Key words

For some time, bullying was virtually synonymous with childhood, with some viewing bullying as simply a rite of passage that youth must invariably endure. Since the shootings at Columbine High School, however, researchers have devoted increased attention to the topic of bullying, with almost 5000 articles, books, and book chapters published on the topic as listed just in PsycINFO. Additionally, post-Columbine, 49 states have passed legislation detailing school policies related to bullying (U.S. Department of Education, 2011). With the advent of cyberbullying in the last 10 years, bullying has adopted a new facade that is both similar to and different from traditional bullying (Kowalski, Limber, & Agatston, 2012; Kowalski, Giumetti, Schroeder, & Lattanner, 2014; Madden, Lenhart, Duggan, Cortesi, & Gasser, 2013).

Importantly, however, much of the research on both traditional bullying and cyberbullying has been conducted using samples in urban and suburban areas to the virtual exclusion of rural populations . Very few researchers have discussed how bullying among individuals in urban areas compares with bullying among individuals in non-urban areas. Within the United States alone, rural areas contain approximately 19% of the population (“2010 Census,” 2010). Rural and urban areas differ from one another along a number of important variables including economic growth, unemployment, socioeconomic status, liberalism/conservatism, and poverty rates (Bishop & Casida, 2011; Dulmus, Sowers, & Theriot, 2006). Because of these features, not only might prevalence rates of involvement in bullying differ between urban and rural areas, but also prevention and intervention strategies may differ somewhat. Thus, the current chapter examines what we know about both traditional bullying and cyberbullying in both urban and rural areas.

Defining Bullying

Bullying has been defined as an aggressive behavior that is intentional, that is typically repeated over time, and that occurs between individuals whose relationship is characterized by a power imbalance (Olweus, 1993, 2013; Olweus & Limber, 2010). Depending on the type of bullying being discussed, this power imbalance can take a number of different forms including differences in physical size, strength, social prowess, numbers, abilities, or technological expertise, among others (Dooley, Pyżalski, & Cross, 2009; Kowalski, Giumetti et al., 2014; Olweus, 2013). Traditional forms of bullying can be either direct (e.g., hitting, kicking, property damage, name-calling) or indirect (e.g., rumor-spreading, excluding others from groups or activities, manipulating relationships; Kowalski, Limber et al., 2012; Thomlison, Thomlison, Sowers, Theriot, & Dulmus, 2004).

Cyberbullying is broadly defined as bullying that occurs through the use of technology via instant messaging, chat rooms, websites, online games, e-mail, social networking sites, or through digital images or messages sent to a cellular phone (Kowalski, Limber et al., 2012). In spite of agreement among researchers with this general definition of cyberbullying, however, there remains disagreement regarding the specific parameters by which cyberbullying should be identified (Kowalski, Giumetti et al., 2014; Olweus, 2013; Smith, del Barrio, & Tokunaga, 2012; Ybarra, Boyd, Korchmaros, & Oppenheim, 2012). The fact that researchers are using even slightly different definitions of cyberbullying (e.g., some using general definitions, others using specific venues by which cyberbullying might occur) has implications for the ways in which cyberbullying is measured, which, in turn, has implications for the prevalence rates that are reported.

