Old Wine,New Bottle? Comparing Interpersonal Bullying and Cyberbullying Victimization

Abstract

This study uses a large nationally representative sample to compare and contrast interpersonal bullying and cyberbullying by asking the following questions: (a) How does the prevalence of cyberbullying victimization compare with the prevalence of interpersonal bullying victimization? (b) How does the relationship between demographic predictors and cyberbullying victimization compare with the relationship between these predictors and interpersonal bullying victimization? and (c) How does the relationship between cyberbullying victimization and avoidance behaviors compare with the relationship between interpersonal bullying victimization and avoidance behaviors? Findings demonstrate that interpersonal bullying victimization is far more prevalent than cyberbullying victimization. Results also illustrate differences in the relationships between demographics and bullying victimization. Finally, students who are a victim of either form of bullying are more likely to engage in school avoidance behaviors. These results highlight the need for comprehensive and preventive programs that can reduce the negative consequences of bullying victimization.

Keywords

delinquency peers bullying school-based programs victimization

Bullying victimization has been a long-standing problem among adolescents in school and occurs when an individual “ . . . is exposed, repeatedly and over time, to negative actions on the part of one or more other persons, and he or she has difficulty defending himself or herself” (Olweus, 1991a, p. 47). Harassment that happens when students are bullied in person by their peers at school or on the way to and from school is often referred to as interpersonal bullying (Farrington & Ttofi, 2009). This can include both direct bullying behaviors, such as hitting, and indirect bullying behaviors, such as spreading rumors about someone (Greeff & Grobler, 2008).

In recent years, rapidly developing technologies have allowed for the expansion of bullying into the complex realm of cyberspace. Cyberbullying, also referred to as electronic bullying, has been defined as “willful and repeated harm inflicted through the medium of electronic text” (Patchin & Hinduja, 2006, p. 152). This type of bullying is sometimes anonymous on the part of the bully and primarily occurs through the use of cellular phones and Internet-enabled computers, as these devices present an opportunity for a victim to receive hurtful messages or inappropriate content by way of electronic mail (email), text messaging, website postings, and other electronic media (Ahlfors, 2010; Hinduja & Patchin, 2008a). Cyberbullying, like interpersonal bullying, can involve both direct behaviors, such as hurtful messages that are transmitted directly from the bully to the victim, and indirect behaviors, such as spreading rumors about someone through an online forum (Snakenborg, Van Acker, & Gable, 2011). However, certain characteristics distinguish cyberbullying from interpersonal bullying, including (a) the potential for an infinite audience; (b) an altered balance of power between the bully and the victim, rendering the victim unable to effectively defend himself or herself against the bullying behaviors; (c) an inability for the bully to observe the immediate reaction of the victim; (d) the absence of space and time constraints on bullying; and (e) the perception of anonymity on the part of the bully (Bauman, 2010; Slonje & Smith, 2008). These differences may make cyberbullying more difficult to address, given the potential of an anonymous offender, an infinite audience, and a lack of physical constraints on the bullying event.

In contrast to the body of research that has been conducted on traditional interpersonal bullying, very little research has studied cyberbullying in detail and even less has compared the two types of victimization. In addition, the research that has been conducted on both types of bullying suffers from some severe limitations. This study will use a large nationally representative sample to compare and contrast interpersonal bullying and cyberbullying, thus providing a clearer picture of overall bullying victimization. The frequency and demographic predictors of interpersonal bullying and cyberbullying will be compared. In addition, the relationship between bullying and school avoidance, an immediate and influential negative consequence, will be examined for both interpersonal bullying and cyberbullying. Understanding the similarities and differences between these forms of bullying will allow for more informed choices when it comes to prevention; knowing what factors increase the likelihood of these bullying behaviors and what those behaviors’ impact is on school avoidance will indicate the best strategies for reducing both forms of bullying victimization.

Prevalence of Interpersonal Bullying and Cyberbullying Victimization

Despite evidence over the past several decades suggesting that schools are generally safe places for students (Calhoun & Daniels, 2008), this environment provides a physical and social setting that is conducive to victimization through bullying. In 2009, evidence from the School Crime Supplement (SCS) to the National Crime Victimization Survey (NCVS) demonstrated that approximately 28% of students 12 to 18 years of age reported having experienced interpersonal bullying at school during the school year (DeVoe & Murphy, 2011). Of those students who have been a victim of bullying, the majority experience interpersonal bullying victimization at school or on the way to and from school (Farrington & Ttofi, 2009; Olweus 2012). Interpersonal bullying victimization can include direct behaviors (such as hitting, kicking, or punching) or indirect behaviors (such as malicious gossip or spreading rumors). Whereas boys tend to use more direct forms of bullying (Baldry & Farrington, 2000; Greeff & Grobler, 2008; Nansel et al., 2001), girls tend to use more indirect forms of bullying (Greeff & Grobler, 2008; Selekman & Vessey, 2004).

Individuals who experience cyberbullying victimization are often referred to as “cyber targets” or “cyber victims” (Ahlfors, 2010). Limited research has been conducted on cyberbullying, but some tentative conclusions can be drawn. Existing estimates of cyberbullying in the United States range from around 6% to more than 40% depending on how this victimization is formally defined and the age of the individuals studied (Bauman, 2010; Hinduja & Patchin, 2007; Kowalski & Limber, 2007; Kraft, 2006; Li, 2007; Patchin & Hinduja, 2006; Patchin & Hinduja, 2010; Williams & Guerra, 2007; Ybarra & Mitchell, 2004a).

