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Children's Experiences of Cyber Bullying: A Canadian National Study

Beran, Tanya; Mishna, Faye; McInroy, Lauren B.; Shariff, Shaheen

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Citation (published version)

Beran, T., Mishna, F., Mcinroy, L. B. and Shariff, S. (2015). Children's Experiences of Cyberbullying: A Canadian National Study. Children & Schools 37(4), 207-214. doi: 10.1093/cs/cdv024.

Publisher’s Statement This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Children & Schools following peer review. The definitive publisher-authenticated version Beran, T., Mishna, F., Mcinroy, L. B. and Shariff, S. (2015). Children's Experiences of Cyberbullying: A Canadian National Study. Children & Schools 37(4), 207-214. doi: 10.1093/cs/cdv024 is available online at: http://cs.oxfordjournals.org/content/37/4/207.

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<title>Children’s Experiences of Cyberbullying: A Canadian National Study

<byline>Tanya Beran, Faye Mishna, Lauren B. McInroy, and Shaheen Shariff

<abstract>This national study reports the prevalence of cyberbullying among youths in Canada

according to demographic characteristics, its impact, and its relationship to six forms of

victimization and perpetration. Cross-sectional data were obtained from a national household panel

of families living in all Canadian provinces. The sample included 1,001 children aged 10 to 17

years. Frequency and multivariate analyses determined the rate and impact of cyberbullying as

reported by children. Correlation analyses examined the extent to which cyberbullying was related

to other types of bullying. Overall, 13.99 percent of children had been cyberbullied once or more in

the past month, varying according to gender. Children who were cyberbullied were likely to

experience negative outcomes on all eight domains measured. The vast majority who were

cyberbullied (94.28 percent) were also targeted through at least one other type of bullying, and over

a third (33.57 percent) perpetrated at least one other type of bullying. Approximately one in seven

Canadian children between the ages of 10 and 17 years is cyber-victimized, and 1 in 13 children

cyber-perpetrates. These rates are similar across demographic groups, and children who are

cyberbullied or cyberbully others are likely to be involved in other forms of bullying. Authors

conclude that bullying prevention and management strategies must include children’s cyber

experiences.

Key words: bullying; childhood aggression; cyber harassment; cyberbullying; mental health

Her Excellency the Right Honourable Michaëlle Jean, Canada’s former governor general, has stated

that “[f]inding ways of predicting and preventing the development of . . . [bullying] is a necessity.

Bullying is not only about threats and intimidation, it is foremost about contempt and injustice”

(cited in Craig, Cummings, & Pepler, 2010). Despite this resolve, no nationally representative

studies of cyberbullying have been conducted in Canada, and few exist around the world. To date,

global research efforts have been fragmentary, though they have also increased rapidly in the last

decade (Cassidy, Faucher, & Jackson, 2013; Kowalski, Giumetti, Schroeder, & Lattanner, 2014). To

determine the prevalence and relevance of these experiences, a national study was conducted of the

rates and impact of cyberbullying, as well as its relationship to other forms of bullying among

children ages 10 to 17 years living in Canada.

Aggression targeted at a person through the use of a technology (for example, cell phone,

computer) and/or within a digital context (for example, online) is identified as cyberbullying

(Kowalski et al., 2014; Patchin & Hinduja, 2012). Reports of its occurrence and outcomes vary

significantly, likely due to inconsistent definitions, measurement, reference periods, and sample

characteristics such as age, geographic location, and gender (Cassidy et al., 2013; Cornell &

Bandyopadhyay, 2010; Cornell & Cole, 2012; Kowalski et al., 2014; Menesini & Nocentini, 2009;

Patchin & Hinduja, 2012). A recent international meta-analysis of 131 studies on cyberbullying

among youths found prevalence for victimization generally ranged between 10 percent and 40

percent (Kowalski et al., 2014). Another recent meta-analysis of 35 studies found a total

victimization range of approximately 5 percent to 70 percent, with a mean of 24 percent and most of

the studies falling into a range between 6 percent and 30 percent. That same meta-analysis found in

27 studies a total perpetration range of approximately 3 percent to 44 percent, with a mean of 18

percent (Patchin & Hinduja, 2012). Recently a large Canadian study of 26,078 students from 436

schools in grades 6 to 10 found that 11 percent to 19 percent of boys and 17 percent to 19 percent of

girls reported cyber-victimization, with older children reporting higher levels. However, rates of

perpetration were not reported (Craig & McCuaig Edge, 2011).

