Moral Disengagement About Cyberbullying and Parental Monitoring: Effects on Traditional Bullying and Victimization via Cyberbullying Involvement

Abstract

The indirect effects of moral disengagement about cyberbullying and parental monitoring on traditional victimization and bullying via cyberbullying involvement were examined in a diverse sample of 800 youth in Grades 3 to 8. After controlling for grade and gender, moral disengagement about cyberbullying and parental monitoring had an indirect effect on traditional victimization and bullying through cyberbullying involvement. Moral disengagement about cyberbullying and parental monitoring had a direct effect on traditional bullying. Results suggest that moral disengagement about cyberbullying and parental monitoring affect cyberbullying involvement and additionally impact experiences beyond the cyber context.

Keywords

cyberbullying bullying peer victimization moral disengagement parental monitoring

Bullying is often defined as repetitious aggression by one individual against another, with the requirement of a power differential between the perpetrator and victim (U.S. Department of Health and Human Services, 2014). Although differences in use of terminology exist, aggression and bullying are terms that have both been used to describe aggression aimed at harming another (Casper, Meter, & Card, 2015; Gladden, Vivolo-Kantor, Hamburger, & Lumpkin, 2014). Traditional bullying can be enacted in different ways, including physical and verbal assaults and social aggression, but definitions of all forms of bullying share the defining characteristics of occurring repeatedly over time, between individuals of differing power (Olweus, 1991). Cyberbullying, similar to traditional bullying, encompasses a variety of deliberate and repeated actions against another. What differentiates cyberbullying from traditional bullying is the electronic means used to enact the aggression. Cyberbullying can occur through email and text messages, chat rooms, social media, or instant messaging, and via unwelcome distribution of photos or videos, exclusion from online social networks, or theft of online identity (Menesini & Spiel, 2012).

Although rates of cyberbullying and victimization are lower than rates of traditional bullying and victimization (Bauman, 2013), about 10% of children and adolescents in a nationally representative sample reported experiencing online bullying; 8% were bullied via texts, and 7% were bullied by phone (Ybarra, Boyd, Korchmaros, & Oppenheim, 2012). In a younger sample of children and early adolescents, about half the sample self-identified as a victim of traditional bullying, compared with 21% who reported being cyberbullied. While about 18% of the same sample reported bullying their peers through traditional means, only 5% reported bullying in the cyber context (Monks, Robinson, & Worlidge, 2012).

Traditional bullying (Jimerson, Swearer, & Espelage, 2010; Smith et al., 1999) and cyberbullying are problems in the most developed countries (Bauman, 2013). The negative effects of involvement in traditional bullying on children’s psychosocial and academic adjustment have been described and reviewed by numerous researchers (Card, Isaacs, & Hodges, 2007; Card, Stucky, Sawalani, & Little, 2008; Hawker, & Boulton, 2000). Research on cyberbullying involvement specifically has shown similarities to the traditional bullying literature in that both victims and aggressors of cyberbullying are at risk of maladjustment. Victims of cyberaggression have been found to have problems similar to those who are victimized through traditional means, including social problems (Ybarra, Mitchell, Wolak, & Finkelhor, 2006), social anxiety (Juvonen & Gross, 2008), declining academic performance, and problems at home (Tokunaga, 2010). Cyberbullies perform more poorly academically (Zhou et al., 2013). The negative impact of involvement in cyberbullying, as either an aggressor or victim, is over and above the negative effects of traditional bullying (Bonanno & Hymel, 2013).

A sizable body of research has shown that individuals who reframe behavior that is contrary to their personal moral standards in order to make it acceptable, that is, those who morally disengage, are more likely to be aggressive (Bandura, Barbaranelli, Caprara, & Pastorella, 1996). Moral disengagement in regard to cyberbullying specifically may predict cyberbullying involvement. Involvement with cyberbullying and victimization may also be predicted by parental behavior. Parental monitoring strategies used to control adolescents’ online behavior might decrease children’s cyberbullying involvement. In the current study, cyberbullying experiences were hypothesized to indirectly affect the relation between moral disengagement about cyberbullying and parental monitoring of online behavior, and two outcomes, traditional victimization and traditional bullying, with the expectation that child moral cognitions and parental behavior would impact behavior in cyberspace, which would then affect adolescents’ involvement in the traditional bullying context.

Overlap of Bullying Involvement

Previous research on traditional bullying has suggested that there is a small percentage of youth who are both perpetrators and victims of aggression, referred to as bully victims (e.g., Haynie et al., 2001). Unlike traditional bullying, this group of victim-perpetrators is larger in the cyber context, evidenced by sizable associations between cyberbullying perpetration and victimization (Bauman & Pero, 2011; Kowalski, Morgan, & Limber, 2012), although not all studies have found this overlap (Kowalski & Limber, 2013), perhaps due to methodological differences, or a difference in the composition of samples. In one study that assessed the factor structure of cyber and traditional bullying and victimization, the best-fitting model included cyberbullying/victimization as a single factor, while traditional bullying and victimization were separate factors (Law, Shapka, Hymel, Olson, & Waterhouse, 2012). Previous research has shown large correlations between cyberbullying and cybervictimization (e.g., Balakrishnan, 2015; Erdu-Baker, 2010; Fanti, Demetriou, & Hawa, 2012; ŞAHİN, 2012). The correlation between cyberbullying and cybervictimization has been shown to be stronger than that of traditional bullying and victimization (Bauman, Toomey, & Walker, 2013).

