Cyberbullying Victimization and Adolescent Mental Health: Evidence of Differential Effects by Sex and Mental Health Problem Type

Abstract

The use of electronic communication technologies has become a core method for adolescent communication. While there are many benefits to such technologies, cyberbullying has emerged as a potential harm. This study examines the association between cyberbullying and adolescent mental health problems and the extent to which this association differs by sex and mental health problem type. A clustered sample of 31,148 students in grades 6–12 (Female = 51.9%; 56.5% Caucasian, 10.2% South Asian) completed an anonymous survey asking their frequency of exposure to traditional forms of bullying, cyberbullying, and experiences of mental health problems over the past 6 months. Multilevel structural equation modelling was used to examine the associations. Controlling for age and traditional forms of bullying, cyberbullying was a significant predictor of adolescents’ emotional and behavioral problems. Cyberbullying was more strongly associated with emotional problems for females and with behavioral problems for males. This evidence identifies unique adverse effects associated with cyberbullying on both emotional and behavioural problems and sex differences in the strength of these associations.

Full text

J Youth Adolescence
DOI 10.1007/s10964-017-0678-4
EMPIRICAL RESEARCH
Cyberbullying Victimization and Adolescent Mental Health:
Evidence of Differential Effects by Sex and Mental Health Problem
Type
Soyeon Kim
1
●Scott R. Colwell
2
●Anna Kata
1
●Michael H. Boyle
1
●
Katholiki Georgiades
1
Received: 6 April 2017 / Accepted: 17 April 2017
© Springer Science+Business Media New York 2017
Abstract The use of electronic communication technolo-
gies has become a core method for adolescent commu-
nication. While there are many benefits to such
technologies, cyberbullying has emerged as a potential
harm. This study examines the association between cyber-
bullying and adolescent mental health problems and the
extent to which this association differs by sex and mental
health problem type. A clustered sample of 31,148 students
in grades 6–12 (Female =51.9%; 56.5% Caucasian, 10.2%
South Asian) completed an anonymous survey asking their
frequency of exposure to traditional forms of bullying,
cyberbullying, and experiences of mental health problems
over the past 6 months. Multilevel structural equation
modelling was used to examine the associations. Control-
ling for age and traditional forms of bullying, cyberbullying
was a significant predictor of adolescents’emotional and
behavioral problems. Cyberbullying was more strongly
associated with emotional problems for females and with
behavioral problems for males. This evidence identifies
unique adverse effects associated with cyberbullying on
both emotional and behavioural problems and sex differ-
ences in the strength of these associations.
Keywords Cyberbullying ●Emotional problems ●
Behavioral problems ●Adolescents ●Sex difference
Introduction
The use of electronic communication technologies such as
social networking services, instant messaging, chat rooms,
and text messaging have revolutionized the ways in which
adolescents communicate with each other (Boak et al. 2014;
Holfeld and Leadbeater 2014). While there are many ben-
efits, such technologies may increase the risk of exposure to
cyberbullying—defined generally as aggression that is
intentionally and repeatedly carried out in an electronic
context where a power imbalance exists between the per-
petrator and victim (Smith et al. 2008). Prevalence estimates
of cyberbullying victimization range from 10 to 40%
(Kowalski et al. 2014), depending on sampling and mea-
surement approaches. While evidence suggests that cyber-
bullying victimization is associated with adolescents’
emotional problems (Gamez-Guadix et al. 2013; Hamm
et al. 2015), its association with behavioral problems is less
investigated and remains unclear (Fisher et al. 2016;
Kowalski et al. 2014). Furthermore, higher susceptibility to
interpersonal stress in adolescent females compared to
males raises the possibility of sex differences in the strength
*Soyeon Kim
kims102@mcmaster.ca
Scott R. Colwell
scolwell@uoguelph.ca
Anna Kata
kataa@mcmaster.ca
Michael H. Boyle
boylem@mcmaster.ca
Katholiki Georgiades
georgik@mcmaster.ca
1
McMaster University, McMaster Innovation Park, Suite 201A,
1280 Main Street West, Hamilton L8S 4K1 ON, Canada
2
University of Guelph, 50 Stone Road East, Guelph N0B 1E0 ON,
Canada
Electronic supplementary material The online version of this article
(doi:10.1007/s10964-017-0678-4) contains supplementary material,
which is available to authorized users.

of these associations. The objectives of the present study are
to examine the association between cyberbullying victimi-
zation and adolescents’emotional and behavioral problems,
and the extent to which sex modifies the strength of these
associations. Moreover, given the high co-occurrence of
multiple forms of victimization (Li 2007; Modecki et al.
2014; Payne and Hutzell 2015; Wang et al. 2009), we
account for the frequency of exposure to traditional forms of
bullying victimization, and contrast the magnitude of the
associations between cyberbullying and traditional forms of
bullying.
Adolescence represents a critical developmental period
for the emergence of mental disorders. Nationally repre-
sentative household surveys of adults in the United States
suggest that half of all lifetime cases of DSM-IV disorders
start by age 14, and three-fourths by age 24 (Kessler et al.
