BULLYING VICTIMIZATION, HEALTH STRAINS AND
JUVENILE DELINQUENCY IN GHANA
A Dissertation
Presented to
The Graduate Faculty of the University of Akron
In Partial Fulfilment of the Requirement for the Degree
Doctor of Philosophy
BULLYING VICTIMIZATION, HEALTH STRAINS AND
JUVENILE DELINQUENCY IN GHANA
Ebenezer Duah
Dissertation
Approved: Accepted:
Advisor Department Chair Dr. Robert Peralta Dr. Rebecca Erickson
Committee Member Dean of College Dr. Stacey Nofziger Dr. Joe Urgo
Committee Member Interim Director of Graduate School Dr. Juan Xi Dr. Marnie Saunders
Committee Member Date Dr. Pamela Tontodonato
Committee Member Dr. Shernavaz Vakil
ii ABSTRACT
This dissertation investigates the relationship between bullying victimization, health strains and adolescent delinquency. A growing body of research has shown that undergoing bullying victimization and health strains significantly predict juvenile delinquency. Research on types of bullying experiences and their connection to juvenile offending has produced inconsistent results. However, most research has been conducted in industrial countries, including the USA, Italy, England, and Germany. Little is known about whether the relationship between being bullied, having health strains, and offending in adolescence is applicable in Sub-Saharan Africa. In addition, research on sex differences in the association between being bullied, health strains, and juvenile offending is scarce.
Using General Strain Theory as a general theoretical framework, I analyze data from the
2012 Global School-Based Health Survey of Ghana. My analysis focuses on sex differences in the bullying, health strain, and juvenile offending relationship.
Measurements of bullying victimization include physical and verbal victimization.
Results suggest that bullying victimization and experiencing health strains significantly increased juvenile delinquency. Additionally, physical and verbal bullying experiences increased juvenile offending. The effect of bullying victimization and heath strains on delinquency was similar for both males and females. Based on my findings, I suggest that the impact of bullying and health strains on delinquency can be reduced through the introduction of anti-bullying regulations in schools, implementation of anti-
iii bullying prevention programs, recreational programs, extended paid parental leave, and home visitation by health professions. Implications for criminological theory, strengths and limitations of the current study, and suggestions for future research conclude this dissertation.
iv
ACKNOWLEDGEMENTS
I want to express my gratitude to many people who have contributed to the successful completion of this dissertation. First, I want to express my sincere gratitude to my dissertation chair, Dr. Robert Peralta, for his guidance and support. At the beginning of 2021, I was not sure that I would finish the dissertation this spring. However, Dr.
Peralta took the baton and guided me successfully to the finishing line. I am also grateful to my late advisor and dissertation chair, Dr. Baffour Takyi, for guiding me throughout this research from the proposal stage to the final stages before he passed on.
I would also like to thank Dr. Juan Xi for her support throughout my graduate career and the dissertation process. Her criticisms and suggestions were very helpful for the successful completion of this dissertation. I am also grateful to Dr. Pamela
Tontodonato for her remarkable contribution to this research. She recognized some of the shortcomings in my work and directed me as to how to fix them.
I want to give special thanks to Dr. Stacey Nofziger for becoming a member of my dissertation committee after the demise of Dr. Takyi. I also appreciate her comments and suggestions, which have helped to improve the document. I will like to acknowledge
Dr. Shernavaz Vakil for accepting to be an external member of the dissertation
v committee. Although she left the University of Akron to another institution, she still stayed on the committee.
Besides my committee members, I want to thank other faculty members who have contributed to my success in graduate school, including Dr. Rebecca Erickson, Dr. John
Zipp, Dr. Matthew Lee, and Dr. Kathryn Feltey. Special thanks to Dr. Enoch Lamptey and Dr. Chris Opoku-Agyeman for the advice, logistic and emotional support. I am also grateful to all my cohort members, including Eric Victory, Scott Swiatek, Dr. Matt
Williamson, T.J. Snyder, Helen Fischer, Davishay Laurence, and Aaron Carmichael, for their help and encouragement.
I am thankful to my late father, Mr. Emmanuel Kwadjo Buabeng, for his supervision which prevented me from going wayward. I also thank him for his financial support for my education. Also, I thank my mother (Elizabeth Frema) and my sister
(Felicia Gyankoma) for being there for me all these years. Finally, I thank my daughter,
Emmanuella Adjoa Buabeng Duah, for always keeping me on my toes.
vi DEDICATION First, I want to dedicate all my accomplishments to my late father, Mr. Emmanuel
Kwadwo Buabeng, for all the financial and emotional support he gave to me throughout my education. Second I dedicate this dissertation to my late academic advisor and dissertation chair, Dr. Baffour Takyi, for both the academic and non-academic advice he offered me. Third, I dedicate this research to my mother, Ms Elizabeth Fremah, and my sister, Felicia Gyankoma, for their financial and emotional contribution to my education.
Finally, I dedicate this dissertation to my daughter, Emmanuella Adjoa Buabeng Duah.
vii TABLE OF CONTENTS
Page
LIST OF TABLES ...... xii
LIST OF FIGURES ...... xiii
CHAPTER
I. THE RESEARCH CONTEXT: BULLYING, HEALTH AND DELINQUENCY ...1
Introduction ...... 1
Problem Statement ...... 8
Significance of the Study ...... 12
Organization of the Study ...... 13
II. LITERATURE REVIEW ...... 15
Introduction ...... 15
Theoretical Framework: Agnew’s General Strain Theory ...... 15
Bullying Victimization and the General Strain Theory ...... 18
Health and the General Strain Theory ...... 19
Gender and the General Strain Theory ...... 20
Other Risk Factors of Juvenile Delinquency ...... 22
Bullying Victimization and Delinquency ...... 26
Frequency of Bullying Victimization and Delinquency ...... 28
Types of Bullying and Its Relationship with Delinquency ...... 29
viii Gender Differences in the Association between, Bullying and Juvenile Delinquency ...... 30
Health and Juvenile Delinquency ...... 31
Health Behaviors and Delinquency ...... 33
Mental Health and Delinquency ...... 38
Gender Health and Delinquency ...... 40
Summary ...... 41
III. SOCIAL SETTING ...... 45
Ghana – An Overview ...... 45
Juvenile Delinquency in Ghana ...... 48
Bullying Victimization in Ghana ...... 51
Adolescent Health in Ghana ...... 52
Bullying Victimization and Juvenile Delinquency in Ghana ...... 54
Juvenile Justice in Ghana ...... 55
Summary ...... 60
IV. METHODOLOGY ...... 61
Introduction ...... 61
Data and Sampling ...... 61
Dependent Variable ...... 62
Independent Variables ...... 64
Control Variables ...... 68
ix Analytic Strategy ...... 70
Missing Data ...... 72
V. RESULTS ...... 74
Introduction ...... 74
Descriptive Statistics ...... 74
Bivariate Correlation among the Variables ...... 81
Bullying Victimization and Juvenile Delinquency ...... 81
Sex, Bullying Victimization, and Delinquency ...... 85
Types of Bullying Victimization and Juvenile Delinquency ...... 88
Physical Health and Juvenile Delinquency ...... 92
Mental Health and Juvenile Delinquency ...... 94
Sex, Physical Health and Juvenile Delinquency ...... 96
Sex, Mental Health and Delinquency ...... 100
Summary ...... 102
VI. DISCUSSIONS AND CONCLUSIONS ...... 105
Overview ...... 105
Research Questions and Hypotheses: Interpretation of Results ...... 106
Limitations and Strengths ...... 110
Contributions to Theory ...... 111
Policy Implications ...... 112
Future Research ...... 114 x Conclusion ...... 116
REFERENCES ...... 117
xi LIST OF TABLES
Table Page
1. Average Daily Lock-up of Juveniles in Ghana ...... 49
2. Missing Data ...... 73
3. Descriptive Statistics for the Total Sample ...... 75
4 Cross-Tabulation of Sociodemographic and Bullying Victimization ...... 77
5 Cross-Tabulation of Sociodemographic and Health Variables ...... 79
6 Bivariate Correlation among the Variables ...... 82
7 Negative Binomial Regression of Bullying Victimization Predicting Delinquency ...... 84
8 Sex, Differences in the Relationship between Bullying Victimization and Juvenile Delinquency ...... 86
9 T-Test for Differences in Effects of Bulling Victimization for Males And Females ...... 87
10 Types of Bullying Predicting Juvenile Delinquency ...... 89
11 Sex, Types of Bullying and Delinquency ...... 90
12 T-Test for Differences in Effects of Physical and Verbal Bullying Victimization for Males and Females ...... 91
13 Physical Health Predicting Juvenile Delinquency ...... 93
14 Mental Health Predicting Juvenile Delinquency ...... 95
15 Sex Differences in Physical Health Predicting Delinquency ...... 98 xii
16 T-Test for Differences in Effects of Physical Health Strains Predicting Delinquency for Males and Females ...... 99
17 Sex Differences in Mental Health Predicting Juvenile Delinquency ...... 101
18 T-Test for Differences in Effects of Mental Health Strains Predicting Delinquency for Males and Females ...... 102
19 Summary of Findings ...... 104
LIST OF FIGURES
Figure Page
1 Bullying Victimization will Positively Predict Juvenile Delinquency ...... 43
2 Physical and Verbal Bullying Victimization Predicting Juvenile Delinquency ....44
3 Physical and Mental Health-Strains will Positively Predict Adolescent Delinquency Behavior ...... 44
4 Map of Ghana ...... 46
xiii
CHAPTER I
THE RESEARCH CONTEXT: BULLYING, HEALTH AND DELINQUENCY
Introduction
Bullying can have destructive consequences for our young people. And it’s not something we have to accept. As parents and students; teachers and communities, we can take steps that will help prevent bullying and create a climate in our schools in which all of our children can feel safe. (Barrack Obama, 2011) Bullying is a serious problem among adolescents worldwide (Cross et al., 2009;
Marees and Petermann, 2010). Globally, the rate of bullying victimization ranges from less than 10% to over 65% in some countries (UNESCO, 2017). In Ghana, about 61% of adolescents have reported being victims of bullying (Owusu et al. 2011). The available evidence shows that bullying victimization is associated with low educational achievement
(Strom et al., 2013; Townsend et al., 2008). Also, victims of bullying are at a higher risk of becoming juvenile delinquents (Bender and Losel, 2011; Sigurdsson et a. 2010). Also, adolescents who experience bullying are likely to report poor health (Copeland et al., 2013;
Gini and Pozzoli, 2009).
Poor health among adolescents is a global problem. In the United States, it is estimated that approximately 43% of juveniles have chronic health conditions (Bethell
2011). The situation is worse in Ghana: 62% and 60% of adolescents have mental illness and physical health conditions (Glozah and Pevalin, 2016). Adolescent ill-health can lead to grave ramifications, including death (WHO, 2018), low educational attainment
1
(Carlson et al. 2008; Rasberry et al. 2017), and juvenile offending (Oshima et al. 2010;
Stogner and Gibson, 2010).
A substantial body of research from western countries have documented that bullying victimization and health strains are associated with delinquency. Many of these studies have found that victims of bullying are more likely to become juvenile offenders than non-victims (Sigfusdottir et al. 2010; Moon, Morash, and McCluskey 2012; Piquero et al. 2017). Also, adolescents who suffer from poor health are at a higher risk of becoming delinquents (Oshima et al. 2010; Stogner and Gibson 2011; Kort-Butler 2015).
Although several studies have found that bullying victimization and health significantly predict delinquency, the focus has always been on Western countries’ and
Asian samples. Empirical research on this topic from sub-Saharan Africa is almost nonexistent. General Strain Theory argues how we can use coping mechanisms to adapt to influences of strain on the likelihood of offending (Agnew 2001). There are many strains in the lives of the youth. Bullying victimization is one of such strains. The latter strain is a negative stimuli presented to victims, which leads to negative emotions.
Without the appropriate coping mechanisms, bullying victimization leads to adolescent offending (Cullen et al. 2008; Sigfusdottir, Gudjonsson and Sigurdsson 2010; Glassner
2020). Another strain is physical and mental health. Good health is a positively valued goal that individuals aim to achieve. Poor health is a loss of positive stimulus, which results in negative emotions and delinquency (Schroeder, Hill, Haynes, and Bradley
2011; Stogner and Gibson 2010, 2011). Past research has been limited to westernized and
Asian groups. In Ghana, the strains of bullying and health are higher than in these areas.
2 So there is the need to examine how such strains might influence Juvenile Delinquency in
Ghana.
Bullying victimization.
Bullying, which is a deliberate, repetitive and unwarranted act of aggression by an
individual or a group of people against others who cannot defend themselves, is a serious
issue among students and adolescents throughout the world (Jankauskiene,
Kardelis,Sukys, and Kardeliene 2008; Olweus and Limber 2010). Prior research indicates
that bullying has negative ramifications for the victims (Kowalski, Agatston, and Limber
2012; Olweus 1993; Wolke et al. 2014; Rivers et al. 2009). There are consequences that
include mental and physical health, educational outcomes, and deviant and criminal
behavior. Past studies have found that victims sometimes experience mental health
problems such as depression, anxiety, suicidal ideations, and paranoia (Copeland et al.
2013; Stapinski et al. 2015; Takizawa et al. 2014). Also, it has been observed that victims
of bullying have a higher risk of experiencing somatic symptoms such as colds,
headaches, and stomach aches (Gini and Pozzoli 2009; van Dam et al. 2012). In addition,
according to some studies, bullying affects the education of victims by increasing school
dropouts, absenteeism, and learning challenges (Strom et al. 2013; Townsend et al.
2008). Many studies have also found an association between bullying victimization and
antisocial behaviors (Bender and Losel 2011; Higgins, Khey, Dawson-Edwards, and
Marcum 2012; Sigfusdottir, Gudjonsson, and Sigurdsson 2010).
Because of its ramifications, it is no surprise that the bullying phenomenon has attracted attention from scholars all over the globe; and from different disciplines including sociology (Agnew et al. 2008; Bender and Losel, 2011; Sigurdsson et al. 2010),
3 psychology (Owusu et al. 2011; Arseneault 2017), and education (Strom et al., 2013;
Townsend et al., 2008). Some scholars and national and international institutions have investigated the prevalence of bullying and reported that the behavior is quite widespread.
For instance, Modecki, Minchin, Harbaugh, Guerra, and Reunions (2014) conducted a meta-analysis of 80 existing studies on bullying. They found that the average prevalence rate of bullying perpetration was 35% and that bullying victimization was 36%. In the
United States, the National Center for Educational Statistics (2016) reported that, during the 2015 school year, approximately 21% of students between the ages of twelve and twenty reported that they had been bullied at their school premises. In the United
Kingdom, a study conducted by the Young Men’s Christian Association (YMCA)(2018) also found that 40% of juveniles reported experiencing bullying once a week, with 54% of them experiencing it as early as age 10 (YMCA 2018).
The story is no different in Asia. Sittichai and Smith (2015) reviewed studies on bullying in South-East Asian Countries and found the prevalence rate ranged from 7-
59%: specifically, Philippines (30–59%), Singapore (12–37%), Indonesia (13–36%) and
Malaysia (7–30%). The situation is worse on the African continent. According to the available data from the African countries that participated in the Global School-based
Student Health Survey, the prevalence rates of bullying ranged from 65% in Zambia to
28% in Tanzania (Owusu et al. 2011).
Another interesting finding from existing studies concerns the pattern of bullying victimization. In general, studies show that bullying victimization is higher among males than females. For instance, Cook et al. (2010) found from their meta-analysis of 153 studies that boys were more likely than girls to be involved in perpetration, victimization,
4 and to be bullies and victims simultaneously. In another study that involved 40 countries, the rate of being bullied ranged from 8.6% to 45.2% for boys and 4.8% to 35.8% for girls
(Currie 2008). In contrast to these findings, Craig et al. (2009) found in their study of bullying victimization among adolescents in 40 countries that females were more likely to be victims of bullying. Since men and women vary in the prevalence of bullying victimization, it is imperative to examine sex differences in the effect of bullying victimization on delinquency.
Juvenile delinquency.
Juvenile delinquency is one of the world’s most pressing issues (WHO 2011;
Sickmund and Puzzanchera 2014; WHO 2015). Young, Greer and Church (2017) defined juvenile delinquency as a criminal offense committed by a young person. The World
Health Organization (WHO) has documented the prevalence of various forms of delinquency globally. United Nations Office on Drugs and Crime (2015) reported that about 60% of the suspects of homicide in the Americas are below age thirty. In addition, approximately 150, 000 juveniles use tobacco, and one out of every five youth have used cannabis in the past month (United Nations 2015). Lastly, 18% of boys and 14% of girls have reported using alcohol (WHO 2011).
In the United States, Sickmund and Puzzanchera (2014) reported that one out of every 8 students (1/8) had been involved in a fight, and one out of every four students
(1/4) had engaged in property offenses. Also, 37% of 10th graders and 21% of 8th graders reported that they have used illicit drugs at least once. Lastly, one out of every twelve murders are committed by juveniles (Sickmund and Puzzanchera 2014).
5 Although the preceding statistics indicate the prevalence of juvenile delinquency, it is also essential to understand how it can have deleterious effects on the individual and society. Youth crime can result in huge financial costs for nations around the world. For instance, in the United States, it is estimated that juvenile delinquency cost taxpayers $8 to $21 billion annually (Justice Policy Institute 2014). In addition, juvenile antisocial behaviors have led, in some cases, to increased mortality or morbidity for those involved
(WHO 2016). And finally, as noted above, teen misconduct can result in health issues such as drug addiction and physical injuries (WHO 2011).
Health and delinquency.
Many studies from the United States and other Western countries continue to document health predicts juvenile delinquency (Semenza 2017; Stogner and Gibson
2010; Clinkinbeard et al. 2011). The association between the two variables apply to a range of outcomes, including health behaviors, physical health, and mental health.
Regarding health behaviors, studies have found that proper diet reduces delinquency
(Gesch, Hammond, Hampson, Eves, and Crowder 2002; Schoenthaler and Doraz 1983;
Schoenthaler 1985). For example, Schoenthaler and Doraz (1983) found that offending behavior plummets when fruit juice and snacks replace junk foods in prisons. Other studies have also found a negative relationship between exercise and delinquency
(Duncan et al. 2002; Escobedo et al. 1993). For instance, Duncan et al. (2002) found that the number of physical activity days was negatively linked to substance abuse. Unhealthy alcohol use has long been associated with violence victimization and perpetration (Peralta et al. 2011). Extant research also indicates a negative association between the quality of sleep an individual gets and the likelihood of engaging in antisocial behavior
6 (Clinkinbeard, Simi, Evans, and Anderson 2011; O’Brien and Mindell 2005; Peach and
Gaultney 2013).
Concerning physical health outcomes and delinquency, research has shown a significant positive association between illnesses such as frequent headaches, colds, aching, general pain, skin problems, dizziness, and joint aches and delinquency in adolescent populations (Ford 2014; Stogner and Gibson 2010, 2011; Oshima et al. 2010).
In other words, juveniles who are disabled or suffer from chronic illnesses are more likely to become delinquents. For example, Oshima et al. (2010) found that adolescents who had functional limitations were likely to come into contact with the justice system than those without disabilities.
Studies have demonstrated a relationship between mental health outcomes and antisocial behaviors (see Anderson, Cesur, and Tekin 2015; Rowe, Maughan, and Eley
2006; Kofler et al., 2011). Some of the mental health conditions are less likely to result in delinquency, whereas others are more likely to do so. Anxiety disorders among youth, for instance, have been found in several studies to be less likely to result in antisocial behavior (Jolliffe et al. 2018; Walker et al. 1991). However, juveniles who suffer
Attention Deficit Hyperactivity Disorder (ADHD) and Oppositional Defiant Disorder
(ODD) during infancy exhibit delinquency initiation at an early age, a greater variety of offending, and a higher prevalence of severe delinquency (Foley, Carlton, and Howell
1996; Sibley et al. 2011). Moreover, depressive disorders in juveniles have been found to be related to suicidal attempts, violent offenses, delinquency, and homicidal thoughts
(Anderson, Cesur, and Tekin 2015; Rowe, Maughan, and Eley 2006; Kofler et al. 2011).
7 Problem Statement
Several studies have found a positive association between bullying victimization and delinquency (Bender and Losel 2011; Cullen et al. 2008; Hemphill et al. 2011;
Higgins, Khey, Dawson-Edwards and Marcum 2012; Sigfusdottir, Gudjonsson, and
Sigurdsson 2010; Sourander, Jensen, and Ronning 2007). In addition to bullying victimization, many of the existing studies have also documented that health strains are significantly related to delinquency (Anderson, Cesur, and Tekin 2015; Rowe, Maugham, and Eley 2006; Kofler et al. 2011; Jolliffe et al. 2018; Loeber et al. 2008; Walker et al.,
1991; Miauton, Narring, and Michaud 2003; Suris et al. 2008). The latter results occur because adolescents who have poor health tend to absent themselves from school or become truants (Kort-Butler 2015). Also, juveniles who lack access to healthcare are more likely to have behavioral problems at school (Kort-Butler 2015). Adolescents’ absence from school, academic problems, and behavioral problems at school increase their chances of getting involved in delinquency behaviors (Kort-Butler 2015).
Although existing studies have demonstrated that bullying victimization and health are significantly related to delinquency, the majority of these studies focus mostly on adolescents in developed countries, including the United States (Khey, Dawson-
Edwards, and Marcum 2012; Wong and Schonlau 2013), Canada (Canadian Institute for
Health Information, 2008), Italy (Vieno, Gini, and Santinello 2011), Iceland (Sigfusdottir,
Gudjonsson, and Sigurdsson 2010), and Australia (Walters 2021). In contrast, the literature on the relationship between bullying victimization and delinquency, as well as health outcomes and delinquency in African countries is for the most part limited or nonexistent. As a result, very little is known about how bullying victimization, health,
8 and delinquency are related in the context of Africa. The lack of studies on the bullying-
delinquency relationship as well as the health-delinquency relationship in many African
countries, including Ghana, is surprising since it reduces our understanding of the link
between these issues within different cultural environments.
