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Bidirectional associations between bullying and cyberbullying have consistently identified during adolescence. However, little is known about how this relationship works on the within-person level, after controlling for potential overlap at the between-person level. This study examined the bidirectional longitudinal associations between bullying and cyberbullying perpetration and victimization during 18-month period over four time points. A total of 2835 participants, aged 11 to 16 years in time 1 (50% girls; M age = 13.13, SD = 1.06) were surveyed. Random intercept cross-lagged analyses revealed the stability of bullying perpetration and victimization. Cyberbullying victimization predicted inversely bullying and cyberbullying perpetration. The results indicate spirals of positive long-term associations between bullying (perpetration and victimization) and cyberbullying perpetration but no long-terms spirals of victimization.
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Journal of Youth and Adolescence (2023) 52:406418
https://doi.org/10.1007/s10964-022-01704-3
EMPIRICAL RESEARCH
Bullying and Cyberbullying Perpetration and Victimization:
Prospective Within-Person Associations
Antonio Camacho1Kevin Runions2Rosario Ortega-Ruiz1Eva M. Romera 1
Received: 26 August 2022 / Accepted: 1 November 2022 / Published online: 17 November 2022
© The Author(s) 2022
Abstract
Bidirectional associations between bullying and cyberbullying have consistently identied during adolescence. However,
little is known about how this relationship works on the within-person level, after controlling for potential overlap at the
between-person level. This study examined the bidirectional longitudinal associations between bullying and cyberbullying
perpetration and victimization during 18-month period over four time points. A total of 2835 participants, aged 11 to 16
years in time 1 (50% girls; Mage =13.13, SD =1.06) were surveyed. Random intercept cross-lagged analyses revealed the
stability of bullying perpetration and victimization. Cyberbullying victimization predicted inversely bullying and
cyberbullying perpetration. The results indicate spirals of positive long-term associations between bullying (perpetration
and victimization) and cyberbullying perpetration but no long-terms spirals of victimization.
Keywords Within-person Longitudinal study Adolescents Bullying Cyberbullying
Introduction
Bullying has been dened as repeated and intentional
aggression by one or more individuals against the victim
who is unable to effectively defend himself/herself (Olweus,
1993; Volk et al., 2014). The new realities of information
and communication technologies have given rise to cyber-
bullying, intentional aggressive acts conducted through these
technologies (Kowalski et al., 2014). Researchers have
recognized the complexity in discriminating between the
two forms of violence in adolescents to the point that an
outstanding question in the literature is the extent of overlap
between bullying and cyberbullying. These questions of
overlap are important to understand the developmental
sequencing of bullying involvement - as perpetrator and as
target of bullying - over time. Questions of overlap also get
at issues of role continuity (e.g., are young people who
engage in cyberbullying more likely to become perpetrators
of ofine bullying?) and role inversion (e.g., are targets of
bullying more likely to become perpetrators of bullying?).
Given the negative impact on psychosocial adjustment of
involvement over time in these phenomena there are some
research questions that need to be addressed. Is there stabi-
lity of involvement over time? Does a particular experience
provide an entry pointinto further bullying involvement?
Does being a target of bullying lead to perpetration? Does
traditional bullying perpetration tend to migrate to the online
setting, or does cyberbullying prepare students for more
direct acts of bullying? Denitive answers to these questions
have eluded researchers to date, and (largely unrecognized)
limitations of the common methodologies pose severe
challenges to those existing preliminary conclusions held by
researchers. Designed effective intervention for adolescents
involved in bullying requires a clear natural history of the
phenomenon of bullying involvement. This study deploys
recent developments in cross-lagged panel modeling to
provide a close examination of bullying perpetration and
victimization both online and off over time, enabling con-
clusions that do not conate inter-individual change over
time (relative to one another) and intra-individual change
(relative to oneself). Given that bullying (about the rst
years of middle school) and cyberbullying (about the last
years of middle school) peak during adolescence (Kowalski
et al., 2014; Pabian & Vandebosch, 2016), the present study
was a four-wave panel study among middle school adoles-
cents aged 11-16 which tends to peak in around middle
adolescence (Kowalski et al., 2014).
*Eva M. Romera
eva.romera@uco.es
1Universidad de Córdoba, Córdoba, Spain
2The University of Western Australia, Perth, Australia
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Stability, Role Continuity and Role Inversion in
Online and Ofine Bullying Involvement
An overlap between bullying and cyberbullying has been
mainly established by correlational cross-sectional studies
(Baldry et al., 2017; Del Rey et al., 2012; Kowalski et al.,
2014; Modecki et al., 2014). A meta-analysis of long-
itudinal studies provided estimates of the association from
victimization to subsequent perpetration and perpetration to
subsequent victimization (Walters, 2021). But the estimates
from this meta-analysis are based on correlations analyses.
Researchers seeking to understand possible causal inu-
ences on behavior are often interested in cross-lagged path
modeling (CLPM), that estimates concurrent, autoregres-
sion and lagged associations. Based on extant research
using CLPM, autoregressive paths have shown that online
and ofine perpetration and victimization are relatively
stable over time (Camacho et al., 2021; Giumetti et al.,
2022; Pabian & Vandebosch, 2016). These studies have
also provided estimates of signicant lagged associations
between perpetration and victimization conducted online
and ofine. Research on different aspects of bullying (e.g.,
bullying and cyberbullying; perpetration and victimization)
has developed two lines of hypothesizing: role continuity
and role inversion.
In the role continuity hypothesis, involvement in bully-
ing continue and extend it via cyberbullying (Baldry et al.,
2016). Based on research to date, adolescents involved in
bullying perpetration are more likely to engage in cyber-
bullying perpetration later (Chu et al., 2018; Giumetti et al.,
2022; Pabian & Vandebosch, 2016). Similarly, those
involved in cyberbullying perpetration have also been
found to have greater subsequent involvement in ofine
perpetration (Pabian & Vandebosch, 2016). Similarly,
higher levels of bullying victimization predict subsequent
increased cyberbullying victimization (Giumetti et al.,
2022; Pabian & Vandebosch, 2016), and vice versa (Chu
et al., 2018; Pabian & Vandebosch, 2016).
Role inversion indicates a process in which those
involved in one role are more likely to become involved in
the other role: victimization may subsequently become
perpetrators, and vice versa (Falla et al., 2022; Lee et al.,
2021; Pabian & Vandebosch, 2016). Perpetrators of bully-
ing may unknowingly make enemies of people more pow-
erful than themselves, thereby becoming targets of bullying
(Malamut et al., 2022). Victims of bullying may come to
learn that perpetration is a path toward perceived popularity
(if not peer acceptance; Strindberg et al., 2020) and begin
bullying other less powerful them themselves. Others may
desire revenge but nd retaliation in kindtoo difcult due
to imbalances in that modality of power (e.g., physical,
psychological, and social); consequently, the victims may
seek a modality where they are not weak. For example,
young people who experience face-to-face victimization
may use the Internet to bully others and take revenge on
those perceived as bullying them at school (Chu et al.,
2018; Runions et al., 2018). The Internet is potentially
signicant for adolescents who perceive themselves as less
empowered, as power imbalance becomes less prominent in
the cyberspace which provides other factors such as anon-
ymity or the technical skills to react to damaging inter-
personal experiences in the ofine context. An example
of this is the nding that adolescents with higher levels of
bullying perpetration are at increased subsequent risk of
cyberbullying victimization (Chu et al., 2018).
