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Does Cyberbullying overlap with school bullying when taking modality of involvement into account?

This is the post-peer-review, pre-copyedit version of the following article:
Kubiszewski, V., Fontaine, R. Potard, C., Auzoult, L. (2015). Does Cyberbullying overlap with school bullying
when taking modality of involvement into account? Computers in Human Behavior. 43, 49-57.
The final authenticated version is available online at:
Does Cyberbullying overlap with school bullying when taking
modality of involvement into account?
Violaine Kubiszewskia, Roger Fontaineb, Catherine Potardc, Laurent Auzoulta
Journal: Computers in Human Behavior
a EA 3188, Psychology Laboratory and FR EDUC, University of Franche-Comté, Besançon, France
b EA 2114, “Psychology of the Various Stages of Life (PAV)” laboratory, François Rabelais
University, Tours, France
c EA 6291, “Cognition, Health & Socialization laboratory, University of Reims, France
Corresponding author:
Violaine Kubiszewski
EA 3188, Laboratoire de Psychologie
Université de Franche-Comté
30, rue Mégevand
25 230 Besançon CEDEX
Does cyberbullying overlap with school bullying when taking
modality of involvement into account?
Education professionals and researchers are concerned by school bullying and cyberbullying
because of its repercussions on students health and the school climate. However, only a few
studies investigating the impact of school versus cyberbullying have systematically explored
whether student victims and perpetrators are involved in school bullying only, cyberbullying
only, or both. The aim of the present study was thus to examine the possible overlap, as well as
the similarities and/or differences, between these two forms of bullying when taking modality
of involvement into account. Individual interviews were conducted with 1422 junior high- and
high-school students (girls = 43%, boys = 57%, mean age = 14.3 ± 2.7 years). Results showed
that cyberbullying and school bullying overlapped very little. The majority of students involved
in cyberbullying were not simultaneously involved in school bullying. Moreover, results
indicated that psychosocial problems (psychological distress, social disintegration, general
aggression) varied according to the form of bullying. Victims of school bullying had greater
internalizing problems than cybervictims, while school bullies were more aggressive than
cyberbullies. Given the sizable proportion of adolescents involved in bullying (school and
cyber) and its significant relationship with mental health, the issue warrants serious attention
from school and public health authorities.
Keywords: Cyberbullying; School bullying; Media; Externalizing behaviors; Internalizing
1. Introduction
1.1. School bullying and cyberbullying
Bullying is a pervasive form of aggressive behavior that has been studied in many countries
(Craig et al., 2009; Menesini et al., 2012) and many different research areas, including
psychology, medicine and biology, etc. It is devastating for the school climate and more
especially for students wellbeing, leading it to be classified as a major public-health problem
in schools (Steffgen, Recchia, & Viechtbauer, 2013; Turner, Exum, Brame, & Holt, 2013).
Bullying is an intentional strategy engaged in by one or more student(s), who set up an
asymmetrical relationship with a classmate based on physical or psychological power. Olweus
(1993) identified three criteria to define bullying: 1) it is an aggressive behavior that is
intentional; 2) it is repetitive; and 3) it is an interpersonal relation characterized by a systematic
imbalance of power and domination. Four profiles can be identified in this kind of relationship:
neutral, victim, bully, and bully-victim, this last profile referring to students who are the victims
of bullying and who bully classmates other than their own aggressors. In schools, bullying can
manifest itself either in direct behaviors, be they physical (slapping, pushing, etc.) or verbal
(insults; etc.), or in indirect attacks (spreading rumors about a student and/or organizing his/her
social exclusion) (Stassen-Berger, 2007).
Over the past few years, a new form of bullying has emerged and caught the attention of
researchers and education professionals. The huge advances in digital technology have given
young people new means of communicating, but also brought some deleterious social
interactions such as cyberbullying (Kowalski & Limber, 2007). Most definitions of
cyberbullying come from definitions of school bullying. Thus, this conduct is often described
as an intentional aggressive behavior that takes place via new technologies, during which
groups or individuals hurt classmates who cannot easily defend themselves (Kowalski &
Limber, 2007; Law, Shapka, & Olson, 2010; Slonje, Smith, & Frisén, 2013). Cyberbullying
events can occur via cellphones or computers, by means of text messages, e-mails, online social
networks (e.g., Facebook®, Twitter®), chatrooms or blogs (Kowalski & Limber, 2007). Like
the bullying that occurs in school, the following four profiles have been identified: cyberneutral,
cyberbully, cybervictim and cyberbully-victim.
Although there are many cyberbullying strategies around, Cowie (2013) has identified some of
the most frequently occurring ones. Denigration, for instance, consists in posting false
information, gossip or rumors about a classmate on a blog or an online social network in order
to damage his/her reputation or friendships. Entering the mailbox or the personal online space
of a classmate and then usurping his/her identity to send or post material to get that person into
trouble or damage his/her reputation or friendships is another strategy that is used. Repeatedly
sending mean, insulting or threatening messages is also an example of cyberbullying.
Contrary to the consensus on the three criteria for defining school bullying, there is no single
clear and consistent definition of cyberbullying (Patchin & Hinduja, 2013). Moreover, many
different words are used in the literature to depict these online practices besides the term
cyberbullying, including online harassment (Wolak, Mitchell, & Finkelhor, 2007), electronic
bullying (Kowalski & Limber, 2007; Raskauskas & Stoltz, 2007), Internet harassment (Ybarra,
Espelage, & Mitchell, 2007; Ybarra, Mitchell, Wolak, & Finkelhor, 2006) and e-bullying (Lam
& Li, 2013). However, it should be noted that some of the researchers who initially used these
other notions, now employ the word cyberbullying (e.g., Kowalski, Giumetti, Schroeder, &
Lattanner, 2014; Ybarra, Boyd, Korchmaros, & Oppenheim, 2012).
The heterogeneity of the devices considered in studies of cyberbullying is another example of
the divergence in definitions. Some researchers have investigated behaviors via cellphones
and/or computers (Kowalski & Limber, 2007; Ortega, Elipe, Mora-Merchán, Calmaestra, &
Vega, 2009; Raskauskas & Stoltz, 2007), others have only taken one of these devices into
account (Aricak et al., 2008; Wolak et al., 2007).
