Cyberbullying perpetration in the COVID 19 era An application of general strain theory

THE JOURNAL OF SOCIAL PSYCHOLOGY
2021, VOL. 161, NO. 4, 466–476
https://doi.org/10.1080/00224545.2021.1883503
Cyberbullying perpetration in the COVID-19 era: An application of
general strain theory
Christopher P. Barlett, Alexis Rinker, and Brendan Roth
Gettysburg College
ABSTRACT
ARTICLE HISTORY
The world is currently grappling with the medical, psychological, economic,
and behavioral consequences of the COVID-19 global pandemic. The existing
research has rightly been focused on the medical contributions – treatment,
symptoms, prevalence, etc. – which are paramount. A paucity of research has
tested the psychological and behavioral consequences of COVID-19. In two
cross-sectional studies of US adults, we posited that personal (e.g., being
diagnosed with COVID-19) and proximal (e.g., knowing people with COVID19) experiences with COVID-19 would be related to cyberbullying perpetra­
tion due to an increase in stress. Using path modeling, results showed that (a)
personal and proximal COVID-19 experiences positively correlated with
cyberbullying (Studies 1 and 2) and (b) personal COVID-19 experiences
were indirectly related to cyberbullying through stress, but not proximal
experiences (Study 2).
Received 1 July 2020
Accepted 14 January 2021
KEYWORDS
COVID-19; cyberbullying;
stress; general strain theory
Cyberbullying – repeatedly and purposefully harming others through electronic mediums
(Englander et al., 2017) – has emerged as an important societal issue across the industrialized
world – for children and adults alike. Understanding the variables and psychological processes
involved in predicting cyberbullying is paramount for prevention and intervention efforts. Recent
myriad positive strides have been made in these research endeavors. Indeed, much scholarship has
been devoted to developing and validating cyberbullying intervention programs (e.g., Gaffney et al.,
2019), quality meta-analytic findings have elucidated the variables germane to cyberbullying (e.g.,
Kowalski et al., 2014), and theory has continued to be developed and refined to explain why and for
whom cyberbullying is likely (c.f., Barlett, 2019). Despite such progress, global and societal-level
changes often present challenges in predicting cyberbullying perpetration. The waxing and waning
of social networking platforms, changes to (cyber)bullying laws, instances of social injustice, and
others may present unintended difficulties in predicting cyberbullying perpetration by possibly
changing the strength and/or direction of existing cyberbullying relationships. Currently, the
world is experiencing the health, economic, psychological, and sociological consequences of the
2019 novel coronavirus (SARS-cov-2; here in referred to as COVID-19). The World Health
Organization declared COVID-19 an international public emergency on January 30, 2020 and, as
the current writing of this paper (11/4/20), there are currently over 47 million cases worldwide
(World Health Organization (WHO), 2020) with over 9 million cases in the United States alone
(CDC, 2020a). The current research presents two studies aimed at understanding how COVID-19
has impacted cyberbullying perpetration in a sample of US adults.
There have been numerous academic papers written about the coronavirus – predominantly from
medical researchers – that largely addresses important issues surrounding this virus, such as pre­
valence rates (Ceylan, 2020), treatment (Sanders et al., 2020), antibody testing (Xiang et al., 2020),
CONTACT Christopher P. Barlett
[email protected]
Washington St., Gettysburg, PA, 17325
© 2021 Taylor & Francis
Department of Psychology, Gettysburg College 300 N.
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lifetime of the virus (Lippi et al., 2020), symptomatology (Sohrabi et al., 2020), who is at risk (CDC,
2020b), and others. Social scientists – psychology included – have an equally important, albeit
different, role in the accumulation of coronavirus knowledge – testing the psychological and beha­
vioral predictors and outcomes associated with the experiences of COVID-19. Given the timing of the
virus, it is unsurprising that a paucity of social scientific research exists, but some findings are
emerging. Indeed, research has shown psychological impacts of COVID-19 including stress, depres­
sion, and anxiety (Wang et al., 2020), and citizen engagement (Chen et al., 2020). We believe that an
accumulated knowledge of COVID-19 effects across multiple disciplines is germane, and the current
research focuses on understanding the behavioral outcomes associated with COVID-19 experiences
and the psychological mediators that explain such effects.