Addressing Methodological Limitations to
Differentiate Between- and Within-Person Change
To date, most studies examining these hypotheses have
used methodological strategies without a capacity to dif-
ferentiate between inter-individual change and intra-
individual change. Specically, CLPM has important
methodological concerns as it cannot account for trait-like
(time-invariant) and state-like (time-variant) individual dif-
ferences (Hamaker et al., 2015). Data simulation studies
have shown that this limitation can result in inaccurate
models and thus mistaken conclusions regarding the exis-
tence of a lagged path from one variable to another or may
result in incorrect estimates of the direction of the causal
relationship (Hamaker et al., 2015). Thus, CLPM may
indicate a signicant positive path from one variable at one
time to another variable at the subsequent time, but the real
relationship may in fact be negative. Obviously, inaccura-
cies of this scale will lead to the wrong conclusions
regarding causal processes. For an accurate insight into the
causal directionality of effects, a differentiation of the
between- and within-person level over time is needed.
Hamaker et al. (2015) have proposed the random intercept
cross-lagged panel model (RI-CLPM), which account for
the individual differences by distinguishing among
between- and within-person level. By incorporating random
intercepts, the RI-CLPM is able to avoid spurious ndings
regarding the presence of any causal relationships, the
temporal priority (and hence likely causal priority) of dif-
ferent variables, and the direction / sign of the estimated
lagged relationship (Hamaker et al., 2015).
To date, few studies of bullying and cyberbullying have
addressed the inuence between perpetration and victimi-
zation without confounding inter- and intra-individual. The
stability of traditional (ofine) bullying victimization and
perpetration (autoregressive paths) has been reported con-
sistently positive in both CLPM and RI-CLPM (Davis et al.,
2022; Pabian & Vandebosch, 2016; Romera et al., 2021;
Zhu et al., 2022). However, the stability apparent in
cyberbullying perpetration and cyberbullying victimization
Journal of Youth and Adolescence (2023) 52:406418 407
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has been based on studies using traditional CLPM (Cama-
cho et al., 2021; Giumetti et al., 2022). Two studies using
RI-CLPM (Boer et al., 2021; Erreygers et al., 2018) have
not replicated this stability, which suggests that the evident
continuity of cyberbullying involvement may be a spurious
nding based on awed treatment of variance. This nding
- that bullying involvement is relatively stable, whereas
cyberbullying involvement is more sporadic- may be based
on the specic characteristics of cyberbullying. In cyber-
bullying, the aggression not necessarily has to be repeated
over time by the same person (resending videos or images
by others is a way of repeating the aggressive action), there
is a higher probability to disengagement chances, and also
there is not a clear interdependence between the victim and
the aggressor (Huang et al., 2022).
Cross-lagged associations between perpetration and vic-
timization, and the possibility of role inversion, have also
been examined using RI-CLPM. Zhou et al. (2022) exam-
ined prospective reciprocal associations between perpetra-
tion and victimization at the within-person level in a sample
of children. Consistent with previous ndings in CLPM,
they found that an increase from T3 in bullying victimiza-
tion compared with their average in the study predicted
higher involvement in bullying perpetration at time 4.
Moreover, bullying perpetration at time 4 also positively
predicted bullying victimization in time 5. These ndings
suggest that once the between- and within-person variance
in victimization and perpetration are disaggregated, bidir-
ectional relationships exist. Examining cyberbullying over
three-waves six months apart, Erreygers et al. (2018) also
explored the bidirectional association between perpetration
and victimization for adolescents. In contrast to previous
ndings using CLPM (Akgül & Artar, 2020; Camacho
et al., 2021), they did not nd signicant association
between cyberbullying victimization and subsequent chan-
ges in cyberbullying perpetration. The cross-lagged path
from cyber-perpetration to cyber-victimization was also not
signicant. This provides important information on the
possible causal development sequencing of cyberbullying
involvement. To date, however, no studies have examined
the interrelationships of both online and ofine bullying
perpetration and victimization, and thus testing the role
continuity and role inversion hypotheses.
Current Study
Although extensive evidence concerning the association
between traditional and cyberbullying perpetration and
victimization has been reported, almost all prior studies
have used statistical approaches that fail to account for
within-person variability (such as CLPM). The dynamic
processes of adolescent bullying and cyberbullying
involvement at the intra-individual level remain unclear. To
address this gap, the aim of the present study was to explore
the bidirectional longitudinal associations between ofine
and online bullying perpetration and victimization using
RI-CLPM that accounts for within-person processes. Based
on prior research, it was expected that ofine bullying
perpetration and victimization, and cyberbullying perpe-
tration and victimization would show positive between-
individual associations (Hypothesis 1), such that indivi-
duals who score higher than their peers on one at one time
will tend to score higher at subsequent points. At within-
individual stability, it was expected that involvement in
ofine bullying perpetration and victimization would be
stable over time, but cyberbullying would not (Hypothesis
2). Based on the cross-lagged paths and the role continuity
hypothesis, it was predicted that online and ofine bullying
perpetration would be reciprocally inuenced over time
(Hypothesis 3) and bullying and cyberbullying victimiza-
tion would also show signicant bidirectionality over time
(Hypothesis 4) (e.g., an increase in bullying perpetration at
one time relative to their average across the four time points
would be associated with higher levels at a later time in
cyberbullying perpetration and vice versa). Finally,
according with role inversion hypothesis, it was expected
that ofine victimization and perpetration would be bidir-
ectionally positive associated over time, but not cyberbul-
lying perpetration and victimization (Hypothesis 5). It was
also expected that bullying victimization would predict
cyberbullying perpetration (Hypothesis 6) and bullying
perpetration would be associated with later cyberbullying
victimization (Hypothesis 7).
Methods
Participants and Procedure
The data were drawn from a larger longitudinal study
focusing on characteristics associated with the bullying and
cyberbullying, using a sample of adolescents in secondary
schools in Southern Spain. The study was approved by the
Ethical Committee of the institution of the Spanish authors.
To recruit participants, schools were invited to collaborate
and were informed of the purpose of the project. Once the
management team of each school agreed to participate,
parental consent was obtained (5% of parents did not con-
sent for their children to participate in the study). The
convenience sample comprised 2835 adolescents (50%
girls) between 11- and 16-years old attending Grades 79,
recruited in 13 middle schools. Data collection occurred
during school hours via trained psychologists with research
experience. Participants were provided the purpose of the
study and standardized information on the study and their
408 Journal of Youth and Adolescence (2023) 52:406418
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participation, emphasizing the voluntary, condential, and
anonymous nature of the data collection. The procedure was
identical at each time point. Participants completed the
paper-and-pencil questionnaire in approximately 30 min.
