Transcending Beyond the Schoolyard

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2012 Transcending Beyond the Schoolyard: A Multilevel Examination of the Environmental Influences and Prevalence of Traditional and Cyber Bullying Perpetration Karla Johanna Dhungana

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COLLEGE OF CRIMINOLOGY AND CRIMINAL JUSTICE

TRANSCENDING BEYOND THE SCHOOLYARD: A MULTILEVEL EXAMINATION OF

THE ENVIRONMENTAL INFLUENCES AND PREVALENCE OF TRADITIONAL AND

CYBER BULLYING PERPETRATION

KARLA JOHANNA DHUNGANA

A Dissertation submitted to the College of Criminology and Criminal Justice in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2012 Karla Dhungana defended this dissertation on June, 26, 2012.

The members of the supervisory committee were:

Brian Stults Professor Directing Dissertation

Martell Teasley University Representative

Eric Stewart Committee Member

Sonja Sienneck Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

To the original Dr. Dhungana:

For as long as I can remember, I’ve aspired to be like you. For being my first and (to this day) biggest source of inspiration, I am in your debt. Thank you for paving the way, Daddy; I love you and I hope I have made you proud.

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ACKNOWLEDGEMENTS

While works like this may seem like a solitary undertaking, I recognize that it took a small army of amazing and gracious cheerleaders to get me to this culmination of my graduate career. My sincerest gratitude is due to my major professor and committee chair, Dr. Brian Stults, for his commitment to the study from its conception and his un-wavering faith and support throughout. My appreciation also goes out to my dissertation committee members, Dr. Eric Stewart, Dr. Sonja Sienneck and Dr. Martell Teasley for helping shape this study and getting it to the final product.

Mom and Dad, thank you for giving me the blessing of roots; for letting me chase my dreams but never letting me lose sight of the road that leads back home. My dear brothers, Rajan dai and Carlo, you’ve watched me grow and shared in nostalgic childhood memories, my everyday adventures and now the fulfillment of my grown-up dreams. Thank you for always believing in your baby sister.

To my lifetime friends, who are like family – Anjali, Kat, Lori, Christina, Sweta, Pranita, Monica, Meenal, Elina, and Meredith – thank you for our beautiful friendship and for pushing me through some of my toughest days during this process.

And finally, to Saugar – thank you a thousand times over. This process brought out the best (and the worst) of me; thank you for being my voice of reason, my constant source of motivation and for believing in me when I needed it most.

TABLE OF CONTENTS

List of Tables ...... vii List of Figures ...... viii Abstract ...... ix 1. CHAPTER ONE: INTRODUCTION ...... 1

2. CHAPTER 2: LITERATURE REVIEW ...... 5 2.1. Defining Bullying ...... 6 2.1.1. Physical Bullying ...... 7 2.1.2. Verbal Bullying ...... 10 2.1.3. Cyber Bullying ...... 11 2.2. Who are the Bully and the Bully Victim? ...... 15 2.2.1. The Bully ...... 15 2.2.2. The Bully Victim ...... 18 2.3. Theoretical Considerations...... 22 2.3.1. Control Theory ...... 22 2.3.2. Social Disorganization Theory ...... 25 2.3.3. Additional Theoretical Considerations...... 28 2.3.4. Demographic Characteristics ...... 29

3. RESEARCH DESIGN AND METHODOLOGY...... 31 3.1. Data Sources ...... 31 3.2. Measures ...... 33 3.2.1. Outcome Variables ...... 33 3.2.2. Predictor Variables ...... 34 3.2.3. Individual-Level Predictors ...... 34 3.2.4. School-Level Predictors ...... 36 3.2.5. School District Level Predictors ...... 36 3.3. Analytic Methods ...... 38

4. DISCUSSION & RESULTS: PREVALENCE & COMPARISON OF MEANS ...... 42 4.1. Prevalence ...... 42 4.1.1. Bullying Perpetration ...... 42 4.1.2. Bullying Victimization ...... 43 4.2. Bullying Location ...... 45 4.3. Comparison of Bullying Experiences across Groups ...... 47 4.3.1. Gender ...... 47 4.3.2. Grade Level ...... 50 4.3.3. Ethnicity ...... 52 4.4. Summary of Bullying Prevalence ...... 54

5. DISCUSSION & RESULTS: MULTILEVEL ANALYSIS ...... 56 5.1. Verbal Bullying ...... 57

5.2. Physical Bullying ...... 61 5.3. Cyber Bullying ...... 65 5.4. Supplementary Analysis of Indirect Effects ...... 67 5.5. Discussion ...... 69

6. CONCLUSION ...... 77 6.1. Policy Implications ...... 80 6.2. Limitations ...... 83 6.3. Directions for Future Research ...... 85

APPENDICES

A. List of Variables and Scales ...... 88

B. Correlation Matrix of Dependent and Independent Variables ...... 91

C. Florida State University Human Subject Committee Approval ...... 93

D. Department of Children and Families Data Approval Letter ...... 95

REFERENCES ...... 97

BIOGRAPHICAL SKETCH ...... 113

LIST OF TABLES

3.1 Demographic Characteristics ...... 33

3.2 Descriptives of Outcome, Level-1, Level-2 and Level-3 Variables ...... 37

5.1 Hierarchical Linear Model ANOVA for Verbal Bullying ...... 58

5.2 Random Intercept Model with Level-1 Covariates for Verbal Bullying ...... 59

5.3 Multilevel Analysis adding Level-2 Predictors of Verbal Bullying ...... 60

5.4 Multilevel Analysis adding Level-3 Predictors of Verbal Bullying ...... 61

5.5 Hierarchical Linear Model ANOVA for Physical Bullying ...... 62

5.6 Random Intercept Model with Level-1 Covariates for Physical Bullying ...... 63

5.7 Multilevel Analysis adding Level-2 Predictors of Physical Bullying ...... 64

5.8 Multilevel Analysis adding Level-3 Predictors of Physical Bullying ...... 65

5.9 Hierarchical Linear Model ANOVA for Cyber Bullying ...... 66

5.10 Logistic Regression Model for Cyber Bullying ...... 67

5.11 Preliminary Indirect Effects Analysis for Verbal and Physical Bullying ...... 68

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LIST OF FIGURES

4.1 Self Reported Rates of Bullying Incidents ...... 44

4.2 Location of Bullying Victimizations...... 46

4.3 Bullying Perpetration by Gender ...... 48

4.4 Bullying Victimization by Gender ...... 48

4.5 Bullying Perpetration by Grade Level ...... 50

4.6 Bullying Victimization by Grade Level ...... 51

4.7 Bullying Perpetration by Ethnicity ...... 52

4.8 Bullying Victimization by Race ...... 53

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ABSTRACT

The general purpose of this study is to provide a multi-level examination of the prevalence and contextual influences of traditional and cyber bullying perpetration through a criminological perspective. Bullying and harassment in our schools has become a growing national epidemic and has caught the attention of various disciplines such as education, psychology, sociology and medicine. However, the use of criminological theories to examine the phenomenon of bullying has been limited. Given the link between deviance and bullying behaviors, leading criminological theories could provide valuable nuances to what we already know about bullying. Using a state-wide representative sample of Florida, the present study provides rich and detailed insights into bullying prevalence in Florida schools by examining the incidence rates for verbal, physical and cyber bullying, where bullying takes place as well as a comparison of involvement among various demographic groups. Using hierarchical linear modeling, the study also examines the fit of four criminological theories – social bond theory, social learning theory, general strain theory and social disorganization theory in explaining traditional and cyber bullying. Results found some distinct factors associated with each type of bullying. Furthermore, the findings indicate that while several key individual level significant effects were found, contextual level variables are still important components to consider. In particular, indirect contextual effects could determine the conditions under which certain individual-level characteristics may function. Based on the findings implications for bullying prevention and intervention programs for bullying behaviors are discussed.

CHAPTER ONE

INTRODUCTION

The general purpose of this study is to provide a multi-level examination of the prevalence and contextual influences of bullying perpetration through a criminological perspective. Bullying and harassment in our schools has become a growing national epidemic and the spectrum of victimization is increasing with the growth of technology, social networks and the anonymity it affords bullies (Eaton et al., 2010; Finkelhor et al., 2009). Bullying can have serious implications for students’ emotional and physical well being, self-esteem, academic achievement and mental health (Nansel et al., 2001; Kaltiala-Heino et al., 2002). Research on bullying has been conducted worldwide and been shown to exist in almost all westernized countries (Bosworth et al., 1999; Moon et al., 2011; Olweus, 1978, 1991; Rigby & Slee, 1991), with the United States reporting some of the highest rates (Duncan, 1999; Hoover et al., 1992). Surveys of students in the United States have shown that 15-35% of students report being bullied in the past 12 months on school property (Eaton et al., 2010, Dinkes, Kemp, & Baum, 2009, Nansel et al., 2001). Bullying has grown to become such a pervasive part of our school culture that it could very well be presenting our youth with their first exposure to violence, delinquency and aggression and translate into severe long term consequences for them. In fact, it is estimated that nearly all students will have some type of exposure to bullying by the time they graduate high school (Dinkes, Kemp, and Baum, 2009; Hoover et al., 1992). President Obama has recently called for greater awareness of the problem, saying that the nation must "dispel the myth that bullying is just a normal rite of passage or an inevitable part of growing up" (Obama, 2010). Most previous bullying studies have focused on the prevalence of bullying and victimization in general (Finkelhor et al., 2005; Nansel et al., 2001; Olweus, 1993; Seals & Young, 2003). Few studies have examined the multiple forms of bullying that are emerging within our schools (Finkelhor et al., 2009; Swearer et al., 2008; Wang, Iannotti, & Nansel, 2009) while some studies from the field of psychology have explored the psychological impact of bullying (Boulton & Smith, 2001; Fekkes et al., 2004; Nansel et al., 2001; Kaltiala-Heino et al., 2002). Empirical studies employing criminological theories to examine bullying however, are limited (Bradshaw et al., 2009; Espelange et al., 2000; Hay & Meldrum, 2010; Moon et al., 2011) and even fewer studies have explored the influences of contextual factors on bullying

1 using multi-level modeling techniques (Bradshaw et al., 2009, Due et al., 2009, Elgar et al., 2009, Pickett & Wilkinson, 2007). Given the relationship between delinquency and bullying, the application of criminological theories to this type of deviance needs to be further explored. However, the issue of bullying is also becoming more nuanced and complex than most previous research have conceptualized and examined. Prior research suggests that there is no single cause of bullying. Rather, variables at the individual, familial, peer, school and community may all play a role in youth becoming a bully (Limber, 2000; Olweus, Limber, & Mihalic, 1999). There is also a need to combine previous research on bullying and add on examinations of emerging trends such as cyber bullying as compared to more traditional forms such as verbal and physical bullying and how the prevalence and influences of each kind relates as well as differs. To date, research looking into the link between traditional bullying and cyber bullying has been limited (Hay & Meldrum, 2010; Hinduja & Patchin, 2008; Hinduja & Patchin, 2009; Patchin & Hinduja, 2011; Wang et al., 2009). These few studies have found strong connections between traditional and cyber bullying. Youth exposed to traditional bullying victimization are also more likely to be cyber bullying victims and traditional bullies are also more likely to engage in cyber bullying (Hay & Meldrum, 2010; Hinduja & Patchin, 2008; Hinduja & Patchin, 2009; Patchin & Hinduja, 2011; Wang et al., 2009). However, these studies lack theoretical examinations of the factors associated with traditional versus non-traditional bullying. Thus, this study will attempt to address two gaps: first, with the perspective that a student’s bullying related experiences are not only dependent on what the student brings to the school but also what the school climate provides to the student, the present study will focus on a multi-level examination of the prevalence of three forms of bullying perpetration – verbal bullying, physical bullying and cyber bullying and the contextual influences on these bullying related experiences through a criminological lens. Secondly, given the large sample of the present study and the various theoretical considerations, the present analysis will also provide an examination of the fit of criminological theories in explaining traditional versus cyber bullying. Since most social problems involve multi-faceted relationships, the use of multilevel theories and analytical techniques is appropriate for examining the influences of traditional and non-traditional bullying. This study plans to utilize a state-wide representative sample of Florida high school and middle school students sampling over 41,000 students nested across 681 schools and 62 school districts. Using this sample, it will address the prevalence of bullying perpetration as well as the

2 influences of multi-level environments on these bullying experiences. The state of Florida currently boasts one of the largest school systems in the country and has a high prevalence of bullying incidents. In 2008-2009, the Florida Department of Education recorded 6,308 cases of bullying and harassment in Florida schools. Furthermore, for the past several years the state has been in the media spotlight for severe acts of bullying: in 2005, after enduring two years of taunts and internet attacks, 15-year old Jeff Johnston took his own life. In 2009, Michael Brewer, 15, was set on fire and left to die by his bullies. In the same year 13-year old Hope Witsell committed suicide over text message pictures. And in 2010, Josie Lou Ratley, 15, came close to losing her life after being stomped by another student. These incidents will be discussed further later on in the paper. Given the implications of bullying on our youth’s well-being as well as the size of the school system and growing prevalence of bullying incidents within the state of Florida, the need to identify the prevalence and contextual influences on bullying is thus a timely and critical issue. Identifying key information on the extent of the problem, the characteristics of those involved in bullying, the types of bullying occurring and the contextual influences on bullying experiences can be vital tools in dealing with this epidemic. Specifically, the first objective of this study will be to address the prevalence of different types of bullying perpetration. Descriptive analyses will be conducted to measure the self-reported number of students bullying others as well as to understand the characteristics of those who are bullies. The prevalence of bullying perpetration will be examined through self-reported measures as well as the number of school reported incidents recorded by the Department of Education to compare and assess the extent of the problem. Next, one-way ANOVAs and chi-squares will be conducted to compare means across race, gender and grade levels to see if there is a significant difference in bullying perpetration across these groups. The second objective will be to assess the contextual influences on bullying perpetration for three types of bullying: verbal bullying, physical bullying and cyber bullying. These will be estimated through hierarchical linear modeling, with models being estimated for each type of bullying. The paper is presented as follows: Chapter 2 will provide a thorough literature review as well as the theoretical context for the present study. The chapter will define the various acts of bullying, identify the bully and the bully victim and present a discussion of the theoretical considerations. A discussion of control theory will be provided to address the individual level influences of bullying, while the implications of social disorganization theory will be presented to discuss the role of

3 contextual variables on bullying. The chapter will also include a discussion of additional theoretical explanations that are less central, however, still applicable to the present study. Chapter 3 addresses the research design and methodology employed for the study. The chapter will focus on data sources, descriptions of study measures as well as the analytical methods employed in the study. Chapters 4 and 5 will report the findings of the present study. Chapter 4 will cover the descriptive statistics as well as results from statistics comparing means across groups while chapter 5 include the results and discussion for the multi-level analyses conducted for each type of bullying. Finally, Chapter 6 will conclude with the policy implications of these findings, the areas of limitation that faced the current study and directions for future research.

CHAPTER TWO

LITERATURE REVIEW

A national survey conducted in the United States showed that among children 17 and younger, approximately 1 in 5 reported being physically bullied, about 3 in 10 reported being teased or emotionally harassed and about 8 percent said they had been a victim of internet harassment (Finkelhor et al., 2009). But is bullying just an inevitable part of growing up? After all, the problem of bullying is not an emerging social problem; it is one that has been around for decades. Yet, the growth of technology, the increased amount of anonymity and the changes in ways of communicating and coping are changing the face of bullying. The modern bully isn’t just someone who steals another child’s lunch money or taunts them for their clothes or hairstyle anymore. The modern bully has evolved and presents a much larger scope of victimization techniques. Along with that, the bully victim also faces much deeper and more insidious consequences of the bullying victimization. In a professional training conducted by SuEllen Fried, founder of BullySafeUSA, a nationwide educational program, a counselor pertinently suggested, “I think you should stop using the term bullying and use the term abuse instead. The term bullying is too benign. Too many people associate it with a childhood rite of passage and dismiss the effect. I am convinced that too many children are in terrible pain and we have to use the strongest language possible to convey their trauma” (Fried & Fried, 2003, p. 4). The Office of Justice Programs has recently indicated their commitment towards juvenile justice and an agenda that “protects children and defends the right to a childhood free from violence.” This need to protect our children and their childhood couldn’t be timelier. In response to the recent influx of bullying and the severe consequences associated with it, Care.com conducted a national survey of parents on the topic of bullying. The survey indicated that bullying is the number one fear for parents, with one in three parents surveyed fearing bullying and cyber bullying over kidnapping, domestic terrorism, car accidents or any other incident (www.care.com, 2010). Bullying can have long term psychological, emotional, educational and occupational implications for its victims. On the other hand, as an introduction to violence, aggression and delinquency, the consequences could be just as devastating for a child who engages in the act of bullying. Bullying may begin a trajectory of criminal involvement and

5 delinquency for a child and become their induction into a life of crime. In order for us to break the cycle of bullying, we need to account for the evolving nature of bullying, examine the bully and the bully victim as well as the risk and protective factors associated with bullying in order to mitigate the negative impact and build resistance against the exposure to bullying violence and aggression. 2.1 Defining “Bullying” Bullying involves various negative actions or behaviors that have the intention to cause harm, an imbalance of power and repetition. It comes in many forms, can be acted out by one person or as a group and can affect children of all ages, gender and racial/ethnic make-up. It can happen in the classroom, the cafeteria, the bus stop, the school yard, in the streets, in one’s neighborhood, at home and at after school programs (VYVP, 2009). Bullying, according to Florida Statute 1006.147, is more formally defined as:

“systematically and chronically inflicting physical hurt or psychological distress on one or more students and may involve: 1. Teasing; 2. Social exclusion; 3. Threat; 4. Intimidation; 5. Stalking; 6. Physical violence; 7.Theft; 8. Sexual, religious, or racial harassment; 9. Public humiliation; or 10. Destruction of property.”

Further, the statute goes on to describe harassment as:

“any threatening, insulting, or dehumanizing gesture, use of data or computer software, or written, verbal, or physical conduct directed against a student or school employee that: 1. Places a student or school employee in reasonable fear of harm to his or her person or damage to his or her property; 2. Has the effect of substantially interfering with a student's educational performance, opportunities, or benefits; or 3. Has the effect of substantially disrupting the orderly operation of a school.

But what identifies an incident as a bullying experience? How does one differentiate between children simply joking around or engaging in physical horse-play from a hurtful act of bullying? In order for an incident to be considered bullying certain characteristics must be present. SuEllen Fried and Paula Fried, authors of Bullies, Targets & Witnesses: Helping Children Break the Pain Chain note that behavior is clearly bullying when: 6

1. There is intent to harm – the perpetrator finds pleasure in the taunting and continues even when the target’s distress is obvious. 2. There is intensity and duration – the taunting continues over a long period of time, and the degree of taunting is damaging to the self-esteem of the target. 3. There is abuse of power – the abuser maintains power because of age, strength, size and/or gender. 4. The target is vulnerable – the target is more sensitive to teasing, cannot adequately defend him – or herself, and has physical or psychological qualities that make him or her more prone to vulnerability 5. The target is unsupported – the target feels isolated and exposed. Often the target is afraid to report the abuse for fear of retaliation. 6. The target experiences significant consequences – the damage to self-concept is long lasting, and the target responds to the abuse with either withdrawal or aggression. (Fried & Fried, 2003, p. 28).

The act of bullying is complex, multifaceted and often combined to include several types of bullying (for example, verbal bullying and physical bullying acted out together) (Morgan, 2008). As described above, bullying is intentional, repetitive, hurtful and about power. A discussion of the bully, the bully victim and the consequences for each will be provided later in the chapter. There are many types of bullying, including physical, verbal, emotional, relationship, sexual, social and cyber bullying (Fried & Fried, 2003; McGraw, 2008; www.Stopbullying.gov). While each of these warrants its own response and analysis, focusing on all of these types of bullying are beyond the scope of the present study. Thus, the current study will review the three most common types of bullying: physical bullying, verbal bullying and cyber bullying.

2.1.1 Physical Bullying Deerfield Beach, FL – October, 2009 – Michael Brewer, 15, was doused in rubbing alcohol and set on fire by five teenagers. It occurred as a result of the teens hassling Michael for $40 that they claimed he owed them for a DVD. He recalls them calling out to him, “Come over here, nobody is going to hit you. Then somebody poured something on me and lit me on fire. Then I started running.” Upon hearing his screams for help, a neighbor helped put out the flames with a fire extinguisher. Michael ripped off his shirt and jumped into a nearby pool but still experienced burns on over 65 percent of his body.

The teens responsible for the attack were charged as adults with attempted second-degree murder (Chang, 2010).

Deerfield, FL – March, 2010 – A 15-year-old middle school student, Josie Lou Ratley, was severely beaten and kicked in the head by a fellow student wearing steel-toed boots in reaction to a text message dispute. The attack left Josie severely injured and doctors had to remove a portion of her skull as part of two separate surgeries to relieve pressure on her brain. Furthermore, she suffered through a medically induced coma and major brain damage. Wayne Treacy, her 15-year old attacker was charged as an adult for first- degree attempted murder. (Miller, 2010; Cavaretta, 2010).

