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Exploring the Mediating Roles of State and Trait
Anxiety on the Relationship between Middle
Adolescents’ Cyberbullying and Depression
Ana-Nicoleta Grigore and Alexandra Maftei *
Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, Bulevardul Carol I, Nr.11,
700506 Ia¸si, Romania; firstname.lastname@example.org
Received: 9 October 2020; Accepted: 18 November 2020; Published: 19 November 2020
Cyberbullying is a global problem with signiﬁcant negative implications, especially among
more vulnerable populations, such as adolescents. Previous research suggested that cyberbullying
is signiﬁcantly associated with depression, and anxiety seems to partially or fully mediate this
relationship. We aimed to investigate the prevalence and the relationships between cyberbullying
status (i.e., cyberbully, cyber victim, double role, or non-cyber), gender, and age. We also explored
the mediating roles of state and trait anxiety on the relationship between adolescents’ cyberbullying
victimization, cyber-aggressiveness, and depression. Our sample consisted of 501 middle adolescents
aged from 12 to 15 years (M=14.00; SD =0.80; 51.1% males). The results suggested no signiﬁcant
associations between participants’ status, gender, or age. Mediation analyses suggested that the
relationship between cyber-victimization, cyber-aggressiveness, and depression was mediated by
state anxiety and not trait anxiety. We discuss the implications of the current ﬁndings in understanding
cyberbullying’s psychological consequences and their relevant practical implications for prevention
and intervention programs.
Keywords: cyberbullying; state anxiety; trait anxiety; depression; adolescents
1.1. Bullying and Cyberbullying
The bullying phenomenon has been studied since the early 1970s [
]. Various researchers
worldwide have agreed that bullying is a phenomenon increasingly present and that it has become a
worrying social problem in educational, organizational, or interpersonal settings [
]. In recent years,
with technology development, a new form of bullying emerged, namely cyberbullying,
as electronic harassment or Internet harassment. Cyberbullying, an ongoing public health issue,
is generally deﬁned as the online (i.e., through some media—e.g., text messages, calls, social networks,
or e-mails) aggressive act committed by a person or a group of people to repeatedly harm a person who
cannot defend himself/herself . In addition to intentionality, imbalance of power, and repetition as
common features with traditional bullying, cyberbullying involves modern technology [
such as anonymity, a possible larger audience, and the possibility of perpetuating the aggressive act at
any time, in addition to the physical distance that separates the victim from the aggressor, signiﬁcantly
contribute to the increasing prevalence of the cyberbullying phenomenon [12–15].
All forms of bullying imply three types of participants: aggressors, victims, and witnesses.
In general, aggressors are individuals who are hurt or scare, with pleasure, people they perceive
as weaker or smaller than themselves [
]. They do not like to lose, do play dirty, are aggressive,
Children 2020,7, 240; doi:10.3390/children7110240 www.mdpi.com/journal/children
Children 2020,7, 240 2 of 11
and generally have a low level of empathy [
]. Victims are people who are intentionally harmed
by the aggressor [
]. They are often shy, introverted, or sensitive, while having low self-esteem [
Finally, the bystanders represent those who witness the victim’s aggression. They can, most often,
encourage the aggressor, remain distant and silent, or defend the victim [
]. In the present study,
we were interested in both the aggressors and the victims of cyberbullying.
Cyberbullies usually exhibit school problems (i.e., substance use and aggressive behavior [
they have low self-esteem, show hyperactivity, they have diﬃculty to express emotions appropriately
(e.g., anger), and usually use technology as a way to vent their frustrations [
]. Due to their ability
to keep their identity unknown and the lack of face-to-face contact with the victim, cyberbullies are
mostly unlikely to experience compassion or sympathy toward the victim [
]. On the other hand,
cyberbullying victims “feel less popular, take more internet-related risks, are more often a bystander
and perpetrator of internet and mobile phone bullying, and are less often a perpetrator and more often
a victim of traditional bullying” ([
] p.1349). Cyberbullying victims usually express social problems
and depressive symptoms, along with school refusal and various physical symptoms associated with
increased psychological distress [
]. These adverse eﬀects can have long-term consequences on
both the victim and their family and peers .