It is tempting, when examining definitions of traditional bullying and cyberbullying, to think of them as two sides of the same coin. In keeping with this, research has shown moderate correlations between involvement in the two types of bullying (e.g., Gradinger, Strohmeier, & Spiel, 2009; Kowalski, Morgan, & Limber, 2012; Smith et al., 2008). While it is true that the two types of bullying share in common the three distinguishing features of the Olweus (1993) definition of bullying (i.e., acts of aggression that are typically repeated over time and that occur when there is a power imbalance between the individuals involved), traditional bullying and cyberbullying also differ from one another in several ways. Although these differences are summarized at length elsewhere (see, for example, Kowalski, Giumetti et al., 2014), three key differences will be noted here. First, compared to traditional bullying , perpetrators of cyberbullying often hide behind a veil of anonymity (Kowalski, Limber et al., 2012). Although they are never as anonymous as they think they are, perpetrators, believing themselves to be anonymous, may say things online that they would never say in someone’s physical presence. Indeed, the very nature of some of today’s technologies facilitates this sense of anonymity (e.g., snapchat). Second, most traditional bullying occurs at school during the school day (Nansel et al., 2001). Cyberbullying, on the other hand, can occur any time of the day or night. Even if targets choose not to check their e-mail or access their text messages, this does not mean that messages are not being left. Third, the punitive fears attached to reporting the two types of bullying differ. Targets of any type of bullying show a strong resistance to reporting their victimization (Harris, Petrie, & Willoughby, 2002; Naylor, Cowie, & del Rey, 2001). However, whereas victims of traditional bullying infrequently tell because they fear further victimization by the perpetrator, victims of cyberbullying report that they do not tell because of fears that their technology will be taken away (Kowalski, Limber et al., 2012).

Prevalence Rates of Bullying and Cyberbullying in Rural and Urban Samples

Examining prevalence rates of any form of bullying is often difficult because of differences across studies in the definitions used to conceptualize bullying and, thus, the manner in which bullying is operationalized. Prevalence rates also vary depending on whose perspective is being assessed. Students often report greater prevalence rates of bullying than either teachers or parents (Stockdale, Hangaduambo, Duys, Larson, & Sarvela, 2002). Furthermore, when considering urban/rural distinctions in regard to bullying, definitional issues again enter in as studies differ in how they define rural (Kulig, Hall, & Kalischuk, 2008). As will be seen, the end result is that prevalence rates are highly variable across studies.

A comparison of some of the key studies of traditional bullying (Limber, Olweus, & Luxenberg, 2013; Nansel et al., 2001; Robers, Kemp, & Truman, 2013) shows that prevalence rates of victimization range from a low of 9% (Olweus, 1993) to a high of 28% (Robers et al., 2013). Similarly, rates of perpetrating bullying range from 9% (Olweus, 1993) to 19% (Nansel et al., 2001). Importantly, however, the samples used in the different studies varied greatly in terms of the ages sampled, the time parameters used (e.g., last couple of months; year), frequency of bullying (e.g., once versus repeated), and the countries in which the data were collected.

Robers et al. (2013) observed that prevalence rates of traditional bullying varied as a function of the urban/rural nature of the sample. Specifically, they found lower rates of being bullied at school in urban areas (25%) than in rural (30%) or suburban (29%) areas. Similarly, Dulmus, Theriot, Sowers, and Blackburn (2004) found very high rates of bullying in three rural areas in Appalachia among youth in third through eighth grade, with 82.3% of the youth responding that they were victims of bullying at least once in the previous 3 months (see also Stockdale et al., 2002). Two years later, Dulmus et al. (2006), in a comparison of victims and bully/victims among elementary and middle school students in Appalachia, observed that 43% reported being a victim of bullying at least 2–3 times a month, with 11.5% being labeled as bully/victims. A similar study in 2005, also conducted with students in rural schools in Appalachia , found that 21.9% of the students reported being victims of traditional bullying at least 2–3 times a month during the past 3 months. Importantly, another 22.9% met victimization criteria established by the researchers, but did not personally identify themselves as victims (Theriot, Dulmus, Sowers, & Johnson, 2005). It is important to note that, among these studies, different frequency criteria were used to assess whether bullying had occurred, likely accounting for some of the observed variability in prevalence rates. Nevertheless, given the variability in prevalence rates of bullying across these three studies with students in Appalachia in as many years, it is hardly surprising that such great variability is also observed when comparing rural and urban samples.