Additional studies with small, randomly selected samples reveal prevalence rates of 21

percent to 25 percent for children ages 12 to 14 years for victimization, and 3 percent to 17 percent

for perpetration (Beran & Li, 2005). In two major metropolitan cities in Canada approximately half

of children ages 11 to 12 years and 15 to 16 years reported cyber-victimization, and a third of them

reported cyber-perpetration (Beran & Li, 2005). The literature thus suggests that prevalence rates

vary widely. Scholars disagree over whether prevalence is increasing or holding steady, but the lack

of cohesive research on the point requires that further study be undertaken (Cassidy et al., 2013;

Kowalski et al., 2014; Rigby & Smith, 2011; Ybarra, Mitchell, Wolak, & Finkelhor, 2006). It is

important to obtain current, nationally representative information about the prevalence of

cyberbullying based on a stratified random sample (Craig & McCuaig Edge, 2011; Hinduja &

Patchin, 2008; Smith et al., 2008). In the present study, a clear and age-appropriate definition of

bullying and cyberbullying is provided along with specific examples.

Research has shown that children who are bullied offline are at risk of experiencing a

multitude of negative consequences (Fekkes, Pijpers, Fredriks, Vogels, & Verloove-Vanhorick,

2006; Gibb, Horwood, & Fergusson, 2011; Gini & Pozzoli, 2009; Hawker & Boulton, 2000;

Reijntjes, Kamphuis, Prinzie, & Telch, 2010). Children who are cyberbullied are also at risk for

negative outcomes such as loneliness, distress, loss or lack of friendships, lack of acceptance by

peers, anger, lack of safety at school, low self-esteem, physical injuries, drug and alcohol use,

weapons possession, and eating disorders (Dehue, Bolman, & Vollink, 2008; Fosse & Holen, 2006;

Jackson & Cohen, 2012; Ybarra & Mitchell, 2007). Despite high-profile media attention linking

cyberbullying to suicide, this outcome is far from the most prevalent and may in fact often be

indicative of a much more complex situation (Cassidy et al., 2013). It is expected that, consistent

with previous research regarding offline bullying, children who are cyberbullied will experience

elevated levels of difficulties in many or all of these areas.

The similarities and differences between offline bullying and cyberbullying are

complicated and contentious. However, in the existing literature there is general agreement that

the two types of bullying share several key features, including intentionality, repetition, and a

power imbalance between victim(s) and perpetrator(s). Key differences between the two types of

bullying include the potential for reduced empathy by cyberbullying perpetrators due to their

inability to witness their victims’ reactions, the relative and perceived anonymity the cyber

context offers cyberbullying perpetrators (which may also skew the victims’ judgement of

threat), and the potential in cyberbullying for a much larger audience (Cassidy et al., 2013;

Kowalski et al., 2014; Patchin & Hinduja, 2012). However, despite the different environments

(that is, offline versus cyber space), there is clearly a significant inter-relationship between

offline and cyberbullying that requires further investigation (Patchin & Hinduja, 2012). The

percentage of children involved in both reportedly ranges from 25 percent to 41 (Beran & Li,

2005; Craig & McCuaig Edge, 2011; Ybarra et al., 2006; Ybarra & Mitchell, 2004).

Although several forms of bullying have been identified in the literature, including

physical (for example, hitting), verbal (for example, name calling), social (for example, gossip,

exclusion), racial (for example, ethnic slurs), sexual (for example, sexual comments), and cyber

(for example, using a communication devices) (Craig et al., 2009; Craig & McCuaig Edge, 2011;

Wang, Iannotti, & Luk, 2012; Wang, Iannotti, & Nansel, 2009), it is not known whether children

who bully and/or are bullied online are also involved in these other forms of victimization and

perpetration. It is important to note that there are many different types of cyberbullying, which

range in online contact, intent, duration, and impact, and also have the potential to include social,

racial, or sexual biases (Willard, 2007). With 99 percent of children ages in grades 4 through 11

in Canada using the Internet (Steeves, 2014), it is important to determine the risk of

cyberbullying. This study contributes new information by documenting the extent of

cyberbullying in Canada, and whether it varies according to demographic characteristics. The

study also determines children’s vulnerability to cyberbullying by assessing its impact across

eight areas of functioning rather than a few, as typically reported. Finally, our results will extend

existing research by identifying the types of offline victimization and perpetration experiences

that are related to cyberbullying.

