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Impacts of traditional bullying and cyberbullying on the

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DOI: 10.1002/pits.21841

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Psychology in the Schools, Vol. 00(0), 2015 ⓍC 2015 Wiley Periodicals, Inc.
View this article online at wileyonlinelibrary.com/journal/pits DOI: 10.1002/pits.21841

IMPACTS OF TRADITIONAL BULLYING AND CYBERBULLYING ON THE MENTAL

HEALTH OF MIDDLE SCHOOL AND HIGH SCHOOL STUDENTS
CRAIG N. HASE AND SIMON B. GOLDBERG
University of Wisconsin

DOUGLAS SMITH, ANDREW STUCK, AND JESSICA CAMPAIN

Southern Oregon University

A critical debate within the field of school psychology has centered on the relationship between
bullying and cyberbullying in terms of prevalence, overlap, and impact. The current study
sought to address the following questions: (1) Does cyberbullying create new victims or merely
a new means of victimization? (2) Does cyberbullying uniquely contribute to negative outcomes
above and beyond those of traditional bullying? Utilizing an anonymous survey to examine
students’ experiences with cyberbullying, traditional bullying, and negative psychological
symptoms, it was found that the vast majority of students who were bullied online were also
victims of in-person bullying. Both forms of victimization were independently associated with
negative outcomes. However, when controlling for traditional bullying, cyberbullying did not
remain a predictor of negative mental health outcomes. In contrast, when controlling for
cyberbullying, traditional bully- ing remained a significant predictor of negative mental health
outcomes. These results suggest that although traditional and cyber forms of bullying tend to
target the same victims, traditional bullying is more uniquely associated with negative
psychological outcomes. ⓍC 2015 Wiley Periodicals, Inc.

In the last 5 years, cyberbullying has captured considerable media attention. Based on
empirical reports of the prevalence and outcomes associated with cyberbullying, as well as several
now-famous suicides related to online victimization, media outlets have labeled this form of
relational aggression an “epidemic” (Gomez, 2010; Yu, 2013). Likewise, some scholars have
theorized that cyberbullying may be more harmful than traditional bullying (Bonanno & Hymel,
2013; Campbell, Spears, Slee, Butler, & Kift, 2012), due to the possibility of a large audience,
anonymity, nearly unlimited access to victims, and lower levels of supervision (Sticca & Perren,
2013).
Alternatively, Olweus (2012) and others have argued that cyberbullying may be an “over-
rated phenomenon” (p. 1). This conclusion is based, in part, on previous research that estab-
lished a large overlap between victims of traditional and cyberbullying (Hinduja & Patchin,
2008), as well as evidence suggesting that online victimization might be better understood
as an extension of in-person bullying (Li, 2007). In addition, Olweus found that, when sta-
tistically modeling traditional and cyberbullying together, traditional bullying was a more ro-
bust predictor of negative outcomes than cyberbullying. A major thrust of the current research
is to examine more closely the overlapping nature of traditional and cyber forms of bully-
ing among middle school and high school students and the impact of each on victims’ mental
health.

The authors wish to thank William T. Hoyt and Stephen M. Quintana for contributions to the present work, as
well as a seed grant from the President’s office at Southern Oregon University. The authors are not aware of any
conflicts of interest in regard to the present study.
Correspondence to: Craig N. Hase, Department of Counseling Psychology, University of Wisconsin-Madison,
335 Education Building, 1000 Bascom Mall, Madison, WI 53706. E-mail: cnhase@wisc.edu

1
2 Hase et al.

