The first known, reported research of cyberbullying in the UK was conducted by the National Children’s Home (NCH, 2002, as cited in Li, 2007a), which was the first organisation to take up the issue of cyberbullying on a national level after pinpointing text bullying as a new and modern problem. This research reported that 25% of 11–19 year olds were victims of cyberbullying, whilst 11% engaged in cyberbullying others. Neither the sample size nor the definition of cyberbullying used in the research was reported. In 2005, in partnership with Tesco Mobile, the NCH (2005) surveyed 770 young people aged 11–19 years old, 97% of whom owned a mobile phone. The research found that 20% of the sample reported experiencing ‘some sort of digital bullying’ (p.3), which was lower than in their 2002 research. The cyber-perpetration rate—where respondents admitted
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to sending ‘a bullying or threatening message to someone else’ (p.3)—was 11%, notably consistent with their 2002 research. In the 2005 study, the researchers defined ‘text bullying’ as follows:
One or more unwelcome text messages that the recipient finds threatening, or causes discomfort in some way (p.3).
The choice of wording in the definition is important to consider; there is no reference to power imbalance, although this may be implied, but reference is made to repetition and to causing harm. Interestingly, the researchers use a definition that does not require more than one incident to qualify as text bullying, which is seemingly inconsistent with the apparent requirement of repetition, considered earlier. The definition is also narrow in that it only considered text messaging, which could have affected the prevalence rate reported, as other modalities, such as email and chatrooms were available at the time this study was conducted. However, this focus is understandable given the business context of Tesco Mobile.
The definition of—or even the use of the term—‘cyberbullying’ is not consistent across a number of studies in the field. For example, Mishna et al. (2010, p.364) asked participants to consider their ‘online behaviour’, but they may not have considered their behaviour as constituting ‘cyberbullying’ as this term was not explicitly mentioned. Consequently, Mishna et al. (2010) reported a high rate of cyber victimisation (49.5%) and cyber perpetration (33.7%). The definition of cyberbullying provided can influence the prevalence level, for example by including or excluding some aspect of it. Ofsted (2012) did not define bullying in their research, stating it was important to consider what pupils who had
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experienced such interactions defined as bullying for themselves. However, measuring bullying or cyberbullying without providing participants without some general guidance as to the meanings of the terms could affect the validity of the research.
The conceptual gap between researchers and participants concerning what cyberbullying means and how it is measured may also explain the differences in prevalence rates. In the absence (or even inclusion) of a definition, the perception of the participant in terms of what may be defined as cyberbullying, both in regard to victimisation and perpetration, may be different. For example, a participant who has been called a nasty name once may consider this cyberbullying (and maybe it is), but the name caller might not consider their behaviour as such. Alternatively, there may be some researchers who might discount this particular incident on the basis that there is no repetition. The perspective of an alleged bully might be that the alleged victim is sensitive, and a particular situation might have been intended as banter rather than intent to do harm, as discussed.
Ybarra and Mitchell (2004, p.1308) used ‘aggression’ as a measure rather than ‘cyberbullying’, and a timescale of ‘in the previous year’. They found that, of 1,501 participants in the USA aged 10–17 years, 4% had been a victim of aggression. This rate is lower than both NCH studies, but Ybarra and Mitchell (ibid) measured aggression as opposed to cyberbullying, which was what they claimed to be investigating in their research. The researchers also reported that 12% of their sample admitted to being aggressive towards someone else. This is similar to Campbell (2005), who reported a cyber perpetration rate of 11%, and is also consistent with the studies considered above. This shows the uniformity of
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cyber perpetration across studies so far, both in the UK and Australia. Campbell (ibid) surveyed 120 pupils aged 12 and 13 years old from Australia, 14% of whom reported being a victim of cyberbullying.