<a>Method

<b>Participants

A stratified random sample of 1,001 children (488 girls, 513 boys) from all 10 provinces of Canada

was obtained (the three northern territories were excluded, however). It was expected that this large

sample size would provide enough power to detect differences across demographic characteristics

for cyber-victimization and cyber-perpetration. The sample was stratified based on age to obtain a

representative sample of children ages 10 to 17 years. The mean age of the sample is 13.62 years

(SD = 2.26). The demographic characteristics of the sample shown in Table 1 are representative of

children living in Canada (Statistics Canada, 2012).

<b>Procedure

The following procedure was conducted in compliance with the ethics review boards represented by

the institutions of all the authors. Parents with children ages 10 to 17 years belonging to a national

research panel representative of Canadian youths (Statistics Canada, 2012) were contacted by e-mail

and asked to give consent for one of their children to answer a survey online. The families were

selected from census data, and parents had the option to participate or decline the panel on behalf of

themselves and their children. The consent rate was 96.25 percent, and the sample is considered

representative of the Canadian population (excluding the Northern Territories). Children then read

the purpose and terms of the study and provided assent to complete the scale. Administration time

was a mean of 21.12 minutes. Children read the following definition of bullying:

<bq>There are lots of ways to hurt someone. A person who bullies wants to hurt

the other person. A person who bullies does it because they can. They may be

older, stronger, bigger, or have other students on their side. There are different

kinds of bullying: 1. physical, such as, hitting, or spitting; 2. verbal, such as,

name-calling, or mocking; 3. social, such as, leaving someone out, or gossiping.

4. electronic, such as, Facebook, or email; 5. racial, such as, saying hurtful

things about someone whose skin is a different colour; 6. sexual, such as,

grabbing, or saying something sexual; and 7. sexual preference, such as, teasing

someone for being gay whether they are or not. (Pepler, Craig, Ziegler, &

Charach, 1993)

<text>Children then responded to one question about being bullied within the last month and one

question about bullying others within the last month. Follow-up questions about the seven types of

bullying they may have experienced or perpetrated were then administered. For example, the

question about physical bullying experienced was phrased as, “Have you been kicked, hit, or

pushed, or spat on in the last month?” The question about physical bullying perpetrated was worded

as, “Have you shoved, hit, kicked or spat at anyone in the last month?” Responses to these 16

questions were rated on a five-point Likert-type scale from 1 = no to 5 = several times a week. The

Cronbach’s alphas of the seven types of victimization and seven types of perpetration items were .74

and .86, respectively, indicating good inter-item reliability.

The subsequent section included eight scales that examine the cognitive, psychological, and

behavioral impact of bullying. The Relationships subscale measured children’s reports of positive

interactions among peers (six items); Anger measured angry feelings and actions (six items);

Anxiety measured nervous thoughts (seven items); Self-Esteem measured self-worth (three items);

Risk measured involvement in criminal behaviors (five items); Physical Injury measured physical

harm sustained from victimization (three items); Drug Use measured drug consumption (three

items); and Eating Problems measured problematic eating behaviors (four items). All 37 items were

rated on a five-point frequency scale: 1 = never, 2 = only once or twice, 3 = sometimes, 4 = about

once a week, and 5 = several times a week. For a complete description of the psychometric

properties of a similar version of this scale see Beran, Stanton, Hetherington, Mishna, & Shariff

(2012). The inter-item consistency according to Cronbach’s alpha for the eight scales ranges from

.69 to .88, indicating good reliability.

Children who indicated that bullying had not happened to them, or who had not bullied

others within the last month, were not administered any subsequent questions about bullying. Their

responses were coded as “no” to the questions about types of bullying. The overall rate of missing

values was less than 3 percent; therefore, no values were imputed.