We chose to study the relationship between COVID-19 experiences (e.g., being diagnosed and/or
being in close proximity with infected others) and cyberbullying perpetration. Although some could
argue that cyberbullying perpetration is not as important to study as other behaviors, we contend that
understanding the processes that predict antisocial online behavior due to a global pandemic is
imperative. A potential fallout from COVID-19 is the likely increase in stress caused by imminent
unexpected life changes, and cyberbullying perpetration may be how some individuals react to said
stress. Recently, Conway et al. (2020) created and validated various scales that purportedly measure the
personal (e.g., “I have been diagnosed with coronavirus [COVID-19]”) and proximal (e.g., “I know
someone who has had coronavirus-like symptoms in the past four months”) experiences with COVID19. The measurement of these constructs enables social psychologists to study whether, why, and for
whom COVID-19 experiences predict behavior, such as cyberbullying. In Study 1 we test if COVID-19
experiences correlate with cyberbullying perpetration, and, if such relationships are discovered, then
we test if stress was an indirect cause in these relationships in Study 2.
General strain theory
We hypothesized an indirect relationship between COVID-19 experiences and cyberbullying through
stress. Indeed, myriad stressors due to COVID-19 have influenced people across the world. For
instance, schools across the world have closed and students are completing their academic year
from home putting new pressure on parents/guardians. Also, there is an increased likelihood of job
loss, being furloughed, or working remotely which likely increases stress. Theoretically, the General
Strain Theory (GST; Agnew, 1992) can explain such relationships. GST posits that stressors (or
strains) may either (a) prevent, or threaten to prevent, people from attaining positively valued goals,
(b) reduce the likelihood of access to positive stimuli, and/or (c) increase negative affect due to
exposure to negative stimuli (Agnew, 1992). Stressors, such as those explicated by GST, have been
shown to predict broader aggressive behaviors (Berkowitz, 1988). Indeed, one common reaction to the
presence of a strain is anger and aggression with the goal of eliminating the strain (Agnew, 1992). GST
has been applied to the study of cyberbullying. Patchin and Hinduja (2011) showed a positive
relationship between the number of strains (e.g., disagreement with family, money problems, being
victimized, etc.) and cyberbullying. Similarly, Lianos and McGrath (2018) showed that academic and
financial strains juxtaposed with prior cyberbullying experiences predicted later cyberbullying perpe­
tration; a finding conceptually replicated by Paez (2018) with social strains. In short, GST posits that
the presence of strains (COVID-19 experiences in our study) predicts stress (our proposed mediator)
to yield antisocial behavior (cyberbullying perpetration).
Overview of the current research
The primary objective of our research is to examine the direct relationship between COVID-19
experiences and cyberbullying perpetration. Two correlational studies sampling US adults from
Amazon’s Mechanical Turk tested these relationships. Study 1 examined the simple relationships
between COVID-19 proximal and personal experiences and cyberbullying perpetration. Study 2
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C. BARLETT ET AL.
examined the same relationships; however, we also tested the mediating role of stress. Amazon’s
Mechanical Turk was an ideal location to recruit participants for several reasons. First, research has
shown that cyberbullying frequency is higher in adulthood than youth (Barlett & Chamberlin, 2017).
Mechanical Turk only allows adults to be sampled. Second, to date COVID-19 has impacted partici­
pants across the 50 US states differently. Mechanical Turk samples people across the US allowing for
variability in COVID-19 experiences, which is important for our statistical analyses. The data for both
studies are available upon request from the corresponding author. The data are not readily available
due to ethical constraints.
Study 1
The purpose of Study 1 was to examine the correlations between proximal COVID-19 experiences,
personal COVID-19 experiences, and cyberbullying perpetration. We hypothesize positive correla­
tions between these variables. Personal COVID-19 experiences, according to Conway et al. (2020),
refer to whether the participant her/himself has been diagnosed with COVID-19 and/or had symp­
toms of COVID-19, etc. Proximate experiences, in contrast, refer to whether or not close others to the
participant have had COVID-19 symptoms or diagnoses. These measures do not directly ask about the
degree of symptoms (e.g., asymptomatic vs. severe symptoms), number of diagnoses, or the relation­
ship between the participant and the others close to them who are sick (for proximate experiences).
Although such extensions could be considered in future work and questionnaires, the current study
used a reliable and valid measure of our constructs.
Method
Participants
One hundred and ninety-four US participants (64.9% male) were recruited from Amazon’s
Mechanical Turk. The average age of the sample was 37.15 (SD = 10.33) years. The majority of
participants were White (69.6%). The only selection criterion in MTURK was to limit our sample to
live in the US.
Materials
Materials for both Studies 1 and 2 are registered at https://osf.io/2jvgr.