2005). Several cognitive and social processes are associated
with this high prevalence and speak to the elevated sus-
ceptibility of adolescents to the adverse effects of cyber-
bullying. First, there are milestones that adolescents achieve
in this period that enable them to engage in bullying. For
instance, advanced abstract thinking allows adolescents to
construct self-concept within their peer group, family and
world. Particularly, adolescents have developed sufficient
social skills to explore the exercise of power and social
engagement (Pepler et al. 2006; Shaffer 2008; Swearer and
Hymel 2015). However, their abilities are not yet con-
solidated as it is a sensitive period for reorganization of
regulatory systems. This period lacks coordination of
emotional, intellectual and behavioral proclivities and cap-
abilities (Blakemore and Choudhury 2006). Furthermore,
from a moral development perspective they are in the pre-
conventional stage, where one focuses primarily on self,
rather than focusing on larger entities (Swearer and Hymel
2015). This self-focused sense of morality and lack of
coordination in cognition makes them vulnerable to mental
health problems as bullying victims. Understanding the
prevalence and the impact of cyberbullying in mental health
in the adolescent group is particularly critical.
The adverse effects of traditional forms of bullying vic-
timization on adolescents’emotional and behavioral pro-
blems are well documented (Arseneault et al. 2010; Craig
et al. 2009; Hawker and Boulton 2000; Reijntjes et al. 2011,
2010; Storch and Ledley 2005; Ttofiand Farrington 2010;
Vaillancourt et al. 2013). For instance, based on a long-
itudinal study, Vaillancourt et al. (2013) reported that bul-
lying victimization predicts emotional and behavioral
problems in adolescents. However, the evidence specificto
cyberbullying focuses primarily on associations with emo-
tional problems and suicidality (Elgar et al. 2014; Gamez-
Guadix et al. 2013; Hamm et al. 2015). Recent meta-
analyses and scoping reviews indicate that cyberbullying
victimization is associated with adolescent depression
(Fisher et al. 2016; Hamm et al. 2015; Kowalski and Limber
2013). Evidence documenting the strength of association
between cyberbullying victimization and adolescent beha-
vioral problems is more limited. A recent meta-analysis
examining the strength of association between peer cyber
victimization and externalizing behaviors reported a mean
effect size estimate of Pearson’sr=0.23 for overall exter-
nalizing problems. However, further differentiating the
types of externalizing behavior problems revealed statisti-
cally significant associations between peer cyber victimi-
zation and self-harm (r=0.34), substance use (r=0.18)
and social problems (r=0.15). The models for risky
sexual behavior and aggression failed to reach statistical
significance, which may be due to a lack of statistical
power and the limited number of studies examining these
outcomes (Fisher et al. 2016; Kowalski et al. 2014).
Moreover, only a few studies of cyberbullying have
attempted to statistically control for other forms of bullying
when examining the strength of association between
cyberbullying and adolescent mental health problems
(Bonanno and Hymel 2013; Campbell et al. 2012; Wang
et al. 2011). This statistical control is important as it pro-
vides an opportunity to address a longstanding concerns
about the distinctiveness of cyberbullying victimization
relative to other forms of bullying (Dooley et al. 2009; Law
et al. 2012; Modecki et al. 2014; Olweus 2012). Therefore,
the current study aims to address these limitations by
examining the association between cyberbullying and both
emotional and behavioral problems, controlling for other
types of bullying. Indeed, the current study explicitly con-
trasts the strength of association between traditional forms
of bullying, cyberbullying, and adolescents’emotional and
behavioral problems.
Concerns exist that the intensity and impact of cyber-
bullying victimization may be greater than traditional forms
of bullying because of the unique features that set it apart,
including: (1) ease of reproducibility (i.e., the perpetrator
can easily copy and paste aggressive messages on multiple
websites, texts, and blogs), (2) widespread reach (i.e., the
perpetrator can easily reach a larger audience by targeting a
social networking site in which most of their friends have
membership), (3) lack of face-to-face contact (i.e., the
perpetrator does not see the immediate effect of their
aggressive behavior), (4) perceived anonymity (i.e., the
perpetrator can hide his/her identity), (5) relative perma-
nence (i.e., aggressive messages on websites tend to stay
visible once posted), (6) limited likelihood for intervention,
and (7) constant accessibility in terms of time and location.
In a qualitative study that investigated how participants
ranked the severity of hypothesized bullying situations
(traditional vs. cyber), cyber scenarios were perceived as
worse than traditional forms of bullying (Sticca and Perren
2013).
J Youth Adolescence

The limited number of studies attempting to control for
other forms of bullying when examining the association
between cyberbullying and mental health problems report
the following. Based on a qualitative study, cyberbullying
victims reported more social difficulties and higher levels of
anxiety and depression than traditional bullying victims
(Campbell et al. 2012). A quantitative study by Bonanno
and Hymel (2013) showed that cyberbullying predicted
depression and suicidal ideation over and above the impact
of traditional forms of bullying. Similarly, Wigderson and
Lynch (2013) reported that cyberbullying was associated
with adolescents’depression and anxiety after accounting
for traditional types of bullying. Taking a different
approach, Wang et al. (2010) incorporated cyberbullying
into other measures of victimization (physical, verbal, social
exclusion, spreading rumors) to create three latent groups of
adolescents based on their reported experiences. Cyberbul-
lying was part of an all class group linked with depression,
suggesting that its association with adolescent internalizing
problems might be indistinguishable from other forms of
bullying. It is notable that all of these studies focused on
“internalizing”problems of youth, leaving open the question
about the association between cyberbullying and externa-
lizing vs. internalizing problems. Furthermore, none of the
studies formally examined the comparative strength of
association between adolescent mental health problems and
different forms of bullying, leaving open the question about
the importance of cyberbullying in relation to other forms of
bullying.