Traditional Bullying
Bullying has been defined as aggressive behavior intended to harm another that is repeated
over time and involves a power imbalance (Olweus, 1993). Over 40 years of research has
documented that bullying occurs frequently in schools in the United States and internationally.
One landmark study (Nansel et al., 2001) of over 15,000 sixth through tenth graders in the United
States found that 16% of those surveyed reported being victims of bullying. A recent large
international study (N 202,056) found that although bullying rates vary by country and gender, on
= 26% of adolescents were involved as a bully, a victim, or as both (Craig et al., 2009).
average,
The deleterious effects of bullying are also well established. Adolescents who are bullied
miss more school (Kochenderfer & Ladd, 1997), show signs of poorer school achievement
(Nakamoto & Schwartz, 2010), and report loneliness (Olenik-Shemesh, Heiman, & Eden, 2012),
poor health (Fekkes, Pijpers, Fredriks, Vogels, & Verloove-Vanhorick, 2006), and greater levels of
anxiety (Juvonen, Graham, & Schuster, 2003) and depression (Fekkes, Pijpers, & Verloove-
Vanhorick, 2004) than their nonvictimized peers. Studies have linked bullying to suicidal ideation
(van der Wal, de Wit, & Hirasing, 2003), while showing a strong relationship between frequency
of bullying episodes and risk of suicidal ideation and suicide attempts (Klomek, Marrocco,
Kleinman, Schonfeld, & Gould, 2007).

Cyberbullying
By comparison, the study of cyberbullying is still relatively new. Cyberbullying has been
defined as “bullying through the use of electronic venues, such as instant messaging, e-mail, chat
rooms, websites, online games, social networking sites, and text messaging” (Kowalski & Limber,
2013, p. S13). Due to discrepant definitions, differences in age groups studied, and a host of
newly developed measures for assessing victimization, prevalence estimates range from as low as
4% (Ybarra & Mitchell, 2004) to as high as 72% (Juvonen & Gross, 2008), with most studies
reporting that about 20–40% of adolescents have been victimized online or by text (Tokunaga,
2010).
In the past decade, a number of negative outcomes have been associated with being
victimized online or by text (for a review see Kowalski, Giumetti, Schroeder, & Lattanner, 2014).
Cyberbully- ing has been linked to depression (Ga´mez-Guadix, Orue, Smith, & Calvete, 2013),
social anxiety (Schenk & Fremouw, 2012), reduced self-esteem (Katzer, Fetchenhauer, &
Belschak, 2009), in- creased substance use (Ybarra, Diener-West, & Leaf, 2007), and academic
problems (Patchin & Hinduja, 2006). Moreover, several studies have found higher incidence of
suicidality among victims of cyberbullying (Bauman, Toomey, & Walker, 2013).

Overlap Between Bullying and Cyberbullying

The overlap between bullying and cyberbullying has recently become a topic of interest.
Although reported overlap rates vary somewhat, there is a growing consensus that many, if not
most, adolescents who are victimized online or by text messaging also experience traditional
forms of bullying at school (Hinduja & Patchin, 2012). Ybarra et al. (2007) reported an overlap of
only 36% between the two groups. However, the researchers assessed overlap in this study via a
single item, and only queried those students who had indicated being the victim of cyberbullying
in the past year. More comprehensive comparisons, assessed with multiple items and administered
to both traditionally bullied and cyberbullied victims simultaneously, have yielded overlaps of
65% (Juvonen & Gross, 2008) and 85% (Hinduja & Patchin, 2008), with some investigators
reporting overlap rates as high as 88% and 93% (Olweus, 2012; Smith et al., 2008).
Given the overlap in students who are bullied at school and students who are victimized online
and by text, some scholars have begun to question the additive harm of cyberbullying. As mentioned
Psychology in the Schools DOI: 10.1002/pits
 Impacts of Traditional Bullying and Cyberbullying 3

previously, Olweus (2012) in particular, has challenged the more sensationalized accounts of the
cyberbullying phenomenon. Referencing several large samples, Olweus claims that, far from
being a qualitatively distinct phenomenon, cyberbullying is simply an extension of in-person
victimization. Moreover, he asserts that cyberbullying does little to intensify the effects of
traditional bullying, and that its associations with negative outcomes fall to nonsignificance when
modeled in the same analysis with in-person victimization.
Olweus’s (2012) claims have been challenged (Hinduja & Patchin, 2012; Menesini, 2012;
Smith, 2012). In fact, in comparisons of outcomes associated with bullying and cyberbullying,
three recent studies have found that cyberbullying predicts negative outcomes above and beyond
traditional bullying (Bonanno & Hymel, 2013; Campbell et al., 2012; Perren, Dooley, Shaw, &
Cross, 2010). Findings remain mixed, however, as a study reported that, when controlling for
bullying, cyberbullying no longer predicts depression, and associations between cyberbullying
and anxiety symptoms attenuate to the point of near nonsignificance (Dempsey, Sulkowski,
Nichols, & Storch, 2009). In addition, several studies (Bauman & Newman, 2013; Sticca &
Perren, 2013) have reported that cyberbullying, in and of itself, is no worse than traditional
bullying, with most students in these samples reporting that they perceive bullying as more
harmful than cyberbullying.

