ArticlePDF Available

Abstract

Adolescents' involvement in cyberbullying has been a growing public health concern for some time. Cybervictimization and cyberaggression are two phenomena that previous research has often shown to be associated. However, longitudinal research into these associations and also into potential risk factors for these phenomena is less common. Anger rumination is a proven risk factor for aggressive behavior, but the relationship between anger rumination and victimization is not clear. The present longitudinal study investigated the associations between cybervictimization, anger rumination and cyberbullying in a sample of 3017 adolescents (MW1 = 13.15; SD = 1.09; 49% girls) from 7th to 9th grade. The European Cyberbullying Intervention Project Questionnaire and the Anger Rumination Scale were administered in four waves with 6 months intervals over a total period of 18 months. The associations between the variables were analyzed with a cross-lagged model. We found that: cybervictimization predicted anger rumination and cyberaggression; anger rumination was associated with later increases in both cybervictimization and cyberaggression: but involvement in cyberaggression predicted neither subsequent involvement in cybervictimization, nor in anger rumination. In addition, cybervictimization was found to mediate the association between anger rumination and cyberaggression. This study expands the understanding of the factors associated with cybervictimization and cyberaggression, and its results indicate that intervention programs should focus on boosting self-control to decrease impulsive behavior and protocols to prevent and intervene in cyberbullying.
Aggressive Behavior. 2021;111. wileyonlinelibrary.com/journal/ab
|
1
Received: 21 May 2020
|
Revised: 12 February 2021
|
Accepted: 15 February 2021
DOI: 10.1002/ab.21958
RESEARCH ARTICLE
Longitudinal associations between cybervictimization, anger
rumination, and cyberaggression
Antonio Camacho |Rosario OrtegaRuiz |Eva M. Romera
Universidad de Córdoba, Cordoba, Spain
Correspondence
Eva M. Romera, Department of Psychology,
Universidad de Córdoba, Ave San Alberto
Magno, s/n, 14071, Cordoba, Spain.
Email: eva.romera@uco.es
Funding information
Ministerio de Ciencia e Innovación,
Grant/Award Number: PSI201674871R
Abstract
Adolescents' involvement in cyberbullying has been a growing public health concern
for some time. Cybervictimization and cyberaggression are two phenomena that
previous research has often shown to be associated. However, longitudinal research
into these associations and also into potential risk factors for these phenomena is
less common. Anger rumination is a proven risk factor for aggressive behavior, but
the relationship between anger rumination and victimization is not clear. The pre-
sent longitudinal study investigated the associations between cybervictimization,
anger rumination and cyberbullying in a sample of 3017 adolescents (M
W1
= 13.15;
SD = 1.09; 49% girls) from 7th to 9th grade. The European Cyberbullying Inter-
vention Project Questionnaire and the Anger Rumination Scale were administered
in four waves with 6 months intervals over a total period of 18 months. The as-
sociations between the variables were analyzed with a crosslagged model. We
found that: cybervictimization predicted anger rumination and cyberaggression;
anger rumination was associated with later increases in both cybervictimization and
cyberaggression: but involvement in cyberaggression predicted neither subsequent
involvement in cybervictimization, nor in anger rumination. In addition, cybervicti-
mization was found to mediate the association between anger rumination and cy-
beraggression. This study expands the understanding of the factors associated with
cybervictimization and cyberaggression, and its results indicate that intervention
programs should focus on boosting selfcontrol to decrease impulsive behavior and
protocols to prevent and intervene in cyberbullying.
KEYWORDS
adolescents, anger rumination, crosslagged model, cyberbullying, longitudinal study
1|INTRODUCTION
Cyberbullying is often described as an intentional and aggressive
behavior perpetrated by an individual or a group of individuals
through the use of information and communication technologies
(Smith et al., 2008). Previous research has tried to identify char-
acteristics in adolescents associated with a heightened involvement
in cybervictimization and cyberaggression. There is a wide body of
evidence showing that cybervictimization and cyberaggression are
associated (r= .21 to r= .80) (see metaanalysis by LozanoBlasco
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2021 The Authors. Aggressive Behavior published by Wiley Periodicals LLC.
et al., 2020). A metaanalysis of the risk factors for cyberbullying
found cybervictimization to be its strongest predictor (r= .51), but
cyberaggression was not a risk factor for cybervictimization
(Kowalski et al., 2014). There are different explanations for the as-
sociation between victimization and aggression. The taxonomy of
reasons (TOR) for involvement in aggressive behavior
(Baumeister, 2001; Pinker, 2011) includes revenge (as planned be-
havior). Depending on their ability to cope with the negative emo-
tions produced by cybervictimization, victims may experience a
desire to take revenge and consequently get involved in reactive
cyberaggression (Martins et al., 2019). Furthermore, aggression
motivated by anger can also be impulsive, a form of selfdefense
(Connor et al., 2019). Therefore, when victims feel threatened and
attacked, especially if they feel the attack is unjustified, some may
respond with anger and aggression (Fluck, 2017). Finally, negative
emotions produced by online victimization (including anger) could
weaken the ability to deal with social stress efficiently, which may
lead to hostile processing of social information, which in turn can
lead to cyberaggression (Ak et al., 2015; MarínLópez et al., 2019).
To design effective prevention programs, it is necessary not only
to identify the risk and protective factors affecting involvement in
cyberbullying, but also to understand the mechanisms underlying
those relationships, and these remain largely unexplored (Romera
et al., 2021). Anger rumination has received attention with regard to
its association with (online) aggression, but its association with vic-
timization remains less clear. This study examined the association of
anger rumination, as a mechanism of internal state, with cybervicti-
mization and cyberaggression. The possible mediating effects invol-
ving the association between variables were explored.
1.1 |Anger rumination and cybervictimization
Theresponsestylestheory(NolenHoeksema, 1991) is often applied to
study the negative effects of traumatic events, such as victimization.
Responses to victimization are classified as emotionfocused coping,
aimed at minimizing distress by focusing on the affect related to the
stressor, and problemfocusedcoping,aimedatremovingor,when
unavoidable, minimizing the impact of the experience by focusing on
the stressor (Lazarus & Folkman, 1984). While problemfocused coping
has been associated with prosocial and adaptive behavior, emotion
focused coping has been linked to antisocial and aggressive behavior
(Eisenberg et al., 2006). Within the response styles theory, emotion
focused coping includes rumination, a cognitive process aimed at coping
with negative experiences and feelings by repetitively and passively
thinking about symptoms, causes and consequences (Nolen
Hoeksema, 1991). The literature has differentiated trait rumination, the
tendency to ruminate as a stable personality characteristic (Just &
Alloy, 1997) and state rumination, referring to a focus on negative
feelings and problems at a given point in time (NolenHoeksema &
Morrow, 1993).Theruminationreferredtointheremainderofthis
article relates most closely to trait rumination. Previous studies about
cyberbullying have found that anger was a common emotional response
of adolescent victims (Ak et al., 2015;Ortegaetal.,2012). In general,
adolescents tend to regulate their emotions of anger, but the regulatory
mechanisms do not always lead to an adaptive response. If an emotional
state such as anger is retained over time, it can lead to rumination as a
way to cope with the negative experience (Ray et al., 2008). Anger
rumination can be understood as the tendency to focus on internal
staterelated thoughts during an anger episode (Sukhodolsky
et al., 2001). After such angerinducing incidents, some adolescents
succeed in managing the situation, while others cannot stop thinking
about the episode and how it came about (Li et al., 2019). Anger ru-
mination has been shown to reduce the scope for adjusted response,
such as reappraisal and problem solving (Lyubomirsky et al., 2015).
Previous research has indeed shown that cybervictimization predicted
rumination (Liu et al., 2020), but so far, the specific association with
anger rumination remains largely unexplored. Only one descriptive
study showed that adolescents and adults who were cybervictimized
reported higher levels of anger rumination compared to those not in-
volved in cyberbullying (Zsila et al., 2018).
Whether a reverse relationship also exists, that is, whether an-
ger rumination predicts an increase in (cyber)victimization has not
been studied yet, but this might be expected. Individuals who engage
in anger rumination may be more inclined to focus on the negative
feelings caused by the stressful event, rather than on addressing the
problem. Previous research suggests that such emotionfocused
coping is associated with an increase in anxiety and depression
(Izadpanah et al., 2017), which in turn are associated with cyber-
victimization (Wright & Wachs, 2019). Moreover, a deficit in self
control, which is widely associated with anger rumination (White &
Turner, 2014), is a proven risk factor for victimization online
(ÁlvarezGarcía et al., 2019). A metaanalysis found that internalizing
problems predict increased peer victimization during youth (Reijntjes
et al., 2010). This may be explained by the fact that impulsive in-
dividuals tend not to consider the consequences of their actions
when engaging in risky behaviors (Gottfredson & Hirschi, 1990). It
should be noted that in Pratt et al. (2014) metaanalysis the pre-
dictive effects of lacking selfcontrol on victimization proved greater
with noncontact forms of victimization, such as in cyberspace. Based
on the discussion above, it is expected that anger rumination is a risk
factor for and predicts a subsequent increase in cybervictimization.
1.2 |Anger rumination and cyberaggression
Emotionfocused coping strategies activated by an angerraising event
aim at managing the intensity of the anger experience, reducing angry
thoughts and avoiding impulsive actions to prevent aggression (Denson
et al., 2011). The multiple systems model of anger rumination (MSM)
(Denson, 2013) has been applied in explaining how anger rumination
might disturb the mechanisms of emotion regulation and thereby fa-
cilitate aggressive behavior. Through different levels of analysis
(cognitive, neurobiological, affective, executive control, and behavioral,
the MSM aims to understand why people engage in such cognitive
processing after identifying an event as provocative. According to the
2
|
CAMACHO ET AL.
model, the affective and neurobiological response, moderated by the
cognitiveresponse(e.g.,throughthemodeofprocessing),influence
executive control and aggressive behavior. The repetitive and passive
thinking within anger rumination can overload cognitive processing
(Denson, 2013) and consequently selfcontrol (White & Turner, 2014).