This dissertation begins to fill the gap in our knowledge about bullying
victimization, health and delinquency in Africa. Specifically, the study will investigate
the bullying delinquency relationship and health delinquency relationship in
Ghana. Ghana is the setting for the study for a variety of reasons. First, official statistics
and existing research suggest that the rate of juvenile delinquency is soaring in that
country. For example, statistics from the Ghana Prison Service showed that on the
average, about 98 juveniles are locked up on a daily basis for delinquency acts (Ghana
Prison Service 2013). Some studies on juvenile delinquency in Ghana have also observed
that delinquency is on the rise (Barnie, Nyarko, Dapaah, Appiah, and Awuviry-Newton
2017; Boakye 2013).
The second reason for focusing on Ghana is that empirical research and newspaper reports in Ghana show that bullying is quite widespread in Ghanaian schools
(Antiri 2016; Bosomtwi, Sabates, Owusu, and Dunne 2010; Owusu, Hart, Oliver, and
Kang 2011). In 2013, the Daily Graphic newspaper reported about the bullying that first- year high school students experience. The victimization included beatings, older students seizing their belongings, being forced to flush the toilets of senior students and other forms of ill-treatment (Frimpong 2013). In 2015, the Daily Graphic newspaper reported that 19 final year students of the Sandema Senior High School were suspended because they had bullied another student until he collapsed (Amenuveve 2015). Furthermore,
9 studies by Owusu et al., (2011), show that 40.1% of high school students had experienced bullying (Owusu et al., 2011), whereas Antiri (2016) found that 47.9 % and 37.2% of students have been victims of physical and verbal bullying respectively.
Third, studies on the health of adolescents in Ghana show that a substantial number suffer from both physical and mental health problems (Alicke et al. 2017; Glozah and Pevalin 2016; Owusu et al. 2011). Alicke et al. (2017) reported that the youth of
Ghana suffer physical health challenges such as chronic malnutrition, obesity, hypertension, Type 2 diabetes, and malaria infection. Glozah and Pevalin (2016) also found that 62% and 60% of adolescents experience mental illness and psychosomatic symptoms respectively.
Fourth, aside from the general lack of information about the bullying-delinquency relationship and health-delinquency relationship in African societies, there is also not enough literature about the relationship between the various types of bullying (physical, verbal, social, and psychological) and delinquency in Ghana. The few studies on the bullying-antisocial behavior relationship tend to use either a single dichotomous variable or a summarized scale for the analysis instead of independently analyzing each type of bullying victimization. Examining each type of bullying victimization would be more useful to researchers and policymakers who seek to identify the types of bullying that robustly predict delinquency. To the best of my knowledge, only three studies exist that have investigated these various types of bullying victimization and their relationship with offending behaviors (see Bender and Losel 2011; Espelage and Holt 2013; Vieno, Gini, and Santinello 2011). The current dissertation research will help fill this gap.
10 Fifth, concerning the health-delinquency relationship, studies have shown that there is an association between different types of health outcomes (health behaviors, physical and mental) and delinquency. For example, Kort-Butler (2015) investigated associations between health-strains and delinquency and marijuana use using a sample of
12,247 respondents, finding that physical health-strains were positively linked to antisocial juvenile delinquency and marijuana use. Similarly, Anderson et al. (2015) also studied peer depression and future delinquency. They found that adolescents who experience depression were more likely to engage in property crime than those who did not experience depression. Likewise, Catrett and Gaultney (2009) explored the effects of insomnia on risky behaviors among adolescents in the United States. They reported that adolescents who suffer insomnia were more likely to engage in smoking tobacco, delinquency, and alcoholism. However, most of the studies focus on only one type of health strains (e.g., Birmaher et al. 1996; Anderson et al. 2011; Goldstein, Walton,
Cunningham, Trowbridge, and Maio 2007; Kort-Butler 2015; Semenza 2017; Sibley et al.
2011; Stogner and Gibson 2010). To the best of my knowledge, few studies have investigated at least two or more health outcomes and their relationships with antisocial behaviors, as is done in the current dissertation research. This research seeks to address the following questions.
1. What is the relationship between bullying and juvenile delinquency behaviors in
Ghana?
2. Do the various types of bullying (i.e., physical, verbal and social) significantly
predict juvenile delinquency in Ghana?
11 3. For each of the two questions noted above, how do these relationships differ for
males and females for juveniles in Ghana?
4. What is the relationship between health and juvenile delinquency behaviors in
Ghana?
5. How does the relationship between health and delinquency behaviors differ for
males and females?
Significance of the Study
The primary objective of this study is to examine the extent to which bullying victimization and health strains are associated with juvenile delinquency in Ghana. This research contributes to the existing literature in four critical ways. First, the emphasis of the study on Ghana. Most prior empirical research to date uses almost exclusively
Western samples (e.g., the United States of America, the United Kingdom, Iceland,
Canada), and a few East Asian cultural settings (China, Korea, Taiwan) in their studies
(Bender and Losel 2011; Cullen et al. 2008; Hemphill et al. 2011; Higgins, Khey,
Dawson-Edwards and Marcum 2012; Sigfusdottir, Gudjonsson, and Sigurdsson 2010;
Sourander, Jensen, and Ronning 2007). Studies that investigate the bullying-delinquency and health-delinquency relationship in Sub-Saharan Africa are nonexistent. This study thus fills this void in knowledge by using samples from Sub-Saharan Africa, specifically
Ghana. In turn, it helps contribute to the cross-cultural literature on bullying and health.
Second, existing studies have established that bullying victimization is associated with delinquency. However, the literature on the relationship between various types of bullying victimization (physical, verbal, social, and others) and delinquency are limited.
12 This study attempts to fill the gap in the literature as it focuses on the various types of bullying (physical and verbal) in examining delinquency
Third, most of the existing studies on the health-delinquency relationship tend to focus on one aspect of health (either physical, or mental, or health behaviors). To date, few studies have focused on more than one health condition and its relationship to juvenile offending. I will examine the health-delinquency relationship using two health conditions (physical and mental).
Finally, research has established that there are sex differences in delinquency and health (Rieker and Bird 2005; Matud 2017). Studies have reported that females have a higher life expectancy and lower mortality rate than males in most countries. Also, males have a lower morbidity rate than females. Again, females tend to suffer more from depression and anxiety disorder while men suffer from substance abuse and personality disorders (Rieker and Bird 2005; Matud 2017). However, when it comes to the health- delinquency and bullying victimization-delinquency relationship (e.g., Cullen et al.
2008), sex differences have been ignored for the most part. This dissertation contributes to this literature by conducting a separate analysis of the health-delinquency and bullying-delinquency relationship for males and females. In this way, we would establish whether the relationship is the same or varies by sex.
Organization of the Study
This dissertation is organized into six chapters. Chapter I has focused on the introduction, problem statement, research questions, the significance of the study and organization of the study. Chapter II provides the theoretical framework for the study as well as a more in-depth examination of prior empirical research on the relationships
13 among bullying victimization, health strains, and juvenile antisocial behavior. In this study, I did not test Robert Agnew’s general strain theory. Instead, I used the theory as a framework to analyze data and interpret results. I used this theory because it provides a unique explanation of the relationship between strain and delinquency. Chapter III focuses on the social setting of the study. I discussed bullying victimization, adolescent health, and delinquency in Ghana. Although the available literature reviewed on these topics mainly deals with Western samples, Chapter III presents literature about bullying victimization, adolescent health strains, and delinquency (including substance abuse) in
Ghana – given that this is the empirical context of the current dissertation research. I concluded the chapter by looking at the juvenile justice system in the country.
In Chapter IV, I discussed the source, type, and method of data collection used in the current study and a description of how missing data were managed, and the statistical techniques used to answer the empirical questions posed above. Chapter V then presents the quantitative results of these analyses. I used frequency tables, mean averages, proportions, standard deviations, and cross-tabulations to describe the data. In addition, I used Pearson’s product-moment correlation to establish bivariate associations among the variables. Finally, I used negative binomial regression to establish the relationship between bullying victimization and delinquency and between health strains and delinquency. In Chapter VI, I discuss the results, draw conclusions, suggest some policy implications, and reflect on the study's limitations, strengths, theory implications, etc.
14
CHAPTER II
LITERATURE REVIEW
Introduction
This dissertation investigates the associations between bullying victimization, health strains, and juvenile delinquency in Ghana. This chapter analyzes the existing empirical research available on the subject. First, I reviewed how Robert Agnew’s general strain theory (1992) can be applied to form the theoretical basis for the study, along with some of the relevant empirical tests conducted using the theory. General strain theory will be the theoretical framework for this study because it has received much empirical support from previous studies investigating the strain-delinquency relationship
(Cullen et al. 2008; Sigfusdottir, Gudjonsson and Sigurdsson 2010; Glassner 2020). This study is not aimed at testing the general strain theory. Instead, it will serve as a guide to analyze the data and interpret the results.In addition, I assess the literature outlining the predictors of delinquency. I also examine previous research on the associations among bullying victimization, health strains, and juvenile delinquency in greater detail.
Theoretical Framework: Agnew’s General Strain Theory Agnew (1992) developed the General Strain Theory to respond to the shortcomings of the classical strain theories. The classical strain theories emphasized a mismatch between culturally defined goals, middle-class expectations or social advancement, and legitimate means of achieving them (Cloward and Ohlin 1960; Cohen
15
1955; Merton 1938). This mismatch, the argument goes, causes people to become frustrated, and they cope by becoming rebels, retreatists, conformists, innovationists, or ritualists.
Classic strain theories have come under attack for their failure to adequately explain crimes of the middle-class, their neglect of goals other than monetary success, and the lack of empirical support (Brezina 2017). Agnew (1992: 4) expanded the definition of strain to encompass “events or conditions that individuals dislike.” Even though his definition comprises the forms of strain indicated in the classical strain theories, it further incorporates an extensive host of stressors not examined in previous theories. Overall, he identified three different types of strains: failure to achieve positively valued goals, removal of positively valued stimuli, and confrontation with negative stimuli (Agnew, 1992).
The first strain identified by Agnew (1992) was a failure to achieve positively valued goals. This strain consists of three sub-types. The first sub-type is the disjunction between aspirations and expectations of goal achievements. Here the focus is on the variation between what individuals aspire to achieve and what they expect to achieve.
The second sub-type deals with a disjunction between expectations and actual achievements. The emphasis here is the gap between what individuals expect to achieve and what they are actually able to accomplish. The third sub-type, which is a disjunction between just/fair outcomes and actual outcomes, is concerned with comparing an individual’s effort and outcomes. When individual views the outcome to be unfair, the social process is theorized to lead to strain.
16 The second strain identified by Agnew deals with the loss of positively valued stimuli. This type of strain, according to him, involves the loss of something cherished by the individual. This type of strain can be social, material, or emotional. It may include, for example, the loss of a friend or a significant other (social), theft of personal property
(material), loss of affection from significant others, and marital breakdown (emotional).
Those who encounter this type of strain try to ratify through revenge, retrieve the lost stimuli, or obtain a replacement. Health strain is the loss of positively valued stimuli in this study. Health conditions remove positive stimuli by preventing people from engaging in activities they would have loved to do. Some health conditions can even restrict an individual’s liberty (Stogner and Gibson 2010).
The third strain identified by Agnew concerns the presentation of negative stimuli. This type of strain, Agnew contends, includes experiences where the individual is exposed to undesirable circumstances or is the recipient of negative treatment by others.
People who are exposed to negative stimuli endeavor to overcome the condition by seeking revenge against the stimuli or dealing with it through the use of substances
(Agnew 1992). In this study, bullying victimization is the negative stimuli that is presented to participants. Participants who are victims of bullying will try to cope with it by either fighting the perpetrator, skipping school, or abusing substances.
The theory maintains that the various strains increase the possibility that an individual will encounter negative emotions. These emotions include anger, frustration, disappointment, depression, anxiety, and fear. Once these emotions emerge, the individual attempts to take action, escape, avoid, or cope with the strain. In Agnew’s
17 view, negative emotions play an essential role in explaining the effect of strains on other types of criminal behavior (Agnew 2006).
The theory also contends that positive coping mechanisms such as intelligence, creativity, self-efficacy, social support, social control, and self-esteem can help people to positively cope with strain. These traits affect the individual’s ability to engage in cognitive, emotional, and behavioral coping. Individuals with positive coping mechanisms will be less likely to participate in offending behavior because they will view the cost as higher than the rewards (Agnew 2006).
Bullying Victimization and the General Strain Theory
Most of the early research on bullying victimization focused on its prevalence and ramifications. Bullying victimization was not considered a type of strain until 2001 when
Robert Agnew revised the general strain theory and incorporated peer abuse. According to Agnew (2001), peer abuse is strongly associated with juvenile offending. Since bullying victimization is a type of peer abuse, several researchers have used it as a strain to predict juvenile delinquency.
Bullying victimization fits into one of Agnew’s strains because it is a negative stimulus presented to adolescents. It can be physical, verbal, social, or psychological maltreatment of one adolescent by another. Victims of bullying are likely to experience negative emotions, which can lead to negative coping mechanisms. Adolescents who experience bullying might rely on either internal or external harmful coping mechanisms to deal with their negative emotions. The internal mechanisms involve using drugs or alcohol to deal with strains, while the external mechanisms involve violent behaviors.
18 Many researchers have used bullying victimization as a strain to predict delinquency among adolescents. Most researchers have found that bullying victimization significantly predicts juvenile delinquency (Cullen et al. 2008; Sigfusdottir, Gudjonsson and Sigurdsson 2010; Glassner 2020). A couple of reasons have been offered for bullying victimization predicting juvenile offending. First, victims of bullying become defenseless against the perpetrator and want to find alternative ways to become tough. Victimized teenagers engage in fighting and substance use to prove that they are strong (Olweus,
1993; Pellegrini & Bartini, 2000; Wong & Schonlau, 2013). Second, some studies have observed that bullied juveniles who associate with aggressive peers are more likely to engage in antisocial behaviors (Bukowski, Sippola, & Newcomb, 2000; Wong &
Schonlau, 2013). For example, in a study of 217 juveniles, Bukowski et al. (2000) reported that victimized adolescents who did not have aggressive friends were less likely to engage in delinquency behaviors than those who do not have belligerent peers.
Health and the General Strain Theory
Scholars have used the general strain theory to explain various types of strains
(financial strain, domestic violence, victimization) and their relationship with offending behavior (Blom, Weijters, and Van der Laan 2011; Zara and Farrington 2010; Eriksson and Mazerolle 2013; Cyr et al. 2017). However, Agnew did not include health as a source of strain but it fits well into this framework.
There are three reasons why health as a strain fits into Agnew’s general strain theory. First, good health is a positively valued goal that people want to achieve
(Schroeder, Hill, Haynes, and Bradley 2011; Stogner and Gibson 2010, 2011). Good health is a positively valued goal because it helps people attain other positively valued
19 goals, such as good academic performance, income, employment, and general well-being.
Failure to achieve good health can result in strain. Second, poor health is a negative stimulus (Schroeder, Hill, Haynes, and Bradley 2011; Stogner and Gibson 2010, 2011). It exposes people to unwanted circumstances and might compel them to cope with these through substance abuse. Also, some health conditions are long-lasting and so can become chronic stressors. Lastly, poor health is a loss of a positively valued goal
(Schroeder, Hill, Haynes, and Bradley 2011; Stogner and Gibson 2010, 2011). It can prevent people from participating in activities that they cherish, which includes going to school and playing with peers.
Some researchers have used the general strain theory to explain the relationship between health strains and adolescent delinquency (Ford 2014; Grosholz and Semenza
2018; Kort-Butler 2015; Schroeder, Hill, Haynes, and Bradley 2011; Stogner and Gibson
2010, 2011; Stogner, Gibson and Miller 2014). For instance, Sogner and Gibson (2010) used the general strain theory to establish a relationship between poor health and delinquency. They used data from the National Longitudinal Study of Adolescent Health.
Their report indicated that poor health predicted non-violent delinquency. Kort-Butler
(2015) also examined health strains and delinquency in the United States. She found that health strains were positively linked to juvenile offending.
Gender and General Strain Theory
General Strain Theory explicitly acknowledges that strains, and the mechanisms available to cope with them, are not evenly distributed across the population and that sex is one characteristic that may influence both strains and coping. Scholars have reported that strain measures affected males’ violent delinquency significantly more than females’
20 violent offending (Morash and Moon, 2007; Piquero and Sealock, 2004; Moon and
Morash, 2017; Pesta, Peralta and Novisky 2019). Moon and Morash (2017) used the general strain theory to test the gender differences in delinquency in South Korea. They found that the strains that males encountered were positively related to violent and property offenses, whereas females' strains were strongly associated with status offending. Moon and Morash (2007) also found that some strains, including financial strains, parental maltreatment, and negative life events, were more strongly associated with delinquency for females than for males.
Some researchers have also used GST to investigate gender differences in the association between bullying victimization and juvenile delinquency, in addition to health strains and juvenile offending (Cullen et al., 2008; Glassner and Cho 2018; Strohacker,
Wright, and Watts, 2019; Brady, Baker, and Pelfrey 2020; Glassner 2020). Some studies have reported that bullying victimization affects males more than females (Glassner and
Cho 2018; Brady, Baker, and Pelfrey 2019) Cullen et al. (2008) also discovered that bullying victimization was strongly associated with delinquency for both males and females. Concerning the gender differences in health strains and delinquency, the results are inconsistent. (Jang 2007; Hoffman and Susan 1997; De Coster and Zito 2010;
Schroeder, Hill, Haynes, & Bradley, 2011). Some have found that health strains leads to delinquency for only males (Jang 2007; De Coster and Zito 2010). Schroeder et al. (2011) also found that poor physical health significantly predicted the onset and escalation of crime among low-income females.
In summary, research using the general strain theory have shown that bullying victimization and health are strains that predict juvenile delinquency. However, the
21 theory has only received empirical support regarding the gender differences in the bullying victimization and delinquency relationship from industrialized countries. This dissertation aims not to test the general strain theory since some of the relevant variables are not available in my data set. Notwithstanding, this theory will serve as a framework to analyze and interpret the data for this research. The theory will serve as a guide to analyze and interpret how bullying victimization and health strains predict delinquency.
Other Risk Factors of Juvenile Delinquency
There are several risk factors of juvenile delinquency. Nonetheless, for the purpose of this study, I will focus on age, parental monitoring, peer influence and school bond.
Age and delinquency.
The age-delinquency association is one of the most vigorous associations in criminological literature (Rocque, Posick, and Hoyle 2015). Research on the association between age and delinquency has shown that antisocial behavior begins to soar prior to adolescence, reaches its zenith during late adolescence, and then plummets at young adulthood (Farrington 1986; Moffitt 1993; McCord et al. 2001). The age-delinquency relationship has shown consistent results over the years, regardless of socioeconomic status (income, education, and neighborhood), race/ethnicity and gender (see Farrington
1986; Moffitt 1993; Piquero et al. 2003; Stolzenberg and D’ Alessio 2008) with other researchers similarly finding changes in health behavior by age (Christie-Mizell and
Peralta 2009).
While many juveniles engage in delinquency, there are differences in when they start and the long term patterns. Researchers have established that those who start early
22 are more likely to pursue a criminal career (Moffitt 1993; McCord et al. 2001).
Nonetheless, the initial delinquency behavior does not necessarily result in adult offending. For example, Sickmund and Puzzanchera (2014) found that only 10 percent of those who commit a first offense end up becoming perpetual offenders in subsequent years. Scholars have attributed the onset of juvenile antisocial behaviors to poor school skills, bad grades, less commitment to school, more attachment to peers, access to marijuana, more interaction with antisocial peers, and stringent laws against drugs (Ayers et al. 1999), weak or broken informal social control (Sampson and Laub, 2016) and lack of self-control (Gottfredson and Hirschi 1990). With respect to the persistence of juvenile delinquency, scholars have determined that access to drugs or continuous illicit drug use
(Sickmund and Puzzanchera 2014), family inclusiveness and interaction, deviant peers, positive sanctions for antisocial behavior (Ayers et al. 1999) and repeated victimization helps to escalate delinquency (Chang et al. 2003).
Grade level and delinquency.
Some scholars have focused on the relationship between school grade levels and delinquency behavior. The scholars have found a positive linear association between grade levels and juvenile offending (Paetsch & Bertrand, 1997; Pollard, Hawkins, and
Arthur, 1999: Wong 2005; Lo et al. 2011). That is, as grade level rises, delinquency increases. For example, Wong (2005) examined the link between adolescent activities and teenage offending in Canada. The results showed that delinquency increased with the rise in grade levels and peaked at grades 11 to 12. In a study of the effect of grade level on student offending in Alabama, Lo et al. (2011) found a positive association between
23 school grade level and juvenile delinquency. However, the relationship depended on the school type.
Parental monitoring and delinquency.
Monitoring, supervision, and child disclosure is also one of the parental predictors of juvenile delinquency. The empirical literature has shown that parents who monitor their children reduce the chances of their offspring becoming delinquents (Laird, Pettit,
Bates, and Dodge 2003, Nofziger, 2008). For example, Laird et al. (2003) found that parental monitoring was negatively associated with child and parent-reported antisocial behavior. In another study Laird, Criss, Petit, Bates, and Dodge (2008) found that adolescents whose parents have less knowledge of their whereabouts and what they do, are more likely to become delinquents and associate with delinquent peers.
