland’s (1997) theory speculates that educational
achievement is related to the following personality pattern order: Investigative, Social, Artistic,
Conventional, Enterprising, Realistic, little empirical evidence has been gathered to support this
view. In response to the general acceptance of Holland’s (1997) theory, the purpose of this study is
to investigate an alternative possibility, namely that there is a relationship between subject content-
vocational interest fit and academic performance. Specifically, school students experiencing a high
degree of correspondence between vocational interest and subject content will perform better
academically than students experiencing a lower degree of correspondence between vocational
interest and subject content.
In addition, the study investigates the predictive influence of certain academic attitudes and study
behaviours on academic performance. In this regard, the study will provide information about six
other non-cognitive factors that may affect academic performance, namely self-efficacy, person-
environment fit, achievement motivation, coping, self-directedness in learning and procrastination
behaviour. Knowledge of these factors and their impact on academic performance may allow
students to develop positive academic attitudes and study skills, thereby facilitating greater
academic success at high school. Increased academic performance may create more educational and
58
occupational opportunity for individuals who want to enter certain career fields but are denied entry
due to low marks at school.
3.4 PARTICIPANTS
The participants consisted of students from a government secondary school in Gauteng, South
Africa with over 1500 students. The school was chosen because it represented a wide range of
school students from different contexts and socio-economic backgrounds. In addition, a large
sample could be drawn from a single grade due to the size of the school. The sample consisted of
285 Grade 10 students, 132 of which were male and 153 of which were female. The average age of
the participants was 16 years. The sample was multicultural with representation from Black (n =
70), Coloured (n = 15), Indian (n = 17) and White (n = 183) racial designations. All Grade 10
students were encouraged to participate in the study but it was explained to them during a parent-
student information evening and in a letter addressed to their parents that participation was
voluntary. Parental consent was obtained before the research was conducted and parents were
assured that all participants would remain anonymous.
3.5 MEASUREMENT INSTRUMENTS
A description of the measurement instruments used, including their purpose, uses in different
contexts and descriptions of the various subtests are highlighted in the following sections. The
reliability and validity of the instruments are also discussed.
59
3.5.1 General Scholastic Aptitude Test (GSAT)
The current study aims to investigate the influence of vocational interest and other non-cognitive
factors on the academic performance of high school students. It was therefore necessary to control
for the cognitive factors, more specifically academic ability, that has been found by other
researchers to strongly predict academic performance. The General Scholastic Aptitude Test
(GSAT) (Senior Series), developed by Claassen et al. (1998) in conjunction with the Human
Sciences Research Council (HSRC), was used as a measure of the academic ability of participants.
The GSAT was selected because it was developed specifically for South African high school
students and can be administered in a group context.
The GSAT measures various reasoning and problem-solving abilities associated with academic
performance in a high school setting. Although the test comprises of a variety of item types, it
specifically measures cognitive academic ability and does not aim to provide a differentiated picture
of a broad range of intellectual functioning (Claassen et al., 1998). The GSAT (Senior Series) was
developed for Afrikaans-speaking and English-speaking South African school students from the age
of 13 years and 6 months to 18 years. Three versions of the test are available, namely a full version,
a shortened version and a shortened speeded version in which the time for completion of the
subtests is limited. The GSAT can be administered in English or Afrikaans and two alternate forms
(Forms A and B) of equal difficulty and with one set of norms between them are available. For
purposes of this research, the shortened speeded version of the test was used as it was able to elicit
reliable information about general scholastic aptitude as well as verbal and non-verbal intelligence
factors, and it was also able to fit within the limited time constraints that were set out by the
60
authorities at the school. Since all of the participants had English as their language of instruction,
Forms A and B of the English version were used.
3.5.1.1 Uses of the GSAT
The GSAT can be used as an objective aid in determining an individual’s general level of academic
ability in order to guide and direct educators with regard to the academic abilities of their students.
It has been used as a screening test for admission to secondary schooling and university and as an
objective measure to determine the placement of students in special programmes such as those for
gifted school students or those with intellectual limitations.
3.5.1.2 Description of the subtests
The shortened speeded version of the GSAT (Senior Series) consists of four subtests, two each of
which provide information about the verbal and non-verbal academic abilities of a student. The
subtests which make up the verbal academic ability component include:
• Subtest 1 : Word Analogies – a measure of the ability to observe the relation between two
words and to use this relation to complete another word pair by analogy as an aspect of
verbal reasoning ability.
• Subtest 3 : Verbal Reasoning – a measure of the ability to determine relations, form new
concepts and manipulate them in a logical manner as an aspect of verbal reasoning ability.
61
The subtests which make up the non-verbal academic ability component include:
• Subtest 2 : Number Series – a measure of the ability to determine relations between numbers
in a series, to deduce the rule applicable to a particular number series and apply it to
complete the number series. This provides a good measure of non-verbal reasoning ability.
• Subtest 4 : Pattern Completion – a measure of the ability to observe figures accurately, to
determine the relation between the figures and apply the rules to complete the patterns. This
provides a measure of non-verbal reasoning ability.
3.5.1.3 Reliability and validity of the GSAT
The GSAT has proved to be both a reliable and valid measure of academic ability within a South
African context. Jooste (2004) highlights that the reliability test-retest coefficients of the test vary
between 0.84 and 0.96. In terms of the content and construct validity, this has been fairly well-
established with a fair predictive validity coefficient of 0.54 for scholastic achievement (Jooste,
2004). Predictive validity for academic performance in examinations has also been established for
the GSAT. Table 3.1 shows research by Claassen et al. (1998) in which high scores on the
shortened version of the GSAT were positively correlated with final examination marks for
Standard 7 (Grade 9) students.
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Table 3.1 Correlation coefficients for shortened GSAT and examination marks
Final Exam Subject N Correlation (r)
Afrikaans (1st language) 462 0.67
Afrikaans (2nd language) 280 0.49
English (1st language) 296 0.52
English (2nd language) 448 0.64
Mathematics 725 0.64
Physical Science 733 0.60
History 715 0.50
Geography 724 0.57
Typing 206 0.43
3.5.2 Self-Directed Search (SDS)
The current study aims to investigate the relationship between vocational interest and academic
performance. It was therefore necessary to include an objective measure of vocational interest. The
Self-Directed Search (SDS), developed by John Holland in 1985 was chosen as a measure of
vocational interest because of evidence for its cross-cultural validity as well as its reliability and
applicability in the field of career psychology.
3.5.2.1 Purpose of the SDS
The SDS is a measure of vocational interest and personality type that was developed according to
Holland’s (1973) theory of personality type and work environments. It also provides information
about a person’s career orientations and aims to establish a correlation between the personal aspects
63
of individual and career information. The items of the SDS measure a preference for certain
activities, the skills they are familiar or competent in, their interest in a variety of occupations and
their assessment of their own abilities. Results reveal information categorised according to six
occupational themes, namely Realistic, Investigative, Artistic, Social, Enterprising and
Conventional (RIASEC).
3.5.2.2 Uses of the SDS
The SDS has been used with high school students, university students and adults and can be used as
an aid in a number of different contexts, including the determination of vocational interests for
career counselling purposes. It has also been used in the selection, placement and occupation
classification in business and industry as well as for investigating alternate career possibilities with
the aid of occupational codes. Additionally, it has assisted in the determination of an individual’s
personal development with more than one administration over time (Nel, 2006).
3.5.2.3 Description of the subtests
The SDS comprises of four sections, each of which measures the six RIASEC interest fields. The
four sections are described according to:
1. Activities: This subtest comprises of 66 items which represent the six (RIASEC) interest fields.
The respondent indicates his or her interest in a variety of activities in the workplace.
2. Competencies: This subtest comprises of 66 items which represent the six (RIASEC) interest
fields. The respondent indicates whether he or she has a working knowledge of an activity or is
competent in a particular activity.
64
3. Occupations: This subtest comprises of 84 items, which represent the six (RIASEC) interest
fields. The respondent indicates his or her feelings and attitudes toward a variety of occupations.
4. Self-rating of abilities or skills: This section consists of two groups (I and II), each comprising
of six abilities or skills correlating with the six (RIASEC) interest fields. The respondent uses a
six-point scale to rate his or her mechanical, scientific, artistic, teaching, sales and clerical
abilities.
With regard to this study, only the total scores for the SDS were used. This score is calculated from
the subscales mentioned above.
3.5.2.4 Reliability and validity of the SDS
Gevers, Du Toit and Harilall (1997) report that there have been a number of studies in which the
SDS was used in South Africa. Of particular importance to the current research is a study that was
conducted in 1997 which aimed to adapt the SDS for use in the South African context. Using the
latest version, the same as used in this study, the SDS was administered to 4573 Standard 7 (Grade
9) and Standard 9 (Grade 11) high school students from English, Afrikaans, Nguni and Sotho
backgrounds (Gevers et al., 1987). The coefficients of reliability were computed and are displayed
in Table 3.2.
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Table 3.2 Reliability coefficients for SDS adaptation study (Sichel formula)
SDS fields Reliability Coefficients
Realistic 0.88
Investigative 0.85
Artistic 0.87
Social 0.85
Enterprising 0.77
Conventional 0.82
Regarding the validity of the SDS, the results of the item analysis of the 1997 South African study
supported the content validity of the questionnaire. The structural relationship between the SDS
fields were also determined and results confirmed the structure of occupational interest as defined
by Holland whereby he states that adjacent fields have more in common (RI, IA, AS, SE, EC, CR)
while opposite fields have less in common (RS, IE, AC) (Gevers et al., 1997). Table 3.3 shows the
intercorrelations of the SDS fields from the 1987 study.