Adolescents were assessed four times in 18 months (six
months after each time point). For the purpose of linking
surveys over time, participants were instructed to develop
their own personal code with the initial letters of their name
and date of birth, so that only they would know this iden-
tication and data anonymity would be guaranteed. In the
baseline, the mean age of students in November 2017
(Time 1; T1) was 13.10 years (SD =1.06). The socio-
demographic characteristics for each time point are shown
in Table 1. The average response rate was 88%. Sample
attrition was principally due to students absent from school
on the day of data collection or having moved schools. Due
to the longitudinal design of the study, a test was performed
to examine whether non-participation might be associated
with any of the study variables. Through logistic regression,
no signicant differences were reported on the basis of
gender, age, cyberbullying and bullying (both perpetration
and victimization; at each time) predicting higher or lower
participation over time (all ps > 0.05).
Measures
Bullying
The European Bullying Intervention Project Questionnaire
(Ortega-Ruiz et al., 2016) was applied to measure bullying
victimization and perpetration. The questionnaire included
14 items, 7 for each subscale: victimization (e.g., Someone
has said mean things about me to other people) and per-
petration (e.g., I have stolen or broken someones things).
Before completing the questionnaire, participants were
provided the characteristics of bullying (intentionality,
power imbalance, repetition over time) to differentiate it
from other aggressive behaviors. The frequency of the
adolescentsbehaviors was addressed with a ve response
options as never (0), once or twice (1), once or twice a
month (2), about once a week (3), and more than once a
week (4), and continuous scores were used for analyses. For
each time, internal reliability using McDonalds omega was
0.86, 0.86, 0.86 and 0.85 for victimization and 0.81, 0.82,
0.81 and 0.77 for perpetration, respectively. The con-
rmatory factor analysis showed good psychometric prop-
erties of the two-factor structure, as proposed in the original
study, with the current sample at T1: χ2=678.430, df =76,
p< 0.001; CFI =0.958, TLI =0.949, RMSEA =0.055,
90% CI [0.051, 0.059], SRMR =0.064.
Cyberbullying
The European Cyberbullying Intervention Project Ques-
tionnaire (Ortega-Ruiz et al., 2016) was applied to mea-
sure cyberbullying victimization and perpetration. The
questionnaire included 22 items, 11 for each subscale:
victimization (e.g., Someone threatened me through texts
or online messages) and perpetration (e.g., Iposted
personal information about someone online). Adoles-
cents were asked to the frequency of cyberbullying from 0
(never)to4(more than once a week). For each time,
McDonalds omega was 0.87, 0.85, 0.88 and 0.89 for
victimization and 0.87, 0.89, 0.89 and 0.89 for perpetra-
tion, respectively. The conrmatory factor analysis
showed good psychometric properties of the two-factor
structure, as proposed in the original study, with the
current sample at T1: χ2=1085.311, df =288, p< 0.001;
Table 1 Sample distribution NTime 1 Time 2 Time 3 Time 4
November 2017 May 2018 November 2018 May 2019
n(p.r.) 2835 2657 (94%) 2515 (89%) 2461 (87%) 2357 (83%)
Gender
Girls 50% 50% 50% 51% 51%
Boys 50% 50% 50% 49% 49%
Age (SD) 13.10 (1.06) 13.60 (1.12) 14.01 (1.05) 14.54 (1.06)
School
Rural 52% 52% 52% 52% 53%
Urban 48% 48% 48% 48% 47%
Grade
1 35% 35% 3% 2%
2 34% 34% 36% 37%
3 31% 31% 32% 31%
4 29% 29%
p.r. participation rate
Journal of Youth and Adolescence (2023) 52:406418 409
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CFI =0.956, TLI =0.952, RMSEA =0.040, 90% CI
[0.038, 0.043], SRMR =0.067.
Covariates
Age and gender were collected as socio-demographic
information to address any differences in variables based
on these characteristics.
Data Analytic Strategy
Spearman correlations and the Intraclass Correlation
Coefcient were performed in preliminary analyses. To
analyze the prospective relationships between bullying and
cyberbullying by disaggregating the between- and within-
person variance a random intercept cross-lagged panel
model (RI-CLPM; Hamaker et al., 2015) was estimated
using Mplus 8.7 (Muthén & Muthén, 1998). The between-
person level (time-invariant characteristics) captures the
stability in each construct through the random intercept that
is estimated with the score across the four times. The
between-person level includes covariances between the
random intercepts of variables of the study. The variance at
the within-person level (time-variant characteristics) is
captured by the participantstime-to-time deviation from
the individual expected score. These analyses permit con-
clusions regarding whether previous increases or decreases
of face-to-face and online victimization and perpetration
from their own average level across the four time points are
associated with subsequent changes in bullying and
cyberbullying involvement. The within-person level
includes autoregressive paths (e.g., cyberbullying victimi-
zation at T1 on cyberbullying victimization at T2), cross-
lagged paths (e.g., bullying victimization at T1 on cyber-
bullying perpetration at T2), covariances between variables
in T1 (e.g., cyberbullying perpetration at T1 with bullying
perpetration at Time 1), and the residual covariances of the
variables at T2, T3 and T4. Gender and age were included
in the model as time-invariant predictor of observed vari-
ables, as boys have been found to have a higher prevalence
of involvement in perpetration (Smith et al., 2019), while
middle adolescents tend to have higher involvement in
cyberbullying (Camacho et al., 2021).
The data were tested for missingness. Littles MCAR
(Little, 1988) test was signicant (p< 0.001) indicating that
the data were not missing completely at random (MCAR).
Based on the low normed chi-square (χ2/df =1.79) the data
were deemed to be missing at random (MAR) (Bollen,
1989). Therefore, missing data were addressed with full
information maximum likelihood (FIML). Maximum like-
lihood estimation with robust standard errors (MLR) was
used to address the non-normally distributed nature of the
variables. For optimal standard model t indices,
comparative t index (CFI) should be above 0.90, and root
mean square error of approximation (RMSEA) should not
exceed 0.08 (Hu & Bentler, 1999). Because the adolescents
were placed in schools, the command type =complex
was used to handle the clustering effects on standard errors.
The associations between the study variables were ana-
lyzed through a set of models with the aim to choose the most
parsimonious model whose change in model twasnotsig-
nicant (Kline, 2015). First, an unconstrained model (model
1) where the components of the RI-CLPM were freely esti-
mated. Then, a stepwise series of constraints were added to
match the paths over time: autoregressive paths (model 2),
cross-lagged paths (model 3) and correlated changes within-
time (model 4). Signicant differences between model t
comparisons were considered when two of the following
criteria were attained: chi-square difference test at p< 0.05
(Satorra & Bentler, 2001), ΔCFI 0.01 and ΔRMSEA 0.015
(Chen, 2007). In the lack of differences between the nested
models, the model with the most constraints was retained.
Results
Preliminary Analyses
Descriptive statistics and Spearmans bivariate correlations
among study variables are reported in Table 2. The cross-
sectional and longitudinal association between bullying and
cyberbullying perpetration and victimization were low-
moderate positive.
In relation to the Intraclass Correlation Coefcient, the
within-person level variance in each measure was higher
than 10% (58% for bullying perpetration; 49% for bullying
victimization; 63% for cyberbullying perpetration; 54% for
cyberbullying victimization). This warrants use of RI-
CLPM to disaggregate the between- and within-person
variance (Hamaker et al., 2015).