1.2. Anxiety, Depression, and Cyberbullying
Previous research suggested that cyberbullying perpetration is signiﬁcantly associated with
depression, and anxiety seems to partially or fully mediate this relationship in both clinical and
non-clinical populations [
]. High levels of depression and stress were found to increase the
probability of being both a cyberbullying victim as well as a cyberbullying perpetrator [
cyber victims and cyber aggressors seem to have similar levels of depression [
]. A recent meta-analysis
conducted by Marciano, Schultz, and Camerini [
] explored the longitudinal data available concerning
cyberbullying perpetration and victimization in youth. Their study involved 56 longitudinal studies,
and the results suggested that cyberbullying perpetration generally determines externalizing problem
behaviors over time, while cyberbullying victimization represents a risk factor for depression and
anxiety. Additionally, depression, anxiety, and Internet use were found to be signiﬁcant cyberbullying
victimization predictors over time.
Jenaro, Flores, and Fr
] explored the medium- and long-term impacts of past experiences of
cyberbullying on students, and their results suggested that students who have been cyberbullied scored
signiﬁcantly higher for anxiety and depression. Moreover, those students who were cyberbullied in
secondary school had a signiﬁcantly lower adjustment at the university level. Similar associations
between cyberbullying victimization, depression, and anxiety were found in various samples of
children and adolescents worldwide [31–33].
In line with previous ﬁndings reported by Na, Dancy, and Park [
], Wang, Xie, Wang, Lei,
and Jiang [
] suggested that cyberbullying victimization predicts depression directly and indirectly,
through the mediating role of social anxiety. Similarly, according to Mart
s, and Escortell [
], cyberbullying victims score highly for social avoidance and social distress.
One of the most worrying eﬀects of cyberbullying victimization is related to suicidal thinking and
], emphasizing the importance of cyberbullying research, prediction, and intervention
strategies to promote positive mental health, especially at younger ages, such as in childhood and
1.3. Cyberbullying, Gender, and Age
Several researchers highlighted the importance of gender in the emergence of bullying behavior [
generally suggesting that both girls and boys engage in this type of behavior [
]. However, studies
have shown that most perpetrators are males, whereas in victims, there is no link between gender and
the likelihood of them becoming victims [
]. Von Mar
es and Petermann [
] suggested that males
Children 2020,7, 240 3 of 11
are more likely to become aggressors as their age increases, in line with similar results presented by
Sentse, Kretschmer, and Salmivalli  or Tustin, Zulu, and Basson .
Barlett and Coyne [
] conducted a meta-analysis to explore whether there are signiﬁcant gender
diﬀerences in the occurrence of cyberbullying behaviors and the potential moderating role of age within
this relationship. Their results showed that male subjects were less prone to cyberbullying behaviors
than women, compared to other ﬁndings [
]. Age moderated this relationship; female participants
were found to be more likely to engage in cyberbullying behaviors during early adolescence compared
to male subjects, who had higher levels of cyberbullying in late adolescence, supporting other similar
results in this area [48,49].
Other studies suggested no gender diﬀerences in victimization rates [
] and reported conﬂicting
results. For example, Kowalski and Limber [
] suggested that girls are more likely to experience
cyberbullying (as both victims and perpetrators), while other authors found no signiﬁcant diﬀerence
between girls and boys in neither cyberbullying perpetration nor victimization [
behaviors (i.e., aggressors) and victimization are subject to developmental variations. Cyber aggressors’
behaviors seem to increase in adolescent ages 12 to 16 [
], while cyber victimization seems to decline
]. However, according to Sim
es and Matos [
], school satisfaction is more important
for oﬀending and teenagers with double roles (both victims and aggressors), as the subjective health
complaints seem to be risk factors for all three proﬁles (i.e., victims, aggressors, and double roles).
1.4. The Present Study
According to a recent Eu Kids Online report [
], 84% of Romanian children access the Internet
through smartphones, with an average of 178 min spent online daily. Half of the youth (i.e., in the Eu
Kids Online sample) reported that negative experiences increased from 21% in 2010 to 33% in 2018.