In addition, adolescents who disclose their activities to their parents reduce their possibility of indulging in delinquent behaviors. Keijsers, Frijns, Branje, and
Meeus(2009) studied adolescents’ disclosure and delinquency among 309 Dutch adolescents, finding that an increase in adolescent delinquency was linked to a decrease in child and parent disclosure. However, it has also been noted that children who are delinquent tend not to disclose to their parents where they are (Hoffman 2015).
Peer influence.
Criminogenic literature has established a strong association between peer influences and juvenile delinquency. These influences emanate from peer delinquents and time spent with peers (McCord et al. 2001). Studies have shown that adolescents who associate with delinquent peers are more likely to be involved in antisocial behavior. For example, Thornberry et al. (1994) examined delinquent peers, beliefs, and antisocial
24 behavior using data from the Rochester Youth Development Study, finding that juveniles who associate with delinquent peers are at a greater risk of becoming delinquents. In another study on delinquent peer groups, delinquency and substance use, Moratta (2017) found that affiliation with delinquent peers increases the likelihood of getting involved in alcohol and substance use.
Time spent with peers has also been documented by researchers as being related to antisocial behavior. Agnew (1991) found that general time spent with friends (e.g., how many days, evenings, and weekend days) had a significant effect on delinquency independently from other peer-related factors. More recently, Meldrum, Young, and
Weerman (2009) reported a general effect of time spent with peers, independent from the delinquency of friends, bond with school, and level of self-control. Other studies have also shown that unsupervised time spent with peers can result in delinquency. Flannery,
Williams, and Vazsonyi (1999) studied antisocial behavior and delinquency after school time among 1170 adolescents. They found that delinquency was high among adolescents who spent unsupervised time with their peers than those with their parents at home.
Weerman et al. (2015) examined time spent with peers and its association with delinquency by focusing on the importance of where, what, and with whom the time is spent within the Netherlands. They found that time spent with peers is linked to delinquency only when the time is spent just socializing, being in public, and being unsupervised.
It must be noted, however, that the extent to which peers influence adolescents to become delinquent depends on family factors and processes. Peer influence tends to be greater when there is family discord. In addition, adolescents who have a good interaction
25 with their parents are less likely to be influenced by their peers. Moreover, juveniles who
are poorly monitored by their parents are at a greater risk of being influenced by friends
to engage in antisocial behavior (McCord et al. 2001).
School bond and delinquency.
Several authors have also identified school bonds as a risk factor to adolescent
delinquency. Studies have documented that adolescents who have a strong bond with
their school are less likely to engage in offending behaviors (Jenkins 1997; Ozbay and
Ozcan 2006; Hart and Mueller 2013). Research on this subject has mostly focused on
school bond aspects, including school commitment, attachment to school, school
involvement, and belief in school rules. For example, Jenkins (1997) explored the impact
of school social bonds on delinquency. She reported that measures of school bonds
significantly predicted school offending. In a recent study, Hart and Mueller (2013)
analyzed the social bond and delinquency relationship among students in the United
States. They reported that school bonds accounted for differences in delinquency among
students.
Bullying Victimization and Delinquency
Studies in criminology conducted in many cultures have consistently found that
bullying victimization and delinquency behaviors are positively related. For instance, in
the United States, Wong and Schonlau (2013) studied bullying victimization and future
delinquency using data from the National Longitudinal Survey of Youth 1997 (NLSY97)
with a sample size of 8833 respondents. The independent variable was bullying
victimization, whereas the dependent variable was delinquency. They also controlled for
demographics, early childhood, school variables, home environment, and behavioral
26 variables. They used a propensity score matching technique to evaluate the effect of bullying victimization on a range of delinquency outcomes. The results showed that early victimization predicted delinquent behaviors, including assault, vandalism, theft, other property crimes, selling drugs, and running away from home.
Similarly, Higgins, Khey, Dawson-Edwards, and Marcum (2012) examined the link between bullying and delinquency among African Americans using a sample of 725
African-Americans derived from the National Longitudinal Survey of Youth 1997
(NLSY97). The data set for this sample was from four rounds of the survey (1997-2000) and were collected using interviews. The study used delinquency as the independent variable and six independent variables. Data for this study were analyzed using multinomial logistic regression analysis. The researchers found that being a victim of repeated bullying is an important measure associated with a trajectory of delinquent activity.
In Europe, Sigfusdottir, Gudjonsson and Sigurdsson (2010) investigated bullying in Iceland. The data for the study were drawn from a cross-sectional, population-based sample of 7149 adolescents, 15 and 16 years old. The data were collected using a questionnaire, which involved 3507 (49.9%) boys and 3528 (50.1%) girls. The study had bullying as the independent variable, delinquency as the controlled variable and controlled for sex, parental education and family structure in the models. Structural equation model was used to analyze the data. They discovered that bullying behavior and bullying victimization both increased the likelihood of delinquent behavior.
In Asia, Moon, Morash, and McCluskey (2012) studied bullying in South Korea using longitudinal data with a sample size of 2,817. The independent variable used was strain
27 which was measured with part-time work, victimization stress, family conflict, financial stress, examination-related stress, conflict with parents, and negative interaction with fellow students, whereas the dependent variable was bullying which was measured using severely beating others, forcefully taking away another’s money or goods, severely teasing and mocking others, threatening others, and bullying others. The study controlled for gender, parental income, and academic performance. The data were analyzed using negative binomial regression, revealing that youths with prior bullying victimization are most likely to engage in delinquency behaviors.
Furthermore, research on bullying and delinquency shows that being a victim of bullying and being a perpetrator of bullying vary in their prediction of delinquency behaviors. For instance, Sigfusdottir et al. (2010) found that bullying behavior was a stronger predictor of delinquent behavior compared to bullying victimization. Similarly,
Piquero et al. (2017) investigated the correlates and consequences of bully-victims in a sample of serious adolescent offenders. The study involved 1,354 serious youthful offenders in Philadelphia. The dependent variable was bullying, which was measured via whether they had ever been bullied or bullied someone. The independent variables included gender, race, temperance, intelligence, exposure to violence, mental health, prior arrest, and family member arrest history. The study was analyzed using multinomial logistic regression. It was found that being both a bully and a victim increases the risk of being arrested.
Frequency of Bullying Victimization and Delinquency
Moreover, research focusing on the frequency of bullying and its association with delinquency has shown that repeated bullying results in delinquency behaviors. For
28 example, Higgins et al. (2012) reported that juveniles who experience repeated bullying are more likely to engage in delinquent activities. Similarly, Sourander et al. (2007) studied childhood bullies and victims and their risk of criminality in late adolescence.
The study had a sample size of 2551 respondents drawn from the longitudinal birth cohort in Finland. The dependent variable was delinquency which included drug, violent, property, traffic offenses, and drunk driving. The independent variable was bullying which was measured via such items as whether a person has been bullied or had bullied others. The study controlled for socioeconomic status, parental education, and psychiatric symptoms. Multinomial logistic regression and binary logistic regression were used to analyze the data. The researchers reported that juveniles who experience perpetual bullying or engage in repeated bullying are likely to engage in delinquency behaviors.
Types of Bullying and Its Relationship with Delinquency
Past research has established the association between bullying and delinquency in advanced countries. In spite of that, few have attempted to investigate how each type of bullying predicts offending behavior. Currently, three studies have focused on that subject. The results from those studies have been inconsistent: some of those studies found that physical bullying better predicts delinquency compared to the other forms of bullying. For example, Bender and Losel (2011) examined bullying and its association with violence and delinquency in Germany. They found that physical bullying better predicted antisocial behavior compared to verbal. In another study, Espelage and Holt,
(2013) investigated school bullying and suicidal behaviors of middle students using a sample of 661, finding that physical bullies were at a higher risk of engaging in suicidal behaviors compared to verbal bullies.
29 One study also found that racist bullying better predicted antisocial behavior.
Vieno, Gini, and Santinello (2011) explored different forms of bullying and its association with substance use, using a sample of 2667 Italian middle and secondary school students, finding that all the forms of bullying predicted substance use.
Nonetheless, racist bullying better predicted substance use compared to the other forms of bullying.
Gender Differences in the Association between Bullying and Juvenile Delinquency
Some researchers have reported gender differences in the association between bullying and antisocial behaviors (Johnston, Doumas, Midgett and Moro 2017; Connolly
2017; Niemela et al. 2011; Cullen et al. 2008; Rosen and Nofziger 2019; Peralta and
Tuttle 2013). However, the use of gender in the context of some studies is debatable.
Some of the studies tend to interchange sex for gender, although they are not the same construct (Peralta and Tuttle 2013). Even studies that focus on gender primarily focus on males and females. In this section of the review, I will look at both sex and gender differences. However, in my analysis, the focus will be on sex since my data does not provide for gender.
On the one hand, some of the past research have shown that bullied males are more likely to engage in substance use (Johnston, Doumas, Midgett and Moro 2017;
Niemela et al. 2011). Conversely, other studies have reported that substance abuse is high among bullied females and males (Connolly 2017). For example, Johnston, Doumas,
Midgett, and Moro (2017) examined the gender variations in bullying and substance abuse among high school students. They found that substance abuse was high among males as compared to females. In contrast, Connolly (2017) investigated sex differences
30 in childhood bullying and substance use in adulthood. The author found that bullied males experienced high usage of cigarettes and marijuana, whereas bullied females reported using more cigarettes.
The literature on sex differences in the correlation between bullying victimization and delinquency is limited. Despite the limited literature, one study has also found that bullied males are more likely to become deviants than their female counterparts, while another has found out that the effect is the same for both genders. Hay, Meldrum, and
Mann (2010) analyzed the association between bullying and deviance. They found that bullying predicted self-harm among males. Similarly, Cullen et al. (2008) explored the effect of bullying victimization on delinquency for both males and females. They found that bullying significantly predicted delinquency among both genders.
Health and Juvenile Delinquency
Past research has shown that health is related to delinquency behaviors
(Clinkinbeard et al. 2011; Semenza 2017; Stogner and Gibson 2010). Below, I will provide an overview of the different aspects of health, including health behaviors,
‘physical health’ and ‘mental health.’ Health is a complex concept that is hard to define.
Because of its complexity, it is subject to change over time. Four different models have been used to conceptualize health in medical research: the medical model, the wellness model, the environmental model, and the holistic model. The medical model views health as the “absence of diseases or infirmity” (Larson 1999). The wellness model defines health as the ability to overcome illness and encompasses the absence of the diseases and the spiritual and social well-being physicians mostly ignored by physicians (Williams,
1993; Levin 1994). The environment model defines health as the adaptation to physical
31 and social surroundings (Larson 1994). The holistic model, which is also called the WHO model, defined health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (United Nations 1948). For the purpose of this study, I define health as the state of spiritual, emotional, and social well-being and the absence of diseases.
Health consists of various aspects; however, the focus is on health behaviors, physical health, and mental health in this section. Various scholars have defined health behaviors in different ways. Conner and Norman (1996) define health behavior as any action aimed at preventing or detecting diseases or improving health and wellness.
Gochman (1997) defines it as patterns of behavior purposefully aimed at maintaining, restoring, and improving health. Health behaviors include diet, exercise, smoking, alcohol usage, vaccination, hospital visits, and many others (Connor and Norman 1996).
From my viewpoint, I define health behaviors as activities aimed at preventing, maintaining, restoring wellness and health. Health scholars have not provided a concrete definition of physical health. For the purpose of this research, I define physical health as a state of complete physical wellbeing, including the absence of diseases which enables individuals to perform physical activities without limitations.
Mental health is a construct that attracted the attention of sociologists, including
Parsons (1991), Foucault (2001), and Szasz (1970). However, they viewed mental health as a deviation from the standard of behavior of a society. The World Health Organization
(WHO) defined mental health is "a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community" (WHO 2018).
32 Health Behaviors and Delinquency
An abundance of research has demonstrated a significant association between health behaviors and delinquency. The relationship between the two variables can be applied to a range of outcomes, including quality of sleep, exercise, quality of diet, and vitamin or mineral supplementation. From GST perspectives, health behaviors predict delinquency because they fit into one of the types of strains. Poor diet, lack of sleep and lack of exercise can be viewed as loss of positively valued goal. Adolescents who experience the aforementioned health problems will experience negative emotions such as depression, anxiety and sadness. Without the appropriate coping mechanisms, the unhealthy adolescents might engage in offending behaviors.
Researchers have found that when adolescents are given vitamins or mineral supplements, it reduces the risk of being involved in antisocial behavior (Schoenthaler et al. 1997; Schoenthaler and Bier 2000; Gesch et al. 2002). For example, Schoenthaler and
Bier (2000) investigated the effect of vitamin-mineral supplementation on antisocial behavior among American adolescents, using a pretest and post-test experimental design in which some students were given daily vitamin-mineral supplements, and others were given a placebo for four months. The researchers found that students who were given the supplements were 47% less likely to engage in delinquent behaviors than those in the controlled group. In a recent study, Gesch et al. (2002) explored the effects of vitamins, minerals and essential fatty acid supplements on delinquency behaviors in the United
Kingdom. They used an experimental design with a sample of 2314 young prisoners, finding that those given the vitamins and mineral supplements committed 26.3% fewer offenses than those given a placebo.
33 Extant studies on the association between association diet and delinquency have provided mixed results. While some studies have shown that proper diet is inversely related to delinquency (Schoenthaler 1985; Schoenthaler and Doraz 1983), other studies have also reported that there is no relationship between the two variables. These studies dwell mostly on the impact of proper diet and blood sugar levels and how they affect antisocial behavior. Researchers have established that juveniles who have a good diet are less likely to be involved in delinquency behaviors. Schoenthaler and Doraz (1983) investigated the association between diet and antisocial behavior among incarcerated juvenile offenders in Alabama, the United States. The study used an experimental design, which involved 276 participants. The researchers introduced fruit juice and snacks to replace the junk foods that were previously given to the participants. They reported that assault, theft, horseplay, failure to obey orders, widespread violations and fighting reduced by 82%, 77%, 65%, 55%, 23%, and 13% respectively. In a recent study on soft drinks and behavioral conducts among adolescents in Norway, Lien et al. (2006) found that conduct problems were high among adolescents who consumed four or more glass of sugar-containing soft drinks.
Researchers have documented that high blood sugar levels are linked to delinquency behavior. It has been established that juveniles who have high blood sugar levels are more likely to engage in delinquency behaviors compared to those with low blood sugar levels. Schoenthaler (1985) investigated diet and antisocial behavior among juveniles in the United States, finding a weak association between low blood sugar and delinquent behaviors. In a meta-analysis blood sugar and aggression, Dewall et al. (2011) found that homicide was high among countries with a high rate of glucose levels. On the
34 contrary, Gans et al. (1990) did not find support for the impact of sugar levels on antisocial behavior.
Extant research also indicates a negative association between quality of sleep an individual gets and the possibility of engaging in antisocial behavior (Clinkinbeard et al.
2011; O’Brien and Mindell 2005; Peach and Gaultney 2013). Research in these areas focuses on lack of sleep and risk-taking behaviors and lack of sleep and delinquency.
Research has shown that lack of sleep significantly predicts delinquency (O’Brien and
Mindell 2005; Rusnac and Spitzenstetter and Tassi 2019; Womack, Reyna, Hook, and
Ramos 2013). O’Brien and Mindell (2005) used data from 388 high school students in
Philadelphia to examine the relationship between sleep problems (i.e., total sleep time, weekend delay and oversleep, daytime sleepiness, and sleep-wake problems) and several measures of risk-taking, which included measures of adolescent violence. They found a significant relationship between sleep-wake problems (e.g., being late to class because of oversleeping, staying up late, trouble falling asleep) and risky behaviors. Concerning the amount of sleep, the authors found that low-end sleepers participated in significantly more dangerous alcohol and sexual behaviors than high-end sleepers. Total sleep time, however, was not related to violence, safety behaviors, or drug use.
Studies on the amount of sleep and antisocial behavior show that there is a significant association. Adolescents who get less sleep are more likely to engage in delinquency behavior (Catrett and Gaultney 2009; Clinkinbeard et al. 2011; Peach and
Gaultney 2013). Catrett and Gaultney (2009) explored the effects of insomnia on risky behaviors among adolescents in the United States. Using data from Add Health which
35 involved 4353 respondents, they reported that adolescents who suffer insomnia were more likely to engage in the smoking of tobacco, delinquency, and alcoholism.
It is generally believed that participation in exercises and other forms of physical activities helps adolescents’ development. It improves their health, promotes solidarity, and keeps them occupied. Hence, youth who undertake regular physical activities are less likely to be involved in antisocial behavior than those who do not participate in regular physical activities (Faulkner et al. 2007). However, the relationship is not consistent: while some studies have found that physical activities can result in decreased delinquency
(Duncan et al. 2002; Escobedo et al. 1993), others have also found that regular exercise is positively associated with delinquency (Begg et al. 1996; Endresen and Olweus 2005;
Faulkner et al. 2007; Thames and Vaisman-Tzachor 2009). Other studies have also indicated that physical activities have no relationship at all with delinquency (Spruit et al.
2016). For instance, Thames and Vaisman-Tzachor (2009) found that engaging in physical activities does not deter students from becoming delinquents. In a cross-sectional study of the association between physical activity and delinquency in Canada, Faulkner et al. (2007) found that physical activity was positively related to delinquency among males.
On the contrary, Duncan et al. (2002) investigated the association between prosocial behavior and delinquency using 356 youths in the United States. They found that days of physical activity were negatively linked to substance abuse.
Physical health and delinquency.
Adolescence is the period where teens become more concerned about their physical health conditions. During this stage, they try to eat well, keep fit, and try to do routine medical checkups. With this in mind, it is expected that adolescents whose health
36 condition is poor would not be motivated or have the ability to be delinquent because of functional limitations (Schroeder et al., 2011). However, the criminological literature has shown that adolescents facing poor health and other related problems tend to report more acts of crime and deviance than their healthier counterparts. These research mostly focus on chronic illness, disability, and somatic symptoms (Miauton, Narring, and Michaud
2003; Suris et al. 2008; Blum, Kelly, and Ireland 2001; Oshima et al. 2010). Physical health leads to delinquency because chronic illness, disability, and somatic symptoms conforms to the strains identified by Agnew in GST. Poor physical health is obnoxious stimulus since it exposes people to unwanted circumstances and might compel them to cope through substance abuse.
Research has shown that chronic illness is linked to antisocial behavior.
Adolescents who suffer chronic illnesses are more likely to become delinquents compared to those who do not (Miauton, Narring, and Michaud 2003; Suris et al. 2008).
Miauton et al. (2003) investigated the chronic illnesses and lifestyles of adolescents in
Switzerland. They used cross-sectional data which involved 9268 participants, finding that adolescents who suffer chronic illness are more likely to drive without a seatbelt, engage in drunk driving, and misuse substances. Studies on disabilities and antisocial behavior have also shown that adolescents who suffer disabilities are more likely to become delinquents (Blum, Kelly, and Ireland 2001; Oshima et al. 2010). Oshima et al.
(2010) investigated disability and juvenile offending in the United States using a sample of 1568 youth. They found that youth with functional limitations were more likely to be summoned before the court than those without disabilities.
37 A growing body of research indicates that adolescents with somatic symptoms such as frequent headaches, colds, aching, general pain, skin problems, dizziness, and joint aches are at a higher risk of becoming delinquents compared to those who do not suffer such symptoms. (Farrington 1995; Shepherd, Farrington, and Potts 2002; Stogner and Gibson, 2010, 2011). For example, Shepherd et al., (2002) investigated the association between physical injuries, illness and antisocial behavior among adolescent males in London. They used longitudinal data which involved 411 participants, reporting that the physical health conditions such as injuries, accidents, and hospital treatments predicted delinquency. Using Add Health data, Stogner and Gibson (2011) found that somatic symptoms such as headaches, stomach aches, and joint pain are related to the onset of substance abuse. In a recent study, Kort-Butler (2015) investigated associations between health-strains and delinquency and marijuana use using a sample of 12,247 respondents, finding that health-strains were positively linked to antisocial juvenile delinquency and marijuana use.
Mental Health and Delinquency
The existing literature has shown a relationship between mental health outcomes
and antisocial behaviors. Some of the mental health outcomes are less likely to result in
delinquency, whereas others are more likely to do so. Such studies tend to look at
anxiety, depression, ADHD, ODD, and stress disorder. Mental health results in
delinquency because it involves loss of a positively valued stimulus which was identified
in the strain theory. Adolescents who experience mental disorders might not be able to
attain their objectives. The poor mental health might lead to negative emotions such as
38 depression, anxiety, and sadness. If there are no positive coping mechanisms, it might lead to delinquency.
Anxiety disorders among the youth, for instance, have been found in several studies to be less likely result in antisocial behaviors (Jolliffe et al. 2018; Loeber et al.
2008; Walker et al. 1991). For example, Walker et al. (1991) investigated anxiety and conduct disorder among adolescents in the United States using a sample of 177 participants, finding that adolescents with an anxiety disorder were less likely to be deviants. In a recent study, using data from the Pittsburgh Youth Study which involved
503 respondents, Jolliffe et al. (2018) found no sufficient evidence that anxiety disorder leads to later delinquency.
Studies which have investigated the link between depression and delinquency have also provided mixed results. Some studies have shown that depression is significantly related to delinquency (see Anderson, Cesur, and Tekin 2015; Rowe,
Maughan, and Eley 2006; Kofler et al. 2011), whereas others have shown the inverse to be the case (e.g., Piquero and Sealock 2000; Sigfusdottir, Farkas, and Silver 2004). Rowe et al. (2006) investigated links between antisocial behavior and depressed mood in the
United Kingdom. Using data from a community survey, with a sample size of 2409 participants, they found that depression was associated with delinquency. Anderson et al.
(2015) also studied peer depression and future delinquency. Using data from the National
Longitudinal Study of Adolescent Health (Add Health), they observed that adolescents who experience depression were more likely to engage in property crime than those who did not experience depression.