Still other studies have reported prevalence rates of involvement in bullying in rural areas that are lower than data reported by those focused on urban samples. For example, Mlisa, Ward, Flisher, and Lombard (2008), in a sample of 1565 rural South African eleventh graders, found that 16.5% reported being victims of bullying, 3.9% reported perpetrating traditional bullying, and 5.5% reported being bully/victims. Similarly, Pellegrini, Bartini, and Brooks (1999) found, in a rural sample of fifth graders in the US, that 18% reported being victims of bullying, 14% reported perpetrating bullying, and 5% being bully/victims. Additionally, Klein and Cornell (2010) found that urbanicity (i.e., a measure of the number of people per square mile in the school zone) was positively related to teacher perceptions of bullying victimization, meaning that the more urban the school setting was, the higher the rate of bullying victimization reported by teachers in this study.

Finally, additional studies have found few differences in prevalence rates of bullying between urban and rural areas. Estell, Farmer, and Cairns (2007) concluded that there were few differences between perpetration and victimization rates in their rural sample compared to published findings with more urban samples. Specifically, they found that, among rural minority youth, 13.4% were victims, 11.7% were perpetrators, and 3.6% were bully/victims. Similar results were observed by Laeheem, Kuning, McNeil, and Besag (2009), who also found that prevalence rates did not differ as a result of rural vs. urban context.

Importantly, however, variables other than rural/urban status differentiate many of these studies, including the culture in which the data are collected. The data collected by Dulmus et al. (2004, 2006) were collected in the United States, whereas the data collected by Laeheem et al. (2009) were collected in Thailand. Additionally, some researchers have suggested that prevalence rates of bullying in rural areas may be underreported because of the close relationships among the individuals and families of the victims and perpetrators. For example, MacIntosh (2005) noted that prevalence rates of workplace bullying, in particular, may be underreported because employers (i.e., perpetrators) may also be neighbors of the victims. Finally, whether researchers concluded that rates in rural areas were greater than, less than, or equal to prevalence rates in urban areas depended on the urban samples they were using for comparison.

Prevalence rates of cyberbullying are highly variable across studies due to a number of different variables including how cyberbullying is defined, if it is defined at all, the ages of the participants sampled, the time parameter assessed (e.g., previous 2 months, 6 months, 1 year, lifetime), and the venue being assessed (e.g., chat rooms, e-mail, social networking sites) to name a few. For example, a survey of 655 youth aged 13–18 found that 15% said that they had ever been cyberbullied, and another 7% said that they had ever cyberbullied others (Cox Communications, 2009). A survey of middle school students’ experiences with cyberbullying resulted in data showing that 9% had been victims of cyberbullying within the last 30 days, with 17% saying they had been cyberbullied in their lifetime (Hinduja & Patchin, 2009). Kowalski and Limber (2007) found that 18% of middle school students had experienced cybervictimization within the previous 2 months; another 11% had perpetrated cyberbullying during that same time frame. Importantly, surveys that simply ask about the overall prevalence rate of cyberbullying (e.g., “How often have you been cyberbullied?”) yield different prevalence rates than assessments that inquire about the extent to which participants have been cyberbullied via e-mail, instant messaging, chat rooms, text messaging, etc. Although prevalence rates in the published literature are highly variable, they typically range between 10% and 40% (e.g., Lenhart, 2010; O’Brennan, Bradshaw, & Sawyer, 2009; see, however, Aftab, 2011; Juvonen & Gross, 2008).