In their research in the UK, Smith et al. (2006), considered a wider, yet still exclusive, list of four technological modalities: email, text message, phone call and video clip. They asked their 92 participants aged 11–16 years old, enrolled across 14 schools in England, to consider whether they had been cyberbullied by any of the four listed mediums ‘at least once…over the last couple of months’ (p.2). Notably, the inclusion of ‘at least once’ goes against the requirement of repetition that many researchers advocate as part of the criteria of bullying. This inclusion of at least once can lead to tensions with the theoretical framework used for determining what constitutes cyberbullying. However, the researchers did provide the frequency categories of ‘at least once’ and ‘2–3 times per month’ (ibid) for participants to select and reported both sets of findings. Such categorisation is valuable in so far as a distinction can be made between those who have been cyberbullied only once—and therefore not meeting the repetition criterion claimed in the literature—and those who have been repeatedly targeted. The researchers found 22% of their sample had been targeted at least once, and 6.6% had been more frequent targets. The difference in these prevalence rates reinforces the importance of researchers being clear with how they define and measure cyberbullying, since if those experiences of only once were discounted on the basis of a lack of repetition, then the implication would have been a lower prevalence rate and most of the cybervictims’ voices and experiences would have been lost in measurement criteria.
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Research carried out in the USA by Patchin and Hinduja (2006), involving 384 participants under the age of 18, revealed a cyber perpetration rate of 11%, which is comparable to studies considered so far, and a cyber victimisation rate of 29%. The rate of victimisation is relatively higher, however, this could be due to the researchers’ methodology: the researchers posted their survey as a link on the website of a popular female vocalist, and they were open to anyone responding. This convenience sample might have led people who had been affected by cyberbullying to take part—and therefore could have biased the results. This was recognised by the researchers in the limitations to their study.
Research by Slonje and Smith (2008) reported an overall prevalence of cyber perpetration of 10.3%. The researchers surveyed 360 adolescents in Sweden, made up of 210 in secondary school and 150 in sixth form college. The researchers asked participants to consider their experiences ‘in the last couple of months’ (p.150), which coincided with the start of the school year, since the questionnaire was distributed in November. The modalities of cyberbullying considered in this research were limited to text message, email, phone calls and picture/video clips. The researchers intentionally limited their consideration to these methods following the relatively high prevalence rates through these methods in the work of Smith et al. (2006). This meant that other categories, such as chat rooms, instant messaging and websites, were not included owing to the researchers’ view of the apparently low incidence levels of these methods, combined with the desire to keep the questionnaire at an appropriate length. However, this represented a missed opportunity in collecting data on a wider range of modalities used in cyberbullying.
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The overall perpetration rate in Slonje and Smith (2008) of 10.3% was an aggregation of 11.9% amongst secondary school pupils (aged 12-15 years) and 8% of sixth form students (aged 15-20 years – it is possible to start sixth form in Sweden at age 15). With regard to cybervictims, the researchers reported an overall prevalence rate of 11.7% (an aggregation of 17.6% amongst secondary school pupils and 3.3% amongst sixth form students). These findings show prevalence rates for both cyber perpetration and cyber victimisation as lower amongst sixth form students when compared with secondary school pupils. Slonje and Smith (2008) argued that the reason for this was that those in sixth form were more interested in educational achievement. On this, they commented:
By this stage in education, only students interested in educational achievement are more likely to be attending…combined with the general age decline in reported victim rates, suggests that the problem is much more acute during the period of compulsory schooling, even for cyberbullying that escapes the school boundaries (p.152).
This comments suggests that age is a factor in bullying and cyberbullying; this is considered in more detail below in a separate section. Since Slonje and Smith (2008) conducted their research, the age of education participation has increased, and despite theirs being a Swedish study, the claim that those attending post-16 education are more interested in educational achievement and how this relates to prevalence of cyberbullying is an interesting one to investigate. This is because even though this claim may hold true, it may not be the case for all that attend college now, since they may be reluctant learners who now must participate in further education, as it is now compulsory, rather than having to for progression
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into higher education, or wanting to for the experience. In any case, Slonje and Smith’s (2008) research is important as sixth form students were included and their involvement in cyberbullying was reported separately. This is a rare and unfortunate feature in the literature reviewed.