Therefore, people with a high level of anger rumination and weak ex-
ecutive control have greater difficulties implementing the emotional
regulation strategies that seek to decrease the arousal level, and
thereby a greater propensity to behave aggressively, either impulsively
or deliberately. Moreover, anger rumination can take the form of angry
afterthoughts, thoughts of revenge, angry memories, and a focus on
causes. Such processes tend to exacerbate and extend the anger
emotion, and reconstructing the background of the threat or injury can
create a willingness to engage in subsequent aggressive behavior
(Denson et al., 2011).
On the empirical level, many studies have shown that anger
rumination predicts higher levels of aggression (Quan et al., 2020;
Salguero et al., 2020; Wang et al., 2019), but the opposite associa-
tion, whether involvement in aggressive behavior predicts an in-
crease in anger rumination, has not been analyzed. Moreover, with
regard to cyberaggression specifically, as far as we know only one
studywith a crosssectional design and using middle adolescents
has found an association with anger rumination (Yang et al., 2020).
The scarcity of evidence relating to the association with cyberbul-
lying perpetration is surprising, given cyberspace's abovementioned
nature of anonymity (Barlett, 2015), which facilitates the possibility
to take revenge with a lower probability of retaliation (Wright &
Li, 2012). This warrants further investigation into anger rumination
as a risk factor for cyberbullying perpetration, especially through
longitudinal analysis.
1.3 |The present study
Using the MSM as a theoretical foundation, the present panel study
into the associations between cyberaggression, anger rumination and
cybervictimization, therefore offers an important contribution to the
existing literature about risk factors for cyberbullying among
adolescents.
With the analysis of these associations, worth considering is also
whether other factors, such as gender and age, influence these re-
lationships. In a review of risk and protective factors for cyberbullying a
definite relationship between gender and cyberbullying was not found,
but the studies in this review that did report gender differences showed
boys were more likely to be cyberbullying perpetrators, while girls were
more likely to be cyberbullied (Kowalski et al., 2019). Regarding age, a
recent study comparing preadolescents and later adolescents found
higher levels of involvement in both cybervictimization and cyberag-
gression in the older group (GonzálezCabrera et al., 2019). With regard
to anger rumination, higher levels have been found in girls than in boys
(Zsila et al., 2019), and there is no existing information about differ-
ences between early and middle adolescents. Although there is some
prior information to guide our expectations about leveldifferences in
these variables between boys and girls, and between early and middle
adolescents, very little is known about the effect of gender and age, as
moderating factors, on the associations between cybervictimization,
anger rumination and cyberaggression. The limited studies that exist
suggest gender does not influence the association between cybervicti-
mization and cyberaggression (Chan et al., 2019), and between anger
rumination and aggressive behavior (e.g., Guerra & White, 2017;White
& Turner, 2014). While gender differences in the association between
anger rumination and cybervictimization have not been explored yet.
In this study, we focus on adolescence because it is a life stage in
which the presence of stressors increases (e.g., the unfair treatment
from peers) (LucasThompson et al., 2018) and depending on attri-
bution and coping style, a time when beneficial or maladaptive traits
that affect later life are often adopted (SeiffgeKrenke, 2013). The
prevalence of cyberbullying increases during adolescence (González
Cabrera et al., 2019), reaching its peak in the later phases of middle
school (Kowalski et al., 2014).
Based on the abovementioned theoretical and empirical re-
search, we formulated the following hypotheses: Adolescents who
have been cybervictimized subsequently become more involved
in cyberaggression (Hypothesis 1a); involvement in cyberaggression
does not lead to a later increase or reduction in cybervictimization
(Hypothesis 1b); cybervictimized adolescents show an increase
in anger rumination (Hypothesis 2a), and anger rumination predicts
an increase in cybervictimization (Hypothesis 2b); anger rumination
predicts an increase in cyberaggression (Hypothesis 3a), but there is
no reverse relationship (Hypothesis 3b); Finally, we expect that boys
and girls do not differ in the associations between cybervictimiza-
tion, anger rumination and cyberaggression (Hypothesis 4a), and
that, due to the narrow age range of the study's participants (1116
years, 7th9th grade) there are no differences in the associations
between early and middle adolescents (Hypothesis 4b).
The possible indirect effects were analyzed. We hypothesized
that anger rumination would mediate the association between cy-
bervictimization and cyberaggression (Hypothesis 5a). This is con-
sistent with previous studies which found the mediating role of anger
rumination as a risk mechanism of aggression with trait selfcontrol,
trait anger and hostile attribution bias as predictors (Li et al., 2019;
Quan et al., 2019; Wang et al., 2018). In a recent study, indirect
effects of victimization and perpetration via anger rumination were
found (Malamut & Salmivalli, 2021). In addition, as found in a long-
itudinal study the mediating role of victimization between depressive
symptoms and violent behavior (Yu et al., 2018), we hypothesized
that cybervictimization would mediate the association between an-
ger rumination and cyberaggression (Hypothesis 5b).
2|METHODS
2.1 |Participants
The participants were drawn from a large longitudinal study into
personal and ecological developmental risks and protective factors
CAMACHO ET AL.
|
3
during adolescence. The convenience sample comprised 3,017 ado-
lescents (49% girls; 51% boys) between 11 and 16 years old, at-
tending Grades 79, and included 115 classes from 13 middle
schools in Southern Spain. In this study, we analyzed four waves of
data collected during the years 20172019 at 6month intervals. At
each data collection point the sample varied due to temporary ab-
sence or changes of school. Wave 1 (W1 hereafter) in November
2017 included 2790 adolescents (49% girls, M
age
= 13.15, SD = 1.09,
92% participation rate); Wave 2 (W2) in May 2018 included 2553
(50% girls, M
age
= 13.61, SD = 1.13, 85% participation rate); Wave 3
(W3) in November 2018 included adolescents 2362 (51% girls,
M
age
= 14.03, SD = 1.05, 78% participation rate); and Wave 4 (W4) in
May 2019 included 2361 adolescents (50% girls, M
age
= 14.55, SD =
1.06, 78% participation rate). Of the total sample, 59% participated
in all four waves, 22% participated at three time points, 11% at two
time points, and 7% took part at only one time point.
2.2 |Measures
2.2.1 |Cyberbullying
We measured cybervictimization and cyberaggression using the
European Cyberbullying Intervention Project Questionnaire (Del Rey
et al., 2015). This scale has shown good validity and reliability in a
Spanish population (OrtegaRuiz et al., 2016) and in crosscultural
populations (HerreraLópez et al., 2017). The questionnaire includes
22 items that assess the frequency of cyberbullying behavior in two
dimensions: 11 items assess cybervictimization (e.g., Someone said
nasty things about me to others either online or through text mes-
sages) and 11 items assess cyberaggression (e.g., I posted embar-
rassing videos or pictures of someone online). The items were all
answered on a 5point scale, ranging from 0 (no)to4(yes, more than
once a week). Responses to the items were averaged within each
dimension. Higher scores correspond to higher levels of cybervicti-
mization and cyberaggression. The internal consistency of the scale
in our study is presented in the Results section.
2.2.2 |Anger rumination
Anger rumination was measured with the Anger Rumination Scale
(Sukhodolsky et al., 2001). This scale has shown good validity and
reliability in Spanish populations (Uceda et al., 2016). The ques-
tionnaire includes 19 items (e.g., When something makes me angry, I
turn this matter over and over again in my mind,”“When someone
provokes me, I keep wondering why this should have happened to
me). The items were answered on a 4point scale, ranging from 1
(almost never) to 4 (almost always). In line with previous studies
using this scale, items were averaged to extract a global anger ru-
mination score (Wang et al., 2019). Higher scores correspond to a
higher level of anger rumination. The internal consistency of the
scale in our study is presented in the Results section.
2.3 |Procedure
Ethical approval was obtained from the research ethics committee of
the corresponding author's institution. Before data collection, in-
formed consent was obtained from government and school autho-
rities, as well as from the participants' parents. The instruments were
implemented in selfreport form in the classroom during regular
school hours and included instructions on how to complete the
questionnaire. Interviewers trained and experienced in psychological
research supervised the data collection using standardized instruc-
tions. These included the assurance to participants that there were
no right or wrong answers, that the data would be anonymous and
treated confidentially, that participation was voluntary, and they
could stop participating at any time. The researchers provided verbal
reading support for those students with reading difficulties. The
questionnaires were administered in paperandpencil format. Data
from different waves was linked through a code composed of the
first characters of the participants' given names and surnames, to-
gether with their dates of birth. On average, it took 30 min to answer
the questionnaires.
2.4 |Statistical analyses
Preliminary steps in the analysis included running descriptive sta-
tistics, correlations and independent ttests to explore gender
(1 = boys; 2 = girls) and age (1 = early adolescents: 2 = middle ado-
lescents) differences. The internal consistency of the scales was as-
sessed with Cronbach's alpha. Longitudinal measurement invariance
was analyzed to verify the consistency of the constructs over time
(Little et al., 2013). Anger rumination was considered a global con-
struct, while cybervictimization and cyberaggression were analyzed
on the cyberbullying scale as two independent and correlated fac-
tors. It was done in a confirmatory factorial analysis by comparing
three increasingly restrictive models. First, to test for configural
invariance the model was estimated with the factor loadings and
intercepts allowed to vary freely without restrictions. Then, metric
invariance (weak) was analyzed after imposing equal factor loadings
across time. Finally, scalar invariance (strong) was explored by im-
posing equal intercepts across time. Model fit of the three con-
secutive models was compared with determine the degree of
invariance of the constructs. With regard to the comparison between
models, ΔCFI < 0.01 and ΔRMSEA < 0.015 (Chen, 2007), they were
considered to represent a statistically nonsignificant difference in
model fit.