All of these statistics are based on urban or suburban samples. Significantly less attention has been focused on prevalence rates of cyberbullying among rural populations (see, however, Bauman, 2010; Navarro, Serna, Martínez, & Ruiz-Oliva, 2013; Price, Chin, Higa-McMillan, Kim, & Christopher Frueh, 2013), and even fewer studies have directly compared cyberbullying as it occurs in rural and urban samples. Among those studies that did include rural samples, prevalence rates are, again, highly variable across studies. In the majority of published studies, prevalence rates of cyberbullying in rural areas are lower than those reported by individuals living in urban areas. For example, Bauman (2010) observed that, among individuals in rural areas, 1.5% were classified as cyberbullies only, 3% as cybervictims only, and 8.6% as cyber bully/victims. Similarly, Price et al. (2013), in an examination of involvement in cyberbullying among sixth- and seventh-grade students in a rural school in Hawaii, found that 7% reported being victims of cyberbullying and 4% reported perpetrating cyberbullying (see, however, Navarro et al., 2013). These prevalence rates are markedly lower than those reported in research using data collected with non-rural samples. Perhaps, individuals in rural areas may have less access to technology than those in urban areas. Given that time spent online is correlated with involvement in cyberbullying (e.g., see Navarro et al., 2013), one would then expect prevalence rates of cyberbullying to be lower in rural samples. However, in their nationally representative study of 12–18 year olds, Robers et al. (2013) observed that students in urban areas reported lower cyberbullying than students in suburban areas (7% vs. 10%, respectively), but did not find significant differences among students from rural communities. Because so few studies have examined prevalence rates of cyberbullying in rural samples, drawing firm conclusions regarding comparisons between urban and rural samples would be premature. Alternatively, even with equal amounts of technology use, perhaps individuals in rural areas are spending their time online engaged in activities that are less conducive to cyberbullying behavior (e.g., spending less time on social media sites). More research attention is clearly needed to examine amounts and forms of technology use in rural populations.

Characteristics of Victims and Perpetrators in Rural and Urban Populations

A number of variables have been linked with a greater likelihood of being a victim or perpetrator of bullying. These factors can be grouped into personal characteristics (such as anxiety or moral disengagement), family characteristics (such as SES or parental monitoring), and community/school characteristics.

Victims of Traditional Bullying

Victims of traditional bullying have typically been classified as either “passive” victims or as “provocative” victims (Kowalski, Limber et al., 2012). Although anyone can be a victim of bullying, passive bullying victims do seem to share particular traits in common including being quiet, sensitive, insecure, socially isolated, anxious, and depressed (Cook, Williams, Guerra, Kim, & Sadek, 2010; Olweus, 1993). Children who are bullied are also more likely than children not involved in bullying to experience psychosomatic complaints, such as headaches, backaches, stomach pain, sleeping problems, and poor appetites (Gini & Pozzoli, 2009). Importantly, however, it is difficult to determine whether some of these characteristics, such as anxiety and depression, are antecedents of bullying victimization, consequences of it, or both (see, however, Cluver, Bowes, & Gardner, 2010).

Provocative victims (also known as bully/victims), on the other hand, tend to be hyperactive (Kumpulainen & Raasnen, 2000) and have trouble concentrating (Olweus, 1993). They are often impulsive and quick-tempered, leading them to react quickly to perceived slights by others. They tend to exhibit internalizing problems (such as anxiety and reduced self-esteem) similar to those of other children who have been bullied, but also externalizing problems associated with children who bully others.

D’Esposito, Blake, and Riccio (2011) examined a sample of 243 sixth- through eighth-grade students from the rural southwestern U.S. and found that greater victimization was linked with higher levels of anxiety , more depressive symptoms, and lower levels of self-esteem. Estell et al. (2007) also found that victims tend to be less popular and more socially rejected. Dulmus et al. (2004) found that victims may have fewer friends than those who have not been bullied.

While being more anxious, having lower self-esteem, and being socially isolated are related to (and may predispose a student to) victimization, having parental support may serve to reduce the likelihood of victimization. Conners-Burrow, Johnson, Whiteside-Mansell, McKelvey, and Gargus (2009) studied 977 middle school and high school students from the rural south and found that students who were not involved with bullying had higher levels of social support from their parents and teachers than perpetrators and bully/victims.

Still another characteristic that may predispose someone to becoming a victim of bullying is having a disability . A study by Farmer et al. (2012) examined 1389 students from rural school districts across the U.S. and found that disabled children receiving special education services were several times more likely than nondisabled children to be victims or bully/victims. Similar findings have been observed for children with disabilities in more urban samples (see, for example, Kowalski & Fedina, 2011).

One final personal characteristic that has been found to be predictive of involvement as a victim is racial group. A study by Goldweber, Waasdorp, and Bradshaw (2013) found that African American youth were more likely to be targeted, regardless of whether they were from an urban or non-urban location. However, others have pointed out that variables such as ethnic density and diversity within a school building may be more informative than prevalence rates by ethnic group (Wang, 2013).