Physical bullying is action oriented, meaning there is some sort of physical action involved. This type of bullying can include punching, kicking, shoving, slapping, tripping, pinching, scratching, biting, hair pulling, stabbing, choking and as described in the extreme examples above even being severely beaten and burned (Fried & Fried, 2003; Quiroz et al., 2006, McGraw, 2008). Studies have found that in general boys are more likely to be involved in physical bullying than girls (Harris et al., 2002; Wang, Iannotti, & Nansel, 2009), however another study also indicated that while girls are less openly violent in their physical bullying, when fights do occur between girls they are very serious (Glover et al., 2000). Girls are more likely to slap, pull hair, scratch, pinch, dig their fingernails or bite someone while boys are more likely to punch, choke, kick, throw objects and use weapons (Fried & Fried, 2003). Recently, more incidents of physical bullying have come to the public’s attention due to the trend of bullies posting videos of their physical attacks online. In February, 2011, police arrested seven teenagers in suburban Philadelphia after a video posted on YouTube by one of the assailants showed them beating a 13-year-old boy, tossing him in a tree and then hanging him from a wrought-iron fence by the hood of his coat. The victim, Nadin Khoury told reporters “There's nothing you can do. You can't fight back or it comes down even worse. You can't say anything except call for help. They do it for no reason. Anybody that's smaller than them, just to get their kicks” (Gay, 2011). This trend of posting online footage of attacks is becoming increasingly common, especially among girls. In April, 2008, a video was posted online showing 16-year old Victoria “Tori” Lindsay, from Lakeland, FL being punched, kneed and slapped by six other girls. The victim is shown huddled in the fetal position, standing and screaming at one point while the assailants continue to beat her. The teens were reportedly retaliating for insults posted on the internet by the attack victim (CNN, 2008). In 2011, a Lifetime movie by the name

8 of Girl Fight was released which is based on the true story of Lindsay. A search for “girl fight” or “girl fight at school” on the internet will produce hundreds of hits and numerous videos similar to the one described above. There are even several websites devoted exclusively to videos of girls fighting. Another incident of a girl being beaten occurred on January, 2012, when a 13-year old from Marion Oaks, FL found herself beaten to the point of unconsciousness by five girls and two boys aged 12 through 15. The incident occurred on a school bus causing her to suffer from a concussion, severe bruising to her head and muscle spasms and was captured on the bus security video (Miller, 2012). The sheriff’s office reported that the victim was riding the school bus for the first time and none of the other students would let her sit down. All seven students have been charged – the five girls each charged with one count of battery and disorderly conduct, one boy with assault and disorderly conduct and the final boy with only disorderly conduct (Persaud, 2012). The use of guns within school grounds has also dramatically increased. In 1999, Elliot et al., reported that the odds of dying a violent death in school were 1 in 2 million. However, in a report released by the Bureau of Alcohol Tobacco Firearms and Explosives, between 2005-2007, school firearms violence investigation was led by actual shootings (54.8%), followed by firearms recovered on school property (22.6%) (ATF, 2008). Ironically, a majority of those involved in school shootings are likely to have endured severe bullying victimization themselves prior to their shooting rampage. A study conducted by the Secret Service and the U.S Department of Education found that on 37 school shootings, including Columbine, almost three quarters of student shooters reported being bullied, threatened, attacked or injured by others; several of them reporting long-term and severe bullying and harassment (Vossekui et al., 2002). In 2001, Time magazine published an article documenting the imprisonment of 12 convicted school shooters, who in total fired 135 shots, killing 21 people and wounding 62. The authors of the article note that, “Almost all the shooters were expressing rage, either against a particular person for a particular affront or, more often, against a whole cohort of bullying classmates” (Roche & Bower, 2001). Among the shooters interviewed was Evan Ramsey, who in 1997 brought a .12 gauge shotgun and opened fire at his high school killing the principle and one other student. He is currently serving a 210-year term in a maximum security prison in Alaska. Ramsey admitted to the authors that he committed his rampage because he was sick of being picked on at school where “Nobody liked me, and I could never understand why” and

9 where his classmates referred to him as “Screech” in reference to a geeky character in the TV show Saved by the Bell (Roche & Bower, 2001). Michael Carneal is another shooter interviewed who was convicted when he was 14 for killing three classmates in West Paducah, KY in 1997 and currently serving a life sentence without a chance of parole for at least 25 years. He pointed out that at the time of his crime he was a freshman and got incessantly picked on for his small stature and quiet manner; he felt going to prison would be better than continuing to endure the bullying at school (Roche & Bower, 2001).

2.1.2 Verbal Bullying Sundance, FL – September, 2009 – Hope Witsell, a 13-year-old, texted a topless photo of herself hoping to get attention from a boy she liked. What transpired as a result of the text was 11 weeks of taunts and bullying that eventually led to Hope taking her own life. The picture she sent was intercepted by another teenager while using the boy’s cell phone and sent it to others. The picture went viral and reached students in Hope’s middle school as well as the nearby high school. She experienced taunts and vulgar remarks thrown at her and noted in her diary (discovered after her death), “Tons of people talk about me behind my back and I hate it because they call me a whore! And I can’t be a whore. I’m too inexperienced. So secretly, TONS of people hate me.” The issue escalated when school officials heard of Hope’s cell phone picture incident and notified her and her parents of a 1 week suspension at the start of the following school year. Further, Hope was excluded from school activities and continued to experience persistent taunts at a summer convention she attended. The day before she took her own life, she noted in her journal, “I’m done for sure now. I can feel it in my stomach. I’m going to try and strangle myself. I hope it works.” She was found by her mother with a pink scarf tied around her neck and the other end around the canopy of her bed. After being taken by ambulance to the local hospital, she was pronounced dead (Inbar, 2009).

Verbal bullying is the use of any language or words to hurt someone (Fried & Fried, 2003). This can include: name-calling, teasing, put-downs, threats, cursing, swearing, yelling, making up stories, gossiping, spreading rumors, racial slurs and insults (www.Stopbullying.gov; Fried & Fried, 2003; Quiroz et al., 2006). A study conducted by the Office for Standards in Education, Children’s Services and Skills in England reported that the most common type of bullying experienced by young children was name calling, with 42% reporting that they were verbally bullied the last time they were bullied (Morgan, 2008). Similar results were found in a study conducted in the United States; among a national sample of school aged students, 53.6% reported being verbally abused in the last 2 months (Wang, Iannotti, & Nansel, 2009). In the example given above, the verbal bullying was a result of “sexting” – the act of sending explicitly

10 sexual messages or imagery electronically, mostly via cell phones. The use of technology and ‘sexting’ also provoked a barrage of verbal taunts for 18-year old Jessica Logan of Cincinnati, OH in 2008 when her ex-boyfriend forwarded explicit photos of her to other girls in their high school following their break-up. She was harassed by the girls who called her names like “slut” and “whore” making her depressed and afraid to attend school (Celizic, 2009). Jessica even conducted an interview on a local Cincinnati television station sharing her story, noting that “I just want to make sure no one else will have to go through this again.” Two months after the interview, Jessica hung herself in her bedroom (Celizic, 2009; Morgan, 2008). Contrary to the popular phrase used in kids playground, “Sticks and stones may break my bones but words will never hurt me,” examples such as the ones above prove that while verbal slings and arrows may not leave any immediate physical scars, it can cause severe damage to one’s self-esteem and confidence and even provoke suicide. Unfortunately, because verbal attacks don’t leave scratches or bruises and often happen when there are no adults around, they can also go unnoticed by parents or teachers. Males have been reported to experience physical and verbal bullying most often while verbal bullying (taunting, sexual insults and spreading rumors) was most common among females (Nansel et al., 2001). In the case of Jessica Logan, her mother states that she did not find out until it was too late. She became aware of the issue when she started receiving letters from the school informing her that her daughter had been skipping school. In fact, she claims that school officials were aware of the harassment but did not do enough to stop it – they simply offered to tell one of the girls, a 16 year old, who had the pictures to delete it and never to speak to Jessica again (Celizic, 2009).

2.1.3 Cyber Bullying Cape Coral, FL – June, 2005 – Jeff Johnston, 15, an honor student was found by his mother and older brother in his bedroom closet where he had hanged himself during the night. Jeff had endured several years of verbal taunts, rumors and cyber bullying at the hands of a fellow classmate. He recalls the bullying having started in the fall of 2002 noting, “out of the blue, this kid I barely knew started trashing me at school, cursing me out under his breath, and telling everyone I was gay." The bully used the internet to post derogatory comments about Jeff, including on the bully’s own blog where he wrote, "...jeff is a fagget [sic]. he needs to die...it seems everythime [sic] i write on the computer i build up rage." Weeks after the suicide, Jeff’s mother found a suicide note in the “trash” of Jeff’s computer: “Hello Friends, I'm just writing to tell you all I won't be in school anymore. I decided to commit suicide because my life is too hard...It's just difficult to explain...I hope none of you miss me...I'm really sorry." (Polk County, 2006).

Estero, FL – January, 2011 – Two teenage girls, 15 and 16 years old, created a Facebook account in a classmate’s name and posted a fake nude photograph of her. The victim’s face was pasted onto the body of a nude female. The victim claims to have suffered through severe ridicule and humiliation from classmates due to the fake profile account (Jabali-Nash, 2011).

Cyber bullying is the repeated use of the internet, mobile phones or other digital technologies to inflict intentional harm upon others (www.Stopbullying.gov, Hinduja & Patchin, 2009). Examples of this type of bullying can include sending mean text messages, writing hurtful or threatening emails, posting negative and defamatory stories online, creating fake social networking accounts, ranking or rating using online websites, blackmailing or terrorizing someone using pictures and rejecting, isolating or excluding someone using technological tools (Quiroz et al., 2006; www.Stopbullying.gov; McGraw, 2008). The example provided above of Jeff Johnston, Hope Witsell and Jessica Logan show how quickly technology can be used to hurt and bully a victim. The key difference to note with this type of bullying and the other types already discussed is the use of technology. Teens today are entrenched in their use and dependency on technology more than ever before. In fact, this generation of teens is the most digitally connected generation. A recent study conducted by the Neilsen Company, How Teens Interact with Media, provides valuable insight into the digital connectedness of today’s teens. The study indicates that among 13-17 year old teens, 83% use their cell phones for text messaging, with a significant gap between the average monthly numbers of text messages vs. phone calls – 2,899 texts vs. 191 phone calls (Neilson, 2011). The usage of text messages has shown a dramatic increase, with average monthly usage growing from 435 texts in 2007 to 2,899 in 2011 (Neilson, 2011). Another study conducted by the Pew Internet and American Life Foundation (Lenhart et al., 2010) indicates that one in three teens send more than 100 texts per day with older teen girls ages 14-17 averaging the most (100 texts per day) and young teen boys the least (average of 20 text messages per day). Text messaging was reported as the primary way that teens reached out to their friends, surpassing face-to-face contact, email, instant messaging and voice calls (Lenhart et al., 2010). The consumption of media has also shown a dramatic increase among teens in recent years. The Henry J. Kaiser Family Foundation recently released a study on the media consumption of 8-18 year olds and identified that the average teen media exposure went up from 12

7 hours 29 minutes in 1999 to 10 hours and 45 minutes in 2009 (Rideout et al., 2010). The report also indicated that nearly half (47%) of the heavy media users reported fair or poor grades (mostly C’s or lower), compared to 23% of light media users. Heavy media users were also found to get in trouble, be sad or unhappy and be bored more often (Rideout et al., 2010). There are also racial disparities in media consumption, Hispanic and Black youth average about 13 hours of media exposure daily, compared to just over 8½ hours among their Whites counterparts (Rideout et al., 2010). In regards to computer related activities, visiting social networking sites such as MySpace or Facebook has been found to be the most popular activity among 8-18 year olds (Rideout et al., 2010). On a typical day, 40% of teens are reported to go on a social networking site and those that do will spend an average of 54 minutes on the site (Rideout et al., 2010). A gender difference is also apparent, boys spend an average of 15 minutes more per day than girls on computers (Rideout et al., 2010). This difference was attributed to boys playing more video games and watching videos on sites such as YouTube. The exception being that girls were found to devote more time than boys while visiting social media sites (Rideout et al., 2010). In the last several years, the increased popularity and usage of social networking sites such as Facebook, MySpace, Twitter and YouTube have made a huge impact on teen’s behaviors, communication and socialization techniques. This technological communication portal is available to teens 24/7 and allows for the development of teen identities in ways not available before. A national poll conducted by Common Sense Media (2009) reveals that 22% of teens check their social networking sites more than 10 times a day while 51% report checking more than once a day. 28% admit that they have shared personal information that they normally wouldn’t have shared in public and another 39% claim to have posted something they regretted (Common Sense, 2009). These sites were also reportedly used for negative purposes – 25% claimed to have shared a profile with a false identify, 26% have pretended to be someone else online, 24% have hacked into someone else’s social networking account, 37% admitted to using the site to make fun of others and 13% claim to have posted nude or seminude pictures or videos of themselves or others online (Common Sense, 2009). There has also been a sharp increase of teens creating and sharing digital materials online. A report from 2007 reveals that among teens aged 12-17, 64% report engaging in online sharing/content creating, with girls dominating over boys (Lenhart et al., 2007). Girl’s blog more

13 than boys as well as post more photos online. Boys however, post more video content online than girls. The report found that 39% of teens have shared their artistic creations such as artwork, photos, stories and videos online, 33% create or work on webpages or blogs for others, 28% have their own online journal or blog, 27% maintain their own personal webpage and 26% claim that they remix content they find online into their own creations (Lenhart et al., 2007). There is also said to be a subset of teens who are super-communicators – those who have access to a multitude of technology options such as landline phones, cell phones, social networking sites, emails and instant messaging. These represent about 28% of the teen population and are most likely to be older teen girls (Lenhart et al., 2007). Given this dramatic shift in teen technology usage it comes as no surprise that there has been a rise in incidents of cyber bullying. Unlike the other two types of bullying already discussed, cyber bullying has some unique advantages for bullies and consequently some detrimental costs to its victims. First, cyber bullying affords anonymity and pseudonymity to bullies. The internet can provide a way for bullies to set-up accounts with false identities, use pseudonyms in chatrooms, leave anonymous comments on blogs and similarly phone numbers can be blocked to send anonymous text messages and make prank calls. This sense of anonymity may embolden bullies; making them exchange more hurtful and malicious comments than they may have been able to face-to-face (Hinduja & Patchin, 2009). Another aspect of cyber bullying is disinhibition (Hinduja & Patchin, 2009; Suler, 2004). As mentioned above, teens may say or do things online that they normally may not do in the real world. This loosening-up, more open expression and less inhibited characteristic is known as the “disinhibition effect” (Suler, 2004). This disinhibition may occur because communication online is asynchronous, meaning people do not interact in real time. Cyberbullies do not have to deal with the immediate reactions of their victims when they bully online (Hinduja & Patchin, 2009; Suler, 2004). There are no physical cues of hurt, dangers of a fight breaking out, verbal backlash or any other immediate consequence (Hinduja & Patchin, 2009; Suler, 2004). Even if the bullying is taking place over instant message exchanges, the lack of physical proximity will provide the bully this sense of disinhibition (Hinduja & Patchin, 2009). Another key feature of cyber bullying is the viral nature of technology (Hinduja & Patchin, 2009). As depicted in the tragic case of Hope Witsell, one picture can instantly go viral and spread like wildfire among an enormous number of recipients in a very short amount of time.

This process is akin to rumors spreading via verbal communication, except this is expedited through technology. This feature also goes with the repetitive characteristic of bullying. Once gone viral, it becomes hard for a victim to control the harassment from not one but now a multitude of sources. Cyber bullying has also created a limitless spatial boundary for bullies. Bullies no longer have to be in close proximity to their victim to cause significant harm and inflict pain. A child who used to avoid attending school to hide from verbal or physical taunts can now be accessed through technology such as his/her cell phone or the internet. Gone is the safety and security associated with a “bully-free” environment such as their home or a religious space; the victim may now feel an increased sense of helplessness and vulnerability and think that there is no way to escape. Experiences such as Jeff Johnston’s have helped bring cyber bullying to the public’s attention. In 2008, three years after his death, the Jeffrey Johnston Stand Up for All Students Act was passed in the Florida legislature requiring all schools in the state of Florida to adopt policies that discourage bullying in person and online or risk losing state funding. In fact, a sign of the pervasiveness and immediacy required is apparent through the fact that currently 35 states have cyber bullying legislature signed into law. The majority of these state laws establish sanctions for all forms of cyber bullying on school property, school buses and official school functions. However, some have also extended sanctions to include cyber bullying activities that originate off-campus, believing that activities off-campus can also have detrimental effects on children's learning environment. The sanctions for cyber bullying range from school/parent interventions to misdemeanors and felonies with detention, suspension, and expulsion in between. Some of these laws also promote Internet safety education or curricula that cover cyber bullying (NCSL, 2011).

2.2 Who are the Bully and the Bully Victim? 2.2.1 The Bully A bully is someone who engages in the act of bullying - a repeated set of intentional behaviors that hurt or harm others and establish a power differential. A bully can be a boy or a girl, can target a single victim or multiple victims and as discussed above can bully in various ways. Bullies may be outgoing and aggressive or a bully can appear reserved on the surface but may try to manipulate people in subtle, deceptive ways, like anonymously starting a damaging rumor just to see what happens. Researchers have studied the thought processes of bullies and

15 categorized various types of bullies. Psychologists have identified between reactive and proactive aggression. Reactively aggressive children are emotional, have poor impulse control, feel constantly threatened and thus reactive in an aggressive way (Dodge & Coie, 1987; Price & Dodge, 1989). They do not see themselves as bullies, rather they feel the need to protect their personal space against any assaults or provocations and instead see the other person as the troublemaker (Dodge & Coie, 1987; Price & Dodge, 1989; Fried & Fried, 2003). In contrast, the proactive bully is non-emotional, controlled and deliberate in their actions (Fried & Fried, 2003). They are selective when picking a target, receive satisfaction from their choosing and enact their aggression with the hopes of achieving an internal goal, such as dominance or coercion, rather than in response to some external threat like the reactive bully (Dodge & Coie, 1987; Price & Dodge, 1989; Fried & Fried, 2003). As discussed above there can be physical bullies who are action-oriented, verbal bullies who use words to attack others, cyber bullies who use technology to inflict their abuse and those that combine and engage in several types of bullying simultaneously. A type of bully that is also associated with verbal bullying is relationship bullies (McGraw, 2008). Relationship bullies use their social groups to exclude or reject a certain person or a group of people and cut off their victims from certain social interactions and connections (McGraw, 2008). This bully can employ several tactics such as stonewalling or the silent treatment where they ignore another child, exclusions from certain peer or social groups, spreading rumors and gossip, taunting someone or making friendships conditional upon certain acts or behaviors in order to prove themselves or become “one of them.” This treatment is commonly seen among girls and among a kind of bully that can be referred to as the elite bully (Fried & Fried, 2003). This bully is one with high status, often based on an attractive appearance, athletic ability, affluence or privileged circumstances due to their parents’ financial or political standing. They use their status to ridicule others, make others feel inferior or excluded and can use their “clique” or social circle to inflict these actions and behaviors (Fried & Fried, 2003; McGraw, 2008). For example a “jock” can ridicule and harass new team recruits and a popular girl may be the leader of a clique/group and determine who makes it in and who stays out of the group. In a study of the popularity of middle school bullies it was found that bullies were among the most popular students in school and female bullies had a greater likelihood of being popular than their male counterparts (Thunfors & Cornell, 2008).

Several studies suggest that bullying in early childhood may increase the risk of negative developmental outcomes. It has been shown that elementary students who were bullies attended school less frequently and were more likely to drop out than other students (Byrne, 1994). Bullies are also more likely to be involved in behaviors such as smoking and drinking alcohol and to do poorly academically (Nansel et al., 2001). Persistent bullies have been found to have the most aggressive attitudes and more likely to get into trouble in school (Carlson & Cornell, 2008). Studies also indicate that bullying can serve as a risk factor for the development of future involvement in delinquency and violent behavior. It has been found that children who are the more aggressive ones at 8 years old were also found to be the more aggressive ones at 30 years old (Husemann et al., 1984; Eron et al., 1987). This early aggressiveness has been shown to be predictive of later antisocial behavior, including criminal behavior, spousal abuse, traffic violations and self-reported physical aggression (Husemann et al., 1984; Eron et al., 1987). Using data from longitudinal studies of students, Husemann et al (1984) note that there are three ideal conditions for learning aggressive behavior: watching others act aggressively (which includes watching aggression on television), being rewarded for acting aggressively and being treated aggressively. The role of aggression within the family and home environment as well as aggressive peers will be discussed later in the chapter. Research has also shown a link between bullying and general misconduct (Bosworth et al., 1999). The National Youth Violence Prevention Resource Center reports that 60 percent of bullies will have a criminal record before age 24, with others studies also reporting similar results - that bullies were several times more likely than their non-bullying peers to commit antisocial acts including vandalism, fighting, theft, drunkenness, truancy and to have an arrest by young adulthood (Farrington, 1991; Olweus, 1993). Bullies can also face legal consequences for their actions. According to the Bully Police Organization (www.bullypolice.org), the foremost lobbying organization in the United States that pressures states to pass school anti-bully laws, 47 states currently have anti-bullying laws. Bullies can be charged for various crimes associated with bullying and there has been an increase in states bringing charges against those involved in bullying. In the bullying and suicide of Phoebe Prince, a 15 year who had moved from Ireland to South Hadley, MA criminal charges was brought against 6 of her bullies. Phoebe’s taunts started when she had a brief relationship

17 with a popular senior boy; some students called her an “Irish slut,” knocked books out of her hand, sent her threatening messages each day and plotted against her using social networking websites (Elkholm & Zezima, 2010a; Elkholm & Zezima, 2010b).). On the day of Prince's suicide, three of the accused, including the male football player who had earlier had the relationship with Prince, allegedly engaged in persistent taunting and harassment of Phoebe at school, in the library and school auditorium. One of the accused allegedly followed her home from school in a friend's car, threw an empty can at her, and yelled an insult. Later that day she hung herself from the stairwell of her home and was found by her 12-year old sister (Elkholm & Zezima, 2010a; Elkholm & Zezima, 2010b). Her assailants, two boys and four girls, ages 16 to 18, faced felony charges that included statutory rape, violation of civil rights with bodily injury, harassment, stalking and disturbing a school assembly (Elkholm & Zezima, 2010a; Elkholm & Zezima, 2010b). Similarly, in the case of Josie Lou Ratley her attacker was charged as an adult with first-degree attempted murder.

2.2.2 The Bullying Victim A victim of bullying can be anyone – boys and girls, of all ages and racial backgrounds. However, certain characteristics have been identified that may make it more likely for someone to become a target of a bully (Perry et al., 1988; Fried & Fried, 2003). Those described as victims tend to be more likely to “reward” their bullies with tangible resources, like giving up lunch money or giving up a table at the cafeteria. They are also more likely to show distress, thus gratifying the bully and letting them feel and see their power. They are also less likely to retaliate against the bully and they are more likely to react on cue to the bullying, thus further encouraging the bully’s behavior (Perry et al., 1988; Fried & Fried, 2003). Fried and Fried (2003) refer to this as the Cry, Comply, Deny, Fly off the Handle Syndrome. They note that bullies thrive on tears – the obvious distress of crying makes bullies feel powerful and more likely to return. Victims that comply – giving up their money, their seat on the bus or their lunch will have bullies return to them due to their lack of resistance. Those that deny abuse by not identifying their bullies due to fear of retaliation will further encourage bullies as they face no consequences for their actions. Finally, victims that unravel and explode as the bully pushes their abuse further and further will have their bully return for more (Fried & Fried, 2003).