Random-Intercept Cross-Lagged Panel Model
Steps were followed to choose the most parsimonious
model before analyzing the prospective relationships
between the study variables (see Table 3). In model 1, paths
were allowed to vary over time. This unconstrained model
showed an excellent model t. In model 2, the auto-
regressive paths were constrained to be equivalent over
time. This model was not signicantly different compared
to the unconstrained model since at least two of the model
t comparative criteria were not violated (see Table 3). In
model 3, imposing further constraints on cross-lagged paths
did not result in signicant differences compared to model
2. For model 4 within-time correlated changes were con-
straining, with no signicant differences in model t
410 Journal of Youth and Adolescence (2023) 52:406418
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compared to model 3. Thus model 4 was retained as the
most parsimonious to explore the prospective association
between bullying and cyberbullying.
The results of the RI-CLPM were reported in STDYX
standardized estimates (see Fig. 1). At the between-person
level, the signicant positive covariances between random
intercepts of bullying and cyberbullying perpetration and
victimization suggest that adolescents who reported higher
levels of one variable at the four time points also reported
higher levels in other variables compared to other adoles-
cents (Hypothesis 1). At the within-person level, signicant
and positive autoregressive paths for both bullying perpe-
tration and victimization indicate that adolescents who
report a higher-than-expected score are likely to report a
higher-than-expected score on subsequent times (Hypoth-
esis 2). Notably, the autoregressive paths of cyberbullying
perpetration and victimization both were not signicant (see
Table 4; Hypothesis 2). In relation to cross-lagged paths,
positive cross-lagged reciprocal effects were found across
time between bullying and cyberbullying perpetration
(Hypothesis 3). Ofine bullying victimization predicted
subsequent cyberbullying victimization (Hypothesis 4),
providing support for the role continuity hypothesis. With
regard to the role inversion hypotheses (Hypotheses 5-7),
signicant negative paths from online victimization to both
bullying and cyberbullying perpetration were discovered
(Hypotheses 5 and 7), while face-to-face victimization
predicted subsequent increases in cyberbullying perpetra-
tion (Hypothesis 6). Regarding the association between
variables within-time, the four study variables were posi-
tively associated (see Table 4).
Based on time-invariant predictors, boys show higher
involvement than girls in both bullying and cyberbullying
perpetration, as in online and face-to-face victimization at T1
Table 2 Spearmans bivariate correlations and descriptive statistics of study variables
12345678910111213141516
1. Bullying perpetration T1
2. Bullying perpetration T2 0.51
3. Bullying perpetration T3 0.42 0.47
4. Bullying perpetration T4 0.40 0.45 0.50
5. Bullying victimization T1 0.55 0.33 0.28 0.29
6. Bullying victimization T2 0.31 0.53 0.29 0.29 0.52
7. Bullying victimization T3 0.28 0.32 0.55 0.35 0.42 0.49
8. Bullying victimization T4 0.25 0.29 0.33 0.56 0.40 0.45 0.54
9. Cyberbullying perpetration T1 0.47 0.37 0.31 0.33 0.34 0.22 0.21 0.21
10. Cyberbullying perpetration T2 0.38 0.52 0.35 0.34 0.22 0.30 0.21 0.18 0.44
11. Cyberbullying perpetration T3 0.31 0.39 0.50 0.38 0.18 0.21 0.31 0.22 0.43 0.47
12. Cyberbullying perpetration T4 0.32 0.33 0.39 0.50 0.18 0.16 0.25 0.29 0.36 0.40 0.48
13. Cyberbullying victimization T1 0.42 0.30 0.27 0.29 0.53 0.36 0.34 0.32 0.59 0.36 0.32 0.29
14. Cyberbullying victimization T2 0.29 0.41 0.28 0.28 0.33 0.46 0.33 0.33 0.34 0.57 0.39 0.34 0.48
15. Cyberbullying victimization T3 0.26 0.33 0.38 0.33 0.31 0.36 0.49 0.37 0.34 0.37 0.60 0.37 0.44 0.54
16. Cyberbullying victimization T4 0.25 0.28 0.31 0.43 0.31 0.32 0.37 0.50 0.32 0.31 0.37 0.60 0.42 0.45 0.50
M0.25 0.28 0.20 0.21 0.55 0.56 0.40 0.42 0.14 0.14 0.12 0.12 0.23 0.21 0.20 0.20
SD 0.42 0.45 0.38 0.36 0.70 0.67 0.58 0.56 0.34 0.36 0.32 0.31 0.43 0.37 0.39 0.38
Range 04040404040404040404040404040404
All correlations were signicant at p< 0.001
Table 3 Model t statistics of
random intercept cross-lagged
panel model
Model Model t Model t comparison
χ2df CFI RMSEA [90% CI] Δχ2(Δdf) ΔCFI ΔRMSEA
Model 1 48.320 38 0.998 0.010 [0.000, 0.018] ––
Model 2 80.170** 46 0.994 0.017 [0.010, 0.023] 24.192 (8)** 0.004 0.007
Model 3 120.834*** 70 0.991 0.017 [0.011, 0.021] 40.605 (24)* 0.003 0.000
Model 4 126.449** 82 0.992 0.014 [0.009, 0.019] 14.496 (12) 0.001 0.003
CFI comparative t index, CI condence interval, RMSEA root mean square error of approximation
*p< 0.05; **p< 0.01; ***p< 0.001
Journal of Youth and Adolescence (2023) 52:406418 411
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(see Table 5). Older participants in the study showed higher
involvement than younger participants on bullying perpetration
(at T1 and T2), cyberbullying perpetration (from T1 to T3) and
cyberbullying victimization (from T1 to T4) (see Table 5).
Discussion
Research has suggested the role continuity and inversion
hypotheses about the phenomena of bullying and cyberbullying
on the involvement of adolescents in perpetration and victi-
mization. However, the bidirectional inuence association has
mainly focused on approaches that may be misleading because
associations within- and between-person were not dis-
aggregated. This study used cutting-edge within-person statis-
tics to correct for limitations with traditional modeling that may
have led to spurious ndings. The present research captured
intra-individual longitudinal uctuations between bullying
perpetration, bullying victimization, cyberbullying perpetration,
and cyberbullying victimization over time after controlling for
time-invariant associations at the between-person level by
using RI-CLPM.
The overlap between bullying perpetration, bullying vic-
timization, cyberbullying perpetration and cyberbullying vic-
timization were reported by the positive associations at a
stable between-person level (Hypothesis 1). These moderate/
strong correlations of random intercepts indicated an overall
time invariance of adolescent bullying involvement of ado-
lescents. Consistent with previous cross-sectional research
(Baldry et al., 2017;DelReyetal.,2012), higher involvement
in one aspect of bullying across the four time points (e.g.,
ofine victimization) was associated with high involvement in
the other aspects (e.g., cyberbullying perpetration) overall.
Associations strong overall (ranging from rof 0.680.82)
except for the association of bullying victimization and
cyberbullying perpetration, which had a notably more modest
association. However, these ndings do not clarify which
features are antecedent or consequence.