There is also uncertainty about the prevalence and adverse
effects of cyberbullying victimization in adolescent males vs.
females (Barlett and Coyne 2014;Kowalskietal.2014;
Mishna et al. 2012;Wangetal.2009). Specifically, some
studies report that females are more likely to experience and
report cyberbullying than males (Beckman et al. 2013;Li
2006), but other studies fail to replicate these findings
(Hinduja and Patchin 2010;Mishnaetal.2010). Differences
in prevalence between the sexes may arise in part from the
way cyberbullying victimization is characterized (threatening
vs. isolating). For example, male adolescents are more likely
to be victims of direct threats, while female adolescents are
more likely to be victims of malicious rumours (Mishna et al.
2010). Furthermore, studies that examined the extent to
which sex moderates the strength of association between
cyberbullying victimization and adolescent mental health
problems are very limited. A 2 year follow-up study reported
that cyberbullying was associated with adolescent mental
health problems among females but not males (Bannink et al.
2014). On the other hand, Wigderson and Lynch (2013)
found no sex difference in the association between cyber-
bullying and mental health outcomes.
Despite the inconclusive findings regarding the sex dif-
ference in prevalence and its association with mental health
problems, there are reasons to believe that both the pre-
valence and strength of association with mental health
problems will be greater for females compared to males.
First, females use more features associated with social
relationships in cyberspace than males (Barlett and Coyne
2014) and communicate more using text messaging and
emails (Dooley et al. 2009). Accordingly, their increased
time online makes them more likely to be exposed to
cyberbullying. Second, they are more likely to be victims of
relational aggression in offline settings (Carbone-Lopez
et al. 2010; Mishna et al. 2010) which is likely to transfer to
online settings. Third, adolescent females are more sus-
ceptible to interpersonal stress (Hammen 2009; Rudolph
et al. 2008), suggesting that they may be at greater risk for
depressive symptoms and disorders after experiencing
relational aggression online (i.e., cyberbullying) (Bor et al.
2014; Hammen 2009; Kuehner 2003; Nolen-Hoeksema and
Girgus 1994). Fourth, female adolescents experience more
intense and prolonged tension as a result of interpersonal
stress compared to males (Hankin et al. 1998; Kessler and
McLeod 1984), which is strongly associated with depres-
sive disorders and recurrence, particularly among females
(Rudolph et al. 2008) (Hammen 2009). Fifth, female ado-
lescents are much more likely to ruminate on the source of
their relational aggression experiences (Nolen-Hoeksema
and Girgus 1994). In light of the disproportionate increases
during the past two decades in Major Depressive Disorder
(MDD) among females (13.1 to 17.3%) compared to males
(4.5 to 5.7%) (Boak et al. 2014; Bor et al. 2014; Mojtabai
et al. 2016), examining sex differences in the strength of
association between cyberbullying victimization and ado-
lescent mental health problems may shed insights into the
possible contributors to these concerning trends. Under-
standing sex differences on the impact of cyberbullying in
emotional and behavioral problems may reinforce the
importance of developing more tailored intervention
approaches/strategies based on sex.
The Current Study
Emerging evidence consistently demonstrates a positive
association between cyberbullying victimization and emo-
tional problems. The evidence for behavioral problems is
less conclusive. Moreover, there are methodological lim-
itations and evidence gaps to be addressed as follows: (1)
most studies have failed to account for other forms of
bullying when estimating the association between cyber-
bullying victimization and adolescent mental health pro-
blems; (2) the extent to which sex modifies the association
between cyberbullying victimization and adolescent mental
health problems has yet to be examined; and (3) very few
studies have conducted an explicit comparison of the
J Youth Adolescence

strength of association between adolescent mental health
problems and cyberbullying victimization, compared to
other forms of bullying. To address these gaps, the current
study examined: (1) the extent to which sex modifies
the strength of association between cyberbullying
victimization and adolescents’emotional and behavioral
problems, and (2) compared the magnitude of associations
between adolescents’emotional and behavioral problems
and cyberbullying victimization vs. traditional forms of
bullying (social, verbal, and physical bullying). The
dataset used in this study includes a probability sample of
schools (cluster samples) and students in grades 6–12. We
use a multilevel structural modeling approach with a
representative adolescent sample to address the study
objectives.
Method
Sampling and Procedures
The selection of schools was based on the sampling design
of the 2014 Ontario Child Health Study (OCHS), a large-
scale general population survey of 10,530 children and
youth, in 7020 families and 180 communities in the pro-
vince of Ontario . Schools located in the 180 communities
that had a high likelihood of having students participating in
the 2014 OCHS were sampled. In total, 360 schools
were selected. Stratification of the 180 communities by
median family income (low, medium and high) resulted in
over-sampling of schools from poorer and wealthier
neighbourhoods.
A total of 248 schools participated out of the 360 schools
selected (68.9%). Of these, 180 were elementary schools
(72.6%) and 68 were secondary schools (27.4%). The
majority of schools were English-speaking (239; 96.4%),
while 9 French schools participated (3.6%).