Characteristics of Interpersonal Bullying and Cyberbullying Victims

Targets of direct and indirect interpersonal bullying behaviors can include both passive and provocative victims (Olweus 1984, 1991b; Parault, Davis, & Pellegrini, 2007; Pellegrini, 2002). Passive victims represent the majority of interpersonal bullying targets, with more than 80% of individuals associated with this characterization of bully victims (Olweus, 1984). Passive victims often feel abandoned and lonely at school, report few aggressive tendencies and view themselves as failures. These individuals tend to be anxious, insecure, and physically frail; they often report feeling that they have been victimized without provocation and generally withdraw when attacked (Olweus, 1991b; Parault et al., 2007). Provocative victims, however, tend to display aggressive tendencies and are discussed in the research literature as both bullies and bully victims. These individuals have been described as anxious, hot-tempered, and restless, and tend to be disliked by members of their peer group (Parault et al., 2007; Pellegrini, 2002). Unlike passive victims, provocative victims often choose to retaliate with violence that is typically reactive in nature (Parault et al., 2007; Schwartz, Proctor, & Chien, 2001).

A marked decline in interpersonal bullying victimization is often seen as students advance to higher grades (Greeff & Grobler, 2008), such that victimization peaks during middle school years and decreases thereafter (Espelage & Horne, 2008; Nansel et al., 2001; Pellegrini & Bartini, 2000). Some challenge these findings (Varjas, Henrich, & Meyers, 2009), however, and suggest that bullying behaviors merely change from direct, aggressive forms of peer victimization to more indirect or passive forms of peer victimization as students age. Males are more likely to be bully victims than females, and are more likely to be the targets of same sex interpersonal bullying victimization (Greeff & Grobler, 2008; Juvonen, Nishina, & Graham, 2000; Olweus, 1994). Whereas male bully victims largely experience direct forms of peer harassment (e.g., hitting, kicking, or punching), female bully victims tend to report more indirect victimization incidents (e.g., peer group exclusion or social isolation/rejection; Olweus, 1993b, 1994; Van der Wal, De Wit, & Hirasing, 2003; Varjas et al., 2009; Wolke, Woods, Bloomfield, & Karstadt, 2001). A few studies, however, have found males and females experience interpersonal bullying victimization equally with regard to prevalence and severity (Greeff & Grobler, 2008; Lerner & Lerner, 2001). As opposed to age and gender, the role of race in interpersonal bullying victimization has received little empirical attention, and the evidence that does exist provides mixed findings. Some find no significant differences when comparing the prevalence of interpersonal bullying victimization among different racial groups (Dake, Price, & Telljohann, 2003; Nansel et al., 2001; Seals & Young, 2003). Other studies, however, report significant differences in the frequency with which interpersonal bullying victimization occurs among different racial groups (Graham & Juvonen, 2002; Hanish & Guerra, 2000), suggesting that White students are significantly more likely to be bully victims than Black students. There are also indications that the association between race and bullying may be influenced by the racial composition of the community, school, or classroom (Hong & Espelage, 2012; Juvonen et al., 2000).

Some studies suggest that “cyber targets” or “cyber victims” are often the same individuals who are victims of interpersonal bullying (Didden et al., 2009; Juvonen & Gross, 2008; Katzer, Fetchenhauer, & Belschak, 2009; Kowalski & Limber, 2007; Slonje & Smith, 2008; Tokunaga, 2010; Twyman, Saylor, Taylor, & Comeaux, 2010; Ybarra, Diener-West, & Leaf, 2007). Other findings regarding demographics on cyberbullying victims are less clear, however. For example, although some studies indicate an increase in victimization as students age (Kowalski & Limber, 2007; Ybarra, Mitchell, Wolak, & Finkelhor, 2006) and others show a decrease (Dehue, Bolman, & Völlink, 2008; Slonje & Smith, 2008; Williams & Guerra, 2007), the majority of studies actually find a negligible association between age and being a victim of cyberbullying (Beran & Li, 2007; Didden et al., 2009; Juvonen & Gross, 2008; Katzer et al., 2009; Patchin & Hinduja, 2006; Smith et al., 2008; Varjas et al., 2009; Wolak, Mitchell, & Finkelhor, 2007; Ybarra, 2004). Similarly, research on gender differences is also inconsistent. A small number of studies suggest that females are more likely to experience cyberbullying victimization than males (Dehue et al., 2008; Kowalski & Limber, 2007; Ybarra et al., 2007; Ybarra & Mitchell, 2008), whereas others suggest that males are more likely than females to be victims (Boulton & Underwood, 1992; Olweus, 1987; Tokunaga, 2010). However, the majority of research on gender differences in cyberbullying indicates that males and females experience victimization at a similar rate (Beran & Li, 2007; Didden et al., 2009; Hinduja & Patchin, 2008b; Juvonen & Gross, 2008; Katzer et al., 2009; Li, 2006, 2007b; Patchin & Hinduja, 2006; Slonje & Smith, 2008; Smith et al., 2008; Topçu, Erdur-Baker, & Capa-Aydin, 2008; Varjas et al., 2009; Williams & Guerra, 2007; Wolak et al., 2007; Ybarra, 2004; Ybarra et al., 2007). Finally, the role of race in cyberbullying victimization has received little empirical attention. The evidence that does exist is conflicting, with some studies showing that victims of cyberbullying are more likely to be White (Li, 2007; Ybarra et al., 2006) and others reporting no statistically significant racial differences (Hinduja & Patchin, 2008b).