Cyberbullying perpetration
The cyberbullying perpetration scale from the Cyberbullying Experiences Survey (Doane et al., 2013)
instructs participants to report how frequently they have engaged in various cyberbullying behaviors
using a 1 (never) to 6 (everyday/almost everyday) rating scale. A sample item is “Have you sent a rude
message to someone electronically?” The 25 items were summed such that higher scores indicate
higher levels of cyberbullying perpetration (α = .99). We modified this scale by asking about the
frequency of the behaviors in the past three months, rather than the past year, in order to ensure that
the cyberbullying behaviors occurred concurrently with COVID-19.
COVID-19 experiences
The Coronavirus Experiences Questionnaire (Conway et al., 2020) was modified for the purposes of
the current study. This is a 10-item measure that asks participants about their experiences with the
coronavirus using two subscales. The first subscale is the three-item Personal Experience (α = .79)
subscale (e.g., “I have been diagnosed with coronavirus”) and the second subscale is the four-item
Proximal Experience (α = .74) subscale (e.g., “I know someone who has had coronavirus-like
symptoms in the last three months”). We modified the measure to have the response options be in
THE JOURNAL OF SOCIAL PSYCHOLOGY
469
a yes/no format (coded as 1 = yes, 0 = no). Certain items were reverse coded and summed, such that
higher scores indicate higher personal or proximate experiences with COVID-19. The remaining three
items assessed news exposure, which were not analyzed in the current study.
Demographics
Participants were asked about their age, gender, and ethnicity. Income, education level, socioeconomic
status, and other demographic information were not ascertained. We also included an attention check
question to track which participants dutifully completed the study. This question asked which sports
the participant played in high school, but also explained that some individuals do not fully attend to
survey questions, then the question instructed participants to select “soccer” and “tennis”. Those who
correctly answered the question were retained for our analyses. After excluding those who failed the
attention check (10.8%), the sample included 173 participants (64.2% male) and the average age of the
sample was 37.23 (SD = 10.10) years. The majority of participants were White (70.5%). There was no
difference in age, t(192) = 0.30, p = .768, or sex X2(1) = .43, p = .510, between the included and
excluded participants.
Procedure
Institutional review board approval was granted for this study. Data was collected as part of a larger
study.1 US participants were recruited using Amazon’s Mechanical Turk. The data were collected in
one day in April 2020. Participants completed the online informed consent before completing the
aforementioned questionnaires. Participants were then thanked, fully debriefed, and compensated
($1US) for their time.
Results
Correlations
Table 1 displays the parametric (Pearson) and non-parametric (Spearman Rank-Ordered) correlations
between the variables of interest. Of theoretical interest, results showed that cyberbullying perpetra­
tion was related to personal (r = .64, p < .001) and proximal COVID-19 experiences (r = .56, p < .001).
Table 1 also shows that all the variables were significantly not normal (e.g., skewed and/or kurtotic).
Sex differences
Due to research showing sex differences in cyberbullying (Barlett & Coyne, 2014), we used both
parametric (independent samples t-test) and non-parametric (Mann-Whitney U) tests to examine sex
differences on the variables of interest to check if we needed to statistically control for such variation.
Table 1. Correlations between relevant variables in study 1.
Variable
1: Cyberbullying
2: COVID-19 experiences (personal)
3: COVID-19 experiences (proximity)
M
SD
Shapiro-Wilk
Skewness (Z)
Kurtosis (Z)
1
–
64***
.56***
56.63
33.19
.83***
.79
−4.45***
2
.58***
–
.69***
.93
1.17
.73***
4.05***
−2.76**
3
.56***
.65***
–
1.52
1.45
.83***
2.00*
3.24**
Numbers below the diagonal are Pearson correlation coefficients and numbers above the diagonal are
Spearman rank-ordered correlation coefficients.
* p < .05; ** p < .01; *** p < .001
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C. BARLETT ET AL.
Table 2. Sex differences in key variables in study.
Variable
Cyberbullying
COVID personal
COVID proximity
Male Mean (SD)
56.70 (33.40)
.95 (1.20)
1.56 (1.47)
Female Mean (SD)
56.52 (33.10)
.90 (1.11)
1.44 (1.42)
t
.03
.22
.53
Z
−.52
−.03
−.52
Results are displayed in Table 2 and showed that males and females responded similarly on all
variables. Therefore, sex of participant was not treated as a covariate in our primary analyses.
Multiple regression
We tested whether personal and proximal COVID-19 experiences predicted cyberbullying using
a multiple regression. Results showed that the model accounted for a significant proportion of
variances in cyberbullying perpetration, R2 = .44, F(2,155) = 60.40, p < .001. Examination of the
unique effects showed that both personal COVID-19 experiences (B = 14.34, SE = 2.43, t(155) = 5.98,
p < .001) and proximal COVID-19 experiences (B = 4.64, SE = 1.94, t(155) = 2.39, p = .018) predicted
cyberbullying perpetration. In both cases, as experiences with COVID-19 increased, cyberbullying
increased.