Within participating schools, anonymous student surveys
were administered to consenting youth. In elementary
schools this included all students in grade 6–8 classrooms;
in secondary schools, this included stratified (by classroom)
random samples of about 240 students. Surveys were
completed during school hours, using either paper-and-
pencil questionnaires or secure internet-based technology
(SNAP Surveys). A total of 31,124 out of 50,495 eligible
students completed surveys (response rate =61.6%).
All study procedures, including consent and con-
fidentiality requirements, were approved by the Hamilton
Integrated Research Ethics Board at McMaster University
and the Research Ethics Committees of the School Boards
involved in the study. School participation took place
between December 2014 and May 2015.
Measures
Emotional and behavioral problems
Emotional problems were measured using 9 items from the
Emotional Problems Scale (depression and anxiety) and 13
items from the Conduct Problems Scale (conduct disorder
and oppositional defiant disorder) developed initially to
screen for psychiatric disorder among children and adoles-
cents in the general population participating in the 1983
Ontario Child Health Study (Boyle et al. 1987,1993) and
updated in the 2014 Ontario Child Health Study (Statistics
Canada 2014). Three additional items on conduct disorder
were added (1) I use weapons when fighting, (2) I steal
things from places other than home, and (3) I have broken
into someone else’s house) (Tremblay et al. 1987). Students
were asked to report on a 3-point frequency/intensity scale
(0, never or not true;1, sometimes or somewhat true;2,
often or very true) the extent to which they have experi-
enced emotional and behavioral problems in the past
6 months. Sample items for emotional problems include “I
am unhappy, sad or depressed”,“I am moody or irritable”,“I
feel worthless or inferior”,“I am too fearful or anxious”,“I
am nervous or tense”, and “Ifind it hard to stop worrying”.
Sample items for behavioral problems include “I threaten to
hurt people”,“I get in many fights”,“I damage schools or
other property”,“I disobey at school”,“I steal things from
places other than home”,“I lose my temper”,“I argue a lot
with adults”,“Iamdefiant and talk back to people”and “I
am angry and resentful”. For descriptive purposes (Table 1)
items are summed to create two scales, one for emotional
problems that ranges from 0–18, with a mean of 4.87 (SD
=4.55) and one for behavioral problems, range 0–26, mean
=2.89 (SD =3.81). For the structural equation modelling
(SEM), the items were used to construct separate latent
variables.
Bullying
Adefinition of bullying was provided in the survey and
included three characteristics of bullying suggested by
Olweus (Olweus 1993,1994): (1) power imbalance; (2)
repetitiveness; and (3) intention to harm. After defining
bullying, each type of victimization was presented—phy-
sical, verbal, social, and cyberbullying—with specific
examples. Students were asked to indicate the frequency in
which they have experienced each form of victimization in
the past 6 months using a 5-point scale (never, once or a few
times, once or twice a month, once or twice a week, almost
every day). These items come from the Ontario Ministry of
Education’s Safe Schools Survey . The exact items appear in
the Appendix.
J Youth Adolescence

Statistical Analysis
Structural equation modelling was used in the analysis
because it (a) provides the ability to model the behavioral
and emotional problems as latent variables which allows for
the inclusion of measurement error (thus correcting for
attenuation), and (b) allows for the simultaneous estimation
of all paths, including the covariate of age on bullying
types.
Cluster sampling in this study (31,021 students nested in
classrooms across 248 schools) often violates the assump-
tion of independence. Failure to account for non-
independence can cause the standard errors of the esti-
mates to be underestimated in comparison to those esti-
mated under a simple random sample (Muthén and Satorra
1995).
To test for this potential bias in standard error estimates,
the intraclass correlations coefficients (ICC) and the design
effect (DEFF)—estimated as [1 +(average cluster size—1)
*ICC]—were estimated for all variables in the model. As
noted by Maas and Hox (2005), in cases such as this DEFF
is more relevant than just the ICC values as it provides an
indication of the extent to which the standard errors may be
biased. DEFF values greater than two suggest the need for
an analytical strategy that accounts for the non-
independence of the data in order to correct for the bias
in standard error estimates (Muthén and Satorra 1995). The
DEFF values for the classroom-to-school clustering were
below 2, indicating that classrooms within schools could be
considered independent. However, the DEFF values for
student-to-school clustering were above 2, indicating that
students within schools could not be considered
independent.
