Download - Cyberbullying Among High School Students
Cyberbullying 1
RUNNING HEAD: CYBERBULLYING
Cyberbullying among high school students: Cluster analysis of sex and age differences and the
level of parental monitoring.
Paper accepted for publication in the International Journal of Cyber Behavior, Psychology and
Learning (IJCBPL). (in press)
Baylor University
One Bear Place 97301
Waco TX 76798-7301
Ikuko Aoyama [email protected]
Tel. 254-652-5356 /Fax. 254-710-3265
Lucy Barnard-Brak [email protected]
Tel. 254-710-4234 /Fax. 254-710-3265
Tony Talbert [email protected]
Tel. 254-710-7417 /Fax 254-710-3160
Cyberbullying 2
Abstract
Bullying, a once typical occurrence in schools, has gone digital. As a result, cyberbullying has
become ever more present among youth. The current study aimed to classify high school
students into four groups based on their cyberbullying experiences and to examine the
characteristics of these groups based on the sex and age of the participants and the level of
parental monitoring. Participants were 133 high school students located in central Texas. A
cluster analysis revealed four distinct groups of students who were: ―highly involved both as
bully and victim,‖ ―more victim than bully,‖ ―more bully than victim,‖ or ―least involved.‖
Significantly more girls and more students in lower grades were classified into the ―more victim
than bully group‖ while older students were more likely to be classified into the ―more bully than
victim‖ group. No significant differences were found between cluster membership and the
degree of parental monitoring.
Cyberbullying 3
Introduction
Cyberbullying among youth has been becoming a serious societal and educational
concern internationally. The media, educators, and parents have been paying great attention to
the phenomena for the past few years because researchers from various countries have revealed
the relatively high prevalence of cyberbullying among youth. For example, approximately 30%
of youth (N=384) surveyed in 2004 reported their victimization, and 11% have cyberbullied
others (Hinduja & Patchin, 2009). The more recent study shows that 72% of the youth
(N=1,454) were victimized at least once in the past year, and 13% of them reported frequent
victimization (Juvonen & Gross, 2008).
Theoretical Background of Cyberbullying
Researchers have linked bullying behaviors with theories of human behaviors and
communication. For example, the well- known theory is the social cognitive theory, which
argues that adolescents model their parents or friends’ aggressive behaviors (Duncan, 2004;
Mouttapa, Valente, Gallaher, Rohrbach, & Unger, 2004). ―The effect [of the model] will be
stronger if the observer has a positive evaluation of the model, for example, perceive, him/herself
as tough, fearless, and strong‖ (Olweus, 1993, p. 43). In other words, observing an aggressive
model makes aggressive behaviors less inhibited if observers see a model getting rewarded for
the aggressive actions. In these cases, the reward means the bullies’ victory over the victims.
Thus, all forms of bullying may be learned actions (Hinduja & Patchin, 2008) because bullying
is a type of peer aggression.
One theoretical model that can possibly explain cyberbullying is desinhibited behavioral
effects on the Internet (Hinduja & Patchin, 2009; Kowalski et al., 2008). Joinson (1998) argues
Cyberbullying 4
that people in cyberspace behave in a way they do not in real life because of the effects of
disinhibition: ―Disinhibition means that normal behavioral restraint can become lost or
disregarded‖ (Mason, 2008, p. 328). For example, researchers have demonstrated that people
tend to behave more bluntly when communicating by e-mail or in other electronic venues.
Moreover, misunderstandings, greater hostility, aggressive responses, and nonconforming
behaviors are more likely in computer-mediated communication than in face-to-face
communication (McKenna & Bargh, 2000). In face-to-face interaction, people read the
emotional reactions of others and modulate their own behavior in response to the consequences
(Kowalski et al., 2008). In other words, human behaviors are inhibited by social situations and
public evaluations (Joinson, 1998). In cyberspace context, on the other hand, people have less
social, contextual, and affective signs than in face-to-face communication; thus, they are less
sensitive and remorseful for the types of behaviors that they exhibit (Mason, 2008). In
cyberbullying, perpetrators have no direct social disapproval and punishment for engaging in
bullying others and do not see that victims suffer (Willard, 2007). As a result, their behaviors are
often disinhibited and become ruder, harsher, and more difficult to control (Hinduja & Patchin,
2009).