Discussion
Results from Study 1 showed that COVID-19 experiences (both personal and proximal) were
correlated and predicted cyberbullying perpetration. This suggests that when individuals have more
personal experiences with the COVID-19 pandemic or know close others who have been infected with
COVID-19 their likelihood of cyberbullying others increases.
Study 2
Since the results from Study 1 showed the COVID-19 experiences predicted cyberbullying perpetra­
tion in US adults, Study 2 was conducted to (a) replicate the correlations uncovered in Study 1 and (b)
test the mediating effect of perceived stress in the relationships between COVID-19 experiences and
cyberbullying perpetration. The former rationale is especially important since different regions were
having very different COVID infection rates, COVID-related restrictions, and COVID-related online
debates that might invite cyberbullying in April 2020 (when data from Study 1 was collected). We
collected data over one month later where more of the US was entrenched in COVID-19 effects to see
if the findings from Study 1 replicated.
Method
Participants
One hundred and eighty-eight US participants (65.6% male) were recruited from Amazon’s
Mechanical Turk. The average age of the sample was 37.96 (SD = 11.29) years. The majority of
participants were White (80.70%). The only selection criterion in MTURK was to limit our sample to
live in the US.2
Materials
The same cyberbullying perpetration (α = .99), COVID-19 experiences (α = .79), and demographic
questionnaire from Study 1 were used in Study 2. In addition, we adapted the Perceived Stress Scale (S.
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471
Cohen et al., 1983) to assess stress. We adapted the measure by changing the time predicate of each
item from “last month” to “last five months” in order to ensure that we were assessing stress during
COVID-19 in the US and to temporally match the cyberbullying perpetration measure. This is a 10item questionnaire that asks participants to rate their level of agreement with the items on a 1 (strongly
disagree) to 5 (strongly agree) rating scale. A sample item is “In the last 5 months, I have you been upset
because of something that happened unexpectedly?” Certain items were reverse coded before sum­
ming, such that higher scores indicate higher levels of perceived stress (α = .80). Finally, analysis of the
attention check item on the demographic questionnaire revealed that 23 (12%) of the sample were
removed from the analyses. Thus, the final sample consisted of 169 (65.7% male) participants with an
average age of 37.38 (SD = 11.04) and the majority White (81.7%). There was no difference in age, t
(186) = 1.90, p = .059, or sex, X2(1) = .00, p = .965, between the included and excluded participants.
Procedure
Institutional review board approval was granted for this study. Data was collected as part of a larger
study.3 US participants were recruited using Amazon’s Mechanical Turk. The data were collected in
one day in June 2020. Participants completed the online informed consent before completing the
aforementioned questionnaires. Participants were then thanked, fully debriefed, and compensated
($2US) for their time.
Results
Correlations
Table 3 displays the parametric (Pearson) and non-parametric (Spearman Rank-Ordered) correlations
between the variables of interest. Of theoretical interest, results showed that cyberbullying perpetra­
tion was related to personal (r = .56, p < .001) and proximal COVID-19 experiences (r = .40, p < .001)
and perceived stress (r = .44, p < .001). Moreover, perceived stress correlated with personal (r = .33, p <
.001) and proximal (r = .32, p < .001) COVID-19 experiences. Finally, both types of COVID-10
experiences were correlated (r = .66, p < .001).
Sex differences
Replicating Study 1, parametric (independent samples t-test) and non-parametric (Mann-Whitney U)
tests showed that males and females responded similarly on all variables. Therefore, sex of participant
was not treated as a covariate in our primary analyses (see Table 4).
Table 3. Correlations between relevant variables in study 2.
Variable
1: Cyberbullying
2: COVID-19 experiences (personal)
3: COVID-19 experiences (proximity)
4: Perceived Stress
M
SD
Shapiro-Wilk
Skewness (Z)
Kurtosis (Z)
1
–
.56***
.40***
.44***
48.68
30.73
.81***
3.00**
−3.39***
2
.51***
–
66***
.33***
.99
1.15
.77***
3.42***
3.05***
3
.43***
.65***
–
.32***
1.62
1.53
.83***
1.53
−3.58***
4
.49***
.32***
.32***
–
27.77
7.08
.96***
−2.26*
1.16
Numbers below the diagonal are Pearson correlation coefficients and numbers above the diagonal are Spearman rankordered correlation coefficients.