Given these results, two analytical approaches were
possible. The first involves multilevel modeling whereby a
model is specified for both the within-school variation and
between-school variation. This approach is particularly
useful if the goal is to understand both within- and between-
school differences. The second approach is to estimate the
standard errors while taking into account the non-
independence but without modeling each level (Muthén
and Asparouhov 2011). Given that the research question in
this study does not involve the study of within-school
variation, the latter approach was taken using the Type =
Complex function in Mplus, version 7.4 (Muthén and
Muthén 2012). In short, a structural equation model with
students nested in schools was used to evaluate the afore-
mentioned research objectives. This approach provided two
advantages. First, it allowed for the simultaneous estimation
of all paths in the model while correcting standard error bias
due to non-independence. Second, it afforded the opportu-
nity to (1) test for sex difference in the association of
Table 1 Descriptive statistics by type of bullying
Total Cyberbullying Verbal Social Physical None
(N =
30,471)
(N =2761,
9.1%)
(N =5196,
25.8%)
(N =6637,
21.9%)
(N =2854,
9.5%)
(N =20153)
Age 13.52 ±2.04 13.61 ±2.16 13.22 ±1.97 13.29 ±2.03 12.92 ±2.01 13.66 ±2.06
Sex (M) 48.1% 39.0% 49.2% 38.6% 64.5% 49.4%
Parent education
Below high school 4.2% 9.2% 5.8% 6.0% 8.0% 2.8 %
High school 12.3% 13.2% 13.4% 13.1% 10.8% 9.3%
College 25.8% 27.2% 26.0% 25.6% 25.9% 20.4%
University 57.7% 50.4% 54.8% 55.2% 55.3% 46.5%
Ethnicity
White 56.5% 57.4% 57.3% 57.9% 55.5% 56.1%
East Asian 4.6% 3.5% 3.3% 3.0% 3.6% 5.2%
Southeast Asian 3.8% 3.0% 3.2% 3.0% 3.2% 4.1%
South Asian 10.2% 7.1% 9.3% 9.1% 9.3% 10.6%
West Asian or Arab 2.0% 1.9% 1.9% 1.8% 1.7% 2.1%
Black African Caribbean or Canadian
American
5.7% 6.4% 5.7% 5.3% 6.8% 5.8%
Latin American Central American South
American
2.2% 2.0% 1.9% 1.9% 1.6% 2.4%
Aboriginal/Native 1.1% 1.7% 1.6% 1.4% 1.9% 1.0%
Other 4.4% 5.4% 4.8% 4.4% 5.4% 4.2%
Multi-racial selected more than 1 group 9.4% 11.5% 11.1% 12.0% 11.1% 8.6%
J Youth Adolescence

cyberbullying victimization and adolescent mental health
(emotional and behavioral problems), and (2) for males and
females, test whether the strength of association between
cyberbullying victimization and adolescent mental health
problems differs from the associations with traditional types
of bullying (social, verbal, and physical).
Prior to estimating the structural effects, the measure-
ment model was assessed by conducting a confirmatory
factor analysis (accounting for the clustered sampling)
which included all latent and observed variables in the
structural model. The latent variables Behavioral Problems
and Emotional Problems were modeled as second-order
factors with the first order factors oppositional defiant dis-
order and conduct disorder measuring Behavioral Problems
and depression and anxiety measuring Emotional Problems.
The measurement model fit the data well [x
2
(208)
=
16,941.009, p=.000, CFI =.922, TLI =.913; RMSEA
=.051] indicating uni-dimensionality of the constructs.
Convergent validity was also achieved as all items loaded
significantly at p<.001 onto their respective constructs
(standardized factor loadings ranged from .666 to .906).
Discriminant validity was achieved as the pairwise con-
straining of the correlations between the constructs sig-
nificantly worsened the fit of the model. Finally, all
constructs achieved reliability as their respective Cronbach’s
alpha coefficients were greater than 0.70 (Emotional Pro-
blems =.901, Behavioral Problems =.871).
For the substantive part of the analysis, two structural
equation models (again accounting for the clustered sampling)
were estimated to examine the direct path of bullying on
Emotional Problems (Fig. 1) and Behavioral Problems (Fig.
2). In both models, age was added as a covariate based on
previous studies that inform the moderating role of age in
cyberbullying (Barlett and Coyne 2014;Kowalskietal.2014).
The model fit statistics indicated that the model fit the data
well for both the Emotional Problems (Fig. 1) [x
2
(59)
=
11,336.780, p=.000, CFI =.920; TLI =.902; RMSEA
=.080; SRMR =.044] and Behavioral Problems (Fig. 2)
[x
2
(113)
=16,089.890, p=.000, CFI =.828; TLI =.802;
RMSEA =.069; SRMR =.079].
To probe the aforementioned sex differences, the Wald
Chi-square Test of Parameter Constraints was employed, by
constraining the direct path coefficient of cyberbullying on
emotional problems (and behavioral problems) to be equal
across the sex categories. Similarly, the Wald Chi-square
test was employed to test whether the direct path coefficient
of cyberbullying on emotional and behavioral problems
differs from the coefficients associated with social, verbal,
and physical bullying within sex. A significant Wald Chi-
square indicates that the beta coefficients are not equal and
consequently that differences exist. Note that power may
not have implications in interpreting Wald-test results.
When comparing two chi-squares, sample size does not
factor into whether the difference is significant. It is a
function of 1 degree of freedom. Furthermore, both the
constrained and unconstrained chi-squares are equally
influenced by sample size, so comparing one vs. the other at
1df due to the contrast is not influenced by sample size.
Result
Demographics
Table 1presents descriptive statistics for the total sample
and separately for students who endorsed being bullied once
or twice a month or more in the past 6 months by form of
bullying. It is important to note that the bullying types are
Fig. 1 Standardized coefficients
for the model of the association
between the four types of
bullying and emotional
problems. Sex-specific
coefficients were estimated for
all the paths, but sex differences
were only examined for the path
between bullying and outcome
variables. Female coefficients
appear first followed by male
coefficients directly below.