Different types of bully targets have also been identified. Olweus (1993) identifies two types of victims – passive and provocative. The passive target is anxious and insecure; they do not invite the bully’s aggression nor do they attempt to defend themselves when attacked. The provocative targets on the other hand create tension by irritating and annoying their bullies and are likely to fight back when attacked. Provocative targets may suffer from ADHD, Asperger’s syndrome or learning disabilities, thus making them unable to pick up on social cues (Fried & Fried, 2003). Research has shown that attention-deficit-hyperactivity disorder (ADHD) is associated with both being a bully as well as being bullied (Holmberg & Hjern, 2008; Unnever & Cornell, 2003). One study reported that children with ADHD are four times more likely than others to be bullies as well as 10 times as likely to have been regular targets of bullies prior to the onset of their symptoms (Holmberg & Hjern, 2008). Obesity has also been linked to bullying (Janssen et al., 2004; Griffiths et al., 2006). Overweight and obese school-aged children are more likely to be both bullies and victims of bullying than their normal weight peers (Janssen et al., 2004). Obesity as a predictor of bullying applies to both boys and girls; preadolescent obese boys and girls are more likely to be victims due to their deviation from expected appearance ideals whereas adolescent obese boys are more likely to be bullies possibly due to their physical size and dominance as compared to others (Griffiths et al., 2006). Another group at high risk of being bully targets is gay/bisexual/lesbian/transgendered (GBLT) teens (Chase, 2001; Kosciw et al., 2010; Norton & Vare, 1998; Berlan et al., 2010). The 2009 National School Climate Survey published by the Gay, Lesbian and Straight Education Network surveyed more than 7,000 GBLT students aged 13-21 over a ten-year period and found that GLBT teens are at higher risk for bullying victimization. Eight in ten GLBT students reported being verbally harassed at school, four in ten had been physically harassed, one in five had been the victim of a physical assault, six in ten felt unsafe at school and seven in ten reported hearing homophobic remarks such as “faggot” or “dyke” directed at them (Kosciw et al., 2010). Furthermore, the reported grade point average of students who were more frequently harassed because of their sexual orientation or gender expression was almost half a grade lower than for students who were less often harassed and increased levels of victimization were related to increased levels of depression and anxiety and decreased levels of self-esteem among GLBT teens (Kosciw et al., 2010).

In a 2001 article written for In These Times, a news magazine published by the Institute for Public Affairs in Chicago, titled “Violent Reaction: What Do Teen Killers Have in Common?” the author addresses the link between violent reactions and labeling of sexual orientation. He notes that the Columbine shooters, Eric Harris and Dylan Klebold had endured repeated harassment due to rumors that they were gay; 14-year old Barry Loukaitis who killed a teacher and two students in Moses Lake, WA had been taunted by jocks who called him a “faggot”; 16-year old Luke Woodham who killed two students and wounded seven others in Pearl, MS was repeatedly called “gay” by his classmates; 14-year old Michael Carneal who killed three students and wounded five others in West Paducah, KY said he had actually been called “gay” in the school newspaper and 15-year old Charles “Andy” Williams who shot 15 students and killed two at Santee, CA was reportedly tormented by his classmates for being a “skinny faggot” (Chase, 2001). Victims of bullying can suffer in a variety of ways including feeling depressed, scared, having low self-esteem and confidence, health problems, physical pains, emotional turmoil, poor grades, suicidal thoughts and suicide (Olweus, 2011). A study noted that students who were bullied reported having greater difficulty making friends and had poorer relationships with their classmates. They were also much more likely than other students to report feelings of loneliness (Nansel et al., 2001). Additionally, youth who are frequently involved in bullying, either as perpetrators or as victims, are more than twice as likely to report having depressive symptoms as those who are not involved in bullying (Saluja et al., 2004). Bullying has also been found to be related to the onset of various mental disorders during adolescence such as psychosomatic symptoms, depression, anxiety, eating disorders and substance use (Kaltiala-Heino et al., 2002). A longitudinal study of 5,813 children born in Finland recently examined whether bullying behaviors at age 8 predicted psychiatric outcomes in adolescence and young adulthood after controlling for psychiatric symptoms at age 8. The study results suggest that bullying may play a role in the development of psychiatric problems during adolescence and young adulthood (Sourander et al., 2009). Among females, being a victim (but not a bully) was associated with a significant increase in the risk for later psychiatric hospitalization and psychiatric medication use. Among males, being a victim (whether alone or when the child is also a bully) was associated with a significant increase in the risk for later psychiatric hospitalization. However, when the authors controlled for

20 psychiatric symptoms at age 8, being a victim of bullying no longer predicted psychiatric hospitalizations or medication use for males (Sourander et al., 2009). Perhaps the most tragic consequence of bullying victimization is suicide. The term ‘bullycide’, referring to suicide that is a result of bullying, unfortunately has now become an occurrence that is happening at increasing frequency. Suicide is the eleventh leading cause of death overall in the United States and the third leading cause of death for people 15 to 24 years old, with more than 4,000 youth dying by suicide each year and many more who consider suicide, make plans to kill themselves, or attempt suicide (Centers for Disease Control and Prevention, 2007). Cases such as Jeff Johnston, Hope Witsell and Jessica Logan’s are tragic examples of teens who committed bullycide. One of the strongest predictors of suicide is one or more prior suicide attempts. The Youth Risk Behavior Survey (YRBS) results from October 2004 to January 2006 indicated that 8.4 percent of all students in grades 9-12 reported having attempted suicide at least once in the 12 months before the survey (Eaton et al., 2006). Studies that compare the rate of suicide attempts among GLBT youth with those among heterosexual youth show significantly higher rates for GLBT youth ((Eisenberg & Resnick, 2006; Remafedi et al., 1998; Safren & Heimberg, 1999). Neil Marr and Tim Field, authors of ‘Bullycide: Death at Playtime’ (2001) investigate the scope of suicide caused by bullying in the UK. They note that every year, at least 16 families in the UK will deal with the tragedy of having their child taking their own lives due to the torment of bullying. It poses a sad reality, that young pre-teens and teens do not see any other option to escape their bullies but through the sacrifice of their own lives. In April, 2011, two 14-year olds, Haylee Fentress and Paige Morvetz hung themselves during a sleepover in what authorities believe was a suicide pact. They were found by Haylee’s mother along with brief suicide notes (McGraw, 2011). In the 2007 book, ‘Bullycide in America’ mothers of children who took their own lives speak out to share their stories and address the painful consequence of bullying that their children had to suffer. Included are the stories of Corinne Wilson (09/30/91 – 10/06/04) who endured relationship bullying and girl on girl bullying through rumors, secrets and exclusion, April Himes (4/27/86 – 2/14/00) who took her own life after enduring 8 months of bullying and many others (High, 2007).

2.3 Theoretical Considerations There have been relatively few theoretical and empirical criminological studies conducted on the predictors of bullying (Bradshaw et al., 2009; Espelange et al., 2000; Hay & Meldrum, 2010; Moon et al., 2011). However, as the discussion below will show, numerous individual and contextual influences could potentially be applied to our understanding of bullying. In order to bridge the gap of criminological research on bullying, the current study will focus on two particularly relevant criminological theories, control theory and social disorganization theory to inform the individual and contextual perspectives respectively. Control theory states that delinquent acts occur when an individual’s bond to society is either weakened or broken. In contrast, social disorganization theory is concerned with where crime occurs. It examines crime and deviance through the social norms, activities and characteristics of the community. Also included is a discussion of additional theories that may be applicable to the examination of bullying from a criminological perspective.

2.3.1 Control Theory The presumption that rule-breaking will occur unless there is something to stop it is the defining characteristic of control theory. Thus, the theory views humans as inherently evil; everyone would act selfishly and take advantage of others if we thought we could get away with it. As Travis Hirschi (1969, p. 34) put it, “The question ‘Why do they do it?’ is simply not the answer the theory is designed to answer. The question is ‘Why don’t we do it?’ There is much evidence that we would if we dared.” The historical roots of the theory can be traced back to the nineteenth century sociologist Emile Durkheim. His basic argument was that as social beings, humans have socially created desires, but no naturally occurring restraints over their appetites. The only possible source of restraint or regulation must be social and can be achieved through the connectedness to a social group (Durkheim, 1897). Thus, moral discipline occurs through our attachment to society, aptly summed by Durkheim when he declared that “we are moral beings only to the extent that we are social beings” (1925, p.64). Travis Hirschi’s (1969) conceptualization of his social bond theory has come to be one of the most prominent formulations of control theory. Hirschi’s theory states that social bonds can come from institutions such as the family, school and the community and emphasize indirect controls that follow from relationships, commitments and values. He identifies four key

22 elements that make up a social bond: attachment to pro-social others, commitment to conventional goals, involvement in conventional activities and belief in conventional rules. Attachment refers to the affective element of the social bond; the desire for the bond and respect of others. The more an individual cares for the opinion of others, the less likely they are to commit delinquent acts. For youth, the most obvious attachment will likely be with their parents. The communication between children and parents as well as values held within a family can influence delinquent behavior. Another source of attachment can be to peers. With youth spending 6-8 hours a day in school surrounded by their peers and social circles, it serves as an important source of influence. In relation, attachment to school can also be an important influence for youth. The quality of the relationship between youth and their teachers or the school in general can make them consider the consequences of engaging in delinquency. Commitment to conventional goals represents the material element of the social bond; the greater an individual’s stake in conformity (Toby, 1957) the more they have to lose by committing delinquent acts. For youth this commitment can come in the form of conventional goals such as graduating from school, maintaining good grades, doing their homework in time, seeking out involvement in conventional activities such as school sports, school clubs, community organizations etc. Involvement is the temporal element; engaging in conventional activities restricts time to commit delinquent acts. The saying “idle hands are the devils workshop” sums up the notion that rule breaking is more likely for those with more un-structured free time. Additionally, it suggests that being committed and subsequently pursing conventional goals would deter someone from delinquency due to the likelihood of jeopardizing the achievement of these goals. Finally, belief can be thought of as the moral element; belief in the legitimacy of the laws and rules of a community restrains us from breaking the law. This implies that people are less likely to engage in delinquent acts if they believe in the validity and justness of the law. If a child does not believe in the rules set by their parents they will be more likely to break it. Similarly, a child may not necessarily agree with the rules at school but still obey the rules because he or she believes that the school has the right to make and enforce these rules. The assumption is that each of these four elements should be negatively related to delinquency because they create “a stake in conformity.” Research has investigated the link between social bonds and general delinquency (Hagan & Simpson, 1978; Kempf, 1993; Krohn &

Massey, 1980; Liska & Reed, 1985; Thornberry et al., 1991) and found correlations between delinquent behavior and one or more of Hirschi’s social bond elements. Kempf (1993) reviewed 71 studies testing Hirschi’s social bond theory and found that overall attachment was the most frequently tested and correlated to delinquency. Attenuated social bonds, such as holding conventional beliefs and belief in conventional rules, has been linked to youth delinquency (Hirschi, 1969; Hill et al., 1999). To examine the role of social bonds in predicting bullying perpetration, the present study includes several measures tapping into the four elements. Bullies have been found to have more supportive beliefs of aggression and violence than non-bullies (Bentley & Li, 1995; Bosworth et al., 1999) and Olweus (1980) noted that the bully is "characterized by an aggressive personality pattern, with a tendency to react aggressively in many different situations, with fairly weak controls or inhibitions against aggressive tendencies, and with a positive attitude towards violence" (Olweus, 1980: 644). It has been shown that bullies are also more likely to influence their peers to engage in bullying others over time (Espelage et al., 2003). The present study thus includes a measure of youth belief (measured through deviant attitudes). Studies have also found that family bonds influence bullying behavior (Bowes et al., 2009; Carney & Merrell, 2001; Espelage et al., 2000; Roberts, 2000; Spriggs et al., 2007; Strassberg et el., 1994). Research on family characteristics of bullies shows that supervision of children’s activities and whereabouts tend to be minimal (Espelage et al., 2000; Roberts, 1988), discipline within the home is inconsistent (Carney & Merrell, 2001), punishment is often physical or in the form of an angry, emotional outburst (Espelage et al., 2000; Roberts, 2000) and the presence of parents in the child’s life is limited (Curtner-Smith, 2000). In order to examine the role of family bonds on bullying behavior, measures such as family opportunities and rewards for pro-social involvement and family attachment are included. The school system can also play a major role in the socialization of youth. A meta- analysis of longitudinal and experimental studies assessing the risk factors of school delinquency has shown that as an instrument of socialization it can serve as a protective factor, putting youth on a pro-social trajectory and preventing them from delinquency; however, it can also serve as a negative socialization tool, increasing the likelihood of following a delinquent and anti-social trajectory (Maguin & Loeber, 1996). Research has shown that misbehavior inside school is related to delinquency elsewhere (Weerman, Harland & Van Der Laan, 2007) and these school offenses can also act as gateways to the juvenile justice system (Greenwald, 2009). Additionally,

24 delinquent students have been shown to have lower educational attainment, which in turn could negatively impact their adult occupational status (Monk-Turner, 1989). Furthermore, a study reviewing the literature on the predictors of youth violence found that serious and violent delinquent youth had more school-related problems (ex: low grades, truancy, suspension, dropping out) than nonviolent youth (Hawkins et al., 1998). A sense of belonging in school has been shown to be negatively associated with bullying involvement (Bosworth et al., 1999) while bullying among middle school boys has be linked to unhappiness at school, disliking school, and depressive symptoms (Slee, 1995; Slee & Rigby, 1993). Attachment and commitment to school have also been related to school misbehavior (Byrne, 1994; Bosworth et al., 1999; Simons-Morton et al., 1999; Stewart, 2003). Bullies have lower commitment to school evident through lower school attendance, a greater likelihood of dropping out, get poorer grades and are more likely to get in trouble at school (Byrne, 1994; Nansel et al., 2001; Carlson & Cornell, 2008). These studies suggest that positive attitudes towards school may be a protective factor against engaging in delinquent behaviors such as bullying in school. To assess the importance of bonding to school in relationship to bullying, variables such as individual attachment to school, school rewards for pro-social involvement, and school grades will be examined.

2.3.2. Social Disorganization Theory In contrast to control theory discussed above, social disorganization is a macro social theory that asks what it is about community structures and cultures that produces different rates of crime. The neighborhood is seen as an important social context within which individual behavior unfolds and the key emphasis is the geographical and spatial distribution of crime. The theory views humans as inherently social, abiding by the norms and values of the groups to which we belong. Society is characterized by some degree of normative variation and people are said to commit delinquent acts if they get involved with groups that promote deviant norms and values. Social disorganization theory’s historical roots go back to the early 20th century to sociologists from the University of Chicago who were collectively known as the Chicago school. These sociologists were interested in correlating the crime trends of neighborhoods to the characteristics of those neighborhoods. Sociologists Clifford Shaw and Henry McKay (1942) conducted the first large scale study of crime in the U.S. by collecting official data on

25 delinquents in Chicago and corresponding the home addresses of each delinquent on the map. Their study found that three key structural variables, residential mobility, racial heterogeneity and concentrated poverty led to community social disorganization, which in turn increases crime and delinquency rates (Shaw & McKay, 1942). They found that delinquency had a clear geographical distribution and that the neighborhoods with high rates of delinquency were characterized by distinct social conditions such as concentrated poverty, strong racial and ethnic segregation and rapid population turnover. Furthermore, they noted that despite the high population turnover, the areas with high delinquency continued to remain high regardless of who lived there. This suggested that characteristics of the area, not the composition of the residents, were linked to crime and delinquency (Shaw & McKay, 1942). In general terms, social disorganization refers to the diminished capacity of a community to realize the common values of its residents and maintain effective social controls. Delinquency is thus rooted in the dynamic life of a community. A socially organized community is said to have solidarity through an internal consensus on values and norms, cohesion among neighbors and integration through social interaction (Kornhauser, 1978; Shaw & McKay, 1942). Shaw and McKay (1942) noted that higher delinquency occurred within low-income, ethnically heterogeneous and unstable locations. Low income neighborhoods may make some feel safe only within their own home (Rainwater, 1966), ethnic heterogeneity may increase language and cultural barriers and residential instability will make it difficult to know and rely on your neighbors. A study has shown that student success is dependent on not only what the student brings to the school environment but also what the school environment provides to the student (Gentle-Genitty, 2009). Thus, an environment that promotes social bonding within the schools is an important part of an individual’s social bond to school and could shed light on a deeper understanding on the environmental influences on bullying behaviors. It has been suggested that disorganized school environments similar to disorganized neighborhoods will be unable to maintain effective social controls. School-level indicators of disorder such as student-teacher ratio, concentration of student poverty, student mobility, school size and school location has been linked to diminished school climates and increased risk of violence (Bevans et al., 2007; Birnbaum et al., 2003; Bradshaw, Sawyer & O’Brennan, 2009; Stewart, 2003). However, there are very few studies that have specifically examined the role of school-level factors on bullying related behaviors and attitudes.

Research indicates that the social context of schools is associated with levels of delinquency and victimization (Anderson, 1982; Astor et al., 2002; Stewart, 2003; Wilcox & Clayton, 2001) and recently, studies have also begun to examine the role of school climates in supporting bullying attitudes and behaviors (Charach et al., 1995; Cowie & Olafsson, 2000; Cullingforda & Morrisona, 1995; Naylor & Cowie, 1999; Unever & Cornell, 2003). This bullying culture, a school climate that supports bullying behaviors (Unever & Cornell, 2003) can be defined as a multidimensional phenomenon characterized by a normative set of shared beliefs that support or encourage bullying behavior (Smith & Brain, 2000; Unever & Cornell, 2003). It has been shown that it is not a problem of isolated incidents and circumstances rather it is a pervasive part of the school social life affecting all students in one form or another (Cullingforda & Morrisona, 1995). Given that the school is the most commonly cited location of both bullying victimization and engaging in acts of bullying, the role of school and school district characteristics on bullying related experiences is important to understand. Residential mobility has been found to be a key component of social disorganization (Shaw & McKay, 1942). Similarly, student mobility has also been linked to higher likelihood of involvement in delinquency (Wilson, 2004). However a study assessing the influence of student mobility on bullying found that it reduced the likelihood of bullying involvement. This may be due to the fact that bullying often requires a social hierarchy and an established social network (Thunfors & Cornell, 2008). To further examine this variable, the present study will include an aggregate level measure of student mobility across each school. School size has also been linked to school delinquency, with increased likelihoods of peer victimizations, reduced perceptions of safety and higher rates of aggressive behaviors found within larger schools (Stewart, 2003; Warner et al., 1999; Wilson, 2004). Larger schools may alienate more students, possibly leading to lower levels of school connectedness and belonging (Warner et al., 1999). To examine the influence of school size on bullying behaviors, the enrollment size of each school district is included in the present study. Some researchers however, have suggested that a measurement of the ratio of students to teachers is a better predictor of the school environment rather than school size due to a decrease in the effectiveness to manage student behavior (Olweaus, 1993, Unever & Cornell, 2003). It has been found that high student-teacher ratio decreases feelings of safety at school and increases likelihood of

27 victimization (Unever & Cornell, 2003). Thus, a measure of instructional staff for each school district will also be included. Structural aspects of a neighborhood such as concentration of poverty have been found to affect the level of organization in a neighborhood as well as the collective efficacy of its residents (Sampson et al., 1997). Similarly, a high concentration of student poverty has been found to diminish school climate and promote a higher likelihood of school violence (Barnes et al., 2006; Bevans et al., 2007; Welch et al., 1999). Several recent studies (Due et al., 2009; Pickett & Wilkinson, 2007) have found that income inequality increased the odds of bullying victimization and another found an even stronger association between income inequality and bullying perpetration (Eglar et al., 2009). These findings provide support to the argument that bullying can be sensitive to class differences and social stratifications. A measure commonly used to establish student SES is the number of students eligible to receive free or reduced lunches at the school. Thus, a measure of the concentration of student poverty will be included at the school district level.

2.3.3. Additional Theoretical Considerations Another theory that has received some criminological attention in relation to bullying and can be applied to the present study is general strain theory. General strain theory posits that several kinds of strain, such as failure to achieve positively valued goals, the loss of positive- valued stimuli and the presentation of negative stimuli can lead to negative affective states including anger, fear, frustration or depression that can then lead to delinquency (Agnew, 1992, 2001). At the individual level it has been found that youths’ experience of physical punishment, maltreatment and rejection by parents, peers and teachers are related to bullying (Espelage et al., 2000; Moon et al., 2011; Olweus, 1993; Patchin & Hinduja, 2011) and anger has been shown to be a powerful predictor of bullying behavior (Bosworth et al., 1999; Espelage et al., 2000). Behavioral problems were also linked to bullying – those with internalizing behavioral problems such as anxiety and depression had a 20% increase in the likelihood of becoming a victim but a 20% decrease in the risk of becoming a bully. On the other hand, children with externalized behavior problems such as aggression were associated with a 120% increase in the likelihood of becoming a bully (Bowes et al., 2009). Another study looking at peer victimization and feelings of self-worth found that high levels of self worth served as a protective factor

28 against anxiety after victimization experiences, while low levels of self-worth served as a risk factor for anxiety (Grills and Ollendick, 2002). Furthermore, children who utilized emotionally oriented coping skills such as avoidance behavior and non-cognitive problem solving was positively associated with bullying and victimization whereas problem solving coping skills that use pro-active strategies usually involving thought and planning were negatively associated with bullying and victimization (Baldry & Farrington, 2005). A measure of students’ level of self- esteem will be included as part of the variables assessed. Social learning theory on the other hand notes that all behavior, including criminal behavior is learned, in particular through the principles of definitions, differential associations, differential reinforcement and imitation (Akers, 1998). Family, friends, teachers and authority figures can all serve as models of behavior. It has been found that the severity in parental punishment practices is associated with higher levels of children's subsequent aggression toward peers (Strassberg et el., 1994) and sibling involvement in delinquency has also been shown to be related to a child’s involvement in delinquency (Hill et al., 1999). Parental spanking and other physical punishment was found to be related for both boys and girls to childhood aggression and bullying (Strassberg et el., 1994). On the other hand, positive parental support and positive adult role models has been shown to be negatively associated with involvement in bullying (Espelage et al., 2000; Wang, Iannotti, & Nansel, 2009) and the likelihood of bullying is significantly reduced for children exposed to adults who suggest nonviolent strategies to manage conflicts. Given the role and effect of the family environment on a child’s decision to imitate and act out aggression the present study includes several family characteristics that could influence bullying behaviors such family conflict and family members with alcohol and drug problems.