A number of risk factors increase the likelihood that a student will experience interpersonal bullying victimization. These include having fewer friends, lacking teacher support, class size and school size, attending schools with a negative school environment, and living in an unsafe neighborhood (Barboza et al., 2009; Espelage, Bosworth, & Simon, 2000; Hong & Espelage, 2012; Khoury-Kassabri, Benbenishty, Astor, & Zeira, 2004; Nansel, Overpeck, Haynie, Ruan, & Scheidt, 2003; Olweus, 1993a; Scholte, Engels, Overbeek, de Kemp, & Haselager, 2007; Swearer, Espelage, Vaillancourt, & Hymel, 2010; Wang & Iannotti, 2012; Wang, Iannotti, & Nansel, 2009; Wienke Totura et al., 2008). For instance, peers can have a considerable influence on the likelihood that a student will be victimized: Students who experience interpersonal bullying victimization tend to be socially isolated and rejected by peers (Scholte et al., 2007; Wang, Iannotti, & Nansel, 2009). Interpersonal bullying victimization also increases among students who lack teacher support (Barboza et al., 2009), and among students who attend larger schools and with a greater student–teacher ratio (Khoury-Kassabri et al., 2004; Olweus, 1993a). School safety influences interpersonal bullying as well, such that students who report lower perceptions of school safety also report higher levels of peer victimization (Nansel et al., 2001; Varjas et al., 2009). Finally, studies suggest that adolescents living in unsafe areas are more likely to experience interpersonal bullying victimization than youth living in safer places, as these neighborhoods may reflect a larger environment that is conducive to bullying behaviors and violence (Espelage et al., 2000; Hong & Espelage, 2012; Khoury-Kassabri et al., 2004).

Unlike interpersonal bullying, being a victim of cyberbullying is not associated with factors such as physical appearance (Wang, Iannotti, & Luk, 2010) or number of friends (Wang, Iannotti, & Nansel, 2009). This type of victimization is, however, associated with the frequency with which youth use electronic devices, such that the more time an individual spends using electronic devices increases the chances that he or she will be a victim of cyberbullying (Ahlfors, 2010; Hinduja & Patchin, 2008b; Juvonen & Gross, 2008; Li, 2007; Wang, Iannotti, & Luk, 2009).

Bullying Victimization and School Avoidance

Although interpersonal bullying victimization affects each adolescent differently, youth who are targeted by these behaviors tend to use a number of negative coping mechanisms. These include depression, fear of school, fighting, loneliness, lower school bonding or school disengagement, stunted academic progress, and suicidal ideations and behaviors (Attwood & Croll, 2006; Glew, Fan, Katon, Rivara, & Kernic, 2005; Harper, Parris, Henrich, Varjas, & Meyers, 2012; Kim, Koh, & Leventhal, 2005; Meyer-Adams & Conner, 2008; Townsend, Flisher, Chikobvu, Lombard, & King, 2008). Interpersonal bullying victimization can also have lasting negative effects on victimized youth, including dropping out of school, later social and psychological maladjustment, and lower occupational stability and success (DeVoe, Kaffenberger, & Chandler, 2005; Hawker & Boulton, 2000; Kumpulainen & Räsänen, 2000; Parault et al., 2007; Townsend et al., 2008).

An immediate and influential negative consequence of interpersonal bullying victimization often seen is the avoidance of places in school or school social situations due to fear of attack or harm (Greeff & Grobler, 2008; Hutzell & Payne, 2012; Juvonen et al., 2000; Mahady Wilton & Craig, 2000; Rapp-Paglicci, Dulmus, Sowers, & Theriot, 2004; Sapouna, 2008; Storch, Brassard, & Masia-Warner, 2003). The majority of research demonstrates an association between interpersonal bullying victimization and avoidance behaviors (DeVoe et al., 2005; DeVoe, Murphy, American Institutes for Research, & Center for Effective Collaboration and Practice, 2011; Hutzell, 2014; Hutzell & Payne, 2012; Juvonen et al., 2000; Mahady Wilton & Craig, 2000; Meyer-Adams & Conner, 2008; Parault et al., 2007; Puhl & Luedicke, 2012; Skrzypiec, Slee, Murray-Harvey, & Pereira, 2011; Storch et al., 2003; Storch & Esposito, 2003; Townsend et al., 2008). Avoidance behaviors are generally associated with increased feelings of loneliness in the school environment and psychological difficulties that result in poor school functioning and absenteeism from school (Juvonen et al., 2000; Townsend et al., 2008). These feelings tend to lead victims of interpersonal bullying to avoid both school-related activities and actual locations in school (DeVoe et al., 2005; Hutzell, 2014; Hutzell & Payne, 2012; Meyer-Adams & Conner, 2008; Parault et al., 2007). Although most studies support the relationship between victimization and avoidance, a few studies have reported mixed findings. Some suggest the possibility that avoidance behaviors differ by grade level, with elementary and middle school students more likely than high school students to avoid locations in school due to fear of attack or harm (Addington, Ruddy, Miller, & DeVoe, 2002; Attwood & Croll, 2006). Finally, other studies simply do not show a relationship between interpersonal bullying victimization and avoidance (Glew et al., 2005; Wolke et al., 2001).

As with interpersonal bullying, cyberbullying victimization is associated with a number of negative adaptations or coping mechanisms. These coping mechanisms can include bringing a weapon to school and participating in other forms of school violence (Mason, 2008; Pelfrey & Weber, 2013; Stover, 2006; Willard, 2006; Ybarra et al., 2007), depression (Bauman, Toomey, & Walker, 2013; Ybarra & Mitchell, 2004b; Ybarra et al., 2006), greater involvement in drinking and smoking (Ybarra & Mitchell, 2004b), low self-esteem (Kowalski & Limber, 2007; Ybarra et al., 2006), and suicidal ideations and behaviors (Bauman et al., 2013; Hinduja & Patchin, 2010; Patchin & Hinduja, 2006). Moreover, Strom and Strom (2005) find that individuals who experience cyberbullying victimization are more likely to become adults who are in need of mental health services and have unstable relationships.