These observations probably go some way to explaining the disparity in the figures for
cyberbullying prevalence. Estimated rates of cyberbullying vary from 11% to more than 50%
in studies considering cybervictimization, cyberperpetration, and both (Kowalski et al., 2014).
Despite the disparities in estimated cyberbullying prevalence around the world, one finding that
appears to be common and convergent is that involvement in bullying in cyberspace is
associated with psychosocial problems, problematic Internet use and poor school performances
(Gámez-Guadix, Orue, Smith, & Calvete, 2013; Kowalski & Limber, 2013).
In France, very few data are available concerning the number of students involved in
cyberbullying and the attendant psychosocial difficulties. Even so, this form of aggressive
behavior may well affect many French adolescents, as in France, 95% of 9- to 16-year-olds use
the Internet at home (Livingstone, Haddon, Görzig, & Òlafsson, 2011), thus increasing the risk
of being involved in cyberattacks (Kwan & Skoric, 2013).
1.2. Divergent considerations
Currently, one of the main questions being explored in the scientific literature concerns the
degree of overlap between cyberbullying and school bullying: do they constitute the same kind
of aggressive behavior, with cyberbullying being a modern and electronic form of school
bullying? Or are they two forms of aggressive behavior that need to be differentiated? Studies
have yielded very divergent results. Some of them suggest that cyberbullying is closely linked
to school bullying, possibly constituting an extension of it, whereas other studies indicate that
cyberbullying does not mirror school bullying. The arguments evoked in these studies cite
observed prevalence, as well as the psychosocial characteristics associated with the various
profiles in these two forms of bullying.
Studies suggesting that school bullying and cyberbullying considerably overlap (Juvonen &
Gross, 2008; Raskauskas & Stoltz, 2007; Smith et al., 2008; Wang, Iannotti, & Nansel, 2009)
have shown that victims in schools tend also to be victims in cyberspace, and cyberbullies are
often students who perpetrate bullying at school. For example, in the study by Raskauskas and
Stoltz (2007), 94% of cyberbullies were also school bullies, and 85% of cybervictims had a
victim profile at school. One year later, Juvonen and Gross (2008) published results revealing
similar tendencies: among the 1454 adolescents they sampled, 85% of those involved in
cyberbullying were also involved in school bullying. These data led the authors of these articles
to hypothesize that cyberbullying is the cyberspace extension of school bullying. In line with
this hypothesis, other surveys have revealed that students who are victims of school bullying
also engage in cyberbullying as bully, often attacking their school aggressors in cyberspace
(König, Gollwitzer, & Steffgen, 2010). The fact that more than half of all cyberbullies or
cyberbullies/victims are the target of bullying in schools further supports the idea of extension
(Ybarra & Mitchell, 2004).
Other arguments help to sustain the overlap hypotheses. Thus, some studies have shown that
students matching the different school and cyberbullying profiles share similar psychosocial
difficulties. As an illustration, it appear that being a cybervictim and being a victim of school
bullying are both significant predictors of social anxiety (Juvonen & Gross, 2008). Moreover,
both forms of bullying lead to the same distress for victims (Smith et al., 2008) and share
interrelated predictors (Casas, Del Rey, & Ortega-Ruiz, 2013).
However, all too few studies have carefully considered the modalities of bullying involvement,
that is, whether students engage in school bullying only, cyberbullying only, or both (Kowalski
et al., 2014; Olweus, 2012). Research in this area of investigation needs to control for the fact
that a student involved in cyberbullying could also be engaged in school bullying, but this
precaution is rarely taken. As a consequence, in many studies that fail to control for involvement
in both forms of bullying, the psychosocial problems found to be associated with cyberbullying
could, in fact, be mainly linked to school bullying (or vice-versa). As stated by Olweus (2012),
there is a need to find out the effects of cyberbullying independently of the possible effects of
school bullying. However, this issue has not received “much systematic and useful research
attention so far” (Olweus, 2012).
Whereas some studies have shown a close relationship between school bullying and
cyberbullying, others led to differentiate these two forms of aggressive behavior. Contradicting
the prevalence estimates mentioned above, Ybarra, Diener-West, and Leaf (2007) demonstrated
that most victims of cyberbullying are not victims at school. Similarly, Kowalski and Limber
(2013) found that most students involved in school bullying (77% of school victims, 74% of
school bullies and 52% of school bully-victims) are not concerned by cyberbullying at all.
Moreover, if cyberbullying were indeed an extension of school bullying, then homeschooled
young people would be protected from cyberbullying. However, cybervictimization rates do
not differ significantly between homeschooling and public/private schooling (Ybarra et al.,
This second consideration is also supported by research on the difficulties associated with
school bullying and cyberbullying. Thus, Wang, Nansel, and Iannotti (2011) found a differential
association of depression with each of these aggressive behaviors: in school bullying, both
victims and bully-victims had higher levels of depression than bullies, whereas in
cyberbullying, only cybervictims exhibited higher levels of depression, and to a far greater
degree. Moreover, in a study by Ortega et al. (2009), victims were revealed to be less
emotionally affected in cases of cyberbullying than in cases of school bullying. By contrast,
Hay, Meldrum, and Mann (2010) described higher levels of psychosocial problems in
cyberbullying than in school bullying. Again, it should be noted that most of the studies yielding
these kinds of results failed to control for the co-occurrence of bullying and cyberbullying.
1.3. Singularity of cyberbullying
Above and beyond the above-mentioned divergences regarding prevalence and psychosocial
problems, cyberbullying can be distinguished from school bullying on many other dimensions.
Although many cyberattacks are similar to those perpetrated in schools (threats, insults, etc.)
some have no equivalent in school bullying. This is the case for the creation of a virtual group
targeting a schoolmate or the hacking of someone’s personal space.
Moreover, cyberbullying is not limited in either time or place (Kowalski & Limber, 2007;
Raskauskas & Stoltz, 2007). While school bullying is often restricted to the time when the
victim is present in school, aggression via new technologies can occur at any time of the day or
night, and can pursue students into their homes, and even into their bedrooms.
In schools, bullies can often be identified and, to a certain extent, avoided. In cyberspace,
attacks can be rendered anonymous through the use of pseudonyms, causing considerable
distress for victims (Kowalski & Limber, 2007; Li, 2007; Mishna, Saini, & Solomon, 2009;
Smith et al., 2008; Ybarra et al., 2007).