The associations between cybervictimization, anger rumination
and cyberaggression were explored in a crosslagged model. This
included the following paths: (a) autoregressive paths within the
same variable over adjacent waves (e.g., anger rumination W1
anger rumination W2); (b) crosslagged paths between different
variables in adjacent waves (e.g., cybervictimization W1
anger
rumination W2); and (c) covariances between different variables
measured at the same wave (e.g., anger rumination W1
4
|
CAMACHO ET AL.
cyberaggression W1), from W2 to W4, the covariances are based on
the residual variances. To allow an efficient and systematic inter-
pretation of the associations, we compared several models with
decreasing constraints imposed on the estimation of above-
mentioned paths. Models were built in four steps: in Model 1 the
crosslagged paths, autoregressive paths, and the residual covar-
iances between the variables in the same wave were constrained to
be equal over time (from W2 to W4); in Model 2 the residual cov-
ariances were freely estimated; in Model 3 the residual covariances
and crosslagged paths were also freely estimated; and in addition, in
Model 4 the residual covariances, crosslagged paths and auto-
regressive paths were freely estimated. The Scaled χ
2
Difference test
(Satorra & Bentler, 2001) was considered to analyze whether the less
constraint model fit better. In case of an improvement, the model
with fewer constraints is retained, while no differences between the
models are found, the model with higher constraints is used for
further comparison with the next model. To analyze whether the
longitudinal associations between cybervictimization, anger rumina-
tion, and cyberaggression differed between boys and girls, and early
and middle adolescents, we ran multigroup analyses and verified
results using Wald tests (Muthén & Muthén, 2017).
Analyses were conducted in Mplus Version 8.4 (Muthén &
Muthén, 2017). Models were estimated using the Maximum Like-
lihood Robust estimator (Satorra & Bentler, 2001) to account for
nonnormality of the data. We reported standard fit indices, includ-
ing the root mean square error of approximation (RMSEA), the
comparative fit index (CFI), and the TuckerLewis index (TLI).
RMSEA values < .08 and .05, and CFI and TLI values > .90 and .95
indicated acceptable and good model fit, respectively. To adjust the
standard errors, we employed a type = complexsampling estimator,
with classroom as a cluster variable, as adolescents were nested
within previously defined groups. 5000 bootstrapping samples were
conducted to estimate the confidence intervals for indirect effects
through using INDIRECT model test in Mplus to analyze the possible
mediations between the variables from Time 1 to Time 4 (i.e., Time 1
Time 2
Time 3
Time 4). Missing data character was explored
though the Missing Completely at Random test (MCAR). Although
Little's MCAR test provided a significant result (p< .001), correction
of this result for sensitivity to sample size through the normed χ
2
(χ
2
/
df = 1.34) (Bollen, 1989) suggests data were missing at random
(MAR). Full Information Maximum Likelihood (FIML) estimation was
used to handle missing data, meaning all participants of the study
were included in the analyses. By using all available data, FIML
overcomes concerns associated with traditional missing data tech-
niques, and provides an efficient estimation in longitudinal designs
(Graham et al., 2001).
3|RESULTS
3.1 |Preliminary steps
The means, standard deviations, and Cronbach's alpha of the main
variables are displayed in Table 1, together with the independent
ttest results used to analyze gender and age differences. Girls
scored higher on anger rumination than boys, while boys had higher
cyberaggression scores in all waves and higher cybervictimization
scores in W1. Two age groups were created to explore age differ-
ences, representing early (1113 years) and middle (1416 years)
adolescence. Middle adolescents reported more cybervictimization,
TABLE 1 Descriptive statistics and
gender and age differences Gender Age
Variable MSDαttest dttest d
1. Cybervictimization (T1) 0.24 0.45 .88 2.04*0.09 5.10*** 0.21
2. Cybervictimization (T2) 0.21 0.38 .86 0.98 4.01*** 0.18
3. Cybervictimization (T3) 0.19 0.39 .87 0.26 3.57*** 0.16
4. Cybervictimization (T4) 0.20 0.38 .88 0.22 2.72*** 0.13
5. Anger rumination (T1) 2.03 0.67 .92 3.94*** 0.17 5.67*** 0.25
6. Anger rumination (T2) 2.08 0.72 .93 6.11*** 0.27 2.80** 0.13
7. Anger rumination (T3) 2.04 0.71 .94 5.71*** 0.25 2.69** 0.13
8. Anger rumination (T4) 2.11 0.74 .95 6.40*** 0.28 1.97*0.09
9. Cyberaggression (T1) 0.15 0.35 .87 5.36*** 0.21 7.16*** 0.29
10. Cyberaggression (T2) 0.15 0.36 .90 2.79** 0.11 3.43*** 0.15
11. Cyberaggression (T3) 0.12 0.32 .90 2.35*0.10 3.61*** 0.16
12. Cyberaggression (T4) 0.12 0.31 .90 2.02*0.09 2.27*0.10
Note: The ttest results show the differences of girls compared to boys and middle adolescents
compared with early adolescents.
*p< .05.
**p< .01.
***p< .001.
CAMACHO ET AL.
|
5
anger rumination, and cyberaggression than early adolescents.
Following Cohen (1977), these effect sizes are considered small.
Correlation analyses (see Table 2) showed that all variables were
stable over time: r= .39 to r= .45 for cybervictimization; r= .47 to
r= .62 for anger rumination; r= .24 to r= .36 for cyberaggression. All
variables were significantly correlated crosssectionally and long-
itudinally in all waves. Respective coefficients of crosssectional and
longitudinal correlation had the following ranges: for cybervictimi-
zation and anger rumination: r= .22 to r= .33; r= .12 to r= .24; for
cybervictimization and cyberaggression: r= .64 to r= .72; r= .19 to
r= .35; and for anger rumination and cyberaggression: r= .14 to
r= .28; r= .10 to r= .16.
To test for longitudinal measurement invariance of the scales,
the factor loadings and intercepts were constrained to be equal over
time in increasingly restrictive steps. The results indicated a good
model fit for both scales (see Table 3). The nested model compar-
isons (Configural vs. Metric; Metric vs. Scalar) showed the increased
constraints did not significantly affect model fit (ΔCFI < 0.01 and
ΔRMSEA < 0.015).
3.2 |Crosslagged model
We estimated and compared hierarchical crosslagged models to
which constraints were introduced in a stepwise manner. Model
1 (with all paths constrained) had good model fit: χ²
(57) = 165.090, p< .001; CFI = 0.979, TLI = 0.975; and RMSEA =
0.025, 90% CI [0.0210.030]. After the covariances between
variables in the same wave were allowed to vary over time,
Model 2 did not reveal fit better than Model 1, consequently
Model 1 was retained: (51) = 157.135, p< .001; CFI = 0.979,
TLI = 0.973; and RMSEA = 0.026, 90% CI [0.0220.031], Δχ²
(6) = 9.24, p> .05. Model 3, with additional unconstraint to the
crosslagged paths, again showed good fit: χ² (40) = 136.671,
p< .001; CFI = 0.981, TLI = 0.969; and RMSEA = 0.028, 90% CI
[0.0230.034]. Fit indices did not improve significantly from
Model 1, Δχ² (11) = 20.31, p> .05. Finally, Model 4, in which au-
toregressive paths were allowed to vary over time once more
showed good fit: χ² (34) = 118.020, p< .001; CFI = 0.984, TLI =
0.968; and RMSEA = 0.029, 90% CI [0.0230.035]. Model fit did
not improve in comparison with Model 1, Δχ² (23) = 33.1, p>.05.
Given the lack of significant differences between the models, the
Model 1 was used to analyze the associations between the
variables.
The results of the crosslagged model are shown in Figure 1. The
autoregressive paths were significant for all variables, as were all
associations between variables within the same wave (W1) and the
residual covariances (from W2 to W4). The crosslagged associations
between different variables in adjacent waves indicate that: (a) cy-
bervictimization predicted later anger rumination and cyberaggres-
sion, (b) anger rumination predicted later cybervictimization and
cyberaggression; and (c) cyberaggression neither predicted later
cybervictimization nor anger rumination. Sensitivity analyses were
performed using multigroup modeling to test for gender and age
differences: this implied constraining the crosslagged paths to be
equal between: (a) boys and girls; and (b) early and middle adoles-
cents. This did not lead to significant differences in any of the paths
(ps > .05 for all Wald tests), indicating an absence of gender and age
differences with regard to the associations. Based on the results
found, the INDIRECT models were added to analyze the possible
mediations between variables. The statistically significant mediated
paths are presented in Table 4. The associations found in the cross
lagged between the variables at two subsequent times are again
confirmed through the indirect effects between Time 1 and Time 4.
Furthermore, cybervictimization was found to mediate the associa-
tion between anger rumination and cyberaggression.
TABLE 2 Correlations between variables
Variables 1 234567891011
1. Cybervictimization (T1)
2. Cybervictimization (T2) .39***
3. Cybervictimization (T3) .44*** .45***
4. Cybervictimization (T4) .37*** .41*** .45***
5. Anger rumination (T1) .33*** .24*** .22*** .20***
6. Anger rumination (T2) .21*** .27*** .20*** .20*** .56***
7. Anger rumination (T3) .20*** .18*** .26*** .19*** .51*** .61***
8. Anger rumination (T4) .14*** .12*** .19*** .22*** .47*** .55*** .62***
9. Cyberaggression (T1) .64*** .23*** .35*** .24*** .28*** .16*** .16*** .16***
10. Cyberaggression (T2) .24*** .70*** .25*** .22*** .12*** .20*** .14*** .13*** .24***
11. Cyberaggression (T3) .30*** .29*** .72*** .32*** .13*** .13*** .17*** .13*** .32*** .33***
12. Cyberaggression (T4) .19*** .25*** .28*** .62*** .12*** .10*** .13*** .14*** .24*** .24*** .36***
***p< .001.
6
|
CAMACHO ET AL.
4|DISCUSSION
We looked at the longitudinal associations between anger rumina-
tion, cybervictimization and cyberaggression, as the possible med-
iation effects.