Table 3.3 Intercorrelations of the SDS fields (1987 study, N = 4573)
RI IA AS SE EC CR Adjacent
fields 0.32 0.30 0.60 0.59 0.62 0.24
RA IS AE SC ER CI Alternate
fields 0.12 0.33 0.44 0.48 0.27 0.33
RS IE AC Opposite
fields 0.04 0.32 0.33
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With regard to the current study, Table 3.4 shows the reliability statistics, estimated by means of
Cronbachs alpha, for the SDS subtests.
Table 3.4 SDS reliability statistics for a sample of high school students (N=285)
SDS Interest Field Cronbach’s Alpha Number of items
Realistic 0.905 38
Investigative 0.893 38
Artistic 0.890 38
Social 0.889 38
Enterprising 0.885 38
Conventional 0.866 38
From Table 3.4 it is clear that the subtests of the SDS yielded satisfactory reliabilities for the
sample in the current study.
3.5.3 Academic Behaviours and Attitudes Questionnaire (ABAQ)
The study aims to investigate additional non-cognitive factors besides vocational interest that may
influence academic performance. It was therefore necessary to include a measure which could elicit
information about other important determinants of academic performance.
3.5.3.1 Purpose of the ABAQ
The ABAQ was developed based on a need to understand non-cognitive aspects that account for
differences in the academic achievement of students at institutions of higher learning (De Bruin, De
Bruin, Schoeman & Hardy, 2005). The ABAQ measures six non-cognitive aspects related to the
67
academic behaviour and attitudes of students, which are represented in the following subtests,
namely Self-efficacy expectations, Person-environment fit, Achievement motivation, Social coping,
Self-directedness in learning, and Academic procrastination.
3.5.3.2 Uses of the ABAQ
The ABAQ was originally developed to be administered in university settings, however, for
purposes of the present study, some of the items were adjusted for high school students. The
instrument provides useful information to students, parents, teachers and guidance counsellors on
some of the attitudes and behaviours that high academic achievers may be adopting to facilitate
their success. It may also highlight areas of concern for those individuals that are academically
under-achieving.
3.5.3.3 Description of the subtests
The ABAQ comprises of 60 individual items which are divided into six subtests which represent the
constructs of Self-efficacy, Person-environment fit, Achievement motivation, Coping, Self-directed
learning and Avoidance of procrastination respectively (De Bruin et al., 2005). Respondents rate
their attitudes and behaviours on a five point Likert-type scale ranging from Strongly Agree to
Strongly Disagree.
3.5.3.4 Reliability and validity of the ABAQ
De Bruin et al. (2005) report the reliability and validity for the ABAQ for university populations.
Table 3.5 shows test-retest reliability coefficients of the ABAQ subscales between genders and
across different language groups using Cronbach’s coefficient alpha:
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Table 3.5 Reliability coefficients for the ABAQ across language and gender groups
Afrikaans English Sotho Nguni Men WomenABAQ subscale
n=724 n=1020 n=427 n=299 n=1432 n=1771
Self efficacy 0.82 0.83 0.80 0.81 0.82 0.84
Person-environment fit 0.86 0.87 0.84 0.82 0.86 0.86
Achievement Motivation 0.77 0.76 0.63 0.65 0.74 0.74
Coping 0.78 0.77 0.73 0.71 0.76 0.80
Self-directedness in learning 0.68 0.70 0.58 0.65 0.65 0.66
Avoidance of Procrastination 0.87 0.88 0.86 0.86 0.88 0.87
In terms of the validity of the instrument, De Bruin et al. (2005) explored the validity of the ABAQ
in predicting performance in a first year university Economics examination across racial
designations. They state that the ABAQ scales jointly accounted for approximately eight percent of
the variance of the examination scores for White respondents and three percent for Black
respondents. They also explored the validity of the ABAQ in predicting academic performance after
controlling for mental ability using the General Scholastic Aptitude Test (GSAT) and found that the
GSAT accounted for approximately 20 percent of the variance in an Economics examination while
the ABAQ accounted for a further 15 percent of the variance in a sample of 136 respondents.
Cronbach’s alpha coefficients of the six subscales for the participants in the current study are
reported as follows: Self-efficacy (α = 0.85), Person-environment fit (α = 0.83), Achievement
69
motivation (α = 0.76), Coping (α = 0.74), Self-directed learning (α = 0.67) and Avoidance of
procrastination (α = 0.84). Not all of these coefficients appear to be acceptable (α <= 0.70),
however they all compare well to the coefficients reported by De Bruin et al. (2005).
3.6 PROCEDURE
When considering that most of the participants in this study were school students under the age of
16 years, care was taken to ensure that the participants, their parents, teachers and other significant
role-players were fully aware of the research which was to be conducted. Firstly, permission was
obtained by the Principal and Head of Department: Life Orientation for the research to be
conducted at the school. Special arrangements were made with the four Grade 10 Life Orientation
teachers to utilise a period of teaching time for the administration of the psychometric instruments.
A full brief regarding the research project was given by the researcher at a Grade 10 parents
information evening. Prospective participants, their parents and teachers were informed about the
nature of the project, the procedures that would be followed with regard to psychometric evaluation,
the implications for the participants during the time of research and the benefits of participation. It
was explained that participation was entirely voluntary and that results would be made available as
soon as the data was analysed and reported.
Following the information evening, students were given a letter which further explained the
procedures and implications of the research project as well as the right to refuse participation. It was
discussed in the letter that parents needed to give consent to the researcher in order for their child to
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participate. It was explained to both students and parents that individual results of the psychometric
instruments would not be disclosed to anyone.
Once the consent forms had been gathered from the parents, the researcher arranged two time slots
during which the participants completed the psychometric instruments. The first instrument to be
completed was the Self-Directed Search (SDS) which was done on a class-by-class basis during a
Life Orientation lesson. The researcher and a registered psychometrist administered the
questionnaire over eleven sessions with classes of approximately 30 respondents each. At the
beginning of each session students were asked if there were any one of them who decided not to
participate. The students who indicated that they do not want to take part in the study, were not
required to complete the questionnaire.
During the second session the General Scholastic Aptitude Test (GSAT) and the Academic
Attitudes and Behaviours Questionnaire (ABAQ) were administered. This involved gathering all
the participants together in the school hall and gym with the support of the teachers and the Grade
10 tutors. The researcher and a registered psychologist administered these instruments. Desks were
spaced far enough apart to prevent plagiarism and a fifteen minute break was given in between the
administration of the GSAT and ABAQ to prevent any confounding factors such as fatigue or lack
of concentration.
Following the completion of the psychometric instruments, the results of all the Grade 10 school
students for the mid-year and final examination were obtained from the school administrator with
permission from the Principal and Head of Department: Life Orientation. These results were
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combined into an averaged result for each student. The psychometric instruments were scored by
the researcher and captured by the Statistical Consultation Services (STATCON) at the University
of Johannesburg. Once the data was captured, the researcher analysed the data in consultation with
STATCON. Following the initial data analysis, school students and parents were informed about
the findings which were reproduced in a document distributed to each Grade 10 learner.
3.7 HYPOTHESES
3.7.1 Research Hypothesis 1
It is hypothesised that academic ability, as measured by the General Scholastic Aptitude Test, has a
statistically significant relationship with academic performance. More specifically, it is
hypothesised that as academic ability increases, the overall academic result will also increase.
3.7.2 Research Hypothesis 2
It is hypothesised that school students who show vocational interest patterns that correspond with
specific subject content, perform academically better than school students who do not have interests
that are in line with the content of the subjects they study. Specifically, those school students who
have vocational interest patterns that have much in common with the content of a specified subject,
will achieve higher academic results as represented by their overall examination percentage in that
subject, when controlling for academic ability.
3.7.3 Research Hypothesis 3
It is hypothesised that school students who show positive academic attitudes and study behaviours
perform academically better than students who show negative academic attitudes and study
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behaviours. Specifically, those school students who show higher scores with respect to self-
efficacy, person-environment fit, achievement motivation, coping, self-directedness in learning and
avoidance of procrastination will achieve higher academic results, as reflected by their overall
averaged result, than those school students who show lower scores with respect to self-efficacy,
person-environment fit, achievement motivation, coping, self-directedness in learning and
avoidance of procrastination.
3.8 STATISTICAL ANALYSIS
The research design employed in this study is a non-experimental survey design, intended to
provide information about the relationships between vocational interest and other non-cognitive
factors affecting academic performance. In accordance with the stated research problem and
purpose of the study, the data analysis was divided into three sections, namely:
(a) The analysis of the data pertaining to Hypothesis 1: Investigating the relationship between
cognitive ability and academic performance.
(b) The analysis of the data pertaining to Hypothesis 2: Investigating the relationships between
vocational interests and academic performance in subjects that correspond with the specified
interests.
(c) The analysis of the data pertaining to Hypothesis 3: Investigating the relationship between non-
cognitive factors and academic performance.
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The statistical analysis of the data was performed using the Statistical Package for Social Sciences
(SPSS, version 14). Descriptive and inferential statistics were employed in the analysis of the data
of this study. The statistical methods selected in the analysis of the data are discussed below.