* p < .05; ** p < .01; ** p < .001
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C. BARLETT ET AL.
Table 4. Sex differences in key variables in study 2.
Variable
Cyberbullying
COVID personal
COVID proximity
Stress
Male Mean (SD)
50.01 (29.28)
.97 (1.12)
1.72 (1.53)
27.95 (6.83)
Female Mean (SD)
46.18 (33.43)
1.03 (1.23)
1.45 (1.51)
27.42 (7.61)
t
.76
−.33
1.08
.46
Z
−1.34
−.16
−1.12
−.09
1.15 (.89 to 1.17)
Proximal
COVID-19
Experiences
Personal
COVID-19
Experiences
1.38 (.21 to 2.55)
.75 (-.27 to 1.72)
Perceived
Stress
.23 (-3.17 to 3.66)
12.05 (7.30 to 16.57)
1.24 (.63 to 1.94)
Cyberbullying
Perpetration
Figure 1. Path modeling results with stress in study 2. Dashed lines are not significant.
Path modeling
We tested the relationships between COVID-19 experiences, perceived stress, and cyberbully­
ing perpetration using path modeling procedures in MPLUS with maximum likelihood estima­
tion. Due to the skewed nature of the data, we used 5000 bootstrapped samples to estimate
unstandardized regression coefficients with 95% confidence intervals. We tested the postulates
of the General Strain Model by examining whether stress was the indirect cause of the effect
between COVID-19 experiences and cyberbullying perpetration; our model first had COVID19 personal and proximity subscales correlated and predicting perceived stress that predicted
cyberbullying perpetration. Finally, COVID-19 personal and proximal experiences predicted
cyberbullying perpetration. Consistent with Study 1, all paths and relationships were estimated
leaving no degrees of freedom to estimate model fit, and, thus, the data was a perfect fit for
the data.
Figure 1 displays the unstandardized path coefficients with 95% confidence intervals. Results
showed that perceived stress and personal COVID-19 experiences predicted cyberbullying per­
petration. Moreover, personal COVID-19 experiences predicted perceived stress. Proximal
COVID-19 experiences correlated with personal COVID-19 experiences, but did not predict
stress or cyberbullying perpetration. INDIRECT model statements were added to test the
mediating effects between COVID-19 experiences and cyberbullying perpetration through stress.
Consistent with the previous analysis, stress was the indirect causal variable in the relationship
between personal COVID-19 experiences and cyberbullying perpetration (Indirect B = 1.71, 95%
CI: .26 to 3.60), but not proximal COVID-19 experiences and cyberbullying perpetration
(Indirect B = .93, 95% CI: −.32 to 2.43).
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Discussion
Replicating the results of Study 1, results from Study 2 showed that personal and proximal COVID-19
experiences correlated with cyberbullying perpetration. Building upon these effects, Study 2 showed
that perceived stress was the mediating variable in the effect of COVID-19 personal experiences and
cyberbullying perpetration. Specifically, as COVID-19 personal experiences increased stress also
increased to yield higher levels of cyberbullying perpetration.
General discussion
The COVID-19 global pandemic has undoubtedly changed everyday life for billions of people across
the globe. The majority of countries, states, and territories have “locked down” their citizens in order
to reduce the spread of COVID-19. Schools (daycares, primary, secondary, and colleges/universities)
have closed and moved to mostly remote instruction, offices have shifted from corporate buildings to
employee residences, jobs have been lost or furloughed, and restaurants have moved exclusively to
drive thru or carryout only (for those that stayed in business). The psychological and behavioral
impact of these, and other, consequences of COVID-19 are not yet well understood; however, such
understanding is imperative for informing parents, spouses, children, counselors, clinicians, and
others how COVID-19 has affected people.
The current research examined the direct (Studies 1 and 2) and indirect relationship (Study 2) between
personal and proximal experiences with the novel coronavirus, stress, and cyberbullying perpetration in
a sample of US adults. First, we wanted to understand if COVID-19 experiences would correlate with
cyberbullying perpetration. Results from both studies showed that as personal and proximal experiences
with the virus increased so did the frequency of cyberbullying perpetration. In other words, the more
experiences people have with COVID-19 the more frequent cyber-attacks are committed.