In previous research, cybervictimization and cyberaggression
were strongly associated (Brewer & Kerslake, 2015), and our results
support this: cybervictimization and cyberaggression would be po-
sitive and unidirectional. Our results supported that cybervictimi-
zation predicted further involvement in cyberaggression, not only
through crosslagged effects but also through indirect effects be-
tween Time 1 and Time 4. However, a significant reverse relationship
was not found. These findings are in line with Kowalski et al. (2014).
Some adolescents may try to cope with the negative emotions
caused by victimization through hostile reactions, either impulsively
or deliberately. An additional explanation of the association is that
the stress produced by victimization may result in an overly hostile
interpretation of other social situations, which may then lead to
cyberaggression that is not necessarily targeted at the original ag-
gressor (Ak et al., 2015).
In our second hypothesis we formulated the expectation of a
bidirectional association between anger rumination and
cybervictimization. Via indirect and crosslagged effects, we indeed
found that cybervictimization predicted a later increase in anger
rumination, but perhaps more importantly as this had not been ex-
plored in the literature before, also found evidence of the reverse
relationship: anger rumination predicted later victimization. With
regard to the first of these associations, it was already known that
some adolescents faced with cybervictimization will think repeti-
tively about the experience and its causes, that is, that they turn to
rumination (Liu et al., 2020). Furthermore, anger rumination also, or
subsequently, predicts a greater likelihood of cybervictimization.
This may be because adolescents who ruminate may be more vul-
nerable to impulsivity and consequently more likely to engage in
risky behavior (Gottfredson & Hirschi, 1990), which in turn may
upset or provoke others (e.g., teasing or joking with others), poten-
tially leading to new episodes of victimization. The suggested ex-
planation through impulsivity and risky behavior finds support in a
result by Pratt et al. (2014) whose metaanalysis showed that lower
selfcontrol predicted subsequent cybervictimization. Our findings
therefore highlight that the activation of anger rumination could be a
strategy that leads to maladaptive behaviors, such as social anxiety
or social maladjustment (Romera et al., 2016). An interesting element
for future study related to this finding but not explored here, is how
TABLE 3 Model fit: Testing for longitudinal measurement invariance
Model fit indices Model comparison
Model tested χ²(df)pvalue CFI TLI RMSEA [90% CI] Δχ²
SB
(df)pvalue ΔCFI ΔRMSEA
Cyberbullying
Configural 4913.270 (3728)*** 0.976 0.976 0.010 [0.0100.011] ––
Metric 4952.604 (3783)*** 0.977 0.976 0.010 [0.0090.011] 91.75 (55)** 0.001 0.000
Scalar 5138.342 (3978)*** 0.977 0.978 0.010 [0.0090.011] 311.310 (195)*** 0.000 0.000
Anger rumination
Configural 7136.792 (2775)*** 0.970 0.969 0.023 [0.0220.024] ––
Metric 7198.826 (2826)*** 0.970 0.970 0.023 [0.0220.023] 127.149 (51)*** .0000 0.000
Scalar 7337.146 (2940)*** 0.970 0.971 0.023 [0.0220.023] 389.668 (114)*** 0.000 0.000
Abbreviations: CFI, comparative fit index; CI, confidence interval; RMSEA, root mean square error of approximation; TLI, TuckerLewis index.
**p< .01.
***p< .001.
FIGURE 1 Crosslagged model. Note: The coefficients provided are the standardized values. Dashed arrows show nonsignificant paths.
*p< .05, **p<.01, ***p< .001
CAMACHO ET AL.
|
7
the level of social support and the role in/of the peer group (Romera
et al., 2020) affects the association between anger rumination and
cybervictimization. It can be imagined that an increase in risky be-
havior or a drop in selfcontrol is more likely to lead to further
episodes of victimization for those adolescents with lower social
support in their peer group.
In our third hypothesis we expected that anger rumination
would predict a later increase in cyberaggression, but not the
reverse. This pattern is indeed what we found through indirect
and crosslagged effects, and is consistent with other studies
(Yang et al., 2020). Our results highlight the importance of seeing
anger rumination as a cognitive mechanism that increases the
risk of adolescents turning to cyberaggression. According to the
MSM, anger rumination may aggravate and sustain an internal
state of aggressive thoughts and high arousal, and thereby lead
cognitive processes to overload, which undermines appraisal and
decisionmaking abilities, and hence decreases the likelihood of
selfregulation and increases the likelihood of impulsive behavior
(Denson, 2013). Anger rumination retrieves the offensive fact
that caused the anger, leading to its intensification, which in-
creases the probability of aggression. The anonymity and re-
sulting reduced probability of retaliation offered by cyberspace
lowers the bar for aggressive behavior. Cyberaggression offers
adolescents an outlet to cope with the strained challenges they
face on a daily basis, and this is an indication of the effects that
their worries, perceptions and expectations have on the ability to
process adverse experiences.
The expectations in the fourth hypothesis were met: the re-
lationships between these variables did not differ between age
groups or between boys and girls. This suggests that intervention
programs aimed at anger rumination as a risk factor for cyberbullying
should equally benefit boys and girls, as well as early and middle
adolescents. While gender and age did not influence the associations,
prevalence of rumination and bullying did differ between these
groups: boys were more involved in cyberaggression, and girls more
frequently reported rumination. Results for cybervictimization are
ambivalent as there were differences at only one wave, with boys
showing greater involvement than girls. With respect to age, middle
adolescents showed greater involvement in all three study variables.
Finally, the present study extends the scope of the analysis be-
yond the reciprocal relationships between the variables, and fur-
thermore the indirect effects revealed cybervictimization as a
mediator, while anger rumination did not. In contrast to expectations
and a recent study on facetoface bullying (Malamut &
Salmivalli, 2021), the association between cybervictimization and
cyberaggression was not mediated by anger rumination (Hypothesis
5a). These considerations should remain cautious, as this study
analyses rumination from a trait approach. Future research could
clarify whether state anger rumination may act as a mediator, by
activating such cognitive processes after the cybervictimization ex-
perience and subsequently lead to an increased probability of being
involved in online aggression. On the other hand, despite extensive
evidence of the association between anger rumination and ag-
gressive behavior, the pathways linking them remain largely
TABLE 4 Significant indirect paths
using bootstrap analysis
b[95% CI] SE t p
CV T1
CV T2
CV T3
CV T4 0.130 [0.0870.172] 0.02 55.59 <.001
CV T1
CV T2
CV T3
AR T4 0.011 [0.0010.021] 0.01 2.25 <.02
CV T1
CV T2
AR T3
AR T4 0.015 [0.0010.028] 0.01 2.15 <.05
CV T1
AR T2
AR T3
AR T4 0.019 [0.0000.038] 0.01 1.97 <.05
CV T1
CV T2
CV T3
CA T4 0.022 [0.0130.031] 0.01 4.69 <.001
CV T1
CV T2
CA T3
CA T4 0.013 [0.0060.020] 0.00 3.44 <.01
CV T1
CA T2
CA T3
CA T4 0.008 [0.0010.014] 0.00 2.21 <.05
AR T1
AR T2
AR T3
AR T4 0.230 [0.2000.261] 0.02 14.30 <.001
AR T1
CV T2
CV T3
CA T4 0.003 [0.0020.005] 0.00 3.72 <.001
AR T1
CV T2
CA T3
CA T4 0.002 [0.0010.003] 0.00 7.06 <.001
AR T1
AR T2
CV T3
CA T4 0.004 [0.0020.007] 0.00 3.22 <.01
AR T1
AR T2
AR T3
CA T4 0.026 [0.0050.046] 0.00 2.41 <.05
AR T1
AR T2
CA T3
CA T4 0.012 [0.0080.015] 0.00 6.16 <.001
AR T1
CA T2
CA T3
CA T4 0.005 [0.0030.007] 0.00 5.48 <.001
AR T1
AR T2
AR T3
CV T4 0.035 [0.0130.058] 0.01 3.08 <.01
AR T1
AR T2
CV T3
CV T4 0.027 [0.0150.040] 0.01 4.17 <.001
AR T1
CV T2
CV T3
CV T4 0.021 [0.0140.028] 0.00 5.73 <.001
Abbreviations: AR, anger rumination; CA, cyberaggression; CV, cybervictimization.
8
|
CAMACHO ET AL.
unknown. Our findings suggest that trait anger rumination is asso-
ciated with aggression via victimization in the online context
(Hypothesis 5b). This result extends beyond the current literature to
understand why anger rumination is associated with cyberaggres-
sion. Adolescents at higher levels of anger rumination were at more
risk of getting victimized online, and victimization experiences fur-
ther increased the risk of aggression.
The results of this study should be interpreted in light of several
limitations. First, although the sample of adolescents was large, we
did not use a random sample. In future studies, our findings ideally
should be replicated with a stratified random sample to ensure
representativeness. Second, the novel study results need to be
examined through other research techniques such as qualitative
studies for a better understanding. This study only used selfreport
instruments, which in future could be improved by the inclusion of
multiinformant data (e.g., from peers or family members). Finally,
the students in this study all fell into a relatively narrow age range,
which limits the ability to generalize our results to other age groups.
Future studies may also want to assess whether the associations
between cybervictimization, anger rumination and cyberaggression
differ between those just starting to use technology as pre
adolescents and later adolescents or emergent adults with more
experience in cyberspace.
In spite of these limitations, this study offers a contribution to
the growing body of research into factors associated with cybervic-
timization and cyberaggression among adolescents. As an empirical
contribution, our findings show that anger rumination predicts an
increase in later involvement in cybervictimization and cyberag-
gression, and that cybervictimization experiences and the anger they
cause will in some adolescents lead to (higher levels) of anger ru-
mination and cyberaggression. Finally, the study contributes to fur-
ther insights into the association between anger rumination and
cyberaggression by highlighting cybervictimization as a mediating
mechanism between both.
The results of our study also have practical implications; they
highlight the importance to develop cognitive strategies that im-
prove selfcontrol to decrease impulsive and risky behavior and
consequently victimization and aggression online. Denson et al.
