Running head: Bullying across contexts.
Bullying prevalence across contexts: A meta-analysis measuring cyber and traditional
Kathryn L. Modecki 1, 2; Jeannie Minchin1; Allen G. Harbaugh3; Nancy Guerra4; Kevin
1Murdoch University, Australia; 2Arizona State University; 3Boston University;
4University of Delaware; 5University of Victoria
Citation: Modecki, K. L., Minchin, J., Harbaugh, A. G., Guerra, N. G., & Runions, K. C.
(2014). Bullying prevalence across contexts: A meta-analysis measuring cyber and traditional
bullying. Journal of Adolescent Health, 55(5), 602-611.
Purpose: Bullying involvement in any form can have lasting physical and emotional
consequences for youth. For programs and policies to best safeguard youth, it is important to
understand prevalence of bullying across cyber and traditional contexts.
Methods: We conducted a thorough review of the literature and identified 80 studies that reported
corresponding prevalence rates for cyber and traditional bullying/aggression in adolescents.
Weighted-mean effect sizes were calculated and measurement features were entered as
moderators to explain variation in prevalence rates and in traditional-cyber correlations within
the sample of studies.
Results: Prevalence rates for cyber bullying were lower than for traditional bullying, and cyber
and traditional bullying were highly correlated. A number of measurement features moderated
variability in bullying prevalence; whereas a focus on traditional relational aggression was the
key factor that increased correlations between cyber and traditional aggression.
Conclusion: In our meta-analytic review, traditional bullying was twice as common as cyber
bullying. Cyber and traditional bullying were also highly correlated, suggesting that poly-
aggression involvement should be a primary target for interventions and policy. Results of
moderation analyses highlight the need for greater consensus in measurement approaches for
both cyber and traditional bullying.
Implications and contribution summary statement: To safeguard youth against harmful effects
from bullying, policies and programs require a clear picture of prevalence across on-line and off-
line contexts. Meta-analytic findings indicate cyber bullying is considerably less prevalent than
traditional bullying and they are highly correlated. Interventions should focus on reducing
malicious behaviors wherever they occur.
Bullying prevalence across contexts: A meta-analytic review of studies measuring cyber and
Scholars have long recognized that prevention of youth aggression and bullying is a
major public health issue (Olweus & Limber, 2010). Over the last decade, research emphasis has
shifted to the prevention of aggression on-line, where adolescents can both bully and be
victimized (e.g. Erdur-Baker, 2010; Juvonen & Gross, 2008). Data from published studies have
helped illustrate that bullying involvement, either on- or off-line, adversely affects youth
adjustment (Copeland et al., 2014; van Geel, Vedder, & Tanilon, 2014). The realization that
bullying in any context can have lasting physical and emotional consequences has lead parents,
educators and policy makers to embrace intervention efforts, and there is now substantial
educational and clinical interest in programs that help to mitigate bullying’s harmful outcomes
(Olweus & Limber, 2010; Williford et al., 2013). In an effort to better understand the relative
prevalence of on-line and off-line bullying, a growing body of research has examined both in the
same study, yielding evidence useful for designing prevention efforts (see Olweus, 2012; 2013).
However, these studies present mixed findings and vary in their approaches to measurement and
methodological rigor. As a result, scholars lack a clear picture of the extent of the bullying
problem on-line relative to off-line, and how measurement approaches influence reported figures.
In order for policies and programs to safeguard youth against detrimental consequences
from bullying, it first is crucial to delineate the settings in which bullying occurs. Unfortunately,
although numerous studies have examined prevalence of cyber bullying and related constructs
(e.g. cyber aggression, electronic aggression), and a sub-set of work has contrasted prevalence of
cyber bullying with traditional bullying and related constructs (e.g. relational aggression, verbal
aggression), the extent to which adolescents differentially engage in aggression across on-line
and off-line contexts remains ambiguous. In the current meta-analysis, we take stock of findings
to date to understand prevalence rates of both modes of bullying within the context of one
another (Olweus, 2012; 2013). We use the general term “bullying” to describe harmful behaviors
that occur in both cyber and non-cyber contexts, because in practice the distinction between
bullying/aggression labels is not always clear (Finkelhor et al., 2012). Consequently, an
expansive set of behaviors must be captured to empirically distill the literature on harmful
behaviors that occur on- and off-line (Cook et al., 2010). Moreover, whether variability in
prevalence rates is partially due to differences in measurement and/or operationalization of the
bullying/aggression construct remains an empirical question (e.g. Cook et al. 2009; Ybarra et al.,
2012). For example, explicit references to bullying and bullying definitions likely influence study
results (Cook et al., 2009), and these and other measurement factors are considered as moderators
that may help explain variability in rates across studies.