Research Questions
Debate continues among researchers concerning the degree and significance of the overlap
between cyberbullying and traditional forms of bullying. The current study sought to address
these questions in a large sample (N 1,225) of middle and high school students in the Pacific
Northwest. The intention of the=researchers was twofold. Hypothesis 1: It was hypothesized that
there would be statistically significant and moderately large overlap between those victimized
online or by text and those victimized in-person. Hypothesis 2: Based on Olweus’s (2012)
findings, it was hypothesized that in-person bullying would be a more robust predictor of negative
outcomes than cyberbullying.

METHOD
Participants
One thousand two hundred and twenty-five adolescents from five middle and high schools in
Southern Oregon (586 female, 632 male, 7 did not report gender) participated in a school-based
survey in the fall of 2012 (Table 1). Although the geographic region from which the sample was
drawn was primarily rural, it also included a university community and a more populous middle-
sized city. The mean age was 14.15 years old (SD 1.94, range 12–18). The sample included
= was predominantly
students registered in grades 6 through 12 and = Caucasian (69.80%, n 855),
=
reflecting the demographics of the communities sampled. Other racial/ethnic minority individuals
included were Latino (13.31%, n 163), Asian or Asian American (4.24%, n 52), African
American (3.35%, n= 41), or bi- or multiracial (7.35%, n 90).=The study followed procedures
=
approved by the IRB at Southern Oregon = University. Passive consent was obtained from parents
for all participants.

Measures
California Bullying Victimization Scale. The eight-item California Bullying Victimization
Scale (Felix, Sharkey, Green, Furlong, & Tanigawa, 2011) was used to measure victims of
bullying. Items included experiences perpetrated in the past month “where the intent was mean or
hurtful,” such as teasing, rumors, exclusion, physical assault, physical threats, sexual jokes, and
damage to belongings. Students indicated on a 5-point Likert scale whether they experienced
Psychology in the Schools DOI: 10.1002/pits
4 Hase et al.
these acts not in the past month, once in the past month, 2 or 3 times in the past month, about
once a week, or several times a week. A total bullying victim score was calculated by averaging
across all items.

Psychology in the Schools DOI: 10.1002/pits

 Impacts of Traditional Bullying and Cyberbullying 5

Table 1
Sample Demographics

Variable n Mean (SD) Percentage

Gender
Female 587 47.88
Male 632 51.55
Missing 7 0.56
Age 14.15 (1.94)
Ethnicity
Asian or Asian American 52 4.24
Caucasian or White 856 69.80
Black or African American 41 3.35
Hispanic, Latino (e.g., Mexican American) 163 13.31
Other 90 7.35
Missing 24 1.96

Higher scores indicated greater incidence of victimization. The possible range of mean scores was
0 to 4. Cronbach’s α reliability estimate was acceptable (α .82). In addition, a categorical variable
=
(indicating bullied or not bullied) was computed. Based on prior work (Olweus, 2012), a student
is classified as traditionally bullied if she or he reported experiencing one or more forms of in-
person bullying two or three times or more in the past month.
Cyberbullying Questionnaire. The nine-item Cyberbullying Questionnaire (Ang & Goh,
2010) was used to measure cyberbullying victimization. Items included experiences perpetrated
in the last month “where intent was mean or hurtful,” such as having online jokes posted, being
purposely left out of an online group, cruel or untrue postings, having a computer or website
hacked, online threats, posts meant to get the victim in trouble, rude messages, posting of online
rumors or gossip, and being tricked into providing private information. Students indicated on a 5-
point Likert scale whether they experienced these acts not in the past month, once in the past
month, 2 or 3 times in the past month, about once a week, or several times a week. A total
cyberbullying victim score was then calculated. Higher scores indicated greater frequency of
victimization. The possible range of mean scores was 0 to 4. Cronbach’s α reliability estimate
=
was acceptable (α .90). As above, a categorical variable (indicating bullied or not bullied) was
computed. A student is classified as cyberbullied if she or he reported experiencing one or more
forms of cyberbullying two or three times or more in the past month (Olweus, 2012).
Strengths and Difficulties Questionnaire. Psychological symptoms were measured using the
Psychological Symptoms subscale of the Strengths and Difficulties Questionnaire – U.S.
Normative Sample (Bourdon, Goodman, Rae, Simpson, & Koretz, 2005). Students answered
questions on a 4-point Likert scale ranging from “not at all true” to “very much true.” There were
five items related to psychological symptoms (“I get a lot of headaches, stomachaches, and
sickness,” “I worry a lot,” “I am often unhappy, downhearted, or tearful,” “I am nervous in new
situations,” “I have many fears, I am easily scared.”). Internal consistency reliability was
= of coverage for the
acceptable (α .75), particularly given the small number of items and breadth
items.