39 In contrast to studies that generally find a positive association between depression and delinquency. Piquero, and Sealock (2000) used the General Strain Theory to examine an offending population of young people who had been detained at juvenile detention facilities in a mid-Atlantic state. They used interviews as well as secondary data regarding an arrest for the study. Findings from the study suggest that depression did not have a significant effect on interpersonal or property offending. In another study,
Sigfusdottir, Farkas, and Silver (2004) investigated the role of depression and anger in the association between family friction and antisocial behavior. In this study that used data from the Icelandic secondary school system and involved 7758 students ages 14 and 16,
Sigfusdottir et al.(2004) reported that a depressed mood did not affect delinquency.
Researchers have also documented that juveniles who suffer from Attention
Deficit Hyperactivity Disorder (ADHD) and Oppositional Defiant Disorder (ODD)
during infancy exhibit delinquency initiation at an early age, and a greater variety of
offending. Foley, Carlton, and Howell (1996) found that antisocial behavior was high
among boys who had been diagnosed with ADHD compared to those who had not. In a
recent study, Sibley et al. (2011) examined ADHD and delinquency among teenagers in
Pittsburgh using a sample of 497, finding that delinquency onset and prevalence was high
among teenagers with ADHD.
Gender, Health and Delinquency
Research on the gender differences in health and delinquency are not many.
Nonetheless, the few that are available have mainly focus on physical health and mental health. The few available research on physical health and delinquency have produced mixed results (Faulkner et al. 2007; Schroeder et al. 2011). For example, Faulkner et al.
40 (2007) investigated physical activity and delinquency adolescents in Ontario, Canada.
They found that rigorous physical activity is positively associated with delinquency among only males. On the contrary, Schroeder et al. (2011) found that physical health was significantly related to females' offending behaviors.
Research on male and female differences in mental health and delinquency have also produced inconsistent results. Some studies found that mental health is linked to offending among females (Obeidallah and Earl 1999; Vaske and Gehring 2010), whiles other researchers report otherwise (Elonheimo et al. (2007). Vaske and Gehring (2010) reported that depression predicted delinquency for females but not males. Elonheimo et al. (2007) also investigated psychiatric disorder and crime among males in Finland. They reported that mental illnesses were linked to offending among males.
Summary
This research examines whether bullying and health have the same effect on
delinquency in Ghana. In developed countries, existing studies have reported that
bullying is associated with juvenile delinquency. That is, bullied adolescents are more
likely to engage in antisocial behaviors than those who do not have bullying encounters.
Additionally, the literature shows that juveniles who experience physical bullying are at a
higher risk of becoming delinquents than those who come up against other forms of
bullying. Also, existing studies on sex differences in bullying- delinquency relationship
have reported that bullying strongly predicts delinquency for both males and females.
Past studies have also linked health to antisocial behaviors. In other words, teenagers
with poor health conditions are at a higher risk of becoming delinquents in contrast with
those who are healthy. Studies of various health outcomes and delinquency have shown
41 that physical health, mental health, and health behaviors vigorously predict delinquency.
Concerning male and female differences in the correlation between health and offending, the literature seems to produce mixed results. Some studies report that unhealthy females are more likely to become delinquents, whereas others suggest that ailing males and females are likely to engage in antisocial behaviors.
Even though bullying and health have been found to predict delinquency in western countries robustly, it has not been established whether the hypothesized relationship is applicable in Ghana and sub-Saharan Africa. In this study, I examine whether bullied, and unhealthy juveniles are more likely to engage in delinquency behaviors than not bullied or healthy youths. Besides, I investigate whether there are gender differences in the link between bullying victimization, health, and delinquency.
Hence the study addresses the following research questions:
1) What is the relationship between bullying and juvenile delinquency behaviors in Ghana?
2) Do the various types of bullying (i.e., physical and verbal) significantly predict juvenile delinquency in Ghana?
3) For each of the three questions noted above, how do these relationships differ for males and females for juveniles in Ghana?
4) What is the relationship between health and juvenile delinquency behaviors in
Ghana?
5) How does the relationship between health and delinquency behaviors differ for males and females?
As such, my dissertation will aim at testing the following hypotheses:
42 1. Bullying victimization will positively predict juvenile delinquency.
2. Bullied males will have a higher probability of reporting delinquency than
females.
3. Type of bullying victimization will positively predict juvenile delinquency.
a. Physical bullying will be positively associated with delinquency
b. Verbal bullying will be positively associated with delinquency
c. Males who suffer from physical bullying will report more delinquency
behaviors than females
d. Females who experience verbal bullying will report more offending
behaviors compared to males
4. Physical health strains will positively be associated with delinquency
5. Mental health strains will positively predict delinquency
6. Females who experience physical health strain will report less offending
behaviors than males.
7. Males who suffer from mental health disorders will report more delinquency
behaviors than females.
Bullying Victimization Juvenile Delinquency
Fig 1: Bullying victimization will positively predict juvenile delinquency
43
Physical bullying Victimization
Verbal bullying Juvenile delinquency victimization
Fig 2 : Physical and verbal bullying victimization predicting juvenile delinquency
Physical health strains
Juvenile delinquency
Mental health strains
Fig 3: Physical and mental health-strains will positively predict adolescent delinquency behavior
44
CHAPTER III
SOCIAL SETTING
Ghana- An Overview
Ghana was the first sub-Saharan African country to attain independence from its
colonial masters in 1957. The country shares borders with Burkina Faso in the north,
Togo in the east, Côte d’Ivoire in the west, and the Atlantic Ocean in the south. The
country has an area of 239,540 square kilometers or 92,486 square miles (Ghana
Statistical Service 2012). Ghana is divided into 16 regions: Western North, Western,
Volta, Greater Accra, Eastern, Ashanti, Central, Northern, Upper East, Upper West, Oti,
Bono East, Ahafo, Bono, North East, and Savannah. Currently, the population is
estimated at 30,955,204, which is made up of 15,231,057 males and 15,724,147 females.
Accra is the country’s capital city and has a population of 5,055,883. There are over 75
ethnic groups in Ghana, including Akans, Moshi-Dagbani, Ewe, Ga-Dangme, Guan,
Grunsi, and Mande-Busanga. The Akans constitute 47.5 percent of the entire population
(Ghana Statistical Service 2012). Prior to attaining independence in 1957, the country
was colonized by several European countries, including the Netherlands, Sweden,
Denmark, and Britain (Gyimah-Boadi and Debrah 2008).
After attaining independence in 1957, Ghana went through several political
uprisings and coups d’état until 1992. During the first republic, Kwame Nkrumah and the
ruling party enacted laws that prohibited dissenting views, which they thought were 45
Fig. 4: The map of Ghana injurious to national unity. Hence, laws such as the Preventive Detention Act (PDA) and
Avoidance of Discrimination Act were passed to control the people who were antagonistic to the government. These laws led to the oppression of the opposition parties in the country. Hence, the opposition parties became weak, which resulted in a one-party state. These developments led to the overthrow of Nkrumah’s government in 1966 by the
National Liberation Council (NLC) led by General E. K. Kotoka (Gyimah-Boadi and
Debrah 2008).
Following the overthrow of Nkrumah’s government in 1966, the National
Liberation Council (NLC) suspended the 1960 constitution and also banned all political activities. In 1968, a constitutional committee was formed to draft a new constitution for the republic. The committee completed their work in 1969, and a new constitution was ratified. The National Liberation Council later lifted the ban on political activities, which allowed for the formation of new political parties. Elections were held, and the Progress
Party, led by Kofi Abrefa Busia, won the elections. However, Busia lost favor in the eyes of the Ghanaian public because he was accused of being tribalistic. In 1972, the government was toppled by a military junta, the National Redemption Council (NRC),
46 led by General Kutu Acheampong (Gyimah-Boadi and Debrah 2008). The Acheampong regime was also overthrown by another junta, National Redemption Council (NRC), in a palace coup led by General F. W. K. Akuffo. This government was characterized by corruption and a failing economy. Hence, in 1979, the National Redemption Council
(NRC) was also overthrown by the Armed Forces Revolutionary Council (AFRC) in
1979, led by Flt. - Lt. Jerry John Rawlings. The Armed Forces Revolutionary Council held an election in the same year. The election was won by the People’s National Party, led by Dr. Hilla Limann (Gyimah-Boadi and Debrah 2008). In 1981, the Limann government was overthrown by the Provisional National Defense Ruling Council
(PNDC), led again by Flt. - Lt. Jerry John Rawlings. Rawlings cited corruption and economic downturn as the reasons for the coup. Rawlings’s regime implemented IMF and World Bank programs such as the Economic Recovery Program and structural adjustment programs to restore the ailing economy. In 1992, Jerry Rawlings restored democratic governance and became the first president of the fourth republic. Since 1992,
Ghana has maintained a stable political environment and has produced five presidents in the fourth republic (Gyimah-Boadi and Debrah 2008).
Ghana’s economy was in bad shape because of the bad policies of political leaders in the 1970s and 1980s. The ailing economy compelled the country to enroll in
IMF and World Bank programs such as the Economic Recovery Program and structural adjustment programs. Economic growth declined considerably from the 1980s until 1995, but has been growing steadily since 1996 (Osei, Atta-Ankomah, and Lambon-Quayefio
2020). The contribution of each economic sector towards the overall economy has varied over the years. While the contributions of some sectors have been rising, others’ have
47 been falling. The share of the manufacturing sector has declined from 13.3 percent in
1989 to 8.8 percent in 2010. Agriculture has also declined from 41 percent in 1984 to
29.5 percent in 2010. Meanwhile, the service sector has grown from 38.3 percent in 1984 to 48.3 percent in 2010 (Osei et al. 2020).
In terms of living standards, available statistics show that the poverty level in
Ghana has been plummeting since 1991. From 1991 to 2006, the poverty level dropped
from 51.7 percent to 28.3 percent. By 2012, the poverty level was reduced to 24.1 percent
(Huq and Tribe 2018). The poverty level is high in the five northern regions and low in
the middle and southern parts of the country (Huq and Tribe, 2018). Poverty in Ghana is
gendered: females are poorer and possess less property than males (Oxfam, 2020).
Statistics further show that the inequality gap was very high from 1992 to 2006.
However, the gap seems to have shrunk from 2006 to 2013 (Huq and Tribe 2018). The
richest 10 percent of the Ghanaian population consume 32 percent of total consumption,
while the destitute consume 2 percent of the total consumption (Oxfam 2020).
Juvenile Delinquency in Ghana
Juvenile delinquency in Ghana has been on the rise in recent times. Although
official statistics available about the subject are rare, the few existing ones show a
gloomy picture. Statistics from the Ghana Prison Service (2015) show that average daily
lock-ups of juveniles have been on the rise since 2014. The average number of daily lock-
ups was high in 2012 (117), but it plummeted in 2013. However, it rose again in 2014
(103) and reached its peak in 2015(125) (Ghana Prison Service 2015).
48 Table 1: Average Daily Lock-up of Juveniles In Ghana Month Average monthly daily lock-up 2015 2014 2013 2012 January 117 93 120 127 February 120 91 99 128 March 124 92 98 127 April 126 99 97 121 May 125 105 97 120 June 125 105 93 118 July 123 106 93 117 August 127 105 94 110 September 126 106 95 113 October 124 108 96 112 November 128 114 99 109 December 131 114 97 106 Average daily lock-up 125 103 98 117 Source: Ghana Prison Service, 2015
In addition to official statistics showing that juvenile antisocial behavior is high,
empirical research conducted in the country also tells the same story (Barnie, Nyarko,
Dapaah, Appiah, and Awuviry-Newton 2017; Boakye 2013). Using a cross-sectional
survey, Boakye (2013) found theft to be the main delinquency behavior in Ghana. In a
recent study of youth violence in Kumasi, Barnie et al. (2017) reported that 54.9 percent
of the youth had a record of involvement in criminality in the past year, including
stealing, drug abuse, robbery, and sexual offenses. Adu-Mireku (2003) investigated
substance abuse among senior high school students in areas in Ghana. Using a sample of
894 participants, Adu-Mireku reported that lifetime alcohol use was 25.1 percent, tobacco
use was 7.5 percent, and marijuana use was 2.6 percent.
Juvenile delinquency in Ghana has been attributed to family factors such as the
parent-child relationship (Weinberg 1964), weak parental attachment (Baffour and
Abasss 2016), dysfunction of the nuclear family, lack of extended family support
49 (Boakye 2012), as well as association with delinquent peers and substance use (Boakye,
2012; Bosiakoh and Andoh 2010). Research has shown that Ghanaian adolescents who lack parental attachment are more likely to become delinquents (Baffour and Abasss
2016; Weinberg 1964). Such teenagers tend not to live with both parents. Instead, they live with guardians, other family members, or a single parent. This causes the children to lose attachment as well as the supervision needed to ensure conformity to norms (Baffour and Abasss 2016; Weinberg 1964).
In addition, researchers have reported that association with delinquent peers is a leading cause of delinquency in the country (Bosiakoh and Andoh 2010). Bosiakoh and
Andoh (2010) investigated the role of differential association theory in juvenile delinquency among 60 inmates of the Ghana Borstal Institute. They found that adolescents who come into contact with delinquent peers over a long period are at a higher risk of becoming delinquents.
Lack of extended family support has also contributed to the rising juvenile delinquency in Ghana (Boakye 2012). Extended family in Ghana mostly consists of parents, children, and other family members from both the matrilineal and the patrilineal lineage, including grandparents, aunts, uncles, nieces, nephews, and cousins. Members of the extended family have financial and moral obligations beyond their nuclear families.
Therefore, these family members help to socialize children in the family (Nukunya 2003).
However, modernity, Western education, as well as the harsh economic system have compelled family members to relinquish the social support that they give to other family members (Dzramedo, Amoako, and Amos 2018). Lastly, some scholars have attributed the rising juvenile antisocial behavior to poverty.
50 Poor households tend to have low income and education and poor diet (Anim,
Mariwa, and Sebu 2012). Therefore, children from poverty-stricken homes have to work to augment the household income. This exposes them to money, which later makes it difficult for parents to exert control over them (Baffour and Abasss 2016).
Bullying Victimization in Ghana
Research on bullying victimization in Ghana has shown that it is rampant.
Empirical research has shown that the rate of bullying victimization ranges from 38 to 62 percent. For example, Owusu et al. (2011) investigated bullying victimization and psychological health among high school students in Ghana. They found that 40.1 percent of students had been victims of bullying. Similarly, the Global School-based Student
Health Survey (2013) found that 61.6 percent and 49.1 percent of junior (JHS) and senior high school (SHS) students, respectively, had experienced bullying in the past month.
Research on sex differences in bullying victimization have produced mixed results. Some studies have found that males are more likely to be victims, while others have reported that females are at a higher risk of encountering bullying. For instance, the GSSHS
(2012) found that 61.1 percent of males and 62.4 females in JHS as well as 47.3 percent males and 51.1 percent in SHS had experienced bullying. Owusu et al. (2011) also found that 41.2 percent of males and 38.8 percent of females had been victims of bullying.
Research on the types of bullying in Ghana is relatively scarce. However, the few existing studies seem to suggest that physical bullying is rampant in the country. For example, Antiri (2016) explored the types of bullying in Ghanaian senior high schools using a sample of 354 students. He reported that 47.5 percent of the students had experienced physical bullying, 37.2 percent had been victims of verbal bullying, and 8.1
51 percent had encountered social bullying. Kyere, Kumah, and Adutwum (2010)
investigated the effects of bullying on academic performance and personality in senior
high school. They found that verbal bullying was the most prevalent. Not many studies
have been done about cyberbullying in Ghana. Two studies have been conducted about
the subject (Sam, Bruce, Agyemang, Amponsah, and Arkorful 2017; Agbeko, and Kwaa-
Aidoo 2018). Using a sample of 844 students, Sam et al. (2017) reported that most of the
students had been victims of cyberbullying no less than once in the past 6 months.
Similarly, Agbeko, and Kwaa-Aidoo (2018) explored cyberbullying in Southern Ghana
using a sample of 233 teenagers. They found that the majority of the teenagers had
experienced cyberbullying in the form of framing and harassment.
Adolescent Health in Ghana
Adolescent health in Ghana has attracted the attention of many scholars. Research
on this subject has focused on both physical and mental health. Concerning physical
health, researchers have focused on adolescent mortality, lifestyle diseases, obesity, and
physical activities. Research on the cause of adolescent mortality in Ghana is rare. The
known literature about this subject was conducted by Ohene, Tettey, and Kumoji (2011)
using 14,034 Korle-bu Teaching Hospital autopsy reports from 2001 to 2003. They
reported that 7 percent (882) of the total deaths were adolescents. According to the
authors, 41.4 percent of adolescents died from communicable diseases, nutritional
disorders, and reproductive health diseases. The report further showed that non-
communicable diseases caused 41 percent of the deaths, and the remainder were ascribed
to injuries and other external causes.
52 Research on lifestyle diseases among adolescents have reported that cardiovascular diseases are relatively low among adolescents in Ghana. Ghana’s
Demographic and Health Survey (2015) reported that the prevalence of hypertension among adolescents was about 3.4 percent; however, the rate was higher for males (4.1%) than for females (3.2%). Research on obesity among teenagers in Ghana has shown that the illness is on the rise. Akowuah and Kobia-Acquah (2019) did a meta-analysis of adolescent obesity and overweight in Ghana using a sample of 16 studies. They found that about 19 percent of teenagers were either obese or overweight.
Some studies have also investigated the physical activity of adolescents in Ghana and have produced mixed results (Ocansey et al., 2016; Seidu et al., 2020). For instance,
Ocansey et al. (2016) investigated the physical activities of Ghanaian youth, finding that youth participation in physical activities was very low. In a recent study, Seidu et al.
(2020) also found that 25 percent of the participants were physically active. On the contrary, Nyawornota et al. (2013) investigated participation in physical activities and overweight among teenagers in Ghana. They found that 83 percent of the participants engaged in moderate to high-level physical activity.
Literature in Ghana has shown that mental illness is relatively high among adolescents. For example, Ghana’s Global School-based Student Health Survey (2012) reported that 22 percent of adolescents had considered attempting suicide, and 29.5 percent had actually attempted suicide. Similarly, Glozah and Pevalin (2016) investigated the relationship between psychosomatic symptoms and mental illness in Ghana using a sample of 770 adolescents. They found that 62 percent of the respondents reported moderate to severe mental disorders.
53 Bullying Victimization and Juvenile Delinquency in Ghana
Research on the relationship between bullying victimization and delinquency in
Ghana is scarce. Most of the available research on this subject matter tends to focus on deviance: only a single study has focused on delinquency. Harvey and Owusu (2014) investigated bullying victimization and substance abuse among Senior high school students in Ghana. Using the Ghana Global School-based Student Health Survey and a sample of 1,984 students, they found a strong association between bullying victimization and substance abuse. However, this study has a couple of shortcomings. First, the study focused only on senior high school students and ignored those in junior high school.
Although it is a fact that a lot of bullying victimization have been taking place in senior high schools, statistics also show that a significant amount of it takes place in Junior High
Schools. Second, the study did not investigate how the various types of bullying predict substance use.
Two studies have focused on the relationship between bullying victimization, mental health and suicidal behaviors. For example, Owusu et al. (2011) explored the relationship between bullying and mental disorders in Ghana. They used a sample of
7,137 from the 2008 Ghana Global School‐based Student Health Survey (GSHS). They found that victims of bullying victimization reported poor mental health than those who were not bullied. Nonetheless, this study has a couple of limitations. First, the findings of the study can only be generalized to high school students. Second, the authors did not analyze the types of the bullying (whether it was physical, verbal, social, or psychological). Third, the study did not provide a gender-disaggregated analysis of the relationship between the variables. Fourth, the data used were cross-sectional, which
54 makes it difficult to establish cause and effect. On the other hand, another study in Ghana has found that the relationship between bullying victimization and mental illness is not unidirectional but bidirectional (Arhin, Asante, Kugbey, and Oti-Boadi 2019). Using a sample of 198 students from Ghana, Arhin et al. (2019) found that mental health significantly predicted bullying victimization. Yet, the study also had a few shortcomings. First, the sample size was only 198, which makes it difficult to make generalizations. Second, the study was limited to the Greater Accra Region of Ghana.
Third, the data used were cross-sectional, so cause and effect cannot be established.
Other researchers have focused on the relationship between bullying victimization and suicidal ideation (Asante, Kugbey, Osafo, Quarshie, and Sarfo 2017; Baiden et al.
2019). Asante et al. (2017) investigated the predictors of suicidal attempts among adolescents in Ghana using a sample of 1,984 students. They found that bullying victimization was a significant predictor of suicidal ideation and attempts. In a recent study, Baiden et al. (2019) also found that victims of bullying victimization were more likely to have suicidal thoughts and attempt suicide.
Juvenile Justice in Ghana
During the precolonial era, there was no juvenile justice system in the country.
This is because the Gold Coast colony was not a country: it was rather a group of tribes and ethnic groups living in their own states. Also, each tribe or ethnic group did not have a separate justice system for adults and juveniles: they were treated in the same manner
(Ame 2018). At the time, crime or delinquency was seen as an offense against society and one’s ancestors. The main agents of social control within the societies were the nuclear family, the extended family, the council of elders, and the paramount chief of the
55 tribe. Sanctions were meant to promote unity in the community and appease the gods as well as the ancestors. The sanctions included pleading, animal sacrifice, and banishment from the community. The kind of sanctions given to a person depended on gravity of the offense (Ame 2018).
