Article
Development of the Cyberbullying
Experiences Survey
Emerging Adulthood
1(3) 207-218
ª 2013 Society for the
Study of Emerging Adulthood
and SAGE Publications
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DOI: 10.1177/2167696813479584
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Ashley N. Doane1, Michelle L. Kelley2, Evelyn S. Chiang3, and
Miguel A. Padilla2
Abstract
The majority of cyberbullying studies have examined middle and high school students. The purpose of the present study was to
develop a multifactor cyberbullying victimization and perpetration survey for use with an emerging adult population. The initial
88-item preliminary survey (44 victimization and 44 perpetration items) was administered to 538 college students (421 females).
Exploratory factor analyses revealed four-factor (i.e., malice, public humiliation, unwanted contact, and deception) victimization
and perpetration scales. A confirmatory factor analysis was then performed on the Cyberbullying Experiences Survey (CES) factor
structure with a separate sample of 638 college students (446 females). Results indicated a final 21-item victimization scale and 20item perpetration scale consisting of the same four factors. The CES has adequate internal consistency and convergent validity
with other measures of cyberbullying and Internet harassment and may provide a promising multifactor method of measuring
cyberbullying victimization and perpetration.
Keywords
cyberbullying, Internet harassment, victimization, survey development
Face-to-face bullying in educational settings has received considerable attention. In particular, attention has been given to
distinguishing between direct (e.g., attacking another person)
and indirect (e.g., excluding a classmate from the larger group)
bullying (Olweus, 1991), recognizing negative outcomes (e.g.,
depression) associated with being a victim (Austin & Joseph,
1996), and identifying characteristics of bullies and victims
(Nansel et al., 2001). Given increased types and availability
of electronic forms of communication in the past decade, a
growing body of research has identified cyberbullying as a
significant concern. However, to date, a multifactor measure
that assesses cyberbullying for emerging adults has not been
developed. A survey that assesses specific forms of cyberbullying may increase our understanding of these behaviors. Thus,
the purpose of the present study was to develop and validate
a measure of cyberbullying perpetration and victimization for
use with a college student population.
Hinduja and Patchin (2009) define cyberbullying as intentionally and repeatedly harming others through the use of electronic devices, such as computers or cell phones. Although
traditional bullying must be repeated (e.g., Olweus, 2003),
whether cyberbullying has to be repeated, and what constitutes
repeated, are not clear (e.g., Bauman, 2011; Slonje, Smith, &
Frisén, 2013; Smith, 2011, 2012). For instance, a single post
on a victim’s Facebook page can be viewed by many, or a
malicious comment can be forwarded often. Slonje, Smith, and
Frisén (2012) found that the majority (72%) of students who
had received cyberbullying information as bystanders did not
forward the information; however, 9% forwarded the information to others, 6% forwarded it to the victim with the intent to
bully the person more, and 13% forwarded it to the victim with
the intent to help. Therefore, the intent behind forwarding
information further confounds the definition of ‘‘repeated’’
experience in cyberbullying. Although Wolak, Mitchell, and
Finkelhor (2007) found the majority of online harassment
Issues Related to the Definition of Cyberbullying
Although cyberbullying has gained attention, it is a relatively
new phenomenon. As such, the term itself is not without
controversy. While we acknowledge that researchers have not
yet settled on a single best term (see Bauman, 2011 or Smith,
2011 for a discussion of this issue), for the purposes of the
present study, we have elected to use cyberbullying, as it is the
most common term used to date.
1
Chowan University, Murfreesboro, NC, USA
Old Dominion University, Norfolk, VA, USA
3
University of North Carolina at Asheville, NC, USA
2
Corresponding Author:
Ashley N. Doane, PhD, Chowan University, One University Place, Murfreesboro,
NC 27855, USA.
Email: doanea@chowan.edu
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208
Emerging Adulthood 1(3)
identified in their survey did not meet the standard definition of
bullying (i.e., the behavior was not repeated), Li and Fung
(2012) found most victims of cyberbullying had been bullied
multiple times. Another issue that distinguishes cyberbullying
from traditional bullying is that while victims of traditional
bullying know their harassers, cybervictims may not (Kowalski
& Limber, 2007; Wolak, Mitchell, & Finkelhor, 2007). For
instance, a study conducted in the Czech Republic found that
approximately 19% of young adults (ages 20–26) knew their
online harasser in person; in contrast, over 40% of adolescents
(ages 12–19) knew their online harasser personally (Ševčı́ková
& Šmahel, 2009).
Cyberbullying Among College Students
Nearly 70% of high school graduates enroll in college (Bureau
of Labor Statistics, 2012). The majority of emerging adults,
then, are in college during this important period of the life span.
Importantly, compared to adolescents, college students are vulnerable to many high-risk behaviors. These behaviors include
greater alcohol use, heavy drinking, and binging (Cleveland,
Lanza, Ray, Turrisi, & Mallett, 2012; Dawson, Grant, Stinson,
& Chou, 2004), increased incidence of hazing (Denmark,
Klara, & Baron, 2008), and for women, greater risk of sexual
assault including rape (e.g., Fisher, Cullen, & Turner, 2000;
Koss, Gidycz, & Wisniewski, 1987).
There are many reasons why college students are at
increased risk of being the victim of negative behaviors. College students meet new peers on campus, in the new community, and online. Interacting with many new acquaintances
may in fact increase risk of bullying (e.g., Denmark et al.,
2008). At the same time, emerging adulthood is characterized
by considerable freedoms, relatively few adult responsibilities,
and greater independence from parents.
Moreover, parents typically reduce parental monitoring just
as emerging adults have greater opportunity to engage in a wide
range of risk behavior. With respect to violence specifically,
Conyne (2010) contends that freshmen, in particular, may be
at risk of being victims of all forms of violence because they
lack self-protection strategies. At the same time, there is
evidence that, contrary to expectations, college students do not
have greater knowledge about Internet safety compared to high
school students (Yan, 2009).
There are a number of reasons why emerging adults may
experience cyberbullying as often as younger individuals. For
instance, 93% of young adults own cell phones compared to
75% of teenagers (Lenhart, Purcell, Smith, & Zichuhr, 2010).
With regard to the prevalence of cyberbullying, studies on
college populations have yielded varied results. Walker, Sockman, and Koehn (2011) found 11% of students had been cyberbullied while at their university. Similarly, Finn (2004) found
that approximately 1 in 10 college students had experienced
repeated harassment, insults, or threats via e-mail or instant
messaging. However, in studies of Turkish college students,
Aricak (2009) and Dilmaç (2009) found 54.4% and 55.3%,
respectively, of college students had experienced at least one
instance of cyberbullying and approximately one fifth (19.7%
and 22.5%, respectively) had cyberbullied others.
Previous Surveys of Cyberbullying Victimization and
Perpetration Assessment
Although previous studies have identified cyberbullying as a
serious problem among college students, the variability in
prevalence of cyberbullying across studies suggests the need
to more closely examine the instruments used to assess cyberbullying. For instance, the lower prevalence of cyberbullying
reported by Finn (2004) may reflect that participants were
asked to report on whether they had experienced repeated harassment, insults, or threats via two specific forms of Internet
communication (i.e., e-mail or instant messaging). In contrast,
Aricak (2009) and Dilmaç (2009) asked whether students had
ever (i.e., lifetime) experienced cyberbullying.