2.3.4. Demographic Characteristics Age, sex, and race are all significant predictors of youth violence (Braithwaite, 1989). Bullying has been shown to be experienced by both boys and girls, with boys reporting more physical and verbal bullying and girls noting that they experience more verbal/relationship bullying (Bradshaw, Sawyer & O’Brennan, 2009; Finkelhor et al., 2009; Harris et al., 2002; Wang, Iannotti, & Nansel, 2009). In line with the types of bullying involving each gender, it has been shown that power-related proactive aggressiveness was found to be predictive of bullying among boys, whereas affiliation-related proactive aggression was found to be a stronger

29 predictor of bullying among girls (Roland & Idsøe, 2001). Additionally, girls are more commonly involved with forms of verbal aggression while boys are more likely to show physical aggression (Crick & Nelson, 2002; Wang, Iannotti, & Nansel, 2009). Girls have also been more likely to be involved with cyber bullying than boys (Hinduja & Patchin, 2009). Traditional forms of bullying has been shown to peak among middle school aged children and decreases through high school (Bradshaw, Sawyer & O’Brennan, 2009; Nansel et al., 2001). Cyber bullying, in contrast, tends to peak later in middle school and high school (Patchin & Hinduja, 2011; Williams & Guerra, 2007; Wolak et al., 2007). Previous studies on racial differences in bullying have been mixed (Bradshaw, Sawyer & O’Brennan, 2009; Nansel et al., 2001; Seals & Young, 2003; Ybarra et al., 2007). Some studies indicate that minority youth are more likely to be victimized (Nansel et al., 2001) and African American and Hispanic students were less likely to report being a victim of bullying while their White counterparts were less likely to report being a bully (Bradshaw, Sawyer & O’Brennan, 2009). A study on cyber bullying found no racial differences (Ybarra et al., 2007) while another (Hinduja & Patchin, 2009) found that white students were slightly more likely to experience cyber bullying both as a victim and offender.

CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

The present study seeks to accomplish two objectives. The first objective is to address the prevalence of bullying perpetration; who is involved in bullying, where does bullying take place and are certain demographic characteristics associated with higher involvement in bullying? The second objective is to identify the individual and environmental influences that contribute to bullying perpetration. Both objectives will seek to identify the prevalence, individual and environmental influences for three types of bullying – verbal bullying, physical bullying and cyber bullying. The research questions are drawn from the existing theoretical and empirical literature and link individual level as well as school and school district-level characteristics to bullying perpetration.

Q1: What is the prevalence of bullying perpetration among Florida middle and high school students? Q2: How are individual level social bond, strain and social learning characteristics associated with bullying perpetration? Q3: Given that school-level variation exists, what is the influence of school level indicators of disorganization on bullying perpetration? Q4: Given that school-district variation exists, what is the influence of school-district level indicators of disorganization on bullying perpetration?

3.1 Data Sources Data for this study comes from multiple sources. The outcome variables – bullying perpetration as well as individual level predictors utilizes self-report student data from the 2010 Florida Youth Substance Abuse Survey (FYSAS). Level-2 data are at the school level and include aggregate level data from the FYSAS. The level-3 contextual variables are derived from the Florida Department of Education. The Florida Youth Substance Abuse Survey (FYSAS) is a survey administered annually throughout the state of Florida and is a collaborative effort between the Florida Departments of Children and Families, Health, Education and Juvenile Justice under the leadership of the

Governor’s Office of Drug Control. It is based on the Communities That Care Youth Survey, developed from the works of Dr. J. David Hawkins and Dr. Richard F. Catalano. The Communities that Care Youth Survey was designed to identify risk and protective factors across the domains of community, school, family, peer and individuals to predict alcohol, tobacco, other drug use and delinquent behavior (Arthur et al., 2002). Thus the FYSAS survey was originally intended to determine the levels of risk and protective factors faced by Florida youth and correlate those levels to alcohol, tobacco and other drug use rates (Florida Department of Children and Families, 2008). The survey was first administered to Florida's middle and high school students during the 1999-2000 school year and is repeated in the spring, annually. In the spring of even years, the survey is administered simultaneously with the Florida Youth Tobacco Survey, sampling enough students to generate data applicable at the county and DCF district level. In odd years the Youth Risk Behavior Survey and the Youth Physical Activity and Nutrition Survey are also added. All surveys are administered to a statewide sample of students (FYSAS, 2010). The data utilized for this study is from the 2010 FYSAS dataset. The dataset is a representative sample of the state of Florida and includes all 67 school districts, covering middle school and high school students from grades 6 thru 12. After dropping missing cases using listwise deletion, the final sample size of the present study is N= 41,098 students, nested across 681 schools and 62 school districts. The sample has approximately 46 percent males and 54 percent females. The original ethnicity options for the survey included White/Caucasian, African American/Black, Asian, Hispanic/Latino, Native American, Native Hawaiian/Pacific Islander and Other. For the purposes of this study, ethnicity was collapsed into the following five categories for analysis: White, African America, Hispanic, Asian and Other (Native American, Hawaiian/Pacific Islander and Other). Approximately 57 percent of the sample self-identified themselves as White. Hispanics were the largest minority group at 19 percent, followed by 17 percent who self-identified as African American, 3 percent as Asian, and 4 percent as Other. For the analysis, the reference group for all ethnic groups was White (=0). Grade cohorts were roughly evenly distributed across middle school grades (6 though 8) at about 17 percent for each grade and high school respondents sampled amounted to 12, 14, 13 and 10 percent respectively for grades 9 through 12. Table 1 presents demographic information for the sample of this study.

Table 3.1: Demographic Characteristics Number of Youth (Percentage) Gender Male 18,905 (46%) Female 22,193 (54%) Race White (reference group) 23,425 (57 %) African American 6,987 (17 %) Hispanic 7,809 (19 %) Asian 1,233 (3 %) Other 1,644 (4 %) Grade Level 6th Grade 7,068 (17.2%) 7th Grade 6,987 (17%) 8th Grade 6,905 (16.8%) 9th Grade 4,932 (12%) 10th Grade 5,754 (14%) 11th Grade 5,343 (13%) 12th Grade 4,109 (10%)

3.2 Measures 3.2.1 Outcome Variables The outcome variable for the present study is bullying perpetration with three types of bullying assessed – physical bullying, verbal bullying and cyber bullying. Bullying perpetration was measured by self-report questions asked of each respondent. Each respondent was asked the following three questions in regards to bullying: Q1: During the past 30 days, have you repeatedly taunted, teased, name called, excluded or ignored another person in a mean way? (Verbal Bullying) Q2: During the past 30 days, have you repeatedly hit, kicked, shoved someone, caused someone physical harm/injury or taken someone’s money or belongings without their permission? (Physical Bullying) Q3: During the past 30 days, have you repeatedly sent mean emails, text messages, IM’s or posted hurtful information on the Internet about another person? (Cyber Bullying)

Similar self-report measures have been used in previous studies addressing the prevalence of bullying related incidents (Eaton et al., 2010, Dinkes, Kemp, & Baum, 2009, Elger et al., 2009, Nansel et al., 2001; Seals & Young, 2003). Bullying participation responses were coded as 0= No, 1= Yes.

3.2.2 Predictor Variables Several multi-item scales were created for the level-1 explanatory variables. In creating the scales, correlation matrices were created to observe associations between the single items. This was followed by a principle components analysis to verify the uni-dimensionality of each scale. Items that needed re-coding were re-coded and items for each scale were then standardized and summed to create an index. Lastly, alpha reliability coefficients were calculated for each multiple item scale to measure its internal reliability. The level-2 data (school level) was also acquired from the FYSAS self-report data and includes aggregate level variables at the school level. Data for the final level, level 3 was acquired the Florida Department of Education. The data was converted from their original file forms into files that could be loaded into the appropriate statistical packages. Once all individual, level 2 and level 3 data were gathered, time was spent accessing and cleaning the data for each “level” of the data in a data management program. While doing this a thorough evaluation of the data was conducted at each level, looking for coding errors, unusual values, assessing normality, etc. This increased familiarity with the data in a univariate, bivariate and multivariate context. The statistical software that is utilized to estimate the descriptive statistics to measure prevalence, one-way ANOVAs and data preparation were carried out using SPSS 18 (SPSS Inc.) while the multi-level analyses and models of this study were carried out using Hierarchical Linear and Nonlinear Modeling 7 (HLM) for Windows (SSI Inc.). For the final stage of the data preparation, a multilevel system file was created to merge all levels to load from SPSS into HLM.

3.2.3 Individual-Level Predictors (Level 1 Data) Several individual level predictors are included in the present study. Individual level predictors included scales measuring respondents prior delinquency (α = .77) tapping into the number of times the respondent has engaged in delinquents acts in the past 12 months. Prior delinquency was measured using 8 items: “Number of times in the last 12 months: been suspended / carried handgun / sold illegal drugs / stolen vehicle / been arrested / attacked to hurt / drunk or high at school/ taken a handgun to school?” Several scales were created to tap into Hirchi’s elements of a social bond. A scale measuring deviant attitudes (α = .79) is also included which includes 5 items asking respondents “How wrong is it: to take a handgun to school / to steal an item worth more than $5 / to pick a fight / to attack with the intention of hurting / to stay away

34 from school all day?” School opportunities for pro-social involvement was measured using a 5- item scale (α = 0.65), “In my school, students have lots of chances to decide things like class activities and rules,” “There are lots of chances for students in my school to talk with a teacher one-on-one,” “Teachers ask me to work on special classroom projects,” “There are lots of chances for students in my school to get involved in sports, clubs and other school activities outside of class,” and “I have lots of chances to be part of class discussions and activities.” School rewards for pro-social involvement (α = 0.70) was measured using a 4-item scale asking students the following statements: “My teachers notice when I am doing a good job and let me know about it,” “The school lets my parents know when I have done something well,” “I feel safe in my school,” and “My teachers praise me when I work hard in school.” School attachment (α = .79) was measured using a 7-item scale: “How often do you feel that the school work you are assigned is meaningful and important?” “How interesting are most of your courses to you?” “How important do you think the things you are learning in school are going to be for your later life?” “Thinking back to the past year in school, how often did you enjoy being in school? / Hate being in school? / Try to do your best in school?” “During the last four weeks, how many whole days have you missed because you skipped or cut school?” School mobility (r = .57**) was measured using 2-items: “Have you changed schools in the past year?” and “How many times have you changed schools since kindergarten?”School grades (r = .48**) were also measured using 2-items: “What were your last year’s grades?” and “Are your grades better than others?” Several measures of family social bonds are also included. Family opportunities for pro- social involvement (α = .77) was measured using a 3-item scale asking “My parents give me lots of chances to do fun things with them,” “My parents ask me what I think before most family decisions,” and “If I had a personal problem, I could ask my mom or dad for help.” Family reward for pro-social involvement is measured using a 4-item scale: “My parents’ notices when I am doing a good job and let me know about it,” “How often do your parents tell you they’re proud of you for something you’ve done?” “Do you enjoy spending time with your mother?” “Do you enjoy spending time with your father?” Lastly, family attachment (α = .77) was measured using a 5-item scale which included the following statements: “Parents ask if I’ve gotten my homework done,” “When I am not at home, one of my parents knows where I am and who,”“Parents would know if I did not come home in time,” “Rules in my family are clear,” and “My family has clear rules about alcohol and drug use.”

A measure of emotional state related to strain is also included. A self-esteem scale (α = .84) consisted of 3-items asking respondents to agree or disagree with “Life not worth it,” “I think I am no good” and “I am a failure.” Additionally, two variables related to social learning are also included. Family conflict (α = .80) was measured using a 3-item scale: “Family has serious arguments,” “Family insults or yells at each other” and “Family argues about the same things over and over.” Family members with alcohol or drug problems were measured by asking the respondents, “Has anyone in your family ever had a severe alcohol or drug problem?” Gender was a dichotomous dummy variable with males (= 1) and females (= 0) as the comparison group. Ethnicity was measured by a set of dichotomous variables: 1 = African American, 1 = Hispanic, 1 = Asian and 1 = Other. White (=0) was the reference group for all ethnicities. For the full list of questions included in each scale and the operationalization of each scale refer to Appendix A.

3.2.4 School-Level Predictors (Level 2 Data) To measure school level influences on bullying, the present study employs school-level means (individual responses averaged within schools). By utilizing both individual scale scores and school-level aggregate measures, estimates for both the individual (within schools and between schools) and the contextual influences of school predictors can be obtained and examined. Past studies that examine schools as social contexts that vary on youth delinquency have used similar aggregate analysis (Ousey &Wilcox, 2005, Felson et al., 1994). Multiple school level measures were aggregated from the individual level scale (see above and Appendix A for the scale description) and averages were created for each school district. These aggregate measures included measures of school opportunities for pro-social involvement, school rewards for pro-social involvement, low school attachment, school mobility and school grades at the school level.

3.2.5 School District-Level Predictors (Level 3 Data) School district level data was obtained for the 2009-2010 school year from the Florida Department of Education. The measure for concentration of student poverty is derived through the percentage of students eligible for free or reduced-price lunch. Enrollment size is measured using the total number of students in school as measured during the fall survey period in October; also known as fall membership. The data utilized for this study uses fall 2010 membership rates. Instructional staff was measured using the total number of instructional staff 36 employed at each school district during the 2009-2010 school year. Table 2 displays descriptives for all measures utilized in the present study.

Table 3.2: Descriptives of Outcome, Level-1, Level-2 and Level-3 Variables

VARIABLES Mean Standard Minimum Maximum Valid Deviation N Perpetrating Bullying (Outcome Variables) Verbal Bullying (0= No, 1= Yes) 0.19 0.40 0 1 41,098 Physical Bullying (0= No, 1= Yes) 0.10 0.31 0 1 41,098 Cyber Bullying (0= No, 1= Yes) 0.06 0.23 0 1 41,098 LEVEL 1: Individual Level Variables Prior delinquency 1.05 3.11 0.00 45.00 41,098 Deviant attitudes 3.09 2.92 0.00 15.00 41,098 School opportunities for pro-social 8.88 2.66 0.00 15.00 41,098 involvement School rewards for pro-social 6.29 2.57 0.00 12.00 41,098 involvement School attachment 10.01 4.73 0.00 24.00 41,098 School mobility 1.98 1.29 0.00 5.00 41,098 School grades 4.84 1.50 0.00 7.00 41,098 Low self esteem 2.33 2.48 0.00 9.00 41,098 Family attachment 11.31 3.21 0.00 15.00 41,098 Family opportunities for pro-social 5.84 2.42 0.00 9.00 41,098 involvement Family rewards for pro-social 8.03 3.02 0.00 12.00 41,098 involvement Family conflict 3.53 2.52 0.00 9.00 41,098 Family members with alcohol or drug 0.37 0.48 0.00 1.00 41,098 problems LEVEL 2: School Level Variables School opportunities for pro-social 8.82 0.63 6.29 11.38 681 involvement School rewards for pro-social 6.33 0.67 4.11 10.15 681 involvement School attachment 9.83 1.36 4.91 13.07 681 School mobility 1.94 0.36 0.73 2.93 681 School grades 4.75 0.35 3.82 5.86 681 LEVEL 3: School District Level Variables Concentration of student poverty 22,785 40,477 683 243,751 62 Enrollment size 40,226 65,607 1,104 347,406 62 Instructional staff 2,880 4,586 79 23,902 62 37

3.3 Analytic Methods Most social science relationships are often multifaceted, representing relationships between individuals and society and structured in a hierarchical fashion (in this case, students nested within schools and school districts/counties). Individuals can be influenced by the social groups they encounter and/or the environments they inhabit. Similarly, these groups and environments can also be influenced by the individuals living within them (Hox, 2010). Thus, for the purposes of the study, multi-level modeling techniques will be utilized which will allow for the variance in the outcome variable (bullying perpetration across the different types of bullying) to be assessed at multiple hierarchical levels. This method provides benefits that methods such as simple or multiple linear regressions cannot provide. In multilevel modeling, level 1 is the individual variables or within group differences and level 2 and above consists of the contextual variables or between group effects. If a researcher were to disaggregate all higher order variables to the individual level then it violates the assumption of independence of observation stipulated in ordinary least squares regression that all individuals in the same group have the same values in the group level variables. Similarly, if a researcher aggregated all individual level characteristics to the higher level and does the analysis on the higher level then they would lose all the within-group information which may account for a large percentage of the variance from the outcome variable. Also, by interpreting aggregate data on the individual level it can distort the interpretation, leading to an ecological fallacy. This is an error where inferences about the nature of specific individuals are based solely upon aggregate statistics collected for the group to which those individuals belong. As a result the researcher assumes that individual members of a group have the average characteristics of the group at large (Raudenbush and Byrk, 2002). First, to measure the prevalence of bullying perpetration, descriptive statistics will be conducted using the statistical software SPSS 18 (SPSS Inc.) This will provide a summary of the self-reported data from the respondents about the frequency of bullying experiences they encounter. Additionally, one-way ANOVAs will be conducted to test whether there is a difference in bullying perpetration across race, gender and grade levels for each type of bullying. Simply put, this will allow us to compare means of the bullying experiences across these groups and to see if there are any significant differences. Next, the HLM 7 for Windows (Hierarchical Linear and Nonlinear Modeling) statistical package software made by Scientific Software International will be used for the multi-level data

38 analysis. For each of the three dependent variables – verbal, physical and cyber bullying – four models using the HLM software will be predicted. A three-level model will be utilized using a single cross-section of data consisting of students (level 1), nested within schools (level 2) nested within school districts (level 3). Since all three outcomes were dichotomous, a Bernoulli model and a full penalized quasi-likelihood (PQL) (Raudenbush et al., 2004) with LaPlace estimation of maximum likelihood (Raudenbush et al., 2000) was utilized. Estimates are reported from the population average models with robust standard errors. Population-average models are reported as opposed to the unit-specific model since this study is interested in estimated predicted probabilities for the whole population. The aim of the study is to identity the differences between those who do and those who do not bully, thus a population-average estimate is appropriate. Population-average inferences are based on fewer assumptions than unit-specific inferences and are therefore more robust to erroneous assumptions about the random effects in the model (Raudenbush & Bryk 2002). The first model will be the unconditional model or the one-way ANOVA model with random effects which will be used as a baseline to predict the outcome variables based on an intercept term and an error term at level 1. In this model, there will be no predictors/explanatory variables; however, the level 1 intercept will be predicted as a function of the level two and level three grouping variables. This model provides a useful baseline for purposes of comparison with the subsequent models, and also allows for the partition of the total variability from each of the three components (Raudenbush and Byrk, 2002):

representing the variability of level-1 units in outcome representing the variability of level-2 units in outcome representing the variance component associated with level-3 units

Note that the variance component for binary outcomes does not contain an estimate of level 1 variance (2). That is because in logistic regression models, it is not possible to estimate both the coefficients and the error variance; therefore, in all logistic regression models, the error variance is always fixed to the same number which is 2/3 = 3.29 (Snijders & Bosker, 1999).That rule also applies to multilevel models, but only to their level 1 residuals. To estimate the proportion of variance at each of the levels, the following formulas will be used:

The proportion of variance over level-1 units

The proportion of variance over level-2 units

The proportion of variance over level-3 units

The intercept-only model also gives us an estimate of the interclass correlation (ICC). Using the between unit variance and within unit variance, the ICC will be calculated, which is a measure of the correlation among the individual observations within the groups. In other words, it will tell us the percentage of variance between the groups. For this study, the ICC is defined at the second and third levels. If the ICC is close to 0 and is not statistically significant then it indicates that there is no significant variation in the dependent variable across groups. In this case there would be no difference in results between conducting an ordinary regression and a multilevel model. However, if the ICC is statistically significant it notes that there is statistically significant variance across groups in the dependent variable. Then in this case it will be appropriate to utilize hierarchical linear modeling and investigate how much of the variance is explained by level 1, level 2 and level 3 variables. The models thereafter are general structural models at each level. The second model, the conditional model or a random intercept model with level-1 covariates includes all the level 1 variables varying randomly over level 2 and level 3. This level of analysis will show the effects of each level-1 explanatory variable and whether each predictor is significantly related to the 2 2 type of bullying perpetration. The proportion of variance explained can be calculated as:  F /  F

2 +  + 0 + In addition to the level 2 intercept variance 0, level 3 variance and level 1 2 2 variance  R = 3.29, we needed to know the variance of fitted values  F. (Snijders & Bosker, 1999).To obtain the variance of fitted values, a level 1 residual file was created through HLM and the variance of the FITVAL variable containing the linear predictor values was calculated 2 using SPSS. These R square values are typically lower than values used with OLS because  R is a fixed number.

In the third model, level 2 predictors are added and show whether the variability across schools remain after controlling for individual-level characteristics as well as the influence of the level-2 predictors. Lastly, the final fully conditional model will be included if there is between- school variation and to assess the influence of school district-level measures. It will include levels 1 and 2 and add in level-3 variables. Variables at all levels were grand-mean centered. In multiple regression analysis, the intercept is interpreted as the expected value of the outcome variable, when all explanatory variables are zero. However, in many cases zero may not be a possible value. To make ‘zero’ a legitimate, observable value we perform a linear transformation and center the explanatory variables. By grand centering, the 0 values will fall in the middle of the distribution of the predictors, the intercept estimates will be estimated with much more precision. It will also make the intercept interpretable. Specifically, it will represent the group-mean value for each student with a (grand) average on every predictor. Since I was interested in the absolute values of level 1 and 2 variables instead of the relative position of each person with regards to their group’s mean, grand-mean centering was utilized instead of group-centering (Hox, 2010). Researchers have also noted that group-centering is more suited when testing hypotheses about the relationship between two level 1 variables and when the hypotheses involve interactions among level 1 variables (Enders & Tofighi, 2007). Since the present study did not examine interaction effects, grand-mean centering was most appropriate.

CHAPTER FOUR

DISCUSSION & RESULTS: PREVALENCE & COMPARISON OF MEANS

4.1. Prevalence This chapter addresses the first research question and presents the results of the prevalence of bullying perpetration among a state-wide representative sample of Florida middle and high school students. This chapter provides some of the most in-depth information to date on bullying prevalence across the state of Florida. The use of recent data from 2010 and the examination of the incidence rates for verbal, physical and cyber bullying, where bullying takes place as well as a comparison of involvement among various demographic groups provides rich and detailed insights into bullying prevalence in Florida schools. Among the fifty largest school districts in the nation, ten are within the state of Florida. With the diverse and large population of students covered in this study, these findings can contribute to the theoretical framework and support the need to examine the influencing characteristics of bullying. It can also inform key components that should be part of bullying prevention and intervention programs.