Instant and massive dissemination is another particular feature of cyberbullying. Most episodes
of school bullying occur in front of only a few schoolmates (Olweus & Limber, 2010), whereas
in cyberspace, information and files can be spread quickly and remain available for a long time,
thereby increasing the number of potential bystanders (Kowalski et al., 2014; Li, 2007).
Lastly, unlike face-to-face relationships, the use of media such as cellphones or computers lacks
nonverbal communication (Kowalski et al., 2014). Cyberbullies cannot visualize the emotional
state and pain they inflict on their victims. Many authors have suggested that the lack of direct
feedback, combined with the possibility of retaining one’s anonymity, prompts individuals to
engage in even more hostile and aggressive behaviors (Postmes, Spears, & Lea, 1998). One of
the variants of Milgram’s famous experiment (1974) incidentally corroborates this notion. He
showed that when participants could not see or hear the suffering simulated by the actor to
whom they had to administer electric shocks, these mock electric shocks more often reached a
lethal intensity, compared with situations in which the participants received emotional
feedback. Cyberspace therefore fosters disinhibition, as neatly illustrated in a study by Aricak
et al. (2008), who found that 59% of students in their sample admitted to saying things online
that they would not say face to face.
1.4. Objective and hypotheses
Although several studies have already explored the overlap between school bullying and
cyberbullying, the results are often divergent and need to be confirmed by further quantitative
and qualitative analyses. Moreover, as mentioned earlier, many studies have investigated the
problems associated with school bullying and cyberbullying without controlling for the effect
of being involved in both forms of bullying.
The main aim of the present study was thus to measure the degree of overlap between school
bullying and cyberbullying regarding prevalence, and identify the psychosocial problems
associated with each of these aggressive behaviors. To this end, we took the precaution of
controlling for simultaneous involvement in school and cyberbullying, and carried out
quantitative and more qualitative analyses.
In the light of the literature mentioned in Section 1.3., showing the singularity of cyberbullying
at several different levels, we hypothesized that it constitutes a different form of aggressive
behavior that needs to be distinguished from school bullying.
More precisely, we tested the following hypotheses:
H1: The majority of students involved in cyberbullying are not simultaneously involved in
school bullying;
H2: The psychosocial characteristics (internalizing problems and externalizing behaviors) of
students involved in cyberbullying differ from those of students involved in school bullying.
2. Methods
2.1. Procedure
Adolescents from three junior high schools and two high schools in Tours (Indre et Loire,
France) and its suburbs took part in this study. All the students attending these five schools
were invited to take part (N = 1646). The occupation of the head of the household was used to
ascertain the adolescents’ social background, based on the classification system used by the
French authorities (High income A, High income B, Average, and Disadvantaged) (Ministry of
National Education, 2011). The data for the students invited to take part matched national
averages: 17.2% in the High income A group (national = 17.5%); 16.3% in the High income B
group (national = 12.7%); 26.1% in the Average group (national = 27.0%); and 40.5% in the
Disadvantaged group (national = 42.7%).
As recommended in France (Kubiszewski, Fontaine, Chasseigne, & Rusch, 2013), data were
collected during individual, anonymous interviews conducted by trained researchers who
administered the measures used in this study. Parents were informed and sent a passive consent
form via their son/daughter. On prearranged dates, those students who had agreed to take part
were given a 30-minute appointment by the school staff and were met individually in quiet,
private rooms that had been made available for the present study. Before each interview, the
adolescents were told that their participation was voluntary and that their responses would be
kept confidential. The protocol was submitted to and approved by the school health services of
the education district in the Indre-et-Loire département (France) and by the schools’ governing
board. Data were collected two weeks after the start of the second term of the 2010/2011 and
2011/2012 school years.
Of the 1646 students, 187 did not take part for two main reasons: parental or adolescent refusal,
and absence of the student on the day fixed for the interview. The final participation rate was
88.6% (N = 1459). A few (n = 37) of the participants were not sufficiently familiar with the
French language, and their data were therefore excluded from the analyses.
2.2. Sample
The final sample consisted of 1422 students (boys = 57%, n = 808; girls = 43%, n = 614) in
Grades Six to 12 of junior high school and vocational high school (n = 257 in Gr 6, n = 219 in
Gr 7, n = 227 in Gr 8, n = 233 in Gr 9, n = 165 in Gr 10, n = 171 in Gr 11 and n = 150 in Gr
12). Ages ranged from 10 to 18 years. Mean age was 14.3 years (SD = 2.7).
2.3. Measures
2.3.1. School bullying
The revised Bully/Victim Questionnaire (rBVQ; Solberg & Olweus, 2003) is a reliable and
valid self-report measure of bullying in France (Kubiszewski et al., 2013) that assesses the
participants’ experience of bullying at school. A detailed definition of bullying (see Solberg &
Olweus, 2003) is read aloud to the participant by the investigator. Students are asked to think
of bullying events that have occurred at school in the last 2-3 months. The rBVQ comes in two
parts: the first part has seven questions about situations experienced as a victim, and the second
has seven questions about situations experienced as a bully. The response alternatives are: “I
haven’t bullied/been bullied by other students”, “I have bullied/been bullied by other students
only once or twice”, “……2-3 times a month”, “……about once a week”, and “……several
times a week”. As recommended (Solberg & Olweus, 2003), we considered that an adolescent
was involved in bullying if she/he answered “2-3 times a month” or more. Students who
reported that they had both been bullied and had bullied other students 2-3 times a month or
more were identified as bullies/victims, those who reported that they had solely been bullied
were classified as victims, those who had solely bullied were classified as bullies, and those
who were neither victims nor bullies were considered to be neutral.
