How the Act of Bullying is Transforming in Today’s High Schools

Abstract: 

The following paper discusses how bullying is evolving in high schools today. Bullying and aggressive social interactions are affecting today’s youth more than ever and we believe that looking at the problem from a sociological perspective will not only help to understand this social problem, but also help with finding solutions for our schools and students. Our methodology consisted of surveys from high schools asking students and teachers twenty open/closed ended questions. The results show new ways bullying occurs through negative social interaction in classrooms, hallways, and social media. Our research will help society understand that this is a daily battle for kids and how to prepare for stepping in and finding a solution to this problem.

Table of Contents: 

    Introduction

     We believe bullying is a problem in our society because of how kids interact and communicate in schools, which is vastly different than it was twenty years ago. The communications between peers have changed from face to face interactions to a virtual language that is in many ways much more damaging than any other form of bullying. It is more than a peer attacking a peer or a group attacking a single person. Thousands of people can be reached through the Internet and through other forms of social media which are changing the lives of young adolescents. We believe this problem needs to be studied because learning the effects of bullying can help our younger generations in personality development, social and learning skills, and help with many disabilities that come with the mental attacks of bullies. We can also provide our children with the necessary tools to stand up for themselves and be ready for when this type of social interaction takes place in our schools. Learning the changes of high school social life can help parents and teachers avoid and break down the issue of bullying in our high schools.

    Others should care about this problem because much of our social interaction is done virtually resulting in the need to learn the pros and the cons of this type of communication. How is virtual bullying and classroom bullying affecting our kids? Our families? Marriages and co-habitating couples? We have to relearn appropriate and inappropriate behavior in high schools and how adolescents treat one another in the classroom and on social media websites. A huge strength that bullies have is that they can go online and attack others in ways that cannot be undone and taken back. We need to take charge this issue and learn a new way to protect our young people from others in this new virtual environment.

    An aspect of this problem that is not well understood is that bullying is evolving and changing into something that is more harmful to those being targeted than ever before. Children that are “nerds” or not the ones being pinpointed anymore; conversely, everyone has something that bullies will notice and attack. The names that are being hurled at the victims are more brutal with labels that stick for years. People still think that there is no big issue in our schools, but kids that physically harm themselves or even commit suicide are proving us wrong.

    We want to explore how bullying takes place in the classroom when teachers are or are not present and we want to look at what drives bullies to revert to the internet and attack others on social media sites. We also want to look at the differences between males and females, their patterns and behavior on bullying, and how that information can be used to help as well.

    We will review the literature on bullying, including resources such as journal articles, statistics, and documentaries. We will then analyze secondary data from the Health Behavior in School-Aged Children (HBSC), 2009-2010. The survey is conducted every four years in forty-two countries and data on health- related behaviors and lifestyle issues among children in grades five through ten. Our sample consisted of 11,010 students ranging from ages ten to seventeen.

    The following literature review focuses on several broad questions that we are attempting to answer with thorough investigation. The main focus to our research is finding out how bullying is changing in today's society and also what preventative methods might be used to prevent childhood bullying. Questions to be addressed include the following. Why do children bully? What are the consequences to bullying? What are some preventative measures for teachers in the classrooms? How has bullying changed? How is bullying different between age groups? How is bullying different between boys and girls? How has social media affected bullying and the tactics of bullying? Is bullying more prevalent in rural or suburban areas and if so, how are they different? Does family life or socioeconomic status have an effect? Are there any differences between small and large schools? Public school or private school? If so what are the differences?

    The two research goals that we will focus on will include identifying general patterns and relationships and making predictions. Identifying the general patterns of bullying and studying how the process of bullying occurs and where it takes place will help us figure out ways to prevent bullying from happening in our schools and also make a difference when high school kids are at home surfing social media sites. Making predictions will be a primary goal in our research. By making accurate predictions, we can understand general patterns associated with bullying and obtain knowledge of its general characteristics which can help us create awareness and programs to help victims in our schools. (H1): Boys between the ages of fourteen and eighteen years of age are more likely to be the bully and/or be victims than girls. (H2): The impact of cyber bullying for students between the ages of fourteen and eighteen years of age are more likely to increase anxiety and lack of social growth than face-to-face bullying. (H3): If we train and prepare teachers on bullying tactics then teachers will be able to make counter attacks on bullying from within the classroom and lower the chances of bullying that takes place under their authority.