In terms of family variables , youth report a higher level of victimization if their parents are overprotective (Smokowski & Kopasz, 2005) and if there is a history of domestic violence and child neglect (Bowes et al., 2009; Cluver et al., 2010; Kowalski, Limber et al., 2012).

Additionally, going to school in a community in which students transition to a new school in the middle grades may be another factor that can lessen the likelihood of bullying and victimization (Farmer, Hamm, Leung, Lambert, & Gravelle, 2011). Specifically, Farmer et al. (2011) found that schools with a transition between fifth and sixth grade in which students changed buildings had fewer children who bullied compared to schools that did not have a transition. Further, the social forces in schools with a transition appeared to be less supportive of bullying than schools that did not have such a transition.

Perpetrators of Traditional Bullying

Perpetrators of traditional bullying have been described as often having a positive view of violence, feeling a need to control others in a negative way, showing little empathy for those who are bullied, being aggressive with peers and adults , having friends who bully others, and (among boys) being physically stronger than their peers (Federal Partners in Bullying Prevention, n.d.; Olweus, 1993). Research indicates that these children and youth are also more likely than their peers to display a variety of other antisocial, violent, or troubling behaviors, such as fighting, stealing, vandalizing property, carrying weapons, and dropping out of school, in addition to having school adjustment difficulties and poor academic achievement (Byrne, 1994; Gini & Pozzoli, 2009; Haynie et al., 2001; Nansel et al., 2001 ; Olweus, 1993). For example, children who bully are more likely than others to drink alcohol and smoke (Nansel et al., 2001; Olweus, 1993) and own a gun for risky reasons, such as to gain respect or to frighten others (Cunningham, Henggeler, Limber, Melton, & Nation, 2000).

In a traditional bullying context, several research studies have been conducted in rural settings that identify personal and family characteristics associated with being a perpetrator of bullying. For example, Burton, Florell, and Gore (2013) examined 851 middle school students from six rural schools in the U.S. and found that the personal characteristics of proactive and reactive aggression tended to be higher among bully/victims than among perpetrators or those not involved in the bullying incident (see also Jansen, Veenstra, Ormel, Verhulst, & Reijneveld, 2011). Aggression was also found to be higher among perpetrators than victims or non-involved students in a sample of African American rural middle school students (Estell et al., 2007), and perpetrators were more often viewed as the group leaders than students in the other groups. In rural samples, perpetrators of bullying have also been identified as lacking empathy. Out of a sample of 192 rural K-8 students from the southeastern U.S., Rowe, Theriot, Sowers, and Dulmus (2004) found no differences in involvement as a function of age or gender, but did find that perpetrators tended to have lower empathy and a reduced willingness to help when another student was being bullied (see also Dulmus et al., 2006).

Certain family characteristics have also been associated with a higher likelihood of involvement in bullying behavior, including coming from a lower SES group (Jansen et al., 2011) and presence of family physical abuse (Limber, Kowalski, & Agatston, 2008). However, other studies have not found a role for family variables in predicting involvement in bullying behavior, including a study by Mlisa et al. (2008). In this study, the authors examined the following family variables: poor family management, a family history of antisocial behavior, and not living with both parents. No significant differences emerged among perpetrators, victims, and bully/victims on any of these variables. This study was conducted in rural South Africa, so the sample may be different than rural populations from the U.S. or other parts of the world.

Few studies have directly compared rural and urban settings, and much of the research examining predictors of bullying victimization and perpetration has focused on bullying in urban or suburban samples (e.g., Camodeca, Goossens, Meerum Terwogt, & Schuengel, 2002; Jolliffe & Farrington, 2011; Veenstra et al., 2005). Therefore, it is difficult to make firm conclusions about the unique personal and familial characteristics of perpetrators and victims in rural areas that might predispose them to becoming a perpetrator or victim, relative to those in urban samples. For example, whereas the effects of age and gender on involvement in both traditional bullying and cyberbullying have been found to be highly variable across studies conducted with urban samples, not enough research has been implemented using rural samples examining these variables to even make comparisons. Further research seems to be needed to more directly compare the characteristics of perpetrators and victims in rural and urban settings.