Underlined coefficients indicate
significantly larger coefficient
between the sexes in the
association (p<.05). *<.05;
**<.01; ***<.001
J Youth Adolescence

not mutually exclusive (i.e., the same student may endorse
multiple forms of bullying). As a comparison group, we
present the demographics for students who do not meet our
criteria for being bullied across all 4 types of bullying. The
mean age of the participants (total) was 13.52 years (±2.04),
with 47.8% of the sample identifying as male. 56.5% of the
sample identified as White, 4.6% East Asian, 3.8% South-
east Asian, 10.2% South Asian, 2.0% West Asian or Arab,
5.7% Black African/Caribbean/Canadian American, 2.2%
Latin American/Central American/South American 1.1%
Aboriginal/Native, 4.4% Other, and 9.4% Multi-racial/
selected more than one group. Verbal bullying was the most
frequently experienced type of bullying (N=5196, 25.8%)
followed by social bullying (N=6637, 21.9%), physical
bullying (N=2854, 9.5%), and lastly cyberbullying (N=
2761, 9.1%). Among those who endorsed being physically
bullied, the majority are males (64.5%). In contrast, males
are less likely to endorse social and cyberbullying victimi-
zation (38.6 and 39.0%, respectively) compared to females.
Correlation matrices for the variables used to model emo-
tional and behavioral problems appear in Tables 2and 3.
Sex Difference in the Strength of Association between
Bullying and Adolescent Mental Health Problems
The model results indicate that cyberbullying significantly
contributes to both emotional and behavioral problems in
both sexes (see Figs. 1and 2). Specifically, after controlling
for age and other forms of bullying, the standardized beta
coefficients for cyberbullying on emotional problems were
β=.133 for females and β=.074 for males. The coefficient
for females was significantly stronger compared to males
(p<.001). Cyberbullying also contributes significantly to
behavioral problems for both males and females, but has a
stronger coefficient for males (males: β=.185, females:
β=.143, p<.001).
Significant sex differences were found for the direct path
coefficients associated with traditional forms of bullying on
emotional problems. Specifically, social bullying (female: β
=.227, males: β=.209, p<.001), was more strongly
associated with emotional problems in females than males,
while physical bullying (females: β=.018, males: β=.024,
p<.05) and verbal bullying (females: β=.123, males:
Fig. 2 Standardized coefficients
for the model of the association
between the four types of
bullying and behavioral
problems. Sex-specific
coefficients were estimated for
all the paths, but sex differences
were only examined for the path
between bullying and outcome
variables. Female coefficients
appear first followed by male
coefficients directly below.
Underlined coefficient indicates
significantly larger coefficient
between the sexes in the
association (p<.05). *<.05;
**<.01; ***<.001
Table 2 Correlations among
the variables used in the SEM
for emotional problems, by sex
Emotional Age Physical Social Verbal Cyber
Emotional 1.000 .235*** .189*** .370*** .330*** .307***
Age .112*** 1.000 −.137*** −.063*** −.105*** .023***
Physical .253*** −.139*** 1.000 .379*** .507*** .353***
Social .342*** −.064*** .546*** 1.000 .638*** .506***
Verbal .309*** −.101*** .632*** .633*** 1.000 .497***
Cyber .244*** .045** .429*** .504*** .418*** 1.000
Numbers above the diagonal represent correlation matrix of female adolescents
*<.05; **<.01; ***<.001
J Youth Adolescence

β=.131, p<.001) was more strongly associated with
emotional problems in males compared to females. Sig-
nificant sex differences were also found for the direct path
coefficients associated with traditional forms of bullying on
behavioral problems. Specifically, social bullying (female:
β=.007, males: β=.047, p<.001), and cyberbullying
(females: β=.143, males: β=.185, p<.001) were more
strongly associated with behavioral problems in males than
females.
Differences in the Magnitude of Association between
Cyberbullying vs. Traditional Types of Bullying on
Adolescent Mental Health Problems
For females, social bullying (β=.227), and cyberbullying
(β=.133) had the strongest association with emotional
problems. While there was no significant difference in the
regression coefficients between social bullying and cyber-
bullying (p>0.05), social bullying and cyberbullying had
significantly stronger associations with emotional problems
than verbal and physical bullying. For males, social bully-
ing had a stronger association with emotional problems
compared to cyberbullying (cyberbullying: β=.074, social
bullying: β=.209, p<.001). The magnitude of associa-
tions between cyberbullying and verbal bullying on emo-
tional problems for males were similar.
For females cyberbullying had the strongest association
with behavioral problems compared to verbal bullying
(cyberbullying: β=.143; verbal bullying: β=.037, p
<.001) and social bullying (cyberbullying: β=.143, social
bullying: β=.007, p<.001), but a significantly smaller
association than physical bullying (cyberbullying: β=.143,
physical bullying: β=.193, p<.001). Within males,
cyberbullying had the strongest association with behavioral
problems compared to verbal bullying (cyberbullying: β
=.185; verbal bullying: β=.027, p<.001), social bullying
(cyberbullying: β=.185, social bullying: β=.047, p
<.001), and physical bullying (cyberbullying: β=.185,
physical bullying: β=.114, p<.001).
Discussion
In an attempt to address evidence gaps in our understanding
of adolescent cyberbullying, the current study evaluated a
comprehensive model of cyberbullying victimization in
males and females. First, it compared the strength of asso-
ciation between cyberbullying and both emotional and
behavioral problems in adolescents. Second, it examined
the unique association of cyberbullying with adolescent
mental health problems in comparison with other forms of
bullying. Third, it tested whether or not the association
between cyberbullying and adolescent mental health pro-
blems differed for females vs. males. Finally, it drew on a
large, representative sample of students in the general
population which facilitated the use of SEM to test specific
hypotheses bearing on associations of interest. Cyberbul-
lying was significantly associated with both emotional and
behavioral problems above and beyond traditional forms of
bullying, and being male or female affected the strength of
these associations. Specifically, there was a stronger asso-
ciation between cyberbullying victimization and emotional
problems for females than males while cyberbullying vic-
timization was associated more strongly with behavioral
problems in males than females.