3.8.1 Descriptive statistics
To describe the sample, frequency distributions were utilised for categorical variables such as
gender, racial designation and language group. Frequency distributions were also used to report on
continuous variables such as age, academic results and cognitive ability as well as the participants’
predominant vocational interests, academic attitudes and study behaviours. The minimum and
maximum values as well as the means and standard deviations are provided for these variables.
Cronbach’s alpha coefficients were computed for each of Holland’s interest categories on the SDS
as well as for the six dimensions of the ABAQ, as already reported in sections 3.5.2.4. and 3.5.3.4
respectively.
3.8.2 Inferential statistics pertaining to Hypothesis 1
Hypothesis 1 of the study involved describing the relationship between an independent predictor
variable, namely cognitive ability, and a continuous dependent variable, namely academic
performance. A statistical method that has proved useful in studying a relationship of this nature is a
simple linear regression model (Field, 2005). In simple regression analysis, variable X is used to
predict or explain variable Y and can be described by the formula y = bx + c (Miles & Shevlin,
2001). For the present study, the predictor variable (x) was cognitive ability and the dependent
variable (y) was academic performance. The significant level of the relationship was considered at
the p < 0.05 and p < 0.01 level.
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3.8.2 Inferential statistics pertaining to Hypotheses 2 and 3
Hypotheses 2 and 3 of the study involve describing the relationship between more than one
independent predictor variable and a continuous dependent variable in the presence of a moderator
variable. For Hypothesis 2, this would describe the relationship between vocational interest and
academic performance while controlling for the moderating effect of cognitive ability. A useful
procedure that allows for the description of these relationships is hierarchical multiple regression
analysis (Field, 2005). In hierarchical multiple regression analysis, more than one independent
variable (X1, X2) are used to predict or explain variable Y and can be described by the equation Y
= b1x1 + b2x2 + c (Miles & Shevlin, 2001). Multiple regression can tell us how good the prediction
is and how much of the variance of Y is accounted for by the linear combination of the independent
variables (Field, 2005). The significant level of the relationship was considered at the p < 0.05 and
p < 0.01 level.
It is important to note that the independent variables are entered into the equation based on
theoretical orientation. For the purposes of this study, the theoretical orientation applied is Social
Cognitive Career Theory which states that performance is a function of non-cognitive factors such
as vocational interest while taking into account the mediating effect of cognitive ability. Therefore,
cognitive ability will be entered into the hierarchy of the equation as the first predictor variable,
followed by the non-cognitive factors. Considering that Hypothesis 2 involves describing whether a
vocational interest that is related to a certain subject increases academic performance, two
regression equations for each of the subjects will be computed. Firstly, vocational interests that are
expected to be related to a particular subject based on Holland’s (1997) theory (for example
Investigative interests and Mathematics), will be entered into the multiple regression equation. The
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amount of variance explained by vocational interest with regard to academic performance while
controlling for academic ability will be recorded. Thereafter the remaining vocational interests will
be entered into the first multiple regression equation to establish whether any additional variance
can be explained.
For Hypothesis 3, the relationship between certain academic attitudes and study behaviours, and
academic performance will be described while controlling for the moderating effect of cognitive
ability.
3.9 CHAPTER SUMMARY
This chapter summarises the research method adopted in the study. It would seem that school
students who disregard the importance of non-cognitive factors perform poorer academically. The
purpose of the study was to investigate the relationship between certain non-cognitive factors and
academic performance, with a specific focus on vocational interests. Two hundred and eighty five
Grade 10 students from diverse backgrounds completed a number of instruments to measure
academic ability, vocational interests and certain academic attitudes and study skills. The data was
analysed using a series of multiple regression techniques, the results of which are reported in the
next chapter.
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CHAPTER FOUR
RESULTS
4.1 INTRODUCTION
Chapter Four provides an overview of the results of the study in terms of descriptive and inferential
statistics. All statistical procedures were performed using SPSS (version 14). The results are
reported as they relate to the various hypotheses stated in Chapter Three.
4.2 DESCRIPTIVE STATISTICS
The sample yielded a total of 285 Grade 10 participants who consisted of males and females
between the ages of 13 and 18 from across four different racial designations and ten language
groups. Tables 4.1, 4.2, 4.3 and 4.4 provide information regarding age, gender, race and language
variables.
Table 4.1 Age statistics for sample of 285 Grade 10 students
Age Frequency Percent Cumulative Percent 13 1 0.4 0.4 14 9 3.2 3.5 15 197 69.1 72.6 16 67 23.5 96.1 17 10 3.5 99.6 18 1 0.4 100.0 Total 285 100.0
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Table 4.1 shows that the majority of the sample (69.1%) consisted of individuals who were 15 years
old. The next most representative age group was 16 years (23.5%), followed by 17 years (3.5%), 14
years (3.2%) and 18 years (0.4%) respectively.
Table 4.2 Gender statistics for sample of 285 Grade 10 students
Gender Frequency Percent Cumulative Percent Male 132 46.3 46.3 Female 153 53.7 100.0 Total 285 100.0
With regard to gender, Table 4.2 shows the distribution of males and females to be fairly even, with
132 males (46.3%) and 153 females (53.7%) being represented.
Table 4.3 Racial designation statistics for sample of 285 Grade 10 students
Racial Designation Frequency Percent Cumulative Percent Black 70 24.6 24.6 White 183 64.2 88.8 Asian/Indian 17 6.0 94.7 Coloured 15 5.3 100.0 Total 285 100.0
Table 4.3 shows that the majority of the sample (64.2%) was from a White racial background. The
next most represented racial designation was Black (24.6%), followed by Asian/Indian (6%) and
Coloured (5.3%).
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Table 4.4 Home language statistics for sample of 285 Grade 10 students
Frequency Percent Cumulative Percent North Sotho 10 3.5 3.5 South Sotho 2 0.7 4.2 Tswana 18 6.3 10.5 Tsonga 2 0.7 11.2 Venda 1 0.4 11.6 Xhosa 12 4.2 15.8 Zulu 17 6.0 21.8 Afrikaans 4 1.4 23.2 English 208 73.0 96.1 Other 11 3.9 100.0 Total 285 100.0
With regard to home language, the descriptive statistics show that the majority of the sample was
English speaking (73%), followed by Tswana (6.3%), Zulu (6%), Xhosa (4.2%), other languages
(3.9%), North Sotho (3.5%), Afrikaans (1.4%), South Sotho (0.7%), Tsonga (0.7%) and Venda
(0.4%).
4.3 RESULTS PERTAINING TO HYPOTHESIS 1
Hypothesis 1 stated that cognitive ability, as measured by the General Scholastic Aptitude Test, has
a statistically significant relationship with school students’ average academic performance. The
variables that apply to Hypothesis 1 were obtained by using the average academic results and the
academic ability score (as measured by the GSAT) for each participant. The average academic
result is comprised of mid-year and final examination marks from all the school subjects taken. A
simple linear regression using SPSS was performed to investigate the relationship between
academic ability and overall academic performance. The results of this analysis are presented in
Table 4.5.
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Table 4.5 Predictive effect of academic ability on overall academic performance
Model R R2 Adjusted
R2 Change Statistics
R2
Change F
Change df1 df2 Sig. F Change 1 0.542(a) 0.294 0.292 0.294 118.019 1 283 0.000 a Predictors: (Constant), Academic ability
As presented in Table 4.5, the results show a significant positive relationship between academic
ability and overall academic performance. With academic ability as the only predictor of academic
performance, R2 = 0.294, F(1, 283) = 118.019, p < 0.001. Academic ability explained
approximately 29% of the variance in the school students’ overall academic performance. On the
basis of these results, Hypothesis 1 can be regarded as true for this particular sample.
4.4 RESULTS PERTAINING TO HYPOTHESIS 2
Hypothesis 2 stated that school students’ who show vocational interest patterns that correspond with
specific subject content, perform academically better than learners who do not have interests that
are in line with the content of the subjects they study. To investigate Hypothesis 2, the average
marks of the mid-year and final examinations for six individual school subjects as well as academic
ability and vocational interests were taken into account. The school subjects represented were
chosen on the basis that they represented Holland’s RIASEC interest groups while taking into
account the sample size for each subject, those with small sample sizes being omitted. Each subject
was designated one or more fields of vocational interests in which the content of the field of interest
was regarded as being a good fit with the content of the school subject. Interests were assigned to
the subjects on the basis of Holland’s (1997) descriptions of the vocational preferences of the
RIASEC personality types. Table 4.6 shows the subjects considered as part of this research as well
80
as the corresponding fields of vocational interests assigned on the basis of correspondence with
subject content.
Table 4.6 Subjects considered in study with corresponding vocational interests
Subject N Relevant vocational interest
Accounting 90 Conventional
Business Economics 125 Enterprising
English 277 Artistic, Social
Life Orientation 277 Social
Life Sciences 113 Investigative
Mathematics 162 Investigative
A series of hierarchical multiple regression analyses was performed in which the various
independent variables were entered into the regression equation in a hierarchical fashion in order to
predict the dependent variable, namely subject-specific academic performance. Firstly, academic
ability was entered into the equation as a control variable (or the first predictor/independent
variable). Secondly, the vocational interest variable that corresponds with the specific school
subject was entered to establish if it does in fact contribute to any additional variance over and
above academic ability. Finally, the remainder of the RIASEC interest fields were entered, to
establish whether any additional variance was explained. The total contributions for each step in the
equation as well as the part or unique contribution of the variables were considered.