On-line harm towards others has been captured under a broad array of terms including
internet harassment (Ybarra, Diener-West, & Leaf, 2007), internet bullying (Law et al., 2012),
internet aggression (Werner, Bumpus, & Rock, 2010), and cyber bullying (Smith et al., 2008).
Whatever label scholars choose, adolescents likely deploy a core set of behaviors to intentionally
harm others on line (Rivers & Noret, 2010). The prevalence of these behaviors, though, is
inconsistent. Illustratively, rates in the literature for cyber perpetration have ranged from 5.3% to
31.5% (Gradinger et al., 2009; Pornari & Wood, 2010), and for cyber victimization have ranged
from 2.2% to 56.2% (Perren et al., 2010; Pornari & Wood, 2010). Investigations in this arena are
particularly important, because even relatively small prevalence rates may belie harmful effects.
An accumulating body of research suggests concerns about harmful effects of bullying may be
magnified for aggression that transpires on-line (van Geel et al., 2014).
Variability in prevalence rates is not confined to studies of on-line behavior. There are
also wide differences in rates of traditional bullying among studies comparing prevalence of on-
line and off-line behavior. For instance, rates of traditional bullying perpetration have ranged
from 9.68% to 89.6% (Perren et al., 2010; Pornari & Wood, 2010) and there are similar
discrepancies for bullying victimization: 9% to 97.9% (Slonje & Smith, 2008; Pornari & Wood,
2010). As a result of these variations, the pervasiveness of both on-line bullying and off-line
bullying is difficult to ascertain. What could account for such divergent estimates?
Inconsistencies in measurement have featured strongly in explanations of disparate
prevalence rates for both cyber and traditional aggression, and scholars have called for greater
consensus in definition and measurement (Cook et al., 2009; Tokunaga, 2010; Ybarra et al.,
2012). Definitions of bullying vary widely, and not all researchers endorse the most widely cited
characterization of bullying: harmful, repetitive behaviors enacted by a perpetrator who is more
powerful than his or her victim (Olweus, 2012; 2013; Vaillancourt et al., 2008). Definitions of
cyber bullying are particularly controversial and some scholars question the relevance of
imposing these three bullying criteria on the technological realm (Runions et al., 2013; Wolak,
Mitchell, & Finkelhor, 2007). Still, the effect of including a definition on prevalence is
inconclusive (e.g. Espelage & Swearer, 2003). In one experimental study, providing a definition
had no effect on either on-line or off-line victimization rates (Ybarra et al., 2012). However,
several other studies show definitions do affect traditional prevalence rates, increasing
perpetration rates but decreasing victimization rates (Cook et al, 2009; Vaillancourt et al, 2008).
Measurement features may also help to account for variation in figures. First, researchers
operationalize bullying many different ways. Some scholars include behavioral examples thought
to characterize bullying (e.g. “have you posted mean messages on-line ”) and others evoke
implicit definitions (e.g. “have you bullied someone over the internet?” or “have you made fun of
or teased someone on-line”) (Cook et al, 2009; Ybarra et al, 2012). Behavioral examples may
create false positives by priming a broad array of experiences that fall beyond the definition of
bullying (Ybarra et al., 2012). Alternatively, youth may discount experiences that fail to align
with provided examples, and behavioral descriptors could produce lower bullying figures (Cook
et al., 2010). The complexity of measuring bullying is further muddied by use of the very term
“bully”, which may decrease reported prevalence (Cook et al., 2009; Ybarra et al.), and use of
behavioral descriptors such as “made fun of”, and “tease”, without highlighting an explicit
intention to harm, which may increase prevalence (Vaillancourt et al., 2008; Vandebosch & Van
Cleemput, 2009). Finally, sampling bias poses a general challenge to community-based research
and may lead to distorted estimates of prevalence (Olweus & Alsaker, 1991). Thus an analysis of
convenience versus random samples may help to explain variability in rates across studies.