Once Ghana became a British colony, the colonial masters instituted the Western juvenile justice system. In the process, a handful of ordinances were enacted, which included the Courts (Amendment) Ordinance (1944), the Probation of Offenders
Ordinance (1944), the Courts Ordinance 1945, the Industrial Schools and Institutions
Ordinance (1945), and the Courts (Amendment) Ordinance (1946) (Ame 2018). The
Courts (Amendment) Ordinance (1944) created a separate justice system for juveniles and adults. This created a unique justice system that focuses on issues affecting teenagers. The Probation of Offenders Ordinance (1944) was concerned with probation and exoneration of suspects. It further outlines the circumstances under which offenders can be probated, the agencies of probation, as well as the responsibilities of probation officers. The Courts Ordinance (1945) enabled the governors of the colony to establish juvenile courts, assign judges to the juvenile courts, and hold hearings in different buildings on several days. The Industrial Schools and Institutions Ordinance (1945) authorized the institution of Borstal to remand homes and industrial schools. The ordinance further spelled out staff regulation and the training to be provided at the institutions. The Courts (Amendment) Ordinance (1946) focused on juveniles who needed care and protection but had not committed any offense (Ame 2018).
The current juvenile justice system in Ghana is built around the Juvenile Justice
Act of 2003. This act provides the legal basis for juvenile justice administration in the
56 country. The provisions in the act include the welfare principle, juveniles' rights, the
arrest of juveniles, juvenile court, and methods of dealing with juvenile offenders.
The welfare principle.
The Juvenile Justice Act 2003 defined a juvenile as a person under 18 years who is in conflict with the law. Such persons should be treated differently from adults.
According to the Act, the teenager's best interest should be of utmost importance with the juvenile courts and other institutions putting the juvenile’s interest first (Juvenile Justice
Act 2003).
Rights of juveniles.
According to the Act, juveniles have the right to privacy during arrest, investigation, and trial. No information concerning the juvenile will be divulged during arrest, investigation, or trial. People who violate the privacy or disclose the identity of juveniles will be convicted (Juvenile Justice Act 2003).
Arrest of juveniles.
Both police officers and private persons can arrest juveniles. The police can carry out the arrested by touching the body of the juvenile. Also, under some circumstances, reasonable force can be used in the process of arresting a juvenile. The arrest can be made with or without a warrant. An officer can arrest a teenager without a warrant if the offense is committed in his/her presence, the teenager impedes an officer in his/her duties, and the teenager tries to abscond from custody (Juvenile Justice Act 2003).
Magistrates of juvenile court may issue warrants of arrest on the basis of applications made by a police officer, a public prosecutor, or the attorney general. The arrest warrant shall spell out the offense, the jurisdiction of the presiding officer, and the
57 name of the officer. Following the arrest, the parents or guardians of the juvenile should be informed about the situation. If parents or guardians cannot be reached, the police have to inform the district probation officer. The district probation officer shall be responsible for identifying the juvenile's parents or guardians and inform them. After the juvenile is arrested, they shall be detained in areas of the police station separated from adults. In addition, males and females shall be detained in different places. The detained juveniles will have to be given sufficient food and medical treatment and allowed visitation from relatives (Juvenile Justice Act 2003).
Juvenile court.
Juvenile court trials shall occur in buildings separate from other courts and on different days. During the court hearings, except for officers of the court, parties of the case and people directly related to the case, no person shall be present. The juvenile court proceedings shall be informal, and the police officer shall not wear official uniform. The law requires the charge sheet to be in a language that is comprehensible to the accused juvenile. This will help the teenager to admit or deny the charge. The accused juvenile shall be granted bail unless he/she will pose a risk to the community. At the court, the juvenile has the right to remain silent, have parents or guardians at the proceedings, and legal representation or legal aid (Juvenile Justice Act 2003).
Methods of dealing with juvenile offenders.
After the court proceedings, the court might decide to exonerate the accused conditionally or unconditionally. In addition, the offender can also be released on probation or handed over to relative. Offenders can also be sent to a juvenile correctional
58 facility. Finally, some accused teenagers might be required to pay fines or damages
(Juvenile Justice Act 2003).
Agents of juvenile justice in Ghana
Currently, there are five institutions that deal with juvenile justice in Ghana: The
Department of Social Welfare; Ministry of Gender, Children and Social Protection;
Domestic Violence and Victims Support Unit; Judicial Service; and District Assemblies.
The Department of Social Welfare is charged with the duty of protecting all children in
the country. The department is required to establish juvenile remand and correctional
centers. The correctional centers are meant to rejuvenate offenders to reintegrate into
society, while the remand homes provide a temporary place for suspects before they face
trial (Hoffman and Baerg 2011).
The Ministry of Gender, Children and Social Protection is responsible for
ensuring the child's survival, protection, and development. This ministry is divided into
two sections: one for women, and the other for children. The ministry formulates policies
about the welfare of children and coordinates the activities of other agencies involved in
children’s welfare. The Domestic Violence and Victims Support Unit (DOVSU) deals
with any offense that is committed against women and children in the home. Also, they
are responsible for litigating juvenile cases. The Judicial Service is made up of the
Supreme Court, appeal courts, high courts, magistrate courts, and juvenile courts. The
juvenile section of the judicial service is mandated to deal with teenage cases. This
institution provides legal assistance and juvenile reintegration services. District
assemblies are responsible for linking up with other government agencies to protect the
rights of juveniles. In addition, each district is required to institute Child Panels and
59 promote their operation, along with interrogating cases of children’s rights violations
(Hoffman and Baerg 2011).
Summary
This section centered on existing research about bullying victimization, health, and delinquency in Ghana. The review about this subject showed that bullying victimization is rampant among adolescents in Ghana. Also, juvenile offending is rising among the youth in the country. Likewise, physical and mental health illness is high among adolescents in Ghana.
Although research in Ghana has shown that bullying victimization and health strains are high among adolescents, few studies have focused on how they are related to juvenile delinquency. Most of the empirical research about the subject mostly dwelt on how bullying victimization is related to substance use, mental health, and suicidal behaviors.
Besides, research on the relationship between types of bullying and delinquency is non- existent. More so, no study in Ghana has examined the sex differences in the association between bullying victimization and juvenile delinquency. Likewise, research on the relationship between health strains and delinquency is not available in Ghana.
60
CHAPTER IV
METHODOLOGY
Introduction
This dissertation investigates bullying victimization, health strains, and juvenile delinquency in Ghana. Previous studies on this topic have mostly used samples from developed countries. To the best of my knowledge, few studies have focused on this subject in Africa, including Ghana. This chapter deals with the data used for this study, the variables, and the data analysis method.
Data and Sampling
Data used in answering my research questions come from the Global School-
based Student Health Survey of 2012 (GSHS2012). The Global School-based Student
Health Survey (GSHS) was developed by the World Health Organization (WHO) and the
Centers for Disease Control and Prevention (CDC) in partnership with UNICEF,
UNESCO, and UNAIDS. The Global School-based Student Health Survey sought to
provide data about student health and protective factors for improving school and youth
health programs and policies in Ghana.
The 2012 Global School-based Student Health Survey in Ghana Senior High and
Junior High Schools was a school-based survey of students in Grades JHS 1 -3 and SHS
1-4. The typical student in JHS 1 -3 and SHS 1-4 in Ghana is aged between ages 13 and
17. So the survey includes the full range of teenagers. Cluster sampling design was used 61
to select all students in Grades JHS 1-3 and SHS 1-4 in Ghana from about 150 schools drawn from all the 10 regions existing at that time. At the initial stage, participating schools were chosen with the odds corresponding to size of enrollment.
Second, the classes were randomly chosen, and all the students in selected grades qualified to take part. The questions asked these selected students include alcohol usage, dietary practices, drug abuse, hygiene, psychological condition, physical exercise, protective factors, reproductive behaviors, cigarette usage, brutality, and inadvertent injury. Students provided their answers to the questions on sheets that could be scanned using a computer. The response rate was 74% for Senior High and 82% for Junior High.
A total of 3,632(1984 SHS and 1648 JHS) students participated in the Ghana GSHS.
The main strength of this survey is that it provides data that focus on the
Ghanaian context. For years, data on adolescent delinquency have been unavailable in both Ghana and many sub-Saharan African countries. Another strength is its representativeness. The data come from all the regions in Ghana, and the sample include students from different ethnic, religious, and socioeconomic background. Third, the data has most of the variables needed to answer the research questions posed in this study.
One weakness though of this data is that it lacks information on the backgrounds of the students; for example, their parents’ occupation, education, and income. Also, the data did not contain many items related to juvenile delinquency. Finally, the data had only three questions that can be used to measure mental health.
Dependent Variable
The dependent variable of interest in this study is delinquency. Juvenile delinquency can be measured or estimated using official statistics or reports from law
62 enforcement agencies as well as self-reported data. This study uses self-reported data to measure juvenile delinquency. While self-reports of juvenile delinquency provide useful information on the links between bullying victimization and delinquency; it needs to be pointed out that such self-reports are not without their critics. For example, some scholars have found that self-reported measures of delinquency are fraught with problems, including the measurement of delinquency across the life-course, testing effects, reliability, validity, and systematics bias (Hindelang, Hirschi and Weis, 1979; Huizinga and Elliot 1986; Jolliffe et al 2003; Krohn, Thornberry, Gibson, and Baldwin 2010).
Notwithstanding the latter challenges, and in the absence of detailed information on delinquency in Ghana, I use self-reported measure because it provides some account of delinquencies which are not captured by official data in Ghana.
Six items are used for my measure of delinquency: smoking of a cigarette, drinking of alcohol, smoking marijuana, amphetamine usage, truancy and involvement in a physical fight. Cigarette smoking and alcohol consumption were measured using the question: “During the past 30 days, how many days did you smoke a cigarette"? Alcohol is measured by the item: “During the past 30 days, how many days did you have at least one drink containing alcohol?” The original responses for the aforementioned items were,
0 days, 1 or 2 days, 3 to 5 days, 6 to 9 days, 10 to 19 days, 20 to 29 days, and All 30 days.
I recoded the items such that 0 days was coded as never and the remaining were recoded as ever used cigarette and alcohol. Marijuana use was measured using the item: “During the past 30 days, how many times have you used marijuana?” Amphetamine usage was also measured using the questions: “During your life, how many times have you used amphetamines or methamphetamines? The responses for the marijuana and amphetamine
63 were 0 times, 1 or 2 times, 3 to 9 times, 10 to 19 times, and 20 or more times. Zero days was recoded to never and the remaining were recoded to ever use marijuana and amphetamine. Physical fight was measured using the item: “During the past 12 months, how many times were you in a physical fight?” The original responses were: 0 times, 1 time, 2 or 3 times, 4 or 5 times, 6 or 7 times, 8 or 9 times, 10 or 11 times, and 12 or more times. This item was recoded so that zero times became never and the rest were coded as ever fought. Truancy in this study is measured with the item: “During the past 30 days, how many days did you miss classes or school without permission? The responses include “0 days”, “1 or 2 days”, “3 -5 days”, “6-9 days”, and “10 or more days. This item was coded so that “0 days” was coded as never and the remaining were coded as ever missed classes without permission. From these six items I created a delinquency index.
The delinquency index ranged from 0 to 6, where 0 means low or no delinquency and 6 represent high delinquency.
Independent Variables
One of the independent variables used in this study is bullying victimization.
Researchers have documented there are two ways of measuring bullying victimization: 1) self-reported and 2) third-party assessment. In this study, bullying victimization will be measured using self-reported data by students. A self-reported assessment will be used for four reasons. First, it captures the victim’s perspective, which is more likely to be accurate (Furlong et al. 2010). Second, victims may provide information about other forms of bullying not known by the researcher (Espelage and Swearer 2003). Third, the data used for the study did not collect information about third party assessment, making it
64 difficult for the study to include such a measure in the study. Finally, it is easy to collect data from many respondents at a cheaper cost (Griffin and Gross 2004).
In addition to the above, researchers also debate about the items that should be used in measuring bullying victimization. Some scholars contend that bullying victimization should be measured with a single item (Rossiter 2002; Solberg and Olweus
2003), but others support the use of multi-item scales (Peskin et al. 2006). The proponents of a single-item measurement contend that the use of a single item to measure bullying victimization can be more reliable and economical (Solberg and Olweus 2003).
In contrast, advocates of the multi-item measurement argue that a single-item cannot represent a complex construct (Peskin et al. 2006; Espelage and Holt, 2001).
Furthermore, they claim that having several items which measure bullying can provide a more valid and reliable measurement of bullying victimization (Espelage and Holt 2001;
Peskin et al. 2006). Felix et al. (2011) admonished researchers to decide on the measurement from their research objective.
In this research, I have used the multiple items thesis to measure bullying victimization. First, bullying victimization is measured using a single item. The variable was measured using the question: “During the past 30 days, on how many days were you bullied? The responses included: “0 days,” “1 or 2 days,” “3 to 5 days,” “6 to 9 days,”
“10 to 19 days,” “20 to 29 days,” “All 30 days.” The responses were recoded into an ordinal responses including never, one or two days, three to five days and six or more days.
Second, the various types of bullying are measured using a single item with multi- responses. The question used is: “During the past 30 days, how were you bullied most
65 often?” The responses included: “I was not bullied during the past 30 days,” “I was hit, kicked, pushed, shoved around, or locked indoors,” “I was made fun of because of my race, nationality, or color,” “I was made fun of because of my religion,” “I was made fun of with sexual jokes, comments, or gestures,” “I was left out of activities on purpose or completely ignored,” “I was made fun of because of how my body or face looks,” and “I was bullied in some other way.” Physical bullying is measured using the item: “I was hit, kicked, pushed, shoved around, or locked indoors.” Verbal bullying is determined using the following items: “I was made fun of because of my race, nationality, or color,” “I was made fun of because of my religion,” “I was made fun of with sexual jokes, comments, or gestures,” “I was made fun of because of how my body or face looks.”
Another independent variable used in this study health strains. In general, a look at the existing literature shows that two methods have been used to operationalize health:
1) objective measures and 2) subjective (self-reported) measures (Cleary 1997). Some scholars prefer to use subjective health measures because it provides accurate evaluation of objective states. In addition, this measure (subjective) is relatively cheap as compared to the objective measures. Furthermore, subjective health measures can be used to collect large data (Cleary 1997; Vaillant and Wolff 2012). Notwithstanding, self-reported health measures face have some weaknesses. First, this measure is highly subjective compared to objective measures. Also, self-assessed health measures result in a significant over or under estimation of health inequalities (Dowd and Todd 2011).
Some scholars though, advocate for the use of objective measures. To them, objective measures provides a richer measurement. This measure provides a richer measurement because it involves using tools (such as pedometers, glucose meter, heart
66 rate monitors) and direct observation by a medical staff (Welk 2002; Silfee et al. 2018).
In addition, objective measures tend to provide less biased data (Baker, Stabile and Deri
2001). Nevertheless, questions have been raised about the use of objective measures health measures. First, some objective measures derived from medical records are subject to measurement errors and biases. Second, some of the objective measure responses are based on subjective judgements (Cleary 1997).
For this study, the focus is on “self-reported health.” I use subjective health measures for the following reasons: first, several studies have shown that it is a robust predictor of juvenile delinquency such as fighting, running away, and marijuana use (See
Miauton, Narring, and Michaud 2003; Suris et al. 2008; Blum, Kelly, and Ireland 2001;
Oshima et al. 2010). Moreover, it is a valid and effective evaluation of objective conditions. According to some studies, subjective measures can also provide accurate and efficient assessments of objective states (Cleary, 1997).
Physical health conditions are measured using three items from the survey. First, respondents were asked: “During the past 12 months, how many times were you seriously injured?” The responses included: “0 times,” “1 time,” “2 or 3 times,” “4 or 5 times,” “6 or 7 times,” “8 or 9 times,” “10 or 11 times,” and “12 or more times.” The responses are recoded in such a way that “0 times” is coded as “None”, which means no injury; “1 time” is coded as “Once”, and the rest of the responses are coded as “Two or more.” The second item asked the following question: “During the past seven days, how many days were you physically active for a total of at least 60 minutes per day?” The responses encompass: “0 days,” “1 day,” “2 days,” “3 days,” “4 days,” “5 days,” “6 days,” and “7 days.” These responses are recoded so that “0 days” is coded as “None,” which means
67 they were not physically active; “1 day,” and “2 days,” are coded as “1-2days”; while the rest are coded “Two or more.” The third item also asked the following question:
“During this school year, on how many days did you go to physical education (PE) class each week?” Their responses were “0 days,” “1 day,” “2 days,” “3 days,” “4 days,” “5 or more days.” These responses are recoded so that “0 days” is coded as “Never,” which means the participants did not take part in physical education; “1 day” was coded as
“Once”; “2 days,” “3 days,” “4 days,” was coded as “2-4 days”; while the others was coded as “ 5 or more days”.
Mental health is measured using two items. The first asked the following question: “During the past 12 months, how often have you felt lonely?” Their responses were “Never,” “Rarely,” “Sometimes,” “Most of the time” and “Always.” The second item focuses on the (following) question: “During the past 12 months, how often have you been so worried about something that you could not sleep at night?” Their responses include “Never,” “Rarely,” “Sometimes,” “Most of the time,” and “Always.”
Control Variables
The extant literature has shown that a host of variables are relevant to our understanding of the bullying-delinquency relationship. For example, studies have found that gender is a significant predictor of delinquency. Studies have found for example that males are more likely to engage in delinquency behavior as compared to females
(Sigfusdottir et al. 2010; Unnever et al. 2008; Walters and Espelage 2017). Research have also found that age is significantly related to delinquency. Even so, the relationship between age and delinquency is curvilinear: it starts slowly at a young age, peaks at adolescence and plummets during young adulthood (Farrington 1986; Moffitt 1993;
68 Piquero et al. 2003; Stolzenberg and D’ Alessio 2008). Research on grade level and delinquency show that the relationship is positive, although the behavior reaches its zenith in higher grades (Lo, Kim, and Church 2008).
Based on the findings from previous studies, the study controls for the following variables: sex, age, and school grade level (JHS 1-3, SHS 1-3) in the models on the links between bullying and delinquency. Age is a continuous variable, and consisted of two cohorts (1994-1996 and 1997-2001)while sex is a dummy variable 1= male, 0= female).
Aside from the demographic variables, a measure of social bond which has also been found to predict delinquency in several studies (Chapple 2007; Craig 2015; Jenkins
1997), were included in my models. These are peer, school and parental bonds. For instance, Jenkins (1997) found that school bond is partially predicts school crime, and misconduct. Chapple (2007) also found that peer bonds influence delinquency behavior, such that bonding with deviant peers result in delinquent behavior. Concerning parental relationship, Craig (2015) found that adolescents who reported feeling closer to their parents were less likely to be delinquent.
Peer bond is measured using the item: “How many close friends do you have?”
The responses are 0, 1, 2, 3 or more. School bond will be measured using the item:
“During the past 30 days, how often were most of the students in your school kind and helpful?” The responses are Never, Rarely, Sometimes, Most of the time, and Always.
Both Peer and School bonds will be treated as categories in the analysis. Parental bonds are measured with three items: “During the past 30 days, how often did your parents or guardians check to see if your homework was done?” “During the past 30 days, how often did your parents or guardians understand your problems and worries?” “During the
69 past 30 days, how often did your parents or guardians really know what you were doing with your free time?” The responses are Never, Rarely, Sometimes, Most of the time, and
Always.
Analytic Strategy
The data for this study was analyzed using Stata (version 15.1). Both descriptive and multivariate techniques are used in the analyses. The analyses will be conducted at two levels or stages. In stage one, descriptive statistics such as means, proportions, and standard deviations will be used to describe the respondents and also look for variations and patterns. Cross-tabulations and Chi-square tests will be used to determine differences in bullying and health across groups for the categorical variables.
In stage two of the analysis, I used Pearson’s product moment correlation to establish a bivariate correlation among the variables. Negative Binomial regression are used to establish the relationship between bullying, health and delinquency. Negative
Binomial Regression were used because the dependent variable is positively skewed.
Seven main equations for this study are estimated using the negative binomial regression method. The first model evaluates the effects of bullying on delinquency. The second model examines effects of bullying victimization on delinquency for males and females.
The third model assesses the impact of types of bullying victimization on delinquency.
Model four focuses on physical health predicting juvenile offending. The fifth model analyzes the mental health predicting adolescent offending. Model six estimates the effect of physical on delinquency for males and females. The last model explores mental health predicting teenage offending for males and females. I estimated the reduced and full models for each of the main models. The seven models are as follows:
70 (H1) Bullying victimization will positively predict juvenile delinquency.
Delinquencyi = β0 + β1Bullyingvictimizationi + β2Sexi + β3Agei + β4Gradei+
β5Schoolbondi+ β6Peerbondi+ β7Parentalbondi + εi.
(H2) Bullied males will have a higher probability of reporting delinquency than females.
Delinquencymalei= β0 + β1Bullyingi + β2Agei + β3Gradei+ β4Schoolbondi+
β5Peerbondi+ β6Parentalbondi+ εi.
Delinquencyfemalei= β0 + β1Bullyingi + β2Agei + β3Gradei+ β4Schoolbondi+
β5Peerbondi+ β6Parentalbondi+ εi.
(H3) Type of bullying victimization will positively predict juvenile delinquency.
Delinquency = β0 + β1PhysicalBullyingi+ β2VerbalBullyingi + β3Sexi + β4Agei +
β5Gradei+ β6Schoolbondi+ β7Peerbondi+ β8Parentalbondi + εi.
a. Physical bullying will be positively associated with delinquency
Delinquency = β0 + β1PhysicalBullyingi+ β2Sexi + β3Agei + β4Gradei+
β5Schoolbondi+ β6Peerbondi+ β7Parentalbondi + εi.
b. Verbal bullying will be positively associated with delinquency
Delinquency = β0 + β1VerbalBullyingi+ β2Sexi + β3Agei + β4Gradei+
β5Schoolbondi+ β6Peerbondi+ β7Parentalbondi + εi.
c. Males who suffer from physical bullying will report more delinquency
behaviors than females who are bullied.