    Literature Review

    When looking at the large issue of bullying, it is often hard to pinpoint exactly where or how the victims are being bullied. Therefore, we decided to look specifically into three different categories of bullying including gender-based bullying tactics, cyber-bullying, and bullying in the classroom.  

    Gender-Based Bullying Tactics

    This category of bullying mainly focuses on the differences between how girls and boys bully, as well as the difference in genders and victimization. For example, boys are almost twice as likely to report bullying to their superiors as girls (Munro 2004). These six articles give us a more solid idea of the differences between males and females when bullying and victimization take place.

    Munro (2004) focused more on low self-esteem in both sexes and why boys are more likely to bully in an aggressive or direct fashion. Munro noticed that while both boys and girls suffer from low self-esteem at some point in their adolescence, boys are far more likely to bully than girls because “a driving force for many boys is proving their masculinity by being as tough or tougher than their peers…girls are more often the victims of classroom and schoolyard harassment” (Munro 2004, 8) He also found that girls experience far more sexual harassment because of the “driving force” of masculinity since boys feel the need to show their strength and identity. It was also discussed that boys are more likely to report bullying “at nearly twice the rate” as girls. With that being said, “…name calling, teasing, exclusion and rumors bully reports indicate a higher rate for female victims than for males. For hitting, name calling, threats and interfering with clothing and property boy victims were subjected to higher levels by bullies than girl victims.…” (Munro 2004, 12).

    Behm-Morawitz and Mastro (2008) examined how bullying in the media (teen movies) can affect attitudes toward gender-based bullying. They found that, “Female friendships have generally been found to be more supportive than male friendships and are characterized by social cooperation….” (Behm-Morawitz and Mastro 2008, 133). Teenage girls also believed that if they were to climb up in “popularity” or “power,” that would justify the so-called “social aggression.”  Socially aggressive behavior is an indirect type of bullying that includes spreading rumors, note passing, humiliation, and other malicious acts. The three most prominent in existing studies were gossiping, backstabbing, and humiliation.

    Ryalls (2001), demonstrated a lot of parallels to, Behm-Morawitz and Mastro (2008). Emily Davis Ryalls’ dissertation talks about how in today’s world, popular girls will do whatever it takes to keep their “power associated with their elite status” among their peers. Ryalls also touches on the fact that girls use “…indirect aggression, which is defined as a form of social manipulation” (Ryalls 2001, iii). She goes on to discuss the “bullying tactics” that come with this type of aggression. These tactics include, “…gossiping, social exclusion, stealing friends, not talking to someone, and threatening to withdraw friendship. The leader of the clique is the Queen Bee who is able to use boundary maintenance to exclude other girls from her friendship groups…and destructive “(Ryals 2011, iii). She includes a quote from another author to illustrate her point, as follows:

    Girls use backbiting, exclusion, rumors, name-calling, and manipulation to inflict psychological pain on targeted victims. Unlike boys, who tend to bully acquaintances or strangers, girls frequently attack within tightly knit networks of friends, making aggression harder to identify and intensifying the damage to the victims...Behind a façade of female intimacy lies a terrain traveled in secret, marked with anguish, and nourished by silence. (Simmons 2002 quoted in Ryalls 2011, 1)

    It was interesting how throughout her dissertation she did not just use the words “mean girl,” but rather used the description of “Queen Bee.” The “Queen Bee” always seems to have a higher socioeconomic status than the “wannabe” (Ryalls, 2011 iv).

    The focal point of Wang, Iannotti, and Nansel’s (2009) research was how bullying methods and frequency change as one goes from grade to grade in school.  It was interesting that the older one gets, the less one is involved in bullying. When compared to middle school students, “9th/10th graders were less involved in all types of bullying, including physical, verbal, relational, or cyber bullying (Wang, Iannotti, and Nansel 2009, 372) The article also explains a lot of the same things that other studies have found such, as girls are not as likely to be  directly aggressive as boys. For example, in the cyber world “boys were more likely to be bullies, whereas girls were more likely to be victims” (Wang, Iannotti, and Nansel, 2009, 371).