(2012) show how cognitive reappraisal strategies, such as dampening
anger that is induced by flashbacks of anger can be successful. Their
approach achieved adaptive processing of memories and promoted
early reductions in anger experience through distraction strategies.
Furthermore, other treatments such as mindfulness (Wright
et al., 2009) and cognitive behavioral therapy (Querstret &
Cropley, 2013) have proven effective in reducing anger rumination.
In this line, previous studies highlight the main role that maladaptive
cognitive emotion regulation strategies, like rumination and self
blame, might play with regard to cyberbullying episodes (Rey
et al., 2020) and how the promotion of forgiveness may decrease this
association (QuintanaOrts, Rey, 2018). In addition, it is particularly
necessary that schools have evidencebased protocols in place to
prevent cyberbullying, and also intervention through restorative
justice and reparation of damage (Del Rey et al., 2018; Williford
et al., 2013). Our study supports that inclusion of such techniques
and programs to prevent cyberbullying is likely to have beneficial
effects.
ACKNOWLEDGMENTS
We thank Peter K. Smith for comments on an earlier draft of this
manuscript. This study was supported by the Government of Spain, I
+D+I, Ministerio de Ciencia e Innovación (PSI201674871R, PI: Eva
M. Romera) (https://www.ciencia.gob.es).
CONFLICT OF INTERESTS
The authors declare that there are no conflict of interests.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on
request from the corresponding author.
ORCID
Antonio Camacho https://orcid.org/0000-0003-1690-834X
Rosario OrtegaRuiz https://orcid.org/0000-0003-2110-6931
Eva M. Romera https://orcid.org/0000-0002-9414-8019
REFERENCES
Ak, Ş., Özdemir, Y., & Kuzucu, Y. (2015). Cybervictimization and
cyberbullying: The mediating role of anger, don't anger me!
Computers in Human Behavior,49, 437443. https://doi.org/10.
1016/j.chb.2015.03.030
ÁlvarezGarcía, D., Núñez, J. C., GonzálezCastro, P., Rodríguez, C., &
Cerezo, R. (2019). The effect of parental control on cyber
victimization in adolescence: The mediating role of impulsivity and
highrisk behaviors. Frontiers in Psychology,10, 1159. https://doi.org/
10.3389/fpsyg.2019.01159
Barlett, C. P. (2015). Anonymously hurting others online: The effect of
anonymity on cyberbullying frequency. Psychology of Popular Media
Culture,4(2), 7079. https://doi.org/10.1037/a0034335
Baumeister, R. F. (2001). Evil: Inside human violence and cruelty. Holt.
Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
Brewer, G., & Kerslake, J. (2015). Cyberbullying, selfesteem, empathy
and loneliness. Computers in Human Behavior,48, 255260. https://
doi.org/10.1016/j.chb.2015.01.073
Chan, S. F., La Greca, A. M., & Peugh, J. L. (2019). Cyber victimization,
cyber aggression, and adolescent alcohol use: Shortterm
prospective and reciprocal associations. Journal of Adolescence,74,
1323. https://doi.org/10.1016/j.adolescence.2019.05.003
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of
measurement invariance. Structural Equation Modeling,14(3),
464504. https://doi.org/10.1080/10705510701301834
Cohen, J. (1977). Statistical power analysis for the behavioral sciences.
Academic Press.
Connor, D. F., Newcorn, J. H., Saylor, K. E., Amann, B. H., Scahill, L.,
Robb,A.S.,Jensen,P.S.,Vitiello,B.,Findling,R.L.,&
Buitelaar,J.K.(2019).Maladaptiveaggression:Withafocuson
impulsive aggression in children and adolescents. Journal of Child
and Adolescent Psychopharmacology,29(8), 576591. https://doi.
org/10.1089/cap.2019.0039
Del Rey, R., Casas, J. A., OrtegaRuiz, R., SchultzeKrumbholz, A.,
Scheithauer, H., Smith, P., Thompson, F., Barkoukis, V.,
Tsorbatzoudis, H., Brighi, A., Guarini, A., Pyżalski, J., & Plichta, P.
(2015). Structural validation and crosscultural robustness of the
European Cyberbullying Intervention Project Questionnaire.
CAMACHO ET AL.
|
9
Computers in Human Behavior,50, 141147. https://doi.org/10.1016/
j.chb.2015.03.065
Del Rey, R., MoraMerchán, J., Casas, J., OrtegaRuiz, R., & Elipe, P. (2018).
'Asegúrate' program: Effects on cyberaggression and its risk
factors. Comunicar,56,3948. https://doi.org/10.3916/C56-
2018-04
Denson, T. F. (2013). The multiple systems model of angry rumination.
Personality and Social Psychology Review,17(2), 103123. https://doi.
org/10.1177/1088868312467086
Denson, T. F., Moulds, M. L., & Grisham, J. R. (2012). The effects of
analytical rumination, reappraisal, and distraction on anger
experience. Behavior Therapy,43(2), 355364. https://doi.org/10.
1016/j.beth.2011.08.001
Denson, T. F., Pedersen, W. C., Friese, M., Hahm, A., & Roberts, L. (2011).
Understanding impulsive aggression: Angry rumination and reduced
selfcontrol capacity are mechanisms underlying the provocation
aggression relationship. Personality and Social Psychology Bulletin,
37(6), 850862. https://doi.org/10.1177/0146167211401420
Eisenberg, N., Fabes, R. A., & Spinrad, T. L. (2006). Prosocial development,
Handbook of child psychology (6th ed., pp. 646718). Wiley.
Fluck, J. (2017). Why do students bully? An analysis of motives behind
violence in schools. Youth and Society,49(5), 567587. https://doi.
org/10.1177/0044118X14547876
GonzálezCabrera, J., Tourón, J., Machimbarrena, J. M., Gutiérrez
Ortega, M., ÁlvarezBardón, A., & Garaigordobil, M. (2019).
Cyberbullying in gifted students: Prevalence and psychological
wellbeinginaSpanishsample.International Journal of
Environmental Research and Public Health,16(12), 2173. https://
doi.org/10.3390/ijerph16122173
Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford
University Press.
Graham, J. W., Taylor, B. J., & Cumsille, P. E. (2001). Planned missing data
designs in the analysis of change. In (Eds.) Collins, L. M. & Sayer, A.
G., New methods for the analysis of change (pp. 335353). American
Psychological Association. https://doi.org/10.1037/10409-011
Guerra, R. C., & White, B. A. (2017). Psychopathy and functions of
aggression in emerging adulthood: Moderation by anger rumination
and gender. Journal of psychopathology and behavioral assessment,
39(1), 3545. https://doi.org/10.1007/s10862-016-9563-9
HerreraLópez, M., Casas, J. A., Romera, E. M., OrtegaRuiz, R., &
Del Rey, R. (2017). Validation of the European Cyberbullying
Intervention Project Questionnaire for Colombian adolescents.
Cyberpsychology, Behavior and Social Networking,20(2), 117125.
https://doi.org/10.1089/cyber.2016.0414
Izadpanah, S., Schumacher, M., & Barnow, S. (2017). Anger rumination
mediates the relationship between reinforcement sensitivity and
psychopathology: Results of a 5year longitudinal study. Personality
and Individual Differences,110,4954. https://doi.org/10.1016/j.
paid.2017.01.023
Just, N., & Alloy, L. B. (1997). The response styles theory of depression:
Tests and an extension of the theory. Journal of Abnormal Psychology,
106(2), 221229. https://doi.org/10.1037/0021-843X.106.2.221
Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R.
(2014). Bullying in the digital age: A critical review and meta
analysis of cyberbullying research among youth. Psychological
Bulletin,140(4), 10731137. https://doi.org/10.1037/a0035618
Kowalski, R. M., Limber, S. P., & McCord, A. (2019). A developmental
approach to cyberbullying: Prevalence and protective factors.
Aggression and Violent Behavior,45(February 2018), 2032. https://
doi.org/10.1016/j.avb.2018.02.009
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal and coping. Springer
Publishing Company.
Li, J.Bin, Dou, K., Situ, Q. M., Salcuni, S., Wang, Y. J., & Friese, M. (2019).
Anger rumination partly accounts for the association between trait
selfcontrol and aggression. Journal of Research in Personality,81,
207223. https://doi.org/10.1016/j.jrp.2019.06.011
Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (2013). Why
the items versus parcels controversy needn't be one. Psychological
Methods,18(3), 285300. https://doi.org/10.1037/a0033266
Liu, C., Liu, Z., & Yuan, G. (2020). The longitudinal influence of cyberbullying
victimization on depression and posttraumatic stress symptoms: The
mediation role of rumination. Archives of Psychiatric Nursing,34(4),
206210. https://doi.org/10.1016/j.apnu.2020.05.002
LozanoBlasco, R., CortésPascual, A., & LatorreMartínez, M. P. (2020).
Being a cybervictim and a cyberbullyThe duality of cyberbullying:
A metaanalysis. Computers in Human Behavior,111, 106444. https://
doi.org/10.1016/j.chb.2020.106444
LucasThompson, R. G., McKernan, C. J., & Henry, K. L. (2018). Unraveling
current and future adolescent depressive symptoms: The role of
stress reactivity across physiological systems. Developmental
Psychology,54(9), 16501660. https://doi.org/10.1037/dev0000530
Lyubomirsky, S., Layous, K., Chancellor, J., & Nelson, S. K. (2015). Thinking
about rumination: The scholarly contributions and intellectual
legacy of Susan NolenHoeksema. Annual Review of Clinical
Psychology,11(1), 122. https://doi.org/10.1146/annurev-clinpsy-
032814-112733
Malamut, S. T., & Salmivalli, C. (2021). Rumination as a mediator of the
prospective association between victimization and bullying. Research
on Child and Adolescent Psychopathology,49, 339350. https://doi.
org/10.1007/s10802-020-00755-z
Martins, M. J. D., Veiga Simão, A. M., Caetano, A. P., Freire, I., Matos, A.,
Vieira, C. C., & Amado, J. (2019). Cybervictimization and cyber
aggression: Personal and situational factors. In Z. Yan (Ed.),
Analyzing human behavior in cyberspace (pp. 255271). IGI Global.