It is also important to recognize that studies have assessed
cyberbullying with a single broad (e.g., Li, 2007) or specific
item (e.g., Gradinger, Strohmeier, & Spiel, 2009; Williams,
& Guerra, 2007). For instance, in their sample of youth ages
10–17, Wolak et al. (2007) asked: ‘‘In the past year, did you
ever feel worried or threatened because someone was bothering
or harassing you online?’’ and ‘‘In the past year, did anyone
ever use the Internet to threaten or embarrass you by posting
or sending messages about you for other people to see?’’ (p.
S53). In contrast, Ybarra, Diener-West, and Leaf (2007) asked
youth whether they had been threatened or embarrassed by
someone posting or sending messages about them for other
people to see or whether they felt worried or threatened because
someone was bothering or harassing them while online. To
assess perpetration of Internet harassment, Ybarra et al.
reversed these 2 items. Other researchers have assessed cyberbullying using a wider range of behaviors. For instance,
Hinduja and Patchin (2010) asked youth whether they had
experienced nine cyberbullying behaviors (e.g., been made fun
of, been picked on, received an upsetting e-mail) and whether
they had engaged in five cyberbullying behaviors (e.g., posted
a picture online without permission, posted something online
about a person to make others laugh). In recent years, researchers have also developed single-factor cyberbullying scales
appropriate for adolescent samples (e.g., Ang & Goh, 2009;
Bauman, 2010; Calvete, Orue, Estévez, Villardón, & Padilla,
2010).
In one notable study on primary and secondary school
children, Vandebosch and Van Cleemput (2009) demonstrated
that asking participants to respond to specific cyberbullying
behaviors (e.g., threatening or insulting someone via e-mail
or mobile phone) led to more affirmative responses than asking
respondents to indicate whether they had engaged in a single
global form of cyberbullying (i.e., bullying others via the Internet or mobile phone). Thus, assessing a broad range of specific
forms of cyberbullying that include many types of electronic
communication and potentially many aspects of cyberbullying
victimization and perpetration may yield a better understanding
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Doane et al.
209
of these types of behaviors, their frequency, and the modes in
which they occur.
Study 1: Item Development and Exploratory
Phase
To our knowledge, previous cyberbullying surveys have been
designed for use with preadolescent and adolescent samples.
The purpose of Study 1 was to develop a survey to assess frequency of cyberbullying victimization and perpetration via
multiple forms of electronic communication (e.g., Internet, text
messaging) in an undergraduate population. A pool of items
was created for the initial Cyberbullying Experiences Survey
(CES) based on responses to a face-to-face interview study of
college students who were cybervictims, which was conducted
by the first author (Doane, Kelley, & Cornell, 2009), a review
of the literature, and comments on an initial draft of the survey
by a subject matter expert, faculty members, and graduate students. The items generated for the initial survey reflected the
many types of cyberbullying experiences reported in the interview study and identified in previous research. We intentionally sought to include items that reflect different types of
cyberbullying, as the prevalence and severity of cyberbullying
may reflect the items assessed. An exploratory factor analysis
(EFA) was conducted on the pilot survey to establish the factor
structure of the survey. In addition, we sought to examine the
internal consistency as well as convergent and discriminant
validity for the newly developed measure.
Method
Participants
item was presented in a pair to assess experiences of cyberbullying victimization and perpetration (e.g., ‘‘Has someone
spread a rumor about you electronically?’’ ‘‘Have you spread
a rumor about someone electronically?’’). Although Rivers and
Noret (2010) discuss that the prevalence of cyberbullying varies in part due to differences in the time frame assessed, the
majority of previous studies have assessed cyberbullying in the
past 12 months (Li & Fung, 2012). In addition, we sought to
assess a time frame recent enough to allow for accurate recall,
but broad enough to capture experiences throughout various
times of the year (e.g., during school, summer, and breaks).
Therefore, participants were asked to report only behaviors that
occurred in the past year. They rated each behavior on a 6-point
scale adapted from Ybarra et al. (2007) that included: never (0),
less than a few times a year (1), a few times a year (2), once or
twice a month (3), once or twice a week (4), and every day/
almost every day (5).
To assess social desirability, a 10-item version of the Marlowe–Crowne Social Desirability scale (Crowne & Marlowe,
1960), developed by Strahan and Gerbasi (1972), was administered. Fischer and Fick (1993) reported a strong correlation
(r ¼ .96) with the original version of the Marlowe–Crowne
Social Desirability scale. Participants answered each item true
or false. For Study 1, a ¼ .59.
Ybarra et al.’s (2007) 6-item measure of Internet harassment
was completed by 84 participants to determine convergent
validity; 3 items assess victimization (a ¼ .63 for Study 1), and
3 items assess perpetration (a ¼ .78 for Study 1). Each item
was answered on a 6-point scale that ranged from never to
every day/almost every day.
Results
The initial CES was piloted with a convenience sample of 538
college students (112 males and 421 females; 5 participants did
not report their gender) at a large university in the mid-Atlantic
area (M ¼ 20.76 years, standard deviation [SD] ¼ 4.72). Most
were White (58.4%) or African American (25.1%). Of the 537
students who reported their class year, 34.5% were freshmen,
28.5% were sophomores, 20.1% were juniors, 16.0% were
seniors, and .9% were post-bachelor’s students. Interested students read a description of the study and then completed the
survey anonymously via an online survey system. Participants
were told that the survey would ask questions related to their
experiences with electronic forms of communication and were
given examples (i.e., Internet, cell phone text messaging, cell
phone picture messaging, cell phone Internet browsing, and
Blackberry or similar types of devices). As an incentive, participants received research credit in their psychology courses.
Prior to survey administration, the study received Human Subjects Committee approval at the participating university.
Measures
General Analytic Framework. To determine the factor structure of
the CES, a separate EFA was conducted on cyberbullying
victimization and perpetration items. In addition, normal theory
bootstrap confidence intervals (CIs) were computed for all coefficient as (Padilla, Divers, & Newton, 2012). All EFAs were
estimated with Mplus 5.2. Because items were measured on a
Likert-type scale (i.e., ordinal), factors were extracted using
weighted least squares with mean and variance adjustment
(WLSMV; Muthén, 1984, 1993; Muthén & Satorra, 1995). For
EFAs, factors were rotated using an oblique Promax rotation,
which rotates the factor structure based on the assumption that
factors are correlated. The main consideration in all EFAs was
an identifiable factor structure with clear factor loadings (i.e.,
simple structure; Tabachnick & Fidell, 2007). Because of the
large sample size and missing data was not substantial, missing
data in all EFAs were addressed by using WLSMV with pairwise
deletion (Asparouhov & Muthén, 2010). Specifically, the
amount of missing data was 6% and 8% for the victimization and
perpetration items, respectively. Missing data for all other analyses were addressed with listwise deletion.
The initial survey consisted of 88 items (44 victimization and
44 perpetration items). The items were worded to assess experiences of cyberbullying victimization and perpetration. Each
Cyberbullying Victimization Items. Although the scree plot
suggested that the initial victimization scale had three factors,
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210
Emerging Adulthood 1(3)
Table 1. Internal Consistency of Factors.