    The most frequent form of bullying (school violence) is gender-based. According to Anagnostopoulos, Buchanan, Pereira, Lichty, “national surveys indicate that fully 80% of adolescents in the United States will experience some type of gender-based bullying before graduating from high school” (2009, 519). The researchers (2009) really wanted to direct attention to two major themes: (1) male-on-female bullying is the most common form of gender-based bullying, and comes from immature males and quiet, shy females; and (2) males and females view gender-based bullying differently. Male school staff members emphasized their attempts to educate immature and inexperienced male students about bullying. They attributed male bullying behavior as immature rather than malicious (Anagnostopoulos, Buchanan, Pereira, Lichty 2009, 352). Female staff members, on the other hand, tended to see most of the bullying of females by males as a type of sexual harassment.

    Cowie (2000) discusses the different types of participants in bullying. The article expresses that it is not just the bully that does the “dirty” work but it is also the group of peers that goes along with whatever the leader does. Cowie says,

    The most common types of participants are “assistants,” who physically help the bully; “reinforcers,” who incite the bully; “outsiders,” who remain inactive and pretend not to see what is happening; and “defenders,” who provide help for the victim and confront the bully. (Cowie 2000, 36)

    Bystanders or “the group behind the bully” are fearful that if they do not go along with what their so-called leader wants them to do that they will end up being the ones that are bullied.

    Cyber-bullying

    The next six articles go into detail about how social media is affecting the victimization process for children. Bullies are able to extend their reaches to the internet and social media websites to inflict pain, as well as use instant messaging to target their victims. Essentially, there is no escape for those that find themselves the center of the bully attacks. Looking into this information, we see that cyber-bullying is making it that much harder for kids to have a positive high school experience with their peers. This leads to further damage when working with others, social mannerisms, and self-esteem.

    In Cetin (2011), the purpose of this study was to develop a reliable and valid scale, which determines cyber victimization and bullying behaviors of high school students. The research was conducted using 404 high school students, both boys and girls, during the 2009-10 school year. Through the development of the bullying scale, they were able to determine that cyber bullying is growing problem and is becoming an “extension to traditional bullying in schools.” The scale is comprised of 22 items and has 3 sub-factors including cyber verbal bullying, hiding identity, and cyber forgery. A number of methods were employed for determining the reliability of the Scale of Cyber Victim and Bullying. These included internal consistency, split-half reliability and test-retest methods. The scale can be used to do further research and can be helpful in our research efforts.

    DeVoe and Murphy (2011) focuses on cyber-bullying and used surveys to gather information from over 7 million high school students. The National Crime Victimization Survey was given to students ranging in age from 12 to 18 years in the 2008-2009 school years. The survey takes into account different ages, race, household income, sex, and the type of school attended, private or public, to identify the correlates of cyber bullying. The survey shows the relationship between bullying and cyber-bullying victimization and other variables of interest, such as the reported presence of gangs, guns, drugs, and alcohol at school; selected school security measures; student criminal victimization; and personal fear, avoidance behaviors, fighting, and weapon carrying at school. Interviews and surveys were the methods used in this article.

    Kiriakidis and Demarques (2013) conducted a study in one high school in the southern United States. The purpose of this case study was to explore teachers’ experiences in student-to-student cyber bullying. The conceptual framework was based on choice theory. Data were collected from semi-structured interviews with high school teachers. Qualitative data were analyzed through content analysis for emergent themes. A major finding was that the school district administrators and teachers should implement a cyber-bullying intervention program for student-to-student online safety. The study will be useful because it provides a different perspective from the viewpoint of the teacher.

    Allen (2012) is a mixed methods study that explores text messaging and cyber-bullying in a suburban US high school. Students answered survey questions regarding the prevalence of bullying and victimization via text messaging. Students and staff members responded to a survey item regarding perceptions of hostile text messaging. Both students and staff members participated in interviews or focus groups where they discussed bullying, student peer interactions, and social conflict. Prevalence for text messaging that was viewed as bullying was considerably lower than other published rates. Female students perceived more hostile text messaging than male students. Analysis of qualitative data suggests that texting contributes to conflict and to a phenomenon called “drama,” and that conflict or drama may lead to bullying.

    Agatston, Kowalski, and Limber (2007) focuses on the perspective of a student as it relates to bullying. A mixture of 148 middle school and high school students were selected from public schools and asked to participate in a focus group. Students were then separated by gender and asked scripted questions by a counselor of that same gender. They found that students’ comments during the focus groups suggest that students, particularly females, view cyber bullying as a problem, but one rarely discussed at school, and that students do not see the school district personnel as helpful resources when dealing with cyber bullying. The study also concluded that schools districts should share policies regarding cyber-bullying with students and parents and that it should be included in bullying prevention strategies.