2.3.2. Cyberbullying
The cyberbullying questionnaire we used contained the items of the Electronic Bullying
Questionnaire by Kowalski and Limber (2007), and was partly modelled on the rBVQ. This
second questionnaire assessed the occurrence of aggressive behaviors such as insults, teasing,
exclusion, hacking or depreciation that can occur on the Internet or via a cellphone. Each student
was classified as a cybervictim, cyberbully, cyberbully-victim or cyberneutral. In accordance
with many studies, and the recommendations of some authors (Gámez-Guadix et al., 2013;
Juvonen & Gross, 2008; Kowalski & Limber, 2007; Li, 2007; Vieno, Gini, & Santinello, 2011;
Ybarra & Mitchell, 2004) the cut-of “only one or twice” was used to define an attack as
cyberbullying. The choice of this cut-off point was justified by two arguments. The first one
concerns possible prolonged exposure to a single attack. In cyberbullying, attacks can remain
online or in the cellphone for quite a while, and can be consulted frequently and at different
times by the victims, as well as by their schoolmates (Dooley, Pyzalski, & Cross, 2009;
Raskauskas & Stoltz, 2007; Slonje et al., 2013). The other argument concerns the numbers of
cyberbystanders, which can gradually increase if the single attack remains online, thus
increasing and prolonging the victim’s distress (Dooley et al., 2009).
2.3.3. Psychosocial problems
Internalizing problems (perceived social disintegration, psychological distress)
Perceived social disintegration was measured using six items developed by Solberg and Olweus
(2003) (e.g., ‘‘You feel less well liked than other students in your class”). There were six
possible responses to these statements: Doesn’t apply at all, Doesn’t really apply, Applies
somewhat, Applies fairly well, Applies well, and Applies exactly. A high score indicated a high
level of self-perceived social disintegration. Cronbach’s alpha for this variable was .76.
Students’ psychological distress (depression tendencies and low self-esteem) was assessed by
means of 11 items taken from two scales by Alsaker and Olweus (Alsaker, Dundas, & Olweus,
1991; Alsaker & Olweus, 1986) (e.g., “I am often sad without seeing any reason for it”). The
response options were the same as for the perceived social disintegration measure. Cronbach’s
alpha for this scale was .86.
Externalizing behaviors (aggression, antisocial behavior)
The aggression scale we used was developed by Solberg and Olweus (2003) (α = .84). There
were 6 items (e.g., ‘‘If I disagree with a boy or a girl my age, I easily get angry and yell at him
or her”). The response options were the same as before, with a high score indicating a high level
of aggression.
Next, adolescents were asked eight questions developed by Bendixen and Olweus (1999) to
assess antisocial behavior (e.g., “Have you ever scribbled on the school building?” or “Have
you ever skipped school a whole day?”). The response options were: Seldom or never,
Sometimes, Fairly often, Often, and Very often. High scores indicated high levels of antisocial
behavior. The reliability of the total scale was .84.
2.4. Data analysis
2.4.1. Prevalence of cyberbullying and comparisons with school bullying
The overlap between school bullying and cyberbullying was assessed by analyzing the
prevalence of students who were simultaneously involved in both. Then, to test the hypothesis
that these two forms of bullying concern different students, we looked at whether the majority
of students involved in cyberbullying were neutral in school bullying.
2.4.2. Comparison of psychosocial problems associated with cyberbullying and school
bullying, taking modality of involvement into account
Previous studies had failed to consider whether some students simultaneously engage in school
and cyberbullying. In order to overcome this major limitation, we therefore created new groups
of students, according to the different modalities of involvement (i.e., noninvolved,
involvement in school bullying, cyberbullying, or both). This allowed comparing the
psychosocial problems associated with each form of bullying. To make the analyses clearer,
adolescents who were engaged in both school and cyberbullying, but with a different profile in
each, were excluded from the analyses (Table 1).
Table 1
An exploratory approach using multiple correspondence analysis (MCA) enabled to bring to
light the relationships between the different response categories for the variables explored in
this study. It was thus possible to submit the data to an overall qualitative analysis that
simultaneously included the following variable response categories:
- For the bullying profile and modality of involvement variable (cf. Table 1), the response
categories were noninvolved, school victim, cybervictim, cyber&school-victim, school bully,
cyberbully, cyber&school bully, school bully-victim, cyberbully-victim, and cyber&school
- Data on the four psychosocial problems considered in this study were recoded to obtain
dichotomous variables: absence of the problem (mean scores < 90th percentile) or presence of
the problem (mean scores 90th percentile). Thus, for each of the four psychosocial variables
(perceived social disintegration, psychological distress, aggression, and antisocial behavior),
the response categories were presence of the difficulty (+) and absence of the difficulty (-).
Then, ANOVAs were used to conduct a quantitative analysis of internalizing problems and
externalizing behaviors associated with each form of bullying. Students involved in school
bullying, cyberbullying, or both were compared with each other, as well as with the
noninvolved group, by means of Tukey’s HSD post hoc test. This post hoc test is particularly
suitable when large numbers of 2 x 2 comparisons are undertaken as it reduces the risk of
committing Type-1 errors (Howell, 2008).
All the analyses were performed with STATISTICA Version 10. Regarding sample size,
statistical significance was set at p < 0.001.
3. Results
3.1. Prevalence of cyberbullying and comparisons with school bullying
In the sample, 366 students (26%) reported being involved in school bullying: 211 of them were
victims (15% of the sample), 109 were bullies (8% of the sample) and 46 were bullies/victims
(3% of the sample).
Regarding cyberbullying, 386 (27%) students reported being involved in this kind of activity in
cyberspace: 249 of them (18%) were cybervictims, 59 (4%) were cyberbullies and 76 (5%)
were cyberbully-victims.
Concerning the question of profile stability across cyber and school bullying, results showed
that these two forms of bullying overlapped very little. Overall, less than a quarter of students
had the same profile in both cyber and school bullying: 13% (n = 10) of cyberbully-victims
were also bullies/victims at school, 22% (n = 13) of cyberbullies were also school bullies; and
26% (n = 66) of cybervictims were also victims at school. Moreover, in the majority of cases,
adolescents involved in cyberbullying were not the same as those involved in school bullying:
62% (n = 155) of cybervictims, 60% (n = 36) of cyberbullies and 51% (n = 59) of cyberbully-
victims had a neutral profile in school bullying.
3.2. Comparison of psychosocial problems associated with cyberbullying and school bullying,
taking modality of involvement into account
As the students involved in cyberbullying were, in most cases, not the same as those involved
in school bullying, in order to gain a more accurate picture of the potential differences between
cyber and school bullying in terms of attendant psychosocial problems, we divided the students
into four groups according to their modality of involvement: noninvolved, cyberbullying only,
school bullying only, or both (cyber&school) (Table 1).