In the USA, Englander (2009) considered both high school students (14-18 years old) and ‘college’ students (18 – 22 years old, UK equivalent of university) in their investigation into cyberbullying. The findings indicated that cyberbullying others and being a cyber victim occurred less in older age groups; however, this research considered only instant messenger (IM) as the modality for cyberbullying others and being cyberbullied. The prevalence rates reported for cyberbullying others was 23% of high school students and 3% for college students. In relation to being cyberbullied, 36% of high school students were cybervictims compared with a lower rate of 8% for college students. The prevalence rates reported in the research were not disaggregated into specific ages, making it difficult to determine the extent to which 16–19 year olds experienced cyberbullying as either a victim or perpetrator.
Englander stated that she was ‘surprised’ (p.8) to find that cyberbullying had followed young people from high school to college at this rate, as she did not anticipate it would. Englander (2009) hypothesised that the maturity of students in college compared to those in high school might be a reason for the reduced prevalence rate in victimisation. Englander (2009, p.8) commented:
While the frequency of cyberbullying diminished significantly following high school, it did not cease entirely.
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The age and relative maturity of participants could be one factor that explains the differences in prevalence rates across studies. The context in which the learning takes place is also important to consider as those who progress their education from secondary school into college also change education environments, but the impact of this factor has not been the focus of a separate investigation. This is one reason why the scope of this thesis considers only post-16 institutions that are separate from secondary schools. In recognising the limitations of the research, Englander (2009) stated that the findings should only be seen as suggestive, as the absolute number of cyberbullies was only 10 for college students, out of 330 students in college that participated.
In 2009, Cross et al. (2009) published their findings from the first Virtual Violence study conducted by the (former) charity ‘BeatBullying’. This research involved collecting data from 2,094 secondary school children aged 11–16 years old in England. Overall, 33% reported to cyberbullying someone, whilst 30% reported being a victim of cyberbullying. In an attempt to estimate cyberbullying prevalence on a national scale, the researchers extrapolated their findings. They used these figures to suggest that, of around 4,424,000 children in the UK aged 11–16, 1,327,000 have been cyberbullied, with one quarter of these (331,800) experiencing persistent cyberbullying (Cross et al., 2009, emphasis added). The researchers described persistent cyberbullying as lasting one or more years.
Three years later, in 2012, BeatBullying published their second Virtual Violence study (Cross et al., 2012). In this research, the researchers surveyed 4,600 school children aged 11–16 years old in England. The researchers asked participants whether they had been ‘deliberately targeted, threatened or humiliated by an
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individual or group through the use of mobile phones or the internet.’ (p.6). The researchers found 17% had cyberbullied others and 28% reported being a cybervictim, of which 23% of victim had experienced persistent victimisation. The overall cyber victimisation rate in this research represented a 2% decrease from their 2009 research, but in relation to cyberbullying perpetration, a more marked reduction was reported from the 2009 research, from 33% to 17%. The researchers suggested this difference might have been attributable to adolescents believing cyberbullying to be more socially unacceptable. However, this research alone did not provide conclusive evidence that the perpetration of cyberbullying was decreasing. It might have been the case that fewer participants admitted to perpetrating cyberbullying others, again due to the fact that they felt it was more socially unacceptable. Another explanation might have been the work of organisations attempting to reduce cyberbullying could be having an impact, although, this too, is speculation.
One of the highest rates in the literature of those reporting being cyberbullied was Tarapdar and Kellett (2011). The researchers found that 38% of 1,282 school-aged children in the UK in their sample reported being affected by cyberbullying as either a victim or a witness. Cyber perpetration was not measured, without any information provided as to why. The researchers recognised that this prevalence rate was higher than in other studies. This was not surprising when considering that the researchers adopted a different, wider measure; rather than measuring whether or not participants had been cyberbullied, they instead measured their exposure to cyberbullying (as a victim or witness). The researchers justified this measure as it:
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captured behaviour undermining aspects of well-being embedded in [the participants’] environment such as; pressures, circumstances, networks, self-perceptions and quality of relationships with peers (p.18).