4.1.1. Bullying Perpetration The data used for the present study asked students about their bullying participation during a 30 day period. In 2010, during a 30 day time-frame, approximately 20.4% (8,383 students) reported verbally bullying another student, 11.3% (4,644 students) reported physically bullying others and 6.2% (2,548 students) claimed to have used technology to cyber bully others matching bullying prevalence rates found in studies using a national sample of U.S students (Eaton et al., 2010, Dinkes, Kemp, & Baum, 2009, Nansel et al., 2001). Theoretically, one could multiply these numbers by 12 months which could show even higher numbers for an annual count of bullying perpetration incidents. Though it could be argued that not every month during the year will have similar rates of bullying – summers could have lower rates due to school closures and vacations while the beginning of a school year could have increases due to new students or a change in social hierarchy within the school.

Previous studies (Olweus, 1993; Bradshaw et al., 2009) suggest that lower status students and those transitioning from elementary to middle school are most vulnerable to bullying victimization. Support for this can be seen in the present sample as well; the group most worried and fearful about bullying was 6th graders, with 34.8% admitting concern followed by 7th graders (30.3%) and gradually decreasing with each grade level. 12th graders reported the least concern at 14.5%. The actual number of bullying perpetration rates can thus be higher or lower than the ones reported above if an annual rate is to be calculated. In 2008-2009, the Florida Department of Education recorded 6,308 bullying related cases for the entire state. This count includes all types of bullying and based on their data we cannot differentiate between each specific type or between victimization and perpetration. However, it can be argued that because a bully can victimize more than one student at the same time or through the use of more than one method of bullying and due to the issue of under-reporting from bully victims, there may be discrepancies with the officially recorded numbers as well. Previous studies have found similar results, with many students and witnesses failing to report their bullying experiences (Rigby & Slee, 1999; Smith et al., 1999).

4.1.2. Bullying Victimization While the focus of the present study is bullying perpetration, this chapter also includes numbers from self-reported incidents of victimization for descriptive purposes. In 2010, within a 30 day period, 30.6% (12,575 students) reported being victims of verbal bullying, 14.2% (5,835 students) noted being victims of physical bullying and 9.3% (3,822 students) admitted to being victims of cyber bullying. Figure 4.1 compares the number of self report bullying perpetration and victimization. The numbers indicate that there are more self-identified victims than bullies in the present sample. This could be the case for several reasons. As mentioned above, bullies can victimize multiple students and even under-report their bullying behaviors in surveys. Similarly, victims may also be under-reporting their level of victimization as well (Ladd & Ladd, 2001; Olweus, 1993; Rigby & Slee, 1999; Smith et al., 1999). Studies have found that the likelihood of telling an adult about bullying victimizations increased with the frequency of being bullied (Smith, 1999; Unnever & Cornell, 2004); while another argues that chronic victimization can overwhelm a child’s resources, debilitate their coping mechanisms and undermine their trust in school authorities, causing them to feel too isolated to seek help (Ladd

& Ladd, 2001). Students may also be more likely to report their victimization based on the type of bullying encountered (Unnever & Cornell, 2004) and social and situational variables (Newman et al., 2001). Studies have found that students who were physically bullied were slightly more likely to tell someone (Unnever & Cornell, 2004), girls were more willing than boys to seek help (Newman et al., 2001) and victims were less likely to come forward if they felt their teachers and school authorities tolerated bullying (Unnever & Cornell, 2004). Bullying can also cause truancy and school absenteeism among students (Reid, 1989, 2005). According to the National Education Association (NEA), 160,000 kids nationwide stay home from school each day to avoid the physical, emotional and psychological torment of being bullied. Studies have shown that bullying can cause a bully victim to become less engaged in school and more likely to stop attending (Seeley et al., 2009). In this sample of Florida students, 23.0% (12,329 students) reported that bullying caused them to be “somewhat” or “a whole lot” worried/fearful, while 4.1% (1,685 students) admitted to having skipped school due to fear of bullying.

Verbal Bullying Physical Bullying Cyber Bullying

35 30 25 20 15 10

Percentage of Students of Students Percentage 5 0 Self-Identified Bullies Self-Identified Bully Victims

Figure 4.1: Self-Reported Rates of Bullying Incidents

If approximately 6% - 20% of students report bullying others and 9% – 30% admit to being bully victims, then potentially the remaining students are witnesses or bystanders to the bullying incidents. This group is the one that observes the bullying interactions. They are the

44 group that are present during a majority of bullying episodes (Atlas & Pepler, 1998; Craig & Pepler, 1997) and that have the opportunity to intervene. Fried and Fried (2003) identify six different types of witnesses: inactive, angry, fearful, voyeur, accomplice and helpful. The inactive witnesses are aware of the bullying situation but try to avoid them. The angry witnesses become annoyed with the targets and express contempt towards them for not acting against their bully. The fearful witness on the other hand think about confronting the bully or reporting the incident to an adult but are scared that the bully might turn on them or be called a snitch so instead focus on self protection. The voyeur gets sadistic pleasure watching the bullying incident and knowing that they have escaped the bully. The accomplice, in order to avoid being the target, joins in on the bullying incidents and in turn reinforces the status and power of the bully. Finally, the helpful witnesses are the ones that understand the difference between tattling and reporting. They challenge the bully and help cease the bullying episode (Fried and Fried, 2003). Another set of studies looking at the behavior of students involved in bullying (Salmivalli et al., 1996; Salmivalli et al., 1998; Salmivalli et al., 1999) also found that a majority of students are not the bully or the victim but rather the passive bystander. These students are characterized as those who do not take sides and instead passively watch the bullying episode; their actions enable the bully despite reporting that they are against bullying (Salmivalli, 1999). It has been shown that the average bullying episode lasts about thirty-eight seconds and can be stopped within ten seconds of the episode (Craig & Pepler, 1995). Studies that have observed classroom and playground interactions find that peers are present in approximately 85% of bullying episodes in roles ranging from active participants to passive bystanders (Atlas & Pepler, 1998; Craig & Pepler, 1995); yet peers were only found to intervene in 10-19% of all bullying incidents (Atlas & Pepler, 1998; Craig & Pepler, 1995; Hawkins et al., 2001). However, when peers did intervene, they were successful in 57% of the interventions (Hawkins et al., 2001). This suggests that the role of witnesses can be powerful tools to prevent and intervene.

4.2. Bullying Location Students were also asked about the location of where they were victimized. Previous studies have noted that the playground or the classroom is the most likely location for bullying (Fekkes et al., 2004; Olweus, 1991; Whitney & Smith, 1993). Similarly, in this study the most common location of victimization was the classroom while the teacher was present (7.1%),

45 followed by other non-school locations (6.6%) and online, texting or via phone (6.2%). Figure 4.2 highlights the specific locations where students reported being bullied. This provides evidence that bullying is a phenomenon that occurs not just within the school, but also outside the school and through the use of technology. However, figure 4.2 also shows that a large majority of locations identified are still within school grounds. Thus, the identification of the role of school characteristics in bullying is a key element to understanding the bullying phenomenon as well as to address ways of reducing bullying.

Bus Stop

Parking Lot

Restroom

Playground or Athletic Field

After School Activities

Somewhere else in School Building

School Bus

Gym or Locker Room

Classroom while Teacher was out

Cafeteria

Hallways or Stairs

Online, Text or Phone

Other non-school location

Classroom while Teacher was In

0 1 2 3 4 5 6 7 8 Percentage of Students

Figure 4.2: Locations of Bullying Victimizations

Furthermore, for the exception of bullying through the use of technology, most of the bullying locations are likely to have witnesses and bystanders. The most common location of bullying, while the teacher is present in the classroom, points to several possible scenarios: that students are reluctant to seek help from teachers, witnesses do not intervene and/or the teacher does not respond appropriately to the incident. Studies have found that students believe that school authorities do little to intervene in bullying incidents (Olweus, 1993, 1994; Craig et al., 2000) and students may not trust authorities or perceive them to be supportive or receptive to their concerns (Hoover et al., 1992; Newman et al., 2001). However, the decision to seek help from adults can reduce the probability of being victimized in the future (Ladd and Ladd, 2001) and generate immediate health related benefits (Kaukinen, 2002). This further highlights the key role of bystanders and school authorities and their potential to intervene in bullying incidents.

4.3. Comparison of Bullying Experiences across Groups 4.3.1. Gender In the present sample, significant gender differences in bullying involvement were found. Female students reported a higher rate of worry or fear due to bullying (29.1%) as compared to the male students (18.6%). Furthermore, more female students (5.2%) admitted to skipping school due to bullying as compared to male students (2.9%). In order to examine gender differences in perpetration for each type of bullying, chi- square tests were conducted. The findings for verbal bullying, χ² (1, N = 41,098) = 49.716, p >.001, physical bullying χ² (1, N = 41,098) = 365.128, p >.001 and cyber bullying χ² (1, N = 41,098) = 236.263, p >.001 all indicate that there are statistically significant differences between males and females in their bullying perpetration. Findings revealed that males have a higher prevalence of verbal and physical bullying while females have higher involvement in cyber bullying. However, for both genders the most common type of bullying perpetration was verbal bullying. Bullying perpetration by gender is summarized in Figure 4.3.

25.00

20.00

15.00 Males Females 10.00

5.00 Percentage of Students

0.00 Verbal Bullying Physical Bullying Cyber Bullying

Figure 4.3: Bullying Perpetration by Gender

Similarly, chi-square tests were also conducted to compare gender differences in bullying victimization. The result show that victimization for verbal bullying, χ² (1, N = 41,098) = 40.083, p >.001, physical bullying χ² (1, N = 41,098) = 249.358, p >.001 and cyber bullying χ² (1, N = 41,098) = 1042.512, p >.001 are all significantly different among males and females. Among both males and females the most common type of victimization is verbal bullying. However, boys tend to experience more physical victimization while girls reported higher rates of verbal and cyber bullying victimization. Figure 4.4 summarizes victimization by gender.

35.00

30.00

25.00

20.00 Males 15.00 Females

10.00 Percentage ofPercentage Students 5.00

0.00 Verbal Victimization Physical Victimization Cyber Victimization

Figure 4.4: Bullying Victimization by Gender

These findings are consistent with previous research where boys were found to be more engaged with physical bullying, while girls were more likely to be involved in verbal and cyber bullying (Bradshaw, Sawyer & O’Brennan, 2009; Finkelhor et al., 2009; Harris et al., 2002; Hinduja & Patchin, 2009; Wang, Iannotti, & Nansel, 2009) In examining aggression among girls, Simmons (2002) note that girls are taught early on not to express aggression directly or physically, while boys on the other hand are often encouraged to do so. Girls fear that conflict can isolate or ostracize them and it is this fear of being alone and the prohibition of aggression that creates a particular way of managing anger among girls (Simmons, 2002). In particular, this could influence higher involvement of girls as relationship bullies (Fried & Fried, 2003; McGraw, 2008), where social groups, cliques and relationships as used as the means to bully a victim. Researchers have also noted that boys are more likely to bully each other directly while girls are more likely to be indirect and talk behind their victims backs (Fried & Fried, 2003). Boys are also more likely to make up quickly and move on from conflicts while girls tend to continue their conflicts indefinitely (Fried & Fried, 2003). In a review of cyber bullying studies done by Hinduja & Patchin (2009) all but one study found that girls were involved with cyber bullying as a bully and a victim as much as, if not more, than boys. Furthermore, the higher involvement of girls in cyber bullying is also consistent with the finding by the Pew Internet and American Life Project that a subset of the teen population, a majority of who are older teenage girls can be distinguished as a set of super-communicators with access to a whole host of technological options (Lenhart et al., 2007). Several additional characteristics of cyber bullying may explain the higher involvement of girls. Technology supports the indirect, emotional and psychological bullying that girls are involved in by providing them a way to engage in “social sabotage” without having their face their victims or being bound to the social constraints of how they are expected to deal with aggression (Hinduja & Patchin, 2009). On the other hand, technology does not facilitate the physical forms of bullying that are more common in boys (Hinduja & Patchin, 2009). Girls also tend to use social support of their cliques and social networks to gang up on a victim; technology provides an expedited process of enlisting help and tormenting a potential victim (Hinduja & Patchin, 2009).

4.3.2. Grade Level Differences in bullying perpetration by grade were examined by conducting one-way ANOVA’s. Results found that statistically significant differences existed among various grade levels for verbal bullying, F (6, 41098) = 113.72, p < .001, physical bullying F (6, 41098) = 91.46, p < .001 and cyber bullying F (6, 41098) = 12.57, p < .001. The highest rates of verbal bullying occur among 7th and 8th graders, physical bullying was highest among 7th and 8th graders as well, while cyber bullying was most common among 8th and 10th graders. In general, verbal bullying and physical bullying involvement seems to decrease with age; 23.4% and 12.8% of 6th graders report verbally and physically bullying others while only 13.9% and 6.9% of 12th graders report the same behaviors. However, the same pattern does not hold for cyber bullying. Involvement in bullying perpetration by grades is presented in Figure 4.5.

6th Grade 7th Grade 8th Grade 9th Grade 10th Grade 11th Grade 12th Grade

10 Percentage of Students of Students Percentage 5

0 Verbal Bullying Physical Bullying Cyber Bullying

Figure 4.5: Bullying Perpetration by Grade Level

In order to examine victimization differences by grades, additional one-way ANOVA’s were conducted. The results revealed that bullying victimization significantly varied among grade levels for verbal bullying, F (6, 41098) = 439.15, p < .001, physical bullying F (6, 41098) = 341.13, p < .001 and cyber bullying F (6, 41098) = 7.06, p < .001. As with the perpetration

50 patterns across grade levels, the most common form of bullying across all grades is verbal bullying. Both verbal and physical victimization decreased with each grade whereas cyber bullying did not hold to this pattern. Cyber bulling victimization is highest among 8th and 10th graders. Figure 4.6 summarizes bullying victimization across the grade levels.

6th Grade 7th Grade 8th Grade 9th Grade 10th Grade 11th Grade 12th Grade

50 45 40 35 30 25 20 15 Percentage of Students of Students Percentage 10 5 0 Verbal Victimization Physical Victimization Cyber Victimization

Figure 4.6: Bullying Victimization by Grade Level

These findings are also consistent with previous research which show that verbal and physical bullying peak in middle school (Bradshaw, Sawyer & O’Brennan, 2009; Nansel et al., 2001; Seals & Young, 2003) while cyber bullying peaks later in middle school and high school (Patchin & Hinduja, 2011; Williams & Guerra, 2007; Wolak et al., 2007). Studies done on traditional bullying have found links between bullying and higher dropout rates (Byrne, 1994) as well as poor academic performance (Nansel et al., 2001) and getting trouble in school (Carlson & Cornell, 2008). This suggests that youth involvement in traditional forms of bullying could cease due to adverse effects such as dropping out and getting into trouble in school. This could also imply that as youth get older they may grow out of traditional bullying by realizing that the consequences of their actions have more weight. They begin to comprehend the effect that

51 school could have on their future, in that doing poorly in school or getting in trouble can jeopardize their future goals or that their actions might disappoint their parents and teachers. Additionally, the increased enforcement and legal consequences for bullying could also present a strong reinforcement to cease involvement. On the other hand, as youth get older they have increased access to various mediums of technology and get more proficient with using these tools (Kowalski & Limber, 2007). The accessibility and anonymity provided by cyber bullying comes with seemingly less consequences and is far less prohibitive than traditional forms of bullying.

4.3.3. Ethnicity According to student self-reported involvement, African American students reported the highest rates of bullying perpetration while White students reported the lowest rates across all three types of bullying. The most common type of bullying across all ethnic groups was verbal bullying, with 18%-27% reporting to have verbally bullied others. Figure 4.7 shows verbal bullying perpetration across ethnic groups.

White African American Hispanic Asian Other

Percentage of Students 5

0 Verbal Bullying Physical Bullying Cyber Bullying

Figure 4.7: Bullying Perpetration by Ethnicity

With respect to victimization, Figure 4.8 shows that the highest level of verbal and physical bullying victimization was reported by students who self-identified as Other followed 52 closely by Asians. Cyber bullying victimization was highest among Other ethnicities followed by White students. As with bullying perpetration, the most common type of victimization across all racial groups was verbal bullying. In contrast to bullying perpetration where African American students reported the highest rates, African American victims of bullying had the lowest rates.

White African American Hispanic Asian Other

40 35 30 25 20 15 10 Percentage of Students of Percentage 5 0 Verbal Victimization Physical Victimization Cyber Victimization

Figure 4.8: Bullying Victimization by Race

Previous studies that have examined racial differences in bullying have largely been mixed (Bradshaw, Sawyer & O’Brennan, 2009; Hinduja & Patchin, 2009; Nansel et al., 2001; Seals & Young, 2003; Spriggs et al., 2007; Ybarra et al., 2007). Some studies find no significant different in bullying involvement across racial groups (Nansel et al., 2001) while others find that African American and Hispanic youth were less likely to report being a victim while their White counterparts were less likely to report being a bully (Bradshaw, Sawyer & O’Brennan, 2009). Additionally, it has also been found that minority youth are more likely to be victimized which is consistent with the finding of the present study that youth of Asian and Other ethnicities have the highest victimization rates. Additional studies have also found that African American youth are more likely to be aggressors while White youth are more likely to be victimized than minorities (Graham & Juvonen 2002; Sawyer et al. 2008). The findings from this study suggest that not only are there are significant differences across racial groups but also across the different types of bullying behaviors.

Another thing to consider is the relationship between bullying and the ethnic composition of the school or classroom. Some studies have noted that ethnic minorities in ethnically heterogeneous classes display more bullying behavior than ethnic minorities in classes with fewer ethnic minority classmates (Hawley et al., 2002; Jackson et al., 2006; Pellegrini & Long, 2002; Vervoort et al., 2010). The implication is that ethnic minorities in classes with higher proportions of ethnic minorities might feel more confident to challenge the position of the ethnic majority group and obtain more dominance by means of bullying. Additionally, a hierarchy in ethnic status among minority groups may also exist (Verkuyten et al., 1996) and youth of different ethnic backgrounds may bully each other to gain social dominance. Since the present study lacked information on ethnic composition of schools or classrooms, this relationship could not be tested. Future research should continue to examine this relationship and the relationships between ethnic composition of school classes and possible interaction effects between ethnicity and gender. 4.4. Summary of Bullying Prevalence The preceding analysis provides evidence that bullying is a prevalent behavior across Florida schools. The prevalence of bullying in the present study was similar to that of previous studies (Eaton et al., 2010, Dinkes, Kemp, & Baum, 2009, Nansel et al., 2001) conducted using nation-wide samples of students. Approximately 1 in 4 students surveyed reported being worried or scared due to bullying. While students reported engaging and being victims of all three types of bullying, the most common form of bullying was verbal bullying with 20.4% of students reporting being bullies and 30.6% reporting victimization. The most common location of victimization was the classroom while the teacher was present (7.1%). In regards to gender, males reported higher rates of perpetuating verbal and physical bullying while females were more likely to be cyber bullies. As for victimization, boys were more likely to be victims of physical bullying while girls reported higher rates of verbal and cyber bullying victimization. Grade level patterns showed that verbal and physical bullying decrease as students get older. However, the same pattern was not evident in cyber bullying. African American students reported the highest rates of bullying perpetration across all three types of bullying and also the lowest rates of victimization across all types of bullying. The highest levels of victimization occurred among students self-identifying their racial background as Other followed by Asians.

The findings in this chapter provide insightful information on the prevalence of bullying and differences among demographic groups across Florida Schools. It also highlights the need to evaluate and administer appropriate prevention and intervention efforts. However, an analysis of the key influences on the different types of bullying is essential to inform these efforts. Thus, the next chapter will present an examination of the individual and contextual level characteristics that could influence bullying behaviors. It will provide an examination of each type of bullying behavior and key potential influencing factors, net of the groups differences discussed above.

CHAPTER FIVE

DISCUSSION & RESULTS: MULTILEVEL ANALYSIS

In this section, I examine the remaining research questions which relate to the multilevel analysis. The role of individual level social bonds, social learning and strain variables will be examined. Additionally, school level and school district level influences on each type of bullying will also be assessed. Using the HLM 7 for Windows (Hierarchical Linear and Nonlinear Modeling) statistical package a series of four models was estimated for each type of bullying. Since all three outcomes were dichotomous, a Bernoulli model and a full penalized quasi- likelihood (PQL) (Raudenbush et al., 2004) with LaPlace estimation of maximum likelihood (Raudenbush et al., 2000) was utilized. Estimates are reported from the population average models with robust standard errors. The first model – the unconditional model or the one-way ANOVA model with random effects was conducted with no predictors to calculate the interclass correlations (ICC) and to determine the appropriateness of utilizing a multilevel model. The proportion of variance at each level was estimated using the level 1 variance (2) which is ﬁxed to 2/3 = 3.29 in all logistic regression models (Snijders & Bosker 1999), the level 2 intercept variance and level 3 intercept variance. The proportion calculated at the second and third levels are numerically equal to the interclass correlation coefficient (ICC) and can be viewed as the proportion of variance in the dependent variables attributable to differences between schools and between school districts. The second model, a random intercept model with level-1 covariates includes all the level 1 variables varying randomly over level 2 and level 3. The third model adds in level 2 covariates and the final model includes all levels 1, 2 and 3 covariates. Prior to conducting the multilevel analysis, zero order correlations were conducted to examine bivariate relationships between the key independent variables and the three types of bullying. This provides us with an initial indication of whether the individual and contextual characteristics are predictive for each form of bullying. All the independent variables at level 1 except for family members with drug or alcohol problems exhibit significant bivariate relationships with all three types of bullying. As expected, school social bonds such as school opportunity for pro-social involvement, rewards for pro-social involvement, school attachment and school grades were negatively associated with engaging in all three types of bullying. School mobility, on the other hand, was positively related with all types of bullying. Similarly, family 56 bonds such as family opportunities and rewards for pro-social involvement and family attachment were all negatively related to all bullying behaviors. Deviant attitudes, prior delinquency, low self esteem and family conflict were all positively associated with engaging in bullying. At the school level, all variables were significant; school attachment, school mobility and school grades were all negatively associated with being a bully. At the school district level, all variables – concentration of student poverty, school district enrollment size and instructional staff ratio were negatively associated, however, none were found to be significant. The complete correlation table can be found in Appendix B. Additionally, to assess for multicollinearity, regression models were estimated and the variance inflation factor (VIF) was examined. The VIF statistics measure how much of the variance of the estimated coefficients are increased over the case of no correlation among the variables. Typically, VIFs over 4.0 indicate that multicollinearity is present. Since none of the VIFs for the variables in this study were over 2.8, it indicates that multicollinearity was not a concern. It should also be noted that on the basis of preliminary analysis, a considerable majority of the within-school slopes were not found to significantly vary across schools for the outcome variables. Thus, for the present study the main interest lies in exploring fixed effects of social bonds, social learning, strain and social disorganization theory on bullying behaviors.