Cyberbullying Victimization

Given that cyberbullying has several unique features compared to traditional face-to-face bullying (e.g., it is communicated through technology, and there may be a greater perceived anonymity, a lack of reactivity, and easy reproducibility), a different set of predictors may be associated with involvement in cyberbullying. Among urban samples, victimization is inversely related to social intelligence (Hunt, Peters, & Rapee, 2012) and directly related to hyperactivity (Dooley, Shaw, & Cross, 2012). Victims of cyberbullying also engage in riskier online behavior than individuals not involved with cyberbullying (Görzig & Ólafsson, 2013) and have a higher level of exposure to violent video games (Lam, Cheng, & Liu, 2013). Psychologically, victims of cyberbullying demonstrate higher levels of depression, anxiety, and suicidal ideation, as well as lower levels of self-esteem compared to those not involved with cyberbullying (Hinduja & Patchin, 2008; Kowalski & Limber, 2013; Kowalski, Giumetti et al., 2014; Ybarra & Mitchell, 2004).

Cyberbullying Perpetration

Cyberbullying perpetrators, similarly, report higher levels of depression and anxiety relative to individuals not involved with cyberbullying (Didden et al., 2009; Ybarra & Mitchell, 2004). Additionally, they show lower empathy (Ang & Goh, 2010) and higher levels of narcissism (Ang, Tan, & Mansor, 2011; Fanti, Demetriou, & Hawa, 2012). Similar to victims of cyberbullying, perpetrators also report higher levels of depression and anxiety and lower levels of self-esteem (Kowalski, Limber et al., 2012).

In a 2010 study, Bauman focused on a rural sample of 221 fifth- through eighth-grade students in Arizona and found that higher levels of cyberbullying perpetration and victimization were related to greater involvement in risky online behaviors and more frequent use of technology. So it would seem that simply being online more often may be associated with a greater likelihood of involvement in cyberbullying. Indeed, this finding was confirmed in a Spanish rural setting (Navarro et al., 2013). While being online might be a risk factor for involvement in cyberbullying, there may be several features that can mitigate this risk. These include monitoring software installed on the computer and joint creation of rules with parents regarding the time spent online—each can help to lessen the likelihood of cyberbullying victimization (Navarro et al., 2013).

Consequences of Bullying and Cyberbullying in Rural and Urban Populations

Research in urban and suburban settings has consistently shown the adverse effects of involvement in bullying for both victims and perpetrators, with traditional and electronic bully/victims showing the most negative physical and psychological effects (e.g., Henry et al., 2013; Mishna, Cook, Gadalla, Daciuk, & Solomon, 2010; Schneider, O’Donnell, Stueve, & Coulter, 2012). These consequences of bullying (whether face-to-face or cyber) are quite varied, ranging from hurt feelings and decreased life satisfaction to poorer grades in school and behavioral conduct problems to drug and alcohol use, depression, and suicidal ideation (Kowalski & Limber, 2013; Kowalski, Limber et al., 2012; Kowalski, Giumetti et al., 2014). Bullying others has also been found to be associated with delinquency, violence, and aggression later in life (Bender & Lösel, 2011; Olweus, 1993; but see Wolke, Copeland, Angold, & Costello, 2013).

Significantly less research has examined the consequences of bullying in rural settings. However, among those studies that have been conducted in rural settings, several have found that victims or bully/victims are more likely to have poor grades in school (Bradshaw, Waasdorp, Goldweber, & Johnson, 2013), be more anxious and depressed (Conners-Burrow et al., 2009; Crosby, Oehler, & Capaccioli, 2010; D’Esposito et al., 2011; Price et al., 2013), have greater psychological difficulties (Duncan, 1999), have greater internalizing and externalizing problems (Farmer et al., 2012), and be more likely to use alcohol and marijuana than non-victims (Wiens, Haden, Dean, & Sivinski, 2010). Additionally, Hay and Meldrum (2010) found that victims of traditional bullying and cyberbullying were more likely to engage in self-harm and suicidal ideation than those not victimized.