Stability of Bullying and Cyberbullying
Once the stable differences between individuals have been
controlled for, a more accurate understanding may be pro-
vided on inuence of time-variant variables and likely causal
processes involved. In support of Hypothesis 2 and consistent
with previous research (Boer et al., 2021; Cogo-Moreira et al.,
2021; Davis et al., 2022; Romera et al., 2022), involvement in
perpetration and victimization was stable for ofine bullying,
but not in cyberbullying. Such ndings for ofine bullying are
consistent with those studies even when CLPM was
employed (and thus without separating between- and within-
person variance; Chu et al., 2018; Pabian & Vandebosch,
2016). Using RI-CLPM, the ndings of the present study
indicate that there is no direct stability in cyberbullying
involvement over time (Boer et al., 2021; Erreygers et al.,
2018), in contrast with previous research using CLPM
(Camacho et al., 2021;Giumettietal.,2022). The stability
over time of victimization and perpetration may be supported
by the hierarchical nature of ofine bullying as a group pro-
cess. However, the involvement of adolescents in cyberbul-
lying tends to have a more sporadic and less sustainable
character over time (Huang et al., 2022;Smaheletal.,2020).
The role of group dynamics in the relative stability of ofine
bullying remains to tested more directly but may speak to a
more complex developmental phenomenon than current
models of role inversion and continuity (see below).
.76***
.38***
.62***
Bullying
perpetration
T1
Bullying
perpetration
T2
Bullying
victimization
T1
Bullying
victimization
T2
Cyberbullying
perpetration
T1
Cyberbullying
perpetration
T2
Cyberbullying
victimization
T1
Cyberbullying
victimization
T2
Bullying
perpetration
Bullying
victimization
Cyberbullying
perpetration
Cyberbullying
victimization
Bullying
perpetration
T3
Bullying
victimization
T3
Cyberbullying
perpetration
T3
Cyberbullying
victimization
T3
.19**
.22***
.06*
.13*
.08**
.20***
.24***
.07*
.14**
.10**
.18**
.23***
.07*
-.12***-.11**-.13**
-.10*-.10**-.11**
.12*
.11**
Bullying
perpetration
T4
Bullying
victimization
T4
Cyberbullying
perpetration
T4
Cyberbullying
victimization
T4
Between-person level Within-person level
.68***
.67***
.82***
.08**.10**.09**
Fig. 1 Random intercept cross-lagged panel model. The within-time covariances and the non-signicant autoregressive and cross-lagged paths at
the within-person level are not illustrated for simplicity. These associations are reported in Table 4.*p< 0.05; **p< 0.01; ***p< 0.001
412 Journal of Youth and Adolescence (2023) 52:406418
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Continuity of Perpetration and Victimization across
Bullying and Cyberbullying
Although cyberbullying perpetration was not stable per se in
theseanalyses,therewasastable pattern of bidirectionality
between online and ofine bullying at the within-person level,
whereinateachwave,cyberbullyingwaspredictedbysub-
sequent ofine bullying and went on to predict the later
increases in ofine bullying perpetration, as per Hypothesis 3.
This provides a more conservative replication of previous
studies that used CLPM (Chu et al., 2018;Giumettietal.,
2022; Pabian & Vandebosch, 2016). According to the role
continuity hypothesis (Baldry et al., 2016), adolescents are
expected to adopt the same patterns across phenomena.
The co-construction theory has been applied to account for
how adolescents construct their digital social interactions
in a similar way to their non-digital environment (Sub-
rahmanyam et al., 2006). The online phenomenon may spread
face-to-face perpetration by attacking others on the Internet to
further increase the damage. Regarding the opposite inuence,
after online perpetration, adolescents may tend to endorse
surrounding states or normative beliefs that their later invol-
vement in bullying perpetration is an acceptable behavior
(Wright & Li, 2013).
Bullying and cyberbullying victimization were also
expected to be bidirectionally associated (Hypothesis 4). In
Table 4 Standardized
coefcients of within-person
level results in RI-CLPM
Covariances Time 1 Time 2 Time 3 Time 4
Bullying perpetration Bullying victimization 0.41*** 0.41*** 0.52*** 0.55***
Bullying perpetration Cyberbullying perpetration 0.46*** 0.42*** 0.52*** 0.52***
Bullying perpetration Cyberbullying victimization 0.36*** 0.33*** 0.36*** 0.35***
Bullying victimization Cyberbullying perpetration 0.31*** 0.25*** 0.30*** 0.30***
Bullying victimization Cyberbullying victimization 0.52*** 0.35*** 0.39*** 0.37***
Cyberbullying perpetration Cyberbullying
victimization
0.63*** 0.67*** 0.71*** 0.65***
Autoregressive path T1 T2
ß(SE)
T2 T3 ß(SE) T3 T4
ß(SE)
Bullying perpetration Bullying perpetration 0.18** (0.06) 0.20*** (0.05) 0.19** (0.06)
Bullying victimization Bullying victimization 0.23*** (0.04) 0.24*** (0.04) 0.22 (0.04)
Cyberbullying perpetration Cyberbullying
perpetration
0.08 (0.06) 0.08 (0.07) 0.08 (0.07)
Cyberbullying victimization Cyberbullying
victimization
0.01 (0.08) 0.01 (0.06) 0.01 (0.06)
Cross-lagged path T1 T2
ß(SE)
T2 T3 ß(SE) T3 T4
ß(SE)
Bullying perpetration Bullying victimization 0.00 (04) 0.00 (04) 0.00 (04)
Bullying perpetration Cyberbullying perpetration 0.09** (0.03) 0.10** (0.03) 0.08** (0.03)
Bullying perpetration Cyberbullying
victimization
0.02 (0.04) 0.02 (0.04) 0.02 (0.03)
Bullying victimization Bullying perpetration 0.00 (04) 0.00 (04) 0.00 (04)
Bullying victimization Cyberbullying
perpetration
0.07* (0.04) 0.07* (0.04) 0.06* (0.03)
Bullying victimization Cyberbullying
victimization
0.11** (0.04) 0.10** (0.03) 0.08** (0.03)
Cyberbullying perpetration Bullying perpetration 0.12* (0.05) 0.14** (0.05) 0.13* (0.05)
Cyberbullying perpetration Bullying
victimization
0.04 (0.04) 0.05 (0.05) 0.05 (0.04)
Cyberbullying perpetration Cyberbullying
victimization
0.07 (0.07) 0.07 (0.07) 0.06 (0.06)
Cyberbullying victimization Bullying
perpetration
0.13** (0.04) 0.11** (0.04) 0.12** (0.04)
Cyberbullying victimization Bullying
victimization
0.02 (0.05) 0.02 (0.04) 0.02 (0.05)
Cyberbullying victimization Cyberbullying
perpetration
0.11** (0.05) 0.10** (0.04) 0.10* (0.04)
*p< 0.05; **p< 0.01; ***p< 0.001
Journal of Youth and Adolescence (2023) 52:406418 413
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
line with previous research (Giumetti et al., 2022;Pabian&
Vandebosch, 2016), ofine bullying victimization predicted
cyberbullying victimization, suggesting that victimization
may begin in person but migrate to online settings. Among
the possible underlying assumptions for this continuity in
victimization are the spread of ofine interpersonal relation-
ships through the Internet and the victim schema model.As
the Internet during these ages is a setting where peers extend
their face-to-face interpersonal relationships, the perpetrator
may spread online to enhance the potential for harming the
victim (Wright & Li, 2013). In addition, victim schema
models (Rosen et al., 2007) may also contribute to role con-
tinuity, as experience of being a target in one setting may
generate negative cognitive biases and maladaptive coping in
peer relationships (Camacho et al., 2022), that lead to greater
perceptions of threat, mistrust and increased likelihood of
victimization both ofine and virtual phenomena (Chu et al.,
2018; Rodríguez-de Arriba et al., 2022). However, unlike
previous studies in CLPM (Chu et al., 2018;Pabian&
Vandebosch, 2016), when differences in stable victimization
at between-person level were controlled for, an increase in
online victimization within-person level did not predict a
higher involvement in face-to-face victimization. Previous
studies on internalizing problems arising from victimization
have also found differences between online and face-to-face
victimization. While studies have reported that both bullying
and cyberbullying victimization are subsequently associated
with greater depressive symptoms (Fredrick et al., 2022;He
et al., 2022), such associations are consistent when analyzed
at the within-person level in bullying (Li et al., 2021), but not
in cyberbullying (Boer et al., 2021). Such divergences could
be explained by the role of the online context for adolescents.