Three main dimensions were highlighted by the MCA, corresponding to 32.6% of the explained
: 13.3% of the inertia was explained by Dimension 1, 11.3% by Dimension 2 and 7.99%
by Dimension 3 (Fig. 1).
Results presented in Figure 1A show that Dimension 1 contrasted noninvolved students with
those involved in bullying. Psychosocial problems were grouped with the students involved in
bullying. Thus, all the profiles of the students involved in school bullying, cyberbullying or
both were associated with psychosocial problems (right side of Fig. 1A), unlike the noninvolved
student profile (left side of Fig. 1A). Regarding involvement in cyberbullying only, although
cyberbullies stood out on this dimension, the other two profiles (cybervictim and
cyberbully/victim) did not
(Fig. 1A).
Dimension 2 differentiated between school and cyber&school victims at one end, characterized
by internalizing problems (social disintegration and psychological distress), and school, cyber-
and cyber&school bullies, and school bully-victims at the other end, all characterized by
externalizing behaviors (aggression and antisocial behaviors). Cybervictims and cyberbully-
victims were not differentiated on the axis, nor were noninvolved students (Fig. 1B).
Dimension 3 distinguished between the two externalizing behaviors considered in this study.
Aggression was associated with school bullies, cyber&school bully-victims, and cyber&school
Greenacre (2010) recommends only retaining dimensions with eigenvalues > 1/Q, where Q is the number of
variables. As five variables were studied, we retained dimensions with eigenvalues above 0.2.
A response category with coordinates of less than 0.5 on one axis was not discriminated on the dimension
associated with this axis (Husson, Lê, & Pagès, 2009).
victims. By contrast, antisocial behavior was linked to bullies and bully-victims in the cyber
and cyber&school modalities.
Figure 1
Dimension 2 of the MCA revealed an association between externalizing behaviors, bullies and
bully-victims, and an association between internalizing problems and victims. As a
consequence, in the quantitative analysis (Table 2) intended to explore psychosocial problems
as a function of modality of involvement, we only considered internalizing problems for victims
and externalizing behaviors for bullies and bully-victims.
Table 2
Results in Table 2 show that school victims and those who were subjected to both forms of
aggression (cyber&school) had significantly higher levels of perceived social disintegration
than cybervictims (Cohen’s d = 0.75) and noninvolved students (Cohen’s d = 0.95), who did
not differ significantly from each other. Concerning psychological distress, school and
cyber&school victims had significantly higher scores (Cohen’s d = 0.6; 0.96; 1.22; 0.86). Once
again, cybervictims and noninvolved students did not differ significantly from each other on
this psychosocial problem.
For bullies, analyses show that those who are engaged in school and in cyber&school bullying
had significantly higher aggression scores than cyberbullies (Cohen’s d = 0.72 and 1.02) and
noninvolved students (Cohen’s d = 1.26 and 1.61), who did not differ significantly from each
other. Concerning antisocial behaviors, cyber&school bullies had the highest scores, and
noninvolved students the lowest ones. Contrary to the results for aggression, cyber- and school
bullies did not differ from each other on the level of antisocial behavior.
Then, school and cyber&school bullies/victims had higher aggression scores than cyber
bullies/victims (Cohen’s d = 1.18 and 1.36) and noninvolved students (Cohen’s d = 1.60 and
1.71). The level of antisocial behavior was higher for “cyber&school” bully-victims than for
cyberbully-victims (Cohen’s d = 0.81). Nonetheless, school bully-victims did not differ from
either cyber- or cyber&school bully-victims on levels of antisocial behavior.
4. Discussion
The abundant literature on cyberbullying published in the past few years highlights the
importance of improving our knowledge of this aggressive behavior and its underlying
processes. In this context, the aim of the present study was to examine the two forms of bullying
that researchers have been focusing their attention on, namely school bullying and
cyberbullying. More specifically, its purpose was to determine whether these two kinds of
aggression are comparable, or if they should instead be viewed separately, owing to substantial
differences. To answer this question, prevalence of these two forms of bullying and their related
psychosocial problems were examined.
4.1. Prevalence of cyberbullying and comparisons with school bullying
This study suggests that cyberbullying represents just as much a public health problem as school
bullying. Results showed that comparably elevated proportions of students were involved in
each of these forms of bullying: more than one in four. One possible explanation for this result
could be that cyberbullying is an extension of school bullying (Fontaine, 2009; Juvonen &
Gross, 2008). However, results did not confirm this notion, as more than half of the students
involved in cyberbullying were neutral at school.
The distribution of students according to their different cyberbullying and school-bullying
profiles is another aspect of the present study that led to distinguish between these two forms
of aggression. Even though total prevalence and victim prevalence were comparable for both
forms of bullying, more students saw themselves as bully-victims in cyberbullying than in
school bullying. This result is similar to the findings of many other studies, in which the
proportion of students claiming to be cyberbully-victims has been found to be close to or higher
than the proportion of cyberbullies (Kowalski & Limber, 2007; Li, 2007; Vieno et al., 2011;
Wang et al., 2009). A parallel can be drawn between these data and the discourse of students
invited to take part in focus groups on cyberbullying perceptions: for them, the disinhibition
generated by cyberspace and by the opportunity to remain anonymous encourages even students
perceived of as shy or vulnerable at school to persecute their schoolmates (Mishna et al., 2009;
Smith et al., 2008). However, this ease of aggression does not protect them from
cyberaggression, which could explain the higher rate of cyberbully-victims compared with the
rate of school bully-victims.
The first part of our results indicated that students involved in cyberbullying and students
involved in school bullying mostly belonged to different groups. On the whole, the results of
our analyses led to infer that cyberbullying is not an extension of school bullying, but instead
provides other students with new opportunities to become bullies.
Moreover, this study yielded data on the prevalence of cyberbullying and school bullying
among French adolescents that were lacking in the scientific literature.
4.2. Comparison of psychosocial problems associated with cyberbullying and school bullying,
taking modality of involvement into account
A second way of investigating the possible overlap of these two forms of bullying was to
determine whether the adolescents with the different cyberbullying profiles had the same
psychosocial characteristics as their school-bullying counterparts.