Disinhibition effects are caused by deindividuation (Joinson, 1998). Deindividuation can
occur when accountability cues are reduced; in other words, anonymity can reduce concerns
about others’ reactions (Joinson, 1998). Deinvididuation also occurs when an individual’s self-
awareness is blocked or reduced by external factors because ―it decreases the influence of
internal (i.e., self) standards of or guides to behavior, and increases the power of external,
situational cues‖ (McKenna & Bargh, 2000, p. 61- 62).
Students’ Status in a Peer Group
Cyberbullying 5
In traditional bullying studies, differences among children have been conceptualized
through categorizing them into four groups: bullies, victim, bully/victims, and not involved
(Espelage & Holt, 2007). However, little research examining the group differences has been
conducted in cyberbullying research. The discrimination among groups is important because
these subgroups exhibit different patterns of aggression and behavioral and internal problems
(Espelage & Holt, 2007). Understanding the group differences is also necessary to deliver an
effective intervention.
Many cyberbullying studies are currently focusing either victims or bullies. However,
bully/victim students who are involved as both bullies and victims are often overlooked.
Researchers have suggested that a child’s status as a bully or victim could be easily
interchanged; for instance, 35.7% of bullies reported experienced being victimized within the
year, and 15.5% of them were currently being victimized as well (Morita et al., 1999). In
addition, traditional bullying studies have shown that bully/victim students have the highest risk
of behavioral and emotional problems because bully/victims experience double negative effects
as both bullies and victims (Marini, Dane, Bosacki, & Ylc-Cura, 2006). In fact, their evaluation
by teachers and peers are low. For example, bully/victims are seen as ―more clumsy and
immature than their peers [and] not only do peers find it difficult to associate with these children,
but teachers and other school personnel frequently report that these children are among the most
difficult to work with in school settings‖ (Kowalski, Limber, & Agatson, 2008, p. 32). Similarly,
bully/victims report a higher rate of depression, somatization, and psychiatric referrals than all of
their peer groups (Ybarra & Mitchell, 2004). Considering all of the fact, the researchers of the
present study believe it is important to identify the distinct subgroups of youth who are
involved/not involved with cyberbullying.
Cyberbullying 6
Sex Differences in Cyberbullying
In traditional bullying, studies have shown that boys were more likely than girls to be
involved in bullying overall; however, more girls experience indirect and psychological types of
bullying such as rumor spreading and social exclusion (Kowalski et al, 2008; Ma, 2002; Olweus,
1993; Raskauskas & Stoltz, 2007). Therefore, researchers have pointed out that cyberbullying is
more prevalent among girls (Anderson & Sturm, 2007; Willard, 2007) because this
cyberbullying is text-based, and girls tend to be more verbal than boys (Hinduja & Patchin,
2009). However, research findings are inconsistent across studies. Some studies found that boys
were more likely to engage in cyberbullying than girls (Dehue , Bolman, & Völlink, 2008;
Katzer, Fetchenhauer, & Belschak, 2009; Shariff, 2008), and girls were more likely to be
victimized online (Dehue et al., 2008; Smith et al., 2008). On the other hand, Li (2006) argues
that more boys reported being cyberbullied than girls. Other researchers, however, find no
significant sex differences (Arıcak, 2009; Beran & Li, 2005).
Age Differences
Research findings on age differences of youth cyberbullying experiences also vary. While
studies in Britain and Canada found no age effects (Beran & Li, 2005; Smith et al., 2008), other
studies identified differences. For example, researchers have argued that cyberbullying peaks
later in middle school or in high school (Hinduja & Patchin, 2009; Kowalski & Limber, 2007).
A survey conducted by Pew/Internet American Life Project (N=935) also reveals that older girls
aged 15 to17 are more likely to report being bullied online than any other age and gender group
(Lenhart, 2007). Likewise, Japanese high school students reported that cyberbullying was more
prevalent among middle school students (Aoyama & Talbert, 2009). In contrast, primary pupils
Cyberbullying 7
in the Netherlands reported their cyberbullying experiences more often than secondary students
did (Dehue et al., 2008).