<a>Analysis

Both descriptive and inferential statistics were used to address the objectives of the study. Frequency

counts identified the proportion of children involved in various types of bullying, and are reported

within 95 percent confidence intervals (CI). Chi-square analyses were then used to determine if the

number of children involved in bullying varied across all the demographic characteristics measured

in this study. A multivariate analysis of variance identified whether the impact of bullying on the

eight domains measured differed between those children who were, and who were not, cyberbullied.

Finally, the proportion of children involved in cyber-victimization and perpetration was calculated

across other types of bullying, and Pearson’s product-moment correlations determined the

significance and magnitude of these relationships.

<a>Results

<b>Rates

As shown in Table 1, a total of 140 (13.99 percent, 95% CI = 11.83, 16.15) children reported being

cyberbullied once or twice or more often in the last month, and 80 (7.99 percent, 95% CI = 6.31,

9.67) reported cyberbullying others once or twice or more often in the last month. The only

significant demographic difference found was that boys were more likely to report cyberbullying

others than were girls, but the effect size was small (Ф = 0.07).

<b>Impact of Cyber-Victimization

A multivariate analysis of variance was conducted between children who reported cyber-

victimization and those who did not, to determine its impact. Significant differences emerged on all

eight scales, as shown in Table 2. According to Cohen’s (1988) criteria for interpreting effect sizes,

partial eta-squared values are small.

<b>Relationship between Cyber and Other Forms of Perpetration and Victimization

Most children who were cyberbullied (n = 140, 13.99 percent) identified at least one other form of

victimization (n = 133) as occurring once or twice or more often, as shown on the left side of Table

3. In other words, 94.28 percent of children who were cyberbullied were also bullied through some

other form. In fact, only seven children (0.70 percent) in the entire sample reported being solely

cyberbullied. Overall, the vast majority of children were not bullied (n = 739, 73.83 percent). As

shown on the right side of Table 3, a large majority of the 133 children who reported cyber and other

types of victimization also reported verbal (n = 123, 92.48 percent) and social (n = 99, 74.44

percent) victimization once or twice or more often. About half (n = 67, 50.37 percent) reported

physical victimization, and more than a quarter (n = 37, 27.82 percent) reported racial victimization.

Under a quarter of these children reported that sexual comments (n = 27, 20.30 percent) or sexual

behaviors (n = 21, 15.79 percent) were directed toward them.

Regarding perpetration against others, approximately one-third of cyberbullied children (n =

140) reported one or more forms of bullying others once or twice or more often (n = 47, 33.57

percent). As demonstrated in Table 3, about a quarter of these victims cyberbullied others (n = 36,

25.71 percent). Almost a third of victims verbally bullied others (n = 41, 29.28 percent), and less

than a quarter socially (n = 26, 18.57 percent) or physically (n = 29, 20.71 percent). They were also

likely to report racial (n = 17, 12.14 percent) bullying or either type of sexual bullying (comments: n

= 15, 10.71 percent; behavior: n = 11, 7.86 percent) toward others. To determine if there is a

significant relationship between cyber-victimization and each of these forms of victimization and

bullying, point-biserial correlations were computed. Cyber-victimization was coded “yes” if it was

experienced once or twice or more often, and “no” if it was not experienced. Scores for all other

forms of victimization and perpetration range from “never” to “several times a week” on a five-point

scale. All correlations were significant, as shown in Table 3.

<a>Discussion

Four main findings emerged in this study. First, the study established the likelihood that a Canadian

child between the ages of 10 to 17 is cyber-victimized (one in seven) and perpetrates cyberbullying

against others (one in 13). Second, these rates do not vary as a function of the child’s demographic

characteristics with the exception of boys reporting slightly higher rates of perpetration than girls.

Third, regarding impact, youths who were cyberbullied reported significantly poorer outcomes on all

of the eight domains assessed. Fourth, our results identified a significant relationship between being

cyberbullied and all forms of victimization and perpetration.