Exploratory Factor Analysis
Factor
Victimization
1. Public humiliation
2. Malice
3. Unwanted contact
4. Deception
Perpetration
1. Unwanted contact
2. Malice
3. Public humiliation
4. Deception
a
Items
% of Variance
11
6
7
3
39.4
9.7
7.3
4.9
.85
.86
.80
.62
8
7
3
3
46.2
13.3
6.7
5.1
.84
.87
.65
.63
Confirmatory Factor Analysis
n
Items
a
n
[.80, .88]
[.83, .88]
[.74, .85]
[.57, .69]
521
529
526
529
9
5
4
3
.89 [.86, .92]
.87 [.85, .89]
.84 [.81, .88]
.74 [.68, .79]
618
626
628
631
[.74, .91]
[.85, .89]
[.58, .72]
[.56, .70]
516
518
535
532
8
6
3
3
.94 [.91, .97]
.90 [.88, .92]
.83 [.79, .88]
.83 [.78, .87]
625
625
629
627
Note. N ¼ 538 for exploratory factor analysis (EFA) and N ¼ 638 for confirmatory factor analysis (CFA). Numbers in brackets are coefficient a 95% normal
theory bootstrap confidence intervals with 1,000 bootstrap samples and listwise deletion.
Table 2. Correlations of All Scales for Studies 1 and 2.
Variable
1
2
3
4
5
6
7
1. CES victim
—
.78
[.72, .83]
—
.56
[.47, .65]
.55
[.41, .65]
—
.54
[.44, .62]
.59
[.45, .69]
.88
[.84, .92]
—
—
.54
[.45, .63]
.55
[.42, .67]
.69
[.61, .76]
.64
[.55, .72]
—
.14
[.20, .09]
.19
[.25, .13]
.18
[.25, .11]
.23
[.29, .17]
.16
[.24, .08]
.20
[.28, .12]
—
.92
[.87, .96]
.46
[.20, .69]
.52
[.19, .74]
—
.38
[.12, .59]
.59
[.24, .81]
—
.71
[.53, .85]
—
6. CAI perpetrator
—
—
—
—
—
.48
[.39, .56]
.60
[.48, .69]
.66
[.67, .80]
66
[.57, .74]
.74
[.67, .80]
—
7. Social desirability
.24
[.43, .02]
.21
[.40, .01]
.32
[.48, .14]
.25
[.40, .11]
—
—
2. CES perpetrator
3. Ybarra victim
4. Ybarra perpetrator
5. CAI victim
Note. CES ¼ Cyberbullying Experiences Survey; CAI ¼ Cyberbullying Assessment Instrument.
Study 1 estimates are below diagonal and Study 2 estimates are above the diagonal. Numbers in brackets are 95% bias-corrected and accelerated bootstrap
confidence intervals with 1,000 bootstrap samples.
four factors were extracted and rotated because it led to a more
interpretable factor structure. Of the 44 initial items, 17 were
removed due to loadings below .30 (Gorsuch, 1983), cross
loadings, or not fitting the factor conceptually. The 27 item
victimization factors along with internal consistency estimates
are presented in Table 1. The victimization items accounted for
61.3% of the variance, and the factor correlations ranged from
.38 to .53.
Cyberbullying Perpetration Items. Although a scree plot of the
initial perpetration scale suggested two factors, after carefully
considering the possible factors and their interpretability, four
factors were extracted and rotated. Of the 44 initial items, 23
were removed due to factor loadings below .30 (Gorsuch,
1983), cross loadings, or not fitting the factor conceptually. The
21-item perpetration factors along with internal consistency
estimates are presented in Table 1. The perpetration items
accounted for 71.3% of the variance; factor correlations ranged
from .27 to .54.
Other CES Validity Evidence. Several correlational analyses were
conducted to assess the validity of the CES victimization and
perpetration scales (see Table 2). Because of the nonnormality
of scores from the scales, bootstrapping was used to estimate
CIs for the correlation coefficients (Efron & Tibshirani,
1998). The CES victimization and perpetration scales were
highly correlated, indicating that cyberbullying victimization
and perpetration are strongly related (Cohen, 1988). To investigate convergent validity, the CES was correlated with Ybarra
et al.’s (2007) Internet harassment measure. As expected, both
the CES victimization and the perpetration scales were moderately and strongly correlated (Cohen, 1988) with Ybarra et al.’s
Internet harassment victimization and perpetration scales.
Although the CES victimization and perpetration scales were
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Doane et al.
211
Table 3. Victimization Scale Modification Indices.
Steps
Step 1: w2(98) ¼ 755.66, p ¼ .000, CFI ¼ .857, TLI ¼ .965, RMSEA ¼ .102
Has someone insulted you after you posted a message electronically?
Step 2: w2(96) ¼ 658.81, p ¼ .000, CFI ¼ .874, TLI ¼ .968, RMSEA ¼ .096
Has someone you talked to electronically but did not want to meet in person tried
to get you to meet them in person?
Step 3: w2(91) ¼ 638.50, p ¼ .000, CFI ¼ .880, TLI ¼ .968, RMSEA ¼ .097
Has a stranger asked electronically about what you are wearing?
Has a stranger asked electronically about what you are wearing?
Step 4: w2(87) ¼ 618.13, p ¼ .000, CFI ¼ .884, TLI ¼ .968, RMSEA ¼ .098
Have you received a message electronically from a stranger requesting sex?
Have you received a message electronically from a stranger requesting sex?
Step 5: w2(81) ¼ 592.06, p ¼ .000, CFI ¼ .892, TLI ¼ .968, RMSEA ¼ .099
Has someone publicly posted information about you electronically that was not true?
Step 6: w2(77) ¼ 571.91, p ¼ .000, CFI ¼ .896, TLI ¼ .968, RMSEA ¼ 1.00
Have you received a rude message electronically?
Step 7: w2(73) ¼ 447.89, p ¼ .000, CFI ¼ .915, TLI ¼ .975, RMSEA ¼ .090
N/A
Factor
M.I.
StdYX E.P.C
F2
94.76
.60
F4
13.40
.39
F1
F4
20.43
14.65
.32
.37
F1
F4
F4
29.27
24.56
14.67
.38
.48
.45
F3
57.18
.33
Note. N ¼ 638. M.I. ¼ modification index; StdYX E.P.C ¼ standardized expected parameter change; F1 ¼ public humiliation; F2 ¼ malice; F3 ¼ unwanted contact;
F4 ¼ deception; CFI ¼ comparative fit index; TLI ¼ Tucker–Lewis index; RMSEA ¼ root mean square error of approximation. Identified items were removed in
the subsequent step. Step 1 is the initial CFA.
significantly correlated with social desirability, both correlations were associated with small effects (Cohen, 1988; i.e.,
r2 ¼ .06 for victimization and r2 ¼ .04 for perpetration).
Study 2: Confirmatory Phase
In Study 2, the Study 1 CES factor structure for the 27-item victimization and 21-item perpetration scales were tested through
confirmatory factor analyses (CFAs) on a second sample of
participants. In addition, internal consistency, convergent, and
discriminant validity were reexamined. As with Study 1, all
CFAs were estimated with Mplus 5.2 using WLSMV with pairwise deletion because of the large sample size and little missing
data (Asparouhov & Muthén, 2010). For Study 2, the amount of
missing data was 6% and 4% for victimization and perpetration
items, respectively.