In cyberbullying, any person may be exposed to be a victim
even those with greater resilience and adaptative coping
strategies that, in contrast, can be effective to avoid face-to-
face victimization.
Inversion of Victimization and Perpetration across
Bullying and Cyberbullying
According to the role inversion hypothesis (Mishna et al.,
2012), those who become involved in one role (target or
perpetrator of bullying) are more likely to take on the other
role later. Counter to the fth hypothesis and in contrast to
previous research (Zhou et al., 2022), changes in ofine
victimization yielded no direct deviations in ofine bullying
perpetration at any wave. However, there was evidence of a
more indirect path whereby being targeted ofine was
positively associated with subsequent cyberbullying perpe-
tration (providing support for Hypothesis 6 and consonant
with Chu et al., (2018)), which in turn was positive asso-
ciated with face-to-face perpetration. This suggests that
targeted adolescents may use the more covert cyber setting
as an incubator for bullying perpetration and then move on
perhaps with growing condence to engage in overt
face-to-face bullying. As Ybarra and Mitchell (2004) have
conjectured, for victims of ofine bullying, the Internet may
provide a setting for dominating others as compensation for
their own social position. Such a contrast could be based on
the helplessness of the victim in the face-to-face situation.
The power imbalance provides the perpetrator a safe posi-
tion, as the victim may not have enough physical and
psychological strengths to overcome the social gap and
address the perpetrator with revenge. However, the power
imbalance differs in the online phenomenon due to anon-
ymity and disinhibition. The difculty to identify the per-
petrator and the avoidance of retaliation may develop into
the perception of a lack of responsibility and deindividua-
lization of the behavior. This may provide the victim of
bullying with the courage to engage in cyberbullying per-
petration later as a means of revenge (Runions et al., 2018)
or as a bid to obtain power or social status, which they may
have attributed to their own bullies in their past experiences.
In the present study, it was also expected that bullying
perpetration would be associated with subsequent cyberbully-
ing victimization (Hypothesis 7). In contrast with two previous
studies (Lee et al., 2021; Pabian & Vandebosch, 2016), face-to-
face perpetration did not predict online victimization, while an
increase in cyberbullying victimization predicted decreases in
cyber- and ofine bullying perpetration, suggesting that
experiences of cyber-victimization may protect against future
Table 5 Standardized coefcients of the time-invariant predictors in
RI-CLPM
GenderaAge
ßSE ßSE
Bullying perpetration T1 0.13*** 0.02 0.04* 0.03
Bullying perpetration T2 0.12*** 0.03 0.04** 0.01
Bullying perpetration T3 0.12*** 0.03 0.01 0.01
Bullying perpetration T4 0.10*** 0.02 0.01 0.01
Bullying victimization T1 0.11** 0.03 0.01 0.02
Bullying victimization T2 0.03 0.04 0.04 0.02
Bullying victimization T3 0.03 0.02 0.03 0.03
Bullying victimization T4 0.03 0.02 0.03 0.02
Cyberbullying perpetration T1 0.08*** 0.02 0.05*** 0.01
Cyberbullying perpetration T2 0.06** 0.02 0.11*** 0.02
Cyberbullying perpetration T3 0.05* 0.02 0.10*** 0.02
Cyberbullying perpetration T4 0.05** 0.02 0.04 0.03
Cyberbullying victimization T1 0.05* 0.02 0.05*** 0.01
Cyberbullying victimization T2 0.01 0.02 0.04*** 0.01
Cyberbullying victimization T3 0.02 0.02 0.04*** 0.01
Cyberbullying victimization T4 0.02 0.02 0.02* 0.01
*p< 0.05; **p< 0.01; ***p< 0.001
aGender was coded as: 0 =boy and 1 =girl
414 Journal of Youth and Adolescence (2023) 52:406418
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
bullying perpetration both ofine and online at the individual
level. This nding, in contrast to the signicant positive path-
ways from ofine victimization to cyberbullying perpetration,
presents a paradox. Once between-person variance is accounted
for, why would an increase in face-to-face victimization lead to
an increase in the involvement in online perpetration, whereas
online victimization leads to a subsequent decrease in perpe-
tration, online and off?
Compared to online victimization, ofine victimization is
less strongly associated with increased internalizing problems
(e.g., fear, humiliation, anxiety or depression; Dennehy et al.,
2020). Being cyberbullied, with the constant access to the
victim (24 h a day, 7 days a week) and the permanency of the
evidence of bullying existing on the Internet (see Runions
et al., 2013 for a review) could generate in the victim a feeling
of chronic vulnerability, helplessness, and powerlessness to
address the revenge through perpetration. These processes
may lead individuals to become aware of the harm caused by
perpetration, as well as experiencing the emotions of others,
resulting in a decreased likelihood of subsequent online
aggression towards others (see meta-analysis, Kowalski et al.,
2014). An adolescent who experiences victimization for a
limited period may develop some sensitivity to the aggressive
behaviors of bullying, knowing personally the associated
psychological consequences.
An alternate possibility is that targets of cyberbullying do
not see or project the same accrual of social status (e.g.,
social impact or perceived popularity; Guy et al., 2019) to
the perpetrator as do targets of face-to-face bullying, where
the group dynamics are more evident, and where popularity
may be attributed to ones tormentor. The perception that
bullying leads to popularity may drive those who are pre-
dominantly bullied ofine in a way that diverges from the
experience of cyberbullied adolescents.
Future research might address this issue through models
that examine whether internalizing symptoms or coping
strategies with victimization may play a mediating role in
the effects of cyberbullying victimization and bullying
victimization on perpetration at within-person level.
Limitations and Practical Implications
Though the study presents certain strengths (large sample,
longitudinal data, and within- and between-person level), the
ndings bear limitations to be addressed in future research.