As with research on characteristics of children who bully and children who are bullied, few studies have directly compared rural settings to urban settings and examined the possible differences in outcomes among individuals from these settings. Therefore, additional research may be needed that more directly compares rural and urban settings to determine whether the outcomes or sequelae associated with bullying and cyberbullying differ in these contexts.

Prevention and Intervention

Substantial numbers of students indicate that they do not report their victimization to others, particularly to adults at school (Limber et al., 2013). Children’s reluctance to report bullying experiences to school staff may reflect a lack of confidence in their teachers’ (and other school authorities’) handling of bullying incidents and reports. For example, in a survey of high school students in the U.S., two thirds of those who had been bullied believed that school personnel responded poorly to bullying incidents at school, and only 6% felt that school staff handled these problems very well (Hoover, Oliver, & Hazler, 1992). Limber et al. (2013) observed that more than 40% of middle school students and more than 50% of high school students felt that their teachers had done “little or nothing” or “fairly little” to reduce bullying. This may stem, in part, from the perceptions of some adults that bullying is developmentally normative and does not require intervention (Shoko, 2012).

It may also stem from a failure on the part of adults to accurately recognize bullying when it occurs. In a study by Craig and Pepler (1997), bullying episodes on the playground were videotaped. When asked how often they intervened, teachers reported that they intervened in 70% of the bullying episodes, when, in fact, they had intervened in only 4% of the bullying episodes. Thomlison et al. (2004) surveyed teachers, staff, and administrators about their knowledge of bullying that occurred at their schools during the previous 3 months. Sixty-two percent of the teachers and administrators and half of the school staff reported that adults at the school “almost always” intervened to put an end to the bullying. Students at the school, however, might well have given a different perspective had they been asked. Clearly, more needs to be done in the area of prevention and intervention.

The literature on traditional bullying has highlighted the efficacy of a systemic-ecological framework in prevention and intervention efforts (Mishna, 2003), which will be used here as a model for prevention and intervention efforts directed at all types of bullying. This model operates on the premise that bullying, in whatever form it may take, is a behavior that occurs within a larger social context that includes family, school, and community (White, Kowalski, Lyndon, & Valentine, 2000). Thus, prevention and intervention efforts should not focus just on the victim or the perpetrator, but rather on a compilation of “individual characteristics, social interactions, and ecological and cultural conditions … [that] contribute to social behavioral problems” (Mishna, 2003, p. 340). Toward this end, school climate factors need to be examined, parental involvement must be encouraged, and community-wide efforts at increasing awareness initiated. At the level of the school, a school-wide approach must be adopted whereby everyone including students, teachers, administrators, bus drivers, cafeteria workers, etc. is educated on how to identify and report bullying and cyberbullying (Olweus et al., 2007; Smith & Shu, 2000). Training for teachers can help them to identify peer groups which may be at particularly high risk for bullying (Farmer, Hall, Petrin, Hamm, & Dadisman, 2010). School-wide assessments of the prevalence of bullying need to be conducted (Olweus & Limber, 2010). Not only are these assessments useful in helping school officials and parents recognize the extent of the bullying problem, but they also allow school officials to provide more targeted instructional attention (Olweus et al., 2007; Rose, Espelage, & Monda-Amaya, 2009). If, for example, sixth-grade girls are more likely to engage in bullying or cyberbullying than sixth-grade boys, this would be important to assess within a particular school. Another best practice in the prevention of bullying (and cyberbullying) involves regular discussions with children and youth about bullying and peer relations (Federal Partners in Bullying Prevention, n.d.). Class discussions can focus on topics such as defining cyberbullying, school rules and policies regarding cyberbullying, online etiquette and safety, monitoring one’s online reputation, how to best respond to cyberbullying, and the role of bystanders who witness cyberbullying behavior (Limber, Kowalski, & Agatston, 2009; Limber et al., 2008).