Method
Participants. The same recruitment procedure was used as Study
1. Respondents who participated in Study 1 were restricted from
participating in Study 2. A battery of measures was completed
by 638 college students (186 males and 446 females; 6 participants did not report their gender) at the same university as Study
1 (M ¼ 21.95 years, SD ¼ 6.07). The majority were White
(57.4%) or African American (24.3%). Of the 637 students
who reported their class year, 29.1% were freshmen, 25.9%
were sophomores, 24.2% were juniors, 20.3% were seniors,
and .5% were post-bachelor’s students. Participants received
research credit as an incentive to participate.
Measures. As in Study 1, in addition to the CES, the 10-item
version of the Marlowe–Crowne Social Desirability scale
(Strahan & Gerbasi, 1972; for Study 2, a ¼ .48) and Ybarra
et al.’s (2007) Internet harassment measure (a ¼ .82 for victimization; a ¼ .82 for perpetration in Study 2) were included. In
addition, the Cyberbullying Assessment Instrument (Hinduja &
Patchin, 2009) was included in Study 2. The Cyberbullying
Assessment Instrument consists of 10 cyberbullying victimization (a ¼ .90 for Study 2) and six cyberbullying perpetration
(a ¼ .84 for Study 2) items.
Results
Cyberbullying Victimization Items. The CFA post hoc fit and
modification indices (MIs) for each step of the victimization
scale analyses are presented in Table 3. At step 1, the initial
27 item victimization CFA factor loadings indicated a good
factor structure for the four factors. However, the fit indices
showed mediocre fit (comparative fit index [CFI], Tucker–
Lewis index [TLI], and root mean square error of approximation [RMSEA]; Hu & Bentler, 1999). To refine the factor
structure, items with MIs that indicated cross loadings were
removed through successive steps. Post hoc model fitting
ended at Step 7 with improved fit indices. To further establish
that a four-factor solution was preferable to a single-factor
solution, a 21-item single-factor CFA was fitted and compared
to the four-factor model at Step 7. The chi-square difference
test indicated that the four-factor CFA had significantly better
fit than a single factor, w2(5) ¼ 523.83, p ¼ .000.
The factor loadings of the 21-item victimization scale are
presented in Table 4 and internal consistency estimates are presented in Table 1. Factor loadings ranged from .64 to .92 (very
good to excellent; Comrey & Lee, 1992). All subscales had
good internal consistency with as above .70 (Nunnally,
1978). All victimization factors were significantly correlated
with one another (rs ¼ .40 to .50, p < .001 for all).
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212
Emerging Adulthood 1(3)
Table 4. Victimization Scale Final Factor Structure.
Factor
Loading
Public Humiliation
Has someone distributed information electronically while pretending to be you?
Has someone changed a picture of you in a negative way and posted it electronically?
Has someone written mean messages about you publicly electronically?
Has someone logged into your electronic account and changed your information?
Has someone posted a nude picture of you electronically?
Has someone printed out an electronic conversation you had and then showed it to others?
Have you completed an electronic survey that was supposed to remain private but the answers
were sent to someone else?
Has someone logged into your electronic account and pretended to be you?
Has someone posted an embarrassing picture of you electronically where other people could see it?
Malice
Has someone called you mean names electronically?
Has someone been mean to you electronically?
Has someone cursed at you electronically?
Has someone made fun of you electronically?
Has someone teased you electronically?
Unwanted Contact
Have you received a nude or partially nude picture that you did not want from
someone you were talking to electronically?
Have you received a pornographic picture that you did not want from someone electronically
that was not spam?
Have you received an unwanted sexual message from someone electronically?
Have you received an offensive picture electronically that was not spam?
Deception
Has someone pretended to be someone else while talking to you electronically?
Has someone lied about themselves to you electronically?
Have you shared personal information with someone electronically and then later found the person
was not who you thought it was?
.84 (.02)
.82 (.03)
.81 (.02)
.73 (.03)
.92 (.03)
.81 (.02)
.84 (.03)
.81 (.02)
.64 (.03)
.86 (.02)
.85 (.01)
.72 (.02)
.81 (.02)
.81 (.02)
.87 (.02)
.83 (.02)
.85 (.02)
.82 (.02)
.71 (.03)
.77 (.02)
.82 (.03)
Note. Numbers in parentheses are standard errors for factor loadings. Factor structure comparative fit index (CFI) ¼ 0.915, Tucker–Lewis index (TLI) ¼ .975, and
root mean square error of approximation (RMSEA) ¼ .090. N ¼ 638 for the CFA model.
Cyberbullying Perpetration Items. The CFA post hoc fit and MIs
for each step of the perpetration scale analyses are presented
in Table 5. At Step 1, the initial 21-item perpetration CFA
factor loadings indicated a good factor structure for the four
factors. However, only one fit index had acceptable fit (TLI;
Hu & Bentler, 1999). As with the victimization scale, at each
step, identified items were removed because the MIs indicated
cross loadings in addition to the current loadings. The final
CFA at Step 2 showed improvement in the fit indices. Both the
CFI and the TLI had acceptable fit, whereas the RMSEA had
marginal fit (Hu & Bentler, 1999). In addition, MIs in the final
step were small. Therefore, post hoc model fitting ended at Step
2. Finally, a 20-item single-factor CFA was fitted and
compared to the four-factor model at Step 2. The chi-square
difference test indicated that the four-factor CFA at Step 2 had
significantly better fit than a single-factor solution, w2(4) ¼
382.80, p ¼ .000.
The factor loadings of the 20-item perpetration scale are presented in Table 6 and internal consistency estimates in Table 1.
Factor loadings ranged from .76 to .96 (excellent; Comrey &
Lee, 1992). All subscale as were again greater than .70 (Nunnally, 1978). All perpetration factors were significantly correlated with one another (rs ¼ .43 to .66, p < .001 for all).
Other CES Validity Evidence. As in Study 1, several correlational
analyses with bootstrapping were conducted to assess the validity of the CES victimization and perpetration scales (see
Table 2). The reported 95% CIs are based on 1,000 bootstrap
samples. The CES victimization and perpetration scales were
again highly correlated. To assess convergent validity, the CES
was correlated with Ybarra et al.’s (2007) Internet harassment
measure and the Cyberbullying Assessment Instrument
(Hinduja & Patchin, 2009). The CES cyberbullying victimization scale was strongly correlated with Ybarra et al.’s Internet
harassment victimization and perpetration scales as well as the
Cyberbullying Assessment Instrument’s victimization scale
and perpetration scales. The CES cyberbullying perpetration
scale was also significantly related to Ybarra et al.’s Internet
harassment victimization and perpetration scales as well as the
Cyberbullying Assessment Instrument’s victimization and perpetration scales. Because of the strong correlation between the
CES perpetration and victimization scales, partial correlations
with Ybarra et al.’s Internet harassment measure and the
Cyberbullying Assessment Instrument were conducted. When
controlling for the CES perpetration scale, the CES victimization scale was significantly related to both Ybarra et al.’s
victimization scale (r ¼ .25, 95% CI [.13, .37]) and the
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Doane et al.
213
Table 5. Perpetration Scale Modification Indices.