First, given that perpetration and victimization were both
treated as discrete variables, the co-occurrence (bully-victim
status) could not be modeled. This is unfortunate, as dual
involvement in both roles is an important phenomenon that this
work is concerned with. Second, only self-report measures
were used, which may have increased shared method variance
and social desirability bias. This bias could be addressed in
future research by using peer or teacher nomination of victims
and perpetrators of traditional bullying. Although the repeated-
measures time intervals (at the beginning and end of the school
year across two school years) provide insight into potential
effects over time, given the dynamic social relationships during
adolescence, a shorter time frame might capture changes to a
better extent, especially in cyberbullying. The sample (between
11 and 16 years at T1; 13-18 at T4) does not allow extra-
polation to the whole period of adolescence, nor does it capture
specic key developmental periods that may inuence the
association between variables (e.g., transition from primary to
secondary school, pubertal maturational stage, development of
romantic relationships). Furthermore, future studies could also
consider controlling for factors that could inuence the invol-
vement of bullying and cyberbullying (e.g., ethnicity, socio-
economic status, internet use, parental styles, internalizing
symptoms, coping strategies). The sample derives from a
specic context in the south of Spain with a majority Caucasian
population. Future studies could consider more demo-
graphically diverse regions with cross-cultural designs to fur-
ther enhance the relevance of the ndings.
Beyond the limitations, the study provides support for
psychoeducational strategies to address bullying and
cyberbullying. Anti-bullying programs aimed at reducing
perpetration and victimization rates in face-to-face phe-
nomenon should target the involvement of schoolchildren in
cyberbullying (Casas et al., 2018). It is also important to
raise awareness that adolescents who are targets of bullying
may come to the erroneous belief that perpetration of bul-
lying is in their best interests. Special emphasis should be
focused on adolescents who experience ofine bullying
since such experience could be extended online, and
problem-focused coping strategies to deal with the situation
and with attendant emotional problems (e.g., anxiety and
depression) may be of value. Such interventions should also
address issues of moral sensitivity to adhere to moral
standards and decrease the selective deactivation of the
moral self-regulation process as a means to avoiding the
normalization of violence, which may fuel vicious circles of
perpetration and victimization (Romera et al., 2021).
Conclusions
The association between bullying and cyberbullying
(victimization and perpetration) have been accounted
from the role continuity and inversion hypotheses.Froma
longitudinal approach, the role continuity hypothesis
addresses how previous involvement in bullying may
subsequently extend to cyberbullying phenomena.
Whereas the role inversion hypothesis addresses how the
involvement of adolescents in one phenomenon may lead
to the involvement of other behaviors later (rst victimi-
zation and then perpetration or vice versa). The present
Journal of Youth and Adolescence (2023) 52:406418 415
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
study provides a notable contribution to the literature by
addressing these hypotheses longitudinally using new
statistical approaches that disentangle between- and
within-individual variance over time. At the between-
person level, stable differences across individuals have
highlighted the well-known overlap between bullying and
cyberbullying victimization and perpetration. At the
within-person level, the longitudinal inuence between
variables has been established by considering the indivi-
dual and its temporal evolution. Through the state-like
characteristics, the present study considers the inuence
between the variables considering that the involvement of
many adolescents in the phenomena may be spontaneous
and connected to a particular situation, which is in line
with previous descriptive studies on bullying and cyber-
bullying. In sum, three conclusions are noteworthy. First,
being involved in bullying, as victim or bully, is posi-
tively long-term associated to cyberbullying perpetration.
Second, there is no long-terms spirals of victimization off
and online. Third, having experiences of cybervictimiza-
tion may predict not being a bully and cyberbully. Taken
together, the ndings highlight the importance of con-
sidering both aggressive behaviors (off y online) to
understand and prevent studentsinvolvement. Effective
intervention depends upon a thorough causal under-
standing of how involvement in bullying arises and
evolved over time; this study provides an important step
in explicating these processes. Knowledge of interactive
play between the diverse manifestations is an important
contribution to the shaping of preventive and intervention
strategies adapted to the social reality of adolescents in an
online and ofine world that is interconnected in many of
its mechanisms.
AuthorsContributions A.C.: conceived of the manuscript, drafted
the manuscript, performed statistical analyses and participated in the
interpretation of the data; K.R.: participated in the interpretation of the
data, drafted the manuscript and revised the manuscript; R.O.R.:
conceived of the study, drafted the manuscript and revised the
manuscript; E.M.R.: conceived of the study, coordinated the data
collection, conceived of the manuscript, coordinated, and drafted the
manuscript, and participated in the interpretation of the data. All
authors read and approved the nal manuscript.
Funding This study was supported by the Spanish National Research
Agency (PDC2021-121741-I00/AEI/10.13039/501100011033), Eur-
opean UnionNextGenerationEU, and by the University of Córdoba
in the Plan Propio de Investigación 2022.
Data Sharing and Declaration The datasets generated and/or analyzed
during the current study are not publicly available but are available
from the corresponding author on reasonable request.
Compliance with Ethical Standards
Conict of Interest The authors declare no competing interests.
Ethical Approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the 1964 Helsinki declaration and its later amendments or comparable
ethical standards. The project was reviewed and approved by Biosafety
and Bioethics Committee of the University of Cordoba.
Informed Consent Written informed consent was obtained from the
parents.
Publishers note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional afliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
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long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if
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use, you will need to obtain permission directly from the copyright
holder. To view a copy of this license, visit http://creativecommons.
org/licenses/by/4.0/.
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Antonio Camacho is a PhD at the University of Cordoba (Spain). His
major research interests include the risk mechanisms involved in
adolescence aggressive behavior.
Kevin Runions is head of the Social and Emotional Wellbeing team at
the Telethon Kids Institute, University of Western Australia. His
research focuses on child and adolescent social behavior,
disadvantage, and promotion of social and emotional wellbeing
equitably.
Rosario Ortega-Ruiz is an Emeritus Professor at the University of
Cordoba (Spain). Her major research interests include bullying,
cyberbullying and interpersonal violence.
Eva M. Romera is an Associate Professor at the University of
Cordoba (Spain). Her major research interests include social and moral
competence and interpersonal relationships.
418 Journal of Youth and Adolescence (2023) 52:406418
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... Cyberbullying takes the following many forms: text harassment, dissemination of damaging information, and distribution of fake news to inspire fanaticism or slander [8,9]. As this information is shared online, it usually becomes ubiquitous and causes major uncertainty and disturbance. ...
... Cyberbullying is a sort of bullying [8,13,17] wherein one deliberately uses information and communication technology to harm others. Cyberbullying could cause greater harm than more traditional kinds of bullying [15,42]. ...
... Cyberbullying takes many different forms, including verbal abuse, sexual misbehavior, identity theft, and the creation of groups meant to attack people [11,43,44]. Direct cyberbullying, in which the bully interacts directly with the victim, and indirect cyberbullying, in which the attacker publicly posts negative material about the victim on social media sites [8,13,17,45] are possible two classification of cyberbullying. ...
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... 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. ...
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... In fact, one study provided evidence for the temporal relation of cyberbullying predicting in-person bullying at a later time (Jose et al., 2012). At the within-person level, Camacho et al. (2023) demonstrated the cross-lagged bidirectional link between in-person bullying and cyberbullying such that in-person and cyberbullying predicted each other over time. Although evidence for the connection between in-person aggression and cyberbullying is robust, little is known about whether and how peer-targeted cyberbullying is associated with cyber dating aggression over time. ...
... This has also been supported by qualitative data from 21 Norwegian teenagers who discussed the initiation of dating aggression online through text messaging and social media, which subsequently continued in person (Hellevik, 2019). On the other hand, there is substantial evidence to suggest that in-person bullying longitudinally predicts cyberbullying (Camacho et al., 2023;Camerini et al., 2020;Jose et al., 2012), which was not supported in our study. Each of these studies was conducted with samples from other countries (Australia, New Zealand, Spain, and New Zealand, respectively), whereas our sample included participants from the United States. ...