The degree of correlation between on-line and off-line aggression involvement is also of
considerable translational significance and accordingly, has generated substantial scholarly
discussion (e.g. Olweus, 2012; Salmivalli, Sainio, & Hodges, 2013). Evidence of strong
correlations bolsters arguments that policy and interventions should focus on poly-aggression
involvement, targeting how youth treat each other on- and off-line to reduce malicious behaviors,
generally (e.g. Wang, Iannotti, Luk, & Nansel, 2010). Weak correlations, conversely, intimate
technology has expanded the population of aggression-involved youth, and suggest cyber-
focused interventions are a more useful public-health approach. Unfortunately, not unlike studies
of prevalence, the literature characterizing overlap in cyber and traditional bullying is marked by
heavy variation. Illustratively, some studies identify relatively weak correlations between cyber
and traditional involvement, and suggest technology may be galvanizing a new breed of
aggressive perpetrators and/or victims (Dempsey et al., 2009; Ybarra & Mitchell, 2004). Other
studies report very strong correlations (Dempsey et al., 2011; Olweus, 2012), supporting
interventions that target harmful behaviors generally, including but not limited to the cyber
Just as for prevalence, disparate measurement approaches may account for some of the
variation in correlations reported across studies. For instance, type of traditional aggression likely
impacts correlations, and relational aggression and cyber aggression may be especially
concomitant (e.g., Hemphill et al., 2010). Further, many traditional bullying scales investigate
behaviors specific to the school-environment by measuring bullying “at school” or by “students”
(Turner et al., 2011). Distressing on-line interactions can occur with school-based peers, but can
also occur with peers known only through on-line contact, and with complete strangers (Wolak et
al., 2007). Thus studies examining traditional bullying “at school” may describe less
correspondence between traditional and cyber bullying than studies examining traditional
bullying within a wider landscape. Conversely, measures that narrowly probe cyber incidents
with “students” or originating “at school” may yield higher correlations (Agatston et al., 2007).
Finally, just as for prevalence, sampling bias may affect apparent overlap between cyber and
traditional aggression, and needs to be accounted for in considering variation in correlations
In all, these discrepancies are important because policy-makers, schools, and parents are
hungry for information about where to intervene with youth to diminish their harmful behavior
involvement (Sabella et al., 2013). Before translating findings for advocacy and intervention, it is
crucial to first present accurate estimates regarding the extent of the problem and to demonstrate
how varying concepts and measures influence results. In an effort to provide a starting point and
create a context for the literature to date, we conducted a meta-analysis of research measuring
both cyber and traditional bullying in adolescents. Eighty studies reported prevalence rates for
cyber and traditional perpetration, for cyber and traditional victimization, or for all four types of
bullying. Following best practice recommendations from Lipsey and Wilson (2001) we coded
and analyzed studies that together provide a sample of 335,519 youth, to estimate mean
prevalence rates and identify the degree of overlap between cyber and traditional bullying across
studies. Further, we examined whether study features accounted for systematic variability in
prevalence rates and correlations across studies.
Study selection. We employed several methods to locate possible eligible studies. First,
we conducted a database search using PSYCInfo, PubMed, Educational Resources Information
Centre (ERIC), Proquest Dissertations and Theses, Scopus, and Google Scholar entering a
combination of the following keywords: adolescent, juvenile, teenage, bully, victim, perpetrator,
aggression, cyber, on-line, internet, text, and electronic. To decrease publication bias, we
included dissertations and theses. Finally, we reviewed the reference list of each located article
for studies that could potentially be included in our sample. We limited the studies in our
analyses to those that met the following inclusion criteria: written in English; included an
adolescent sample (age range including at least some youth ages 12-18); was self-report with a
recall period immediately preceding data-collection; reported concurrent prevalence rates for
both cyber and traditional bullying; focused on peer (not sibling) bullying; and was not based on
a unique sub-set of youth (e.g. deaf youth). Our original search drew 1,951 studies, and from the
articles originally identified, our final sample consisted of 80 studies that were coded for study
and measurement level effects. Supplementary Table 1 specifies our decision steps for study
exclusion and Supplementary Table 2 outlines the 87 studies that were excluded based on a full
Study coding. First, the first and second authors created a provisional coding form and
manual. Next, they independently pilot-coded five studies, and then revised the coding criteria
based on any identified issues. These same articles were re-coded at the end of study, with
perfect test-retest reliability. After pilot testing, the same researchers independently coded all of
the studies, with a 98% agreement rate between coders. When coding information was missing or
unclear, we contacted the study’s primary author to request the required information. Because we
were primarily interested in the moderating effect of measurement, where possible, we located
the specified instrument from each study to verify definitions, wording, and items. Further,
because sample representativeness arguably affects prevalence estimates, we coded for whether
researchers introduced a degree of randomness into their protocol, such as random-digit dialing,
or randomly selecting classes within schools.