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4.4.1 Results pertaining to Accounting
A hierarchical multiple regression analysis was performed with academic performance in
Accounting as dependent variable and academic ability and the six vocational interest fields as
independent variables. Academic ability was entered into the regression equation first, followed by
the Conventional interest, and then the remaining five vocational interests. The results are
summarised in Table 4.7.
Table 4.7 Predictive effect of vocational interests on academic performance in Accounting
Model R R2 Adjusted
R2 Change Statistics
R2 Change F Change df1 df2 Sig. F
Change 1 .445(a) .198 .189 .198 22.184 1 90 .001 2 .502(b) .252 .235 .054 6.397 1 89 .013 3 .610(c) .372 .320 .121 3.229 5 84 .010 a Predictors: Academic ability b Predictors: Academic ability, C c Predictors: Academic ability, C, A, R, I, E, S
With academic ability as the only predictor, R2 = 0.198, F(1, 90) = 22.184, p < 0.001. The
Conventional interest explained a further 5.4% of the variance in Accounting performance, ΔR2 =
0.054, F(1, 89) = 6.397, p < 0.013. The remaining five vocational interest fields jointly explained a
further 12.1% of the variance in performance in Accounting, ΔR2 = 0.121, F(5, 84) = 3.229, p <
0.01. Jointly, academic ability and the six interests accounted for 37.2% of the variance in
Accounting. The standardised regression weights, t-values, p-levels and semi-partial correlations of
the predictor variables with academic performance in Accounting are summarised in Table 4.8.
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Table 4.8 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Accounting
Model StandardisedCoefficients t p Correlations
ß Zero-order Partial Part
1 Academic ability .445 4.710 .001 .445 .445 .445
2 Academic ability .454 4.945 .001 .445 .464 .453
C .232 2.529 .013 .214 .259 .232
3 Academic ability .335 3.547 .001 .445 .361 .307
C .199 1.847 .068 .214 .198 .160 R -.166 -1.836 .070 -.055 -.196 -.159 I .366 3.434 .001 .467 .351 .297 A -.068 -.666 .507 .031 -.072 -.058 S .033 .281 .780 .194 .031 .024 E -.136 -1.191 .237 .027 -.129 -.103 a Dependent Variable: Accounting mark
Inspection of Table 4.8 shows that in step three of the hierarchical analysis, only the Investigative
interest (β = 0.366, r = 0.297, t = 3.434, p < 0.001) and academic ability (β = 0.335, r = 0.307, t =
3.547, p < 0.001) were significantly related to Accounting in the presence of all the remaining
interests. However, the hierarchical analysis has shown that, as expected, the Conventional interest
does explain a significant portion of the variance in Accounting above and beyond that explained by
academic ability.
4.4.2 Results pertaining to Business Economics
A hierarchical multiple regression analysis was performed with academic performance in Business
Economics as dependent variable and academic ability and the six vocational interest fields as
independent variables. Academic ability was entered into the regression equation first, followed by
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the Enterprising interest, and then the remaining five vocational interests. The results are
summarised in Table 4.9.
Table 4.9 Predictive effects of vocational interests on academic performance in Business
Economics
Model R R2 Adjusted R2 Change Statistics
R2
Change F Change df1 df2 Sig. F
Change 1 .323(a) .104 .097 .104 14.562 1 125 .001 2 .370(b) .137 .123 .033 4.693 1 124 .032 3 .539(c) .290 .248 .153 5.128 5 119 .001
a Predictors: Academic ability b Predictors: Academic ability, E c Predictors: Academic ability, E, R, I, A, C, S With academic ability as the only predictor, R2 = 0.104, F(1, 125) = 14.562, p < 0.001. The
Enterprising interest explained a further 3.3% of the variance in Business Economics performance,
ΔR2 = 0.033, F(1, 124) = 4.693, p < 0.032. The remaining five vocational interest fields jointly
explained a further 15.3% of the variance in performance in Business Economics, ΔR2 = 0.153, F(5,
119) = 5.128, p < 0.001. Jointly, academic ability and the six interests accounted for 29% of the
variance in Business Economics. The standardised regression weights, t-values, p-levels and semi-
partial correlations of the predictor variables with academic performance in Business Economics
are summarised in Table 4.10.
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Table 4.10 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Business Economics
Model Standardised Coefficients t p Correlations
ß Zero-order Partial Part
1 Academic ability .323 3.816 .001 .323 .323 .323
2 Academic ability .299 3.554 .001 .323 .304 .296
E .182 2.166 .032 .222 .191 .181 3 Academic
ability .242 3.031 .003 .323 .268 .234
E .073 .693 .489 .222 .063 .054 R -.228 -2.672 .009 -.137 -.238 -.206 I .291 3.286 .001 .387 .288 .254 A .070 .739 .462 .234 .068 .057 S .008 .081 .936 .268 .007 .006 C .128 1.322 .189 .260 .120 .102
a Dependent Variable: Business Economics mark
Inspection of Table 4.10 shows that in step three of the hierarchical analysis, the Investigative
interest (β = 0.291, r = 0.254, t = 3.286, p < 0.001), Realistic interest (β = -0.228, r = -0.206, t = -
2.672, p < 0.009) and academic ability (β = 0.242, r = 0.234, t = 3.031, p < 0.003) were
significantly related to Business Economics in the presence of all the remaining interests. However,
the hierarchical analysis has shown that, as expected, the Enterprising interest does explain a
significant portion of the variance in Business Economics above and beyond that explained by
academic ability.
4.4.3 Results pertaining to English
A hierarchical multiple regression analysis was performed with academic performance in English as
dependent variable and academic ability and the six vocational interest fields as independent
85
variables. Academic ability was entered into the regression equation first, followed by the Social
and Artistic interests, and then the remaining four vocational interests. The results are summarised
in Table 4.11.
Table 4.11 Predictive effects of vocational interests on academic performance in English
Model R R2 Adjusted R2 Change Statistics
R2
Change F Change df1 df2 Sig. F
Change 1 .526(a) .277 .274 .277 106.109 1 277 .001 2 .564(b) .318 .311 .041 8.360 2 275 .001 3 .696(c) .484 .471 .166 21.792 4 271 .001
a Predictors: Academic ability b Predictors: Academic ability, S, A c Predictors: Academic ability, S, A, R, C, I, E
With academic ability as the only predictor, R2 = 0.277, F(1, 277) = 106.109, p < 0.001. The Social
and Artistic interests explained a further 4.1% of the variance in English performance, ΔR2 = 0.041,
F(2, 275) = 8.360, p < 0.001. The remaining four vocational interest fields jointly explained a
further 16.6% of the variance in performance in English, ΔR2 = 0.166, F(4, 271) = 21.792, p <
0.001. Jointly, academic ability and the six interests accounted for 48.4% of the variance in English.
The standardised regression weights, t-values, p-levels and semi-partial correlations of the predictor
variables with academic performance in English are summarised in Table 4.12.
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Table 4.12 Regression weights, t-tests and effect sizes in the prediction of academic
performance in English
Model Standardised Coefficients t p
Correlations
ß Zero-order Partial Part
1 Academic ability .526 10.301 .001 .526 .526 .526
2 Academic ability .517 10.350 .001 .526 .529 .515
S .199 3.419 .001 .227 .202 .170 A .009 .153 .878 .151 .009 .008
3 Academic ability .396 8.242 .001 .526 .448 .360
S .119 2.003 .046 .227 .121 .087 A -.008 -.152 .880 .151 -.009 -.007 R -.262 -5.391 .001 -.238 -.311 -.235 I .349 6.838 .001 .500 .384 .298 E -.115 -1.907 .058 -.050 -.115 -.083 C .077 1.370 .172 .100 .083 .060
a Dependent Variable: English mark
Inspection of Table 4.12 shows that in step three of the hierarchical analysis, the Investigative
interest (β = 0.349, r = 0.298, t = 6.838, p < 0.001), Realistic interest (β = -0.262, r = -0.235, t = -
5.391, p < 0.001), Social interest (β = 0.119, r = 0.087, t = 2.003, p < 0.046) and academic ability (β
= 0.396, r = 0.360, t = 8.242, p < 0.001) were significantly related to English in the presence of all
the remaining interests. It should be noted that the effect of the Artistic interest became non-
significant when reviewing the semi-partial correlations of the interest groups. However, the
hierarchical analysis has shown that, as expected, both the Social and Artistic interests do explain a
significant portion of the variance in English above and beyond that explained by academic ability.
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4.4.4 Results pertaining to Life Orientation
A hierarchical multiple regression analysis was performed with academic performance in Life
Orientation as dependent variable and academic ability and the six vocational interest fields as
independent variables. Academic ability was entered into the regression equation first, followed by
the Social interest, and then the remaining five vocational interests. The results are summarised in
Table 4.13.
Table 4.13 Predictive effects of vocational interests on academic performance in Life
Orientation
Model R R2 Adjusted R2 Change Statistics
R2
Change F Change df1 df2 Sig. F
Change 1 .444(a) .197 .194 .197 67.873 1 277 .001 2 .476(b) .226 .221 .029 10.493 1 276 .001 3 .655(c) .429 .414 .203 19.241 5 271 .001
a Predictors: Academic ability b Predictors: Academic ability, S c Predictors: Academic ability, S, R, C, I, A, E
With academic ability as the only predictor, R2 = 0.197, F(1, 277) = 67.873, p < 0.001. The Social
interest explained a further 2.9% of the variance in Life Orientation performance, ΔR2 = 0.029, F(1,
276) = 10.493, p < 0.001. The remaining five vocational interest fields jointly explained a further
20.3% of the variance in performance in Life Orientation, ΔR2 = 0.203, F(5, 271) = 19.241, p <
0.001. Jointly, academic ability and the six interests accounted for 42.9% of the variance in Life
Orientation. The standardised regression weights, t-values, p-levels and semi-partial correlations of
the predictor variables with academic performance in Life Orientation are summarised in Table
4.14.