In most countries, 14- to 16-year-old adolescents spend almost twice as much time online than 9- to
10-year-olds. Children and adolescents aged 9 to 16 years ranked as the highest group to access the
Internet and engage in daily online activities. Romanian boys reported being victims more than girls,
and out of those children who witnessed other teens being cyberbullied, half of them tried to help the
victim, 45% did not react, and 7% encouraged the aggressor. Furthermore, one-third of the children
reported negative comments received for personal content shared online (most of it without consent),
a frequent practice in Romania .
Given the signiﬁcant negative consequences that cyberbullying has on one’s mental health,
in addition to the worrying data related to cyberbullying all over the world, Romania included,
in the current research, our aims were (1) to investigate the prevalence and the relationships between
cyberbullying status (i.e., cyberbully, cyber victim, double role, or non-cyber), gender, and age; and (2) to
explore the mediating roles of state anxiety (i.e., the psychological and physiological reactions toward
adverse situations in a speciﬁc moment; [
]) and trait anxiety (i.e., a trait of personality (therefore, stable
over time) describing individual diﬀerences related to one’s tendency to present state anxiety; [
on the relationship between middle adolescents’ cyberbullying victimization, cyber-aggressiveness,
and depression. Furthermore, we also aimed to examine whether the existing shreds of evidence
suggested by fellow researchers from other countries are generalizable across other cultural samples,
such as in Romania.
Based on the earlier studies discussed, we assumed the following: (H1) a high prevalence rate
among cyber victims in both boys and girls [
]; (H2) higher rates of cyber-aggression among
older, male boys [
]; (H3) lower rates of cyber-victimization among older
(H4) signiﬁcant associations between cyber status (i.e., cyberbully, cyber victim, double role,
or non-cyber), and depression, anxiety state, and anxiety trait. Concerning our ﬁfth assumption (H5),
we expected state anxiety and trait anxiety to mediate the relationships between cyber-victimization
and cyber-aggression, in line with previous studies that conﬁrmed partial or total mediation eﬀects of
]. However, we were also interested in exploring which of the two anxiety dimensions
has a more substantial eﬀect on the relationship between cyberbullying and depression.
Children 2020,7, 240 4 of 11
2. Materials and Methods
Our ﬁnal cross-sectional sample consisted of 501 middle adolescents aged 12 to 15 years (M=14.00;
SD =0.80; 51.1% males). They were all students in a north-eastern public school in Romania, with similar
socio-economic backgrounds. The inclusion criteria were related to having and using a personal
smartphone (regardless of the frequency of use). Seven participants from our initial sample were
excluded due to incomplete demographic information (i.e., gender and age) or lack of responses to
one of the questionnaires. Participation was voluntary and all the middle adolescents in our sample
received a colorful sticker at the end of the research procedure as a reward for their participation.
2.2. Research Procedure
This study’s protocol was designed in concordance with the ethical requirements speciﬁc to the
faculty where the authors are aﬃliated. All participants voluntarily participated in the study and
gave written informed consent, following the 2013 Declaration of Helsinki and the national laws from
Romania regarding ethical conduct in scientiﬁc research, technological development, and innovation.
Before administering the questionnaire, the informed consent of the legal representatives of the
adolescents (i.e., parents) was obtained. The research was conducted in late 2019, in participants’ usual
classrooms, following the school principal’s approval. The questionnaires were distributed by the
school psychologist and one research assistant, blind to the study’s aims.
We used an adapted version of The Beck Depression Inventory-II (BDI II; Beck and Steer, [
adapted and validated on the Romanian population by David and Dobrean, [
]) to measure depression.
The scale consists of 21 items that measure depression on a scale from 0 (symptom absence) to
3 (severity of the symptom). We only used the ﬁrst 20 items, given the age of our participants and the
nature of the last dimension (Loss of Interest in Sex). Participants were asked to read each group of
statements carefully and pick out the one statement in each group that best describes how they have
been feeling during the past two weeks, including the present day. Example items included “I feel my
future is hopeless and will only get worse” (a 3-score on the Pessimism dimension); “I dislike myself”
(a 3-score on the Self-Dislike dimension); or “I have much greater diﬃculty in making decisions than
I used to” (a 2-score on the Indecisiveness dimension). Cronbach’s alpha coeﬃcient for the present
study indicated high reliability of 0.915.
We further used the State-Trait Anxiety Inventory (STAI, Spielberger [
]) to measure anxiety.