Steps
Step 1: w2(56) ¼ 289.53, p ¼ .000, CFI ¼ .944, TLI ¼ .988, RMSEA ¼ .081
Have you sent an inappropriate message to someone electronically?
Have you sent an inappropriate message to someone electronically?
Have you sent an inappropriate message to someone electronically?
Step 2: w2(52) ¼ 185.97, p ¼ .000, CFI ¼ .969, TLI ¼ .993, RMSEA ¼ .064
N/A
Factor
M.I.
StdYX E.P.C
F1
F3
F4
96.59
80.60
75.27
.47
.46
.55
Note. M.I. ¼ modification index; StdYX E.P.C ¼ standardized expected parameter change; F1 ¼ unwanted contact; F3 ¼ deception; F4 ¼ public humiliation;
CFI ¼ comparative fit index; TLI ¼ Tucker–Lewis index; RMSEA ¼ root mean square error of approximation. N ¼ 638.
Identified items were removed in the subsequent step. Step 1 is the initial CFA.
Table 6. Perpetration Scale Final Factor Structure.
Factor
Loading
Unwanted contact
Have you sent an unwanted pornographic picture to someone electronically?
Have you tried to meet someone in person that you talked to electronically who did not want to meet you in person?
Have you sent an unwanted sexual message to someone electronically?
Have you sent an unwanted nude or partially nude picture to someone electronically?
Have you sent a message to a person electronically that claimed you would try to find out where they live?
Have you tried to get information from someone you talked to electronically that they did not want to give?
Have you sent a message electronically to a stranger requesting sex?
Have you asked a stranger electronically about what they are wearing?
Malice
Have you sent a rude message to someone electronically?
Have you teased someone electronically?
Have you been mean to someone electronically?
Have you called someone mean names electronically?
Have you made fun of someone electronically?
Have you cursed at someone electronically?
Deception
Have you pretended to be someone else while talking to someone electronically?
Has someone shared personal information with you electronically when you pretended to be someone else?
Have you lied about yourself to someone electronically?
Public humiliation
Have you posted an embarrassing picture of someone electronically where other people could see it?
Have you posted a picture of someone electronically that they did not want others to see?
Have you posted a picture electronically of someone doing something illegal?
.91 (.02)
.91 (.02)
.95 (.01)
.95 (.02)
.91 (.02)
.87 (.02)
.92 (.02)
.89 (.03)
.82 (.02)
.76 (.02)
.87 (.01)
.91 (.01)
.86 (.02)
.82 (.02)
.82 (.02)
.94 (.02)
.83 (.02)
.85 (.02)
.96 (.02)
.82 (.03)
Note. Numbers in parentheses are standard errors for factor loadings. Factor structure comparative fit index (CFI) ¼ 0.969, Tucker–Lewis index (TLI) ¼ .993, and
root mean square error of approximation (RMSEA) ¼ .064. N ¼ 638 for the CFA model.
Cyberbullying Assessment Instrument victimization scale (r ¼
.21, 95% CI [.09, .34]). Furthermore, the CES perpetration
scale was significantly related to both Ybarra et al.’s perpetration scale (r ¼ .32, 95% CI [.15, .45]) and the Cyberbullying
Assessment Instrument perpetration scale (r ¼ .41, 95% CI
[.24, .55]) while controlling for the CES victimization scale.
Discriminant validity with social desirability had a small effect
again. The CES victimization and perpetration scales were significantly correlated with social desirability; however, both
scales yielded smaller effects sizes than in Study 1 (r2 ¼ .02 for
victimization and r2 ¼ .03 for perpetration; Cohen, 1988).
In addition, correlations between the four factors of the CES
victimization and perpetration scales and Ybarra et al.’s (2007)
Internet harassment measure, the Cyberbullying Assessment
Instrument (Hinduja & Patchin, 2009), and social desirability
were examined. Ybarra et al.’s victimization scale was significantly correlated with the four CES victimization scales: public humiliation (r ¼ .46, 95% CI [.34, .58]), malice (r ¼ .52,
95% CI [.44, .58]), unwanted contact (r ¼ .38, 95% CI [.28,
.48]), and deception (r ¼ .43, 95% CI [.33, .53]). Also, Ybarra
et al.’s perpetration scale was correlated with the four CES perpetration scales: public humiliation (r ¼ .44, 95% CI [.33,
.55]), malice (r ¼ .54, 95% CI [.43, .64]), unwanted contact
(r ¼ .46, 95% CI [.32, .58]), and deception (r ¼ .45, 95% CI
[.34, .57]). Likewise, the Cyberbullying Assessment Instrument victimization scale was correlated with all four
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214
Emerging Adulthood 1(3)
Table 7. Means and Gender Differences in the Victimization and
Perpetration Scales.
95% CIs (MD)
Factor
Men
(M)
Women
(M)
Lower
Limit
Upper
Limit
Victimization
Public humiliation
Malice
Deception
Unwanted contact
Perpetration
Unwanted contact
Malice
Deception
Public humiliation
.89
.57
1.45
1.08
.77
.70
.36
1.25
.66
.52
.65
.33
1.15
.78
.64
.42
.11
.86
.43
.37
.12
.12
.11
.12
.03
.15
.15
.20
.09
.01
.36
.35
.50
.45
.28
.40
.37
.60
.37
.29
Note. All 95% confidence intervals (CIs) are bootstrapped using 1,000 samples.
Confidence intervals that do not contain 0 indicate significance at the a ¼ .05
level.
Table 7, for victimization experiences, men reported more frequent public humiliation, malice, and deception compared to
females. No significant differences were found in reports of
victimization by unwanted contact. Men reported significantly
more cyberbullying perpetration on all four CES scales:
unwanted contact, malice, deception, and public humiliation.
Age was negatively correlated with three of the cyberbullying
victimization factors and all four cyberbullying perpetration
factors. Specifically, age was negatively correlated with public
humiliation (r ¼ .16; 95% CI [.20, .12]), malice (r ¼
.20; 95% CI [.26, .14]), and deception (r ¼ .16; 95%
CI [.21, .11]) victimization experiences, as well as perpetration of unwanted contact (r ¼ .10; 95% CI [.12, .07]), malice (r ¼ .16; 95% CI [.21, .10]), deception (r ¼ .17; 95%
CI [.20, .13]), and public humiliation (r ¼ .16; 95% CI
[.19, .13]). Age was not significantly related to unwanted
contact victimization frequency (r ¼ .06; 95% CI [.12, .01]).
General Discussion
victimization scales of the CES: public humiliation (r ¼ .51,
95% CI [.39, .62]), malice (r ¼ .43, 95% CI [.35, .49]),
unwanted contact (r ¼ .40, 95% CI [.29, .50]), and deception
(r ¼ .40, 95% CI [.28, .50]), and the Cyberbullying Assessment
Instrument perpetration scale was correlated with the four CES
perpetration scales: public humiliation (r ¼ .51, 95% CI [.39,
.62]), malice (r ¼ .54, 95% CI [.47, .61]), unwanted contact
(r ¼ .44, 95% CI [.31, .56]), and deception (r ¼ .47, 95% CI
[.36, .57]). Social desirability was related to all four CES victimization scales: public humiliation (r ¼ .09, 95% CI
[.15, .03]), malice (r ¼ .17, 95% CI [.24, .11]),
unwanted contact (r ¼ .08, 95% CI [.15, .002]), and
deception (r ¼ .09, 95% CI [.16, .03]), but only three CES
perpetration scales: public humiliation (r ¼ .12, 95% CI
[18, .06]), malice (r ¼ .24, 95% CI [.30, .18]), and
deception (r ¼ .14, 95% CI [.20, .08]). Social desirability
was not related to the unwanted contact perpetration factor
(r ¼ .06, 95% CI [.13, .01]).