Gap between Adults and Youth
In spite of the high prevalence of cyberbullying among adolescents, studies suggest that
adults underestimate the incidents. For example, ―The percentage of parents reporting that their
child was engaged in bullying on the Internet or via text messages was considerably lower
(4.8%) than the percentage of children reporting to be engaged in bullying on the Internet or via
text messages (17.3%)‖ (Dehue et al., 2008, p. 219). This finding is consistent with a study by
Bradshaw, Sawyer, and O-Brennan (2007) which indicates that adults estimate the incidents of
traditional bullying: over 49% of children (N=15,185) reported being bullied at least once during
the past month; whereas, 71.4% of staff (N=1,547) estimated that 15% or less of the students at
their school were frequently bullied. In addition, only less than 1% of staff members reported
bullying rates similar to those indicated by students. These findings suggest that adults may not
fully aware of bullying/cyberbullying incidents happening to their children.
Parents’ Monitoring Roles
Traditional bullying studies indicate the association between parental monitoring and
bullying behavior. For example, parents with permissive parenting are less likely to acknowledge
their children’s activities (Marini et al., 2006), and parents of bully-victims often display the
indifferent-uninvolved parenting style, neglect, and inconsistently monitor their children
(Duncan, 2004). In addition, Wienke Totura et al (2009) found that the level of adult monitoring
negatively correlated with bullying behaviors.
Cyberbullying 8
The parental monitoring plays key roles because cyberbullying often occurs at home;
however, parental monitoring strategies do not seem to work well. Mason (2008) said about 30%
of adolescents use the Internet for 3 hours or more daily, and during these hours, more than 50%
of them reported poor parental monitoring. Rosen (2007) also pointed out many parents ―were
unsure what their children were doing online, but didn’t know how to approachthe subject with
their teens‖ (p. 80). Similarly, McQuade et al. (2009) found that 93 percent of parents stated they
established Internet rules for their child’s; however, 37 percent of children reported being given
no rules from their parents on the Internet activity. Likewise, Rosen (2007) found that even
though the majority of parents set limits on their children’s Internet use, they are not actually
monitoring those limits. These findings indicate the difficulty of effective parental monitoring. In
fact, Mesch (2009) reanalyzed a large secondary data of nationally representative youth sample
(N=945) and found that parental mediation and monitoring are not very effective.
Purpose of the study
Cyberbullying research is still in its infancy; thus, research findings are inconsistent
across researchers. It is possible that these mixed findings are due to the lack of knowledge
regarding the level of students’ involvement in cyberbullying. Therefore, the purpose of the
present study was to identify the subgroups of youth who are involved with cyberbullying and to
examine any sex and age differences among these groups. The research questions consist of the
following:
1) Can we classify the students based on their cyberbullying experiences?
2) Are there any sex differences among the groups?
3) Are there any age differences among the groups?
4) If there are age differences among the group, does the association indicate a trend?
Cyberbullying 9
5) Are there any significant relationships between the degree of parental monitoring and
cluster membership?
Method
Participants
Participants were selected from a public high school located in central Texas: 133 high
school students (Male = 52.7%, n = 68, Female = 47.3%, n = 61). Students who were taking
computer classes were invited to participate in the study. 89.5% (n = 119) of the participants
were Caucasian, and the remaining 21% (n = 14) were African American, Hispanic, and others.
Of these students, 57 (43.2%) were ninth graders, 20 (15.2%) were tenth graders, 41 (31.1%)
were eleventh graders, and 14 (10.6%) were twelfth graders, and their mean age was 15.7 years
old (SD = 1.25). The school is located in rural area with 85.7% of the students in this school
being Caucasian (SchoolDataDirect, 2009). Thus, the sample of this present study may be
considered representative as a sample of this high school.
Instrument
The self-report survey uploaded on a web-based online survey management tool was
used. The survey was modified from the one created by Willard (2007) and Smith, Mahdavi,
Carvalho, and Tippett (n.d.). The survey consists of 55 questions, including demographic
information, and open-ended questions. Sixteen questions were used for the analysis of the study.
The duration of the survey was approximately 20 minutes to complete. The participants accessed
the online survey during computer classes at the high school.