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Table 4.14 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Life Orientation
Model Standardised Coefficients t p Correlations
ß Zero- order Partial Part
1 Academic ability .444 8.239 .001 .444 .444 .444
2 Academic ability .436 8.226 .001 .444 .444 .436
S .172 3.239 .001 .191 .191 .172
3 Academic ability .313 6.202 .001 .444 .353 .285
S .035 .558 .577 .191 .034 .026 R -.331 -6.478 .001 -.278 -.366 -.297 I .373 6.945 .001 .477 .389 .319 A -.006 -.104 .917 .127 -.006 -.005 E -.057 -.901 .368 -.026 -.055 -.041 C .096 1.627 .105 .132 .098 .075
a Dependent Variable: Life Orientation mark
Inspection of Table 4.14 shows that in step three of the hierarchical analysis, the Investigative
interest (β = 0.373, r = 0.319, t = 6.945, p < 0.001), Realistic interest (β = -0.331, r = -0.297, t = -
6.478, p < 0.001) and academic ability (β = 0.313, r = 0.285, t = 6.202, p < 0.001) were
significantly related to Life Orientation in the presence of all the remaining interests. It should be
noted that the effect of the Social interest became non-significant when reviewing the semi-partial
correlations of the interest groups. However, the hierarchical analysis has shown that, as expected,
the Social interest does explain a significant portion of the variance in Life Orientation above and
beyond that explained by academic ability.
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4.4.5 Results pertaining to Life Sciences
A hierarchical multiple regression analysis was performed with academic performance in Life
Sciences as dependent variable and academic ability and the six vocational interest fields as
independent variables. Academic ability was entered into the regression equation first, followed by
the Investigative interest, and then the remaining five vocational interests. The results are
summarised in Table 4.15.
Table 4.15 Predictive effects of vocational interests on academic performance in Life
Sciences
Change Statistics Model
R
R2
Adjusted R2
R2 Change F Change df1 df2 Sig. F
Change 1 .532(a) .283 .276 .283 44.521 1 113 .001 2 .671(b) .451 .441 .168 34.255 1 112 .001 3 .736(c) .542 .512 .092 4.292 5 107 .001
a Predictors: Academic ability b Predictors: Academic ability, I c Predictors: Academic ability, I, S, R, C, A, E
With academic ability as the only predictor, R2 = 0.283, F(1, 113) = 44.521, p < 0.001. The
Investigative interest explained a further 16.8% of the variance in Life Sciences performance, ΔR2 =
0.168, F(1, 112) = 34.255, p < 0.001. The remaining five vocational interest fields jointly explained
a further 9.2% of the variance in performance in Life Sciences, ΔR2 = 0.292, F(5, 107) = 4.292, p <
0.001. Jointly, academic ability and the six interests accounted for 54.2% of the variance in Life
Sciences. The standardised regression weights, t-values, p-levels and semi-partial correlations of the
predictor variables with academic performance in Life Sciences are summarised in Table 4.16.
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Table 4.16 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Life Sciences
Model StandardisedCoefficients t p Correlations
ß Zero-order Partial Part
1 Academic ability .532 6.672 .001 .532 .532 .532
2 Academic ability .329 4.204 .001 .532 .369 .294
I .457 5.853 .001 .603 .484 .410
3 Academic ability .310 4.234 .001 .532 .379 .277
I .526 6.798 .001 .603 .549 .445 R -.129 -1.630 .106 -.061 -.156 -.107 A -.127 -1.652 .101 -.055 -.158 -.108 S .096 1.077 .284 -.005 .104 .070 E -.253 -2.562 .012 -.135 -.240 -.168 C .097 1.139 .257 .119 .109 .074
a Dependent Variable: Life Sciences mark Inspection of Table 4.16 shows that in step three of the hierarchical analysis, the Investigative
interest (β = 0.526, r = 0.445, t = 6.798, p < 0.001), Enterprising interest (β = -0.253, r = -0.168, t =
-2.562, p < 0.012) and academic ability (β = 0.310, r = 0.277, t = 4.234, p < 0.001) were
significantly related to Life Sciences in the presence of all the remaining interests.
4.4.6 Results pertaining to Mathematics
A hierarchical multiple regression analysis was performed with academic performance in
Mathematics as dependent variable and academic ability and the six vocational interest fields as
independent variables. Academic ability was entered into the regression equation first, followed by
the Investigative interest, and then the remaining five vocational interests. The results are
summarised in Table 4.17.
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Table 4.17 Predictive effects of vocational interests on academic performance in
Mathematics
Change Statistics Model
R
R2
Adjusted R2
R2 Change F Change df1 df2 Sig. F
Change 1 .327(a) .107 .102 .107 19.438 1 162 .001 2 .371(b) .138 .127 .030 5.687 1 161 .018 3 .473(c) .224 .189 .087 3.479 5 156 .005
a Predictors: Academic ability b Predictors: Academic ability, I c Predictors: Academic ability, I, R, E, A, C, S
With academic ability as the only predictor, R2 = 0.107, F(1, 162) = 19.438, p < 0.001. The
Investigative interest explained a further 3.0% of the variance in Mathematics performance, ΔR2 =
0.030, F(1, 161) = 5.687, p < 0.018. The remaining five vocational interest fields jointly explained a
further 8.7% of the variance in performance in Mathematics, ΔR2 = 0.087, F(5, 156) = 3.479, p <
0.005. Jointly, academic ability and the six interests accounted for 18.9% of the variance in
Mathematics. The standardised regression weights, t-values, p-levels and semi-partial correlations
of the predictor variables with academic performance in Mathematics are summarised in Table
4.18.
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Table 4.18 Regression weights, t-tests and effect sizes in the prediction of academic
performance in Mathematics
Model Standardised Coefficients t p Correlations
ß Zero-order Partial Part
1 Academic ability .327 4.409 .001 .327 .327 .327
2 Academic ability .274 3.579 .001 .327 .271 .262
I .183 2.385 .018 .263 .185 .175
3 Academic ability .300 4.011 .001 .327 .306 .283
I .204 2.600 .010 .263 .204 .183 R -.107 -1.360 .176 -.100 -.108 -.096 A -.073 -.871 .385 -.076 -.070 -.061 S -.093 -.983 .327 -.029 -.078 -.069 E -.191 -1.958 .052 -.136 -.155 -.138 C .268 2.882 .005 .101 .225 .203
a Dependent Variable: Mathematics mark
Inspection of Table 4.18 shows that in step three of the hierarchical analysis, the Investigative
interest (β = 0.204, r = 0.183, t = 2.600, p < 0.010), Conventional interest (β = 0.268, r = 0.203, t =
2.882, p < 0.005) and academic ability (β = 0.300, r = 0.283, t = 4.011, p < 0.001) were
significantly related to Mathematics in the presence of all the remaining interests.
It would seem that for all the subjects considered, Hypothesis 2 can be accepted in that vocational
interests which fit with specific subject content explain a meaningful proportion of the variance in
academic performance over and above what is explained by cognitive ability. It should be noted
however that vocational interests that did not correspond with subject specific content also
explained a proportion of the variance in academic performance, particularly the Investigative and
Realistic interest. More will be discussed about this in Chapter Five.
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4.5 RESULTS PERTAINING TO HYPOTHESIS 3
Hypothesis 3 stated that learners who show positive academic attitudes and study behaviours
perform academically better than students who show negative academic attitudes and study
behaviours. The results that apply to Hypothesis 3 were obtained by using average academic results
over all the subjects taken (mid-year and final examination marks), academic ability (as measured
by the GSAT) and the total scores for the six dimension of the ABAQ. A multiple regression
analysis was conducted to investigate the relationship between the various academic attitudes and
study behaviours and overall academic performance while controlling for academic ability. The
results of the analysis are presented in Table 4.19.
Table 4.19 Predictive effects of ABAQ factors on overall academic performance
Change Statistics Model
R
R2
Adjusted R2
R2 Change F Change df1 df2 Sig. F
Change 1 .542(a) .294 .292 .294 118.019 1 283 .001 2 .672(b) .452 .438 .158 13.284 6 277 .001
a Predictors: Academic ability b Predictors: Academic ability, Person-environment fit, Coping, Avoidance of procrastination, Self-efficacy, Self-directed learning, Achievement motivation
With academic ability as the only predictor, R2 = 0.294, F(1, 283) = 118.019, p < 0.001. The ABAQ
factors explained a further 15.8% of the variance in overall academic performance, ΔR2 = 0.158,
F(1, 277) = 13.284, p<0.001. Jointly, academic ability and the ABAQ factors accounted for 45.2%
of the variance in overall academic performance. The standardised regression weights, t-values, p-
levels and semi-partial correlations of the predictor variables with overall academic performance
are summarised in Table 4.20.