Data Analysis
Data were analyzed in the R statistical software program (R Development Core Team, 2013).
Paired t-tests compared reported rates of each type of victimization. The overlap between in-person
Psychology in the Schools DOI: 10.1002/pits
6 Hase et al.

Table 2
Victimization and Psychological Symptoms Descriptive Statistics

Measure n Mean (SD) Range α

In-person victimization 1225 0.53 (0.69) 0–4 .82

Cyber victimization 1219 0.17 (0.46) 0–4 .90
Psychological symptoms 1216 2.09 (0.72) 1–4 .75

Note. In-person victimization assessed using the California Bullying Victimization Scale (Felix et al., 2011). Cyber victim-
ization assessed using the Cyberbullying Questionnaire (Ang & Goh, 2010). Psychological symptoms assessed using the
Strengths and Difficulties Questionnaire – U.S. Normative Sample (Bourdon et al., 2005).

and online victimization was examined with both correlations and frequency tables (with Fisher’s
exact test), as has been recommended (Kowalski et al., 2014; Olweus, 2012). In particular,
continuous and dichotomized variables for both forms of bullying (i.e., 0 did not occur, 1
= using the cutoff
occurred) were used to report overlap. Frequencies were examined = of at least one
experience of a given type of bullying (i.e., traditional, cyber) occurring twice per month or
more. In-person and online victimization rates were entered in regression models as predictors of
psychological symptoms, first individually and then simultaneously. Kernel density plots of these
bivariate and partial regression coefficients were created using R. In addition, models were run
separately for participants of middle and high school age as well as for female and male
participants. Regression models that included formal tests of interaction (gender or age by
bullying or cyberbullying as predictors of emotional symptoms) were conducted as well.

RESULTS
Means, SDs, and internal consistency reliability coefficients are reported in Table 2 for the
two forms of victimization as well as psychological symptoms. Internal consistency was adequate
for all measures (α values > .70). Almost half of the participants (44.57%) were classified as
victims of in-person bullying based on the categories described above, although the overall mean
frequency (mean [M] 0.53, SD 0.69) was between none in the past month and once in the past
= =
month. A minority of participants (16.32%) were classified as victims of cyberbullying. The
average frequency of these events (M 0.17, SD 0.46) was also fairly low (between none in the
= in the past= month). Mean psychological symptom scores were 2.09 (SD
past month and once
=
0.72), indicating participants on average endorsed the five psychological symptoms as “a little
true.” This mean score is within the low-to-medium difficulty range (Bourdon et al., 2005).

Relative Contribution of In-Person and Online Bullying to Psychological Outcomes

Paired t-tests were used to compare rates of in-person versus online victimization. In-person
bullying occurred much more frequently (t[1,218] 25.14, p < .001, d .60). The same pattern
= =
was found suggesting higher rates of in-person bullying (all ps < .001) for all demographic
groups. Following procedures recommended elsewhere (Kowalski et al., 2014; Olweus, 2012), we
ex- amined the overlap between online and cyberbullying. For participants experiencing online
bullying defined as described above (n199), the vast majority also reported experiencing some in-
person bullying (n = 185, 93.0%, odds ratio 24.38 [13.90, 46.18],
= =
p < .001). In addition, treating bullying as a continuous variable, a large correlation was found
between frequency of in-person and online
bullying (r .71, p < .001).
= of regression models examined the relationship between rates of in-person and
A series
online victimization with psychological symptoms. Victimization rates were entered first
Psychology in the Schools DOI: 10.1002/pits
 Impacts of Traditional Bullying and Cyberbullying 7
individually and