5.1. Verbal Bullying The first model, an unconditional model / one-way ANOVA model with random effects was conducted with no predictors. The ICC for verbal bullying at the between schools level is 0.024 (² (619) = 1097.59, p < .000) and the ICC between school districts is 0.0034 (² (61) = 97.75, p < .001). Both the between school and between school districts variances are significant allowing us to reject the null hypothesis that there is no difference in verbal bullying across schools and school districts. Thus, it is appropriate to utilize multilevel modeling and investigate how much of the variance is explained by level 1, level 2 and level 3 variables. It also tells us that a significant majority, 97.3% of the variability in verbal bullying is between students, 2.4% is between schools and only 0.34% is between school districts. Table 5.1 displays the ANOVA model results for verbal bullying.

Table 5.1: Hierarchical Linear Model ANOVA for Verbal Bullying

Fixed Effects Coefficient S.E t-ratio

Grand Mean -1.407975 0.022986 -61.254 Random Effects Variance ² D.F.

Students 3.29 Schools 0.08136 1097.59** 619 School Districts 0.01136 97.75417** 61 ICC By level

Level 2 0.024 Level 3 0.003

** p<.001

Next, a random intercept model with level 1 covariates was calculated. This model estimated the effects of level-1 predictors and whether they are significantly related to the outcome. Twelve of the nineteen predictors were found to be significantly related to verbal bullying. African Americans were 80% more likely and those of Other ethnicities were 17% more likely than their White peers to engage in verbal bullying. Similarly, males (OR= 1.15), those with deviant attitudes (OR= 1.13), family conflict (OR= 1.11), lower levels of self esteem (OR=1.07) and prior delinquency (OR= 1.03) were also more likely to engage in verbal bullying. Contrary to expectations, opportunities for pro-social involvement in school made a student more likely (OR= 1.01) to engage in verbal bullying. On the other hand, students in higher grade levels (OR=.84), with higher levels of school attachment (OR=.98), who received school rewards for pro-social involvement (OR=.96) and had better school grades (OR=.95) were less likely to engage in verbal bullying. By calculating the proportion of variance at level 1, we find that 16.2% of the level 1 variance in verbal bullying is accounted for by level 1 variables. Table 5.2 shows the results of the random intercept model with level-1 covariates for verbal bullying.

Table 5.2: Random Intercept Model with Level-1 Covariates for Verbal Bullying

Coefficient S.E OR L1: Within Schools Grade -.174 .020 0.84** Gender (0= Female, 1 = Male) .141 .032 1.15** Hispanic (1= Hispanic) ƚ .061 .034 1.06 African American (1 = African Am) ƚ .592 .034 1.80* .186 .078 1.20 Asian (1= Asian) ƚ .160 .054 1.17** Other (1= Other) ƚ Prior Delinquency .033 .004 1.03** Deviant Attitudes .122 .004 1.13** School Opportunities for Pro-Social Involvement .018 .005 1.01** School Rewards for Pro-Social Involvement -.040 .005 0.96** School Attachment -.012 .003 0.98** School Mobility .005 .009 1.00 School Grades -.042 .007 0.95** Low Self Esteem .070 .005 1.07** Family Attachment -.005 .005 0.99 Family Opportunities for Pro-Social Involvement .005 .007 1.00 Family Rewards for Pro-Social Involvement .001 .007 1.00 Family Conflict .113 .005 1.11** Family Members with Drug or Alcohol Problems .037 .026 1.03 Conditional Error Variance Components (Random Variance ² D.F. Effects) 2 Students 3.29 ( F = .64) Schools 0.00866 653.7 619 School Districts 0.01021 17.36** 61

Note: Level 1 predictors (within school parameters) were grand mean centered. ** p<.001, * p<.05, ƚ = reference category is White

In Model 3, level 2 variables (school level) were added in. Only one of the five variables was found to be significantly related to engaging in verbal bullying. Schools with higher levels of school attachment (OR = .91) reduced the odds of engaging in verbal bullying. Table 5.3 shows the results of model 3.

Table 5.3: Multilevel Analysis adding Level 2 Predictors of Verbal Bullying

Coefficient S.E OR L1: Within Schools Grade -.132 .010 0.87 ** Gender (0= Female, 1 = Male) .142 .027 1.15 ** Hispanic (1= Hispanic) ƚ .049 .035 1.05 African American (1 = African Am) ƚ .571 .034 1.77** .186 .074 1.20 Asian (1= Asian) ƚ .155 .053 1.16** Other (1= Other) ƚ Prior Delinquency .033 .004 1.03** Deviant Attitudes .122 .005 1.13 ** School Opportunities for Pro-Social Involvement .024 .006 1.02 ** School Rewards for Pro-Social Involvement -.040 .006 0.95 ** School Attachment -.015 .003 0.98** School Mobility .010 .010 1.01 School Grades -.041 .009 0.95 ** Low Self Esteem .070 .005 1.07** Family Attachment -.005 .005 0.99 Family Opportunities for Pro-Social Involvement .005 .008 1.00 Family Rewards for Pro-Social Involvement .001 .007 1.00 Family Conflict .112 .005 1.11 ** Family Members with Drug or Alcohol Problems .037 .027 1.03

L2. School Opportunities for Pro-Social Involvement -.146 .033 0.86 School Rewards for Pro-Social Involvement .053 .040 1.05 School Attachment -.084 .018 0.91 ** School Mobility 0.00 .051 1.00 School Grades -.039 .049 0.96

Conditional Error Variance Components (Random Variance ² D.F. Effects) Students 3.29 Schools 0.00531 634.42 614 School Districts 0.00873 131.39** 61

Note: Level 1 predictors and school level variables were grand mean centered. ** p<.001, * p<.05, ƚ = reference category is White

In the final model, level 3 (school district level) variables were included. None of the three school district level variables were found to be significantly associated with engaging in verbal bullying. Table 5.4 shows the results of this model.

Table 5.4: Multilevel Analysis adding Level 3 Predictors of Verbal Bullying

Coefficient S.E OR L1: Within Schools Grade -.132 .011 0.85** Gender (0= Female, 1 = Male) .141 .032 1.14** Hispanic (1= Hispanic) ƚ .063 .039 0.81** African American (1 = African Am) ƚ .576 .037 0.91 .193 .077 1.52** Asian (1= Asian) ƚ .161 .054 0.94 Other (1= Other) ƚ Prior Delinquency .033 .004 1.03** Deviant Attitudes .122 .004 1.13** School Opportunities for Pro-Social Involvement .024 .006 1.01* School Rewards for Pro-Social Involvement -.042 .005 0.96** School Attachment -.014 .003 0.98** School Mobility .010 .010 1.01 School Grades -.041 .007 0.96** Low Self Esteem .069 .005 1.07** Family Attachment -.005 .005 0.99 Family Opportunities for Pro-Social Involvement .005 .008 1.00 Family Rewards for Pro-Social Involvement .001 .007 1.00 Family Conflict .113 .005 1.12** Family Members with Drug or Alcohol Problems .036 .026 1.03

L2. School Opportunities for Pro-Social Involvement -.142 .033 0.86 School Rewards for Pro-Social Involvement .046 .045 1.04 School Attachment -.089 .019 0.91** School Mobility .023 .054 1.02 School Grades -.037 .056 0.96

L3. Concentration of Student Poverty .000 .000 1.00 Enrollment Size -.000 .000 -0.86 Instructional Staff Ratio .000 .000 0.99

Conditional Error Variance Components (Random Variance ² D.F. Effects) 3.29 Students 0.00557 636.03 614 Schools 0.00599 76.83** 59 School Districts

Note: Level 1 predictors and school level variables were grand mean centered. ** p<.001, * p<.05, ƚ = reference category is White

5.2. Physical Bullying

Results of the physical bullying unconditional ANOVA model indicates that both the variances at the between school level of .040 (² (619) = 1095.45, p < .000) and the between 61 school district level of .003 (² (61) = 85.42, p =.021) are significant allowing us to reject the null hypothesis that there is no difference in physical bullying across schools and school districts. It also shows that a significant amount of variability in physical bullying is between students (95.7%) while the remaining 4% is between schools and 0.3% between school districts. Table 5.5 displays the ANOVA model results for physical bullying.

Table 5.5: Hierarchical Linear Model ANOVA for Physical Bullying

Fixed Effects Coefficient S.E t-ratio

Grand Mean -2.117147 0.026476 -79.965 Random Effects Variance ² D.F.

Students 3.29 Schools 0.13973 1095.45** 619 School Districts 0.01115 85.42* 61 ICC By level

Level 2 .040 Level 3 .003

** p<.001, * p<.05

Next, in the random intercept model level 1 predictors were added in and thirteen of the nineteen predictors were found to be significantly related to physical bullying. Males are 50% more likely than girls to engage in physical bullying. Compared to their White peers, students of African American and Other ethnicities were 58% and 21% more likely to engage in physical bullying. Additionally, having deviant attitudes (OR=1.15), engaging in prior delinquency (OR=1.06), higher rates of school mobility (OR=1.03), having family conflict (OR=1.11) and low self esteem (OR=1.03) were all significantly related to engaging in physical bullying. Factors that were negatively related to physical bullying included being in higher grade levels (OR=.79), having higher school attachment (OR= .98) being rewarded in school for pro-social involvement (OR=.96), having better grades (OR=.93) and higher levels of family attachment (OR=.96). For physical bullying, we find that 22.3% of the level 1 variance is accounted for by the individual level variables. Table 5.6 shows the results of the ANCOVA model for physical bullying

Table 5.6: Random Intercept Model with Level-1 Covariates for Physical Bullying

Coefficient S.E OR L1: Within Schools Grade -.224 .011 0.79** Gender (0= Female, 1 = Male) .410 .036 1.50** Hispanic (1= Hispanic) ƚ .118 .051 1.12 African American (1 = African Am) ƚ .462 .053 1.58** .239 .095 1.27 Asian (1= Asian) ƚ .191 .066 1.21* Other (1= Other) ƚ Prior Delinquency .060 .004 1.06** Deviant Attitudes .147 .006 1.15** School Opportunities for Pro-Social Involvement .0004 .008 1.00 School Rewards for Pro-Social Involvement -.040 .009 0.96** School Attachment -.018 .004 0.98** School Mobility .031 .012 1.03* School Grades -.064 .015 0.93** Low Self Esteem .038 .008 1.03** Family Attachment -.034 .007 0.96** Family Opportunities for Pro-Social Involvement .010 .011 1.01 Family Rewards for Pro-Social Involvement .002 .010 1.00 Family Conflict .109 .007 1.11** Family Members with Drug or Alcohol Problems -.040 .034 0.96 Conditional Error Variance Components (Random Variance ² D.F. Effects) 2 Students 3.29 ( F = .96) Schools 0.03690 742.92** 619 School Districts 0.01281 104.92** 61

Note: Level 1 predictors (within school parameters) were grand mean centered. ** p<.001, * p<.05, ƚ = reference category is White

In Model 3, school level variables were added in. Similar to verbal bullying, only one of the five school level variables was found to be significantly related to physical bullying. Higher levels of school attachment (OR=.90) reduced the likelihood of engaging in physical bullying. Table 5.7 shows the results of Models 3.

Table 5.7: Multilevel Analysis adding Level 2 Predictors of Physical Bullying

Coefficient S.E OR L1: Within Schools Grade -.183 .016 0.83** Gender (0= Female, 1 = Male) .412 .036 1.51** Hispanic (1= Hispanic) ƚ .104 .052 1.10 African American (1 = African Am) ƚ .436 .054 1.54** .245 .093 1.27 Asian (1= Asian) ƚ .187 .066 1.20* Other (1= Other) ƚ Prior Delinquency .060 .004 1.06** Deviant Attitudes .147 .006 1.15** School Opportunities for Pro-Social Involvement .004 .009 1.00 School Rewards for Pro-Social Involvement -.041 .009 0.95** School Attachment -.020 .004 0.98** School Mobility .037 .012 1.03* School Grades -.061 .015 0.93** Low Self Esteem .037 .008 1.03** Family Attachment -.034 .007 0.96** Family Opportunities for Pro-Social Involvement .009 .011 1.01 Family Rewards for Pro-Social Involvement .003 .010 1.00 Family Conflict .109 .007 1.11** Family Members with Drug or Alcohol Problems -.040 .034 0.96

L2. School Opportunities for Pro-Social Involvement -.096 .038 0.90 School Rewards for Pro-Social Involvement .005 .060 1.00 School Attachment -.099 .023 0.90** School Mobility -.034 .067 0.96 School Grades -.113 .067 0.89 Conditional Error Variance Components (Random Variance ² D.F. Effects) Students 3.29 Schools 0.03408 727.66** 614 School Districts 0.01322 106.88** 61

Note: Level 1 predictors and school level variables were grand mean centered. ** p<.001, * p<.05, ƚ = reference category is White

Finally, in Model 4, school district level variables (level 3) were added in and like the verbal bullying outcome none of the variables were significantly related to physical bullying. Table 5.8 shows the results for Model 4.

Table 5.8: Multilevel Analysis adding Level 3 Predictors of Physical Bullying Coefficient S.E OR L1: Within Schools Grade -.183 .016 0.83** Gender (0= Female, 1 = Male) .412 .036 1.51** Hispanic (1= Hispanic) ƚ .118 .054 1.12 African American (1 = African Am) ƚ .442 .055 1.55** .254 .095 1.29 Asian (1= Asian) ƚ .194 .067 1.21* Other (1= Other) ƚ Prior Delinquency .060 .004 1.06** Deviant Attitudes .146 .006 1.15** School Opportunities for Pro-Social Involvement .004 .009 1.00 School Rewards for Pro-Social Involvement -.041 .009 0.95** School Attachment -.020 .004 0.98** School Mobility .037 .012 1.03* School Grades -.061 .015 0.94** Low Self Esteem .037 .008 1.03** Family Attachment -.034 .007 0.96** Family Opportunities for Pro-Social Involvement .009 .011 1.00 Family Rewards for Pro-Social Involvement .003 .010 1.00 Family Conflict .109 .007 1.11** Family Members with Drug or Alcohol Problems -.040 .034 0.96

L2. School Opportunities for Pro-Social Involvement -.090 .038 0.91 School Rewards for Pro-Social Involvement -.005 .061 0.99 School Attachment -.104 .025 0.90** School Mobility -.002 .069 0.99 School Grades -.100 .070 0.90

L3. Concentration of Student Poverty .000 .000 1.00 Enrollment Size -.000 .000 0.75 Instructional Staff Ratio .000 .000 0.99 Conditional Error Variance Components (Random Variance ² D.F. Effects) Students 3.29 Schools 0.03542 730.56** 614 School Districts 0.00853 95.84* 59

Note: Level 1 predictors and school level variables were grand mean centered. ** p<.001, * p<.05, ƚ = reference category is White

5.3. Cyber bullying The first model, the unconditional ANOVA model for cyber bullying allows us to calculate the ICCs at the school and school district levels. However, both the variances at the school level of .003 (² (619) = 638.07, p =.28) and the school district level of .003 (² (61) = 87.14, p =.15) were very close to zero and not statistically significant. Thus, we must accept the null hypothesis that there is no 65 significant difference in cyber bullying across schools and school districts. In this case, it is appropriate to conduct an ordinary logistic regression to examine the effects of level 1 variables and not appropriate to conduct an analysis of the level-2 and level-3 predictors. Table 5.9 shows the results of the ANOVA model for cyber bullying

Table 5.9: Hierarchical Linear Model ANOVA for Cyber Bullying

Fixed Effects Coefficient S.E t-ratio

Grand Mean -2.790487 0.026051 -107.117

Random Effects Variance ² D.F.

Students 3.29 Schools 0.01087 638.07 619 School Districts 0.01058 87.14 61

ICC By level

Level 2 .003 Level 3 .003

Results of the logistic regression with level 1 variables reveal that nine of the nineteen individual level variables are significantly related to engaging in cyber bullying. In contrast to both verbal and physical bullying where females were less likely to engage in bullying, females were 47% more likely (OR=.53) than males to cyber bully others. African Americans (OR=1.39) were 39% more likely to engage in cyber bullying compared to their White counterparts. Additionally, engagement in prior delinquency (OR=1.03), having deviant attitudes (OR=1.13), low levels of self esteem (OR=1.09) and family conflict (OR=1.09) were also associated with cyber bullying others. On the other hand, students who received school rewards for pro-social involvement (OR=.96), those with better grades in school (OR=.94) and had higher levels of family attachment (OR=.95) were also less likely to engage in cyber bullying. The R-square of .112 indicates that the variables included in the model explain 11.2% of the variability in cyber bullying. Results of the logistic regression model for cyber bullying are presented in Table 5.10.

Table 5.10: Logistic Regression Model for Cyber Bullying

Coefficient S.E OR

L1: Within Schools Grade -.014 .012 .98 Gender (0= Female, 1 = Male) -.623 .047 .53** Hispanic (1= Hispanic) ƚ -.084 .055 .92 African American (1 = African Am) ƚ .329 .053 1.39** .113 .116 1.11 Asian (1= Asian) ƚ -.052 .089 .94 Other (1= Other) ƚ Prior Delinquency .035 .005 1.03** Deviant Attitudes .123 .007 1.13** School Opportunities for Pro-Social Involvement .005 .010 1.00 School Rewards for Pro-Social Involvement -.032 .011 .96* School Attachment -.013 .006 .98 School Mobility -.026 .016 .97 School Grades -.053 .014 .94** Low Self Esteem .091 .009 1.09** Family Attachment -.043 .008 .95** Family Opportunities for Pro-Social Involvement .003 .013 1.00 Family Rewards for Pro-Social Involvement .024 .011 1.02 Family Conflict .081 .009 1.09** Family Members with Drug or Alcohol Problems .002 .043 1.00

R-Squared: .112

** p<.001, * p<.05, ƚ = reference category is White

5.4. Supplementary Analysis of Indirect Effects An intended major contribution of this study beyond existing research was the incorporation of contextual characteristics, yet as shown above, little of significance was found in the contextual levels. One possible reason may be that contextual characteristics influence bullying indirectly through their effect on individual-level characteristics. Researchers have noted that while hierarchical linear models are appropriate for the examination of multilevel data and can handle errors of measurement, they do not incorporate simultaneous equations for direct and indirect effects (Raudenbush & Sampson, 1999) and can provide challenges for mediation testing. While a full analysis of causal ordering is beyond the scope of this study, preliminary exploratory analysis was done to examine possible indirect effects of the contextual variables. A single model was estimated which included only level-2 variables and level-3 variables and

67 level-1 variables that could not be influenced by contextual effects such as gender, grade and race. The analysis presented in Table 5.11 shows some evidence of indirect effects for both verbal and physical bullying within schools but not within school districts. Results of the exploratory analysis showed that in addition to school attachment, school grades and school opportunities for pro-social involvement was found to reduce the likelihood of engaging in both verbal and physical bullying at the school level. The results from this additional analysis confirm the possibility that while some contextual variables may not show significance in multilevel models, they may still have indirect effects. Furthermore, it points to how potential confounding in mediation effects can arise and incorrect substantive conclusions can be drawn if researchers are not sensitive to these effects at different levels.