In the context of the current research, cyberbullying victimization can also lead to both school avoidance and avoidance of electronic devices (Pelfrey & Weber, 2013; Stover, 2006; Wolak, Mitchell, & Finkelhor, 2006). Although cyberbullying victimization is a relatively new area of study (Hinduja & Patchin, 2008a; Patchin & Hinduja, 2006; Ybarra & Mitchell, 2004a), there is some suggestion of an association between cyberbullying victimization and avoidance behaviors. In general, the most popular avoidance strategies used by students who experience cyberbullying victimization include blocking messages or identities and changing one’s email address or phone number (Agatston, Kowalski, & Limber, 2007; Dehue et al., 2008; Kowalski et al., 2008; Parris et al., 2012; Schenk & Fremouw, 2012). Other strategies used by students include consciously avoiding the Internet (Beran & Li, 2005) and skipping school (DeVoe et al., 2011; Li, 2010). Hinduja and Patchin (2007) suggest that targets of cyberbullying may, at some point, become preoccupied with plotting ways to avoid certain peers due to fear for their safety. Indeed, victims of cyberbullying may attempt to avoid cyberbullies in Internet chat rooms, email interactions, and instant message conversations. Because these types of online bullying behaviors often manifest offline via actions or words, Hinduja and Patchin (2007) further suggest that cyberbullying victims may use avoidance strategies in the school setting as well to avoid cyberbullies in person.

The Current Study

Although the work discussed above provides valuable insights, the majority suffers from several limitations. First, a large number of this research was conducted in only one geographic location or “cyber place” (Glew et al., 2005; Greeff & Grobler, 2008; Hinduja & Patchin, 2008b, 2010; Juvonen et al., 2000; Mahady Wilton, Craig, & Pepler, 2000; Meyer-Adams & Conner, 2008; Parault et al., 2007; Patchin & Hinduja, 2010; Rapp-Paglicci et al., 2004; Storch et al., 2003; Storch & Esposito, 2003; Townsend et al., 2008). As such, the ability to generalize findings to students in other areas within a particular country or abroad, or even to other online youth, is limited. Similarly, a number of studies utilized a small sample size, also limiting the ability to generalize study results to other populations (Bauman, 2010; Greeff & Grobler, 2008; Hinduja & Patchin, 2008b; Juvonen et al., 2000; Mahady Wilton et al., 2000; Parault et al., 2007; Rapp-Paglicci et al., 2004; Smith et al., 2008; Storch et al., 2003; Storch & Esposito, 2003). An additional limitation pertains to research conducted in locations that are demographically, culturally, and otherwise dissimilar from the United States (Greeff & Grobler, 2008; Mahady Wilton et al., 2000; Sapouna, 2008; Slee, 1994; Smith et al., 2008; Townsend et al., 2008). Due to differences associated with geographic variation, it is possible that findings from these studies are limited in terms of generalizability and may not apply to schools in the United States.

Based on these limitations, as well as the absence of conclusive research examining cyberbullying, this article will use a large nationally representative and reliably strong data set to explore the following three research questions:

Research Question 1: How does the prevalence of cyberbullying victimization compare with the prevalence of interpersonal bullying victimization?

Research Question 2: How does the relationship between demographic predictors such as age, gender, and race and cyberbullying victimization compare with the relationship between these predictors and interpersonal bullying victimization?

Research Question 3: How does the relationship between cyberbullying victimization and avoidance behaviors compare with the relationship between interpersonal bullying victimization and avoidance behaviors?

Method

Data and Sample

Data for these analyses are from the 2011 National Crime Victimization Survey (NCVS): School Crime Supplement (SCS). The NCVS is an ongoing collection of crime victimization in the United States, and has periodically included add-on surveys, of which the SCS is one. The U.S. Census Bureau uses a “rotating panel” design to select respondents for the NCVS each month. Households are randomly selected and divided into groups or rotations. Each age-eligible household member (i.e., individuals who are 12 years old and older) becomes part of the panel. Once in the sample, respondents are interviewed every 6 months over a 3-year period for a total of seven interviews.

In addition to regular NCVS information on the nature and extent of criminal victimization, additional information is periodically obtained about specific issues that relate to crime. This information is collected using a supplemental survey instrument, such as the SCS. The SCS was initially fielded at all NCVS households in 1989, and then again in 1995, 1999, 2001, 2003, 2005, 2007, 2009, and 2011 to produce a cross-section of national estimates of the levels of school-related disorder and victimization (U.S. Department of Justice, 2013). Respondents who were administered the 2011 SCS questionnaire were required to be between the ages of 12 and 18, to attend a primary or secondary education program, and to have been enrolled in school at any time during the 6 months prior to the month of the interview (U.S. Department of Justice, 2013). The 2011 SCS consists of questions that relate to students’ experiences with and perceptions of school crime and safety. For the current study, these data allow a rare opportunity to examine self-reported bullying victimization and the coping strategies that youth use to combat bullying behaviors.¹

After excluding cases of noninterviews as well as missing data, the study sample consists of 3,305 male (50.5%) and 3,242 female (49.5%) students and 5,229 White (79.9%) and 1,318 minority (20.1%) students.² All students who completed the SCS questionnaire were between 12 and 18 years of age. Of the 6,547 students in the sample, 940 (14.4%) were 12, 992 (15.2%) were 13, 917 (14%) were 14, 963 (14.7%) were 15, 969 (14.8%) were 16, 962 (14.7%) were 17, and 804 (12.3%) were 18.

Measures

Interpersonal bullying victimization is measured by a series of questions asking whether another student has bullied the respondent during the current school year. In total, seven binary variables measuring interpersonal bullying victimization were included in the analyses: “Has another student (a) made fun of you, called you names, or insulted you, in a hurtful way; (b) spread rumors about you or tried to make others dislike you; (c) threatened you with harm; (d) pushed you, shoved you, tripped you, or spit on you; (e) tried to make you do things you did not want to do, for example, give them money or other things; (f) excluded you from activities on purpose; and (g) destroyed your property on purpose?” Possible responses to each item were “yes” and “no” (yes = 1, no = 0). In addition, all seven items were combined into one dichotomous variable that represents whether students were bullied or not bullied (“yes” or “no”; yes = 1, no = 0).³ The descriptive statistics for each individual item and the overall interpersonal bullying victimization measure can be seen in Table 1.