<b>Prevalence

Given that nearly all Canadian children use electronic communication devices and engage with the

cyber world (Steeves, 2014), the cyber-victimization rate of 13.99 percent and cyber-perpetration

rate of 7.99 percent applies to the majority of children in Canada (excluding Yukon, Northwest

Territories, and Nunavut). These findings are comparable to national rates of cyberbullying in the

United States (Ybarra et al., 2006), and to the general range of most international findings (Kowalski

et al., 2014). The exception was that boys reported cyberbullying others more often than did girls,

but this difference was small. Existing research on gender and cyberbullying has been inconclusive,

with some studies finding higher rates of perpetration by boys, some by girls, and some finding no

significant difference by gender. Similar inconsistencies are found when examining the relationship

between gender and victimization (Cassidy et al., 2013; Kowalski et al., 2014).

This study expands on the existing research in the Canadian context by determining that

almost all children reported at least one other type of victimization in addition to cyber-

victimization. The vast majority reported being verbally and socially bullied. About half reported

being physically bullied, and more than a quarter reported being racially bullied. Sexual forms of

bullying were less often reported but were also related to cyber-victimization. It was surprising that

less than 1 percent of respondents reported cyber-victimization only. These results correspond to

other findings that suggest cyberbullying often occurs in a relationship context of ongoing

interactions and opportunities for offline bullying (Craig et al., 2009; Craig & McCuaig Edge, 2011;

Craig & Pepler, 2007; Hinduja & Patchin, 2008; Kowalski et al., 2014). It also demonstrates that

cyberbullying is not an isolated experience. It seems that for many, if not most, young people cyber

space is an extension of the interactions that occur in physical space. Cyber-interactions may mirror

the complex dynamics of power and control that exist in some peer relationships at school and in the

neighborhood. The results show that children who reported cyber-victimization were significantly

more likely to identify negative outcomes than children who did not report cyber-victimization. The

magnitude of this difference was small, potentially because other forms of victimization and

individual challenges were experienced in both groups. These results indicate that cyberbullying

must be studied in the context of other forms of victimization within relationships (Craig et al.,

2009; Craig & McCuaig Edge, 2011; Craig & Pepler, 2007).

About a quarter of children who were cyberbullied also reported bullying others in various

ways. Perhaps becoming a target of cyberbullying creates an expectation among peers that

harassment is “normal,” inviting reciprocating bullying behaviors in any form: “Everyone does it.”

Having been bullied may also provide justification for a child contemplating perpetrating: “He

started it.” These results correspond with research involving focus groups with youths, in which the

participants stated that individuals who might be too timid to bully others offline might be

emboldened to do so online, due to the perceived anonymity or lack of cues to see the impact of their

aggressive behavior on others (Mishna, McLuckie, & Saini, 2009). Technical skill and media

adroitness may also play an important role in cyber contexts. It is important to note that

technological adeptness is not universal among young people even in the current context (Cassidy et

al., 2013; Kowalski et al., 2014). In addition to expectation and justification, stimulation may

motivate people to bully and be bullied. Some communication devices have been marketed with

suggestive messages that condone harassment, and children may see this as a source of

entertainment: “It’s fun.” For these reasons, children may engage in bullying behaviors that repeat a

circular pattern of victimization–perpetration.

<b>Implications for Prevention and Intervention in Practice

The findings of this study have significant implications for prevention and intervention in practice

settings. Many of the prevention and intervention efforts traditionally considered appropriate to

address bullying may not be appropriate for cyberbullying because of the key differences between

the two modalities (Faccio, Iudici, Costa, & Belloni, 2014). Yet, given this study’s finding that

cyber-victimization is significantly related to other forms of victimization, it is necessary to consider

prevention and intervention efforts that address both children’s offline and online settings

comprehensively, but also distinctly. Students should be made aware of stratagies to prevent and

respond to bullying that are effective specifically with offline bullying as well as specifically with

cyberbullying. Prevention and intervention initiatives and programs implemented to address bullying

and cyberbullying should also be flexible and contextually responsive to the ongoing issues in

particular settings. It is important to note that the role of adults, both parents and professionals,

remains crucial. Adults should facilitate nonjudgmental dialogue with young people about these

issues, demonstrating engagement and awareness. Students’ perceptions of adults’ knowledge may

be influential in their willingness to share their experiences of victimization (Faccio et al., 2014).