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Table 4.20 Regression weights, t-tests and effect sizes pertaining to the predictive effects of
ABAQ factors on overall academic performance
Model Standardised Coefficients t p Correlations
ß Zero-order Partial Part 1 Academic ability .542 10.864 .001 .542 .542 .542 2 Academic ability .422 8.756 .001 .542 .466 .389 SE .238 3.885 .001 .482 .227 .173 PE -.038 -.707 .480 .177 -.042 -.031 AM .177 2.725 .007 .329 .162 .121 SD .172 2.859 .005 .395 .169 .127 CO .019 .401 .688 .081 .024 .018 AP -.145 -2.401 .017 .090 -.143 -.107
Note: SE = Self-Efficacy, PE = Person-environment fit, AM = Achievement motivation, SD = Self-directed learning, CO = Coping, AP = Avoidance of procrastination
Inspection of Table 4.20 shows that in step two of the hierarchical analysis, Academic ability (β =
0.422, r = 0.389, t = 8.756, p < 0.001), Self-efficacy (β = 0.238, r = 0.173, t = 3.885, p < 0.001),
Achievement motivation (β = 0.177, r = 0.121, t = 2.725, p < 0.007), Self-directedness in learning
(β = 0.172, r = 0.127, t = 2.859, p < 0.005) and Avoidance of procrastination (β = -0.145, r = -
0.107, t = -2.401, p < 0.017) were significantly related to overall academic performance in the
presence of all the remaining ABAQ factors.
4.6 CHAPTER SUMMARY
Chapter Four presented the results of the study that correspond with the stated research hypotheses.
With respect to Hypothesis 1, as expected, it was found that academic ability significantly
contributed to the variance in overall academic performance. For Hypothesis 2, the results showed,
as expected, that interest groups that correspond with subject specific content also contribute
towards the variance in subject specific academic performance over and above what is explained by
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academic ability. In addition however, it was unexpected to find that Investigative and Realistic
interests were associated with positive and negative academic performance respectively, despite a
low fit with specific subject content. With regard to Hypothesis 3 the results showed, as expected,
that certain academic attitudes and behaviours significantly contributed to overall academic
performance over and above academic ability. These results are discussed in Chapter Five.
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CHAPTER FIVE
DISCUSSION OF FINDINGS, LIMITATIONS OF THE STUDY AND
IMPLICATIONS FOR FURTHER RESEARCH
5.1 INTRODUCTION
In this chapter the results of the current research on vocational interests and other non-cognitive
factors affecting academic performance are discussed. Inferences as to what factors may have
specifically affected the outcomes of the study are also highlighted. In addition, an alternative
theoretical framework to Social Cognitive Career Theory for describing factors influencing task
performance is suggested for the high school context. The chapter also deals with the implications
of the results for the various groups which may be affected by the research and limitations of the
study are discussed. Recommendations are made for further research topics based on the study’s
findings and limitations.
The study aimed to provide information about various non-cognitive factors that affect the
academic performance of high school students. Research in this area is important as school
students’ academic results at a high school level are instrumental in shaping their educational and
career pathways. A lack of information about the importance of non-cognitive factors such as
vocational interest, self-efficacy and achievement motivation, negative academic attitudes and study
behaviours, and a pervasive belief amongst school students that intelligence is the predominant
factor affecting academic performance may contribute to poor academic performance. Poor
academic performance denies opportunities for further education and training and consequently
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closes doors on various occupational opportunities which may have been available. The goals of the
study were to provide objective information about the role of various non-cognitive factors,
specifically vocational interest, person-environment fit, self-efficacy, achievement motivation,
coping, self-directedness in learning and avoidance of procrastination in relation to academic
performance. This information could be used by school students and their significant others to
facilitate decisions and programmes aimed at improving academic performance at high school. A
number of research hypotheses were formulated about factors affecting academic performance.
Firstly, it was hypothesised that cognitive factors would contribute significantly toward the variance
in academic performance. Secondly, it was hypothesised that school students who had vocational
interests that correspond with the subjects they are enrolled for would perform better academically
than those school students whose interests do not correspond with their subjects. Lastly, it was
hypothesised that school students who show positive academic attitudes and study behaviours will
perform academically better than students who show negative academic attitudes and study
behaviours. Simple and multiple regression analyses were used to determine which factors
significantly predicted academic performance and what the nature of the prediction was. The
following sections discuss the results of the findings.
5.2 VARIABLES AFFECTING ACADEMIC PERFORMANCE
5.2.1 Academic ability
The relationship between academic ability and academic performance was investigated by means of
a simple regression analysis. The results of this analysis showed that academic ability accounted for
more than 29% of the variance in overall academic performance. This finding seems to be
consistent with other studies (cf. Furnham & Chamorro-Premuzic, 2004; Grobler et al., 2001; Lau
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& Roeser, 2002; Masqud, 1983; Midkiff et al., 1989; Rigdell & Lounsbury, 2004) which also report
significant positive relationships between academic ability and academic performance, particularly
in the area of Mathematics.
It is interesting to note those subjects in which academic ability appears to play a smaller or almost
negligible role. The effect of academic ability on academic performance in these subjects is lower
than the effect of academic ability on overall academic performance, as measured by an average of
all the subjects taken. This seems to be the case for Accounting, Business Economics, Life
Orientation and Mathematics in which the multiple correlation coefficients (R2) were found to be
0.198 (p < 0.001), 0.104 (p < 0.001), 0.197 (p < 0.001) and 0.102 (p < 0.001) respectively (see
Tables 4.7, 4.9, 4.13 and 4.17 respectively). The relationship between academic ability and overall
academic performance where the correlation coefficient was found to be 0.294, appears to be
meaningfully stronger.
With regard to Mathematics, the smaller effect size of academic ability on academic performance
(see Table 4.17) does not seem to be consistent with the literature (Grobler et al., 2001; Midkiff et
al., 1989) which reports significant stronger multiple correlation coefficients. These researchers
found strong correlations among general scholastic aptitude, academic achievement, and
examination performance, with the highest correlations among these variables ranging from 0.69 to
0.74. A reason for the smaller effect of academic ability on academic performance in this sample
could possibly be related to the fact that school students are streamed into one of two Mathematics
subjects, namely Mathematics and Mathematics Literacy. If a student does not achieve a certain
aggregate by the end of Grade 9, he or she is required to do Mathematics Literacy in which the
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content requires less cognitive ability and focuses on functional Mathematics problems. It could be
that the variability in academic ability in school students enrolled for Mathematics is not large due
to the fact that the seemingly more intelligent school students are being streamed into the subject. It
is recommended that further research be done in this regard because various non-cognitive factors
might have a mediating effect on academic performance, thereby influencing the streaming process.
With regard to the Business Economics and Life Orientation results, it is possible that success in
these subjects relies on experiential learning and common sense as well as academic ability. The
subject content is such that the school students may be exposed to the various concepts and learning
material in everyday life and this experiential learning factor may decrease the influence of
academic ability which is more cognitive in nature. The small effect of academic ability on
Accounting performance was unexpected as the subject does require a certain amount of planning
and organisational skills that are expected to be present in individuals with higher cognitive ability.
However, Accounting at a school level does have a large “practice” component in that once a
method is learned, it can be applied to the question to formulate a result. It seems that it does not
require much of the abstract thinking component of academic ability as opposed to subjects such as
English.
5.2.2 Vocational Interest
As mentioned in the chapter introduction, multiple regression analysis was used to investigate the
relationship between vocational interest and academic performance. As was hypothesised, the
results indicated that school students with vocational interests that correspond with specific subject
content, performed better than the students who did not show this high level of fit between interests
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and subjects. This was found to be the case for all the subjects considered, namely, Accounting
(R2= 0.054; p < 0.013), Business Economics (R2= 0.123; p < 0.032), English (R2= 0.318; p <
0.001), Life Orientation (R2= 0.226; p < 0.001), Life Sciences (R2= 0.451; p < 0.001) and
Mathematics (R2= 0.138; p < 0.018). Therefore one can expect that students who have specific
career goals and who are interested in certain types of work may perform better academically when
the subjects that they study relate in some way to their vocational interests. The study showed that
school students’ interests were not independent of achievement, contradicting findings by Ainley et
al. (1990) in a longitudinal study of Australian school students in which no relationship was found.
Even though the results of the present study support what was expected according to Hypothesis 2,
it should be taken into account that only a small amount of variance in academic performance was
explained by the vocational interests which seemed to fit with subject specific content. Most of the
subject specific vocational interests explained less than 6% of the variance in academic
performance, with the exception of Life Sciences in which 16.8% of the variance was explained. In
hypothesising why the contribution of subject specific vocational interests is not higher, it is of
particular interest to note the vocational interests that had a large impact on academic performance,
regardless of whether they correspond with the subject content or not. Specifically, Investigative
interests were significantly and positively associated with performance in Accounting (r = 0.297; p
< 0.001), Business Economics (r = 0.254; p < 0.001), English (r = 0.298; p < 0.001), Life
Orientation (r = 0.319; p < 0.001), Life Sciences (r = 0.445; p < 0.001) and Mathematics (r = 0.183;
p < 0.010). Realistic interests also had a significant negative relationship with academic
performance in Business Economics (r = -0.206; p < 0.009), English (r = -0.235; p < 0.001) and
Life Orientation (r = -0.297; p < 0.001), a total of three out of the six subjects considered. This
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suggests that school students who have an interest in observational, symbolic, systematic and
creative activities involving physical, biological and cultural phenomena may perform better
academically, whether they are interested in the subject or not and whether their interests
correspond to the subject content or not. In contrast, those school students who prefer Realistic
interests and activities that entail the explicit, ordered or systematic manipulation of objects, tools,
machines and animals and who may have an aversion to educational or therapeutic activities, may
not perform academically well, whether they are interested in the subject or not. These findings are
consistent with Holland’s (1997) theory that school students with Investigative interests appear to
perform better academically and those with Realistic interests do not perform well academically.