Table 5.11: Preliminary Indirect Effects Analysis for Verbal and Physical Bullying

Verbal Bullying Physical Bullying Coefficient SE OR Coefficient SE OR

L2. School Opportunities for Pro- -0.146 .03 .86** -0.128 .04 .87* Social Involvement School Rewards for Pro-Social 0.015 .04 1.01 -0.023 .06 .97 Involvement School Attachment -0.050 .01 .95* -.0.05 02 .94* School Mobility 0.107 .05 1.11 0.114 .06 1.12 School Grades -0.204 .05 .81** -0.324 .07 .72**

L3. Concentration of Student Poverty 0.000 0.0 1.00 0.000 0.0 1.00 Enrollment Size -0.000 0.0 0.81 -0.000 0.0 0.75 Instructional Staff Ratio -0.000 0.0 0.99 -0.000 0.0 0.99

Note: Grade, race and gender were also included in the analysis but not shown in the table. ** p<.001, * p<.05

The significance of two additional school level variables beyond what was found in the full models indicate that variables such as contextual level school grades and school opportunities for pro-social involvement may influence individual levels such as school attachment, deviant attitudes or prior delinquency. Likewise, these contextual characteristics may probably also affect the individual-level measures of those same characteristics (for example, contextual levels of school grades might influence individual school grades). Future researchers

68 should be cautious in interpreting multilevel effects across different levels and examine the possible indirect effects that various factors can have on bullying behaviors. In particular, the use of longitudinal data would allow for causal analysis and the identification of mediator and antecedent variables. 5.5. Discussion This chapter utilized multilevel techniques to examine the individual, school and school district level effects on three types of bullying perpetration. Several key findings on the dynamics and possible influences on bullying perpetration emerged from the present analysis. In relation to research question two, the results indicate that various dimensions of the social bond are related to each type of bullying. Deviant attitudes were found to predict all three types of bullying, supporting previous research that bullies in general tend to have stronger beliefs of aggression and violence (Bentley & Li, 1995; Bosworth et al., 1999; Olweus, 1980; Unnever & Cornell, 2003). Bullies with favorable attitudes towards aggression may have fewer reservations about bullying and may not feel constrained by the general set of rules against bullying. This early onset of aggression among bullies could point to key long term implications. The surgeon general’s task force on youth violence examining several longitudinal surveys of violent offending found that the most chronic form of criminal offending came from an early onset trajectory of aggressive behavior in childhood (US DHHS, 2001). Additionally, previous studies have found that 60% of bullies will have a criminal record before age 24 (Farrington, 1991) and are more likely to be involved in acts such as vandalism, fighting, drunkenness and truancy (Olweus, 1993). There is also evidence that children who were bullies are likely to have children who will grow up to be bullies (Carney & Merrell, 2001), be aggressive against their spouse and children (Roberts, 2000) and influence their peers to engage in bullying over time (Espelage et al., 2003). Certain dimensions of social bonds to the school also emerged as a significant protective factor from becoming a bully. Having higher attachment to school, receiving school rewards for pro-social involvement and having better grades was found to reduce the likelihood of engaging in all three types of bullying. These findings are consistent with previous studies which indicate that bullies tend to have poorer school achievement, lower school attendance, lower attachment to the school environment and more likely to get in trouble at school (Bosworth et al., 1999; Byrne, 1994; Carlson & Cornell, 2008; Nansel et al., 2001; Slee, 1995; Slee & Rigby, 1993; US

DHHS, 2001). As predicted by social bond theory, stronger bonds to institutions such as schools emphasize indirect controls that come from relationships, commitments and values (Hirschi, 1969). Students who are doing well in school and have bright prospects ahead will be less likely to engage in deviant acts that could potentially jeopardize their futures. This bond to the school can be seen as a form of social investment or stake in conformity; thus students with higher stakes in conformity will have more to lose and are less likely to break the law by engaging in acts of bullying. However, contrary to expectations, one aspect of school bonds - having opportunities for pro-social involvement in school, increased the likelihood of becoming a verbal bully. This could point to the unique characteristic of verbal bullying and its link to popularity and school involvement. The positive effect of opportunities for pro-social involvement could be an opportunity effect, such that students who are more engaged in group activities like sports and campus organizations have more opportunity to engage in verbal bullying. For example, a student who serves as Captain of a sports team/ cheer squad or is the president of a student organization may reinforce their status and position by engaging in verbal bullying behavior such as ridicule (making fun of a bad play or lack of skill), humiliation (singling out a student for public embarrassment or blame for a game) or intimidation (using threats and yelling at someone to gain submission), equating being respected with being feared. These verbal bullying behaviors may be overlooked or ignored due to the status of the bully. As previous research has suggested, popularity of a student has been linked to engaging in bullying behaviors (Olweus, 1993; Salmivalli et al., 1996; Thunfors & Cornell, 2008) and “elite bullies” use their high status as a platform for engaging in bullying (Fried & Fried, 2003; McGraw, 2008). Although, it is not certain whether bullying is a cause or effect of popularity. Also, consider this finding in light of what was reported in Chapter 4. Students surveyed in this study reported that the most common type of bullying experienced was verbal bullying and the location where they were victimized the most was in the classroom while the teacher was present. One study noted that most students believed that neither teachers nor their classmates would do much to counter bullying episodes, with many students joining in on the bullying (Unnever & Cornell, 2003). The authority of a bully is strengthened by silence; thus the status of bullies involved in pro-social activities may be further reinforced by victims who do not retaliate, the compliance of bystanders and the lack of intervention from teachers and school authorities. Often,

70 students involved in school activities such as sports and student organizations are admired and looked up to by both peers and staff. As such, students may be less likely to speak out against this type of bully for fear of social retaliation and teachers and school authorities may mistake their acts of verbal bullying as signs of being assertive or having a leadership personality. In examining social bonds within the family, the present study found that opportunities and rewards for pro-social involvement within the family did not emerge as significant protective factors. However, strong levels of family attachment were found to be related to the reduced likelihood of becoming a physical and a cyber bully. This suggests that youth who come from households where parents supervise their activities, enforce consistent and fair discipline and are active in the child’s life will be less likely to engage in physical or cyber bullying (Bowes et al., 2009; Carney & Merrell, 2001; Curtner-Smith, 2000; Espelage et al., 2000; Roberts, 1988). On the other hand, children whose parents continually dismiss or tolerate their aggressive and deviant behaviors will continue to increase their levels of aggression towards their peers, siblings and teachers (Olweus, 1993). This finding once again highlights the difference in verbal bullying from other types of bullying. While family attachment reduced the likelihood of youth engaging in physical and cyber bullying, no significant influence was found for verbal bullying. As mentioned above, there are no physical scars or evidence of verbal bullying and it often happens quickly and under the radar, making it harder to detect and stop. This suggests that even if families are attentive and pro-actively engaged in their children’s lives, they may not be aware of their child’s involvement in verbal bullying. Furthermore, given that opportunities for pro-social involvement in school and perhaps popularity increases the likelihood that youth would be involved in verbal bullying, parents may equate their child’s involvement in school as a marker of their success and be less likely to suspect or discover their child’s involvement in verbal bullying. While physically beating up another child or continually harassing someone using technology may be brought to a parent’s attention and stopped quickly, the lack of detection and dismissive attitudes of school authorities towards verbal bullying may make it less likely for this type of bullying to be noticed. Some support was also found for social learning and the family environment’s influence on bullying behaviors. Family conflict was predictive of all three types of bullying supporting the concept of social learning that children imitate role models such as their parents, to use aggressive means to achieve their goals (Akers, 1998). In essence, if a child grows up in an

71 environment where conflicts are resolved in a violent and aggressive manner then they are more likely to engage in similar behaviors outside of their homes as opposed to those who are exposed to adults who use nonviolent strategies to manage conflict (Espelange et al., 2000; Wang, Iannotti, & Nansel, 2009). This implies that the family environment may provide a conventional socializing agent against bullying involvement by promoting anti-deviant definitions, the reinforcement of conformity through parental discipline and the development of self-control (Akers, 1998). Children can be exposed to deviant attitudes and have reinforced aggressive behaviors if they are continuously exposed to an environment of ineffective and erratic parental supervision and discipline and the endorsement of favorable attitudes and behavior towards deviance. Studies have found that in general, parental deviance and criminality is predictive of a child’s future delinquency and crime (McCord 1991; Heimer, 1997). Another study (Brucacher et al., 2009) found that higher appraisals of procedural justice during family conflict resolution were associated with lower frequencies of bullying by youth. If a child believed that their parents were asserting power over them unfairly, the more likely they were to assert their power over someone weaker and smaller through the form of bullying (Brucacher et al., 2009). This suggests that if parents want to teach their child about nonviolent strategies of dealing with conflict and treating others fairly and justly, they need to set the example within their home first. It also points to the important role that parents can play with reducing the likelihood of their child being involved with bullying. Low levels of self-esteem were also found to be predictive of all three types of bullying behavior. This finding lends some support to general strain theory which posit that strains create negative emotions (such as anger, anxiety, depression etc), which in turn influence involvement in delinquency (Agnew, 1992). Individuals are said to experience these negative emotions when they are treated unfairly and unjustly or exposed to negative stimuli. As a way to escape or deal with these emotions, these strained individuals engage in delinquent behaviors. Previous research has shown that youths’ experience of physical punishment, maltreatment and rejection by parents, peers and teachers are related to bullying (Espelage et al., 2000; Moon et al., 2011; Olweus, 1993; Patchin & Hinduja, 2011). Additionally, it has also be found that youth who engage in frequent bullying (as perpetrators or victims) are twice as likely to report having

72 depressive symptoms (Saluja et al., 2004) and utilize emotional oriented coping skills like avoidance behavior and non-cognitive problem solving (Baldry & Farrington, 2005). Some previous studies (Duncan, 1999; Rigby & Slee, 1991; Tritt & Duncan, 1997) have found that victims of bullying have lower levels of self-esteem than bullies, however others (O'Moore & Hillery, 1989; O’Moore & Kirkham, 2001) have found that bullies have lower levels of self-esteem and were more depressed (Rigby & Slee, 1991) than those who were neither bullies nor victims of bullying. Some researchers (O’Moore & Kirkham, 2001) have also suggested that what protects bullies from victimization is their value placed on physical attractiveness and popularity while others also imply that bullying may have the effect of restoring self-esteem (Rigby & Slee, 1992). As mentioned above, while the link between popularity and bullying has been found in some studies (Olweus, 1993; Salmivalli et al., 1996; Thunfors & Cornell, 2008) it is not certain which causal direction it takes. If bullying is said to restore self-esteem, then it would suggest that continuous bullying would lead to higher levels of self-esteem among bullies. There were no indicators of duration of bullying in the present study and since bullying was defined as a dummy variable (0= not involved, 1 = involved) instead of a continuous variable we could not examine this relationship. Future studies should use longitudinal data to examine the link between bullying, popularity and self-esteem and how it is related to the onset and duration of different types of bullying. Demographic characteristics were also predictive of being a bully. Males were more likely to engage in verbal and physical bullying while females were more likely to engage in cyber bullying. As noted in the previous chapter, girls and boys have been shown to bully in different ways (Fried & Fried, 2003; McGraw, 2008; Simmons, 2002; Roland & Idsøe, 2001). Relationships play a central role in the development of girls and this focus combined with cultural expectations of how they ought to deal with anger creates the foundation for a specific kind of abuse. With girls’ focus on relationships, the use of technology allows them to express their aggression without dealing with direct conflict. Furthermore, the influx of teen technology usage, especially among older teen girls (Lenhart et al., 2007) also supports the higher rate of cyber bullying among girls. On the other hand, while physical bullying has consistently been linked more to boys (Bradshaw, Sawyer & O’Brennan, 2009; Finkelhor et al., 2009; Harris et al., 2002; Wang, Iannotti, & Nansel, 2009), there has been recent research which points to new trends among

73 boys: the use of homophobic language and access to weapons (Crick & Bigbee, 1998; Fried & Fried, 2003; Kindlon & Thompson, 1999; Levant, 1998; Murray, 1999). As with girls, boys grow up with a set of expectations of how to deal with their emotions. They are taught to conform to a set of masculine behaviors, discouraging the expression of vulnerable emotions, while their sisters are encouraged to do just that (DeAngelis, 2001). Just as relationships form the core of girls’ developmental pathway, the culture around boys is particularly cruel to those who don’t conform to the male norms of toughness, stoicism and competition. Furthermore, sexuality and sexual identity is a major concern for young boys who are just beginning their sexual development. The use of sexual name-calling through words like fag, queer, gay and homo among boys creates a way to target another for deviating from the expectation of a “man.” Physical bullying is also more likely to be noticed and stopped immediately; sexual name-calling may provide a way to provoke an attack and still “hurt” their targets over an extended period of time. For victims of sexual name-calling, the effects can be destructive. As noted in an interview with an 8th grade boy by Fried & Fried (2003), “When someone calls you ‘gay’ or ‘faggot’ day after day, you begin to wonder if they know something about you that you don’t know yourself.” The link between violent reactions and labeling of sexual orientation was examined by Chase (2001) who noted that teen school shooters such as Eric Harris and Dylan Klebold of Columbine were repeated harassed due to rumors of being gay, Barry Loukaitis of Moses Lake, WA was taunted by jocks by calling him a “faggot”, Pearl, MS teen Luke Woodham was repeatedly called “gay”, Michael Carneal from West Paducah, KY was called “gay” in his school newspaper and Charles “Andy” Williams of Santee, CA was repeatedly harassed for being a “skinny faggot.” With regard to ethnicity, compared to White students, African American students were found to be more likely to engage in all three types of bullying. Students of Other ethnicities were also found to engage more in verbal and physical bullying compared to White students. As noted in Chapter 4, the findings of the present study are both similar and contrary to previous studies that have examined the link between race and bullying (Bradshaw, Sawyer & O’Brennan, 2009; Hinduja & Patchin, 2009; Nansel et al., 2001; Seals & Young, 2003; Spriggs et al., 2007; Ybarra et al., 2007). This suggests that cultural and social contexts may vary among racial groups and could play a critical role in explaining bullying involvement. Additional research needs to be done to examine further nuances between race and bullying such as the role of ethnic

74 composition in relationship to bullying as well as what some researchers have noted as a “digital- divide,” where certain racial and economic groups may have less access to technology when explaining race differences in cyber bullying (Norris, 2001). Alternatively, it may also be that previously disadvantaged or less socially powerful groups may find an avenue to stand up and attempt to gain social power through the medium of technology (Hinduja & Patchin, 2009). Moreover, examining cultural differences in variables such as values and belief systems, defensive vs. compliant cultures, whether aggression is internalized and exhibited through psychological complications or handled in an aggressive externalized manner and the overall perceptions towards aggression and violence across cultures could lead to a better understanding of racial and ethnic differences in bullying. With respect to school level indicators of disorganization and contextual factors, only one variable at the school level was found to be significantly associated with verbal and physical bullying. School attachment was found to be predictive at both the individual and school level. This indicates that school attachment is not just important at the individual level, but rather that it has a contextual effect as well. So, if a student, whether high or low in attachment themselves and net of individual level effects attends a school that has low levels of school attachment, they will be more likely to engage in bullying behaviors. This suggests that schools where students value being in school, feel connected, place importance on the educational opportunities provided and have respect for school rules, teachers and/or authorities are less likely to engage in verbal and physical bullying. Thus, pro-social school bonding created through developing school connectedness and engagement could be an important school element that can reduce verbal and physical bullying behaviors. This also points to previous findings that school climate is important to consider when examining bullying attitudes and behaviors (Charach et al., 1995; Cowie & Olafsson, 2000; Cullingforda & Morrisona, 1995; Naylor & Cowie, 1999; Unever & Cornell, 2003). Students who attend schools with a positive climate will feel a stronger sense of connectedness and belonging, while schools with a strong bullying culture will reduce their trust in authorities and their likelihood of seeking help (Olweus, 1993; Hoover et al., 1992; Newman et al., 2001). This bullying climate is fostered by a set of shared beliefs that support or encourage bullying behavior. While most schools will have anti-bullying messages, the present finding suggests that

75 schools that have a greater general sense of attachment may have students that will be more likely adhere to these messages, despite their own degree of attachment. The present study found notable differences between verbal bullying and physical and cyber bullying, thus future research should also examine the nuances of school climates on the different types of bullying. While most schools employ anti-bullying policies and initiatives, the dismissive and trivializing nature of verbal bullying may still be a serious problem that is not being addressed. Research shows that the bullying climate is not due to one or two isolated incidents; it is a pervasive part of the school social life and affects all students in some form or another (Cullingforda & Morrisona, 1995). A number of bullying studies, including the present study, note that verbal bullying is the most common form of bullying experienced by youth (Finkelhor et al., 2009; Morgan, 2008; Wang, Iannotti, & Nansel, 2009). Given the links between pro-social involvement in school, popularity and school attachment it provides even more reason to examine these relationships. Lastly, with regard to the final research question, none of the school-district level indicators of disorganization were found to be predictive of any type of bullying. Only marginal percentages of variation were found at the school district level for verbal and physical bullying and no significant variation for cyber bullying, so these results are not surprising. It could be that much of the variation in bullying lies between students and some between schools; school districts may be too large to nest schools within. While the variables assessed at the school district level were included on a theoretical basis, previous support for these variables have been at smaller units such as the school and classroom (Bradshaw, Sawyer & O’Brennan, 2009; Stewart, 2003; Unever & Cornell, 2003). Due to data limitations, the variables of concentration of student poverty, enrollment size and instructional staff were not available at the school level for the present study. Future studies should examine these variables in relation to bullying and social disorganization theory at a smaller unit than the present study.

CHAPTER SIX

CONCLUSION

The problem of bullying has been a growing topic of concern and has garnered attention from various disciplines such as education, psychology, sociology and medicine. However, the use of criminological theories to examine the phenomenon of bullying has been limited. Given the link between deviance and bullying behaviors, leading criminological theories could provide valuable nuances to what we already know about bullying. Furthermore, while we refer to bullying as a national epidemic and talk about it as a general problem, findings from the present study have shown that there are several types of bullying, each with some distinct influencing factors that will require empirical consideration and unique strategies to provide effective interventions. Thus, this study provides a criminological take on this important issue and examines the influence of individual and contextual level variables on three major types of bullying behaviors. First, the present study sought to examine the prevalence of each type of bullying behaviors among Florida middle and high school students. The incidence rates for this study were similar to ones conducted on a national scale, indicating that the problem of bullying is as prevalent in the state of Florida as it is throughout the rest of the country. Among the notable findings were that verbal bullying was the most form of bullying, both in perpetration and victimization. Additionally, the most common location of victimization was in the classroom while the teacher was present. Girls were more likely to perpetrate cyber bullying and experience verbal and cyber victimizations while boys engaged in verbal and physical bullying and experienced higher levels of physical victimization. African American students reported the highest rates of bullying perpetration across all three types and also the lowest rate of victimizations. Furthermore, verbal and physical bullying significantly decreased after middle school, while cyber bullying peaked among high school students. By utilizing a large state-wide representative sample, recent data from 2010 and identifying information on bullies, victims and location of bullying incidents, this study provides a rich source of information on the problem of bullying in Florida schools. Secondly, the influence of individual level characteristics on bullying behaviors drawn from three criminological theories – social bond theory, social learning theory and general strain

77 theory were examined. The present study improves on past research by examining different types of bullying behaviors using these theories; previous criminological studies of bullying have utilized a composite measure of bullying involvement (Moon et al., 2011), a single general question asking students how often they have bullied (Bradshaw et al., 2009) or one where verbal and physical bullying is combined into a single measure of traditional bullying and compared against cyber bullying (Hay & Meldrum, 2010). Support was found for social bonds at both the school and family level suggesting that elements of Hirschi’s (1969) social bond are important variables in creating a stake in conformity and reducing bullying behaviors. The present study also found some support for social learning and general strain theories indicating that the family environment plays a foundational role in reducing or increasing the likelihood of bullying involvement. The home environment will typically provide the first glimpse of conflict and lessons in conflict resolution for a child. Children will learn about communication, conflict resolution, respect and decision making by watching their parents on a daily basis. Lessons learned within the home may be transferred outside into the schoolyard and children will deal with conflict in the same manner as what they are exposed to at home (Espelange et al., 2000; Wang, Iannotti, & Nansel, 2009). Parents can also play a critical role in developing their child’s self-esteem. Genuine and fair treatment as well as active supervision and involvement by parents will translate into stronger role models and more self-assured children. However, conflict within the home can lead to low levels of self- esteem, depressive symptoms and the utility of emotional oriented coping skills like avoidance behavior and non-cognitive problem solving (Baldry & Farrington, 2005; Saluja et al., 2004) and some children may go on to use bullying as a platform to create a false reality to gain and restore their self-esteem (Rigby & Slee, 1992). Some interesting nuances were found in the effects of family variables across the different types of bullying. The likelihood of involvement in all three types of bullying was increased by family conflict but family attachment only reduced the likelihood of engaging in physical and cyber bullying. This suggests that certain family-based strategies may work for physical and cyber bullying, but not for verbal bullying. Additionally, school variables were also shown to have some differences across different types of bullying. Opportunities for pro-social involvement in school were found to create an opportunity effect, increasing the likelihood of engaging in verbal bullying. This suggests that contrary to pro-social involvement serving as an

78 expected protective factor, in the case of verbal bullying it may have an inverse effect. The more a student becomes central to the social network of the school, the more opportunities they may have to engage in forms of verbal bullying. This points to the need to examine the role of social hierarchy and the role of victims and bystanders who provide this sense of status to verbal bullies. The remaining research questions addressed the possibility that bullying perpetration could be influenced by contextual characteristics of schools and school-districts. Only one variable at the school level, attachment to school, was found to be significant in reducing the likelihood of engaging in verbal and physical bullying. This suggests that school engagement and connectedness, net of individual effects, plays an important role in reducing the likelihood of traditional forms of bullying. However, further analysis exploring the possible indirect effects also point to the importance of contextual level school grades and school opportunities for pro- social involvement in reducing the likelihood of traditional bullying. This suggests that while bullying has been shown to have many individual level factors, contextual level variables are still important components to consider. In particular, these direct and indirect contextual effects need to be further examined to determine the conditions under which certain individual-level characteristics may function. Finally, this study also assessed the role of school-district level indicators of disorganization on bullying perpetration. None of the variables assessed at this level were found to be significant. Similarly, nothing of significance was found in the preliminary analysis of indirect effects either. This suggests that variability in bullying behavior may not be at the school district level, but rather at smaller units such as schools or the classroom. One of the most important advancements of this study over prior research was the distinction between types of bullying. Throughout the analysis, interesting similarities and differences were observed across the bullying types with regard to characteristics of people involved in bullying as well the characteristics that influence each type of bullying. Verbal bullying was found to be the most common form of bullying, with 20% of students admitting to bullying others and 30% of students reporting being bullied. This is about twice the numbers reported for physical bullying (11.3% bullies and 14.2% victims) and about three times the size of reported cyber bullying prevalence (6.2% bullies and 9.3% victims). In contrast to physical and cyber bullying, expected protective factors against bullying involvement such as pro-social involvement in school and family attachment were not significant for verbal bullying.

Furthermore, several other findings from this study should also be considered. Take the fact that males were more likely to engage in verbal bullying, the most common location of bullying was the classroom with the teacher present and that the most common type of bullying was verbal bullying - all this points to the need to shift our thinking and ways of dealing with verbal bullying and the notion of power that the bully yields. Teachers and parents need to be more attuned to verbal bullying behaviors, especially among those who have high involvement in school activities and males. Behaviors should not be dismissed or looked over due to the status of the bully. Instead, parents and educators need to actively teach children to use their position of authority to gain respect by encouraging team building and positive interactions instead of through instilling fear of social rejection, isolation and acceptance. Unlike verbal and physical bullying, being a girl and in higher grades made a student more likely to engage in cyber bullying. As a student gets older, their likelihood of involvement in verbal and physical bullying decreases while their involvement with cyber bullying increases. This could be in part due to the increase in access to technology as well as their increased proficiency with various devices. However, the question here is also whether verbal and physical bullies truly desist from bullying behavior or do they simply age out of traditional bullying and move on to a different method of bullying such as cyber bullying. Previous research has shown a correlation between between traditional and cyber bullies (Hay & Meldrum, 2010; Hinduja & Patchin, 2008; Hinduja & Patchin, 2009; Patchin & Hinduja, 2011). Similarly, in this study we find that verbal bullying is significantly correlated to physical bullying (.40) and cyber bullying (.21). Among those who reported verbally bullying others, 20.7% also reported engaging in physical bullying and 10.7% in cyber bullying. This overlap in bullying behavior suggests that while certain factors are distinct to each type of bullying, these behaviors also do not occur in isolation. Future research should examine the causal order of bullying behavior and whether traditional bullying causes cyber bullying or vice versa.