Participants responded to the CES items on a 6-point Likerttype scale, rating each behavior in terms of frequency of experience ranging from never to every day/almost every day. The
results of the present study indicate that 96.1% of respondents
had been the victim of some form of cyberbullying less than a
few times a year or more. With respect to the frequency with
which participants experienced different forms of cyberbullying,
73.2% reported experiencing public humiliation, 87.8% malice,
65.9% unwanted contact, and 78.4% deception. In addition,
84.2% of respondents reported that they had perpetrated at least
one form of cyberbullying less than a few times a year or more.
Specifically, 38.0% reported public humiliation, 77.8% malice,
29.4% unwanted contact, and 53.1% deception.
Gender and Age. To examine for possible gender and age
associations with each CES factor, independent measures
t-tests and correlations were conducted. As before, the reported
95% CIs are based on 1,000 bootstrap samples. As shown in
The purpose of this study was to develop a reliable and valid
multifactor survey to assess cyberbullying victimization and
perpetration among emerging adults. To generate initial survey
items for the CES, a review of the literature was conducted and
behaviors were included that were generated from an interview
study of cybervictims (Doane et al., 2009).
The results of the two EFAs on the initial victimization and
perpetration scales as well as the two CFAs on a separate
sample indicated four factors: malice, public humiliation,
unwanted contact, and deception. The final CES scale resulted
in 21 victimization items and 20 perpetration items. Importantly, the four factors identified (malice, public humiliation,
unwanted contact, and deception) reflected the four most
frequently reported types of incidents reported by cybervictims
in face-to-face interviews conducted by the first author (Doane
et al., 2009; i.e., teased/insulted, online deception, inappropriate message, private photo posted). Slonje et al., 2013 stated
that examining different types of cyberbullying may be more
appropriate than as one construct because many facets of
cyberbullying differ depending on the type. For example, in a
large sample of Italian adolescents, Menesini, Nocentini, and
Calussi (2011) found that for both males and females, unpleasant pictures on websites and pictures or videos of intimate or
violent scenes taken with a phone were the most severe acts of
cyberbullying, whereas silent or prank calls and insults via
instant messaging were least severe.
To assess convergent validity and social desirability of the
CES, participants were administered two alternative measures
of cyberbullying. Both the victimization and the perpetration
scales of the CES were correlated to scores from Ybarra
et al.’s (2007) Internet harassment measure in both Study 1 and
2. In addition, the CES was correlated with the Cyberbullying
Assessment Instrument (Hinduja & Patchin, 2009) in Study 2.
These findings provide evidence that the CES has convergent
validity with other instruments that assess Internet harassment
and cyberbullying. However, unlike Ybarra et al.’s measure
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Doane et al.
215
and Hinduja and Patchin’s measure, the CES is a multifactor
measure that assesses a wide range of emerging adults’
cyberbullying experiences.
In both the initial questionnaire development phase (Study
1) and the confirmatory phase of survey development (Study
2), the CES victimization and perpetration scores were modestly correlated with a short version of the Marlowe–Crowne
Social Desirability Scale (Strahan & Gerbasi, 1972). The
Marlowe–Crowne was designed to include items that are typically untrue for all respondents and that have little pathological
or abnormal implications and is based on the premise that some
people describe themselves in a socially desirable way (Waltz,
Strickland, & Lenz, 2004). Because the Marlowe–Crowne was
negatively associated with reports of cyberbullying victimization and perpetration behaviors, it appears that respondents
who need greater approval from others may be expected to
endorse slightly fewer cyberbullying victimization and perpetration behaviors. Although those who behave in a socially
desirable way may be more reluctant to report instances of
cyberbullying victimization or perpetration, it is possible that
they have not had as many cyberbullying experiences. For
instance, if people have a tendency to present themselves in a
socially acceptable way online, they may be less vulnerable
to being victimized. Likewise, they may not view bullying
others electronically as a socially acceptable behavior, and as
a result, may be less likely to engage in the behavior.
Further support was found for the CES in that the factors
were internally consistent. Although only three of the four
victimization scale factors (i.e., public humiliation, malice, and
unwanted contact) and two of the perpetration scale factors
(i.e., malice and unwanted contact) had acceptable Cronbach’s
as in Study 1, all of the cyberbullying victimization and perpetration factors in Study 2 were found to be internally consistent.
It should be noted that the main consideration in all EFA/
CFA models was to maintain a simple structure in order to
achieve a conceptually meaningful factor structure. Such
endeavors can at times be in conflict with structural equation
modeling statistical standards as was particularly the case for
the victimization items. Within this context, there are three
options when MIs indicate cross loading(s): correlate the items,
add the cross loading, or remove the item. Of these options,
only item removal can help achieve simple structure; however,
it can also be the most statistically detrimental. In addition,
CFA fit indices tend to be negatively impacted as the number
of indicators per construct increases, even with a properly specified model. From a measurement perspective, the items for the
final CFAs all had strong loadings, even though the fit indices
for the victimization items were not ideal. In the long run, it
was decided that having a clear and conceptually meaningful
factor structure was more important than relying heavily on fit
index standards. This is not meant to imply that fit indices were
ignored, just that fit indices were not purely the only consideration in post hoc model modifications. It should also be pointed
out that the EFA/CFA factor structure remained unchanged.
The only difference is that conceptually redundant items were
removed in the CFAs. The loss of items at the CFA stage is
likely the result of being conservative at the EFA stage, in that
items with loadings of .30 or greater were retained. For further
details about these ideas, the reader is referred to Brown and
Moore (2012) and West, Taylor, and Wu (2012).
Cyberbullying Prevalence by Factor
We found that nearly all participants had experienced cyberbullying victimization (96.1%) and perpetration (84.2%) less than
a few times in the previous year or more. An advantage of the
CES is the ability to examine the frequency with which individuals experience different forms of cyberbullying. Given that
participants were much more likely to report that they had
engaged in malice as opposed to public humiliation or
unwanted contact, differences in reports of the perpetration
of the four different forms cyberbullying should be examined
in future research.
Gender and Age Differences by Factor
Gender and age findings were fairly consistent across factors.
Three of the four victimization factors (public humiliation,
malice, and deception) as well as all four perpetration factors
were more frequently reported by men versus women.
Although the literature on the relationship between gender and
cyberbullying are inconclusive (see Slonje et al., 2013), the
present study is consistent with findings of Fanti, Demetriou,
and Hawa (2012) who also found males were more often
victims and perpetrators of cyberbullying. In the present study,
the only factor that did not differ as a function of gender was
unwanted contact victimization. This finding suggests that men
and women may experience unwanted contact equally. In addition, age was related to three of the cyberbullying victimization
factors (public humiliation, malice, and deception) and all four
cyberbullying perpetration factors, such that as age increased,
cyberbullying victimization and perpetration frequency
decreased. Similarly, Ševčı́ková and Šmahel (2009) found that
adolescents (ages 12–19) and young adults (ages 20–26) were
more likely than older adults to be cybervictims, and adolescents were most likely to be aggressors. These results suggest
that involvement in cyberbullying may decrease as age
increases. In contrast, age was not related to the frequency with
which participants reported victimization by unwanted contact.