The researchers did not disaggregate their findings into those who had been victims of cyberbullying from those who had witnessed it. Such a feature made comparison more difficult with other studies.
Further studies also indicated relatively higher levels of prevalence. In Canada, Beran and Li (2007) used a self-completion questionnaire to survey 432 school children aged 12–15, which, amongst other questions, asked, ‘Have harassing behaviours involving technology been directed towards you?’ (p.6), to which 58% participants answered ‘yes’ in regard to events occurring at least once. In terms of measuring perpetration, the researchers asked participants to answer the question, ‘Do you use technology to harass others?’ (ibid), to which 26% answered ‘yes’. However, the researchers measured harassment rather than cyberbullying, or at least made use of this terminology, and this could have affected how participants responded. Nevertheless, the researchers conflated the terms cyberbullying and harassment in their paper. Researchers need to ensure they are providing an appropriate definition that measures what they are claiming and seeking to measure. Such as approach adds to the overall validity of the research, and would facilitate more reliable comparisons between studies. High prevalence rates such as this were not so rare in the literature, as the following studies illustrate.
In Turkey, Aricak (2009) surveyed 695 undergraduates aged between 18 and 22 years old and found that 54.4% were cyberbullied ‘at least once in their lifetime’ (p.171) and 19.7% admitted to cyberbullying others in their lifetime. This relatively high prevalence level can be attributed—at least in some part—to
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measuring the lifetime experience of cyberbullying, rather than, for example, in the last couple of months. Clearly, stark differences in measures such as these are likely to have an impact on findings. The time of year at which data were collected might also affect the prevalence rate: for example, participants who are asked to consider ‘the last 2–3 months’ since September might answer differently to if they are/were asked to consider the same time period if the research had started in March, as in NCH (2005) or June, as in Smith et al. (2008) above. This may be because of the cycle of the academic year, and also that the development or breakdown of peer relationships throughout the course of the academic year might need time to occur. The use of different time periods to measure cyberbullying makes proper comparison between studies difficult.
Further studies of cyberbullying also highlighted inconsistencies in prevalence rates, and differences in the measures and timescales used. In an effort to illustrate this, Hinduja and Patchin (2012) randomly surveyed 4,400 participants aged 11– 18 years old in the USA and found 20% had been cyberbullied at some point in their lives. This contrasts with research in the same year by Slonje et al. (2012), who found that 10.6% of their sample reported being a victim of cyberbullying whilst 9.6% reported being a cyberbully in the last 2–3 months, which notably coincided with the start of the school year.
Vazsonyi et al. (2012) surveyed 25,142 school children aged between 9 and 16 years old across 25 European countries. Surprisingly, the researchers did not report prevalence figures for cyber victimisation or cyber perpetration in their research. They stated in their report:
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The rates of cyberbullying perpetration and victimization were not the goal of this article, and we do not report them because of space constraints (ibid, p.217).
This was an unhelpful feature of this article, as the reporting of prevalence levels would have enabled comparisons to be made with other studies. A cursory online search for further details of this research revealed that the overall prevalence rate for cyber victimisation was 7%, using a time period of over the last year. However, no information was found for the findings of each country. Furthermore, the researchers missed the opportunity to collect data from those aged over 16 years old, which would have been beneficial to include, given the relatively large scale of the research.
Prevalence has become a contentious issue. Olweus (2012a), in his article entitled ‘Cyberbullying: An Over-rated Phenomenon’, used data from very large samples: 450,490 students aged 7–18 years old across 1,349 schools in the USA, and 9,000 students from Norway, aged 11–16 years old. Olweus argued that claims made about cyberbullying in the media and research were greatly exaggerated and based on little empirical support. Olweus suggested that cyberbullying, when studied in a proper context, was a low-prevalence phenomenon. Cyberbullying was defined for participants as:
bullying performed via electronic means such as mobile/cell phones or the internet (p.521).
This definition omits the criteria of bullying to which Olweus subscribed in his earlier research (see Olweus, 1993; 1999). The table of data below summarises the research upon which Olweus based his claims.