Table 1.

Descriptive Statistics for Study Variables.

Measure	M	SD	Range	n
Interpersonal bullying victimization
Made fun of you	.179	.383	0-1	5,793
Spread rumors about you	.186	.389	0-1	5,784
Threatened you with harm	.052	.222	0-1	5,792
Pushed or shoved you	.080	.271	0-1	5,790
Made you do something	.033	.178	0-1	5,788
Excluded you	.055	.228	0-1	5,786
Destroyed your property	.028	.165	0-1	5,785
Overall interpersonal bullying victimization	.282	.450	0-1	5,796
Cyberbullying victimization
Posted hurtful information	.037	.189	0-1	5,766
Shared private information	.011	.104	0-1	5,772
Threatened you over email	.020	.138	0-1	5,778
Threatened you over messaging	.028	.164	0-1	5,779
Threatened you over text	.045	.208	0-1	5,778
Threatened you in online gaming	.014	.119	0-1	5,780
Excluded you online	.012	.107	0-1	5,781
Overall cyberbullying victimization	.091	.287	0-1	5,786
Other measures
Female	.495	.500	0-1	6,547
Age	14.937	1.969	12-18	6,547
Minority	.201	.401	0-1	6,547
Overall school avoidance	.077	.267	0-1	5,789

Cyberbullying victimization is measured by a series of questions asking whether another student has cyberbullied the respondent during the current school year. In total, seven binary variables measuring cyberbullying victimization were included in the analyses: “Has another student (a) posted hurtful information about you on the Internet, for example, on a social networking site like MySpace, Facebook, Formspring, or Twitter; (b) purposely shared your private information, photos, or videos on the Internet or mobile phones in a hurtful way; (c) threatened or insulted you through email; (d) threatened or insulted you through instant messaging or chat; (e) threatened or insulted you thorough text messaging; (f) threatened or insulted you through online gaming, for example, while playing XBOX, World of Warcraft, or similar activities; and (g) purposefully excluded you from online communications?” Possible responses to each item were “yes” and “no” (yes = 1, no = 0). In addition, all seven items were combined into one dichotomous variable that represents whether students were cyberbullied or not bullied (“yes” or “no”; yes = 1, no = 0). The descriptive statistics for each individual item and the overall cyberbullying victimization measure can be seen in Table 1.

Demographic predictors of bullying victimization include gender, age, and race. Gender is a binary variable coded male (0) or female (1). Age is a continuous variable ranging from 12 to 18, and race is a binary variable coded White (0) and minority (1). The descriptive statistics of each variable can be seen in Table 1.

Avoidance behaviors are measured by a series of questions asking whether a student has avoided school because he or she was fearful of being attacked or harmed in that area. A scale has been created from 12 binary variables, which include the avoidance of (a) the shortest route to school, (b) the entrance into the school, (c) hallways or stairs, (d) parts of the school cafeteria, (e) any school restrooms, (f) other places inside the school building, (g) the school parking lot, (h) other places on school grounds, (i) any online activities, (j) any activities at your school, (k) any classes, and (l) school. Possible responses for these items were “yes” and “no” (yes = 1, no = 0). These items were combined into one dichotomous variable that represents whether students did or did not avoid places in school or school altogether (“yes” or “no”; yes = 1, no = 0).⁴ The descriptive statistics for the overall avoidance measure can be seen in Table 1.

Analytical Approach

This study examined three research questions that compare various aspects of interpersonal bullying and cyberbullying victimization. To examine the first question, comparing the prevalence of the two bullying forms, the means were examined for the overall interpersonal bullying victimization measure and the overall cyberbullying victimization measure, as well as the 14 individual bullying victimization items. Due to the dichotomous nature of these variables, the mean represents the proportion of the sample that experienced victimization, thereby showing a clear picture of each type of bullying victimization. In addition, a McNemar chi-square test was run to examine possible significant differences between the two overall bullying victimization measures.

To examine the second question, comparing the effect of demographic predictors on the two forms of bullying victimization, a series of binary logistic regression models were estimated, due to the dichotomous nature of the dependent variables. Hosmer and Lemeshow’s chi-square test was used to examine the overall fit of the models and the Wald chi-square test was used to determine the significance of individual parameter estimates.

Finally, to examine the third research question, which compares the prediction of avoidance behaviors by the two forms of bullying, an additional series of binary logistic regression models were estimated. In these models, the overall school avoidance scale was regressed on the bullying victimization measures. The demographic predictors were also included in these models to control for spuriousness. Again, Hosmer and Lemeshow’s chi-square test was used to examine the overall fit of the models and the Wald chi-square test was used to determine the significance of individual parameter estimates.

Results

The means shown in Table 1 provide answers to the first research question: Interpersonal bullying victimization is far more prevalent than cyberbullying victimization. While 28.2% of students reported that they had been victims of interpersonal bullying, only 9.1% of students reported that they had been victims of cyberbullying. The McNemar chi-square test confirms that a significant difference exists between these percentages (χ2 = 935.457, p < .001). Means of the individual bullying measures show that the most prevalent forms of interpersonal bullying victimization include being made fun of (17.8%) and having rumors spread about you (18.6%), while the least prevalent include having someone make you do something you did not want to do (3.3%) and having your property destroyed (2.8%). The most prevalent forms of cyberbullying include being threatened over text (4.5%), while the least prevalent include being excluded online (1.2%). Note that the most prevalent form of cyberbullying, being threatened over text, is just slightly more prevalent than the least prevalent form of interpersonal bullying, having your property destroyed (4.5% vs. 2.8%). It is also important to note that, although far more prevalent than cyberbullying victimization, even interpersonal bullying victimization is rare, in that the majority of youth do not report being a victim of bullying (71.8%).