Previous studies examining the association between
cyberbullying and adolescent mental health problems con-
cur with several of our findings. For instance, a recent meta-
analysis reported a positive association between cyberbul-
lying and both emotional (e.g., depression, anxiety, anger)
and behavioral problems (e.g., aggression, substance use,
risky sexual behavior) ranging from Person’sr=.14 to .34
(Fisher et al. 2016). Other meta-analyses (Hamm et al.
2015; Kowalski et al. 2014) and a longitudinal study
(Gamez-Guadix et al. 2013) found similar results of the
association between cyberbullying victimization and emo-
tional problems. The influence of cyberbullying victimiza-
tion on depression is the most extensively reported
(Mitchell et al. 2007; Reed et al. 2015; Wang et al. 2011).
Our findings on the association between cyberbullying
victimization and behavioral problems also align with
Table 3 Correlations among
the variables used in the SEM
for behavioral problems by sex
Behavioral Age Physical Social Verbal Cyber
Behavioral 1.000 .017*** .264*** .185*** .210*** .232***
Age .083*** 1.000 −.137*** −.062*** −.105*** .023***
Physical .236*** −.139*** 1.000 .379*** .507*** .353***
Social .219*** −.064*** .546*** 1.000 .638*** .506***
Verbal .206*** −.101*** .632*** .633*** 1.000 .497***
Cyber .268*** .046** .429*** .504*** .419*** 1.000
Numbers above the diagonal represent correlation matrix of female adolescents
*<.05; **<.01; ***<.001
J Youth Adolescence

previous studies suggesting positive associations with
physical violence, substance use and abuse (Litwiller and
Brausch 2013; Reed et al. 2015), delinquency (Mitchell
et al. 2007), and reactive aggression (Sontag et al. 2011). It
is important to note that the present study extends existing
evidence on the association between cyberbullying victi-
mization and behavior problems as (1) it measures a broader
spectrum of behavioral problems (Conduct Problems Scale;
(Boyle et al. 1987,1993)) rather than few isolated beha-
vioral problems such as violence, aggression and risky
sexual behavior; (2) incorporates key characteristics of
bullying (i.e., intentionality, repetition, and power imbal-
ance); and (3) controls for traditional types of bullying. For
instance, findings from a meta-analysis by Kowalski et al.
(2014) reported a positive association between cyberbully-
ing victimization and behavioral problems, including con-
duct problems and substance use, however it has been
criticized for not including studies that use the key char-
acteristics of bullying (Fisher et al. 2016). In a more recent
meta-analysis, Fisher et al. (2016) included a larger number
of studies that were not limited by the key characteristics of
bullying and used variance estimates that include multiple
effect sizes arising from the same study. Although they
reported a positive association between peer cyber victi-
mization and overall behavioral problems, they failed to
find significant associations with risky sexual behavior and
aggression due to the limited number of studies investi-
gating these outcomes.
Our findings demonstrate clear sex differences in
cyberbullying victimization prevalence and the association
between cyberbullying and adolescent mental health. In
terms of prevalence, cyberbullying victimization was more
prevalent in females than males, while males reported more
physical bullying than females. This finding echoes existing
studies that report higher prevalence of direct/physical
forms of bullying in males (Archer 2004; Card et al. 2008),
and higher prevalence of indirect forms of bullying in
females (Carbone-Lopez et al. 2010).
The strength of association between cyberbullying vic-
timization and mental health problems varied by sex and
type of mental health problem. On the one hand, there was a
stronger association between cyberbullying and emotional
problems among female adolescents compared to male
adolescents; on the other hand, the association was stronger
for behavioral problems among male adolescents compared
to female adolescents. Existing studies suggest that female
adolescents are more vulnerable to emotional problems (Bor
et al. 2014; Hammen 2009; Kuehner 2003; Nolen-
Hoeksema and Girgus 1994; Piccinelli and Wilkinson
2000). For example, the incidence and morbidity risk
associated with most depressive disorders are much higher
in females compared to males—differences that begin to
emerge in early adolescence and persist throughout the life
course (Piccinelli and Wilkinson 2000). Also, females may
experience certain stressors more and may be more vul-
nerable to developing depression in response to inter-
personal stress (Nolen-Hoeksema and Girgus 1994). The
bullying literature provides some support for this argument
and our finding. For example, females report higher levels
of cyberbullying victimization compared to males (Mishna
et al. 2012), and the impact of traditional forms of bullying
on depression and suicidal ideation is greater for females
compared to males (Klomek et al. 2007).
Finally, our findings indicate that cyberbullying con-
tributes unique variance to the association with adolescents’
emotional and behavioral problems, over and above tradi-
tional forms of bullying. We are aware of only a few other
studies that took other forms of bullying into account when
examining the association between cyberbullying victimi-
zation and mental health outcomes, and the results of these
studies are consistent with the current findings (Bonanno
and Hymel 2013; Perren and Gutzwiller-Helfenfinger 2012;
Wigderson and Lynch 2013). The current study extended
this line of inquiry by contrasting the magnitude of the
effect of cyberbullying victimization on adolescents’emo-
tional and behavioral problems, compared to other forms of
bullying victimization. For females, cyberbullying victimi-
zation had a significantly stronger association with emo-
tional problems than verbal and physical bullying. For
males, the effect of cyberbullying on emotional problems
was stronger than physical bullying. For both sexes,
cyberbullying had significantly stronger associations with
behavioral problems than other forms of bullying. Our
findings suggest a unique, negative effect of cyberbullying
victimization on adolescent mental health and that the
magnitude of the effect is comparable, and in some
instances, stronger than more traditional forms of bullying
victimization.