<b>Limitations

This study has several limitations. It was not possible to differentiate those children who

experienced cyberbullying from those who experienced no form of bullying to examine impact, as

the group size difference was too high. Identifying children who experienced only one type of

bullying would result in group sizes too small to compare impact. Also, children are likely to be

affected by cyberbullying in additional ways not measured in this study. The sample was delimited

to children living in the provinces, and excludes children living in the Northern Territories, which is

typical of national surveys conducted in Canada. Although there were no significant differences

between reports of children living in urban compared to rural areas, children in northern remote

communities may have different experiences of cyberbullying. Also, although the national panel was

ethnically diverse, we were not able to obtain these data due to privacy concerns.

<a>Conclusion

The current study shows that cyberbullying, like offline bullying, is reported by a significant number

of children in regard to both victimization and perpetration. Moreover, all children in Canada are at

risk for bullying given that differential rates were not found among any demographic groups, with

the exception of boys reporting marginally higher rates. Cyberbullying is an issue that deserves

serious attention, with victimized children reporting that it affects them, at least to some degree, in

many different ways, including in their relationships with their peers and their use of drugs. This

study provides robust evidence for the need to include the cyber world in any future attempts to

address bullying victimization and perpetration. <dgbt>

<a>References

Beran, T. N., & Li, Q. (2005). Cyberharassment: A study of a new method for an old behavior.

Journal of Educational Computing Research, 32(3), 265–277.

Beran, T. N., Stanton, L., Hetherington, R., Mishna, F., & Shariff, S. (2012). Development of the

Bullying and Health Experiences Scale. Interactive Journal of Medical Research, 1(2), e13.

Cassidy, W., Faucher, C., & Jackson, M. (2013). Cyberbullying among youth: A comprehensive

review of current international research and its implications and application to policy and

practice. School Psychology International, 34, 575–612.

Cohen, J. (1988). Statistical power analysis for behavioral sciences (2nd ed.). Hillsdale, NJ:

Erlbaum.

Cornell, D. G., & Bandyopadhyay, S. (2010). The assessment of bullying. In S. R. Jimerson, S. M.

Swearer, & D. L. Espelage (Eds.), The handbook of bullying in schools: An international

perspective (pp. 265–267). New York: Routledge.

Cornell, D. G., & Cole, J. C. (2012). Assessment of bullying. In S. Jimerson, A. Nickelson, M. J.

Mayer, & M. J. Furlong (Eds.), Handbook of school violence and school safety:

International research and practice (2nd ed., pp. 289–303). New York: Routledge.

Craig, W., Cummings, J., & Pepler, D. (2010, November 22). The right to be safe: Bullying is a

human rights issue. The Federation for the Humanities and Social Sciences Blog. Retrieved

from http://www.ideas-idees.ca/blog/right-be-safe-bullying-human-rights-issue

Craig, W., Harel-Fisch, Y., Fogel-Grinvald, H., Dostaler, S., Hetland, J., Simons-Morton, B., et al.

(2009). A cross-national profile of bullying and victimization among adolescents in 40

countries. International Journal of Public Health, 54, 216–224.

Craig, W., & McCuaig Edge, H. (2011). Bullying and fighting. In J. G. Freeman, M. King, W.

Pickett, W. Craig, F. Elgar, I. Janssen, & D. Klinger (Eds.), The health of Canada’s young

people: A mental health focus (pp. 167–183). Ottawa: Public Health Agency of Canada.

Craig, W. M., & Pepler, D. J. (2007). Understanding bullying: From research to practice.

Canadian Psychology, 48, 86–93.

Dehue, F., Bolman, C., & Vollink, T. (2008). Cyberbullying: Youngsters’ experiences and parental

perception. CyberPsychology & Behavior, 11, 1–8.

Faccio, E., Iudici, A., Costa, C., & Belloni, E. (2014). Cyberbullying and interventions programs in

school and clinical setting. Procedia: Social and Behavioral Sciences, 122, 500–505.

Fekkes, M., Pijpers, F.I.M., Fredriks, A. M., Vogels, T., & Verloove-Vanhorick, S. P. (2006). Do

bullied children get ill, or do ill children get bullied? A prospective cohort study on

relationship between bullying and health-related symptoms. Pediatrics, 117, 1568–1574.

