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Aggressive Behavior. 2021;1–11. wileyonlinelibrary.com/journal/ab
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1
Received: 21 May 2020
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Revised: 12 February 2021
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Accepted: 15 February 2021
DOI: 10.1002/ab.21958
RESEARCH ARTICLE
Longitudinal associations between cybervictimization, anger
rumination, and cyberaggression
Antonio Camacho |Rosario Ortega‐Ruiz |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: PSI2016‐74871‐R
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 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, 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 self‐control to decrease impulsive behavior and
protocols to prevent and intervene in cyberbullying.
KEYWORDS
adolescents, anger rumination, cross‐lagged 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 meta‐analysis by Lozano‐Blasco
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 meta‐analysis 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 self‐defense
(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; Marín‐López 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(Nolen‐Hoeksema, 1991) is often applied to
study the negative effects of traumatic events, such as victimization.
Responses to victimization are classified as emotion‐focused coping,
aimed at minimizing distress by focusing on the affect related to the
stressor, and problem‐focusedcoping,aimedatremovingor,when
unavoidable, minimizing the impact of the experience by focusing on
the stressor (Lazarus & Folkman, 1984). While problem‐focused 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 (Nolen‐Hoeksema &
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‐
state‐related thoughts during an anger episode (Sukhodolsky
et al., 2001). After such anger‐inducing 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 emotion‐focused
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
(Álvarez‐García et al., 2019). A meta‐analysis 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) meta‐analysis the pre-
dictive effects of lacking self‐control 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
Emotion‐focused coping strategies activated by an anger‐raising 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
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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 self‐control (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
study—with a cross‐sectional 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 pre‐adolescents and later adolescents found
higher levels of involvement in both cybervictimization and cyberag-
gression in the older group (González‐Cabrera 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 level‐differences 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) (Lucas‐Thompson 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 (Seiffge‐Krenke, 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 cyber‐aggression (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 (11–16
years, 7th–9th 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 self‐control,
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 7–9, and included 115 classes from 13 middle
schools in Southern Spain. In this study, we analyzed four waves of
data collected during the years 2017–2019 at 6‐month 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 (Ortega‐Ruiz et al., 2016) and in cross‐cultural
populations (Herrera‐López 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 5‐point 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 4‐point 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 self‐report 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 paper‐and‐pencil 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 t‐tests 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 cross‐lagged 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) cross‐lagged 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
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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
cross‐lagged 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 cross‐lagged paths were also freely estimated; and in addition, in
Model 4 the residual covariances, cross‐lagged 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
non‐normality 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 Tucker–Lewis 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 = complex”sampling 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
t‐test 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 (11–13 years) and middle (14–16 years)
adolescence. Middle adolescents reported more cybervictimization,
TABLE 1 Descriptive statistics and
gender and age differences Gender Age
Variable MSDαt‐test dt‐test 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 t‐test 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 cross‐sectionally and long-
itudinally in all waves. Respective coefficients of cross‐sectional 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 |Cross‐lagged model
We estimated and compared hierarchical cross‐lagged 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 [0.021–0.030]. 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 [0.022–0.031], Δχ²
(6) = 9.24, p> .05. Model 3, with additional unconstraint to the
cross‐lagged paths, again showed good fit: χ² (40) = 136.671,
p< .001; CFI = 0.981, TLI = 0.969; and RMSEA = 0.028, 90% CI
[0.023–0.034]. 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 [0.023–0.035]. 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 cross‐lagged 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 cross‐lagged 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 cross‐lagged 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 cross‐lagged 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 cross‐lagged 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 meta‐analysis showed that lower
self‐control 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
S‐B
(df)pvalue ΔCFI ΔRMSEA
Cyberbullying
Configural 4913.270 (3728)*** 0.976 0.976 0.010 [0.010–0.011] –––
Metric 4952.604 (3783)*** 0.977 0.976 0.010 [0.009–0.011] 91.75 (55)** 0.001 0.000
Scalar 5138.342 (3978)*** 0.977 0.978 0.010 [0.009–0.011] 311.310 (195)*** 0.000 0.000
Anger rumination
Configural 7136.792 (2775)*** 0.970 0.969 0.023 [0.022–0.024] –––
Metric 7198.826 (2826)*** 0.970 0.970 0.023 [0.022–0.023] 127.149 (51)*** .0000 0.000
Scalar 7337.146 (2940)*** 0.970 0.971 0.023 [0.022–0.023] 389.668 (114)*** 0.000 0.000
Abbreviations: CFI, comparative fit index; CI, confidence interval; RMSEA, root mean square error of approximation; TLI, Tucker–Lewis index.
**p< .01.
***p< .001.
FIGURE 1 Cross‐lagged 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 self‐control 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 cross‐lagged 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
decision‐making abilities, and hence decreases the likelihood of
self‐regulation 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 face‐to‐face 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 [0.087–0.172] 0.02 55.59 <.001
CV T1
→
CV T2
→
CV T3
→
AR T4 0.011 [0.001–0.021] 0.01 2.25 <.02
CV T1
→
CV T2
→
AR T3
→
AR T4 0.015 [0.001–0.028] 0.01 2.15 <.05
CV T1
→
AR T2
→
AR T3
→
AR T4 0.019 [0.000–0.038] 0.01 1.97 <.05
CV T1
→
CV T2
→
CV T3
→
CA T4 0.022 [0.013–0.031] 0.01 4.69 <.001
CV T1
→
CV T2
→
CA T3
→
CA T4 0.013 [0.006–0.020] 0.00 3.44 <.01
CV T1
→
CA T2
→
CA T3
→
CA T4 0.008 [0.001–0.014] 0.00 2.21 <.05
AR T1
→
AR T2
→
AR T3
→
AR T4 0.230 [0.200–0.261] 0.02 14.30 <.001
AR T1
→
CV T2
→
CV T3
→
CA T4 0.003 [0.002–0.005] 0.00 3.72 <.001
AR T1
→
CV T2
→
CA T3
→
CA T4 0.002 [0.001–0.003] 0.00 7.06 <.001
AR T1
→
AR T2
→
CV T3
→
CA T4 0.004 [0.002–0.007] 0.00 3.22 <.01
AR T1
→
AR T2
→
AR T3
→
CA T4 0.026 [0.005–0.046] 0.00 2.41 <.05
AR T1
→
AR T2
→
CA T3
→
CA T4 0.012 [0.008–0.015] 0.00 6.16 <.001
AR T1
→
CA T2
→
CA T3
→
CA T4 0.005 [0.003–0.007] 0.00 5.48 <.001
AR T1
→
AR T2
→
AR T3
→
CV T4 0.035 [0.013–0.058] 0.01 3.08 <.01
AR T1
→
AR T2
→
CV T3
→
CV T4 0.027 [0.015–0.040] 0.01 4.17 <.001
AR T1
→
CV T2
→
CV T3
→
CV T4 0.021 [0.014–0.028] 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 self‐report
instruments, which in future could be improved by the inclusion of
multi‐informant 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 self‐control 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 (Quintana‐Orts, Rey, 2018). In addition, it is particularly
necessary that schools have evidence‐based 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 (PSI2016‐74871‐R, 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 Ortega‐Ruiz https://orcid.org/0000-0003-2110-6931
Eva M. Romera https://orcid.org/0000-0002-9414-8019
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How to cite this article: Camacho, A., Ortega‐Ruiz, R., &
Romera, E. M. (2021). Longitudinal associations between
cybervictimization, anger rumination, and cyberaggression.
Aggressive Behavior,1–11. https://doi.org/10.1002/ab.21958
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