Delinquency = β0 + β1PhysicalBullyingi+ β2Agei + β3Gradei+
β4Schoolbondi+ β5Peerbondi+ β6Parentalbondi + εi.
71 d. Females who experience verbal will report more offending behaviors than
males
Delinquency = β0 + β1VerbalBullyingi + β2Agei + β3Gradei+
β4Schoolbondi+ β4Peerbondi+ β5Parentalbondi + εi.
(H4) Physical health strains will positively be associated with delinquency
Delinquencyi= β0 + β1 PhysicalHealth + β2Sexi + β3Agei + β4Gradei+
β5Schoolbondi+ β6Peerbondi+ β7Parentalbondi+ εi.
(H5) Mental health strains will positively predict delinquency
Delinquencyi= β0 + β1 MentalHealth + β2Sexi + β3Agei + β4Gradei+
β5Schoolbondi+ β6Peerbondi+ β7Parentalbondi+ εi.
(H6) Females who experience physical health strain will report less offending behaviors than males.
Delinquencyi= β0 + β1 MentalHealth + β2Agei + β3Gradei+ β4Schoolbondi+
β5Peerbondi+ β6Parentalbondi+ εi.
(H7) Males who suffer from mental health disorders will report more delinquency behaviors than females.
Delinquencyi= β0 + β1 PhysicalHealth + β2Agei + β3Gradei+ β4Schoolbondi+
β5Peerbondi+ β6Parentalbondi+ εi.
Missing Data
Some of the variables used in the study have a couple of cases missing. I dealt with the missing cases by using Multivariate imputation by chained equations (MICE).
MICE is known to generate results that have better standard errors compared to other methods. Besides, the methods impute missing cases multiple times (Schafer and
72 Graham, 2002). Finally, this method is capable of accommodating various types of variables including continuous, binary, categorical and ordered. A drawback of this method of dealing with missing data is that the researcher needs to be mindful of modelling of conditional specifications. Furthermore, convergence is vital when using
MICE to analyze data which contain missing cases (White et al. 2011). The percentage of missing cases and imputations are presented in table 2.
Table 2: Missing Data Variable Complete Missing cases (%) Imputed Total Sex 3594 1.05 38 3632 Age 3613 0.52 19 3632 Grade level 3617 0.41 15 3632 Peer bond 3587 1.24 45 3632 School bond 3574 1.6 58 3632 parental bond: Parents check homework 3593 1.07 39 3632 Parents understand 3566 1.82 66 3632 child’s troubles Parental know child’s 3499 3.66 133 3632 use of free time Mental health Loneliness 3618 0.39 14 3632 Sleeplessness 3617 0.41 15 3632 Physical health Physically active 3594 1.05 38 3632 Physical Injury 3092 14.87 540 3632 Physical education 3541 2.51 91 3632 classes Bullying victimization 3292 9.36 340 3632 Types of bullying victimization: Physical bullying 3330 8.31 302 3632 Verbal bullying 3330 8.31 302 3632 Delinquency 2918 19.66 714 3632
73
CHAPTER V
RESULTS
Introduction
This chapter presents the results from the analysis of bullying victimization, health strains, and juvenile delinquency in Ghana. The analysis is grouped into three sections. The first section focuses on the descriptive statistics of the variables used in this research. The second presents the findings of the bivariate analysis of the study. The third examines the multivariate analysis of the relationship among bullying victimization, health strains, and juvenile antisocial behavior in Ghana.
Descriptive Statistics
Table 3 presents the descriptive statistics of the participants. Most of the participants were males (53.8%), and the mean age was 16 years. The mode grade level was JHS 1 (18.4%), while the mean delinquency index was 0.9. These results imply that on a delinquency index ranging from 0(no delinquency) to 6(high delinquency), the average delinquency behavior index is 0.9. More than half (59.28%) of the respondents had been bullied at least once in the past 30 days. Specifically, 11.71 percent had experienced physical bullying, and 18.9 percent had been exposed to verbal bullying.
Regarding mental health, the mean score for sleeplessness was 1.43, while the average score for the feeling of loneliness was 1.57. These results connote that the average participant in the study rarely experienced sleeplessness and sometimes felt lonely. 74
Table 3: Descriptive Statistics for the Total Sample Variables Mean/Proportion S.D Min Max Valid N Sex 0 1 3,594 Male 53.8 Female 46.2 Age 16 1.94 11 18 3613 Grade Level 1 7 3617 JHS 1 18.4 JHS 2 15.2 JHS 3 11.9 SHS 1 16 SHS 2 10.7 SHS 3 17.1 SHS 4 10.7 Peer bond 1.7 1.03 0 3 3587 School bond 2.1 1.26 0 4 3574 Parental bond Parents check homework 2.09 1.6 0 4 3593 Parents understand child’s troubles 2.12 1.4 0 4 3566 Parental know what the child does 2.05 4.5 0 4 3499 Bullying 0 3 3292 Never 40.72 One or two days 32.64 Three to five days 12.13 Six or More days 14.51 Types of Bullying Physical 0 1 3330 Yes 11.71 No 88.29 Verbal 0 1 3330 Yes 18.59 No 81.14 Mental Health Loneliness 1.57 1.17 0 4 3618 Sleeplessness 1.43 1.17 0 4 3617 Physical Health 0 2 35.94 Physically active Never 30.63 1-2days 33.61 More than 2 days 35.75 Injury 0 2 3092 Never 37.32 1-2days 31.99 More than 2 days 30.69 Participation in physical education 0 2 3541 Never 28.78 Once 30.19 2-4 days 24.85 More than 4 16.18 Delinquency 0.98 1.16 0 6 2918 75 Concerning physical health, the overwhelming majority had been physically active, and had participated in physical education class no less than once within the past
30 days. Again, most participants had experienced a bodily injury at least once in the last
30 days. Also, the average school bond was 2.1, whereas the mean peer bond was 1.7.
Finally, the mean parental bond are as follows: Parents check homework (x̄ =2.09),
Parents understand their child’s troubles (x̄ =2.12), and Parents know their child’s use of free time (x̄ =2.05). These outcomes imply that parents sometimes checked their wards’ homework, understood their troubles, and knew what they did with their free time.
Cross-tabulation of Sociodemographic and Bullying Victimization
Table 4 shows the cross-tabulation of sex, cohort, education level, and bullying.
Bullying victimization was prevalent among females than among males. Despite that, there was no significant association between sex (X2= 3.6, p > 0.05) and bullying.
Approximately 39% of the 1997–2001 cohort members had never been victims of bullying compared to 53.6% of the 1994–1996 cohort. The chi-square analysis revealed a statically significant association between cohorts (X2= 70.2, p > 0.001) and bullying victimization. In terms of educational level, bullying victimization was common among junior high school students (62.2%) than senior high school (43.9%). The results further show that educational level (X2= 117.0, p > 0.001) and bullying victimization are significantly associated with each other.
The table further shows that 89.8% of females did not been victims physical bullying than 86.9% of males, and verbal bullying was extensive among females (20.6) than males (16.9). Sex was significantly associated with both physical (X2= 7.0, p >
0.001) and verbal bullying victimization (X2 = 7.8, p > 0.001). About 90.4% of the 1994–
76 Table 4: Cross-tabulation of Sociodemographic and Bullying Victimization Variables Sex Cohort Education level
Female Male X2 Total P 1997- 1994- X2 Total P JHS SHS X2 Total P 2001 1996 (%) (%) (%) (%) (%) (%) (%) (%) Bullying Victimization Never 46.4 49.4 3.6 48.0 0.3 38.6 53.6 70.2 48.0 0.0 37.8 56.1 117.0 48.0 0.0
One or two days 29.3 28.0 28.6 34.7 25.0 28.6 36.4 22.6 28.7
Three to five days 11.5 10.1 10.7 12.3 10.0 10.8 12.0 9.7 10.7
Six or More days 12.9 12.5 12.7 14.4 11.5 12.6 13.8 11.6 12.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Types of bullying
Physical bullying No 89.8 86.9 7.0 88.2 0.0 84.6 90.4 25.4 88.2 0.0 84.5 91.2 35.8 88.2 0.0
Yes 10.2 13.1 11.8 15.4 9.6 11.8 15.5 8.8 11.8
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Verbal bullying No 79.4 83.2 7.8 81.4 0.0 79.1 82.7 6.9 81.4 0.0 78.1 84.2 20.2 81.5 0.0
Yes 20.6 16.9 18.6 20.9 17.3 18.7 21.9 15.8 18.5
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 *p < .05 **p< .01 ***p < .001
77
1996 cohort had never experienced physical bullying compared to 84.6% of the 1997-
2001 cohorts. Also, 20.9% of the 1997-2001 cohorts had experienced verbal bullying compared to 17.3% of the 1994–1996 cohort. The table further shows that cohort was significantly associated with both physical (X2 = 25.4, p > 0.001) and verbal bullying victimization (X2= 6.9, p > 0.001). Concerning educational levels, physical bullying was more common among students in junior high school (15.5) than senior high school (8.8).
Also, verbal bullying was frequent for junior high school students (21.9) than senior high school students (15.8). The educational level was significantly associated with both physical (X2= 35.8, p > 0.001) and verbal bullying victimization (X2 = 20.2, p > 0.001).
Cross-tabulation of Sociodemographic and Health Variables
Table 5 shows the cross-tabulation of sociodemographic and health variables. For physical health, the results reveal that 33.9% of females had never been active within the past thirty days than 27.9% of males. Also, 63.8% of females had suffered physical injuries than 61.4% of males. Likewise, 28.9% of males had never participated in physical education class for the past one month compared to 28.7% of females.
Concerning mental health, 74.2% of females had at least rarely experienced loneliness than 69.2% of males. Similarly, 68.8% of females at a minimum rarely experienced sleeplessness compared to 64.4% of males. Sex was significantly associated with being physically active (X2 = 18.1, p > 0.001) and all the measures of mental health {loneliness
(X2 = 11.9, p > 0.001), sleeplessness (X2 = 10.7, p > 0.001)}.
78
Table 5: Cross-tabulation of Sociodemographic and Health Variables Variables Sex Cohort Education level Female Male X2 Total P 1997- 1994- X2 Total P JHS SHS X2 Total P 2001 1996 (%) (%) (%) (%) (%) (%) (%) (%) (%) Physical Health Physically active Never 33.9 27.9 18.1 30.7 0.00 36.6 26.8 38.5 30.5 0.0 37.4 24.9 85.9 30.6 0.0 1-2days 33.4 33.6 33.5 31.5 34.9 33.6 33.8 33.4 33.6 More than 2 days 32.8 38.5 35.8 31.9 38.3 35.9 28.8 41.7 35.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Physical Injury Never 36.2 38.6 2.9 37.5 0.2 31.3 41.0 29.5 37.3 0.0 27.7 45.1 99.7 37.3 0.0 1-2days 33.4 30.7 31.9 35.6 29.8 32.0 36.2 28.7 32.1 More than 2 days 30.4 30.8 30.6 33.1 29.2 30.7 36.1 26.2 30.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Physical Education Never 28.7 28.9 1.1 28.8 0.8 29.4 28.1 21.5 28.6 0.0 29.0 28.6 31.2 28.8 0.0 Once 30.0 30.4 30.2 25.8 33.0 30.3 25.9 33.8 30.2 2-4 days 25.5 24.0 24.7 27.0 23.6 24.9 26.9 23.2 24.9 More than 4 15.8 16.7 16.3 17.7 15.3 16.2 18.3 14.4 16.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Mental Health Loneliness Never 25.8 30.8 11.9 28.5 0.0 35.2 24.2 76.8 28.4 0.0 36.6 21.6 142.9 28.4 0.0 Rarely 8.1 8.1 8.1 10.3 6.8 8.1 10.3 6.4 8.1 Sometimes 49.4 45.6 47.4 40.7 51.5 47.4 39.9 53.7 47.4 Most of the time 10.7 9.5 10.1 8.4 11.1 10.0 7.5 12.2 10.0 Always 6.0 6.1 6.1 5.4 6.5 6.1 5.9 6.2 6.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Sleeplessness Never 31.2 35.6 10.7 33.5 0.0 41.9 28.3 99.5 33.5 0.0 42.0 26.3 131.1 33.5 0.0 Rarely 8.8 9.8 9.4 10.9 8.5 9.4 10.8 8.5 9.6 Sometimes 44.8 40.5 42.5 34.9 47.1 42.5 34.0 49.3 42.4 Most of the time 10.7 10.0 10.3 7.6 12.1 10.3 8.5 11.9 10.3 Always 4.5 4.1 4.3 4.7 4.1 4.3 4.7 4.0 4.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 *p < .05 **p< .01 ***p < .001
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The table shows that physical health strains were frequent among the 1997-2001 cohort than the 1994-1996 cohort. About 36.6% of the 1997-2001 cohort had never been physically active within the past 30 days than 26.8% of the 1994-1996 cohort. Also, physical injuries were prevalent among the 1997-2001 cohort (68.7%) than the 1994-
1996 cohort (59%). Approximately 29% of the 1997-2001 cohort had never participated in physical education classes compared to 28.1% of the 1994-1996 cohort. For mental health, 75.8% of the 1994-1996 cohort had at minimum rarely experienced loneliness compared to 64.8% of the 1997-2001 cohort. Lastly, 71.7% of the 1994-1996 cohort had at least rarely experienced sleeplessness compared to 58.1% of the 1997-2001 cohort.
Cohort was significantly associated with all the measures of physical {physically active
(X2 = 38.5, p > 0.001), physical injury (X2 = 29.5, p > 0.001), and physical education (X2
= 21.5, p > 0.001)} and mental health {loneliness (X2 = 76.8, p > 0.001) and sleeplessness (X2 = 99.5, p > 0.001)}.
Regarding level education, the table reveals that for physical health, 37.4% of students in JHS had never been physically active within the last 30 days compared to
24.9% of SHS students. Also, physical injuries were widespread among JHS students
(72.3%) than SHS students (54.9%). Besides, 29.0% of JHS students had never participated in physical education class than 28.6% of SHS students. For mental health,
78.4% of SHS students had at least rarely experienced loneliness than 63.4% of JHS students. Besides, 73.7% of the SHS students had at minimum experienced sleeplessness than 58% of JHS students. Finally, education level was significantly associated to all the measures of physical {physically active (X2 = 85.9, p > 0.001), physical injury (X2 = 99.7,
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p > 0.001), and physical education (X2 = 142.9, p > 0.001)} and mental health {loneliness
(X2 = 76.8, p > 0.001) and sleeplessness (X2 = 131.1, p > 0.001)}.
Bivariate Correlation among the Variables
Table 6 presents the bivariate correlation among the variables used in the study.
The dependent variable, juvenile delinquency, was positively associated with bullying victimization and health strains. Nevertheless, it was inversely related to most of the demographic and social bond variables. Specifically, juvenile delinquency had a very weak negative relationship with age, grade level, peer bond, school bond, parent check homework, parents understand child’s troubles and parental knowledge of child’s use of free time. However, delinquency had a weak positive relationship with bullying victimization and physical injury. Similarly, delinquency had a very weak positive association with physical bullying, verbal bullying, loneliness, sleeplessness, physically active, and physical education.
Bullying victimization was positively related to health strains, but was negatively linked to demographic and social bond variables. For instance, bullying victimization had a very weak inverse relationship with age, grade level, and school bond. On the other hand, being victimized had a weak positive relationship with physical bullying, verbal bullying, and physical injury.
Bullying Victimization and Juvenile Delinquency
After exploring the bivariate relationship among the variables used in the study, I now focus on the effect of bullying victimization on juvenile delinquency in Ghana. My first hypotheses states that: (H1) bullying victimization will positively predict juvenile delinquency.
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Table 6 Bivariate Correlation among the Variables Variables 1 2 3 4 5 6 7 8 Age 1 Sex 0.071 *** 1.000 Grade level 0.742 *** 0.013 1.000 Peer bond -0.096 *** 0.171 *** -0.055 *** 1.000 School bond 0.059 *** 0.020 0.121 *** 0.112 *** 1.000 Parental bond: Parents check homework -0.095 *** -0.039 * -0.030 0.028 0.226 *** 1.000 Parents understand child’s troubles 0.064 *** -0.001 0.154 *** 0.066 *** 0.248 *** 0.397 *** 1.000 Parents know child’s use of free time -0.016 -0.063 *** 0.049 ** 0.038 * 0.220 *** 0.455 *** 0.433 *** 1.000 Juvenile Delinquency -0.089 *** 0.021 -0.116 *** 0.079 *** -0.069 *** -0.049 ** -0.080 *** -0.073 *** Bullying victimization -0.123 *** -0.025 -0.177 *** 0.029 -0.059 * 0.012 -0.024 0.004 Types of bullying victimization Physical bullying -0.096 *** 0.045 ** -0.109 *** -0.001 -0.043 0.018 0.004 0.019 Verbal bullying -0.058 *** -0.049 ** -0.093 *** 0.003 -0.014 0.052 ** -0.001 -0.015 Mental Health Loneliness 0.143 *** -0.047 ** 0.163 *** -0.031 0.014 -0.006 0.005 0.013 Sleeplessness 0.156 *** -0.048 ** 0.156 *** -0.023 0.004 -0.011 -0.002 0.014 Physical Health: Physically active 0.113 *** 0.071 *** 0.149 *** 0.064 *** 0.118 *** 0.003 0.097 *** 0.080 *** Physical Injury -0.103 *** -0.012 -0.168 *** 0.028 -0.066 -0.006 -0.061 *** -0.024 Physical education -0.031 0.001 -0.037 * 0.024 0.086 0.129 *** 0.061 *** 0.076 ***
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Table 4 Bivariate Correlation among the Variables (Continuation) Variables 9 10 11 12 13 14 15 16 17 Juvenile Delinquency 1.000 Bullying victimization 0.306 *** 1.000 Types of bullying victimization Physical bullying 0.100 *** 0.270 *** 1.000 Verbal bullying 0.186 *** 0.385 *** -0.174 *** 1.000 Mental Health Loneliness 0.094 *** 0.141 *** 0.047 ** 0.068 *** 1.000 Sleeplessness 0.133 *** 0.137 *** 0.070 *** 0.089 *** 0.280 *** 1.000 Physical Health: Physically active 0.054 ** 0.013 -0.009 0.007 0.082 *** 0.021 1.000 Physical Injury 0.318 *** 0.348 *** 0.129 *** 0.233 *** 0.105 *** 0.173 *** -0.026 1.000 Physical education 0.090 *** 0.065 *** 0.033 0.064 *** 0.060 *** 0.049 *** 0.140 *** 0.076 *** 1.000 *p < .05 **p< .01 ***p < .001 KEY 1. Age 10. Bullying victimization 2. Gender 11. Physical bullying victimization 3. Grade level 12. Verbal bullying victimization 4. Peer bond 13. Loneliness 5. School bond 14. Sleeplessness 6. Parents check homework 15. Physically active 7. Parents understand child’s troubles 16. Physical injury 8. Parents know child’s use of free time 17. Participation in physical education 9. Juvenile delinquency
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Table 7 shows the negative binomial regression of bullying victimization predicting juvenile delinquency. In the baseline model (model 1), when an adolescent is bullied 1 or
2 days, 3–5 days, and 6 or more days, the expected log count of delinquency increase by
0.441, 0.621 and 0.928 units, respectively, compared to those who were never bullied.
Table 7: Negative Binomial Regression of Bullying Victimization Predicting Delinquency (N=3632) Variables Model 1 Model 2
b SE b SE Sex 0.028 0.042 Age 0.002 0.014 Grade level (Ref: JHS 1) JHS 2 0.107 0.057 JHS 3 0.111 0.065 SHS 1 -0.489 *** 0.075 SHS 2 -0.302 *** 0.082 SHS 3 -0.124 0.077 SHS 4 -0.024 0.088 Peer bond 0.081 *** 0.019 School bond -0.029 0.015 Parental bond Parents check homework -0.013 0.013 Parents understand child’s -0.023 0.016 troubles Parental know child’s use of -0.032 * 0.015 free time Bullying Victimization (Ref: never) One or two days 0.441 *** 0.046 0.427 *** 0.046 Three to five days 0.621 *** 0.065 0.63 *** 0.067 Six or More days 0.928 *** 0.054 0.937 *** 0.054 Constant -0.327 0.032 -0.226 0.091 F-Test F(3, 392.8) = 96.25*** F(16, 3764.6) = 29.48*** *p < .05 **p< .01 ***p < .001
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Model 2 explores how bullying victimization predicts juvenile delinquency, while controlling for sociodemographic and social bond variables. The results show that If a juvenile experience bullying victimization for 1 or 2 days, 3–5 days, and 6 or more days, the expected log count of delinquency increase by 0.427, 0.630, 0.937 units, respectively, than never bullied adolescents, after holding other variables constant. This finding supports the first hypothesis. These results imply that respondents who were bullied were more likely to exhibit delinquency behaviors than those who were not bullied.
Surprisingly, the results further show that being in SHS1 and SHS2 reduces the expected log count of delinquency by -0.489 and -0.302, respectively. The results further reveal that a unit increase in peer bond reduces the expected log count of delinquency by 0.032.
Sex, Bullying Victimization and Delinquency
This section of the analysis is aimed at testing the third hypothesis of this study which states that (H2) bullied males will have a higher probability of reporting delinquency than females. The sex variations in the association between bullying victimization and juvenile delinquency is also presented in table 8. The results in model 1 show that for males who are bullied 1 or 2 days, 3–5 days, and 6 or more days, the expected log count of delinquency increase by 0.452, 0.583 and 0.896 units, respectively, compared to those who were never bullied. Also, the expected log count of delinquency increases by 0.425, 0.617, and 0.918 units for females who are victimized for 1 or 2 days,
3–5 days, and 6 or more days, respectively, than those who were not bullied.