The MCA initially revealed an association between externalizing behaviors and all modalities
of involvement as perpetrators (cyberbullying, school bullying, or both cyber&school bullying).
However, Dimension 3 of the MCA, together with the results of the quantitative analyses,
appeared to nuance this association. Cyberbullies appeared to be less aggressive than school
bullies, but with comparable levels of antisocial behavior. Unlike face-to-face exchanges, the
use of a computer or cellphone is devoid of the nonverbal communication that is supposed to
convey the interlocutor’s emotional state. Many authors have suggested that the lack of direct
emotional feedback, combined with the possibility of remaining anonymous, engenders hostile
behaviors based on decreased inhibition (Livingstone et al., 2011; Postmes et al., 1998). This
dehumanization is a possible explanation for our result showing that cyberbullies are less
aggressive -but not less antisocial- than school bullies. Some adolescents who would not display
aggressive behavior in face-to-face interactions may feel sufficiently unconstrained by social
norms to indulge in these behaviors in cyberspace. This postulate is moreover reinforced by the
results of research demonstrating that young people who perpetrate cyberbullying feel less
guilty and have less of a bad conscience than the perpetrators of school bullying (Wachs, 2012).
The victims of bullying at school or in cyberspace were clearly differentiated. In the MCA, the
cybervictim modality was not differentiated on any of the three dimensions considered, whereas
the school victim modality made an important contribution on two dimensions. Moreover,
quantitative analyses showed that cybervictims were no different from noninvolved students
for perceived social disintegration, and exhibited lower levels of this internalizing problem than
school victims. Previous studies had led to the conclusion that many students know how to end
or restrict cyberbullying, by blocking or printing out the cyberbully’s messages, restricting
access to their personal spaces on online social networking sites, and so on (Agatston,
Kowalski, & Limber, 2007; Fenaughty & Harré, 2013). The EU KIDS ONLINE survey
published in 2011 seemed to confirm this conclusion, as more than half of the students assessed
had the skills needed to protect themselves on the Internet (Livingstone et al., 2011). We can
hypothesize that knowledge of these different strategies, added to the fact that cyberbullying
does not systematically occur directly in front of schoolmates in the playground, could explain
the differences observed between victims in cyberspace only and victims at school only,
regarding perceived social disintegration.
In our sample, cybervictims also had lower levels of psychological distress than school victims,
again lending weight to the idea that cyberbullying and school bullying are different
phenomena. This result is in agreement with a number other studies (Jose, Kljakovic, Scheib,
& Notter, 2012; Livingstone et al., 2011; Ortega et al., 2009; Ybarra et al., 2006). For example,
research by José et al. (2012) suggested that cyberbullying is less devastating on an emotional
level than school bullying. Moreover, a study by Ybarra et al. (2006) indicated that more than
half of the victims of cyberbullying were not emotionally impacted by this kind of aggression.
For the authors, attacks that occur at school are more difficult to avoid than those that occur in
cyberspace, and thus disturb young people more. It may be easier for students to stop this kind
of abusive relationship in cyberspace, using their knowledge of the many strategies mentioned
Nevertheless, it should be noted that, in the present study, cybervictims had higher levels of
psychological distress than noninvolved students. Despite the concrete protection measures that
can be implemented by adolescents, cyberbullying remains an unpleasant and destabilizing
experience for victims.
Finally, results regarding students involved in both school and cyberbullying, and who had the
same profile in each modality, showed that their levels of psychosocial difficulties were
comparable to those of students involved in school bullying only. Thus, for a minority of
students (about one adolescent in 20 in our sample), there is an overlap between cyberbullying
and school bullying, accompanied by weakened psychological adjustment.
The present study’s main limitations stemmed from its reliance on self-report
methodology, which can be subject to a social desirability bias when aggressive behaviors are
addressed. Furthermore, longitudinal research might be useful for testing the robustness of these
results. The present study also failed to take some combinations of bullying involvement into
account (e.g., students who are victims in school bullying and perpetrators in cyberbullying),
because of the small numbers of students who reported these combinations, plus our desire to
be clear. Thus, in future studies, larger samples will be needed to consider the data of students
involved in both school and cyberbullying but with different profiles. Besides, above and
beyond data on involvement in school and/or cyberbullying, young people’s perceptions of the
gravity of these attacks should be investigated, as they may moderate the association between
the two kinds of bullying (in cyberspace and at school) and psychosocial problems in different
ways. Finally, this was a person-centered study, and it dealt with students’ cyber and/or school
bullying profiles at a given point in time. In future studies, a longitudinal approach or structural
equation modeling would provide a rather broader view, by describing the relationship
independently of the person classification. This would be useful for gaining a more dynamic
view of possible changes in the students’ profiles in these two forms of bullying, or for obtaining
more complex interactive models of the factors associated with bullying and cyberbullying. It
would also enable to test moderating and mediating models of school and cyberbullying.
5. Conclusion
The results of this study lead to the conclusion that cyberbullying is a form of aggressive
behavior that is quite distinct from school bullying. These two forms were differentiated both
from the perspective of prevalence and from the perspective of the psychosocial problems
associated with the different profiles. In other words, not only were different students involved
in each of these forms of bullying, but they also had distinct characteristics.
These conclusions could contribute to the design of preventive interventions in schools. Given
the difference between school and cyberbullying, it is difficult to conceive that actions intended
to reduce school bullying could be as efficient in preventing cyberbullying. Because of the
undeniable presence of cyberbullying and the number of young people concerned, students,
together with their parents and teachers, should be regularly informed of the existence of this
practice and any changes it undergoes. Having the means to protect oneself and to deal with
school or cyberbullying are other aspects that need to be tackled, given the psychosocial
problems that can be generated. Despite the differences between these two kinds of bullying,
they need to be combatted simultaneously, through a combination of targeted actions.
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Table 1: Modalities of involvement in school and cyberbullying
Profiles in school bullying
Profiles in cyberbullying
School victim
School bully
Cyber &
School victim
Cyber &
School Bully
Note. “/” indicates combinations excluded from the analysis because they concerned so few
adolescents (n = 5-17) and because we wanted the data to be as clear as possible. It is common
for these combinations to be represented by only a few participants (see also Kowalski &
Limber, 2013)
Table 2: Mean scores (standard deviation) on internalizing problems and externalizing
behaviors as a function of bullying involvement modality for victims, bullies, bully-victims and
noninvolved students.