Since we found reliable relationships between COVID-19 experiences and cyberbullying perpetration,
Study 2 was conducted in order to understand whether stress indirectly causes these relationships. Stress
was chosen because (a) antidotal evidence suggests that COVID-19 has caused much stress and (b)
according to the General Strain Theory (GST; Agnew, 1992), strains (or stressors) can result in
delinquent behaviors, including cyberbullying (e.g., Paez, 2018; Patchin & Hinduja, 2011). Our results
suggest that stress does, indeed, act as the indirect variable in the relationship between COVID-19
experiences and cyberbullying perpetration. Interestingly, this indirect effect only occurred for personal –
not proximate – COVID-19 experiences. This suggests that perceived stress positively predicts cyber­
bullying perpetration only if the novel coronavirus has personally affected an individual, but not their
friends or acquaintances. Although post-hoc, such findings may appear intuitive. One’s own personal
diagnoses with COVID-19 will likely increase the worry and stress regarding their own health and
livelihood, and, in accordance with GST, one way to express or relieve that stress is to harm others online.
While this study mainly intended to simply discover the direct and indirect relationships between
COVID-19 experiences and cyberbullying perpetration from a theoretical point of view, we wanted to
explicate the practical implications. Given the paucity of research examining how COVID-19 experi­
ences yield psychological and behavioral changes, we wanted to better understand how cyberbullying
perpetration is related to such experiences in order to better inform parents, counselors, clinicians,
school administrators, and others about such effects. For instance, findings from our study suggest that
those who encounter and/or attempt to combat cyberbullying and cyber-victimization should target
reducing the impact of stress – as a function of COVID-19 personal experiences. If no measures are
taken to alleviate stress caused by COVID-19, then there will likely be more stress and cyberbullying.
Limitations and future research
Although the current research yielded interesting direct and indirect results, we view this as an
important first step that can inform future studies. Like all psychological studies, there are some
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C. BARLETT ET AL.
limitations that need to be addressed and studied in future research. First, the data is correlational and
causal claims cannot be made. True, we tested a causal model in our primary analyses, based on theory
and past work; however, other research designs should be tested in future work. For instance,
researchers can compare cyberbullying perpetration and levels of stress from 2019 (before COVID19 was rampant in the US) to one year later. Another option is to longitudinally examine cyberbullying
across time to see how cyberbullying perpetration changes with COVID-19 experiences. Indeed, as
more people become infected with the virus, it may be interesting to see how cyberbullying perpetra­
tion changes.
Second, our data collection and analysis only presents the relationships between COVID-19
experiences, stress, and cyberbullying perpetration at a single point in time. The importance of our
findings, therefore, is to demonstrate that under the conditions at the time of data collection in the US,
cyberbullying was predicted by COVID-19 proximal and personal experiences and stress.
Third, General Strain Theory (Agnew, 1992) posits many different types of strains that can
influence behavior, and findings in the cyberbullying literature shows that strains other than work
and family stressors can cause cyberbullying (e.g., Patchin & Hinduja, 2011). We specified our
measurement of strains to be specific to COVID-19 experiences. Future work should attempt to
include other types of stressors or strains that changed as a function of COVID-19 experiences, such
as: feelings of isolation, the difficulty in getting resources from local grocery stores or markets, and
others.
Fourth, our procedures called for the use of self-reported measures of antisocial behaviors (cyber­
bullying perpetration) from a Mechanical Turk sample. Although we have previously argued that for
this project, a Mechanical Turk sample was ideal; however, varying beliefs, attitudes, past behaviors,
living situations, etc. are commonplace when Mechanical Turk users are sampled. We diligently
removed participants from our analyses who were not adequately paying attention; however, webbased psychological research with such a heterogeneous sample is not as ideal as sampling a more
homogeneous participants. Although these differences often are conceptualized in the error term in
our analyses, future work should attempt to replicate our results with either a more homogeneous
sample or statistically address such variation. Related, we relied on self-report methods. Currently, in
the cyberbullying literature, there are no other valid procedures for measuring cyberbullying perpe­
tration (Barlett, 2019); however, future work should attempt to validate our methods using alternative
measures.
Finally, generalizations about our findings to samples beyond US adults are ill-advised. We elected
to sample adults, rather than adolescents, for our study for two reasons. First, the strain items we
created are not appropriate for youth because the items inquire about work and family stressors. We
do acknowledge that youth are likely experiencing the deleterious psychological effects of COVID-19
experiences akin to adults, and future work should test whether cyberbullying perpetration is due to
stressors relevant to you. Second, research has shown that cyberbullying peaks in adulthood, rather
than in adolescence (Barlett & Chamberlin, 2017). Thus, we wanted to sample individuals who are
most likely to cyberbully others in an effort to provide information to those who can help reduce
cyberbullying.