The results in model 2 were not different from those of model 1. Holding, sex, age, grade level, school bond, and peer bonds constant, for females who encountered bullying for 1 or 2 days, 3–5 days, and 6 or more days, the expected log count of
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Table 8: Sex Differences in the Relationship between Bullying Victimization and Juvenile Delinquency Variables Male (N = 1970) Female (N = 1662) Model 1 Model 2 Model1 Model 2 b SE b SE b SE b SE Age -0.013 0.019 0.029 0.020 Grade level JHS2 0.150 0.084 0.144 0.086 JHS3 0.197 * 0.092 0.113 0.091 SHS1 -0.258 ** 0.100 -0.714 *** 0.111 SHS2 -0.177 0.114 -0.441 *** 0.133 SHS3 -0.051 0.104 -0.175 0.122 SHS4 0.067 0.119 -0.086 0.136 Peer bond 0.048 0.026 0.140 *** 0.032 School bond -0.051 * 0.023 -0.009 0.025 Parental bond Parents check homework -0.012 0.019 -0.035 0.019 Parents understand child’s troubles -0.024 0.020 -0.015 0.024 Parents know what the child’s use of free time -0.048 * 0.021 0.002 0.023 Bullying victimization (ref: never) One or two days 0.452 *** 0.062 0.425 *** 0.063 0.435 *** 0.073 0.424 *** 0.072 Three to five days 0.583 *** 0.085 0.571 *** 0.086 0.617 *** 0.090 0.647 *** 0.085 Six or more days 0.896 *** 0.075 0.887 *** 0.076 0.918 *** 0.079 0.925 *** 0.079 Constant -0.281 0.040 -0.016 0.131 -0.354 0.047 -0.505 0.134 F-Test F(3, 330.5)= 53.54*** F(15,2674.7)= 16.27*** F(3,456.0)= 46.08*** F(15, 3060.3)=18.16*** *p < .05 **p< .01 ***p < .001
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delinquency increased by 0.424, 0.647 and 0.925 units, respectively, as compared to juveniles who were never bullied. Similarly, the expected log count for offending behavior increases by 0.425, 0.571 and 0.887 for adolescent males who were victims of bullying for 1 or 2 days, 3–5 days, and 6 or more days, respectively, as compared to juveniles who were not bullied, after holder all other variables constant.
The results further show the expected log count of delinquency reduces by -0.714 and -0.441 units for SHS1 and SHS2 females respectively, whiles being a male student in
SHS1 reduces the expected log count of offending behavior for males by 0.258 units.
Moreover, a unit increase in school bond and parental knowledge of child’s use of free time reduced the expected log counts of offending behavior by -0.051 and 0.048 units, respectively, for males. On the other hand, the expected log counts of delinquency for females increases by 0.140 units for every unit increase in peer bond.
Table 9: T-Test for Differences in Effects of Bullying Victimization for Males and Females Variables df t p Bullying Victimization One or two days 3,628 0.010 0.992 Three to five days 3,628 0.629 0.530 Six or more days 3,628 0.347 0.729
Although the regression coefficients of bullying victimization predicting delinquency is greater than that of males, the t-statistics in table 9 show that the differences in the regression coefficients are not statistically significant. Therefore, I conclude that the effect of bullying victimization on delinquency is similar for males and females. This finding does not support my third hypothesis that (H2) that bullied males are likely to engage in delinquency than females.
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Types of Bullying Victimization and Juvenile Delinquency
In my introduction and literature review, I argued that research on the relationship between bullying types and their relationship with delinquency had produced inconsistent results. Hence, I hypothesized that (H3) the types of bullying victimization will significantly predict juvenile delinquency. Specifically, physical bullying (H3a) and verbal bullying (H3b) will positively predict teenage offending behaviors. The results for the types of bullying predicting juvenile delinquency are presented in table 10. In model
1, the results show that being a victim of physical and verbal bullying increases the expected log count of offending behavior by 0.478 and 0.586 units, respectively, compared to those who are never bullied. Model 2 displays the types of bullying predicting juvenile antisocial behaviors after controlling for sociodemographic and social bond variables. The results indicate that the expected log counts of delinquency behavior increases by 0.460 and 0.590 for adolescents who are physically and verbally bullied, respectively, compared to those who are not victimized. The latter outcomes support my hypothesis that types of bullying victimization (physical and verbal) will positively predict teenage offending. The results from the table further reveal that being in SHS1,
SHS2 and SHS3 reduces the expected log count of delinquency by -0.475, -0.322 and -
0.188 units, sequentially. Furthermore, a unit increase in peer bonds increases the expected log count of delinquency by 0.089 units, while a unit increase in school bond reduced the expected log count of adolescent offending by -0.038 units.
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Table 10: Types of Bullying predicting Juvenile Delinquency (N= 3632) Model 1 Model 2 Variables b SE b SE Sex 0.025 0.043 Age 0.004 0.015 Grade level(Ref: JHS 1) JHS 2 0.132 * 0.059 JHS 3 0.106 0.067 SHS 1 -0.475 *** 0.077 SHS 2 -0.322 *** 0.084 SHS 3 -0.188 * 0.079 SHS 4 -0.112 0.090 Peer bond 0.089 *** 0.020 School bond -0.038 ** 0.016 Parental bond: Parents check homework -0.025 0.013 Parents understand child’s troubles -0.024 0.016 Parental know child’s use of free time -0.024 0.015 Types of bullying victimization Physical bullying 0.478 *** 0.061 0.460 *** 0.059 Verbal bullying 0.586 *** 0.048 0.590 *** 0.049 Constant -0.190 0.030 -0.079 0.092 F-Test F(2, 388.6)=75.34*** F(15, 4529.8)= 20.61*** *p < .05 **p< .01 ***p < .001
Sex, Types of Bullying Victimization, and Delinquency
This section of the analysis focuses on the sex differences in the association between types of bullying victimization and juvenile delinquency. I will test two hypotheses in this section. The first states that, (3c) male victims of physical bullying are more likely to become delinquents. The second states that (H3d) female victims of verbal bullying are at a higher risk of involving in juvenile offending. The results from the analysis are presented in Table 11.
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Table 11: Sex, Types of Bullying and Delinquency Variables Male (N= 1970) Female (N= 1662) Model 1 Model 2 Model 1 Model 2 b SE b SE b SE b SE Age -0.004 0.02 0.028 0.02 Grade level JHS2 0.179 * 0.086 0.146 0.09 JHS3 0.163 0.092 0.132 0.094 SHS1 -0.27 ** 0.102 -0.683 *** 0.115 SHS2 -0.221 0.116 -0.444 *** 0.135 SHS3 -0.16 0.104 -0.187 0.126 SHS4 -0.03 0.119 -0.177 0.14 Peer bond 0.053 * 0.026 0.153 *** 0.032 School bond -0.056 * 0.024 -0.019 0.025 Parental bond Parents check homework -0.019 0.019 -0.049 * 0.02 Parents understand child’s troubles -0.025 0.02 -0.018 0.025 Parental know what the child does -0.045 * 0.021 0.015 0.025 with free time Types of bullying victimization Physical bullying 0.425 *** 0.072 0.422 *** 0.07 0.524 *** 0.093 0.485 *** 0.092 Verbal bullying 0.592 *** 0.065 0.573 *** 0.064 0.558 *** 0.072 0.582 *** 0.071 Constant -0.156 0.034 0.087 0.129 -0.2 0.04 -0.351 0.132 F-Test F(2, 466.2)= 39.94*** F(14, 3375.5)=11.01*** F(2, 141.6)= 33.40*** F(14, 2460.2)=13.31*** *p < .05 **p< .01 ***p < .001
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The baseline model (model 1) results show that being a male victim of physical and verbal bullying increases the expected log count of delinquency by 0.425 and 0.592 units, respectively, compared to non-victims. Also, the expected log counts for offending behavior increases by 0.524 and 0.558 units for females who are bullied physically and verbally, respectively, as compared to those who were never bullied. Model 2 presents the types of bullying predicting antisocial behavior after controlling for demographic variables and social bonds. The results show that physical bullying victimization increases the expected log counts of offending behavior by 0.422 and 0.485 units for males and females respectively, as compared to those who are never bullied. Also, verbal bullying victimization increases the expected log counts of juvenile delinquency by 0.573 and 0.582 units for males and females, respectively, compared to the non-bullied.
Aside from the types of bullying predicting delinquency, the data further reveals that being in SHS1 and SHS2 reduced the expected log count of delinquency by -0.683 and -0.444 units for females. Besides, a unit increase in peer bonds increases the expected log counts of delinquency by 0.053 and 0.153 units for males and females, respectively.
More so, a unit increase in school bond and parental knowledge of their child’s use of free time reduces the expected log counts of offending behavior by -0.056 and -0.045 units, respectively, for males. For females, the results show that parental knowledge of their child’s use of free time reduces the expected log counts of delinquency behavior by
-0.049 units.
Table 12: T-Test for Differences in Effects of Physical and Verbal Bullying Victimization on Delinquency for males and females Types of bullying df t p Physical bullying 3,628.000 0.545 0.586 Verbal bullying 3,628.000 0.094 0.925
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Even though the regression coefficient for females is greater than that of males, the t-test in table 12 shows that the difference is not significant. On the basis of the statistical test in table 9, I concluded that the effect of physical and verbal bullying victimization on juvenile delinquency is similar for males and females. The finding does not support the hypothesis that male victims of physical bullying are more likely to report delinquency than females. Also, the findings do not support the hypothesis that females victims of verbal bullying victimization are more likely to engage in delinquency behaviors than males.
Physical Health and Juvenile Delinquency
This section focuses on physical health predicting juvenile delinquency among adolescents in Ghana. I used negative binomial regression to test my hypothesis (H4), which states that: physical health strains will be positively associated with delinquency.
The findings from the analysis are presented in table 13. The results from model 1 in table
13 show that being physically active for 1–2 days and 3 or more days increases the expected log count of delinquency by 0.177 and 0.137 units respectively, as compared to adolescents who were never active. Also, experiencing bodily injury for 1–2 days and 3 or more days increases the expected log counts of offending behavior by 0.492 and 0.846 units respectively, than the non-injured. The expected log counts of delinquency increases by 0.209 units for adolescents who participated in physical education classes for 2–4 days than those who did not attend physical education classes.
Model 2 explores how physical health predicted juvenile delinquency after holding sociodemographic, social bonds, and mental health variables constant. The results revealed that partaking in physical activities for 1–2 days and 3 or more days increases the expected
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log counts of offending behavior by 0.215 and 0.200 units, respectively, compared to those who were not active.
Table 13 : Physical Health Predicting Juvenile Delinquency (N= 3632) Variables b SE b SE Sex 0.029 0.041 Age -0.014 0.014 Grade level JHS2 0.086 0.056 JHS3 0.053 0.066 SHS1 -0.460 *** 0.076 SHS2 -0.326 *** 0.082 SHS3 -0.188 * 0.078 SHS4 -0.116 0.087 Peer bond 0.081 *** 0.019 School bond -0.040 ** 0.015 Parental bond Parents check homework -0.014 0.013 Parents understand child’s troubles -0.021 0.016 Parental know child’s use of free time -0.032 * 0.015 Mental Health Loneliness 0.049 ** 0.016 Sleeplessness 0.085 *** 0.016 Physical Health Physically active (ref: never) 1-2 days 0.177 *** 0.047 0.215 *** 0.047 3 or more days 0.137 ** 0.048 0.200 *** 0.047 Injury (ref: never) 1-2 days 0.492 *** 0.054 0.416 *** 0.052 3 or more days 0.846 *** 0.049 0.721 *** 0.051 Participation in physical education (ref: never) Once 0.035 0.052 0.095 0.051 2-4 days 0.209 *** 0.053 0.209 *** 0.052 More than 4 days 0.104 0.063 0.103 0.062 Constant -0.635 0.056 -0.577 0.106 F-Test F(7, 1088.9) =47.53*** F(22, 4644.5)= 24.09*** *p < .05 **p< .01 ***p < .001
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Also, being injured for 1–2 days and 3 or more days increases the expected log counts of juvenile delinquency by 0.416 and 0.721 units respectively, than those who have not been exposed to injuries. Lastly, the expected log counts of offending behavior increases by
0.209 units for teens who participated in physical education class for 2–4 than those who did not participate. In summary, the findings indicate that adolescents who were very active and attended physical education classes were at a risk of becoming delinquents than those inactive ones. Also, teenagers who suffered bodily injuries were more likely to become delinquents. Hence, the hypothesis that physical health strains positively predict juvenile offending is partially supported.
The analysis for the mental health variable showed that loneliness and sleeplessness increases the expected log counts of delinquency by 0.049 and 0.085 units, respectively. A unit increase in peer bond increases the expected log counts of delinquency by 0.081 units. Being a student in SHS1, SHS2 and SHS3 reduces the expected log counts of offending behavior by -0.460, -0.326 and -0.188 units, respectively. Finally, a unit increase in school bond and parental knowledge of the child’s use of free time reduces the expected log counts of antisocial behavior by-0.040 and -
0.032, respectively.
Mental Health and Juvenile Delinquency
Table 14 analyzes how mental health predicted juvenile antisocial behavior. This analysis will test the hypothesis (H5): mental health strains will positively predict delinquency. The results in model 1 show that a unit increase in loneliness and sleeplessness increases the expected log counts of delinquency behavior by 0.049 and
0.111 units, respectively. In model 2, the expected log count of offending behaviors
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Table 14: Mental Health Predicting Juvenile delinquency (N= 3632) Variables b SE b SE Sex 0.029 0.041 Age -0.014 0.014 Grade level JHS2 0.086 0.056 JHS3 0.053 0.066 SHS1 -0.460 *** 0.076 SHS2 -0.326 *** 0.082 SHS3 -0.188 * 0.078 SHS4 -0.116 0.087 Peer bond 0.081 *** 0.019 School bond -0.040 ** 0.015 Parental bond Parents check homework -0.014 0.013 Parents understand child’s troubles -0.021 0.016 Parental know child’s use of free time -0.032 * 0.015 Physical health Physically active (ref: never) 1-2 days 0.215 *** 0.047 3 or more days 0.200 *** 0.047 Injury (ref: never) 1-2 days 0.416 *** 0.052 3 or more days 0.721 *** 0.051 Participation in physical education (ref: never) Once 0.095 0.051 2-4 days 0.209 *** 0.052 More than 4 days 0.103 0.062 Mental Health Loneliness 0.049 ** 0.018 0.049 ** 0.016 Sleeplessness 0.111 *** 0.017 0.085 *** 0.016 Constant -0.207 0.040 -0.577 0.106 F-Test F(2, 534.9)= 29.48*** F(22, 4644.5)= 24.09*** *p < .05 **p< .01 ***p < .001
increases by 0.049 and 0.085 units for loneliness and sleeplessness, respectively, after holding sociodemographic, social bond, and physical health variables constant. The results mentioned above support the hypothesis that mental health strains are positively associated with delinquency. As expected, measures of physical health increases
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delinquent behavior. Specifically, being physically active for 1–2 days and 3 or more days increases the expected log counts of delinquency by 0.215 and 0.200 units respectively. Also, the expected log counts of offending behaviors increases by 0.416 and
0.721 for adolescents who were injured 1–2 days and 3 or more days respectively, than those who were not injured.
Participating in physical education class for 2–4 days increases the expected log count of delinquency by 0.209, compared to those who did not participate. On the other hand, the expected log count for delinquency reduces delinquency by -0.460, -0.326 and -
0.188 for students in SHS1, SHS2 and SHS3 students, respectively. Similarly, a unit increase in school bond and parental knowledge of the child’s use of free time increases the expected log count of antisocial behavior by -0.040and -0.032, respectively.
Sex, Physical Health and Juvenile Delinquency
Previous literature has documented that physical health is positively associated with adolescent offending. Little information is available about the sex differences in the relationship between physical health strains and adolescent offending. In this section, I test the hypothesis that: (H6) females who experience physical health strain will report less offending behaviors than males. Table 15 shows the sex differences in physical health predicting adolescent delinquency behavior. The results in model 1 show for males, being injured for 1-2 days and 3 or more days increases the expected log counts of delinquency by 0.477 and 0.804 units, respectively, than those who were not injured.
Also, the expected log count of offending behavior increases by 0.191 units for males who participated in physical education class for 2-4 days than those who did not attend.
For females, participating in physical activities for 1-2 days and 3 or more days increases
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the expected log count of offending behavior by 0.203 and 0.192 units respectively, than the inactive teenagers. Besides, females who experienced injuries for 1-2 days and 3 or more days were vulnerable to an increase in the expected log counts of offending behaviors by
0.518 and 0.888 units respectively, than those who did not suffer any injury. Also, females who took part in physical education classes for 2-4 days are at a risk of an increase in the expected log counts of delinquency by 0.253 units than those who did not attend.
In model 2, the expected log count of delinquency increases by 0.167 and 0.183 units for males who were active for 1-2 days and 3 or more days respectively, than those who were not active. Similarly, females who were active for 1-2 days and 3 or more days were vulnerable to an increase in the expected log count of delinquency by 0.250 and
0.225 units, respectively, than those who inactive juveniles.
Also, males who have been injured for 1-2 days (b=0.398, p<0.001) and 3 or more days (b=0.680, p<0.001)} were at a risk of an expected log count of delinquency by
0.398 and 0.680 units, respectively, compared to those without injuries. Likewise, females who were injured for 1-2 days and 3 or more days increases the expected log counts of delinquency by 0.460 and 0.773 units, respectively, than those who did not suffer any injury. More so, the expected log count for offending behavior increases by 0.211 for males who attended physical education classes for 2-4 days than those who did not participate. Similarly, for females who partake in physical education classes for 2-4 days the expected log count of delinquency increases by 0.253 units, than non-participants. In all, the results showed that the regression coefficients of physical health strains predicting delinquency for females were greater than males.
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Table 15:Sex Differences in Physical Health Predicting Delinquency Variables Male (N=1970) Female (N= 1662) Model 1 Model2 Model 1 Model 2 b SE b SE b SE b SE Age -0.028 0.019 0.017 0.020 Grade level JHS2 0.141 0.083 0.097 0.085 JHS3 0.131 0.091 0.028 0.092 SHS1 -0.247 * 0.101 -0.717 *** 0.112 SHS2 -0.216 0.116 -0.487 *** 0.130 SHS3 -0.132 0.104 -0.243 * 0.122 SHS4 -0.015 0.121 -0.222 0.137 Peer bond 0.046 0.028 0.146 *** 0.033 School bond -0.049 * 0.023 -0.036 0.024 Parental bond Parents check homework -0.008 0.019 -0.037 0.019 Parents understand child’s troubles -0.020 0.020 -0.014 0.024 Parental know child’s use of free time -0.057 ** 0.022 0.007 0.024 Mental health Loneliness 0.034 0.021 0.062 ** 0.025 Sleeplessness 0.084 *** 0.022 0.091 *** 0.023 Physical health Physically active (ref: never) 1-2 days 0.128 0.075 0.167 * 0.073 0.203 *** 0.071 0.250 *** 0.069 3 or more days 0.091 0.074 0.183 * 0.075 0.192 *** 0.067 0.225 *** 0.066 Physically injury (ref: never) 1-2 days 0.477 *** 0.069 0.398 *** 0.070 0.518 *** 0.098 0.460 *** 0.093 3 or more days 0.804 *** 0.065 0.680 *** 0.066 0.888 *** 0.086 0.773 *** 0.084 Participation in physical education (ref: never) Once 0.015 0.070 0.074 0.071 0.072 0.078 0.098 0.077 2-4 days 0.191 ** 0.067 0.211 ** 0.066 0.253 *** 0.073 0.207 ** 0.071 5 or more days 0.089 0.077 0.093 0.076 0.137 0.090 0.121 0.089 Constant -0.532 0.076 -0.330 0.142 -2.345 0.42 -0.950 0.150 T-Test F(7, 1016.5) = 24.35*** F( 21, 3789.7)= 12.51*** F(7, 829.6) = 24.04*** F(21, 3590.4) = 15.43*** *p < .05 **p< .01 ***p < .001
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The results further show that the two measures of mental health increase the expected log count of delinquency for females. However, only sleeplessness increased the expected log count of delinquency for males. Again, being in SHS1, SHS2, and SHS3 reduced delinquency for females but not males. Moreover, peer bond increased offending behavior for females but not males. School bond significantly reduced offending behavior for males but not females. Finally, parents knowing what their children do with their free time reduced teenage antisocial behavior for males.
To test whether the effect is greater for females than males, I conducted a T-test which is presented in table 11. The results from the t-test showed that the difference is not significant. The latter results imply that the effect of physical health strains on delinquency behavior is the same for both males and females. Therefore, I reject the null hypothesis.
Table 16: T-Test for Differences in Effects of physical health strains predicting delinquency for Males and Females Physical health df t p Physically active (ref: never) 1-2 days 3,628 0.82629 0.40869395 3 or more days 3,628 0.4204 0.67421853 Injury (ref: never) 1-2 days 3,628 0.53265 0.59431183 3 or more days 3,628 0.87057 0.38404833 Participation in physical education (ref: never) Once 3,628 0.22914 0.81877008 2-4 days 3,628 0.04126 0.96708825 5 or more days 3,628 0.23925 0.81092803
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Sex, Mental Health and Delinquency
The analysis of the sex differences in mental health predicting juvenile antisocial behavior is displayed in table 17. The analysis in the table is used to test the hypothesis: males who suffer from mental health disorders will report more delinquency behaviors than females. In model 1, a unit increase in loneliness and sleeplessness increases the expected log count of delinquency by 0.059 and 0.114 units, respectively for females. For males, a unit increase in sleeplessness increases the expected log counts of delinquency by 0.112 units.
After controlling for age, social bonds, and physical health in Model 2, a unit increase in loneliness and sleeplessness increases the expected log count of antisocial behavior by 0.062 and 0.025 units, respectively, for females. Also, the expected log count for offending behavior increases by 0.084 units for males who experience sleeplessness.
