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 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/2167696813479584 ea.sagepub.com 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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, Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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). Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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 Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015 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. 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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. Downloaded from eax.sagepub.com at University of Haifa Library on January 5, 2015