Measures
Cyberbullying offending behaviors
Five questions assessed the frequency of cyberbullying offending behaviors (e.g., ―In the
last six months, have you sent mean or nasty messages to someone?‖, and ―In the last six months,
Cyberbullying 10
have you put down someone else online by sending or posting cruel, gossip, rumors, or other
harmful materials?‖). These questions addressed various types of cyberbulllying such as text
message, email, and Internet community. Response choices were ―Yes, 1 to 4 times‖ (coded as
1), ―Yes, more than 5 times‖ (coded as 2), and ―No‖ (coded as 0). Higher scores indicate more
frequent offending behaviors. Cronbach’s alpha was 0.69, and its value is close to the
Cyberbulling Offending Scale (Cronbach’s alpha = 0.76) developed by Hinduja & Patchin (2009).
Cyberbullying victimization
A self-report cyberbullying victimization, including name-calling, social exclusion,
rumor spreading, was measured by the six questions (e.g., ―In the last six months, have you
received online messages that made you fear for your safety?‖ and ―In the last six months, have
you been put down online by someone who has sent or posted cruel, gossip, rumors, or other
harmful materials?‖). Response choices and coding system were the same as above. Higher
scores indicate more frequent victimization. Five questions measured offending behaviors, and
six questions measured victimization; thus, the scores were standardized to provide a standard
metric. Cronbach’s alpha was 0.72, and its value is close to the Cyberbulling Victimization Scale
(Cronbach’s alpha = 0.73) developed by Hinduja & Patchin (2009).
Parental monitoring
Two questions assessed the level of parental monitoring (e.g., ―How often do you discuss
what you are doing online with your parents?‖). Response choices were ―Frequently‖ (coded as
2), ―Occasionally‖ (1), and ―Never‖ (0).
Data Analysis
First, characteristics of the data distribution were evaluated (e.g. skewness). According to
the z-score, both offending and victimization measure data indicated a moderate degree of
Cyberbullying 11
positive skeweness; thus, the data were transformed by square rooting the composite score. This
data transformation procedure has been recommended by Tabachnick and Fidall (2001) when
moderate positive skewness was observed. Second, a k-mean cluster analysis was performed to
classify students into four groups. The number of cluster was determined based on a review of
extant literature. Previous research utilized a k-mean cluster analysis as an appropriate analysis
(Espelage & Holt, 2007). Subsequently, a multivariate analysis of variance (MANOVA) was
conducted to ensure that the cluster analysis had classified the participants accurately. In
conducting our MANOVA, a Box’s test was also conducted to test the assumption of the equality
of covariance matrices. Third, a 2 (sex) x 4 (groups) chi square (χ2) analysis was conducted to
examine the presence of any sex differences. As a measure of effect size, a Phi coefficient (Φ)
was also calculated. Phi values of 0.1, 0.3, and 0.5 may be interpreted as small, medium, and
large association between groups respectively (Green & Salkind, 2004). Then, standardized
residuals in each cell greater or lesser than 1.96 were considered as being statistically significant
at the 0.05 level or less. Fourth, a one-way analysis of variance (ANOVA) was performed to
examine the relationship between age and cluster group membership. A Levene’s F test was
conducted to examine if the assumption of homogeneity of variances was met. Finally,
nonresponse items were handled by a pair-wise deletion method because missing data consisted
of only about 10% for both offending and victimization measures. SPSS 16.0 was used for all
data analyses.
Results
Correlations
A statistically significant, positive correlation between offending and victim scale score
was found (r = 0.62, p < 0.01). This result indicates that students who cyberbullied others are
Cyberbullying 12
likely to be also victimized. In addition, a statistically significant, negative correlation between
offending scale score and sex was observed (r =- 0.24, p < 0.01). This result indicates that girls
are less likely than boys to cyberbully others. As for the level of parental monitoring, age showed
statistically significant, negative correlation (r =- 0.24, p < 0.01) and sex showed a statistically
significant, positive correlation (r =0.21, p < 0.05). These results indicate that the level of
parental monitoring is higher for younger children and girl. The level of parental monitoring was
also negatively correlated with offending scale score (r =- 0.19, p < 0.05). This result indicates
that students are less likely to cyberbully others as the level of parental monitoring increases.