Results for the binary logistic regression models examining the second research question can be seen in Table 2. These results show some similarities and some differences between the demographic predictors’ relationships with the two forms of bullying victimization. Gender is a significant predictor of both interpersonal bullying victimization (b = 0.338, p < .001) and cyberbullying victimization (b = 0.567, p < .001), illustrating that female students are more likely to be victims of both types of bullying. The odds ratios of these estimates show that a girl’s chance of being a victim of interpersonal bullying is 1.402 times greater than a boy’s chance; similarly, a girl’s chance of being a victim of cyberbullying is 1.763 times greater than a boy’s chance. Further exploration of this relationship can be seen in Table 3. Results of the models in which the individual bullying items are regressed on the demographic predictors show that although gender is a significant predictor for most of the individual bullying measures, the direction of that relationship differs depending on the specific form of harassment. For all forms of cyberbullying except being threatened online, gender significantly predicts victimization such that girls are more likely to be victims of cyberbullying. However, for interpersonal bullying, a difference can be seen when examining direct versus indirect forms of interpersonal bullying. Girls are significantly more likely to be excluded (b = 0.358, p < .01), be made fun of (b = 0.188, p < .01), or have rumors spread about them (b = 0.749, p < .001). By contrast, boys are significantly more likely to have their property destroyed (b = −0.443, p < .01) or be pushed (b = −0.313, p < .01). Thus, the results suggest that girls are more likely to experience indirect bullying, whether in person or in cyberspace, whereas boys are more likely to experience direct forms of bullying.

Table 2.

Binary Logistic Regression Results for Demographic Predictors of Overall Bullying Victimization Measures.

	Interpersonal bullying victimization			Cyberbullying victimization
Coefficients	b	SE	exp(b)	b	SE	exp(b)
Constant	0.401*	.236	1.494	−3.406***	.375	0.033
Female	0.338***	.059	1.402	0.567***	.094	1.763
Age	−0.100***	.016	0.905	0.059*	.025	1.061
Minority	−0.190*	.075	0.827	−0.492***	.131	0.611
Model summary
Hosmer and Lemeshow’s χ²	20.497			17.117
df	8			8
p value	.029			.029

p < .05. **p < .01. ***p < .001.

Table 3.

Binary Logistic Regression Results for Individual Bullying Victimization Items.

	Made fun of			Spread rumors			Threatened you			Pushed you			Made do thing			Excluded			Destroyed
Coefficients	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)
Constant	−1.087***	.279	2.966	−1.175***	.274	0.309	−1.931***	.474	0.145	−1.390**	.399	4.013	−2.237***	.591	0.107	−1.300**	.463	0.273	−1.356*	.640	0.258
Female	0.188**	.069	1.206	0.749***	.070	2.115	0.017	.119	1.017	−0.313**	.099	0.731	−0.190	.149	0.827	0.358**	.117	1.430	−0.443**	.164	0.642
Age	−0.183***	.019	0.833	−0.047*	.018	0.954	−0.067*	.032	0.935	−0.256***	.028	0.774	−0.075	.040	0.928	−0.115***	.031	0.892	−0.135**	.044	0.874
Minority	−0.267**	.090	0.766	−0.138	.087	0.871	−0.032	.148	0.969	−0.025	.122	0.975	0.165	.175	1.180	−0.362*	.159	0.696	−0.219	.211	0.804
Model summary
Hosmer and Lemeshow’s χ²	13.254			19.149			4.492			4.585			5.341			8.222			3.583
df	8			8			8			8			8			8			8
p value	.103			.014			.810			.801			.721			.412			.893
	Posted			Shared			Threaten email			Messaging			Threaten text			Threaten game			Exclude online
Coefficients	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)	b	SE	exp(b)
Constant	−4.820***	.579	0.008	−6.319**	1.052	0.002	−4.199***	.771	0.015	−3.668*	0.651	0.026	−4.873***	.528	0.008	−3.897**	.894	0.020	−3.408*	.984	0.033
Female	1.218***	.163	3.380	0.718**	.269	2.051	0.959*	.211	2.609	1.041**	.180	2.833	1.023**	.192	2.781	−2.567**	.425	0.077	−0.535*	.254	1.708
Age	0.057	.037	1.059	0.094	.068	1.099	−0.014	.051	0.986	−0.029	.043	0.971	0.087*	.034	1.091	−0.028	.060	1.028	−0.082	.066	0.921
Minority	−0.298	.191	0.742	−0.070	.323	0.933	−0.594*	.288	0.552	−0.649**	.247	0.522	−0.615**	.192	0.541	−0.792*	.356	0.453	−0.966*	.429	0.381
Model summary
Hosmer and Lemeshow’s χ²	20.014			15.803			4.279			7.070			24.014			1.403			2.107
df	7			8			7			7			7			7			7
p value	.006			.045			.747			.422			.001			.985			.954

p < .05. **p < .01. ***p < .001.

The demographic predictor age is significantly related to both forms of bullying, but in opposite directions: Older students displayed lower chances of being a victim of interpersonal bullying (b = −0.100, p < .001) but greater chances of being a victim of cyberbullying (b = 0.059, p < .001) (Table 2). The odds ratios of these estimates show that each year increase in age decreases the risk of interpersonal bullying victimization by a factor of 0.905 and increases the risk of cyberbullying victimization by a factor of 1.061. Results of the models shown in Table 3, in which the individual bullying items are regressed on the demographic predictors, confirm this pattern: Older students are significantly more likely to be threatened through text (b = 0.087, p < .05) and less likely to have their property destroyed (b = −0.135, p < .01), be excluded (b = −0.115, p < .001), be pushed (b = −0.256, p < .001), be threatened in person (b = −0.067, p < .05), be made fun of (b = −0.183, p < .001), and have rumors spread about them (b = −0.047, p < .05).