A key aspect of school and community-wide approaches toward bullying prevention is empowering bystanders to provide support for targets of bullying (Davis & Nixon, 2011; Kowalski, Schroeder, & Smith, 2013). Many have suggested recently that bystanders might more appropriately be called “upstanders” to encourage them to “stand up” on behalf of victims as opposed to just “standing by” (e.g., “Bully bust,” 2012). Bystanders who do nothing on behalf of the victim appear to both perpetrators and victims to be supporters of the bullying behavior (Olweus, 1993). While understandably, children and youth may not want to get directly involved in a bullying situation, they can offer their support to targets of bullying in other ways (such as being a friend to them or offering sympathy), or they can inform adults of the bullying they have witnessed in the virtual or real worlds.

Youth within schools today face a strong code of silence (Agatston, Kowalski, & Limber, 2007, 2011; Davis & Nixon, 2011; Rigby, 2008). Not only are targets unlikely to report their victimization, but bystanders are equally unlikely to report instances of bullying and cyberbullying of which they become aware. Limber et al. (2013), in an analysis of 20,000 students in third through twelfth grade, found that only 25% of elementary school students, 15% of middle school students, and 12% of high school students believe that other students “often” try to stop bullying that they observe. In a survey of teachers, administrators, and staff by Thomlison et al. (2004), 59% of teachers and administrators and 73% of staff indicated that students had tried to put an end to the bullying “sometimes” or “once in a while.” This number, however, declines as students age through middle and high school. To encourage prosocial behavior on the part of bystanders, teachers should be encouraged to engage students in discussions and role-play activities that encourage positive actions on the part of students who witness bullying (Olweus & Alsaker, 1991; Olweus & Kallestad, 2010). These actions may involve speaking out against bullying, seeking help from adults, and including bullied peers in activities.

As Thomlison et al. (2004) note, while training and education of students are key, training and education of parents and school personnel are equally critical. All individuals need to be educated on how to address bullying appropriately and how to work to prevent it from occurring in the future. If students have difficulty determining how to respond appropriately to bullying that is occurring, how much more difficult it must be for bystanders as well as adults to detect and respond to bullying that may be occurring among those students.

Some training messages and strategies may need to be adjusted depending upon the rural versus urban setting of the school and the viewpoints of staff in these settings. For example, educators in rural and urban settings may have different perceptions about the seriousness of bullying. It is possible that educators native to rural settings, particularly in impoverished areas, may perceive bullying to be more socially acceptable than educators in some urban areas and may view bullying as a necessary means of “toughening kids up” (Shoko, 2012). Research on the negative effects that follow from bullying clearly suggests, however, that this perception is misguided.

One study that highlighted the importance of attending to the values endemic to particular areas focused on perceptions of bullying among sexual minority students (Bishop & Casida, 2011). The authors pointed out that, while sexual minority status places individuals at risk of homophobic bullying in any setting, “students in rural areas with theologically conservative values tend to be at the greatest risk of homophobic retaliation with little to no recourse by the school district” (p. 134). As noted by Bishop and Casida, oftentimes school personnel are uncertain how to respond to bullying that is directed against sexual minority students because of the reactions that may follow from homophobic parents. This research highlights the fact that training in bullying prevention and intervention needs to be sensitive to the views and perceptions of particular audiences.

Conclusion

A clear take-home message from this chapter is that more research is needed that directly compares samples of individuals from urban and non-urban areas. This type of research should lend conceptual clarity to how involvement in both traditional bullying and cyberbullying compares across the two areas. To date, we cannot say conclusively whether rural youth are more or less involved in bullying of any type than urban youth. Although the predictors of involvement in bullying seem to be similar among youth from both rural and urban areas, again we cannot say that with certainty without a more direct comparison. The firm conclusion that can be drawn, however, is that both traditional bullying and cyberbullying are ever present in both rural and urban samples of young people. Comprehensive programs aimed at reducing the prevalence of bullying among all youth are needed.