There were no statistically significant correlations among sex, age, and victimization scale score.
Cluster Analysis
It was hypothesized from literature on traditional bullying that four clusters would
emerge. Cluster one was termed the ―least involved‖ group, and scored the lowest on both
offending and victim scale scores. The group included 68 students (51.1 % of the sample).
Cluster two was termed the ―highly involved both as a bully and a victim‖ group, and scored the
highest scores on both offending and victim scale scores. The group included 17 students
(12.8 % of the sample). Cluster three was termed the ―more bully than victim‖ group and scored
the second highest score on offending scale score and the second lowest on victim scale score.
The group included 14 students (10.5 % of the sample). Cluster four was termed the ―more
victim than bully‖ group and scored the second highest on victim scale score and the lowest on
offending scale score. The group included 13 students (9.8 % of the sample). Of the 133 students,
21 students did not complete the survey; thus, they are treated as missing data. Table 1 contains
the descriptive statistics according to cluster membership while Figure 1 contains a graphic
display of offending and victimization scale scores according to cluster membership.
Cyberbullying 13
[Insert Table 1]
[Insert Figure 1]
Further, results from the MANOVA indicated the distinctions among four subgroups
were significant on both bullying (F(3,108) = 351.72, p < 0.01, η2 = 0.90) and victimization
measures (F(3,108) = 240.65, p < 0.01, η2 =0.87). The Box test was significant (F(3,4) = 6.82, p
< 0.01), thus the assumption of the equality of covariances was violated. As a result, the
MANOVA statistic reported was the Wilks’ Lambda, which was significant (Λ = .02, F(6,214)=
0.20, p < 0.01, η2 = 0.85).
Sex differences
A 2 (sex) x 4(cluster) chi-square analysis revealed statistically significant differences for
sex, χ2 (3, N =133) =11.63, p < 0.05, Φ =0.36). The value of Phi indicated a medium strength of
association between sex and student group membership. In examining the standardized residuals
for each cell in the chi-square analysis, results indicated that significantly more girls (n =11, Std
residual = 2.1) than boys (n = 2, Std residual = -1.9) were classified into the ―more victim than
bullys‖ group. However, no other significant sex differences emerged.
Age differences
A Levene’s F test indicated that the assumption of homogeneity of variance was not met,
F(3, 124) = 3.21, p < 0.05. A one-way ANOVA revealed statistically significant differences for
age (F(3, 124) = 3.96, p < 0.05, η2= 0.87), suggesting that cluster membership was associated
with students’ age. Younger students (M = 14.92, SD = 0.33) were more likely to be in the ―more
victim than bully‖ group, but as they become a little older, the trend seemed to split into two
directions: ―Highly involved‖ (M = 15.76, SD = 0.29) or ―Least involved‖ (M = 15.61, SD =
0.13). Then, older students (M = 16.39, SD = 0.28) were more likely to be in the ―more bully
Cyberbullying 14
than victim‖ group. Both linear and quadratic trend appear to be significant; however, quadratic
trend seems to fit the data slightly better, F(3, 124) = 3.96, p < 0.05. Figure 2 contains a graphic
display of the relationship between age and cluster membership.
[Insert Figure 2]
The level of parental monitoring
A Levene’s F test indicated that the assumption of homogeneity of variance was not met, F(3,
124) = 3.04, p < 0.05. A one-way ANOVA revealed statistically nonsignificant differences for
the level of parental monitoring (F(3, 124) = 2.08, p> 0.05), suggesting that cluster membership
was not associated with the level of parental monitoring.
Discussion
The purpose of the present study was to identify the subgroups of youth who are
involved with cyberbullying and to examine sex and age differences and the level of parental
monitoring among these groups. A cluster analysis identified four groups: ―least involved‖,
―highly involved both as a bully and victim‖, ―more bully than victim‖, and ―more victim than
bully‖ group. Although the majority of students were in the ―least involved‖ group (51.1 %),
about 10% of the students were in the ―highly involved both as a bully and victim‖ group‖, and
the rest of the students (21%) had also experienced cyberbullying at least once. In traditional
bullying studies, differences among children have been conceptualized through categorizing
them into four groups: bullies, victim, bully/victims, and not involved (Espelage & Holt, 2007).