Cyber b
Cyber &
F [η²]
Tukeys HSD
Internalizing problems in
(n = 827)
(n = 154)
(n = 127)
(n = 66)
(3, 1170)
Perceived social
1.67 (.69)
1.84 (.68)
2.40 (1.03)
2.52 (1.09)
54.90*** [0.12]
a, b < c, d
Psychological distress
1.91 (.73)
2.22 (.82)
2.79 (1.06)
3.06 (1.09)
78.05*** [0.17]
a < b < c, d
Externalizing behaviors
in bullies:
(n = 827)
(n = 35)
(n = 64)
(n = 13)
(3, 935)
1.42 (0.47)
1.73 (0.63)
2.26 (0.82)
2.45 (0.77)
71.22*** [0.19]
a < b < c, d
Antisocial behavior
1.36 (0.37)
1.71 (0.53)
1.72 (0.50)
2.38 (1.02)
48.09*** [0.13]
a <b, c< d
Externalizing behaviors
in bully-victims:
(n = 827)
(n = 39)
(n = 20)
(n = 10)
(3, 892)
1.42 (.47)
1.72 (.53)
2.64 (.97)
2.98 (1.20)
73.29*** [0.20]
a <b< c, d
Antisocial behavior
1.36 (.37)
1.68 (.53)
1.93 (.58)
2.16 (.65)
35.27*** [0.11]
a < b~c ;
b < d ; c~d
Note. *** p < 0.001.
a Noninvolved = students involved neither in cyberbullying nor in school bullying
b Cyber only = students involved in cyberbullying but not school bullying
c School only = students involved in school bullying but not cyberbullying
d Cyber & School = students involved in both cyberbullying and school bullying
Figure 1: Exploratory analyses with multiple correspondence analyses of profiles and
modalities of bullying involvement, as well as psychosocial problems (perceived social
disintegration, psychological distress, aggressiveness and antisocial behavior).
Psychosocial problems (+ = presence; - = absence)
○ Profiles of students involved in cyberbullying only
● Profiles of students involved in school bullying only
◊ Similar profiles in school bullying and cyberbullying
Figure 1: Exploratory analyses with multiple correspondence analyses of profiles and
modalities of bullying involvement, as well as psychosocial problems (perceived social
disintegration, psychological distress, aggressiveness and antisocial behavior).
Psychosocial problems (+ = presence; - = absence)
○ Profiles of students involved in cyberbullying only
● Profiles of students involved in school bullying only
◊ Similar profiles in school bullying and cyberbullying
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This paper examines the impact of bullying and cyberbullying victimization on youth health behaviors (smoking, drinking, drugs, sexual intercourse) since the literature has not evaluated within the same framework whether bullying on school grounds and cyberbullying have distinct effects on such behaviors. Using within law heterogeneity in anti-(cyber)bullying laws during 2011--2019, I jointly estimate the decision to adopt a health (dis)accumulating behavior along with a multivariate treatment into four victimization groups: no victimization, bullying only at school, only cyberbullying, both bullying and cyberbullying. Identification of students who face one of the four distinct victimization types reveals heterogeneous effects: cyberbullying has stronger deleterious effects than bullying, and students who experience both accumulate even less health capital. Interestingly, female students respond to victimization by increasing their participation in addictive health behaviors (smoking, excess drinking, marijuana, other illicit drugs), whereas male students are prone to engaging in riskier sexual behaviors (multiple sex partners, unprotected sex). These effects remain even after accounting for mediating effects of student depression and truancy suggesting that public policies should invest in strategies to educate the student's social environment (peers, teachers, parents) about their role in preventing (cyber)bullying and in providing effective counseling for victimized students.
Technological developments enable adolescents to establish crowded peer groups through communication over extensive social networks that are difficult to supervise. However, inappropriate and unsupervised use of information and communication technologies can make adolescents the target of behaviors related to cyberbullying. Cyberbullying and victimization have severe negative effects on individuals' social, academic, and emotional lives. The negative effects of cyberbullying can be experienced more intensely during adolescence as a result of the changes occurring in the cognitive developmental field. The cyberbullying experienced in adolescence is also a predictor of bullying behaviors in social relations and professional lives as adults. The effects of bullying behaviors in adulthood can be an indication of the long-term effects of cyberbullying. Taking this as the starting point, this chapter aims to examine the types, prevalence, short and long-term effects of cyberbullying behaviors during adolescence.
Описывается триада «Преследователь – жертва – наблюдатель» в кибербуллинге. Представлен анализ структурных и динамических характеристик этих позиций в свете особенностей, обусловленных информационными технологиями. На основании анализа англоязычной литературы предлагается дифференцированное видение позиций, учитывающее особенности психической реальности, а также поведенческих проявлений, связанных с каждым типом. Анализируются реактивный и проактивно-агрессивный типы поведения, факторы виктимизации и этапы помогающего поведения наблюдателя. Предполагается, что такой подход может внести вклад как в эмпирические исследования, так и в программы профилактики кибербуллинга.
This chapter deals with serious chronic illnesses among students and what effects and consequences these have for the school. In this context, the school not only bears a great responsibility for paying attention to these problems, but also offers an optimally suitable framework for psychological interventions in dealing with grief.
A lockdown was imposed in Wuhan, China, the alleged epicentre of the COVID-19 outbreak, on 23 January 2020. Rattled by the short notice and severity of the restrictions, many grabbed the last opportunity to escape, an act widely criticised on Weibo, China’s popular microblogging site. This study aims to examine the unsavoury discourse deployed by Weibo users to express impoliteness and discursively construct negative identities of the lockdown escapees. Posts on Weibo criticising, reporting and threatening the escapees were analysed, revealing that the escapees were dehumanised through vivid animal metaphors to highlight their irresponsibility and call for their punishment. Animal metaphors can co-occur with various impoliteness triggers to intensify offensiveness, heightening the hostility of interlocutors towards a target. This use of metaphors also showcases online users’ anger, distrust, and hatred towards the escapees, their solidarity-seeking behaviour online and their irrationality.