The results further show that being physically active and participating in physical education class for 2–4 days increases the expected log counts of delinquency for both males and females than those who were not active nor participated in physical education class.
However, physical injury increased teenage offending for males but not females.
Being an SHS1, SHS2, or SHS3 student reduces the expected log counts of delinquency by -0.720, -0.490 and -0.240 units, respectively, for females. A unit increase in school bond and parental knowledge of child’s use of free time reduces the expected log count of delinquency by -0.050 and -0.060 units, respectively, for males. Also, a unit increase in peer bond increases the expected log count of delinquency by 0.146 units for females.
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Table 17: Sex differences in Mental Health Predicting Juvenile Delinquency Variables Males (N= 1,970) Females (N= 1662) Model 1 Model 2 Model 1 Model 2 b SE b SE b SE b SE Age -0.030 0.019 0.017 0.02 Grade level JHS2 0.141 0.083 0.097 0.085 JHS3 0.131 0.091 0.028 0.092 SHS1 -0.250 * 0.101 -0.720 *** 0.112 SHS2 -0.220 0.116 -0.490 *** 0.130 SHS3 -0.130 0.104 -0.240 * 0.122 SHS4 -0.020 0.121 -0.220 0.137 Peer bond 0.046 0.028 0.146 *** 0.033 School bond -0.050 * 0.023 -0.040 0.024 Parental bond Parents check homework -0.010 0.019 -0.04 0.019 Parents understand child’s troubles -0.020 0.020 -0.010 0.024 Parental know child’s use of free time -0.060 ** 0.022 0.007 0.024 Physically active (ref: never) 1-2days 0.167 * 0.073 0.250 *** 0.069 3 or more days 0.183 * 0.075 0.225 *** 0.066 Physical Injury (ref: never) 1-2 days 0.398 *** 0.070 0.460 0.093 3 or more days 0.680 *** 0.066 0.773 0.084 Participation in physical education
(ref: never) Once 0.074 0.071 0.098 0.077 2-4 days 0.211 ** 0.066 0.207 *** 0.071 More than 4 days 0.093 0.076 0.121 0.089 Physically active Mental health loneliness 0.038 0.021 0.034 0.021 0.059 * 0.027 0.062 ** 0.025 sleeplessness 0.112 *** 0.023 0.084 *** 0.022 0.114 *** 0.025 0.091 *** 0.023 Constant -0.151 0.048 -0.330 0.142 -0.260 0.058 -0.950 0.150 F-Test F(2, 786.6) = 17.65*** F(21, 3789.7) = 12.51*** F(2, 694.0)= 17.00*** F(21, 3590.3)=15.43*** *p < .05 **p< .01 ***p < .001
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Table 14: T-Test for Differences in Effects of Mental Health Strains Predicting Delinquency for Males and Females Mental health df t p Loneliness 3628 0.858 0.391 sleeplessness 3628 0.220 0.826
The t-test statistics in table 14 show no significant difference. Hence the effect of mental health strains on both males and females is similar. On the basis of the t-test, I reject the hypotheses that males who experience mental health strains are likely to become delinquents than females.
Summary
This chapter presented the quantitative analysis of the association between bullying victimization and juvenile delinquency as well as the relationship between health strains and juvenile offending in Ghana. I used cross-tabulations and Chi-square tests to determine differences in bullying and health across groups. In addition, Pearson’s moment correlation was used to establish the bivariate correlations among the variables.
Likewise, I used a t-test to compare the regression coefficients of males and females.
Lastly, I used negative binomial regression to predict the relationships of bullying victimization and health to delinquency.
The cross-tabulation revealed that bullying victimization and health strains were prevalent among females than males. Bullying victimization and physical health strains were common among the 1997–2001 cohort, while mental health strains were frequent among the 1994–1996 cohort. Likewise, bullying victimization was widespread among the JHS students, while health strains were common among the SHS students. The chi- square statistics indicate a statically significant association between sex and types of
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bullying victimization (physical and verbal). Based on sex, there were significant differences in being physically active and mental health. Also, there was a significant difference in bullying victimization health strains across cohorts. Finally, JHS and SHS students differ in the prevalence of bullying victimization and health strains.
Negative binomial regression was used to establish the bullying victimization and delinquency relationship and the health strains and juvenile offending association while controlling for relevant variables such as sex, age, grade level, school bonds, peer bonds, and parental bonds. The multivariate analysis showed that bullying victimization increased juvenile offending. The results further showed that physical and verbal bullying victimization increased adolescent offending. More so, physical health strains increased juvenile offending. Also, mental disorders significantly increased adolescent delinquency.
Lastly, other variables that strongly predicted juvenile delinquency were school bond and peer bond. Grade level and parental bond partially predicted juvenile offending.
I will summarize the results of this research in the next chapter. In addition, the implications of the findings and the conclusions and recommendations will also be discussed.
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Table 13: Summary of findings Hypotheses Results Reasons H1: Bullying victimization will Null hypothesis Delinquent peers, positively predict juvenile delinquency. accepted trying to be tough H2: Bullied males will have a higher Null hypothesis probability of reporting delinquency rejected than females.
H3a: Physical bullying will be Null hypothesis Delinquent peers, positively associated with delinquency Accepted trying to be tough
H3b: Verbal bullying will be positively Null hypothesis Delinquent peers, associated with delinquency accepted trying to be tough H3c: Male victims of physical bullying Null hypothesis will have a higher probability of rejected becoming juvenile delinquents than females H3d: Female victims of verbal bullying Null hypothesis will report more offending behavior rejected than males.
H4: Physical health strains will Null hypothesis Breakdown of the positively be associated with accepted family, availability of delinquency drugs
H5: Mental health strains will positively Null hypothesis Breakdown of the predict delinquency accepted family, availability of illicit drugs
H6: Females who experience physical Null hypothesis health strain will report less offending rejected behaviors than males H7: Males who suffer from mental Null hypothesis health disorders will report more rejected delinquency behaviors than females.
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CHAPTER VI
DISCUSSIONS AND CONCLUSIONS
Overview
The study aimed to ascertain the relationship between bullying victimization, health strains, and juvenile delinquency in Ghana. The study aimed to answer five research questions: (1) What is the relationship between bullying and juvenile delinquency behaviors in Ghana? (2) Do the various bullying types (physical and verbal) significantly predict juvenile delinquency in Ghana? If so, how do they differ by gender, age, and grade level? (3) Are there gender differences in the bullying–delinquency relationship as pertains to Ghana? (4) What is the relationship between health and adolescent delinquency behaviors? (5) Is the relationship between health and delinquency behaviors stronger for males than for females?
Data from the Global School-based Student Health Survey of 2012 (GSHS2012) were used in this study. Negative binomial regression was used to analyze the data while controlling for sex, age, grade level, school bond, and parental bond. In this chapter, I present the discussion of the findings based on each research question posed. Following that, I will conclude this chapter and offer some recommendations for areas of future investigation.
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Research Questions and Hypotheses: Interpretation of Results
First, what is the relationship between bullying and juvenile delinquency behaviors in Ghana? This research question advanced the literature by investigating whether bullying victimization predicts teenage antisocial behavior in the African context. Previous research on this question focused on samples from Western countries
(the United States, Canada, Britain, Korea, and China). General strain theory contends that peer abuse is essential in adolescent offending behavior (Agnew 2001). As such, my first hypothesis stated that (H1) bullying victimization would positively predict juvenile delinquency. I found that bullying victimization increased juvenile antisocial behavior after controlling for sociodemographic variables as well as peer, school, and parental bonds. This finding supports previous research which suggests that victims of bullying are more likely to become delinquents (Bender and Losel 2011; Cullen et al., 2008;
Higgins, Khey, Dawson-Edwards and Marcum 2012; Sigfusdottir, Sigurdsson 2010;
Walters and Espelage 2017). Also, the results provide support for the importance of GST in the explanation of bullying victimization and adolescent offending association.
Two factors can be used to explain why bullying victimization predicts teenage antisocial behavior. First, victims of bullying become defenseless against the perpetrator and want to find alternative ways to become tough. After encountering bullying, teenagers engage in fighting and substance use to prove that they are strong (Olweus
1993; Pellegrini and Bartini 2000; Wong and Schonlau 2013). Second, peer influence is also a possible explanation for bullied juveniles’ involvement in delinquency. Some studies have observed that bullied juveniles who associate with aggressive peers are more likely to engage in antisocial behaviors (Bukowski, Sippola, and Newcomb 2000; Wong
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and Schonlau 2013). For example, in a study of 217 juveniles, Bukowski et al. (2000) reported that victimized adolescents who did not have aggressive friends were less likely to engage in delinquency behaviors. In contrast, those in contact with quarrelsome peers were highly likely to engage in juvenile offending. Finally, there is lack of bullying prevention regulations in schools. Stakeholders in Ghana view bullying as a rite of passage for the adolescents. Therefore, no attempt has been made to design rules and regulations to prevent bullying.
Third, are there sex differences in the bullying–delinquency relationship as it pertains to Ghana? Agnew and Broidy (1997) argued that males and females vary in offending behavior because of differences in types of strains they experience, reactions to negative emotions, and social support. The literature on sex variations in the association between bullying victimization and delinquency have produced mixed results. Literature on this subject is limited in Ghana. Therefore, this research question aimed to find out if bullying victimization predicts delinquency differently for males and females. Hence, my third hypothesis states that (H2) bullied males will have a higher probability of reporting delinquency than females. I found from the analysis that the effect of bullying victimization on delinquency is similar for females and males. This result contradicts the findings of Johnston, Doumas, Midgett, and Moro (2017), who reported that bullied males were more likely to engage in antisocial behaviors compared to females.
Fourth, do the various bullying types (physical and verbal) significantly predict juvenile delinquency in Ghana? This research question aimed to explore the relationship between the types of bullying and teenage offending. The available literature on this subject matter has been inconsistent. Some studies have reported that physical bullying
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significantly predicts juvenile offending, whereas other studies have shown the contrary.
Therefore, I tested two hypotheses: (H3a) physical bullying will be positively associated with delinquency; (H3b) verbal bullying will be positively associated with delinquency.
In this study, I found that physical and verbal bullying victimization increases the risk of becoming juvenile offender after controlling for sociodemographic variables as well as social bond variables. This finding supports the findings of Vieno, Gini, and Santinello
(2010) that all types of bullying victimization positively predict adolescent offending.
The results from this analysis also provides some support for GST with respect to the role of peer abuse in offending behavior.
Aside from the aforementioned hypotheses, I tested for sex differences in types of bullying and their relationship with delinquency. So, I hypothesized that: (H3c) females who experience verbal bullying will report more offending behaviors than males; (H3d) males who suffer from physical bullying will report more delinquency behaviors than females. I found that the effect of both physical and verbal bullying on delinquency for females and males is equal. Thus I did not find support for both hypotheses.
Fifth, what is the relationship between health and adolescent delinquency behaviors? Physical and mental health conditions were not considered as strains in
Agnew’s GST. However, a couple of scholars have used physical and mental health as strains to predict delinquency behavior (Stogner and Gibson 2010; Ford 2014; Kort- butler 2015). This research question was to find out whether the proposed health– delinquency relationship established in Western countries was applicable in sub-Saharan
Africa. I tested two hypotheses: (H4) physical health strains will positively be associated with delinquency; (H5) mental health strains will positively predict delinquency. The
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results revealed that physical health increased delinquency. Also, mental health strains significantly increased adolescent offending. The finding that physical health strains increased juvenile delinquency confirms previous studies which reported that adolescents who have physical health and other related problems tend to report more acts of crime and deviance (Blum, Kelly, and Ireland 2001; Miauton, Narring, and Michaud 2003;
Oshima et al. 2010; Suris et al. 2008). Similarly, the finding that mental health strains significantly increases juvenile delinquency agrees with earlier researchers’ reports of a positive association between mental health and adolescent antisocial behavior (Piquero and Sealock 2000; Sigfusdottir, Farkas, and Silver 2004). The findings that physical and mental health strains predicts juvenile delinquency provides support for the GST. The support to GST provided by this research is important because this one of the few studies that has used samples from Sub-Saharan Africa.
A possible explanation for this result could be the breakdown of the family in
Ghana. The family system in Ghana provides social support for adolescents and helps to care for sick juveniles. However, in recent times, families are breaking down. For example, Clark and Brauner-Otto (2015) reported that Ghana's divorce rate was 33.2% in
2008. A family breakdown could result in poor health for adolescents, which might in turn, lead to juvenile antisocial behavior. For example, Annim, Awusabo-Asare, and
Amo-Adjei (2013) reported that juveniles from nucleated households have better health outcomes than those from non-nucleated households.
Another possible explanation is the availability and cost of illicit substances in
Ghanaian society. In recent times, Ghana has become a fertile ground for transporting illicit drugs to Western countries. In addition, there are reports that some illicit drugs are
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being produced in the country (Bird 2019). Therefore, hard substances are easily accessible to teenagers. For example, Kabore et al. (2019) explored the ecological factors predicting illicit drug use in Ghana. They reported that low drug prices and availability of drugs were significant factors predicting substance abuse.
Finally, is the relationship between health and delinquency behaviors stronger for males than for females? Although existing literature shows that health is positively associated with juvenile offending, little is known about the sex differences. Agnew and
Broidy (1997) contended that males and females differ delinquency behavior due to the differences in strains, response to strains and social support. Nonetheless, less support have been found for their proposition when it comes to sex differences in the effect of health strains and offending behavior. This research question aimed at ascertaining whether unhealthy females are more likely to become delinquents than males. Hence, two hypotheses were developed for this section: (H6) females who experience physical health strain will report less offending behaviors than males; (H7) males who suffer from mental health disorders will report more delinquency behaviors than females. The results showed that the impact of health strains (physical and mental) on delinquency is equal for males and females. Thus I did not find support for both the latter and former hypotheses.
Limitations and Strengths
This dissertation, like any other research, has its strengths and weaknesses. The strength of this dissertation can be grouped into two. First is the use of Non-western and
Asian samples. Most extant empirical studies mostly use Western samples (e.g., USA,
Italy) and Eastern cultural settings (Korea). A study that focuses on sub-Saharan African
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cultures is not available. This study has filled this void by examining the bullying- delinquency relationship in Ghana. Another strength is its representativeness. The data comes from all the ten regions that existed in Ghana when the data was collected.
Representativeness in this data is vital since Ghana comprises different ethnic groups, all of which have different cultures.
Despite the strengths, the study has several shortcomings. First, the study used cross-sectional data, which makes it difficult to establish causal relationships. Cross- sectional data are also susceptible to recall bias; hence the prevalence of bullying and delinquency might be under-or overestimated. Second, mental health was measured with only two items in the dataset. Also, the items captured only some aspects of Center for
Epidemiologic Studies Depression Scale (CES-D). However, mental health is a complex concept that requires several items to measure to ensure validity. Furthermore, juvenile delinquency was measured with items that were related to status offenses. Even so, about half of the measures of status offenses focused on substance misuse. Data on property offenses, violent offenses, and gang membership were not available. Aside from the lack of data on some offenses, delinquency measures covered various time windows. The
Moreover, researchers have shown several variables to predict teenage delinquency which were missing from the Global School-based Student Health Survey (GSHS). I could not control some important demographic variables such as parental education, income, and employment, which are known predictors of delinquency.
Contributions to Theory
The theoretical implications of this dissertation to the general strain theory are in two folds. First, this research provides empirical evidence for strains predicting juvenile
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delinquency from African samples. The available literature has primarily focused on samples from North America, Europe, and Asia. The previous literature has found that both bullying victimization and health strains are positively associated with delinquency.
The findings from this research help to close the research gap. My findings from the
Ghanaian context adds to those scholars who have argued that bullying victimization and health strains positively predict juvenile delinquency.
Second, this study also contributes to strain theory regarding the effects of types of bullying victimization on delinquency behaviors. Existing literature on the subject has produced mixed results. This study provides empirical support for those researchers who found physical and verbal bullying to be a predictor of delinquency behaviors.
Policy Implications
Scholars have documented that bullying victimization and health strains significantly predict juvenile delinquency. However, the overwhelming majority of the studies were conducted in Western countries, while little has been done about the subject matter in sub-Saharan Africa, including Ghana. This research is unique because it provides an insight into the how victimization and health strains affect juvenile delinquency in an African context. One of the key findings of this study is that victims of bullying were more likely to exhibit delinquency behaviors. This finding can be attributed to the lack of a bullying prevention policy in schools. Although bullying has been going on in Ghanaian schools for years, no effort has been made by stakeholders and policymakers to curb the situation. This has allowed bullying to become a normalized behavior in Ghana. Stakeholders in education, and policymakers in the country should draft specific rules and regulations against bullying in schools and communities.
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Bullying literature have documented a couple of anti-bullying programs which have been proven to reduce bullying, including the Olweus bullying prevention program
(Losey 2009), the confident kids program (Berry & Hunt 2009), steps to respect (Brown et al. 2011) and the positive action program (Megha, Liddell, and Ferreira 2018). For instance, Farrington et al. (2016) did a systematic reviews of the efficacy of bullying intervention programs. They found that all types of bullying intervention programs were effective. Similarly, Alford and Derzon (2011) assessed the effectiveness of 24 school- based violence intervention programs. They reported that all the programs reduced school violence significantly. Since bullying is very prevalent in Ghanaian schools, it is necessary to implement positive action programs so that students will learn to do good things and treat others the way they would want to be treated. The Olweus bullying prevention program, which encompasses individual, classroom, school, and community- level components, can also help curb bullying in Ghana.
Another explanation for bullying predicting juvenile antisocial behavior is the association with delinquent peers. General strain theory contends that association with delinquent peers is one of the factors that determine the effect of strains on delinquency
(Agnew 2001). Several studies in Ghana have found that juveniles with delinquent friends are more likely to engage in bullying and delinquency. In this study, the results further showed that peer bonds significantly predicted delinquency. Parental supervision is necessary to dissuade teenagers from bad friends. However, given Ghana's current economic conditions, which require both parents to work full-time, parental supervision is difficult. Thus policy makers can implement an extended paid parental leave program to help some parents or guardians stay home to supervise their wards. In addition,
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community-based recreational programs, including drama, sports, music, arts, and other activities, could help adolescents keep busy after they close from school or during weekends. An alternative explanation for bullying predicting delinquency is victims resorting to ways of getting tough. When victims are bullied, they try to prove to others that they are also strong. The latter compels them to resort to substance abuse and fighting. To prevent this from happening, policymakers can introduce emotional literacy intervention program. The emotional literacy intervention program could help determine the emotional literacy levels of adolescents and provide the necessary intervention.
Another finding from the study concerns health strains predicting juvenile antisocial behaviors. A possible explanation for health strains predicting delinquency is the declining role of the family in helping the sick. Family members should be encouraged to take a keen interest in the health of adolescents. Lack of access to health care services might also play a role. Policy makers could help curb the effect of health on delinquency by introducing home visitation by health professionals. The home visitation could help adolescents have access to healthcare without having to travel a long distance.
Future Research
This dissertation's findings warrant the continuation of research into the bullying victimization, health strains, and delinquency relationship. Given the fact that this research used cross-sectional data, future researchers must use longitudinal data. Such data will help to show whether the relationship among the dependent and independent variables persists over time.
As noted in the latter section, I measured mental health with only two items.
Given that mental health is a complex concept, using two items to measure it is not
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enough. Future scholars should use several mental illness measures to establish the relationship between mental health and juvenile antisocial behavior. Future researchers can use measures of depression, anxiety, eating disorders, addictive disorders, and schizophrenia, among others. Future researchers should also include additional physical health measures such as chronic illnesses and somatic symptoms aside from mental health measures.
Juvenile delinquency can be status offenses, property offenses, violent offenses, and gang membership. However, in this dissertation, the focus was more on status offenses. Future researchers who want to investigate delinquency in Ghana should endeavor to include delinquency measures other than status offenses. Inclusion of property offenses, violent offenses, and gang membership in the study will help improve our understanding of Sub-Saharan Africa's behavior.
In this research, I was able to disaggregate the data based on gender. The data disaggregation helped to provide insight into the sex differences in the association between bullying victimization and delinquency and health strains and juvenile offending. However, I could not test the interaction effect of gender because my data could not converge. Future researchers should test the interaction between gender and bullying victimization, in addition to gender and health.
Variables such as parental income, parents' and socioeconomic status were not included in this research. Controlling for such variables in the study could have helped determine if the same conditions in the western world are applicable in Ghana. Future researchers should endeavor to include the variables mentioned above in their study.
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Conclusion
Notwithstanding this study's limitations, bullying victimization and health strains do appear to predict juvenile delinquency in Ghana. As it currently stands, bullied adolescents are more likely to engage in offending behaviors than non-bullied teenagers.
Also, healthy juveniles are less likely to engage in delinquent behaviors than unhealthy ones. To help ameliorate the situation, policy makers and opinion leader should ensure that schools have anti-bullying rules and regulations. Also, the government should pay parents extended parental leave to enable them have time for supervising adolescents.
Besides, emotional intervention programs and recreational activities could help curb bullying and delinquency. More so, home visitation by health professionals could help improve adolescent health and reduce offending behaviors.
In summary, this dissertation provides an empirical examination of the strain- delinquency relationship from a Ghanaian context. Using the general strain theory as a framework, we can observe the ramifications that strains have on adolescents' offending behaviors in Ghana. Therefore, more research is necessary to develop solutions to palliate the effects of strains on adolescent offending. Even though the underlying mechanisms for the strain-delinquency relationship are available in the western world, sub-Saharan
Africa is yet to be explored. As sociology students and scholars, the onus is on us to investigate the underlying mechanisms for strains impacting delinquency in Sub-Saharan
Africa. For parents, policymakers, and health experts, I hope this project will help us comprehend and combat this complex social problem.
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