Statistical analyses. We used fixed effects models for several reasons. First, we aimed to
understand variability in prevalence rates across existing studies, rather than extrapolate to a
wider population of studies that may not have been included in the analysis. Particularly because
cyber bullying is an emerging area of study, we deemed this a reasonable assumption. Further,
too much between-study variation results in under-powered random effects models, and given the
wide-range in prevalence rates across studies, we presumed that random error would make it
particularly difficult to detect small to moderate moderator effects. Nonetheless, we tested this
last assumption by running exploratory random effects analyses.
Prevalence rates. We divided all prevalence rates by 100, resulting in proportion
estimates for each dependent variable. Only one prevalence rate was recorded for each dependent
variable within each study, to avoid violating independence assumptions. Because calculating an
effect size directly from a proportion underestimates the confidence interval around the mean, we
converted each proportion to a logit using a log transformation (Lipsey & Wilson, 2001). For
each dependent variable, we computed a weighted mean effect size so that studies based on
larger samples were given more weight than those based on smaller samples (Lipsey & Wilson,
2001). Results were transformed back to proportions from logits for ease of interpretation.
Degree of association. Effect sizes for degree of association between cyber and
traditional aggression were represented using Pearson correlations. We converted Spearman’s rho
and Kendal’s tau to Pearson’s r values based on Gilpin (1993) and used beta-coefficients to
estimate correlation coefficients based on Peterson and Brown (2005).
Differences in prevalence rates. To compare whether each moderator variable was more
strongly associated with either cyber or traditional aggression, we calculated Z values using the
z = B1 – B2
se(B1)2 + se(B2)2
The Z values, which can be thought of as beta weights for each independent variable, controlled
for other independent variables in the model.
Mean prevalence rates. As described in the top-half of Table 1, cyber bullying was less
prevalent than non-cyber (traditional) bullying across both perpetration and victimization. The
sample-size weighted mean prevalence rates across contexts were remarkably similar for cyber
perpetration (16; 95% CI: .15-.16) and victimization (.15; 95% CI: .15-.15), and for traditional
perpetration (.35; 95% CI: .34-.35) and victimization (.36; 95% CI: .36-.36). Cochran’s Q
statistics, calculated to assess homogeneity of the effect sizes for each dependent variable and
following a chi-square distribution, indicate substantial variability in prevalence rates across all
four outcomes. Figure 1 displays the prevalence rates graphically in the form of proportion of
youth reporting being a perpetrator and Figure 2 graphically displays proportion of youth
reporting being a victim. Notably, examination of the pattern of study findings across both
figures reveals that the majority of studies report higher offline rates than on-line rates.
Prevalence-moderator effects. The bottom-half of Table 1 describes the results of
moderator analyses focusing on measurement effects. It is noteworthy that almost all of the
measurement effects influenced both cyber and traditional aggression in the same direction.
However, there are clear differences in the strength of measurements effects on cyber and
traditional aggression, and the pattern of strength differences are nearly identical across
perpetration and victimization.
Beginning with the first listed predictor (middle of Table 1), results indicate that using a
definition with a clear reference to intent to harm, repetition, and power imbalance, is related to
higher prevalence rates for both cyber and traditional bullying, though the effect is stronger for
traditional than for cyber. Further, providing respondents with behavioral examples is generally
linked with lower prevalence estimates, with one exception. Behavioral examples are associated
with higher traditional perpetration rates.