MarínLópez,I.,Zych,I.,OrtegaRuiz, R., Llorent, V. J., & Hunter, S. C. (2019).
Relations among online emotional content use, social and emotional
competencies and cyberbullying. Children and Youth Services Review,
108(2020), 104647. https://doi.org/10.1016/j.childyouth.2019.104647
Muthén, L. K., & Muthén, B. O. (2017). Mplus user's guide (8th ed.). Muthén
& Muthén.
NolenHoeksema, S. (1991). Responses to depression and their effects on
the duration of depressive episodes. Journal of Abnormal Psychology,
100(4), 569582. https://doi.org/10.1037/0021-843X.100.4.569
NolenHoeksema, S., & Morrow, J. (1993). Effects of rumination and
distraction on naturally occurring depressed mood. Cognition and
Emotion,7(6), 561570. https://doi.org/10.1080/02699939308409206
Ortega, R., Elipe, P., MoraMerchán, J. A., Genta, M. L., Brighi, A., Guarini, A.,
Smith, P. K., Thompson, F., & Tippett, N. (2012). The emotional impact
of bullying and cyberbullying on victims: A European crossnational
study. Aggressive Behavior,38(5), 342356. https://doi.org/10.1002/
ab.21440
OrtegaRuiz, R., Del Rey, R., & Casas, J. A. (2016). Evaluar el bullying y el
cyberbullying validación española del EBIPQ y del ECIPQ.
Psicologia Educativa,22(1), 7179. https://doi.org/10.1016/j.pse.
2016.01.004
Pinker, S. (2011). The better angels of our nature: Why violence has declined.
Viking.
Pratt, T. C., Turanovic, J. J., Fox, K. A., & Wright, K. A. (2014). Selfcontrol
and victimization: A metaanalysis. Criminology,52(1), 87116.
https://doi.org/10.1111/1745-9125.12030
Quan, F., Yang, R., & Xia, L. X. (2020). The longitudinal relationships
among agreeableness, anger rumination, and aggression. Current
Psychology,https://doi.org/10.1007/s12144-020-01030-6
Quan, F., Yang, R., Zhu, W., Wang, Y., Gong, X., Chen, Y., Dong, Y., &
Xia, L. X. (2019). The relationship between hostile attribution bias
and aggression and the mediating effect of anger rumination.
Personality and Individual Differences,139, 228234. https://doi.org/
10.1016/j.paid.2018.11.029
10
|
CAMACHO ET AL.
Querstret, D., & Cropley, M. (2013). Assessing treatments used to reduce
rumination and/or worry: A systematic review. Clinical Psychology
Review,33(8), 9961009. https://doi.org/10.1016/j.cpr.2013.08.004
Ray, R. D., Wilhelm, F. H., & Gross, J. J. (2008). All in the mind's eye? Anger
rumination and reappraisal. Journal of Personality and Social Psychology,
94(1), 133145. https://doi.org/10.1037/0022-3514.94.1.133
Reijntjes, A., Kamphuis, J. H., Prinzie, P., & Telch, M. J. (2010). Peer
victimization and internalizing problems in children: A metaanalysis
of longitudinal studies. Child Abuse and Neglect,34(4), 244252.
https://doi.org/10.1016/j.chiabu.2009.07.009
Rey, L., Neto, F., & Extremera, N. (2020). Cyberbullying victimization and
somatic complaints: A prospective examination of cognitive emotion
regulation strategies as mediators. International Journal of Clinical
and Health Psychology,20(2), 135139. https://doi.org/10.1016/j.
ijchp.2020.03.003
Romera, E. M., Camacho, A., OrtegaRuiz, R., & Falla, D. (2021). Cybergossip,
cyberaggression, problematic Internet use and family communication.
Comunicar,67.https://doi.org/10.3916/C67-2021-05
Romera, E. M., GómezOrtiz, O., & OrtegaRuiz, R. (2016). The mediating
role of psychological adjustment between peer victimization and
social adjustment in adolescence. Frontiers in Psychology,7,19.
https://doi.org/10.3389/fpsyg.2016.01749
Romera, E. M., Jiménez, C., Bravo, A., & OrtegaRuiz, R. (2020). Social
status and friendship in peer victimization trajectories. International
Journal of Clinical and Health Psychology,21(1), 100191. https://doi.
org/10.1016/j.ijchp.2020.07.003
Salguero, J. M., GarcíaSancho, E., RamosCejudo, J., & KannisDymand, L.
(2020). Individual differences in anger and displaced aggression: The
role of metacognitive beliefs and anger rumination. Aggressive
Behavior,46(2), 162169. https://doi.org/10.1002/ab.21878
Satorra, A., & Bentler, P. M. (2001). A scaled difference chisquare test
statistic for moment structure analysis. Psychometrika,66(4),
507514.
SeiffgeKrenke, I. (2013). Stress, coping, and relationships in adolescence.
Psychology Press.
Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N.
(2008). Cyberbullying: Its nature and impact in secondary school
pupils. Journal of Child Psychology and Psychiatry and Allied Disciplines,
49(4), 376385. https://doi.org/10.1111/j.1469-7610.2007.01846.x
Sukhodolsky, D. G., Golub, A., & Cromwell, E. N. (2001). Development and
validation of the anger rumination scale. Personality and Individual
Differences. Personality and Individual Differences,31(5), 689700.
Uceda, I. M., Bleda, J. H. L., Nieto, M. Á. P., Sukhodolsky, D. G., &
Martínez, A. E. (2016). Psychometric properties of the Spanish
adaptation of the anger rumination scale: Evidence of reliability and
validity in the general population. The Spanish Journal of Psychology,
19,19. https://doi.org/10.1017/sjp.2016.17
Wang, X., Yang, L., Yang, J., Gao, L., Zhao, F., Xie, X., & Lei, L. (2018). Trait
anger and aggression: A moderated mediation model of anger
rumination and moral disengagement. Personality and Individual
Differences,125,4449. https://doi.org/10.1016/j.paid.2017.12.029
Wang, Y., Cao, S., Dong, Y., & Xia, L. X. (2019). Hostile attribution bias and
angry rumination: A longitudinal study of undergraduate students.
PLoS One,14(5), 19. https://doi.org/10.1371/journal.pone.0217759
White, B. A., & Turner, K. A. (2014). Anger rumination and effortful
control: Mediation effects on reactive but not proactive aggression.
Personality and Individual Differences,56(1), 186189. https://doi.
org/10.1016/j.paid.2013.08.012
Williford, A., Elledge, L. C., Boulton, A. J., DePaolis, K. J., Little, T. D., &
Salmivalli, C. (2013). Effects of the KiVa antibullying program on
cyberbullying and cybervictimization frequency among Finnish
youth. Journal of Clinical Child and Adolescent Psychology,42(6),
820833. https://doi.org/10.1080/15374416.2013.787623
Wright, M. F., & Li, Y. (2012). Kicking the digital dog: A longitudinal
investigation of young adults' victimization and cyberdisplaced
aggression. Cyberpsychology, Behavior and Social Networking,15(9),
448454. https://doi.org/10.1089/cyber.2012.0061
Wright, M. F., & Wachs, S. (2019). Adolescents' psychological
consequences and cyber victimization: The moderation of school
belongingness and ethnicity. International Journal of Environmental
Research and Public Health,16, 2493. https://doi.org/10.3390/
ijerph16142493
Wright, S., Day, A., & Howells, K. (2009). Mindfulness and the treatment
of anger problems. Aggression and Violent Behavior,14(5), 396401.
https://doi.org/10.1016/j.avb.2009.06.008
Yang, J., Li, W., Wang, W., Gao, L., & Wang, X. (2020). Anger rumination
and adolescents' cyberbullying perpetration: Moral disengagement
and callousunemotional traits as moderators. Journal of Affective
Disorders.https://doi.org/10.1016/j.jad.2020.08.090
Yu, R., Branje, S., Meeus, W., Koot, H. M., van Lier, P., & Fazel, S. (2018).
Victimization mediates the longitudinal association between
depressive symptoms and violent behaviors in adolescence.
Journal of Abnormal Child Psychology,46(4), 839848. https://doi.
org/10.1007/s10802-017-0325-2
Zsila, Á., Urbán, R., & Demetrovics, Z. (2018). Anger rumination and unjust
world beliefs moderate the association between cyberbullying
victimization and psychiatric symptoms. Psychiatry Research,268,
432440. https://doi.org/10.1016/j.psychres.2018.08.001
Zsila, Á., Urbán, R., Griffiths, M. D., & Demetrovics, Z. (2019). Gender
differences in the association between cyberbullying victimization and
perpetration: The role of anger rumination and traditional bullying
experiences. International Journal of Mental Health and Addiction,17,
12521267. https://doi.org/10.1007/s11469-018-9893-9
How to cite this article: Camacho, A., OrtegaRuiz, R., &
Romera, E. M. (2021). Longitudinal associations between
cybervictimization, anger rumination, and cyberaggression.
Aggressive Behavior,111. https://doi.org/10.1002/ab.21958
CAMACHO ET AL.
|
11
... Adolescence is a time of high incidence of cyberbullying and is often accompanied by role swapping between cyberbullies and cybervictims (Ang, 2015). Two longitudinal studies of cyberbullying among adolescents found that cyberbullying victimization positively predicted later perpetration, while perpetration did not predict later victimization, regardless of whether between-and within-person variance was distinguished (Camacho et al., 2021;2023a), suggesting that the transition from cybervictims to cyberbullies may be an important reason for the difficulty in reducing cyberbullying during adolescence. Although existing research has confirmed cyberbullying victimization as the strongest predictor of cyberbullying perpetration (Kowalski et al., 2014), how cyberbullying victimization is longitudinally associated with cyberbullying perpetration and what factors protect adolescents from cyberbullying remains unclear. ...