Finally, the demographic predictor minority is significantly related to both forms of bullying victimization in the same direction, such that White students are more likely to be victims of both interpersonal bullying (b = −0.190, p < .05) and cyberbullying (b = −0.492, p < .001; Table 2). The odds ratios of these estimates show that being a minority student decreases the chances of being a victim of interpersonal bullying by 0.827 and the chances of being a victim of cyberbullying by 0.611. Again, results of the models shown in Table 3, in which the individual bullying items are regressed on the demographic predictors, confirm this pattern: White students are more likely to be excluded online (b = −0.966, p < .05), threatened online (b = −0.792, p < .05), threatened over text (b = −0.615, p < .01), threatened through instant messaging (b = −0.649, p < .01), threatened over email (b = −0.594, p < .05), made fun of (b = −.267, p < .003), and excluded in person (b = −.362, p < .05). It is interesting to note that there is no significant racial difference when the direct forms of interpersonal bullying victimization are examined: White and minority students are equally likely to be pushed, made to do things, and have their property destroyed.

Results for the binary logistic regression models examining the third research question can be seen in Table 4. In these models, both forms of bullying victimization significantly and positively predict overall school avoidance. Students who are victims of both interpersonal bullying and cyberbullying are significantly more likely to engage in avoidance behaviors than students who have not experienced such victimization (b = 1.820 and b = 1.668, p < .001, respectively). The odds ratios of these estimates show that being a victim of interpersonal bullying increases a student’s odds of school avoidance by a factor of 6.171, while being a victim of cyberbullying increases a student’s odds of school avoidance by a factor of 5.300.

Table 4.

Binary Logistic Regression Results for School Avoidance.

Coefficients	b	SE	exp(b)	b	SE	exp(b)
Constant	−2.552***	.416	0.078	−1.161**	.406	0.313
Interpersonal	1.820***	.107	6.171	—	—	—
Cyber	—	—	—	1.668***	.118	5.300
Female	0.125	.103	1.134	0.134	.102	1.144
Age	−0.057*	.028	0.945	−0.114***	.028	0.892
Minority	0.011	.129	0.931	0.030	.127	1.031
Model summary
Hosmer and Lemeshow’s χ²	7.477			4.688
df	8			8
p value	.486			.790

p < .05. **p < .01. ***p < .001.

Discussion

The current study used a large nationally representative sample to explore the similarities and differences between interpersonal bullying and cyberbullying victimization. Specifically, the following questions were examined: How does the prevalence of cyberbullying victimization compare with the prevalence of interpersonal bullying victimization? How does the relationship between demographic predictors and cyberbullying victimization compare with the relationship between these predictors and interpersonal bullying victimization? And, finally, how does the relationship between cyberbullying victimization and avoidance behaviors compare with the relationship between interpersonal bullying victimization and avoidance behaviors?

Results from analyses exploring the first question indicate that interpersonal bullying victimization is far more prevalent than cyberbullying victimization. The means of the overall scales show that more than 3 times as many students have been victims of interpersonal bullying than cyberbullying (28.2% vs. 9.1%). This difference is made even clearer when one considers that the most prevalent form of cyberbullying, being threatened over text, is just slightly more prevalent than the least prevalent form of interpersonal bullying, having your property destroyed (4.5% vs. 2.8%). By contrast, the most prevalent forms of interpersonal bullying victimization include having rumors spread about you (18.6%) and being made fun of (17.8%). Thus, despite claims that cyberbullying is a frequent phenomenon among youth and that the prevalence has increased in recent years (Olweus, 2012), it is likely that this type of bullying is overrated and occurs less frequently than anticipated. It is also possible, however, that these types of bullying behaviors are becoming normalized in a culture that is increasingly reliant on social technologies, such that students fail to acknowledge and report behaviors that would otherwise be considered cyberbullying victimization. Just as the lack of face-to-face interaction or anonymity can make it easier for cyberbullies to be hurtful to others, so too may these characteristics make it easier for cyberbully victims to ignore such hurtful actions.

Results from analyses examining the second research question illustrate some similarities and some differences in the demographic predictors’ relationships with the two forms of victimization. When the overall bullying victimization scales were examined, girls were more likely to be victims of both types of bullying. Further examination, though, showed that this relationship actually differs based on the type of bullying experienced. Girls are more likely to be victims of most forms of cyberbullying. For interpersonal bullying, however, a difference can be seen between direct and indirect forms. Girls are more likely to be excluded, made fun of, or had rumors spread about them, whereas boys are more likely to have their property destroyed or be pushed. Thus, it appears that girls are more likely to experience indirect bullying, whether in person or in cyberspace, whereas boys are more likely to experience direct forms of bullying.

Strong differences were seen in the relationship between age and bullying victimization. Younger students were more likely to experience interpersonal bullying; specifically, they were more likely to be excluded, pushed, threatened, or made fun of, or to have their property destroyed or rumors spread about them. However, older students were more likely to be threatened through text, which likely has to do with older students’ greater access to electronic devices. In contrast, no difference between the types of bullying was seen in terms of race: White students were more likely to experience both interpersonal bullying and cyberbullying.

Finally, results from analyses exploring the third research question indicate that students who are a victim of either form of bullying are more likely to engage in school avoidance behaviors than students who have not experienced such victimization. It is interesting to note that bullying victimization that occurred in cyberspace (i.e., over text or email or online) still resulted in the negative consequence of physical school avoidance.

Study Limitations

One limitation of this research is the use of secondary data. Although the data used in this research capture some of the objective indicators that someone has been bullied, it is important to note the potential for additional measures, such as intensity or repetition, that could more fully illustrate victimization experiences for bullied youth.

Another limitation involves the cross-sectional nature of the data. It is not possible to clearly determine the causal direction or temporal ordering of the relationships identified in this research, because data for the SCS were collected at a single point in time. For instance, it was hypothesized that students who have experienced bullying victimization are more likely to engage in school avoidance. It is also possible, however, that these behaviors could have preceded the incidents of bullying victimization, suggesting that students were bullied because they avoided specific places in school or school altogether (Hutzell & Payne, 2012).