    Schneider, O'Donnell, Stueve, and Coulter (2012) conducted a regional study assessing roughly 20,000 high school students and their bullying victimization and psychological distress. Students were given surveys and the researchers found that 15.8% of students reported cyber-bullying and 25.9% reported school bullying in the past year. A majority, 59.7%, of cyber-bullying victims were also school bullying victims; 36.3% of school bullying victims were cyber-bullying victims. Victims of bullying reported lower school performance and school attachment. The findings in this article confirm the need for cyber-bullying prevention, as it seems to have an effect on performance and the overall health of the students.

    Bullying in the Classroom

    In our last category of bullying we look at the tactics used in the classrooms when there is an authoritative figure present. We wanted to look at how behavior changed in the bullies and in the victims to see if having surveillance changed the interactions between the two groups. There are six articles that go into detail about how classroom management and control can impact bullying and the occurrences that still happen in what should be protected environments for the students. 

    Banks (1997) focused on the connection between classroom management and classroom bullying. The main factors considered were classroom management, teacher practices, school environment, and student behavior. The researcher concluded that bullying is most likely to occur in classrooms where the punishments are minimal, where the classroom instruction is of lower quality, where the school administration and classrooms are disorganized, and when the school creates an anti-social environment.

    Boulton and Underwood (1992) described a study of incidences of bullying in classroom settings. It was conducted by selecting 27 students (19 boys, 8 girls) and placing them in a classroom where they were being recorded with a camera. The students did not know they were being recorded and were chosen by the teachers because they either had aggressive or non-aggressive tendencies. There were 60 episodes of bullying during the duration of 28 hours of classroom observation. The bullying episodes happened twice every hour and the actual episode was usually short and quick. It was noted that boys and girls were bullied at the same rate even though the number of boys was more than double the number of girls, and the peers that were observing the episode either joined in or just observed; no one tried to stop the actual bullying from happening. The conclusion of the study is that most bullying episodes happen in classroom activities that include group work, where peers are in close proximity to each other. Also the individual traits of the bullies were very important because the episodes with the same bullies were very similar and usually the same kids were getting picked on.

    Bullock (2002) describes bullying as "the systematic abuse of power" and when looking closely at the circumstances in the classroom, is even more strategic when an authority figure is present. A large way that bullying takes place in the classroom is with hand-written notes or texts of which the administrator is unaware. This article states that most adults, including educators, think of verbal bullying as normal and harmless. The interviewed 138 teachers from North England schools and concluded that one in every four teachers did not consider name-calling, spreading rumors, intimidation by staring, and the stealing of other's belongings as bullying. The top three behaviors that were seen as bullying were physical abuse, forcing someone to do something they did not want to do, and threatening others.

    Shields and Cicchetti (1997) found that teachers considered the physical bullying to be much more severe than verbal attacks and emotional abuse. They do not seem to understand the repercussions of social exclusion and psychological terror. The best way to look at this research is by using a dyadic approach; the bully and the victim. In a classroom, the bully has to be very calculating and specific in their attacks. The victim has to appear vulnerable and at the time the bully has to avoid others joining because he does not want to draw the educator’s attention. These bullies in the classroom are skilled at avoiding blame and usually feel no remorse for their actions and their victims.

    Arsenio and Lemerise (2001) discuss most likely scenarios for school bullying to take place. Bullying is more likely to occur in larger schools than smaller schools. Also, the research shows that students with disadvantaged socioeconomic backgrounds are more likely to be victims than those that do not. Victimization is much less likely in schools where parents are involved in the activities and their children's education on a regular basis. When school counselors and educators proactively intervene in any bullying or threatening situation, there is less likely to be victimization. When the school and the educators have intensive supervision and anti-bullying measures or programs for students, teachers, and parents, there is less bullying.

    Barone (1997) asks the research question, "Why should there be an interest in the occurrence of peer harassment in the first place? Previous research suggests that not only are the students who are directly involved in peer harassment incidents at risk for a host of adjustment difficulties, but even bystanders and onlookers who witness such harassment events can be negatively affected." This article is very much focused on the fact that bullying not only affects victims, but also the bullies and the onlookers of bullying. It describes some of the long term damage that can be done and what effects each party will most likely suffer from later in life. The concern is that people are not aware of how damaging this kind of social behavior can be and that everyone is affected by these negative social situations. Studies have found that aggressive children actually experience large amounts of peer rejection and anxiety in classrooms. This can potentially cause higher levels of delinquency and psychosocial maladjustment. Also, bullies have a much harder time in school learning and focusing than other students who are adjusted socially. Many aggressive teens internalize these frustrations and this causes depression and can lead to disengagement from the school and school activities. Also, both indirect and direct links have been found between peer harassment and indicators of school functioning, including decreased interest in school events, lower GPA's and more absenteeism.