Fosse, G. K., & Holen, A. (2006). Childhood maltreatment in adult female psychiatric outpatients

with eating disorders. Eating Behavior, 7, 404–409.

Gibb, S. J., Horwood, L. J., & Fergusson, D. M. (2011). Bullying victimization/perpetration in

childhood and later adjustment: Findings from a 30 year longitudinal study. Journal of

Aggression, Conflict, and Peace Research, 3, 82–88.

Gini, G., & Pozzoli, T. (2009). Association between bullying and psychosomatic problems: A meta-

analysis. Pediatrics, 123, 1059–1065.

Hawker, D.S.J., & Boulton, M. J. (2000). Twenty years’ research on peer victimization and

psychosocial maladjustment: A meta-analytic review of cross-sectional studies. Journal of

Child Psychology and Psychiatry, 41, 441–455.

Hinduja, S., & Patchin, J. (2008). Cyberbullying: An exploratory analysis of factors related to

offending and victimization. Deviant Behaviour, 29, 129–156.

Jackson, C. L., & Cohen, R. (2012). Childhood victimization: Modeling the relation between

classroom victimization, cyber victimization, and psychosocial functioning. Psychology of

Popular Media Culture, 1, 254–269.

Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the

digital age: A critical review and meta-analysis of cyberbullying research among youth.

Psychological Bulletin, 140, 1073–1137.

Menesini, E., & Nocentini, A. (2009). Cyberbullying definition and measurement. Journal of

Psychology, 217, 230–232.

Mishna, F., McLuckie, A., & Saini, M. (2009). Real world dangers in an online reality: A qualitative

study examining online relationships and cyber abuse. Social Work Research, 33, 107–118.

Patchin, J. W., & Hinduja, S. (2012). Cyberbullying: An update and synthesis of the research. In J.

W. Patchin & S. Hinduja (Eds.), Cyberbullying prevention and response: Expert perspectives

(pp. 13–35). New York: Routledge.

Pepler, D. J., Craig, W. M., Ziegler, S., & Charach, A. (1993). A school-based antibullying

intervention: Preliminary evaluation. In D. Tattum (Ed.), Understanding and managing

bullying (pp. 76–91). Oxford, UK: Heinemann Books.

Reijntjes, A., Kamphuis, J. H., Prinzie, P., & Telch, M. J. (2010). Peer victimization and

internalizing problems in children: A meta-analysis of longitudinal studies. Child Abuse &

Neglect, 34, 244–252.

Rigby, K., & Smith, P. K. (2011). Is school bullying really on the rise? Social Psychology of

Education, 14, 441–455.

Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying:

Its nature and impact in secondary school pupils. Journal of Child Psychology and

Psychiatry, 49, 376–385.

Statistics Canada. (2012). Census data products. Retrieved from http://www.statcan.gc.ca

Steeves, V. (2014). Young Canadians in a wired world, phase III: Life online. Ottawa:

MediaSmarts. Retrieved from http://mediasmarts.ca/ycww

Wang, J., Iannotti, R. J., & Luk, J. W. (2012). Patterns of adolescent bullying behaviors: Physical,

verbal, exclusion, rumor, and cyber. Journal of School Psychology, 50, 521–534.

Wang, J., Iannotti, R. J., & Nansel, T. R. (2009). School bullying among adolescents in the United

States: Physical, verbal, relational, and cyber. Journal of Adolescent Health, 45, 368–375.

Willard, N. E. (2007). Cyberbullying and cyberthreats: Responding to the challenge of online social

aggression, threats, and distress. Champaign, IL: Research Press.

Ybarra, M. L., & Mitchell, K. J. (2004). Online aggressors/targets, aggressors, and targets: A

comparison of associated youth characteristics. Journal of Child Psychology & Psychiatry,

45, 1308–1316.

Ybarra, M. L., & Mitchell, K. J. (2007). Prevalence and frequency of Internet harassment

instigation: Implications for adolescent health. Journal of Adolescent Health, 41, 189–195.

Ybarra, M. L., Mitchell, K. J., Wolak, J., & Finkelhor, D. (2006). Examining characteristics and

associated distress related to Internet harassment: Findings from the second youth Internet

safety survey. Pediatrics, 118, 1169–1177.