There are several limitations to this research that must be
acknowledged. First, since cross-sectional data were col-
lected at one point in time rather than at multiple time
points, the current study cannot disentangle the temporal
relationship between cyberbullying victimization and ado-
lescent mental health. A longitudinal study design is needed
to test causal hypotheses. Furthermore, the authors
acknowledge the limitations of relying on a self-report
single item for each of the bullying domains, including
cyberbullying, such that measurement sensitivity can be
lower when using global, single-item questions (Vaillan-
court et al. 2010). However, some researchers note that a
single-item measure is sufficient when the concept has a
single referent that is easy to recognize and understand
(Menesini and Nocentini 2009). We tried to provide a
measure of bullying that captures the full conceptual
domain of the construct incorporating the four components
of cyberbullying as recommended in previous studies
J Youth Adolescence

(Murphy and Davidshofer 2005; Vaillancourt et al. 2008).
Future studies should consider using multiple items to
assess each type of bullying so that shared and non-shared
variance or measurement error could be disaggregated. We
also acknowledge our inability to distinguish whether the
social and verbal forms of bullying are occurring online vs.
offline.
Conclusion
The current study adds to a growing body of evidence that
documents the negative impact of cyberbullying victimi-
zation on emotional problems and behavior problems.
Furthermore, this research provides a better understanding
of the role of sex on the prevalence of cyberbullying and its
association with mental health problems in the context of
other types of bullying. The present research could be very
informative for clinicians and school personnel who deal
with adolescent mental health. Anti-bullying programs are
most effective when targeting specific adolescents at risk
(Bradshaw 2015). Mental health professionals, service
agencies, and school policy makers should take a step
towards paying more attention to cyberbullying victimiza-
tion and consider the role of sex in order to provide more
effective preventative interventions and service. Specifi-
cally, schools should develop, implement, monitor and
evaluate evidence-based bullying prevention programs that
explicitly include teaching students effective strategies for
safe and ethical online behavior. Particularly, a systematic
whole-school approach could effectively prevent and man-
age cyberbullying.
Acknowledgements This research uses data from the 2014 School
Mental Health Surveys (http://www.ontariochildhealthstudy.ca/smhs),
a project led by Drs. Kathy Georgiades and Michael Boyle at
McMaster University. We thank the participating schools, students,
educators and principals and the Ontario Ministry of Education for
their support of the study. We also wish to acknowledge Melissa
Kimber, the study research coordinator, for her tireless efforts in
supporting the study, as well as the rest of the study implementation
team. Thanks also to research assistant Donna Oh, who helped with
literature reviews.
Funding The 2014 School Mental Health Surveys was funded by
the Canadian Institutes of Health Research (www.cihr-irsc.gc.ca;
CIHR Ref #136939).
Author Contributions S.K. conceived of this paper, and contributed
to the design, analysis, interpretation of data, and drafted the manu-
script; S.C. helped with the statistical analysis, interpretation, and
drafting the manuscript; A.K. helped with study implementation,
coordination, and editing the manuscript; M.B. participated in the
interpretation of the data, critical revision, and helped draft the
manuscript; K.G. participated in the statistical analysis, interpretation
of the data, and helped draft the manuscript. All authors read and
approved the final manuscript.
Compliance with Ethical Standards Dr. Boyle is supported by
Canadian Institutes of Health Research (CIHR) Canada Research
Chair in the Social Determinants of Child Health. Dr. Georgiades is
supported by a CIHR New Investigator Award and The David R (Dan)
Offord Chair in Child Studies.
Conflicts of Interest The authors declare that they have no com-
peting interests.
Ethical Approval All necessary ethics approvals were received for
this study. The study procedures, including consent and confidentiality
requirements, were approved by the Hamilton Integrated Research
Ethics Board at McMaster University and the Research Ethics Com-
mittees of the School Boards involved in the study.
Informed Consent Informed consent was obtained from all indivi-
dual participants included in the study.
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Soyeon Kim is a post-doctoral fellow at McMaster University. Her
major research interests include cyber-victimization, mental health,
perception and cognition, and ADHD.
Scott R. Colwell is an Associate Professor at the University of
Guelph. His major research interests include statistical methods as
applied to social and behavioral research, and social issues with
respect to organizational and managerial decision making.
Anna Kata is a research coordinator at McMaster University. Her
research interests include the social determinants of health and public
health issues.
Michael H. Boyle is a Canada Research Chair in the Social
Determinants of Child Health and a Professor at McMaster
University. His research interests include determinants of child
health, measurement of childhood psychopathology, and research
study design.
Katholiki Georgiades is the David R. (Dan) Offord Chair in Child
Studies and an Associate Professor at McMaster University. Her
research interests include inequities in child and adolescent health,
immigrant disparities, and contextual influences (family, school,
community) on child and adolescent health.
J Youth Adolescence