    Ambert (1994) talks about the teacher’s management of the classroom and the social structure of the class and the acts of bullying. In all, 2,002 students and 99 teachers participated in this questionnaire survey. This study shows that classroom management had a direct impact on the occurrence of bullying and the behavior conducted by bullies in the classroom. The students’ socioeconomic status and family life were also factors that affected bullying.  The researchers found a substantial effect of classroom management of the teachers.

    Methodological Review

    Questionnaires and Surveys

    Throughout previous studies of gender-based bullying tactics their measures and methods, there seemed to be the same or very similar use of both qualitative and quantitative methods.  Munro (2004), Cowie (2000), and Anagnostopoulos, Buchanan, Pereira, and Lichty (2008)  used questionnaires and surveys that used ordinal measures through multiple-choice responses such as “never,” “sometimes,” “often,” or “very often.” The sample sizes in each article were all very large. They coded for social aggression, social cooperation, characteristics, and consequences. Ryalls (2001) also analyzed media such as reality TV, movies, magazines, books, etc. The Olweus Bully/ Victim Questionnaire was also used by Munro (2004) and Wang, Iannotti, and Nansel (2009) to measure the different types of bullying such as relational, verbal, and physical. DeVoe (2011), Bullock (2002), Boulton (1992), and Schneider (2012) used surveys in order collect data.

    Mixed-Methods, Focus Groups, Interviews

    Anagnostopoulos, Buchanan, Pereira, and Lichty (2008) also interviewed staff and students for 30-60 minutes. They were asked to explain the “prevalence, severity, and features of gender-based bullying.” In Cetin (2011), methods used included internal consistency, split-half reliability and test-retest methods. Kiriakidis (2013) used a qualitative approach to their research by conducting semi-structured interviews. Allen (2012), Arsenio and Lemerise (2000), and Agatston (2007) conducted interviews and focus groups. Banks (1997) and Barone (1997) used secondary research and literature reviews to conduct their research. Anagnostopoulos, Behm-Morawitz, and Mastro (2008) found the top twenty “grossing” films that best represented their topic of finding how the media depicts the role of girl-to-girl bullying, how demographics may affect bullying, and also how bullying tactics were different. Ambert (1994) took a qualitative approach by videotaping twenty-eight hours of footage in a classroom where twenty seven students were hand-picked by teachers for being aggressive or nonaggressive.

    Methods

    Data

    Information was taken from the study on Health Behavior in School-Aged Children (HBSC), 2009-2010 (Iannotti 2009-2010). The HBSC was initiated in 1982 and has been conducted every four years since then in numerous countries. The study includes forty-two countries and looks at health- related behaviors and lifestyle issues among children in grades five through ten. The sample size for our study consisted of 11,010 students ranging from ages ten to seventeen.

    Dependent Variables

    Our dependent variable is victimization and was created using an index of seven different items. These items look at how often students are bullied. Topics included how often students were called names, excluded from events, hit/kicked/pushed, ied about by others, or discriminated against for their race or religion. A Likert scale was used and ranged from one to five with one being I have not been bullied in this way in the past couple of months; two, only once or twice; three, two or three times a month; four, about once a week; and five, several times a week. The Cronbach’s alpha was .854.

    Independent Variables

    Our independent variables are cyber-bullying, negative classroom environment, and males. Topics for cyber-bullying include how often students were bullied by using a computer or email, using a cell phone, using a computer or email outside of school, and or using a cell phone outside of school.  A Likert scale was used and ranged from one to five with one being I have not been bullied in this way in the past couple of months, two being only once or twice, three being two or three times a month, four being about once a week, and five being several times a week). The Cronbach’s alpha is .943.

    For negative classroom environment, topics were focused on whether or not students in class enjoyed being together, were kind and helpful, and accept the student as he/she was. A Likert scale was used to determine whether students agreed or disagreed ranging from one to five with one being strongly agree; two, agree; three, neither agree nor disagree; four, disagree; and five, strongly disagree. The Cronbach’s alpha is .740.