<authorblurb>Tanya Beran, PhD, is professor, Cumming School of Medicine, University of

Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4Z6, Canada; e-mail:

tnaberan@ucalgary.ca. Faye Mishna, PhD, is dean and professor, and Lauren B. McInroy,

MSW, is a PhD candidate, Factor-Inwentash Faculty of Social Work, University of Toronto,

Ontario. Shaheen Shariff, PhD, is associate professor, Integrated Studies in Education, McGill

University, Montreal, Quebec. This research was supported by a Standard Research Grant #410-

2007-0671 from the Social Sciences and Humanities Research Council of Canada.

Original manuscript received March 5, 2015

Final revision received April 17, 2015

Accepted April 22, 2015

Table 1: Prevalence of Cyber-Victimization and Cyber-Perpetration and Demographic

Characteristics of Sample (N = 1,001)

Full Sample Cyber-Victimization (n =

140)

Cyber-Perpetration (n =

80)

Characteristic n % n % n %

Gender

Male 513 51.25 62 44.28 50 62.50

Female 488 48.75 78 55.71 30 37.50

χ2 = 3.03 (1), p > .05 χ2 = 4.36 (1), p < .05

Age (years)

10 100 9.99 6 4.28 3 3.75

11 132 13.19 18 12.86 11 13.75

12 121 12.09 23 16.43 13 16.25

13 134 13.39 26 18.57 10 12.50

14 123 12.29 16 11.43 11 13.75

15 128 12.78 20 14.28 13 16.25

16 125 12.49 18 12.86 11 13.75

17 138 13.78 13 9.29 8 10.00

χ2 = 13.70 (7), p > .05 χ2 = 6.83 (7), p > .05

Area

Rural 729 72.82 35 25.00 18 22.50

Urban 272 27.18 105 75.00 62 77.50

χ2 = 0.42 (1), p > .05 χ2 = 0.99 (1), p > .05

Birth country

In Canada 900 89.91 122 87.14 73 91.25

Outside Canada 101 10.09 18 12.86 7 8.75

χ2 = 1.45 (1), p > .05 χ2 = 0.18 (1), p > .05

Language

English 753 75.22 106 75.71 65 81.25

French 201 20.08 26 18.57 11 13.75

Other 46 4.60 8 5.72 4 5.00

Missing 1 0.10 0 0.00 0 0.00

χ2 = 0.70 (1), p > .05 χ2 = 2.22 (1), p > .05

Note: Chi-square statistics were conducted on the entire sample.

Table 2: Mean Scores of Impact Subscales for Cyber-Victims and Nonvictims

Subscale Cyber-Victim

(n = 140)

Nonvictim

(n = 861)

F

df (1999)

Partial

Eta-

Squared

Risk 1.34 1.10 50.56* .05

Relationships 3.61 4.08 44.06* .05

Anger 2.28 1.78 56.15* .06

Physical Injury 1.23 1.05 55.72* .06

Anxiety 2.70 2.10 56.62* .06

Self-Esteem 4.13 4.44 22.50* .02

Eating Problems 1.36 1.12 53.68* .06

Drug Use 1.53 1.31 12.62* .01

Note: low scores for relationships and self-esteem scales indicate poor outcomes.

*p < .001.

Table 3: Rates of Cyberbullying in Combination with Other Forms of Victimization and

Perpetration

n % of

total

sample

(N =

1,001)

Cyber and Other

Victimization and

Perpetration Types

Correlation of

Cyber-

Victimization

and Other

Victimization

and

Perpetration

Types (N =

1,001)

n

%

Victimization 140 Victimization (n =

133)

Cyber and other

type

133 13.29 Verbal .61* 123 92.48

Cyber only 7 0.70 Social .58* 99 74.44

Other type only 122 12.19 Physical .44* 67 50.37

None 739 73.82 Racial .32* 37 27.82

Sexual comments .41* 27 20.30

Sexual behaviours .33* 21 15.79

Perpetration (n =

140)

Cyber .23* 36 25.71

Verbal .20* 41 29.28

Social .21* 26 18.57

Physical .18* 29 20.71

Racial .20* 17 12.14

Sexual comments .20* 15 10.71

Sexual behaviours .20* 11 7.86

*p < .001.