Psychology in the Schools DOI: 10.1002/pits

8 Hase et al.

Table 3
In-Person and Cyber Victimization Predicting Psychological Symptoms in Individual and Simultaneous Re-
gression Models

Outcome Model Type Predictor t(df) p β (95% CI)

Psychological symptoms Individual In-person victimization 9.98 (1,213) <.001 0.28 (0.22, 0.33)
Psychological symptoms Individual Cyber victimization 7.33 (1,208) <.001 0.21 (0.15, 0.26)
Psychological symptoms Simultaneous In-person victimization 6.61 (1,207) <.001 0.26 (0.18, 0.34)
Psychological symptoms Simultaneous Cyber victimization 0.590 (1,207) .555 0.02 (−0.05, 0.10)

Note. CI confidence interval. Psychological symptoms assessed using the Strengths and Difficulties Questionnaire – U.S.
=
Note. Psychological Sx psychological symptoms assessed with the Strengths and Difficulties Questionnaire – U.S.
Normative Sample (Bourdon = et al., 2005).

then simultaneously (Table 3). A significant bivariate relationship was found between in-person
victimization and psychological symptoms (β .28, p < .001, Figure 1a) as well as between online
=
victimization and psychological symptoms (β .21, p < .001, Figure 1b). Those reporting more
=
frequent victimization of either type also reported more psychological symptoms. Each form of
bullying also remained a significant predictor of psychological symptoms when controlling for
age, gender, and race/ethnicity.
A simultaneous regression model with both forms of bullying entered as predictors has been
proposed as a method for examining their relative contribution (Kowalski et al., 2014; Olweus,
2012). In this model, only in-person bullying remained a significant predictor of psychological
symptoms (β .26, p < .001, Figure 1c), with the effect size for online victimization
=
nonsignificant and quite small (β .02, p .555, Figure 1d).
= the participant
Because = age range spanned both middle and high schools, models were run
separately for each age grouping. Significance tests for all results reported above (i.e., individual
and simultaneous regression analyses) remained unchanged. Further, models were conducted
including age by bullying (cyber or in-person) interactions. Interaction terms were not significant
in any of these models.
Models were also run separately for female and male participants. Regression models in-
cluding either in-person or cyberbullying alone as predictors of psychological symptoms remained
unchanged, with both forms of bullying significantly predicting symptoms (p < .01). However,
dif- ferences did emerge by gender when the simultaneous models (with both forms of bullying
entered as predictors) were run separately for female and male participants. In a model including
only female participants, in-person bullying remained the only significant predictor of
psychological symptoms (β 0.33 [0.21, 0.45], p < .001), with more bullying associated with
fewer= psychological symp- toms. Cyberbullying was now a marginally significant predictor of
psychological symptoms, but in the unexpected direction (β 0.12 [ 0.23, 0.004], p .058). Further,
in the simultaneous model = −
including −only males, both= in-person bullying and cyberbullying
significantly predicted psychologi- cal symptoms (in-person: β 0.23 [0.13, 0.34], p < .001;
cyberbullying: β 0.11 [0.05, =0.01], p = =
.028). Indeed, subsequent models were run in the full sample (i.e., with both genders) that
included an interaction term for gender along with either in-person or cyberbullying. Although
gender did not significantly interact with in-person bullying (p > .10), a significant interaction
was detected between cyberbullying and gender (estimate 0.19, t[1,198] 2.24, p .025). The
= = =
direction of the interaction term suggests that, when controlling for in-person bullying,
cyberbullying is more closely linked with psychological symptoms for male students than for
Psychology in the Schools DOI: 10.1002/pits
 Impacts of Traditional Bullying and Cyberbullying 9
female.