6.1. Policy Implications In examining ways to combat social issues, prevention is always preferable to intervention. Findings from the present study point to several important policy implications for both aspects of bullying. With regard to prevention, the role of parents and behaviors learned within the home is critical. Programs aimed at prevention need to address the role of parents as the first set of teachers for children. As noted above, parents can teach their children key lessons 80 about conflict resolution, self-esteem building, assertiveness and communication. An example of a current program that incorporates such components is the social learning/behavioral based model called the Common Sense Parenting (CSP) program. This program was developed to enhance parenting skills that encourage positive behavior, discourage negative behavior, and teach alternatives to problem behavior in children. Participating parents learn and practice techniques that address issues of communication, discipline, decision-making, relationships, self- control, and school success. In particular, this program supports the finding of the present study which indicates that family attachment and responses to conflict within the home are significantly related to reducing bullying behaviors. The program has been used effectively with children from all age groups and evaluated to show that parents report significantly fewer child behavior problems and improved parent and family interactions after participating in the program (Burke, R., & Herron, R., 1996; Thompson et al., 2003). On the other hand, given the role of social bonds with the family and schools as well as the influence of family strain and family and school environments, effective bullying intervention programs need to be multi-level and multi-component. Schools should examine the prevalence of bullying, location of incidents, involvement among various groups as well as information on who/how students and adults intervene. As indicated by the present findings, there are multiple environments that influence bullying; programs need to engage all those involved including teachers, parents, caregivers, bullies and victims. The present study found that key aspects of the school bond - higher attachment to school, receiving school rewards for pro-social involvement and having better grades were found to reduce the likelihood of engaging in all three types of bullying. Accordingly, this indicates that educators and school authorities can play a vital role in reducing bullying behaviors. When teachers recognize and encourage positive behaviors with the classroom, students are less likely to engage in acts of deviance. This form of social investment is a powerful tool that protects against deviance since it creates a stake in conformity for the student. Bullying was also found to occur in the classroom while the teacher was present. Prior research notes that victims are less likely to speak out if they believe their school tolerates bullying behaviors (Unnever & Cornell, 2004). This suggests that classroom management by a teacher plays a critical role by having clear rules and consequences and fair and consistent enforcement. A clear message needs to be sent to students that the teacher and the school do not tolerate bullying.

Teachers should also learn to be diligent in their classroom monitoring and observations. Bullying behavior is often discrete and quick. They need to learn to recognize behaviors among students who are being victimized before they escalate by paying close attention to those who start to isolate themselves, start to lose interest in doing school work, appear anxious, sad or depressed or suddenly begin to do poorly in school and have increased absenteeism. As discussed above, the characteristics of verbal bullying point to the distinct role of power among pro-socially involved students, of students who are new to the school environment becoming physical bullies and girls who engage in more relationship focused, discrete and covert forms of cyber bullying. While traditionally bullies are said to be impulsive, aggressive, defiant and likely to be involved in anti-social and rule-breaking activities like vandalism, delinquency and substance abuse, the findings of this study give a reason to also think otherwise. Teachers need to look beyond the usual signs and suspects, and be diligent in their observations about other students who could potentially engage in bullying behaviors. Schools also need to create policies and rules against all forms of bullying and enforce these rules to establish a climate that bullying is not acceptable. This climate is important since levels of school attachment reduce the likelihood of engaging in verbal and physical bullying. Reinforcement of positive interactions and inclusiveness are important components to incorporate in prevention programs. Lastly, bullying prevention materials should be built into the school curriculum and activities. Simply employing zero tolerance policies can often be problematic because they are inflexible when dealing with situations that require discretion on part of the educator. Teachers and staff should be trained appropriately to give them the skills to intervene and enforce the bullying rules and policies according to different types of bullying and circumstances. An example of such a program is the Olweus Bullying Prevention Program originally developed by Dr. Dan Olweus, a psychologist who has devoted nearly 30 years towards the study of bully/victim problems among school aged children. He is credited for conducting the first scientific study on bully/victim problems (Olweus, 1978) and his prevention program has been developed, refined and systematically evaluated over the years (Kallestad & Olweus, 2003; Limber et al., 2004; Olweus, 2004). The program is a multi-level, multi-component school based program designed to restructure the existing school environment to reduce opportunities and rewards for bullying. It targets students aged 6-15 and includes three core components

82 implemented at the school, classroom and individual level. These components address several key findings and variables discussed in this study. At the school level the program calls for the completion of an anonymous survey to assess the nature and prevalence of bullying. This is followed by the formation of a bullying prevention coordinating committee, training for committee members and staff, development of a coordinated system of supervision, adoption of school-wide rules against bullying and the development of appropriate positive and negative consequences for students’ behaviors. At the classroom level, it includes reinforcement of school-wide rules against bullying, holding regular classroom meetings with students to increase knowledge and empathy and informational meetings with parents. And finally, at the individual level it includes interventions aimed at students who are bullies, interventions aimed at victims and a discussion with parents of students who are involved. As suggested by the findings of this study, it examines individual variables within students, the role of the family and the school environment. The program supports the need to create strong attachments to school both at the individual level and at the contextual level by encouraging active involvement among students and teachers. It addresses the role of family functioning as well as school resources in improving academic achievement, which in turn can reduce the likelihood of bullying behavior. The findings of this study also provide a basis for us to re-think a one-size-fits-all bullying prevention and intervention program. Since each type of bullying was shown to have some unique influencing factors, programs should consider adjusting certain components to address students who are most likely to perpetuate each type of bullying. Future studies need to continue examining the nuances between each type of bullying and keep up with the evolving nature of bullying behaviors. These studies should examine for whom and in which context the prevention and interventions are most effective to reduce bullying behaviors. In particular, efforts need to be made to identity and counter forms of verbal bullying since this is the most frequently reported type of bullying, yet may be the one that is the hardest and least likely to be discovered and stopped.

6.2. Limitations The use of a cross-sectional dataset in the present study is a key limitation. Without longitudinal data causal relationships cannot be inferred. While the study found support for several criminological theories and was able to identify variables associated with the

83 involvement in different types of bullying, further research is needed to examine the direction of these associations. For example, social bonds to the school can reduce the likelihood of becoming a bully or someone already in involved in bullying can develop weak school bonds. Determining causal order of key variables such as social bonds to the family and the school, strain from the family environment, social learning and school climate will help in creating more effective and appropriate bullying intervention and prevention programs. Furthermore, the use of longitudinal data to explore the preliminary indirect effects found in the study can help identify the role of antecedent and mediator variables in bullying across contextual levels. Another issue of concern in bullying studies is the method of identifying bullies and victims. Among a majority of bullying studies (Eaton et al., 2010, Dinkes, Kemp, & Baum, 2009, Elger et al., 2009, Nansel et al., 2001; Seals & Young, 2003) including the present study, self-report measures were utilized to assess the prevalence of bullying. This involves administering anonymous surveys to students where they are asked about their involvement in bullying during a specified period of time. They are generally provided a definition of bullying and asked about their engagement as a bully or a victim. This method, however, may provide some potential limitations. Due to the social disapproval associated with being a bully, some students may not disclose being a bully (Cornell, 2006), or alternatively may misrepresented themselves as a bully due to social desirability. Additionally, depending on the survey description, some students may not identify their behavior as bullying based on the definition provided. An alternate method to self-report measures is the use of peer nominations. While this method has not been as widely used (Fox & Boulton, 2005; Toblin et al., 2005), research comparing the two methods (Branson& Cornell, 2009; Cornell & Brockenbrough, 2009; Cornell & Sheras, 2006) have found that the peer nomination method results in higher rates of identifying bullies. In this approach students are asked to list the names of classmates who fit the definitions of a bully or a bully victim. Since the data is gathered from multiple sources, it tends to increase the reliability and because the method provides researchers with names of students they can further verify the validity of these reports. Yet this method may have some limitations as well. Students may judge their peers on reputation, even after their involvement in bullying may have ceased (Fox & Boulton, 2005) while some may identify peers who engage in minor or infrequent acts of bullying or incorrectly nominate a peer to retaliate against a specific student.

Studies assessing the validity of both measures found that due to the strengths and weaknesses associated with each method, it may be best to utilize both to fully grasp the complexities of bullying involvement (Branson & Cornell, 2009). Additionally, since the present study utilized dichotomous outcome variables that differentiated those who are involved and not involved in bullying I was not able to examine the frequency of these bullying experiences. The measure for each type of bullying were also single item indicators with several types of behaviors addressed in a single question. This could also affect student responses; if students did not identify with all of the behaviors listed under, say for example cyber bullying - repeatedly sending mean emails, text messages, IM’s or posted hurtful information on the Internet about another person - they may not report involvement. Multi-item indicators allowing for the identification of the prevalence and frequency of individual bullying behaviors should be utilized in future studies to increase validity of these measures. The third level of analysis, school districts, was not found to be significant in the present study. The findings indicate that a significant level of variance was within students and some between schools. Thus, the factors examined at the school district should perhaps be examined at a smaller unit of analysis like the school level to examine the influences of school-level indicators of disorder. It should also be noted that the present study only examined the main effects of the independent variables. Future studies should examine within and cross-level interactions and test whether relationships between the predictor variables and different types of bullying vary across different contexts like classrooms, schools and school districts. In particular, given the differences found in the influence of pro-social involvement in school and likelihood of engaging in different types of bullying, it would be interesting to examine the cross-level interaction effects of grade, gender and race and levels of school involvement across different types of school environments. This could examine whether high involvement and achievement in schools among various groups, increases or decreases their likelihood on bullying involvement.

6.3. Directions for Future Research A significant majority of the variability in verbal and physical bullying and all of the variability in cyber bullying was within students. Yet, only 16.3% of verbal bullying, 22.3% of physical bullying and 11.2% of cyber bullying was explained by the level-1 variables included. This suggests that there are additional variables that influence bullying behavior which was not

85 examined in this study. Additionally, while this study found some support for criminological theories such as social bond theory, general strain theory and social learning, the study was limited by the available data. Studies should continue to examine the fit of these theories by modeling additional theoretically informed variables not included in the present study. These could include strains experienced within the home such as experiencing higher levels of financial strain, parental punishment or maltreatment and strains experienced in the context of the school environment such as teacher punishment, examination or schoolwork related strain. Another key variable missing from this analysis was the inclusion of peer influences. The peer level question for this dataset was only administered to middle school students (6th- 8th graders). Since the study explored bullying behaviors among middle school and high school students, the variable was chosen to be excluded. Given the role of peer in social learning and social bonds, future studies should include it to examine its role in explaining bullying behaviors. Another missing variable that could provide for a better specified model is the inclusion of social class/socio-economic status. The influences of social class and access to resources should be examined in relation to social exclusion and other types of bullying. These theories were chosen for their empirical foundation within the school delinquency and bullying literature. However, future research should also examine additional criminological perspectives as explanatory frameworks for bullying. It should be assessed whether one theory can adequately explain all three types of bullying or if separate theories better explain each type. Theories that could be considered include routine activities theory, school climate theory and labeling theory. As noted, future studies should also utilize longitudinal data to examine the causal relationships between the key variables in this study. Directional relationships such as those between traditional and cyber bullying, popularity, self esteem and the onset and duration of different types of bullying and the direction between school bonds and bullying should be identified. Lastly, studies should also continue to examine the prevalence of different types of bullying by combining varied methods such as peer and teacher nominations instead of just the commonly used self-report survey method. Data reliability and validity can be increased by gathering data from multiple sources and may enable researchers to rule out alternative explanations for their findings and pose research questions that would probably not be testable with single-source, single-method data sets.

In closing, this study provided an analysis of the prevalence and environmental influences of traditional bullying such as verbal and physical bullying and new and emerging styles of technology based bullying. The prevalence rates for the present study was consistent with previous research showing that verbal bullying was the most common form of bullying, followed by physical and cyber bullying. Boys were most likely to engage in physical and verbal bullying while girls tend to engage in forms of cyber bullying. Traditional forms of bullying decreased with age while cyber bullying was found to have the opposite pattern. The study also examined the fit of several criminological theories in explaining traditional and cyber bullying, adding to the limited literature and expanding empirical inquiries using leading criminological theories to explain the bullying phenomenon. Some support was found for social bonds, social learning, general strain theory and social disorganization theory. While a significant majority of second and third level variables were not found to be significant, the findings provide evidence that contextual level effects, direct and indirect, were an important component to understanding bullying behaviors. Using the theoretical framework of these theories, the present study found some distinct factors associated with each type of bullying. This provides valuable information on the nuances and evolving nature of bullying behaviors. It also provides strong evidence for criminologists, educators, parents and policy makers to continue to examine bullying using criminological frameworks to find appropriate solutions to dealing with this complex social problem.

APPENDIX A

LIST OF VARIABLES AND SCALES

STUDY VARIABLES:

Outcome Variables:

 Verbal Bullying - During the past 30 days, have you repeatedly taunted, teased, name called, excluded or ignored another person in a mean way?  Physical Bullying - During the past 30 days, have you repeatedly hit, kicked, shoved someone, caused someone physical harm/injury or taken someone’s money or belongings without their permission?  Cyber Bullying - During the past 30 days, have you repeatedly sent mean emails, text messages, IM’s or posted hurtful information on the Internet about another person?

Individual-Level Predictors (Level 1)

 Prior Delinquency (α = .77) o Number of times in the last 12 months: been suspended / carried handgun / sold illegal drugs / stolen vehicle / been arrested / attacked to hurt / drunk or high at school/ taken a handgun to school?  Deviant attitudes (α = .79) o How wrong is it: to take a handgun to school / to steal an item worth more than $5 / to pick a fight / to attack with the intention of hurting / to stay away from school all day?  School opportunities for pro-social involvement (α = 0.65) o In my school, students have lots of chances to decide things like class activities and rules o There are lots of chances for students in my school to talk with a teacher one-on-one o Teachers ask me to work on special classroom projects o There are lots of chances for students in my school to get involved in sports, clubs and other school activities outside of class o I have lots of chances to be part of class discussions and activities.  School rewards for pro-social involvement (α = 0.70) o My teachers notice when I am doing a good job and let me know about it o The school lets my parents know when I have done something well o I feel safe in my school o My teachers praise me when I work hard in school  School attachment (α = .79) o How often do you feel that the school work you are assigned is meaningful and important? o How interesting are most of your courses to you? o How important do you think the things you are learning in school are going to be for your later life? o Thinking back to the past year in school, how often did you enjoy being in school?/ Hate being in school? / Try to do your best in school? o During the last four weeks, how many whole days have you missed because you skipped or cut school?  School mobility (r = .57**) o Have you changed schools in the past year?

o How many times have you changed schools since kindergarten?  School grades (r = .48**) o What were your last year’s grades? o Are your grades better than others?  Family opportunities for pro-social involvement (α = .77) o My parents give me lots of chances to do fun things with them o My parents ask me what I think before most family decisions o If I had a personal problem, I could ask my mom or dad for help  Family reward for pro-social involvement o My parents’ notices when I am doing a good job and let me know about it o How often do your parents tell you they’re proud of you for something you’ve done? o Do you enjoy spending time with your mother? o Do you enjoy spending time with your father?  Family attachment (α = .77) o Parents ask if I’ve gotten my homework done o When I am not at home, one of my parents knows where I am and who o Parents would know if I did not come home in time o Rules in my family are clear o My family has clear rules about alcohol and drug use.  Low self esteem (α = .84) o Life not worth it o I think I am no good o I am a failure  Family conflict (α = .80) o Family has serious arguments o Family insults or yells at each other o Family argues about the same things over and over  Family members with alcohol or drug problems o Has anyone in your family ever had a severe alcohol or drug problem?

School-Level Predictors (Level 2 Data)  School level aggregate measures o School opportunities for pro-social involvement o School rewards for pro-social involvement o School attachment o School mobility o School grades

School District-Level Predictors (Level 3 Data)  Concentration of student poverty - percentage of students eligible for free or reduced- price lunch  Enrollment size - the total number of students in school as measured during the fall survey period in October; also known as fall membership.  Instructional staff - the total number of instructional staff employed at each school district during the 2009-2010 school year

APPENDIX B

CORRELATION MATRIX OF DEPENDENT AND INDEPENDENT VARIABLES

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 1 2 .40** 1 3 .27** .21** 1 4 .15** .21** .12** 1

5 .23** .26** .16** .37** 1 6 -.08** -.10** -.07** -.13** -.24** 1 7 -.11** -.12** -.08** -.13** -.26** .60** 1 8 -.12** -.14** -.10** -.21** -.42** .44** .49** 1 9 .03** .03** .02** .08** .08** -.01** -.04** .05** 1 10 -.11** -.13** -.09** -.22** -.26** .19** .19** -.34** -.04** 1 11 -.11** -.11** -.09** -.15** -.29** .30** .33** -.36** -.04** .21** 1 12 -.11** -.11** -.09** -.16** -.31** .29** .35** -.39** -.04** .26** .67** 1 13 .16** .13** .11** .14** .24** -.11** -.17** .22* .16** -.13** -.36** -.36** 1 14 -.09** -.13** -.10** -.22** -.33** .25** .26** -.35** .02** .24** .60** .60** -.17** 1 15 .16** .12** .12** .11** .22** -.17** -.22** .25** .11** -.20** -.33** -.33** .39** -.18** 1 16 .000 -.008 -.001 -.002 .000 .001 -.003 .001 .007 .008* .002 .002 -.007 .007 -.002 1 17 -.04** -.03** -.01** -.02** -.05** .21** .12** -.07** .003 .04** .03** .03** -.01** .03** -.02** -.001 1 18 .01** .006 -.02** -.06** -.07** .12** .21** -.16** -.06** .07** .10** .12** -.05** .12** -.01** -.004 .59** 1 19 -.06** -.04** .01** .07** .08** -.05** -.13** .26** .08** -.06** -.12** -.14** .08** -.15** .01** .009 -.26** -.63** 1 20 -.03** -.02** .007 .04** .05** .003 -.05** .09** .24** -.02** -.07** -.07** .07** -.07** .01** .009 .01** -.25** .34** 1 21 -.02** -.02** -.02** -.08** -.06** .04** .07** -.07** -.03** .21** .08** .11** -.05** .10** -.03** .02** .20** .35** -.29** -.13** 1 22 -.029 -.015 -.116 .023 .060 .003 -.036 .051 -.081 .074 .049 .033 .017 .016 .063 .077 -.32** .197 -.206 .28 .104 1 23 -.038 -.017 -.134 .065 .068 .008 -.043 .075 -.069 .056 .028 -.003 .036 .010 .097 .054 -.28 .187 -.188 .31* .095 .38** 1 24 -.046 -.026 -.137 .047 .062 .021 -.032 .067 -.084 .061 .030 .011 .036 .021 .092 .062 -.28 .182 -.192 .32** .093 .43** .37** 1

** p<.001, * p<.05, 1= Verbal Bullying, 2= Physical Bullying , 3= Cyber Bullying, 4= Prior Delinquency, 5= Deviant Attitudes, 6= School opportunity for pro-social involvement, 7= School rewards for pro-social involvement , 8= School attachment, 9= School mobility, 10= School grades, 11= Family opportunities for pro-social involvement, 12= Family rewards for pro-social involvement, 13= Family conflict, 14= Family attachment, 15= Low self esteem, 16= Family members with drug of alcohol problems, 17= School opportunities for pro-social involvement, 18= School rewards for pro-social involvement mean, 19= School attachment mean, 20= School mobility mean, 21= School grades mean, 22= Concentration of student poverty, 23= School district enrollment Size, 24= Instructional staff ratio 92

APPENDIX C

FLORIDA STATE UNIVERSITY HUMAN SUBJECT COMMITTEE APPROVAL

Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673 · FAX (850) 644-4392

RE-APPROVAL MEMORANDUM

Date: 2/22/2012

To: Karla Johanna Dhungana

Address: Hecht House, 634 W. Call Street, Tallahassee, FL 32306 Dept.: CRIMINOLOGY AND CRIMINAL JUSTICE

From: Thomas L. Jacobson, Chair

Re: Re-approval of Use of Human subjects in Research A Multi-Level Examination of the Prevalence and Environmental Influences on Bullying Related Experiences

Your request to continue the research project listed above involving human subjects has been approved by the Human Subjects Committee. If your project has not been completed by 2/13/2013, you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the committee.

If you submitted a proposed consent form with your renewal request, the approved stamped consent form is attached to this re-approval notice. Only the stamped version of the consent form may be used in recruiting of research subjects. You are reminded that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report in writing, any unanticipated problems or adverse events involving risks to research subjects or others.

By copy of this memorandum, the Chair of your department and/or your major professor are reminded of their responsibility for being informed concerning research projects involving human subjects in their department. They are advised to review the protocols as often as necessary to insure that the project is being conducted in compliance with our institution and with DHHS regulations.

Cc: Brian Stults, Advisor HSC No. 2012.7732

APPENDIX D

DEPARTMENT OF CHILDREN AND FAMILIES DATA APPROVAL LETTER

ｾｾＢ］ＺＺ］＠ . h State of Florida Rick Scott Governor q I? ' Department of Children and Families . David E. Wilkins ｾＭ Secretary "

Aprill3, 2011 The Florida State University Human Subjects Office 20 JO Levy Avenue Suite276-C Tallahassee, FL 32306-2742

Dear Human Subjects Review Committee:

Tllis letter is to provide confirmation that Karla Dllungana, doctoral candidate at the FSU College of Criminology and Criminal Justice, has been given permission to utilize the Florida Youth Substance Abuse Survey (FYSAS) data from the Florida Department of Children and Families for her dissertation proposal. The dataset will be used for the individual level data and analyses in her dissertation titled, "A MultH..evel Examination ofthe Prevalence and Environmental Influences on Bullying Related Experiences." She has utilized the dataset previously for her Master's thesis and is familiar with the variables and format.

Responses to the FYSAS are anonymous and are not individually identifiable. Therefore the survey is not considered research involving human subjects according to 45CFR46.102(f).

I support the proposed research project and welcome any questions you may have regarding the dataset and its use.

Sincerely,

ｈ｡ｬｾ＠ Joson, MPH Man ing Epidemiologist Subs nee Abuse Plogram Office Florida Department of Children and Families Building 6, Room 325 l317Winewood Blvd Tallahassee, FL 32399-0700

1317 Winewood Boulevard, Tallahassee, Florida 32399-0700

Mission: Protect the Vulnerable, Promote Strong and Economically Self-Sufficient Families, and Advance Personal and Family Recovery and Resiliency REFERENCES

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BIOGRAPHICAL SKETCH

Karla Johanna Dhungana completed her Bachelors in Psychology with a minor in Art History from Loyola Marymount University in Los Angeles, CA during fall, 2006. She began attending Florida State University in the fall of 2007 as a graduate student in the College of Criminology and Criminal Justice. Under the advisement of Professor Sarah Bacon, she obtained her Master’s degree in Criminology during the summer of 2009. For her dissertation, she worked under the advisement of Professor Brian Stults.

Her research interests include crime & social institutions, traditional & cyber bullying, women & crime, policing, prisoner re-entry and youth gangs.

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