It is possible that these types of behaviors, many of which are
of a sexual nature, are experienced throughout the college
years. Age differences in cyberbullying among college students
should be further explored.
Limitations
The present study was conducted with college student volunteers. In addition, the sample was largely comprised of females
and all data were self-report. Although weak, a modest correlation was found between social desirability and the CES scales
for both victimization and perpetration. It should be noted that
in both Study 1 and 2, the internal consistency of the social
desirability scale was low. As both initial and CFA were
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216
Emerging Adulthood 1(3)
conducted, this study represents a first step toward establishing
a multifactor structure of cyberbullying victimization and
perpetration for emerging adults.
Future Research
Future research should examine gender and age differences,
antecedents, responses, and consequences of the various forms
of cyberbullying. In addition, future investigations should
examine how different forms of cyberbullying, as well as the
frequency and duration of cyberbullying, may be associated
with the degree to which participants were upset by these
experiences and responses to these events. Although not examined in the present study, further investigations should also
examine weighting items by how much the experience upset
the participant which may result in more accurate composite
cyberbullying scores.
Conclusion
Studies of college students indicate that cyberbullying is a
pervasive problem in the emerging adult population. Consequently, there is a need to better define cyberbullying among
this population as well as examine the effects of being either
a victim or perpetrator. The psychometric properties of the CES
and its convergent validity with other measures of Internet harassment and cyberbullying suggest that the CES is a promising
multifactor measure of assessing cyberbullying victimization
and perpetration among the emerging adult population.
Because the CES assesses different forms of cyberbullying
(e.g., public humiliation, malice, unwanted contact, and deception), it has the potential to promote greater understanding of
the frequency and seriousness of different forms of cyberbullying among an emerging adult population.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The authors received no financial support for the research, authorship,
and/or publication of this article.
References
Ang, R. P., & Goh, D. H. (2009). Cyberbullying among adolescents:
The role of affective and cognitive empathy, and gender. Child
Psychiatry & Human Development, 41, 387–397. doi:10.1007/
s10578-010-0176-3
Aricak, O. T. (2009). Psychiatric symptomatology as a predictor of
cyberbullying among university students. Eurasian Journal of
Education Research, 34, 167–184.
Asparouhov, T., & Muthén, B. (2010). Weighted least squares estimation with missing data. Mplus Technical Appendices.
Retrieved from www.statmodel.com
Austin, S., & Joseph, S. (1996). Assessment of bully/victim problems
in 8 to 11 year-olds. British Journal of Educational Psychology,
66, 447–456. doi:10.1111/j.2044-8279.1996.tb01211.x
Bauman, S. (2010). Cyberbullying in a rural intermediate school: An
exploratory study, The Journal of Early Adolescence, 30, 803–833.
doi:10.1177/0272431609350927
Bauman, S. (2011). Cyberbullying: What counselors need to know.
Alexandria, VA: American Counseling Association.
Brown, T. A., & Moore, M. T. (2012). Confirmatory factor analysis. In
R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp.
361–379). New York, NY: Guilford Press.
Bureau of Labor Statistics. (2012). College enrollment and work activity of 2011 high school graduates. Retrieved from http://www.bls.
gov/news.release/hsgec.nr0.htm
Calvete, E., Orue, I., Estévez, I., Villardón, L., & Padilla, P. (2010).
Cyberbullying in adolescents: Modalities and aggressors’ profile.
Computers in Human Behavior, 26, 1128–1135. doi:10.1016/j.
chb.2010.03.017
Cleveland, M. J., Lanza, S. T., Ray, A. E., Turrisi, R., & Mallett, K. A.
(2012). Transitions in first-year college student drinking behaviors:
Does pre- college drinking moderate the effects of parent- and
peer-based intervention components? Psychology of Addictive
Behaviors, 26, 440–450. doi:10.1037/a0026130
Cohen, J. (1988). Statistical power analysis for the behavioral
sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis
(2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Conyne, R. K. (2010). Prevention program development and evaluation: An incidence reduction, culturally relevant approach.
Thousand Oaks, CA: Sage.
Crowne, D. P., & Marlowe, D. (1960). A scale of social desirability
independent of psychopathology. Journal of Consulting Psychology, 24, 349–354. doi:10.1037/h0047358
Dawson, D. A., Grant, B. F., Stinson, F. S., & Chou, P. S. (2004).
Another look at heavy episodic drinking and alcohol use disorders
among college and noncollege youth. Journal of Studies on
Alcohol, 65, 477–488.
Denmark, F. L., Klara, M. D., & Baron, E. M. (2008). Bullying and
hazing: A form of campus harassment. In M. A. Paludi (Ed.),
Understanding and preventing campus violence (pp. 27–40).
Westport, CO: Praeger.
Dilmaç, D. (2009). Psychological needs as a predictor of cyber bullying: A preliminary report on college students. Educational
Sciences: Theory & Practice, 9, 1307–1325.
Doane, A. N., Kelley, M. L., & Cornell, A. M. (2009, April). Online
bullies: College students’ reports of internet harassment and
cyberbullying. Poster presented at the Society for Research in
Child Development Biennial Meeting, Denver, CO.
Efron, B., & Tibshirani, R. (1998). An introduction to the bootstrap.
Boca Raton, FL: CRC Press.
Fanti, K. A., Demetriou, A. G., & Hawa, V. V. (2012). A longitudinal
study of cyberbullying: Examining risk and protective factors.
European Journal of Developmental Psychology, 9, 168–181.
doi:10.1080/17405629.2011.643169
Finn, J. (2004). A survey of online harassment at a university campus.
Journal of Interpersonal Violence, 19, 468–483. doi:10.1177/
0886260503262083
Fischer, D. G., & Fick, C. (1993). Measuring social desirability: Short
forms of the Marlowe-Crowne Social Desirability Scale.
Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015
Doane et al.
217
Educational and Psychological Measurement, 53, 417–424.
doi:10.1177/0013164493053002011
Fisher, B. S., Cullen, F. T., & Turner, M. G. (2000). The sexual
victimization of college women. Washington, DC: U.S. Department
of Justice, National Institute of Justice and Bureau of Justice Statistics.
Gorsuch, L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Gradinger, P., Strohmeier, D., & Spiel, C. (2009). Traditional bullying
and cyberbullying: Identification of risk groups for adjustment
problems. Journal of Psychology, 217, 205–213. doi:10.1027/
0044-3409.217.4.205
Hinduja, S., & Patchin, J. W. (2009). Bullying beyond the schoolyard:
Preventing and responding to cyberbullying. Thousand Oaks, CA:
Corwin Press.