    Control Variables

    Control variables include age, race, and gender. For our control variable of age, we recorded data from school-aged children ranging from age 10 to 17. Our second control variable of race was split into four groups, including Black or African American, Asian, American Indian or Alaska Native, and Native Hawaiian or other Pacific Islander. The last control variable was gender and examined the differences between male and female students. In Table 1, descriptive statistics show that the 11,010 respondents answered all questions. When looking at bullying and the results from the SPSS analysis using the control variables, we were able to see a significant negative relationship between cyber-bullying and Asian males.

    Results

    Descriptive Analysis

    Table 1 shows the descriptive analysis of the independent and dependent variables. The mean, standard deviation and minimum/maximum values were presented based on the given independent and dependent variables. After listwise deletion, our sample size reduced from 12,642 to 11,010 participants. On average, respondents in the sample size were 13 years old with the standard deviation of 1.73.  Sixty-eight percent of the respondents were White; 20 percent were African American; six percent, Asian; and five percent American Indian or Alaskan Natives. Only two percent were Hawaiian or Pacific Islanders. The sample had equal propostions of males and females. On average, the US adolescents had 2.63 points on the index of victimization, ranging from 0 to 28 with the standard deviation of 4.70. Only a small proportion of adolescents engaged in cyber-bulling behaviors. There average score on the index of cyber-bullying, ranging from 0 to 16 was .43 with the standard deviation of 1.97. On average, respondents reported 2.28 on the index of negative environment in classroom, ranging from 1 to 5 with the standard deviation of .84. 

    Multivariate Analysis

    Table 2 shows the multivariate analysis predicting school victimization. We used OLS regression to analyze the results. We have two models in our analysis. In the first model, independent variables were used to explain school victimization. We introduced control variables in addition independent variables in the second model.  As seen in the Table 2, the second model is best for model since the second model’s R2 (.182) is higher than the R2 of the model 1 (.172). It can be said that 18.2 percent of variation in victimization was explained by independent and control variables. Based on the second model, there is a statistically significant, positive relationship between cyber-bullying and school victimization (p≤.001). On average, each unit increase in cyber-bullying leads to an increase in school victimization by .705, controlling for all other variables (b = .705). Based on the second model, we see that there is a statistically significant, positive relationship between negative classroom environment and school victimization (p ≤ .001). On average, each unit increase in negative environment in the classroom leads to an increase in school victimization by 1.528, controlling for all other variables (b = 1.528) There is a statistically significant relationship between gender and school victimization (p ≤ .001). On average, males reported -.025 points on the victimization index, all else equal (b = -.025). There is also a statistically significant relationship between race and school victimization (p ≤ .001). Blacks or African Americans reported .345 points on the victimization index, all else equal (b = .345). Asians reported .078 points on the victimization index, all else equal (b = .078). American Indians or Alaska Natives reported .483 points on the victimization index, all else equal (b = .483). Native Hawaiians or Pacific Islanders reported .942 points on the victimization index, all else equal (b = .942).

    Findings

    Of the ten to seventeen age ranges the children that reported being bullied the most were within plus/negative one standard deviation of the mean age. The average age for males was thirteen. The majority, 68%, of the males are between 11.3 and 14.76 years of age. The age information was not available for the females. More children reported bullying within a negative classroom environment than through cyber-bullying even though both forms of bullying were reported. The results show that more African American children reported being bullied than Asian, more Asian children reported being bullied than American Indian or Alaska Native children, and more American Indian or Alaska Native children reported being bullied than Hawaiian or Pacific Islander. Hypothesis 1 and 2 were reject because the average age for boys that were bullied was age thirteen and they were also reporting that the incidents of bullying took place in the classroom or in face-to-face altercations. Therefore, we reject our prediction that males would report bullying between the ages of fourteen to eighteen and that they would experience more bullying through cyber connections then face-to-face or classroom environments. Our last hypothesis, we believe, still holds water. If we can prepare our teachers and train them to handle these situations, we should see bullying and victimization lower in our schools. The fact that the results show that more kids are reporting face-to-face bullying and classroom bullying shows that these teachers are ill-equipped to handle these situations. For the benefit of the kids, we need to take charge and train these administrators to handle bullying in schools.