When somebody, usually a teenager, abuses or harasses individual on the internet and other digital places, mainly on social networking platforms, this is termed as cyberbullying. Cyberbullying, like all types of bullying, produces psychological, emotional, and physical distress. Every individual's reaction to being bullied is diverse, but research has discovered certain common patterns. In a recent study, we introduced a technique called Hybrid Firefly Artificial Neural Networks (HFANN) to combat cyberbullying. Nevertheless, without considering the sentiment analysis features, accuracy of cyber bullying identification is lowered in this study. The Sentiment Analysis and Deep Learning based Cyber Bullying Detection (SADL-CDD) approach is used in the suggested research approach to address this issue. The punctuations, urls, html tags, and emoticons from the input tweet comments are removed first in this study project. Sentiment feature extraction is performed after pre-processing to improve classification accuracy. The Modified Fruit Fly Algorithm (MFFA) is used to choose the best features from the extracted features. Following feature selection, cyber bullying detection is carried out using a Hybrid Recurrent Residual Convolutional Neural Network (HRecRCNN). The experimental outcome of this study indicates the efficiency of the suggested approach. In comparison to current algorithms, the SADL-CDD method delivers improved classification performance with respect to reduced time complexity, greater precision, recall, f-measure, and accuracy.
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Although the Internet has transformed the way our world operates, it has also served as a venue for cyberbullying, a serious form of misbehavior among youth. With many of today's youth experiencing acts of cyberbullying, a growing body of literature has begun to document the prevalence, predictors, and outcomes of this behavior, but the literature is highly fragmented and lacks theoretical focus. Therefore, our purpose in the present article is to provide a critical review of the existing cyberbullying research. The general aggression model is proposed as a useful theoretical framework from which to understand this phenomenon. Additionally, results from a meta-analytic review are presented to highlight the size of the relationships between cyberbullying and traditional bullying, as well as relationships between cyberbullying and other meaningful behavioral and psychological variables. Mixed effects meta-analysis results indicate that among the strongest associations with cyberbullying perpetration were normative beliefs about aggression and moral disengagement, and the strongest associations with cyberbullying victimization were stress and suicidal ideation. Several methodological and sample characteristics served as moderators of these relationships. Limitations of the meta-analysis include issues dealing with causality or directionality of these associations as well as generalizability for those meta-analytic estimates that are based on smaller sets of studies (k < 5). Finally, the present results uncover important areas for future research. We provide a relevant agenda, including the need for understanding the incremental impact of cyberbullying (over and above traditional bullying) on key behavioral and psychological outcomes. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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Cette étude a pour but d’évaluer la validité de l’adaptation française du Questionnaire Agresseur/Victime révisé (Bully/Victim Questionnaire revised [BVQr]) administré au cours d’entretiens individuels avec des adolescents. Cet outil permet d’identifier l’implication d’un élève dans une relation de harcèlement scolaire (bullying) en déterminant son profil (« victime », « agresseur », « agresseur/victime », « neutre »). Les 1422 participants sont issus de collèges et lycées français. Le modèle testé (à deux facteurs) satisfait les critères d’adéquation considérés. Les coefficients alpha de Cronbach témoignent d’une bonne consistance interne. L’implication dans du bullying en tant que victime est significativement corrélée avec la détresse psychologique et les difficultés d’intégration sociale. L’implication en tant qu’agresseur est significativement corrélée aux comportements externalisés. Les corrélations croisées indiquent une validité discriminante satisfaisante. Enfin, les caractéristiques psychosociales des élèves impliqués dans les différents profils du bullying sont conformes à celles attendues. Ce travail rend compte d’une bonne validité de l’adaptation française du BVQr pour mesurer l’implication des adolescents dans le bullying.
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Agnew’s general strain theory (GST) has received significant empirical attention, but important issues remain unresolved. This study addresses three such issues. First, the authors examine the effects of bullying—a source of strain that may be consequential, but that has been neglected in GST research to date. Second, drawing from recent research on deliberate self-harm among adolescents, the authors examine the effects of bullying not just on externalizing deviance (aggressive acts committed against others and their property) but also on internalizing deviance directed against the self. Third, the authors examine these relationships separately for males and females to assess sex differences in responses to strain. These three issues are examined with self-report data collected from a sample of middle and high school students in a Southeastern state. The analysis reveals that bullying is consequential for both externalizing and internalizing forms of deviance and that these relationships are in some instances moderated by sex.
Despite significant overlaps between victim status in traditional forms of bullying and cyberbullying, and qualitative results about self-reported reasons for cyberbullying, the role of revenge and retaliation as a motive to engage in acts of cyberbullying has not yet been examined systematically. As a first step, this study investigates whether and to what extent traditional victims, when they become cyberbullies, actually choose their former (traditional) perpetrators as targets of their own cyberbullying behavior. Furthermore, the impact of individual differences in relevant traits, such as vengefulness and justice sensitivity, on the choice of cybervictims is examined. Data from 473 students were collected via an online survey. Of these, 149 were identified as traditional victims/cyberbullies. Results show that traditionally bullied students indeed tend to choose their former perpetrators as cybervictims, and that individual differences play a role in the choice of their victims. Implications for further research, as well as for interventions and prevention programmes, are discussed.
Avec ce manuel, le lecteur dispose d’un équipement complet (bases théoriques, exemples, logiciels) pour analyser des données multidimensionnelles. Son contenu correspond à l’enseignement d’analyse de données proposé à Agrocampus. Il a été conçu pour des étudiants qui ne se destinent pas aux métiers de la statistique mais qui auront à traiter des données dans leurs emplois. Cette édition prend en compte l’actualisation du logiciel R.
The present study investigated the stabilities of and interrelationships among traditional (i.e., face‐to‐face) bullying, traditional victimhood, cyber bullying, and cyber victimhood among adolescents over time. About 1,700 adolescents aged 11–16 years at Time 1 self‐reported levels of both bullying and victimization in four contexts (in school, outside of school, texting, and on‐line) annually for 2 years. Results indicated that all four dynamics were moderately stable over time. The following variables were found to bidirectionally reinforce and predict each other over time: traditional bullying and traditional victimization; traditional bullying and cyber bullying; and traditional victimization and cyber victimization. These results indicate that bullying and victimhood in both face‐to‐face and cyber‐based interactions are related but not identical interpersonal dynamics.