Notes
1. Other trait questionnaires were measured, but not analyzed in this study. These included, COVID-19 concerns
and impact (Conway et al., 2020), anonymity perceptions (Wright, 2013), Belief in the Irrelevance of Muscularity
for Online Bullying (Barlett & Gentile, 2012), cyberbullying attitudes (Barlett et al., 2016), cyber-victimization
(Doane et al., 2013), and various other demographic questionnaires (e.g., number of kids, relationship status,
time spent online, etc.). We do not believe that the inclusion of these questionnaires would alter the results and
there were no experimental manipulations that would have interfered with answers on these measures.
2. A power analysis was conducted using G*Power to test if the sample size used had adequate power. For Study 1,
we derived our effect size from the reported correlation between COVID personal and proximate experiences
from Conway et al. (2020; r = .58) and opted for power of .95, with a two-tailed alpha of .05. Results showed that
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475
we needed 28 total participants. In Study 2, we used the same the correlation between cyberbullying and COVID
experiences reported in Study 1 (r = .56) and results showed that 31 total participants were necessary to achieve
optimal power. In short, both studies are adequately powered.
3. Other trait questionnaires were measured, but not analyzed in this study. These included, the Dirty Dozen
(Jonason & Webster, 2010), questions related to quarantine experiences, the Ten Item Personality Inventory
(Gosling et al., 2003), impulsivity (Steinberg et al., 2013), cyber-aggression (Runions et al., 2017), guilt
(T. R. Cohen et al., 2011), and various other demographic questionnaires (e.g., number of kids, relationship
status, time spent online, etc.). We do not believe that the inclusion of these questionnaires would alter the results
and there were no experimental manipulations that would have interfered with answers on these measures.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Dr. Christopher P. Barlett is an associate professor in the psychology department at Gettysburg College. He studies the
predictors and consequences of cyberbullying perpetration.
Alexis Rinker is currently an undergraduate student at Gettysburg College. She is a psychology major and has worked in
Dr. Barlett's Aggression Research Lab for over a year.
Brendan Roth is currently an undergraduate student at Gettysburg College. He is a psychology major and has worked in
Dr. Barlett's Aggression Research Lab for over a year.
Data availability statement
The data described in this article are openly available in the Open Science Framework at https://osf.io/j78sc/.
Open scholarship
This article has earned the Center for Open Science badge for Open Materials. The materials are openly accessible at
https://osf.io/j78sc/.
References
Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47–87. https://
doi.org/10.1111/j.1745-9125.1992.tb01093.x
Barlett, C. P. (2019). Predicting cyberbullying: Research, theory, and intervention. Elsevier. https://doi.org/10.1016/c20180-00531-9
Barlett, C. P., & Chamberlin, K. (2017). Examining cyberbullying across the lifespan. Computers in Human Behavior, 71,
444–449. https://doi.org/10.1016/j.chb.2017.02.009
Barlett, C. P., & Coyne, S. M. (2014). A meta-analysis of sex differences in cyber-bullying behavior: The moderating role
of age. Aggressive Behavior, 40(5), 474–488. https://doi.org/10.1002/ab.21555
Barlett, C. P., & Gentile, D. A. (2012). Attacking others online: The formation of cyberbullying in late adolescence.
Psychology of Popular Media Culture, 1(2), 123–135. https://doi.org/10.1037/a0028113
Barlett, C. P., Helmstetter, K., & Gentile, D. A. (2016). The development of a new cyberbullying attitude measure.
Computers in Human Behavior, 64, 906–913. https://doi.org/10.1016/j.chb.2016.08.013
Berkowitz, L. (1988). Frustrations, appraisals, and aversively stimulated aggression. Aggressive Behavior, 14(1), 3–11.
https://doi.org/10.1002/1098-2337(1988)14:1<3::AID-AB2480140103>3.0.CO;2-F
Centers for Disease Control and Prevention. (2020a, May 22). Cases in the U.S. https://www.cdc.gov/coronavirus/2019ncov/cases-updates/cases-in-us.html
Centers for Disease Control and Prevention. (2020b). People who need extra precautions. https://www.cdc.gov/corona