... Numerous studies indicate that victims of cyberbullying are more likely to report internalizing problems such as depression, anxiety, and suicidal ideation (e.g., Camerini et al., 2020;Zhou et al., 2017). Additionally, cyberbullying victimization may be associated with externalizing problems like aggression (e.g., cyberbullying perpetration) and antisocial behavior (Camacho et al., 2021). A meta-analysis consist of 131 studies found that cyberbullying victimization was the strongest predictor of cyberbullying perpetration, but cyberbullying perpetration was not a risk factor for cyberbullying victimization (Kowalski et al., 2014). ...
... A meta-analysis consist of 131 studies found that cyberbullying victimization was the strongest predictor of cyberbullying perpetration, but cyberbullying perpetration was not a risk factor for cyberbullying victimization (Kowalski et al., 2014). The unidirectional effects of adolescent cyberbullying victimization on cyberbullying perpetration were confirmed in two studies, using a crosslagged model and a random intercept cross-lagged model respectively (Camacho et al., 2021;2023a). These findings suggest that cyberbullying victimization may be a critical factor in the persistence and spread of cyberbullying perpetration. ...
Article
Full-text available
Although cyberbullying victimization significantly impacts cyberbullying behaviors, research on its longitudinal mechanisms and protective factors remains scarce. A total of 1465 Chinese adolescents (52.2% female) with an average age of 16.14 (SD = 0.40) participated in a three-wave longitudinal study with 3-month intervals. Cyberbullying victimization positively predicted cyberbullying perpetration 6 months later, and this effect was mediated by impairment in personality functioning. Mindfulness buffered the predictive effect of cyberbullying victimization on impairment in personality functioning and mitigated the negative impact of impairment in personality functioning on cyberbullying perpetration. Further findings revealed that the indirect effect of impairment in personality functioning was more pronounced when levels of mindfulness were low, and higher levels of mindfulness could disrupt the mediating pathway of impairment in personality functioning between cyberbullying victimization and perpetration. The findings highlighted the importance of promoting the positive development of adolescents’ personality functioning and fostering mindfulness skills to reduce cyberbullying among adolescents.
... transition into bullying perpetrators, creating a cyclical pattern of campus bullying (Camacho et al., 2021;Walters, 2020). Despite this the question of how prior bullying victimization is highly correlated with subsequent bullying perpetration has not been explicitly discussed. ...
... However, the dichotomous roles of the bully and the victim are not fixed. Previous research has shown that victims often become bullies, contributing to a vicious cycle of bullying (Camacho et al., 2021). For adolescents, being bullied is a socially threatening experience that can undermine their social image within peer groups, disrupt their sense of belonging, and even damage their physical health (Park & Metcalfe, 2020). ...
... Our findings highlighted a critical phenomenon in adolescent development: prior bullying victimization is positively associated with subsequent bullying perpetration. This result aligned with the conclusions of Camacho et al. (2021) and Walters (2020), validating the main hypothesis of the TMM. For adolescents, bullying victimization is a severe negative event that can profoundly impact their social image and status. ...
Article
Full-text available
Introduction Bullying among adolescents is a global public health issue prevalent in schools, posing significant risks to positive adolescent development. Studies have shown that bullied adolescents tend to engage in more bullying perpetration, but this underlying process of longitudinal correlation has not been fully elucidated. Methods Based on two waves of longitudinal data collected from 347 junior and 144 senior high school students in China (Mage = 13.66 years, SDage = 1.46, 59.27% boys) at 1‐year intervals, two moderated chain‐mediation models were used to explore the longitudinal correlations between bullying victimization and bullying perpetration and its underlying processes. Results The results found a significant positive correlation between adolescents’ bullying victimization experiences 1 year prior and bullying perpetration 1 year later. Furthermore, fear of negative evaluation and psychache played a longitudinal chain‐mediating role in the process, with self‐esteem and grade moderating this mediating pathway, either enhancing or weakening the effect. Conclusions This study demonstrates that prior bullying victimization is longitudinally and positively associated with subsequent bullying perpetration among adolescents. This process is mediated by fear of negative evaluation and psychache, with self‐esteem and grade level as moderators. Based on these conclusions, we have formulated the Threat‐Motivation Model, offering a framework to understand the relationship between bullying victimization and bullying perpetration. Practical implications, including strategies to reduce bullying in youth groups, are discussed.
... This phenomenon can be understood through the lens of the "cycle of violence" theory, which suggests that victims of aggression may resort to aggressive behaviors as a coping mechanism or to regain a sense of control and power [20], suggesting that cybervictimization experiences can be viewed as situational factors triggering negative emotional responses, such as anger or distress [21]. Relatively few studies have examined the group that encompasses those who are both victims of cyberbullying and perpetrators (i.e., cyberbully-victims) [22]. In the digital context, the anonymity and perceived detachment provided by online interactions can exacerbate this cycle, making it easier for victims to become perpetrators without immediate social repercussions [23]. ...
Preprint
The spread of Information and Communication Technologies (ICTs) brought advantages and disadvantages, particularly impacting youth, daily involved in using Internet and social media applications. In preadolescents’ social development, problematic social media use (PSMU) and cyberbullying (CB) are potential risk factors across several countries. PSMU is defined as the lack of regulation of social media platforms’ use associated with negative outcomes in everyday life, while CB refers to using digital technology to harass, threaten, or embarass another person. Among preadolescents, CB perpetration is frequently associated with cybervictimization (CV) experiences. The underlying mechanisms that drive this relationship have received limited attention. The aim of the cross-national comparative study, rooted in the General Aggression Model, is to investigate the direct and indirect effects between cyberbullying perpetration and cybervictimization, testing a model involving PSMU and moral disengagement (MD) as serial mediators in this association. 895 Italian and Spanish preadolescents (M_age = 11.23, SD = 1.064) completed a self-report survey during school hours. Descriptive statistics were computed, and a serial mediation model was run. The results show that CV is positively associated to CB, and that PSMU and MD positively serially mediate the CV-CB link. This study’s insights suggest the need for tailored educational interventions targeting European youth, to promote more positive online social interactions and a safer digital environment.
... Some studies have found that PRD is a significant risk factor associated with online aggression [27,28]. The PRD theory posits that individuals or groups become aware of their disadvantages by comparing themselves either horizontally or vertically to a reference group, leading to negative emotions such as depressive symptoms [29][30][31][32] and anger [33][34][35], which can heighten the likelihood of aggressive behavior [36][37][38]. Additionally, fairness theory suggests that when individuals perceive unfair treatment, they not only feel dissatisfaction but also alter their behavior to restore a sense of fairness [39]. ...
Article
Full-text available
While personal relative deprivation (PRD) is recognized as a potential risk factor for aggression, the mechanisms underlying this relationship are not well understood. This study investigates how revenge motivation mediates the link between PRD and online aggression, as well as how a violent attitude moderates this connection. A total of 1004 college students completed self-reported measures on demographic factors, PRD, online aggression, revenge motivation, and violent attitudes. The findings revealed a positive correlation between PRD and online aggression, with revenge motivation serving as a mediating factor. Additionally, a violent attitude was found to moderate the relationship, indicating that PRD had a stronger association with online aggression in individuals with higher violent attitudes compared to those with lower attitudes.
... After being bullied offline, individuals often dwell on past negative events and stressful experiences, which will leading to persistent feelings of anger and anxiety (Borders, 2020), their anger rumination will be reinforced (Hamer and Konijn, 2016). It also awakens the individual's sense of aggression and puts them in a state of anger and resentment causing the individual to develop a state of aggressive and inappropriate behavior repeatedly (Pedersen et al., 2011), leading to increased aggressive behavior (Watkins and Roberts, 2020;Camacho, 2021). ...
Article
Full-text available
This research delves into the correlation between offline bullying and online unethical behavior among college students; and examines the potential mediating influences of anger rumination and perceived relative deprivation. The findings suggest that anger rumination, perceived relative deprivation, offline bullying, and online unethical behavior exhibit significant positive correlations with each other. Offline bullying is a strong predictor of online unethical behavior among college students, with the dual mediating effects of perceived relative deprivation and anger rumination on the relationship between offline bullying and online unethical behavior. This suggests that offline bullying directly influences college students' online unethical behavior and also influences it indirectly through anger rumination and perceived relative deprivation.
... Similarly, traditional bullying was associated with suicidal ideation while cyberbullying was not IeJSME 2024 Vol 18 (1): 66-80 (Bannink et al, 2014). Interestingly, cyber bullying victims had higher levels of anger rumination which also led them to be cyber-aggressors (Camacho et al, 2021). Bullies showed lower academic performance and poorer skills in organising and planning studies than victims or uninvolved students (Aparisi et al, 2021;Morales-Arjona et al, 2022). ...
Article
Research suggests that cyberbullying is more harmful than traditional bullying and can cause more profound harm to individuals. The study aimed to examine the effects of cyberbullying victimization on junior high school students’ cyberbullying over time while exploring the mediating role of loneliness and the moderating role of perceived social support. Four self-report questionnaires were administered to 561 middle school students at three time points. Data were analyzed using SPSS for descriptive statistics and Pearson correlation analysis. Moderated mediated effects tests were performed on the variables using SPSS Process. T1 cyberbullying victimization in middle school students predicts T2 cyberbullying behavior. T2 loneliness mediates the role of T1 cyberbullying victimization in influencing T2 cyberbullying. T2 perceived social support moderated the second half of the mediating role pathway. First, there are many mediators and moderators of cyberbullying victimization that affect cyberbullying, and others need to be examined. Second, only three middle schools in China were selected as investigators for this study, and future studies could select different populations to verify the applicability of the findings. Cyberbullying victimization can increase cyberbullying. Reducing cyberbullying requires attention to psychological and behavioral changes in cyberbullying victims.