Working downward on the table, results demonstrate that including the term “bully” is
related to lower prevalence rates for all four aggression indices, though the effect is stronger for
cyber than for traditional bullying. Inclusion of the term “fun” or “tease” shows an opposite
pattern and is associated with more prevalent aggression, with a stronger effect for traditional
than cyber bullying. Finally, randomization is associated with lower prevalence rates across the
four dependent variables, though the negative effect of researcher-imposed randomization is
stronger for cyber than for traditional aggression.
Mean degree of association between cyber and traditional bullying. Described at the top
of Table 2, the sample-size weighted mean correlation between cyber and traditional perpetration
across studies was 𝑟 = .47 (95% CI: .47-.47), and the association between cyber and traditional
victimization was 𝑟 = .40 (95% CI: .40-.41). These results indicate a moderately strong
association. Again the Q statistics indicate a great deal of variability in degree of association
between cyber and traditional bullying across studies. The correlations are displayed graphically
in Figure 3. The pattern of study findings reveals that the correlations between cyber and
traditional perpetration are generally higher than those between cyber and traditional
Correlation-moderator effects. The bottom of Table 2 describes moderator effects on the
degree of association between cyber and traditional aggression. Accounting for the range of
estimated correlations within these models, the positive coefficients indicate a stronger degree of
association and the negative coefficients indicate a weaker degree of association between cyber
and traditional bullying. Thus, focusing on perpetration (bottom left side of Table 2), associations
based on relational perpetration have stronger correlations between cyber and traditional bullying
(Z = .84; 𝑟 = .69), whereas associations based on general traditional bullying or other bullying
subtypes have relatively weaker, though still positive associations (intercept; Z = .48; 𝑟 = .45).
Further, studies based on school-centered traditional perpetration report weaker correlations (Z =
.41; 𝑟 = .39) relative to studies that do not focus on school-based traditional perpetration
(intercept; Z = .48; 𝑟 = .45). Studies with some degree of researcher-imposed randomization also
report weaker, though still positive associations between cyber and traditional perpetration (Z =
.37; 𝑟 = .35).
The bottom right side of Table 2 describes moderator effects on the correlation between
cyber and traditional victimization. Only relational victimization significantly impacted the
correlation. Specifically, traditional relational measures have stronger, positive correlations
between cyber and traditional victimization (Z = .65; 𝑟 =.57) than do traditional measures that
are not strictly based on relational victimization (intercept; Z = .32; 𝑟 = .31).
Exploratory analyses. Our final set of analyses was exploratory and examined whether
effects would hold under the more conservative random effects models. There were only small
differences in prevalence rates when comparing fixed and random effects models. Finally,
random effects analyses (available upon request) resulted in few statistically or substantively
meaningful moderator effects. There appeared to be too much random variability in prevalence
rates between studies for moderators to systematically explain their variance.
Summary. This meta-analysis is the first to take stock of the literature on prevalence of
cyber relative to traditional bullying. In so doing, we found that in the context of studies that
have measured both forms of aggression to date, cyber bullying was far less prevalent, with rates
that are less than half those of traditional bullying. Moreover, the pattern of study findings was
consistent both within and across studies; the majority of studies reported higher offline rates
than on-line rates. These trends have recently been implied, but not yet tested empirically across
existing studies (Olweus, 2012; Salmivalli et al., 2013). Correlations between traditional and
cyber bullying were also relatively strong, and when relational aggression was considered, the
associations between on-line and off-line bullying were particularly robust. Findings suggest that
cyber and traditional measures may reflect different methods of enacting a similar behavior
(being mean to others) and the form (on-line versus off-line) of bullying may be less important
than the conduct (Williams & Guerra, 2007).
Prevalence and moderator effects. Across eighty studies that report rates for cyber and
traditional perpetration, cyber and traditional victimization, or both, we found mean prevalence
rates of 35% for traditional bullying involvement and 15% for cyber bullying involvement.