virus/2019-ncov/need-extra-precautions/index.html
476
C. BARLETT ET AL.
Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of the Total Environment, 729,
138817. https://doi.org/10.1016/j.scitotenv.2020.138817
Chen, Q., Min, C., Zhang, W., Wang, G., Ma, X., & Evans, R. (2020). Unpacking the black box: How to promote citizen
engagement through government social media during the COVID-19 crisis. Computers in Human Behavior, 110,
106380. https://doi.org/10.1016/j.chb.2020.106380
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social
Behavior, 24(4), 385–396. https://doi.org/10.2307/2136404
Cohen, T. R., Wolf, S. T., Panter, A. T., & Insko, C. A. (2011). Introducing the GASP scale: A new measure of guilt
and shame proneness. Journal of Personality and Social Psychology, 100(5), 947–966. https://doi.org/10.1037/
a0022641
Conway, L. G., III, Woodard, S. R., & Zubrod, A. (2020). Social psychological measurements of Covid-19: Coronavirus
perceived threat, government response, impacts, and experiences questionnaires. https://doi.org/10.31234/osf.io/
z2x9a
Doane, A. N., Kelley, M. L., Chiang, E. S., & Padilla, M. A. (2013). Development of the cyberbullying experiences survey.
Emerging Adulthood, 1(3), 207–218. https://doi.org/10.1177/2167696813479584
Englander, E., Donnerstein, E., Kowalski, R., Lin, C. A., & Parti, K. (2017). Defining cyberbullying. Pediatrics, 140(2),
148–151. https://doi.org/10.1542/peds.2016-1758U
Gaffney, H., Farrington, D. P., Espelage, D. L., & Ttofi, M. M. (2019). Are cyberbullying intervention and prevention
programs effective? A systematic and meta-analytical review. Aggression and Violent Behavior, 45, 134–153. https://
doi.org/10.1016/j.avb.2018.07.002
Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A very brief measure of the big-five personality domains.
Journal of Research in Personality, 37(6), 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1
Jonason, P. K., & Webster, G. D. (2010). The dirty dozen: A concise measure of the dark triad. Psychological Assessment,
22(2), 420–432. https://doi.org/10.1037/a0019265
Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical
review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073–1137. https://
doi.org/10.1037/a0035618
Lianos, H., & McGrath, A. (2018). Can the general theory of crime and general strain theory explain cyberbullying
perpetration? Crime & Delinquency, 64(5), 674–700. https://doi.org/10.1177/0011128717714204
Lippi, G., Sanchis-Gomar, F., & Henry, B. M. (2020). Coronavirus disease 2019 (COVID-19): The portrait of a perfect
storm. Annals of Translational Medicine, 8(7), 497. https://doi.org/10.21037/atm.2020.03.157
Paez, G. R. (2018). Cyberbullying among adolescents: A general strain theory perspective. Journal of School Violence, 17
(1), 74–85. https://doi.org/10.1080/15388220.2016.1220317
Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A test of general strain
theory. Youth and Society, 43(2), 727–751. https://doi.org/10.1177/0044118X10366951
Runions, K. C., Bak, M., & Shaw, T. (2017). Disentangling functions of online aggression: The cyber-aggression typology
questionnaire (CATQ). Aggressive Behavior, 43(1), 74–84. https://doi.org/10.1002/ab.21663
Sanders, J. M., Monogue, M. L., Jodlowski, T. Z., & Cutrell, J. B. (2020). Pharmacologic treatments for coronavirus
disease 2019 (COVID-19): A review. Journal of the American Medical Association, 323(18), 1824–1836. https://doi.
org/10.1001/jama.2020.6019
Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., & Agha, R. (2020). World health
organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal
of Surgery, 76, 71–76. https://doi.org/10.1016/j.ijsu.2020.02.034
Steinberg, L., Sharp, C., Stanford, M. S., & Tharp, A. T. (2013). New tricks for an old measure: The development of the
barratt impulsiveness scale–brief (BIS-brief). Psychological Assessment, 25(1), 216–226. https://doi.org/10.1037/
a0030550
Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate psychological responses and
associated factors during the initial stage of the 2019 Coronavirus disease (COVID-19) epidemic among the general
population in China. International Journal of Environmental Research and Public Health, 17(5), 1729–1753. https://
doi.org/10.3390/ijerph17051729
World Health Organization. (2020, May 22). WHO Coronavirus disease (COVID-19) dashboard. https://covid19.who.int
Wright, M. F. (2013). The relationship between young adults‘ beliefs about anonymity and subsequent cyber aggression.
Cyberpsychology, Behavior, and Social Networking, 16(12), 858–862. https://doi.org/10.1089/cyber.2013.0009
Xiang, F., Wang, X., He, X., Peng, Z., Yang, B., Zhang, J., Zhou, Q., Ye, H., Ma, Y., Li, H., Wei, X., Cai, P., & Ma, W.
(2020). Antibody detection and dynamic characteristics in patients with COVID- 19. Clinical Infectious Diseases, 22,
1930–1934. https://doi.org/10.1093/cid/ciaa461