A further limitation of this research is the absence of information to control for particular key constructs. Three self-report measures were included in the current analyses as demographic variables; however, data to account for additional individual-level control variables as well as the influence of structural factors on school avoidance were not available (Hutzell & Payne, 2012). For instance, the National Audit Office (NAO; 2005) report identifies three categories of factors that can be a precursor to absenteeism from school: (a) home influences, (b) school influences, and (c) student factors. In addition, although school type was not examined in the current study, Chandler et al. (1995) suggest that students who attend private institutions are less likely to engage in avoidance behaviors than students attending public schools.

Conclusion

The current study used a large nationally representative sample to answer exploratory questions about bullying victimization. Results indicate that interpersonal bullying victimization is far more prevalent than cyberbullying victimization, with more than 3 times as many students having been victims of interpersonal bullying than cyberbullying. In addition, findings illustrate some similarities and some differences in the demographic predictors’ relationships with the two forms of victimization. Girls are more likely to be victims of indirect interpersonal bullying and cyberbullying, whereas boys are more likely to be victims of direct interpersonal bullying. Differences were also seen in the relationship between age and bullying victimization, with younger students more likely to experience interpersonal bullying and older students more likely to experience cyberbullying. Finally, results indicate that students who are a victim of either form of bullying are more likely to engage in school avoidance behaviors than students who have not experienced such victimization.

The current work on interpersonal bullying and cyberbullying is timely given the concern from parents, the school community, and legislators surrounding such victimization. Although both types of bullying behaviors can involve direct and indirect victimization, there are several characteristics that distinguish cyberbullying from interpersonal bullying, such as the potential for an infinite audience, an inability for the bully to observe the immediate reaction of the victim, and the perception of anonymity on the part of the bully (Bauman, 2010; Slonje & Smith, 2008). Despite these differences, and despite claims that cyberbullying is a frequent phenomenon among youth (Olweus, 2012), the current work highlights that cyberbullying occurs much less frequently than anticipated and therefore demonstrates the need for prevention programs to focus more on interpersonal bullying behaviors than on those that occur in the realm of cyberspace.

There is also a need to focus on the negative consequences associated with bullying, as results of the current work indicate that youth who are victims of either form of bullying are more likely to engage in school avoidance behaviors than students who have not experienced such victimization. These findings parallel those of previous studies that have found an association between being bullied and avoidance behaviors (Agatston et al., 2007; Beran & Li, 2005; Dehue et al., 2008; DeVoe et al., 2005; DeVoe et al., 2011; Hinduja & Patchin, 2007; Hutzell & Payne, 2012; Juvonen et al., 2000; Kowalski et al., 2008; Li, 2010; Mahady Wilton & Craig, 2000; Meyer-Adams & Conner, 2008; Parault et al., 2007; Parris et al., 2012; Schenk & Fremouw, 2012; Smith et al., 2008; Smith, Talamelli, Cowie, Naylor, & Chauhan, 2004; Storch et al., 2003; Storch & Esposito, 2003; Townsend et al., 2008). This avoidance can significantly hinder academic success and lead to dropping out of school altogether, which can dramatically increase the probability of having economic, emotional, and physical problems later in life (Townsend et al., 2008).

As such, there is an immediate need for the development and implementation of school-based policies and programs to mediate bully/victim issues. Hutzell and Payne (2012) discuss both traditional and more contemporary programs that have been shown to be effective in reducing bullying victimization in schools. In particular, the current research calls for a focus on preventing interpersonal bullying victimization. Even more specifically, there should be a focus on reducing indirect interpersonal bullying of female students, direct interpersonal bullying of male students, and all interpersonal bullying of younger students regardless of gender. Traditional programs designed to mediate bully/victim problems generally follow the Olweus Bullying Prevention Program model, which builds on four principles to be used at the individual, classroom, and school levels (Olweus, 2003). These principles involve establishing a school and home environment characterized by involvement of adults, positive interest, and warmth; placing limits on behaviors that are viewed as unacceptable; consistently applying sanctions that are nonphysical and nonpunitive for unacceptable behavior; and having adults who act as positive role models and authorities (Olweus, 2003). More contemporary initiatives have approached bullying victimization from a restorative justice perspective, using reintegrative shaming techniques to restore the relationship between the victim and the offender as well as forgiveness and reconciliation to reduce the likelihood of future bullying victimization incidents (Ferguson, Miguel, Kilburn, & Sanchez, 2007). Not only have both types of programs been shown to reduce bullying victimization (Ahmed & Braithwaite, 2006; Farrington Ttofi, 2009; Olweus, 2005), but they may have a strong impact on the negative consequences that result from bullying victimization as well, such as school avoidance. Reducing bullying victimization and its negative consequencescan lead to a more favorable climate for all members of the school community that encourages greater connectedness to the school environment.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Allison Ann Payne is an associate professor in the Department of Sociology and Criminology and the associate director of the University Honors Program at Villanova University. Her research interests include juvenile delinquency prevention, school climate and discipline, and criminological theory and she has researched the effect of school climate on school disorder, student racial and ethnic composition and school discipline, predictors of school program implementation, and gender differences in school correlates of delinquency. Allison has been published in multiple journals, including Criminology, Journal of Research in Crime and Delinquency, and Social Problems.

Kirsten L. Hutzell is an Assistant Professor in the Department of Criminal Justice and Criminology at Lycoming College. She received her MA in Criminology, Law and Society from Villanova University in 2010 and her PhD in Criminology, Law and Society from George Mason University in 2014. Kirsten’s research interests include crime and delinquency prevention, school disorder and violence, program evaluation and research methodology, criminological theory, and restorative justice.

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