Psychology in the Schools DOI: 10.1002/pits

10 Hase et al.

FIGURE 1. Kernel density plots of in-person and cyber victimization predicting psychological symptoms. (a and b) Relation-
ships when in-person and cyber victimization are entered individually as predictors. (c) Residualized in-person
victimization (controlling for cyber victimization) predicting psychological symptoms. (d) Residualized cyber
victimization (controlling for in-person victimization) predicting psychological symptoms. Ordinary least squares (OLS)
regression lines are dis- played. Note. Psychological Sx psychological symptoms assessed with the Strengths and
Difficulties Questionnaire – U.S.=Normative Sample (Bourdon et al., 2005).

DISCUSSION
There has been debate in the school psychology literature regarding the relationship between
traditional bullying and cyberbullying. Some have argued that cyberbullying is an extension of
traditional bullying, whereas others point to differences between the two forms of victimization.
In addition, investigators have presented conflicting evidence concerning the negative outcomes
associated with each form of bullying, with some claiming that cyberbullying is a stronger
predictor of negative outcomes, whereas others argue that traditional bullying predicts negative
symptoms above and beyond cyberbullying.
The current study addresses these discrepancies. Our findings confirm that there is
substantial overlap between those who are victimized online and by text and victims of in-person
bullying. In our sample, the vast majority of participants (93.0%) who were cyberbullied also
reported being
Psychology in the Schools DOI: 10.1002/pits
 Impacts of Traditional Bullying and Cyberbullying 1
1
bullied in-person. This finding is consistent with studies (Kowalski & Limber, 2013; Kowalski et
al., 2014; Olweus, 2012; Smith et al., 2008) that have directly compared cyberbullying and
traditional bullying and lends support to researchers who suggest that cyberbullying may be an
extension of traditional bullying (Hinduja & Patchin, 2008; Li, 2007). Further, the current study
confirms prior work reporting that traditional bullying is considerably more frequent than
cyberbullying (Kowalski & Limber, 2013; Olweus, 2012; Smith et al., 2008).
Results from the present sample confirm the strong association between cyberbullying and
psy- chological symptoms. However, the present results also suggest that traditional bullying
uniquely predicts negative psychological symptoms above and beyond cyberbullying. In contrast,
cyberbul- lying was no longer a robust predictor of negative psychological symptoms when
modeled with traditional bullying. This finding is nearly identical with the results from Olweus’s
(2012) con- troversial paper, “Cyberbullying: An overrated phenomenon?” This finding is also
consistent with Dempsey et al. (2009), who reported that the impact of cyberbullying on
depression was no longer significant when controlling for in-person bullying.
These findings stand in contrast to previous studies that did not control for traditional bul-
lying when examining the impact of cyberbullying, and therefore, perhaps erroneously, attributed
participants’ negative outcomes to cyberbullying alone. It also challenges studies that may have
incompletely modeled the overlap between traditional and cyberbullying when concluding that cy-
berbullying is more closely associated with negative outcomes. For example, Campbell et al.
(2012) compared cybervictims’ and traditional victims’ psychological outcomes, but did not
examine the relative impact of each form of bullying in relation to the other. Although Bonanno
and Hymel (2013) examined the relative impact of the two forms of victimization and concluded
that cyberbullying contributed uniquely to outcomes, traditional bullying still tended to produce
twice the effect size of cyberbullying in their sample. Likewise, Dempsey et al. (2009) found that
cyberbullying predicted unique, if greatly attenuated, variance in social anxiety when controlling
for traditional bullying. However, as with Bonnano and Hymel’s (2013) sample, traditional
bullying still tended to produce twice the effect size of cyberbullying.
Differences between Bonanno and Hymel’s (2013) findings and those presented here may
also be due to the relatively low degree of overlap between cyberbullying and traditional bullying
in their sample, which contrasts with the higher degree of overlap reported in the present sample
and in recent meta-analytic reviews (Kowalski et al., 2014). Dempsey et al. (2009), whose results
were more similar to those presented here in terms of the relative contributions of bullying and
cyberbullying to negative outcomes, also reported a larger degree of overlap than Bonanno and
Hymel.
The present findings become somewhat more nuanced when gender is considered. In the
present sample, when both forms of victimization were run simultaneously, there remained a
strong association between in-person victimization and psychological symptoms among girls.
However, cyberbullying was marginally negatively associated with psychological symptoms, such
that more cyberbullying was linked with fewer symptoms. This finding failed to reach
significance (despite the large sample) and should thus be interpreted with caution. Future work
may do well to examine the potentially idiosyncratic impact of cyberbullying specifically in girls.
Among boys, both bullying and cyberbullying, when run in a simultaneous regression model,
emerged as significant predictors of psychological symptoms. As has been noted previously (e.g.,
Bonanno & Hymel, 2013; Dempsey et al., 2009), the effect size for in-person bullying was
roughly twice that of cyberbullying. Never- theless, it seems that, when controlling for traditional
bullying, cyberbullying is more closely linked with psychological symptoms for boys than for
girls.
These findings contribute to the varied and discrepant accounts of gender and cyberbullying
that have been reported in the recent literature. Unlike traditional bullying, in which boys are
Psychology in the Schools DOI: 10.1002/pits
12 Hase et al.
involved much more prevalently than girls (Jolliffe & Farrington, 2006), the relationship between
gender and