A self-report questionnaire was designed to assess levels of state anxiety and trait anxiety. Participants
answered each of the 40 items on a scale from 1 (almost never) to 4 (almost always). Example items
for the trait anxiety subscale include “I worry too much over something that really doesn’t matter”
and “I am content; I am a steady person.” For the state anxiety subscale, example items include
I am tense
”; “I am worried”, and “I feel calm; I feel secure.” Higher scores indicate higher levels of
anxiety. Cronbach’s alpha coeﬃcient for the present study indicated high reliability for both the state
anxiety subscale (0.920) and the trait anxiety subscale (0.893).
Finally, we used the short 9-item form of the European Cyberbullying Intervention Project
Questionnaire (ECIPQ, Brighi et al. [
]), to assess the experience of cyber victimization and cyber
aggression. Participants answered a total of 18 items (nine items for each dimension, i.e., aggressor
or victim) on a Likert-type scale from 1 (never) to 5 (almost daily). The experience as a victim
of cyberbullying dimension explored direct behaviors (i.e., direct aggression—”Someone told me
something nasty or threatened me via the Internet or texting”; posting or editing of embarrassing
personal pictures or videos—“Someone posted online embarrassing pictures or videos or modiﬁed
the one I posted”; identity theft—“Someone illegally logged into my e-mail or social network account
and stole personal information”), or indirect aggressions, such as releasing personal information
Children 2020,7, 240 5 of 11
(e.g., “Someone released personal information on me online”) or spreading rumors (e.g., “Someone
spread rumors about me online”). The same items were used in an active form to assess cyberbullying
behaviors perpetrated by subjects. A high score on each of the two dimensions indicated a higher
experience as a cyberbully or a cyber-victim. Cronbach’s alpha coeﬃcient for the present study indicated
satisfying reliability coeﬃcients for both the cyber-victim subscale (0.856) and the cyber-aggressor
A demographic scale assessed participants’ gender and age. All instruments were ﬁrst pre-tested
in a similar sample of middle adolescents (N=35, Mage =14.01), and no diﬃculties were reported in
terms of understanding the concepts and the words used within the questionnaires.
3.1. Cyber Status, Gender, Age
We used the SPSS 20.0 program to analyze our data. Based on the ECIPQ subscales’ scores
and the median test, participants were grouped in four cyber statuses: cyberbully, cyber victim,
double role (i.e., both cyber bullies and cyber victims), or none of the above (neither a cyber victim
nor a cyber aggressor). To be classiﬁed as a cyberbully, participants need to have higher scores on the
cyber-aggressor subscale (>Mdn =16) and low scores on the cyber victim scale (<Mdn =18). Similarly,
we classiﬁed participants who scored higher >Mdn =18 on the cyber victim subscale and lower than 16
on the cyber aggressor dimension as cyber victims. The adolescents with a double role scored highly
on both dimensions. Higher scores on both subscales implied a double role (both as a victim and a
cyber aggressor). The lowest possible score on the ECIPQ subscales was 9. Therefore, we considered
participants who scored nine or lower than the median on any of the two subscales as non-cybers
(neither a cyber victim nor a cyber aggressor) (see Table 1). Chi-square tests indicated no signiﬁcant
associations between participants’ status (i.e., cyberbully, cyber victim, double role, or non-cyber) and
gender (X2(3, 501) =1.83, p=0.607), nor with participants’ age (X2(3, 501) =5.53, p=0.136).
Participants’ status (i.e., cyberbully, cyber victim, double role, or non-cyber) depending on
gender and age (N=501).
Aggressor Male 39
(15%) * Female 36
Victim Male 43
(18.6%) * Female 50
Double role Male 76
(30.5%) * Female 77
Non-cybers Male 98
(35.9%) * Female 82
* Note: Percentages are calculated from the total sample of participants (N=501).
3.2. Cyber Status, Anxiety, and Depression
Further analyses suggested a signiﬁcant association between participants’ status and state anxiety
(3, 501) =173.635, p<0.001). Participants with higher levels of state anxiety were more likely to
be victims (N=80). In contrast, participants with lower scores on the state anxiety subscale were
more likely to have a double role (N=96), to be aggressors (N=69), or to be non-cybers (N=157).