This finding is also supported by research done by Schneider and Overton (1983) and Sparfeldt
(2007). It seems therefore that in this study, Holland’s (1997) theories about academic performance
appear to be fairly robust.
It is interesting to note the trends in the data when the influence of all the vocational interests on
academic performance are considered, such as in step three of the multiple regression analysis (see
Tables 4.7, 4.9, 4.11 & 4.13 respectively). Conventional, Enterprising and Social interests
significantly explained some of the variance in Accounting, Business Economics, English and Life
Orientation academic performance respectively, when entered into the regression equations on their
own. However this effect seems to be moderated by Investigative and Realistic interests and the
impact of the other subject specific interests becomes of no significance, with the exception of Life
Sciences and Mathematics in which the subject specific interest was Investigative at the outset.
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In attempting to explain why the presence of Investigative and Realistic interests have such an
effect on academic performance, one could speculate that the academic culture of high schools is
suited to individuals with Investigative interests who enjoy researching into their subject matter.
Their scientific approach to class work and homework tasks may be suited to the way in which
lessons are presented and also may be congruent with the interests of the teachers. In contrast, those
with Realistic interests are more practical and hands-on and therefore the classroom style of
teaching evident in most academic high schools may not be in line with their specific interests. This
finding seems to be consistent with research reported by Posthuma and Navran (1970). They
assessed the personalities of academic staff members and first year students at a military college
and found that the highest academic achievers reflected the most amount of congruence between
personality and environment while the lowest academic achievers reflected the lowest amount of
congruence. Schneider and Overton (1993) also state that educational achievement may be related
to the type of environment, which Holland believes to be defined in part by the situation or
atmosphere created by the people who dominate the environment. It could be argued that this
relates to the construct of person-environment fit measured by the ABAQ, however the ABAQ
factor relates more to whether an individual fits with the subject or courses that they have chosen
and does not relate to the general environment as defined by the interests of the people that occupy
it.
It is interesting to note that in one of the scientific subjects, specifically Life Sciences, there appears
to be a negative correlation between Enterprising vocational interests and academic performance (r
= -0.168; p < 0.012). This supports Holland’s (1997) congruence theory whereby people search for
environments that will let them exercise their skills and abilities, express their attitudes and values,
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and take on agreeable problems and roles. He also states that certain personality types will avoid
certain kinds of work and working environments. Holland describes Enterprising individuals as
people who prefer activities that entail the manipulation of others to attain organisational goals or
economic gain and that they may be averse to observational, symbolic and systematic activities.
Observational, symbolic and systematic activities are characteristic of the sciences and school
students who are averse to them may not perform as well academically in science-related subjects.
Another significant and rather unexpected finding was the positive relationship between
Conventional interests and academic performance in Mathematics. According to Holland (1997),
Conventional individuals prefer activities that entail the explicit, ordered, systematic manipulation
of data and they tend to be very organised and systematic. One may attempt to explain the positive
relationship between Conventional interests and Mathematics from the point of view that the
mentioned Conventional attributes may facilitate performance in Mathematics, especially at a
Grade 10 level where there is more emphasis on mathematical procedures and less of a creative or
artistic component.
5.2.3 Academic attitudes and study behaviours
Multiple regression analysis was used to investigate the relationship between the various academic
attitudes, study behaviours and academic performance. The results showed that three of the six
ABAQ factors have meaningful positive relationships with overall academic performance, namely
Self-efficacy, Achievement motivation and Self-directedness in learning. A significant negative
relationship emerged between Avoidance of procrastination and academic performance, while no
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significant relationship between Person-environment fit and performance as well as Coping and
performance could be found.
5.2.3.1 Self-efficacy and academic performance
The findings on the relationship between Self-efficacy and academic performance suggest that this
factor plays an important role in facilitating academic performance. This seems consistent with
Bandura’s (1986) theory that persons with high levels of self-efficacy will produce more success
experiences which in turn reinforce their level of self-efficacy. On the contrary, persons with low
levels of self-efficacy will produce less successful experiences, thereby reducing their self-efficacy
levels (Meyer et al., 1997). The research also seems consistent with a number of quantitative studies
(cf. Andrew, 1998; Lent et al., 1994; Siegel et al., 1985) in which it has been reported that self-
efficacy is positively related to academic performance. As mentioned in Chapter Two, self-efficacy
is a key tenet of Social Cognitive Career Theory (SCCT) and, within this theory, is linked to aspects
of vocational interest. Specifically, SCCT suggests that people develop interests in activities in
which they view themselves to be efficacious and for which they anticipate positive outcomes
(Lopez et al., 1997). In the present study, both vocational interests and self-efficacy were positively
related to academic performance and so seems to be consistent with the social-cognitive interest
model of SCCT. However, the SCCT model may need to be revised for this study considering that
Investigative and Realistic interests had such a profound effect on academic performance. The
formulation of an adjusted explanatory model for this particular sample is discussed in section 5.3
of this chapter and it is recommended that further research be conducted in similar populations and
other contexts to verify the research data.
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5.2.3.2 Person-environment fit and academic performance
With regard to Person-environment fit, the non-significant relationship between this factor and
academic performance was another unexpected finding. A positive relationship between person-
environment fit and academic performance could have contributed to the explanation for the strong
positive effect of the Investigative interest and the strong negative affect of the Realistic interest on
academic performance as mentioned in section 5.2.2, however no significant relationship was
found. It could be that the Person-environment fit factor may be more appropriate for university
students because they have more of a choice regarding faculty or subjects and because the
environments of particular faculties differ, as opposed to a school where the general environment
remains homogenous for the entire group. The ABAQ was originally designed for use in a
university setting and so this factor may need to be revised further to accommodate high school
environments.
5.2.3.3 Achievement motivation and academic performance
As expected, Achievement motivation was positively associated with academic performance. This
finding is supported by studies conducted by Busato et al. (2000), Tavani and Losh (2003) and
Lounsbury et al. (2003). These results suggest that school students with a striving tendency towards
success including the associated positive effects, and a striving towards the avoidance of failure and
the associated negative effects, perform better academically. This may relate to parental influence
and the expectation that parents set for their children to achieve academically. If a child performs
well academically, there may be positive results such as praise or a material reward which in turn
fosters additional motivation. In contrast, poor academic performance may result in negative
outcomes such as punishment. This is in line with research reported by Kaplan et al. (2001) where it
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has been shown that parental academic expectations of their children were positively related to
academic performance.
5.2.3.4 Self-directedness in learning, Coping and academic performance
The results showed that Self-directedness in learning was significantly and positively related to
academic performance. This suggests that school students who exhibit a high degree of self-
management and self-monitoring when confronted with a complex or ambiguous task perform
academically better than those students who do not show these qualities. It is interesting to note that
the Coping factor of the ABAQ was not significantly related to academic performance at a high
school level. The Coping factor on the ABAQ is related to an individual’s ability to gain social
support from significant others to assist in improving their academic performance. The research
shows that in this sample, tactics used to gain social support with the aim to alleviate stresses does
not seem to have an impact on their academic performance. A possibility for this could be that
social support is unavailable due to a number of factors. With the ever increasing emphasis on
administrative duties in the education field and large class sizes, teachers may be too busy to offer
school students individual attention. In addition, the trend towards both caregivers working a full
day may mean that academic support from parents is unavailable to school students. It seems as
though in this study, self management is more important in facilitating good grades than coping
strategies which aim to elicit social support. This may be due to the nature of the education system
in which the students are working in. Because most school students are minors, they experience
little control over how much work they are given by their teachers and their power of negotiation is
limited. Also, most teachers have a set curriculum and a set amount of work which needs to be
completed, and they may not be receptive to modifying the syllabus in order to assist poor academic
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performers. This makes it increasingly difficult for some school students to initiate coping
mechanisms with the intent to modify the external environment so that they can manage their
workload. Those students who manage themselves despite the environmental pressures, in other
words, a more self-directed learner, are more likely to succeed academically in a school
environment.
5.2.3.5 Avoidance of procrastination and academic performance
With regard to avoidance of procrastination, the results were quite unexpected. While it was
predicted that avoidance of procrastination would be significantly associated with academic
performance, it was expected that the less school students engaged in procrastinatory activities, the
better they would perform academically. However, the results showed an inverse relationship
between avoidance of procrastination and academic performance, implying that the more school
students procrastinated, the better they performed academically. This seems inconsistent with the
body of research on procrastination behaviour (cf. Rothblum et al., 1986; Semb et al., 1979;
Solomon & Rothblum, 1984; Tice & Baumeister, 1997; Wolters, 2003) which has noted the adverse
effects or procrastinatory behaviour on academic performance as well as high levels of stress and
poor self-rated health. It is possible that in this study, better academic performance could be as a
result of “active procrastination”, a concept described by Chu and Choi (2005) in which students
will procrastinate so that they will work hard under pressure, thereby facilitating an increase in
academic performance. In view of the fact that a positive relationship between avoidance of
procrastination and academic performance has been extensively reported in the literature, the
current research results pertaining to this factor are treated with a high degree of scepticism and it is
suggested that further research be performed to validate these unusual findings.