    Limitations

    Our limitations for our research were mainly time and limited data available on the topic of bullying. Initially we wanted to conduct interviews and surveys at surrounding high schools with the students and teachers. However, we did not have enough time to conduct a full study with interviews, read, code, and compare themes for this research. With the SPSS data set that we had available to us, we were unable to compare males and females in our results, as well as compare the genders on our three main themes: cyber-bullying, differences in gender bullying, and negative classroom experience.

    Conclusion

    In conclusion, our research will help students, teachers, and parents not only understand that this is a daily battle for our kids, but also how to prepare for stepping in and finding a solution to this social problem. We want people to understand that there are steps that we can take to teach our young people how to defend themselves, each other, and perhaps even help those who have the urge to attack others and bully. We had expected to find that cyber-bullying was a new trend that was taking over the social environment of high schools. However, that was not the case at all. We also did not expect to see significant differences in races and occurrences of bullying but the data showed us that there is evidence that certain races are targeted more than others. In our research, we saw that classroom bullying is reported more than cyber-bullying, and because we were only able to get responses from males. This made it impossible to see the differences in genders and bullying like we originally hoped to see, however we were able to get some great information and results on males that were experiencing victimization. Knowing that kids are still being bullied mainly in the classroom environment tells us that there is something our administrators and parents can do about the situation. Educating teachers on the correct way to handle bullying in the classroom can drastically reduce this kind of terror within schools. Helping parents understand that this is a problem that their children are facing every day will allow them to step up and teach their kids the right way to handle themselves. This will help kids know that their parents are supportive and that they are listening to them, which will help them handle these situations in schools. Cyber-bullying is found less than other types of bullying, but is still surfacing enough for people to become more aware of the dangers of online attacks. Many of the respondents were younger, and we assume that this affected our data on cyber-bullying. If we could add on to our research, we would want to see how text messaging, social media, and other forms of online communication affect older kids. We would look at specific ages and try to get focus groups together to talk about this issue.

    Our research is important to society in so many ways because we were able to see that in many cases African American students are bullied in the classroom more often than other race or ethnic groups. Males reported they were more likely to be bullied at age thirteen than any other year. This knowledge can be taken to schools and educators and steps can be taken to focus on this age group and to become more aware of bullying among different races. If these schools can focus on bullying in the classroom and in the classrooms of the students around age thirteen, we may see bullying statistics drop in the coming years.

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    Table 1: Mean, Standard Deviations (SD) Ranges of Variables Used in the Analysis Health Behavior in School-Aged Children (HBSC), 2009-2010, (N=11,010)
     

    Variables Mean SD Min Max
    Dependent Variable:        
    Victimization 2.63 4.70 0 28
    Independent Variable:        
    Cyber-Bullying .43 1.97 0 16
    Negative Environment in Classroom 2.28 .84 1 5
    Males .50   0 1
    Control Variables:        
    Age 13.03 1.73 10 17
    Race : White .68      
    Race: Black or African American .19   0 1
    Race: Asian .06   0 1
    Race: American Indian or Alaska Native .05   0 1
    Race: Hawaiian or other Pacific Islander .02   0 1
     

    Table 2: Estimates of OLS Regression Models Predicting the Effect of Bullying on Victimization (N=11,010)

      Model 1 Model 1 Model 2 Model 2
    Variables

    B

    (SE)

    Beta

    B

    (SE)

    Beta
    Independent Variables        
    Cyber Bullying

    .707***

    (.021)

    .297

    .705***

    (.021)

    296
    Negative Environment in the Classroom

    1.487***

    (.048)

    .268

    1.528***

    (.048)

    .275
    Males

    -.073***

    (.082)

    -.008

    -.025***

    (.082)

    -.003
    Control Variables        
    Age    

    -.252

    (.024)

    -.093
    Race: Black/African American    

    .345***

    (.104)

    .029
    Race: Asian     078***

    (.178)

    .004
    Race: American Indian/Alaskan Native    

    .483***

    (1.83)

    .023
    Race: Native Hawaiian/Pacific Islander    

    .942***

    (.311)

    .026
    Constant

    -1.037***

    (.127)

     

    2.015***

    (.322)

     
    R2 .172   .182  
    Adjusted R2 .172   .181  
    Model F 762.698   305.674  
    Model Degrees of Freedom 11017   11009  

    Note: B (SE)=unstandardized estimate of the regression coefficient (and its standard error).
    Beta= standardized estimate of the regression coefficient.

    ***p< or equal to 0.001,**p< or equal to 0.01, *p< or equal to 0.05 (two-tailed tests)