We also found a signiﬁcant association between participants’ status and depression (X
(3, 501) =66.12,
p<0.001). Participants with higher levels of depression were more likely to be victims (N=77),
with lower scores on the depression scale were more likely to have a double role
(N=102) or to be non-cybers (N=91).
Children 2020,7, 240 6 of 11
Finally, a signiﬁcant association was found between participants’ status and trait anxiety
(3, 501) =88.89, p<0.001). Participants with higher levels of trait anxiety were more likely
to be victims (N=72) or to have a double role (N=102). In contrast, participants with lower scores on
the trait anxiety subscale were more likely to be aggressors (N=56) or non-cybers (N=124).
3.3. The Mediating Role of Anxiety
To deepen our exploration, we further performed mediation analyses using the Hayes [
SPSS macro program PROCESS. The theoretical hypothesis model was tested by estimating the 95%
conﬁdence interval (CI) for mediation eﬀects with 5000 resampled samples. In the current study,
we selected Model 4 to analyze the mediating eﬀect of state anxiety and trait anxiety on the relationship
between cyber aggressiveness and cyber victimization. We used the total scores of the cyberbullying
subscales (i.e., total scores for cyber victimization and cyber aggression) within the ECIPQ.
Descriptive analyses. The means, standard deviations, and correlation matrix of each variable are
shown in Table 2. We found signiﬁcant positive associations between cyber victims and trait anxiety,
state anxiety, and depression. Similar signiﬁcant positive associations were found between cyber
aggressors, state anxiety, and depression. A negative, signiﬁcant association was found between cyber
aggressors and depression.
Table 2. Means, standard deviations, and correlations between the main variables (N=501).
Variables M SD 12345
1. Cyber victim 18.71 6.61 1 0.324 ** 0.785 ** 0.625 ** 0.516 **
2. Cyber aggressor 16.29 4.07 0.324 ** 1 0.208 ** 0.052 −0.095 *
3. State anxiety 39.99 12.42 0.785 ** 0.208 ** 1 0.673 ** 0.650 **
4. Trait anxiety 44.56 12.44 0.625 ** 0.052 0.673 ** 1 0.478 **
5. Depression 13.97 11.48 0.516 ** −0.095 * 0.650 ** 0.478 ** 1
*p<0.005; ** p<0.001.
Mediation eﬀect analysis
. Model 4 is a simple mediating model in the SPSS macro PROCESS
compiled by Hayes [
]. We used it to test the mediating eﬀect of trait and state anxiety on the
relationship between cyberbullying (i.e., cyber-victimization and cyber-perpetration) and depression.
3.3.1. The Mediating Role of State Anxiety and Trait Anxiety on the Relationship between
Cyber-Victimization and Depression
The results suggested a positive, signiﬁcant eﬀect of cyber-victimization on state anxiety (b=1.47,
t=28.31, p<0.001) and on trait anxiety (b=1.17, t=17.89, p<0.001). State anxiety signiﬁcantly
predicted depression (b=0.55, t=10.18, p<0.001), but trait anxiety did not have a signiﬁcant predictive
eﬀect on depression (b=0.06, t=1.57, p=0.11). The indirect eﬀect of cyber-victimization and depression
was mediated by state anxiety (indirect eﬀect =0.82, SE =0.11, 95% CI =[0.61
1.04]) but not trait
anxiety (indirect eﬀect =0.007, SE =0.06, 95% CI =[−0.004 −0.019]).
3.3.2. The Mediating Role of State Anxiety and Trait Anxiety on the Relationship between
Cyber-Aggressiveness and Depression
The results suggested a positive, signiﬁcant eﬀect of cyber-aggressiveness on state anxiety (b=0.63,
t=4.74, p<0.001) but not on trait anxiety (b=0.15, t =1.17, p =0.24). State anxiety signiﬁcantly
predicted depression (b=0.62, t=15.00, p<0.001), but trait anxiety did not have a signiﬁcant predictive
eﬀect on depression (b=0.03, t=0.80, p=0.42). The indirect eﬀect of cyber- aggressiveness and
depression was mediated by state anxiety (indirect eﬀect =0.39, SE =0.07, 95% CI =[0.259
but not trait anxiety (indirect eﬀect =0.005, SE =0.01, 95% CI =[−0.016 −0.040]).