However, the analyses indicate that it is rare for high school students to be pure cyberbullies
and/or cybervictims.
As for sex differences, girls were more likely to be in the ―more victim than bully‖ group
than boys. This result is consistent with findings arguing that more girls experience indirect types
Cyberbullying 15
of bullying than boys (Kowalski et al, 2008; Ma, 2002; Olweus, 1993), and girls were more
likely to be victimized online (Dehue et al., 2008; Smith et al., 2008). The result also found
significant age differences among groups. Younger students were more likely to be in the ―more
victim than bully‖ group, and older students were more likely to be in the ―more bully than
victim‖ group. In traditional bullying, researchers have argued that younger children are
victimized more often than older children because younger victims were bullied both by older
and same-age pupils. On the other hand, older victims were bullied mainly by same-age (Smith,
Madsen, & Moody, 1999).
Moreover, it is also possible that older students are more technology savvy than younger
students and know how to protect themselves from being cybervictims: blocking unwanted
contacts or limiting friends’ network on social networking sites. Therefore, older students were
less likely to be victimized compared to younger students. In addition, older students have a
larger social circle than younger students at high school. Even though cyberbullying can happen
anonymously, this type of harassment often occurs within the circle of friends. In fact, Smith et
al. (n.d.) report that most of the cyberbullying is done by students in the same class, or in a same
year different class in their study. Similarly, Kowalski & Limber (2007) found that victims were
cyberbullied by a student at school. In other words, students are victimized by someone they
know, not by strangers in a cyberspace. Thus, older students who know more people at school
possible have higher risk of being highly involved as a bully or victim.
In addition, younger students have not acquired assertive skills yet: ―There is evidence
that some victims of bullying come from enmeshed, overprotective family backgrounds in which
skills of assertiveness are not practiced‖ (Smith, Madsen, & Moody, 1999, p. 282). Research
suggests the usefulness of assertive training for victimized students (Smith, Madsen, & Moody,
Cyberbullying 16
1999); thus, teaching younger students how to be assertive and protect themselves online would
be important.
Finally, the level of parental monitoring was not associated with cluster membership. Our
finding is consistent with Mesch (2009)’s study. This result indicates parental monitoring
strategies are not effective and protective as McQuade et al. (2009) pointed out.
Implications & Limitations of the Study
This research extends previous cyberbullying studies by classifying students and
examining sex and age differences and the level of parental monitoring based on the subgroups.
Past research indicated mixed findings on sex and age differences possibly because the
methodology ignored students’ cyberbullying subgroups (e.g., Aricak, 2009; Raskauskas &
Stoltz, 2007; Li, 2006). As discussed earlier, the classification is important for schools to
implement effective prevention and intervention strategies.
In addition, this study also found sex and age differences. Unlike traditional bullying,
physical strength and age do not seem to be a significant predictor in cyberbullying contexts;
however, girls and younger students are still more likely to be victimized than boys and/or older
students. Even though it seems easier for cybervictim to fight back, some victims may not know
how to protect themselves. Therefore, it can be concluded that cyberbullying victimization
pattern is similar to traditional bullying.
Finally, some limitations of the study also need to be addressed. First, this is a cross-
sectional correlational study; thus, causality inferences cannot be made. Second, the sample size
may be considered small, however our analyses revealed acceptable levels of statistical power (1
– β = .95 to .99) and the majority of the students are Caucasian who live in the rural area of
middle class families. Therefore, future studies can include individuals with different ethnic and
Cyberbullying 17
socioeconomic backgrounds. Finally, age differences are compared only among high school
students in the current study. If middle school or elementary school students were included,
analyses can show more distinct age differences and trend. Future studies can replicate the study
with larger and more diverse samples. In addition, future researchers also can examine other
variables such as educational achievement and psychological traits to distinguish among groups.
Despite these limitations, this research adds to a growing literature on cyberbullying among
youth.
Acknowledgement
The authors thank Dr. Willard and Dr. Smith for letting us use part of their
questionnaires for our study.