Consistent with our pattern of findings, Olweus (2012) found that cyber bullying was
considerably less prevalent than traditional bullying within large samples from the US and
Norway. Our findings are also consistent with research generated by Salmivalli and colleagues
(2013), who found that cyber bullying was far less prevalent than traditional bullying within their
KiVa data. Based on disparate prevalence rates, these scholars have asserted that interventions
should not shift attention from traditional to cyber settings. Our findings are consistent with this
hypothesis and suggest interventions that exclusively target cyber contexts are neglecting a
highly salient setting for preventing youthful bullying, at least in terms of prevalence. However,
we maintain that rather than focusing on the setting (e.g. on-line versus off-line) of bullying,
efforts to prevent detrimental effects of bullying may be best served by instead focusing on the
behavior of bullying—working to reduce harmful conduct wherever it occurs (Williams &
Guerra, 2007; Williford et al., 2013).
David-Ferdon and Hertz (2007) noted previously, and our results substantiate, that
measurement factors systematically drive variability in cyber bullying prevalence rates. Notably,
similar measurement dimensions also moderated traditional perpetration results, in the same
direction and often with stronger effects. For example, including a definition with three main
bullying criteria (intentionality, repetition, and power-imbalance) increased prevalence across all
four outcomes. It may be that without a definition, youth conjure aggressive incidents such as
physical fighting (Vaillancourt et al., 2008), which are less common than bullying (Nansel et al.,
2003). In contrast, behavioral examples decreased prevalence for three of four outcomes. One
interpretation of this negative effect is that examples help to further clarify and encapsulate
bullying definitions (Vaillancourt et al., 2010). An alternative interpretation is that youth are
under-reporting harmful incidents because behavioral examples are not representative of
adolescents’ experiences (Card, 2013). Further research is needed to reconcile these different
A number of other measurement features also moderated prevalence rates and in expected
directions. For example, use of the term “bully” also produced lower prevalence rates across
outcomes, and this effect was particularly strong for cyber aggression. Conceivably, the term
“bully” and its associated negative implications may deter youth from conceptualizing their
involvement as such (Guerra et al., 2011), and this hesitancy may be especially salient for cyber
aggression. Not surprisingly, the use of terms such as fun/tease increased rates across outcomes;
perpetrators and victims may be unsure how to characterize ambiguous behaviors such as teasing
or poking-fun (Guerra et al.). Notably, whether or not researchers imposed any randomization
was one of the stronger predictors of prevalence. Randomization lead to lower prevalence and
this effect was particularly strong for cyber aggression. These findings highlight a need for
random rather than convenience samples within the field.
Cyber and traditional correlations and moderator effects. Many previous studies have
measured cyber and traditional aggression, but ignored the overlap between the two (Salmivalli
et al., 2013). We identified mean correlations between cyber and traditional bullying across
studies to date and characterized measurement features that help to account for this relation. Our
meta-analytic results indicate fairly high correspondence between cyber and traditional
perpetration (𝑟 = .47) and between cyber and traditional victimization (𝑟 = .40). The associations
between on-line and off-line aggression were also heavily influenced by type of traditional
aggression in the model and correlations based on traditional relational aggression were
particularly strong, perpetration (𝑟 = .69) and victimization (𝑟 = .57).
These findings suggest more behavioral similarities across on-line and off-line settings
than differences. What we can infer from this heavy overlap is that focusing exclusively on cyber
contexts may not be the optimal approach to reducing harmful behaviors among youth. Instead,
interventions should target how youth treat each other to reduce cruelty and meanness and
increase respectful and positive behaviors, generally, across settings (Salmivalli et al., 2013). Of
course, on-line and off-line bullying were not perfectly correlated and certain new victims may
stem from the on-line environment. There could be many explanations for this, including access
to internet, cell phones, or the ease of cyber communication and its lack of personal cues (Li,
Smith, & Cross, 2012). More typically, however, youth who are involved in cyber bullying are
also involved in traditional bullying.
Only a few other measurement features influenced the degree of association between
cyber and traditional bullying, and these features affected only perpetration, not victimization.
First, because cyber bullying can involve non-school based peers (Turner et al., 2011), we
hypothesized that school-based traditional instruments might diminish correspondence between
cyber and traditional bullying. As expected, school-based measures decreased overlap for
perpetration, though the effect was weak. Second, sampling bias is a practical concern for
community-based research and we hypothesized that researcher-imposed randomization would
affect correspondence between cyber and traditional bullying. Consistent with this notion,
researcher-imposed randomization functioned to decrease overlap in perpetration, though again
this effect was small. These results again underscore the need for random rather than convenience
samples within the literature.