Psychology in the Schools DOI: 10.1002/pits

 Impacts of Traditional Bullying and Cyberbullying 1
3
cyberbullying has been mixed. Smith et al. (2008) found that girls in their sample were more
likely to be victimized online than boys, whereas Li (2006) reported the opposite, and Hinduja and
Patchin (2008) found no significant differences. As yet, literature on which gender might derive
the most harm from cyberbullying is still developing. Therefore, it is the hope of the researchers
that these findings will contribute to this burgeoning discussion.
Research on the link between traditional bullying and cyberbullying with psychological
symp- toms has real-world consequences. As school districts allocate resources and develop
programs to address these overlapping but unique forms of relational aggression, the empirical
literature may be used to guide policy. School psychologists and other mental health professionals
working in school settings have an important role in these policies. At the prevention level, it is
clear that classroom climates that foster group cohesion, support, and respect for individual
differences are much less conducive to bullying and other forms of peer aggression (Frey,
Newman, Nolen, & Hirschstein, 2012; Goldweber, Waasdorp, & Bradshaw, 2013). School
psychologists can assist both teachers and administrators in identifying specific strategies for
creating nurturing and supportive classroom climates that incorporate prosocial norms for
behavior. At the same time, school psychologists, counselors, and other school personnel must
actively address the emotional and mental health needs of students victimized by both traditional
and cyber forms of bullying. It is not the intention of this study, therefore, to downplay the threat
of certain forms of cyberbullying and their impacts on victims. However, it is possible that the
impact of cyberbullying may be better explained by simultaneously examining (and addressing)
traditional bullying.
A number of limitations are important to acknowledge. First, the cross-sectional and corre-
lational nature of the research design limits causal inferences that can be drawn from the data.
Although it appears clear that bullying, both cyber and in-person, is linked with negative
outcomes, a causal direction cannot be confirmed. It is feasible that individuals experiencing more
psycholog- ical symptoms may be more likely to be bullied, just as it is feasible that individuals
being bullied may experience more psychological symptoms. Longitudinal designs are more
appropriate for ad- dressing the complex issue of causality implied in this work. Second, the study
relied exclusively on self-report measures in a relatively homogenous and regionally unique
sample. Third, although both bullying scales have been validated and utilized in previous studies,
the assessment of bullying in these measures covers only bullying and cyberbullying activity “in
the past month,” and therefore may have missed important historical episodes of victimization, as
well as the differential effects those events may have engendered. Fourth, although the mental
health assessment used in this study has been administered to hundreds of thousands of students
over the past decade, the items could be perceived as generic or limited. Lastly, it is known that
the participants in the present study had received education at school about bullying. It is possible
this may have led some students to overreport their bullying experiences.
Strengths of the present study include a large sample, the use of previously validated
measures, and a model that directly compares in-person bullying and cyberbullying. Future
research might establish directionality of effects through the use of longitudinal designs. In
addition, studies might attempt to discern which forms of cyber victimization lead to more severe
outcomes. Another fruitful investigation might explore emotional reactions to bullying as a
potential mediator between being a cyber or traditional victim and associated negative outcomes.
Future studies would do well to assess potential interactions between gender and various forms of
bullying.

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Psychology in the Schools DOI: 10.1002/pits