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... Conversely, because bullying behavior is targeted (Camacho et al., 2023), bullies may also more clearly choose their targets based on status and affection goals, such as adolescents who have been rejected by their peers (Li et al., 2024). Choosing rejected classmates as targets for bullying is a status-seeking strategy by other classmates, because rejected peers lack the attractiveness of social interaction, making them ideal targets for bullies (Kisfalusi et al., 2022). ...
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... As these Japanese students progressed through middle school, their participation in bullying events appeared to be less role-specific, with greater overlap between roles at Time 5. Actions that students take in bullying situations are likely to vary across events depending on who initiated the event (e.g., a friend or antagonist), who was being bullied, who else was present, and how the victimized peer and other bystanders responded to the attack. Variability in roles over time is consistent with both self-reported and observed bullying behaviors (Baldry et al., 2017;Camacho et al., 2023;Frey et al., 2014). These results indicate the need for greater attention to event-specific conditions that may promote support versus additional denigration of victimized youth. ...
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The present study investigates the longitudinal relationships between bullying roles (bullying, passive bystanding, victimization) and moral disengagement to examine influences on the shifting role of bullying in Japanese middle school students. Participants were 271 Japanese students (Time 1: M age = 12.72, SD = .45, 136 boys and 135 girls) recruited from two public middle schools (9 classes). Five data collection occasions in Grades 7 to 9 alternated between surveys of self-reported bullying behavior (three timepoints) and moral disengagement (two timepoints). Structural equation modeling showed that moral disengagement was reciprocally related to bullying and passive bystanding. Victimization was only related to moral disengagement in Grade 9, such that moral disengagement in June predicted increased victimization experiences in December. These findings suggest that intervention and prevention programs in schools need to actively address moral disengagement, with a focus on bystanders as well as perpetrators and victims of bullying.
... The articles focused on a variety of topics and all had compelling narratives, impressive data and cutting-edge analyses. The areas of study included peer relationships (Camacho et al. (2023); Leggett-James, Faur, Kaniušonytė, et al. 2023;Walsh, Lee, Lemmers-Jansen, et al. 2023); mental health (Borg, & Willoughby 2023;Gepty, Lambert, & Ialongo, 2023;Rothenberg, Bizzego, Esposito, et al. 2023;Sun, Jiang, Zilioli, et al. 2023); educational engagement and transitions (Joy, Mathews, Zhao, et al. 2023);Song, Chen, Zhang, et al. 2023); parent-child relationships (Koopmans, Nelemans, Bosmans, et al. 2023;Peng, Hawk, & Wang, 2023), and identity (Sladek, Gusman, & Doane, 2023;Vankerckhoven, Raemen, Claes, et al. 2023). ...
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Prior research has established that being a target of offline and online victimization might function as a significant risk factor that increases the likelihood of adolescents' involvement in cyberhate. Yet, relatively little is known about the mediating and moderating mechanisms underlying this relationship. To fill this important gap in knowledge, the present study aims to examine (1) whether excessive Internet use and contact with unknown people online act as sequential mediators in the relationship between overall victimization and youth's involvement in cyberhate; and (2) whether restrictive parental mediation has any role to play in moderating this relationship. The findings suggest that adolescents who experience victimization are more likely to turn to using the Internet excessively, and consequently interact with strangers online, which in turn makes them more prone to becoming victim to cyberhate or spreading hateful content online themselves. Moreover, restrictive parental mediation was shown to exacerbate the link between excessive Internet use and adolescents' contacts with unknown people online, thereby putting them at higher risk of cyberhate involvement. The current study emphasizes the need for a balanced approach to parental mediation – one that fosters open communication, trust and the development of digital literacy skills.
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Awareness that high-status adolescents can be targets of aggression has grown in recent years. However, questions remain about the associations of the confluence of victimization and popularity with adjustment. The current study fills this gap by examining the joint and unique effects of victimization and popularity on aggression and alcohol use. Participants were 804 Dutch adolescents (50.2% boys, Mage = 13.65) who were followed for one year. High-status victims were more aggressive and drank more alcohol than lower-status victims. High-status victims were also more proactively and indirectly aggressive and self-reported more bullying than high-status non-victims. Thus, the findings demonstrated a conjoined risk of victimization and popularity for some types of aggression.
Article
Full-text available
Background/Objective The present study aims to explore the dynamics of social anxiety profiles in adolescents over time and the psychosocial effects these dynamics have. Method A representative sample of Andalusian (southern Spain) adolescents in Secondary Education was drawn. The study used single-stage stratified cluster sampling. A total of 2,140 students aged 11-16 years (47% girls; MageT1 = 13.68, SD = 1.27) were involved at two time points with a six-month interval. Results The results provided a four-profile structure: low social anxiety, moderate cognitive disturbance, high with difficulties in new situations, and high social anxiety. The latent transition analysis showed a stability in the social anxiety profiles of between 58%-61%. Those adolescents who remained in or transitioned to profiles with higher social anxiety scored worse on peer adjustment, peer victimization and subjective well-being. Conclusions The study may contribute the improvement of the psychological treatments in social anxiety and reduce adverse effects on peer relationships and well-being by distinguishing the profiles and their dynamics.
Article
Full-text available
Normative adjustment stimulates the development of attitudes and behaviours that promote school climate. Previous research has shown that it is a relevant factor in preventing involvement in risk behaviours that affect the quality of peer relationships in classrooms and schools. Previous the development of behaviour adjusted to the norms which promotes interaction processes fostering a positive atmosphere in the classroom and in the school. The aim of this study is to analyse the prospective influence of normative adjustment on bullying perpetration over four time periods spaced six months apart (18 months). A total of 3017 adolescents between 11 and 16 years (49.5% girls; MageT1 = 13.15, SD = 1.09) are involved in the present study. The Random Intercept Cross-Lagged Model results indicate an influential bidirectional association between normative adjustment and bullying perpetration over time. When the adolescents’ normative adjustment increases, their involvement in bullying perpetration decreases six months later. On the other hand, when the adolescents’ bullying perpetration increases over time, a decrease in normative adjustment is evident later. The unconditional univariate growth results report that normative adjustment increases, while bullying perpetration decreases. These findings are discussed in terms of the need to consider contextual factors and how they interact in our understanding and prevention of bullying in schools.
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The saturation of social media use in adolescents’ lives has raised questions about both the risks and positive outcomes that may be associated with use. This study filled this gap by examining longitudinal associations among active social media use and depressive symptoms for male and female adolescents and the mediating role of friend support and cybervictimization. These relations were investigated in a sample of 800 13-15-year-old (M = 14.45) adolescents (57% female, 81% White) across four waves of data over two years. The results indicated that higher levels of active social media use led to reduced depressive symptoms for female adolescents, while active social media use predicted more cybervictimization for male adolescents. In contrast, cybervictimization predicted higher levels of active social media use for female adolescents. Friend support predicted more active social media use for male and female adolescents. Overall, findings reveal a complex picture of social media use for both male and female adolescents and further research is needed which examines types of social media use and their associations with both online and offline experiences.
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