Limitations. Our findings must be considered in light of the study’s limitations. First, only
a subset of studies within the larger literature reported both cyber and traditional bullying
prevalence (n = 80). Because of large between-study variance, we used fixed effects analyses, so
that results must be interpreted only within the context of studies in our sample. As the literature
grows, we encourage further meta-analytic work based on larger samples under a random-effects
It is also worth considering whether traditional estimates describe harmful behaviors that
occur solely off-line. It is possible that, lacking qualifiers to designate off-line events, some
traditional assessments may capture malicious actions that occur on-line. However, this potential
bias may be less likely within our data, because we examined studies that considered both types
of bullying and youth may have been primed to consider these behaviors separately.
Finally, concerns about cyber bullying’s prevalence are magnified by worries about its
particularly deleterious consequences. Illustratively, one recent meta-analysis indicates cyber
bullying represents a particularly problematic mental health risk for victims (van Geel et al.,
2014). This meta-analysis cannot speak to the different effects of cyber relative to traditional
bullying. Nor can results address whether putative mediators of change are similar for both types
of behaviors (e.g. Modecki et al., 2013; Williford et al., 2013). Rather, our findings anchor
prevalence estimates across studies to describe the contexts in which bullying occurs, and further
meta-analytic research must address antecedents and outcomes of both types of bullying.
Implications for policy and prevention. This is the first study to undertake a systematic
review of the literature to uncover mean prevalence rates for cyber relative to traditional bullying.
In doing so, we find that cyber bullying is far less prevalent than traditional bullying.
Notwithstanding bullying within the on-line environment may have particularly pernicious
effects on adolescent’s health (van Geel et al., 2014), their involvement in this phenomenon
remains less common at present. Moreover, cyber and traditional aggression were highly
correlated, indicating that youth are similarly involved in aggression across on-line and off-line
contexts. The implication of this relatively heavy overlap is that the form of communication may
be less important than the behavior itself. Given that traditional bullying is more widespread than
cyber bullying, and given the heavy overlap between both forms of aggression, policies and
programs may be best served by improving overall functioning in youth engaged in aggression or
bullying more broadly, rather than focusing on hurtful behaviors that occur within a specific
setting (Olweus, 2013; Salmivalli et al., 2013).
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Prevalence estimates for cyber and traditional bullying (top) and measurement effects on cyber versus traditional prevalence (bottom).
Cyber Est. (SE)
Trad. Est. (SE)
Cyber Est. (SE)
Trad. Est. (SE)
Note. Estimates for mean confidence intervals are transformed from logits. All Q statistics significant at p < .001. All variables coded as 0 = no; 1
= yes. Definition = measure includes definition with three key bullying criteria. Example = measure includes example of targeted behaviors. Bully
= measure references bully and/or bullying. Fun/Tease = measure includes making fun or teasing without indicating harm or hurt.
***p < .001; **p <0.01; *p <0.05.
Degree of association (top) and predictors of degree of association between cyber and traditional
Cyber & Trad. Perpetration
Cyber & Trad. Victimization
Q(35) = 7821.3
Q(36) = 6290.8
Perpetration Est. (SE)
Victimization Est. (SE)
Note: All Q’s significant at p < .001. All variables coded as 0 = no; 1 = yes. Trad relational =
correlation based on relational bullying vs. combined or other bullying types. School/student trad
= traditional bullying measure references students and/or school. School/student cyber = cyber
bullying measure references students and/or school.
***p < .001.
Figure 1. Error bar charts with effect sizes and 95% confidence intervals from studies reporting
prevalence rates of cyber and traditional perpetration in adolescents.
Note. Size of point scaled based on sample size.
Figure 2. Error bar charts with effect sizes and 95% confidence intervals from studies reporting
prevalence rates of cyber and traditional victimization in adolescents.
Note. Size of point scaled based on sample size.
Figure 3. Error bar charts with effect sizes and 95% confidence intervals from studies reporting
overlap in cyber and traditional bullying in adolescents.
Note. Size of point scaled based on sample size.