Children 2020,7, 240 7 of 11
Our primary ﬁndings suggested no signiﬁcant associations between participants’ status, gender,
or age. Therefore, H1, H2, and H3 were not conﬁrmed. We assumed that our data would suggest
signiﬁcant associations between cyber status (i.e., cyberbully, cyber victim, double role, or non-cyber),
depression, anxiety state, and anxiety trait (H4). The results conﬁrmed this assumption, suggesting that
(a) participants with higher levels of state anxiety were more likely to be victims, while participants
with lower scores on the state anxiety subscale were more likely to have a double role, to be aggressors,
or to be non-cybers; (b) participants with higher levels of depression were more likely to be victims,
and adolescents with lower scores for depression were more likely to have a double role or to be
non-cybers; (c) middle adolescents with higher levels of trait anxiety were more likely to be victims
or to have a double role, while participants with lower scores for trait anxiety were more likely to be
cyber aggressors or to be non-cybers. Mediation analyses suggested that the relationship between
cyber-victimization, cyber-aggressiveness, and depression was mediated by state anxiety and not
The lack of signiﬁcant associations between gender, age, and cyber status seem to conﬁrm
previous results concerning this area of research (i.e., [
]), which might be explained by the
similar percentages in both girls and boys in this speciﬁc age group (12 to 15 years) concerning
cyberbullying exposure, victimization, and perpetration. However, further studies are needed to
better clarify the conﬂicting results concerning cyberbullying, age, and gender. Results concerning the
associations between cyber status and anxiety trait, anxiety state, and depression are probably our most
important ﬁndings, with relevant practical implications. First of all, we found that middle adolescents
with higher levels of depression and state and trait anxiety were more likely to be cyber-victims,
conﬁrming previous ﬁndings in this area (e.g., ). This is an essential insight for future prevention
and intervention programs that would beneﬁt from focusing on cultivating adolescents’ well-being
and emotional balance. Second, mediation analyses suggested the signiﬁcant mediating role of state
anxiety, which adds to the importance of enhancing adolescents’ well-being. We already know that the
most frequently used intervention in the area of cyberbullying generally includes education on coping
skills, empathy training, social skills, and communication training [
]. However, in line with
similar previous ﬁndings [
], our results point out the usefulness of intervention programs aimed at
increasing adolescents’ well-being and to reduce psychological distress and, implicitly, cyberbullying,
such as cognitive behavioral programs [
], along with social support [
] and positive family and
peer relationships [70,71].
Children and adolescents generally spend time online on social media platforms. As we already
know, there might be a direct relationship between anxiety, depressive symptoms, and social media
]. Hunt, Marx, Lipson, and Young [
] suggested that limiting social media use might
decrease loneliness and depression. Furthermore, spending less time online reduces the risks of
becoming a cyber victim and continuing to be a cyberbully. Therefore, intervention strategies in both
school and family settings should also aim to reduce the time spent online by children and teenagers
and replace it with valuable, family and peer oﬄine time.
A series of limitations also need to be addressed. First, all measures were self-reported, which might
have generated biased responses. Second, the school setting might have inhibited participants and,
therefore, it might have also increased the likelihood of desirable answers. Third, we did not account
for other variables that were found to be signiﬁcant for cyberbullying among middle adolescents,
such as diﬃculties in emotion regulation and loneliness, sexual orientation [
], empathy and moral
disengagement , and several other individual, family, and neighborhood characteristics .
Despite these limitations, we consider the current ﬁndings relevant for therapeutical, school,
and family-based intervention strategies to prevent, reduce, and eradicate the risk factors and adverse
eﬀects of cyberbullying.
Children 2020,7, 240 8 of 11
Both authors contributed equally to conceive and design the main goal of the study,
analyze the data, and write the manuscript. All authors have read and agreed to the published version of
This paper was co-ﬁnanced from the European Social Fund, through the Human Capital Operational
Program, project number POCU/380/6/13/123623 ”Doctoral students and postdoctoral researchers ready for the
Conﬂicts of Interest:
The authors declare that there are no potential conﬂicts of interest associated with this
publication and that any ﬁnancial support has been noted in the Funding section.
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