Hinduja, S., & Patchin, J. W. (2010). Bullying, cyberbullying, and suicide. Archives of Suicide Research, 14, 206–221. doi:10.1080/
13811118.2010.494133
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in
covariance structure analysis: Conventional criteria versus new
alternatives. Structural Equation Modeling, 6, 1–55. doi:10.1080/
10705519909540118
Koss, M. P., Gidycz, C. A., & Wisniewski, N. (1987). The scope of
rape: Incidence and prevalence of sexual aggression and victimization in a national sample of higher education students. Journal of
Consulting and Clinical Psychology, 55, 162–170. doi:10.1037/
0022-006X.55.2.162
Kowalski, R. M., & Limber, S. P. (2007). Electronic bullying among
middle school students. Journal of Adolescent Health, 41,
S22–S30. doi:10.1016/j.jadohealth.2007.08.017
Lenhart, A., Purcell, K., Smith, A., & Zichuhr, K. (2010). Social
media & mobile Internet use among teens and young adults.
Retrieved from http://www.pewinternet.org/Reports/2010/SocialMedia-and-Young-Adults.aspx
Li, Q. (2007). New bottle but old wine: A research of cyberbullying in
schools. Computers in Human Behavior, 23, 1777–1791. doi:10.
1016/j.chb.2005.10.005
Li, Q., & Fung, T. (2012). Predicting student behaviors: Cyberbullies,
cybervictims, and bystanders. In Q. Li, D. Cross & P. K. Smith
(Eds.), Cyberbullying in the global playground: Research from
international perspectives (pp. 99–114). Oxford, England:
Wiley-Blackwell.
Menesini, E., Nocentini, A., & Calussi, P. (2011). The measurement of
cyberbullying: Dimensional structure and relative item severity
and discrimination. Cyberpsychology, Behavior, and Social
Networking, 14, 267–274. doi:10.1089/cyber.2010.0002
Muthén, B. (1984). A general structural equation model with
dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115–132. doi:10.1007/
BF02294210
Muthén, B. (1993). Goodness of fit with categorical and other
non-normal variables. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 205–243). Newbury Park,
CA: Sage.
Muthén, B., & Satorra, A. (1995). Technical aspects of Muthén’s LISCOMP approach to estimation of latent variable relations with a
comprehensive measurement model. Psychometrika, 60,
489–503. doi:10.1007/BF02294325
Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton,
B., & Scheidt, P. (2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. The Journal
of the American Medical Association, 285, 2094–2100. doi:10.
1001/jama.285.16.2094
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York, NY:
McGraw-Hill.
Olweus, D. (1991). Bully/victim problems among schoolchildren:
Basic facts and effects of a school based intervention program.
In D. J. Pepler & K. H. Rubin (Eds.), The development and treatment of childhood aggression (pp. 411–448). Hillsdale, NJ:
Lawrence Erlbaum.
Olweus, D. (2003). A profile of bullying at school. Educational
Leadership, 60, 12–17.
Padilla, M. A., Divers, J., & Newton, M. (2012). Coefficient alpha
bootstrap confidence interval under non normality. Applied Psychological Measurement, 36, 331–348. doi:10.1177/014662161
2445470
Rivers, I., & Noret, N. (2010). ‘I h8 u’: Findings from a five-year study
of text and email bullying. British Educational Research Journal,
36, 643–671. doi:10.1080/01411920903071918
Ševčı́ková, A., & Šmahel, D. (2009). Online harassment and cyberbullying in the Czech Republic: Comparison across age groups.
Journal of Psychology, 217, 227–229. doi:10.1027/0044-3409.
217.4.227
Slonje, R., Smith, P. K., & Frisén, A. (2012). Processes of cyberbullying, and feelings of remorse by bullies: A pilot study. European
Journal of Developmental Psychology, 9, 244–259. doi:10.1080/
17405629.2011.643670
Slonje, R., Smith, P. K., & Frisén, A. (2013). The nature of
cyberbullying, and strategies for prevention. Computers in Human
Behavior, 29, 26–32. doi: 10.1016/j.chb.2012.05.024
Smith, P. K. (2011). Bullying in schools: Thirty years of research. In
C. P. Monks & I. Coyne (Eds.), Bullying in different contexts (pp.
36–60). New York, NY: Cambridge University Press.
Smith, P. K. (2012). Cyberbullying: Challenges and opportunities for
a research program—A response to Olweus (2012). European
Journal of Developmental Psychology, 9, 553–558. doi:10.1080/
17405629.2012.689821
Strahan, R., & Gerbasi, K. C. (1972). Short, homogeneous versions of
the Marlowe-Crowne social desirability scale. Journal of Clinical
Psychology, 28, 191–193. doi:10.1002/1097-4679(197204)28:
2<191:AID-JCLP2270280220>3.0.CO;2-G
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics
(5th ed.). New York, NY: Pearson.
Vandebosch, H., & Van Cleemput, K. (2009). Cyberbullying among
youngsters: Profiles of bullies and victims. New Media & Society,
11, 1349–1371. doi:10.1177/1461444809341263
Walker, C. M., Sockman, B. R., & Koehn, S. (2011). An exploratory
study of cyberbullying with undergraduate university students.
TechTrends, 55, 31–38. doi:10.1007/s11528-011-0481-0
Waltz, C. F., Strickland, O. L., & Lenz, E. R. (2004). Measurement in
nursing and health research (3rd ed.). New York, NY: Springer.
Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015
218
Emerging Adulthood 1(3)
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model
selection in structural equation modeling. In R. H. Hoyle (Ed.),
Handbook of structural equation modeling (pp. 209–231).
New York, NY: Guilford Press.
Williams, K. R., & Guerra, N. G. (2007). Prevalence and predictors of
Internet bullying. Journal of Adolescent Health, 41, S14–S21. doi:
10.1016/j.jadohealth.2007.08.018
Wolak, J., Mitchell, K. J., & Finkelhor, D. (2007). Does online
harassment constitute bullying? An exploration of online harassment by known peers and online-only contacts. Journal of
Adolescent Health, 41, S51–S58. doi:10.1016/j.jadohealth.
2007.08.019
Yan, Z. (2009). Differences in high school and college students’ basic
knowledge and perceived education of Internet safety: Do high
school students really benefit from the Children’s Internet Protection Act? Journal of Applied Developmental Psychology, 30,
209–217. doi:10.1016/j.appdev.2008.10.007
Ybarra, M. L., Diener-West, M., & Leaf, P. J. (2007). Examining the
overlap in Internet harassment and school bullying: Implications
for school intervention. Journal of Adolescent Health, 41,
S42–S50. doi:10.1016/j.jadohealth.2007.09.004
Author Biographies
Ashley N. Doane is an assistant professor of psychology at
Chowan University. She earned her PhD in Applied Experimental Psychology from Old Dominion University. Her
primary research interests include the antecedents and consequences of cyberbullying and cyberbullying prevention.
Michelle L. Kelley is professor of psychology at Old Dominion University. Her research interests lie in the area of parenting and children’s social and behavioral development.
Evelyn S. Chiang is an assistant professor of psychology at the
University of North Carolina at Asheville. She earned her PhD in
Educational Psychology from the University of Florida. Her
primary research interests include motivation, metacognition, and
individual differences in learning as well as health contexts.
Miguel A. Padilla is an assistant professor of quantitative
psychology in the Department of Psychology at Old Dominion
University. His research interests are in psychometrics, applied
statistics, and statistical computing.
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