Cyberbullying 18
References
Anderson, T., & Sturm, B. (2007). Cyberbullying: From playground to computer. Young Adult
Library Services, 5, 24-27.
Aoyama, I., & Talbert, L. T. (2009, April). A cross-cultural study on cyber-bullying among high
school students in the Unites States and Japan. Paper presented at the meeting of the
American Educational Research Association (AERA), San Diego, CA.
Arıcak, O. T. (2009). Psychiatric symptomatology as a predictor of cyberbullying among
university students. Eurasian Journal of Educational Research, 34, 167-184.
Barnard-Brak, L., & To, Y. (2009). Examining parental nonresponse to stimulant treatment
questions according to ethnicity. Journal of child and adolescent psychopharmacology,
19, 301-304.
Beran, T., & Li, Q. (2005). Cyber-harassment: A study of a new method for an old behavior.
Journal of Educational Computing Research, 32, 265-277.
Bradshaw, C. P., Sawyer, A.L., & O-Brennan, L. (2007). Bullying and peer victimization at
school: Perceptual differences between students and school staff. School Psychology
Review, 36, 361-382.
Dehue, F., Bolman, C., & Völlink, T. (2008). Cyberbullying: Youngsters' experiences and
parental perception. CyberPsychology & Behavior, 11, 217-223.
Duncan, D. R. (2004). The impact of family relationships on school bullies and victims. In D. L.
Esspelage & S. M. Swearer (Eds.), Bullying in American schools. (pp. 227-244). London:
Lawrence Erlbaum Associates, Publishers.
Espelage, L. D., & Holt, K., M. (2007). Dating violence & sexual harassment across the bully-
Cyberbullying 19
victim continuum among middle and high school students. Journal of Youth and
Adolescence, 36, 799-811.
Green, S. B., & Salkind, J. W. (2004). Using SPSS for Windows and Macintosh: Analyzing and
Understanding Data (4th Edition). London: Pearson.
Hinduja, S., & Patchin, J. W. (2007). Offline consequences of online victimization: School
violence and delinquency. Journal of School Violence, 6, 89-112.
Hinduja, S., & Patchin, J. W. (2008). Cyberbullying: An exploratory analysis of factors related to
offending and victimization. Deviant Behavior, 29, 129-156.
Hinduja, S., & Patchin, J. W. (2009). Bullying beyond the schoolyard: Preventing and
responding to cyberbullying. Thousand Oaks, CA: Crown Press.
Joinson, A. (1998). Causes and implications of behavior on the Internet. In Gackenbach, J. (Ed);
Psychology and the Internet: Intrapersonal, interpersonal, and transpersonal
implications (pp. 43-60). San Diego, CA US: Academic Press.
Juvonen, J., & Gross, E. F. (2008). Extending the school grounds?—Bullying experiences in
cyberspace. Journal of School Health, 78, 496-505.
Katzer, C., Fetchenhauer, D., & Belschak, F. (2009). Cyberbullying: Who are the victims?: A
comparison of victimization in internet chatrooms and victimization in school. Journal of
Media Psychology: Theories, Methods, and Applications, 21, 25-36.
Kowalski, M. R., & Limber, P. S. (2007). Electronic bullying among middle school students.
Journal of Adolescent Health, 41, 22-30.
Kowalski, M. R., Limber, P. S., & Agatson, W. P. (2008). Cyberbullying: Bullying in the digital
Cyberbullying 20
age. Malden, MA: Blackwell Publishing.
Li, Q. (2006). Cyberbullying in schools: A research of gender differences. School Psychology
International, 27, 157-170.
Ma, X. (2002). Bullying in middle school: Individual and school characteristics of victims and
offenders. School Effectiveness and School Improvement, 13, 63 – 89.
Marini, Z., Dane, A., Bosacki, S., & Ylc-Cura, Y. (2006). Direct and indirect bully-victims:
differential psychosocial risk factors associated with adolescents involved in bullying and
victimization. Aggressive Behavior, 32, 551-569.
Mason, K. (2008). Cyberbullying: A preliminary assessment for school personnel. Psychology in
the Schools, 45, 323-348.