Article
Objective: The COVID-19 pandemic and ensuing lockdown disrupted daily life and was related to increased mental health problems across the developmental spectrum, including for emerging adults. Understanding factors that contribute to adjustment during such national crises is critical, and attachment theory may provide a valuable framework for doing so. Participants & Methods: In the current study, 441 U.S. college students completed an online survey of their attachment internal working models (IWMs), anger and depressive rumination, and the psychological impact of COVID-19. Results: More secure IWMs of the mother-child relationship were indirectly associated with lower psychological impact of the pandemic through lower anger and depressive rumination. Although more secure IWMs of the father-child relationship were associated with lower depressive rumination, there were no indirect associations with the impact of the pandemic. Conclusions: Findings demonstrate the utility of attachment theory for understanding the impact of national crises and have implications for preparing and assisting populations at risk.
Article
Full-text available
Research into risky online behaviour among children and adolescents is on the rise, with more studies being conducted into the factors which can influence this phenomenon, above all in relation to school and family life. In the latter sphere, one relevant factor is the degree of genuine trust children have in their parents when using the Internet. The main objective of this study is to verify the effects of child disclosure about cyberaggression, in addition to the mediating role of problematic Internet use and cybergossip, and the moderating role of gender and age. A total of 866 primary school children (53% girls) between 10 and 13 years old (M=11.21; SD=0.90) were surveyed using self-reporting. The data processing followed a moderated serial mediation model using "Process". The results revealed the effects of child disclosure about cyberaggression and the mediation of problematic Internet use and cybergossip. Unlike gender, age moderated the effects of the mediation model. The results highlight the need to foster a climate of trust and communication in the family environment to reduce involvement in risky online behaviour, in which children feel understood and supported by their parents, which in turn encourages open communication about Internet use. Resumen La investigación sobre ciberconductas de riesgo infantil y juvenil se abre paso con estudios sobre factores que puedan influir en estos fenómenos, entre los que se destacan los relacionados con la convivencia escolar y familiar. En esta última, es relevante el nivel de confianza espontánea del hijo hacia su progenitor en el uso Internet. El objetivo de este trabajo es comprobar el efecto de la revelación filial en la ciberagresión, así como el rol mediador del uso problemático de Internet y el cibercotilleo, y el rol moderador del sexo y la edad. Un total de 866 escolares de primaria (53% chicas) de entre 10 y 13 años (M=11,21; D.T.=0,90) fueron encuestados mediante el uso de autoinformes. El tratamiento de datos siguió un modelo de mediación serial moderada a través de «Process». Los resultados evidenciaron los efectos de la revelación filial sobre la ciberagresión, así como la mediación del uso problemático de Internet y el cibercotilleo. A diferencia del sexo, la edad moderó los efectos del modelo de mediación. Los resultados ponen de manifiesto la necesidad de establecer un clima de confianza y comunicación en el entorno familiar para disminuir la implicación en las ciberconductas de riesgo, donde los menores se sientan comprendidos y apoyados por los progenitores, facilitando la comunicación espontánea sobre el uso de Internet.
Article
Full-text available
Although there is evidence of concurrent associations between victimization and bully perpetration, it is still unclear how this relation unfolds over time. This study investigates whether victimization in childhood is a prospective risk factor for bully perpetration in early adolescence, and examines rumination as a socio-cognitive factor that may mediate this association. Participants included 553 third graders (43.2% boys; Mage = 9.85), with follow-up assessments when they were in fourth, seventh, and eighth grade. Results indicated that more frequent victimization in grades 3 and 4 was indirectly associated with bully perpetration in grade 8, through rumination in grade 7 about past victimization experiences in elementary school. This pattern remained regardless of whether the rumination elicited feelings of anger or sadness. Our findings demonstrate one pathway through which frequent victimization can lead to perpetration and underscore the important role of rumination in victims’ subsequent adjustment. Implications for future interventions are discussed.
Article
Full-text available
Aggression is a type of negative social behavior. Agreeableness and anger-related cognition are thought to be important factors that affect aggression. The longitudinal relations among agreeableness, anger-related cognition and aggression, and the affective cognitive path underlying the relationship between agreeableness and aggression are not clear, however. In this study, 942 college students were investigated twice at an interval of six months, using the Buss-Perry Aggression Questionnaire, Anger Rumination Scale, and agreeableness subscale of the NEO Five-Factor Inventory. The results indicate that: agreeableness negatively predicts anger rumination and aggression after six months; anger rumination positively predicts aggression over time; and anger rumination mediates the longitudinal association between agreeableness and aggression. These results suggest that the prosocial personality may withstand aggression through resisting anger-related cognition. This study deepens our understanding of the relationships between personality and aggression, allowing a development of the General Aggression Model, in terms of recognizing the cognitive pathway for personality to influence aggression, and provides theoretical guidance on reducing the generation of aggression in daily life.
Article
Full-text available
Background/Objective: Most studies have evaluated victimization at a single time point, making it difficult to determine the impact of the time during which an individual is victimized. This longitudinal study aims to examine the differences in the levels of social status (social preference and perceived popularity) and friendship in peer victimization trajectories, and to analyse if there were changes over time in the levels of social status and friendship in each trajectory. Method: The final sample was composed of 1,239 students (49% girls) with ages between 9 and 18 (M = 12.23, SD = 1.73), from 22 schools in southern Spain. Peer nominations were collected. Results: The General Linear Model results associated the highest levels of social preference, perceived popularity and friendship with the sporadic victimization profile and the lowest levels of these dimensions with the stable profile. Conclusions:The results are discussed based on important personal aspects of stable victimization that confirms social rejection, unpopularity, and the low social support that victimization causes. This contribution is discussed in terms of health and social welfare in adolescence.
Article
Full-text available
Background/Objective: The main purpose of this study was to examine the relationships among cybervictimization, maladaptive cognitive emotion regulation strategies and somatic complaints in a 4-month follow-up study. Method: A total of 1,024 high school students (456 male and 568 female, M (SD) = 13.69 years (1.3 years), range 12–18 years, voluntarily participated in this study. Measures of cybervictimization and cognitive strategies were obtained at Time 1. Four months later (Time 2), measures of somatic complaints were obtained. Results: Multiple mediation analyses were conducted to determine the mediating roles of maladaptive strategies in the link between cybervictimization and somatic complaints. As expected, path-analytic results showed that cybervictimization predicted somatic symptoms. Furthermore, some maladaptive regulation strategies, namely self-blame and rumination, partially mediated the link between cybervictimization and somatic symptoms evaluated 4-months later. Conclusions: The findings are discussed in terms of the role that maladaptive cognitive emotion regulation strategies might play with regards to physical health in cyberbullying episodes. In general, these findings have important implications for developing an understanding about the affective determinants of somatic health problem initiation and maintenance after a victimization and for developing intervention programs specifically for cybervictimized adolescents.
Article
Full-text available
This chapter review some of the principal personal and situational factors established through recent international research that contribute to explain the phenomenon of cyber-victimization and cyber-aggression among adolescents, as well as its relations with socio-demographic variables (age, sex, grade level). Personal factors, like emotions, motives, normative beliefs, and moral disengagement were discussed jointly with situational factors, as the role of peers, friends, school and family environments, in addition to the possible interactions of these variables on cyber-bullying. The chapter ends with a discussion of future directions about the research on this phenomenon, namely in what concern educational programs that can use digital technology to help adolescents, schools and families to deal with cyber-bullying.
Article
Cyberbullying perpetration has become an international public health concern among adolescents. However, it is less clear whether anger rumination potentially increases adolescents’ cyberbullying perpetration up to now, and there is a limited understanding of factors that may affect this relationship. Therefore, the current study examined the relationship between anger rumination and adolescents’ cyberbullying perpetration and attempts to determine whether moral disengagement and callous-unemotional traits moderated this relationship at the same time. Two thousand four hundred and seven Chinese adolescents completed the measurements of anger rumination, moral disengagement, callous-unemotional traits, and cyberbullying perpetration. Results showed that adolescents with high anger rumination were likely to engage in cyberbullying perpetration, even after controlling age and gender. Compare to low moral disengagement adolescents, high moral disengagement adolescents were more likely to bully others online when they have high levels of anger rumination. Moreover, anger rumination significantly predicted adolescents’ cyberbullying perpetration when their moral disengagement and callous-unemotional traits were both high, or one was high. On the contrary, when adolescents’ moral disengagement and callous-unemotional traits were both low, this effect became nonsignificant. The current study first explored the relationship between anger rumination and adolescents’ cyberbullying perpetration and clarifying the moderating mechanisms underlying this relationship. Adolescents should be taught to manage and express their emotions properly, establish the right moral standards and reduce moral disengagement, as well as care more about others, in order to provide appropriate intervention.
Article
Cyberbullying has been established as a serious problem that affects all countries. However, the phenomenon of duality in cyberbullying, whereby an individual assumes two completely opposite roles, i.e., being a cybervictim and a cyberbully at the same time, has not been sufficiently examined in depth. The study population of this meta-analysis of 22 studies (K = 27) comprised 47,836 adolescents whose mean age was 13.68 years. The effect size of the correlation between being both a cybervictim and a cyberbully was moderate-high (r = 0.428), and its significance was high (p<0.001). The moderator variables sex, age and culture were studied by meta-regression; only culture was found to be significant, explaining 66% of the variance (R² = 66%). It was found in the data that Central European, Mediterranean culture, North American, South America and Asian culture in particular accounted for most of the moderator effect, while the other two variables were insignificant. The systematic review showed that the group of cyberbully-victims was chiefly formed by females with unstable family links (laissez-faire parental style, lack of communication and rules, offensive communication with parents). Lack of clear, appropriate rules and behavioural patterns in this family type reinforces problematic Internet use, which in turn increases the risk of individuals in this group becoming cybervictims. Longitudinal studies have revealed a series of grave problems and a relation between reporting being a cybervictim in the first survey waves and becoming a cyberbully in later waves. The cybervictim-bully population also proved to be more prone to suffer other psychological disorders (depression and anxiety) and emotional difficulties with peers.