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5.3 A NEW EXPLANATORY MODEL FOR ACADEMIC PERFORMANCE
OF HIGH SCHOOL STUDENTS IN THE RESEARCH SAMPLE
As has been discussed in Chapter Four and in the above sections, both self-efficacy and vocational
interests were significantly related to academic performance in the current sample. The results seem
to be consistent with the Social Cognitive Interest and Performance Models of Lopez et al. (1997)
outlined in Chapter Two. According to the Social Cognitive Interest Model, Lopez et al. (1997)
showed that high school students’ self-efficacy and outcome expectations predicted their interest in
Mathematics and that self-efficacy partially mediated the effect of ability on grades in Mathematics.
These models are shown again in Figure 5.1 and 5.2.
Figure 5.1 Social Cognitive Interest Model
Source: Copyright © 1997 by Lopez et al. Reprinted with permission.
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Figure 5.2 Social Cognitive Performance Model
Source: Copyright © 1997 by Lopez et al. Reprinted with permission.
When reviewing the above models, it is evident that self-efficacy influences vocational interest and
academic performance separately, however SCCT maintains that the direct predictive effect of self-
efficacy and vocational interests on academic performance occurs concurrently. This is explained
further in the section below.
The results of the current study also seem consistent with the Social Cognitive Career Theory (Lent
et al., 1996) and the model of person, contextual and experiential factors affecting career related
choice behaviour (Lent et al., 1994), also outlined in Chapter Two. According this model, person
inputs and background contextual factors have an impact on learning experiences which affects a
person’s self-efficacy and outcome expectations. This in turn affects a person’s interests, choice
goals, choice actions and ultimately, performance levels. A diagrammatic representation of the
theory is depicted in Figure 5.3.
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Figure 5.3 Model of person, contextual and experiential factors affecting career related
choice behaviour
Source: Copyright © 1994 by Lent et al. Reprinted with permission by Jossey-Bass.
When reviewing the above model, the vocational interest factor which has been seen to be
important when considering the current study is not divided into general vocational interests and
subject specific vocational interests. Considering the significant effect of subject specific vocational
interests and Investigative and Realistic interests in this sample, it is evident that this model needs
to be revised to explain the academic performance of the high school students in the current study.
Taking this into account, it is proposed that self-efficacy expectations in turn has an effect on both
subject-specific vocational interests and Investigative and Realistic interests, and this in turn affects
a school student’s choice goals, choice actions and academic performance. This model would also
accommodate Holland’s (1997) theory that the presence of Investigative and the absence of
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Realistic interests promotes academic performance. The suggested model is represented in Figure
5.4.
Person inputs - Predispositions - Gender - Race/ethnicity - Disability or health status
Background contextual factors
Learning experiences
Self-efficacy
Outcome expectations
Subject-specific vocational interests
Choice goals
Choice actions
Performance domains and attainment Realistic and
Investigative vocational interests
Contextual influences proximal choice behaviours
Figure 5.4 Explanatory model for academic performance in high school students
From the above model it should be seen that self-efficacy, subject-specific vocational interests and
Investigative and Realistic interests all may have an impact on performance goals directly, thereby
facilitating academic performance. In this model, a school student’s belief in their ability to perform
a certain type of task or job reinforces their interest in the vocations related to those tasks. However
the way in which interests influence academic performance in specific subjects may be regarded as
a two-fold process. The first process involves the presence of two specific interests, namely
Investigative and Realistic interests. According to the model, the presence of these two interests
will affect academic performance in any subject and is not related to subject-specific content. This
part of the theory is in line with Holland’s (1997) theory of vocational interests and academic
performance. The second process involves subject-specific vocational interests. A school student’s
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belief in their ability to perform a specific task will promote particular vocational interests that
promote better academic performance in specific subjects related to those vocational interests. No
formal data analysis was conducted on the relationship between self-efficacy and vocational
interests in this sample, therefore it is recommended that further research be conducted to
empirically validate the proposed model for the broader population of high school students.
5.4 IMPLICATIONS OF THE RESEARCH FINDINGS
The current study has a number of implications for school students who are attempting to better
their academic performance. Firstly, the study shows that non-cognitive factors have an important
part to play in facilitating good grades, dispelling the myth amongst some high school students that
intelligence or academic ability is the only factor affecting academic performance. School students
who take cognisance of the fact that their vocational interests, academic attitudes and study
behaviours affect their academic results may adopt more positive attitudes and behaviours,
facilitating an increase in performance. Of equal importance is the issue of subject choice. School
students who choose subjects in line with their vocational interests have a greater chance of
succeeding academically and therefore this should be an important consideration when selecting
subjects at the end of their Grade 9 year.
An important implication for parents is to encourage the development of Investigative interests, as
this seems to foster good academic performance in a high school setting. In contrast however, those
students with strong Realistic interests may be at a disadvantage in that perhaps they are not suited
to the environment and teaching style associated with an academic focus in high school. Parents,
teachers and guidance counsellors need to be aware that students who are Realistic in their
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vocational orientation may learn differently and might need to be accommodated in terms of
teaching style within the academic environment. Although Outcomes Based Education in South
Africa has implemented policies which aims to transform education into a more hands-on and
experiential type of learning, more needs to be done to accommodate the variety of vocational
personality types in a school setting.
Parents, teachers and guidance counsellors may also facilitate better academic attitudes and study
behaviours by encouraging school students to believe in their ability to achieve at their optimal
level, thereby increasing a student’s self-efficacy. Reward systems could be revised, not only for the
top achievers but for all students who consistently improve their academic performance.
Recognition of academic performance is a positive stimulus which may increase achievement
motivation to strive for higher grades. Encouragement and education pertaining to a student’s level
of self-directedness should also be encouraged, focusing on the student's ability to manage himself
or herself independently when faced with various academic tasks.
5.5 LIMITATIONS OF THE STUDY AND IMPLICATIONS FOR FUTURE
RESEARCH
The study has a number of limitations which need to be taken into consideration when reviewing
the findings. Firstly, according to strict empirical guidelines, the participants should be drawn at
random from the population, whereas in the case of this study, participants from one high school
were used. A sample from a single school needed to be used because the same examination results
for each school subject were required to assess academic performance. However, this does not
enable the study to be generalised to other populations of high school students. The proposed
114
adjusted model of academic performance therefore applies only to the current sample and its
applicability to the population of high school students in South Africa needs to be investigated
further. Secondly, it would be more appropriate to incorporate larger sample sizes per subject,
specifically for Accounting which only had 90 participants. Small sample sizes reduce the statistical
power of the hierarchical regression analysis.
The study also brought up a number of topics and problems that may be the focus of further
research, especially in a South African context. For example, the effect of academic ability and non-
cognitive factors on Mathematics performance needs to be researched, especially considering the
introduction of Mathematics Literacy and the streaming of students into these two subjects based on
their academic performance in Grade 9 Mathematics. This topic should be the subject of further
study as school students who have the ability to perform well academically in Mathematics may be
streamed into Mathematics Literacy classes due to negative academic attitudes and study
behaviours. With regard to the proposed model of self-efficacy and vocational interest affecting
academic performance, limitations exist in that this model has not been empirically validated and
further research needs to be conducted in order provide quantitative data which verifies the
hypothesis that subject-specific interests directly affect performance in the related subjects.
The study of vocational interest and its influence on academic performance has not been well
researched both internationally and in South Africa, and it would be appropriate to investigate the
relationship between these constructs across a diverse range of schools or universities that offer
varying degrees of academic focus. In addition, the effect of vocational interest on subject-choice is
a related topic which warrants further research. School students may choose subjects for which they
115
have no vocational interest and consequently this may affect their academic performance in those
subjects.
Considering the importance of Investigative and Realistic vocational interests in affecting academic
performance, it may be necessary to study different teaching environments and the way in which
schools and teaching staff either promote or inhibit the formulation of Investigative and Realistic
interests in school students. This research has important implications in that students with Realistic
interests may not perform well academically in an Investigative environment. The research may
produce results pertaining to person-environment fit which did not appear to be significant in this
study.
Finally, some of the factors measured by the ABAQ produced unexpected findings and it would
seem that further research needs to be conducted in order to verify the conclusions drawn in this
study. For example, the lack of significant relationship between coping strategies and academic
performance is contradictory to recent research reported by Zuckerman et al. (1998), Nonis et al.
(1998), Malefo (2000) and Collins and Onwuegbuzie (2003) who suggest that these constructs are
related. Furthermore, the unexpected finding that avoidance of procrastination is negatively related
to academic performance in this study also warrants further research as studies by Tice and
Baumeister (1997), Solomon and Rothblum (1984) and Rothblum et al. (1986) provide
contradictory findings.
116
5.6 CONCLUSION
In conclusion, it can be stated that for the high school students in this sample, both cognitive and
non-cognitive factors influence academic performance. Specifically with regard to cognitive
factors, an increase in academic ability has a positive influence on academic performance and these
findings are consistent with a large volume of research. With regard to non-cognitive factors, the
results of this study suggest that vocational interests also influence academic performance. The
nature of this relationship is such that vocational interests which have much in common with
specific subjects will increase academic performance in that subject. However, the presence of
Investigative and Realistic interests will increase and decrease academic performance respectively,
no matter what subject is taken into account. Furthermore, certain academic attitudes and study
behaviours such as self-efficacy, achievement motivation, self-directedness in learning and the
avoidance of procrastination also appear to have had impact on the academic performance of the
high school students. It is important to note that these findings pertain to the participants in this
study. As indicated in preceding paragraphs, further research needs to be conducted to investigate to
what extent these findings can be generalised to the population of high school students in South
Africa.
117
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