McKenna, K. Y. A., & Bargh, J. A. (2000). Plan 9 from cyberspace: The implications of the
internet for personality and social psychology. Personality & Social Psychology Review,
4, 57-75.
McQuade, C. S., Colt, P. J., Meyer, B. N. (2009). Cyber bullying. Protecting kid and adults from
online bullies. Westport, Connecticut: Praeger.
Mesch, S. G. (2009). Parental Mediation, Online Activities, and Cyberbullying.
CyberPsychology & Behavior, 12, 387-393.
Morita, Y., Soeda, H., Soeda, K., & Taki, M. (1999). Japan. In P, Smith., Y, Morita., J, Junger-
Tas., D, Olweus., R, Catalano., & P, Slee. (Eds.), The nature of school bullying: A cross-
national perspective (pp. 309-323). Florence, KY US: Taylor & Frances/Routledge.
Cyberbullying 21
Mouttapa, M., Valente, T., Gallaher, P., Rohrbach, L. A., & Unger, J. B. (2004). Social network
predictors of bullying and victimization. Adolescence, 39, 315-335.
Olweus, D. (1993). Bullying at school. What we know and what we can do. Malden, MA:
Blackwell Publishing.
Raskauskas, J., & Stoltz, A. D. (2007). Involvement in traditional and electronic bullying among
adolescents. Developmental Psychology, 43, 564-575.
Rosen, L. D. (2007) Me, MySpace, and I: Parenting the Net Generation. New York: Palgrave
Macmillan.
School Data Direct Retrieved on Nov 1, 2009, from http://www.schooldatadirect.org/
Shariff, S. (2008). Cyber-Bullying: Issues and solutions for the school, the classroom and the
home. New York: Routledge.
Smith, P. K., Madsen, K. C., & Moody, J. C. (1999). What cause the age decline in reports of
being bullied at school? Towards a developmental analysis of risks of being bullied.
Educational Research, 41 267-285.
Smith, P., Mahdavi, J., Carvalho, M., and Tippett, N. (n.d.). An investigation into cyberbullying,
its forms, awareness and impact, and the relationship between age and gender in
cyberbullying. Retrieved July 11, 2008, from http://www.anti-
bullyingalliance.org.uk/downloads/pdf/cyberbullyingreportfinal230106_000.pdf
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 & Psychiatry, 49, 376-385.
Tabachnick, G. B., & Fidall, S. L. (2001). Using multivariate statistics. (4th
ed.). Boston: Allyn
& Bacon.
Cyberbullying 22
Wienke Totura, M. C. et al. (2009). Bullying and victimization among boys and girls in middle
school: The Influence of perceived family and school contexts. The Journal of Early
Adolescence, 29, 571-609.
Willard, N. (2007). Cyberbullying and cyber-threats: responding to the challenge of online
social aggression, threats, and distress. Champaign, IL: Research Press.
Ybarra, M. L. (2004). Linkages between depressive symptomatology and internet harassment
among young regular internet users. CyberPsychology & Behavior, 7, 247-257.
Ybarra, M. L., & Mitchell, K. J. (2004). Online aggressor/targets, aggressors, and targets: A
comparison of associated youth characteristics. Journal of Child Psychology & Psychiatry,
45, 1308-1316.
Cyberbullying 23
Figure 1. Graphic display of offending and victimization scale scores
More
victim
than
bully
Least
Involved
Highly
Involved
More
bully
than
victim
Offending Score -0.55 -0.55 2.05 1.04
Victimization Score 1.07 -0.68 1.63 0.32
-1
-0.5
0
0.5
1
1.5
2
2.5
Offending Score
Victimization Score
Cyberbullying 24
Table 1. Descriptive statistics according to cluster membership
Offending Score Victimization Score
M SD M SD N
More victim than bully -0.55 0 1.07 0.56 13
Least Involved -0.55 0 -0.68 0 68
Highly Involved 2.05 0.83 1.63 0.34 14
More bully than victim 1.04 0.29 0.32 0.68 17
Cyberbullying 25
Figure 2. The relationship between age and cluster membership
14.92
15.61
15.76
16.39
14
14.5
15
15.5
16
16.5
17
More victim
than bully
Least
Involved
Highly
Involved
More bully
than victim
Mean age