Individuals involved in cyberbullying are the same as those involved in traditional bullying, in that those who are aggressive in cyber contexts tend to also bully their peers in person (Dempsey, Sulkowski, Dempsey, & Storch, 2011; Monks et al., 2012; Olweus, 2012; Perren & Gutzwiller-Helfenfinger, 2012). Furthermore, there is concurrent overlap between victimization in traditional and cyber contexts (Gradinger, Strohmeier, Schiller, Stefanek, & Spiel, 2012; Juvonen & Gross, 2008; Monks et al., 2012). Victimization of others in the cyber context may open the door for victimizing others in the traditional context of school, or vice versa.

Much of the extant research on cyberbullying included adolescents as participants, who are expected to be more engaged with technology, but younger children and early adolescents have access to and use the Internet and mobile phones (Bauman & Tatum, 2009; Mishna, Saini, & Solomon, 2009). Monks and colleagues (2012) found that children as young as 7 years old engage in cyberbullying and are victimized in the cyber context by peers. Even without access to technology, young people can still find themselves the victims of cyberbullying if pictures, emails, or other forms of media that ridicule a victim are shared among peers. Because children as well as adolescents are involved with cyberbullying and traditional bullying as both aggressors and victims, it is important to assess youth from these age groups in studies of cyber and traditional bullying.

Moral Disengagement

Individuals involved in traditional bullying contexts are often the same as those involved in cyberbullying. Thus, it is likely that ways of thinking about cyberbullying involvement, particularly moral disengagement about cyberbullying, may have an impact on traditional bullying involvement. Moral disengagement is a cognitive mechanism whereby a person convinces himself or herself that a behavior that is contrary to their personal moral standards is acceptable. The mechanisms of self-regulation that govern moral conduct, whether they keep an individual’s behavior within a moral realm, or disengage from moral beliefs in order to allow the transgression, are used when they are activated (Bandura, 2002). Youth who morally disengage and then engage in cyberbullying are apt to do the same in the traditional bullying context. There are different ways that an individual can disengage from morally self-sanctioned behavior, causing harm to others. These mechanisms include vilifying victims through attribution of blame (it is the victim’s fault), reconstruing the conduct/moral justification (a higher principle is invoked, for example, saving the reputation of the class), obscuring personal causal agency (no one else tried to stop it), misrepresenting the consequences of one’s actions (it didn’t really hurt them), and using euphemistic labeling (it’s just playful teasing; Bandura, 1999; Bandura et al., 1996; Bussey et al., 2015).

Individuals who morally disengage are thought to engage in patterns of thinking that can lead to aggressive conduct (Bandura et al., 1996). It has been suggested that those who bully their peers morally disengage from moral standards about harming others in order to dissociate their behavior from their self-regard (Bussey et al., 2015). Moral reasoning can lead to moral (or immoral) action through self-regulatory mechanisms used to justify the moral reasoning or distort the implications of engaging in particular behavior (Bandura, 2001). Those who engage in moral disengagement about cyberbullying, therefore, will be more likely to be involved with cyberbullying actions. Numerous research studies have established an association between moral disengagement and bullying (Gini, Pozzoli, & Hymel, 2014; Obermann, 2011).

Of interest is how this social-cognitive process of moral disengagement, when specific to cyberbullying, is associated with cyberbullying involvement. A growing body of research indicates an overlap among those who are victimized by both cyber and traditional means, and those who perpetrate acts of cyber- and traditional bullying (Monks et al., 2012). Moral disengagement about cyberbullying may lead to more cyberbullying involvement, which could then generalize to thinking about bullying behavior in the traditional context as well. The social-cognitive processes, even though specific to the cyber domain, may reflect general attitudes about the acceptability of aggressive interactions in the cyber context, which could impact traditional bullying involvement.

Parental Monitoring of Online Behavior

Adolescents tend to have their own personal devices and social media accounts, (Lenhart, Purcell, Smith, & Zickuhr, 2010). Some parents monitor their children’s online and social networking site behavior. In one sample, overall, fewer than half of children reported that their parents monitored their Internet usage; cyberbullies reported fewer restrictions by their parents than those who did not bully others in the cyber context. No differences in parental monitoring were found between victims and nonvictims of cyberbullying (Zhou et al., 2013). Other studies have shown general parental monitoring (Taiariol, 2010) and parental monitoring specific to online behavior (Vandebosch & Van Cleemput, 2009) to negatively predict cyberbullying. Research has also found less victimization among youth whose parents monitor them (Taiariol, 2010), and monitor them in certain ways, such as restricting which websites they are allowed to visit (Mesch, 2009).

Only a few studies have investigated characteristics of children who are monitored and families who monitor their children’s online behavior and even fewer have investigated predictors of parental monitoring of online behavior, and how it is related to cyberbullying involvement (cf. Mesch, 2009). Mesch (2009) studied parental mediation in a representative sample of U.S. teens (age 12-17). For those in the sample who had not been victimized online, parental rules about online activity were more common, including restrictions on the amount of time spent online and which websites the child was allowed to visit. Online filters installed by parents were also found more often among the participants who had not been victimized. The only parental behavior that was found to be predictive of reduced chances of online bullying was the presence of parental rules about the websites youth were allowed to visit. There is a need for more research on the effect of parental monitoring of online behavior on cyberbullying involvement, and whether this effect indirectly impacts children’s traditional bullying involvement.

Current Study

The current study tested an indirect effects model of the relations between moral disengagement about cyberbullying, parental monitoring of online behavior, and two outcomes: traditional victimization and traditional bullying. We expected that moral disengagement about cyberbullying would be associated with more cyberbullying involvement, that parental monitoring would be associated with less cyberbullying involvement, and that involvement with cyberbullying would predict traditional victimization and bullying in the offline context. First, we tested the direct effect of moral disengagement about cyberbullying and parental monitoring on cyberbullying involvement. Next, we tested the direct effect of moral disengagement about cyberbullying and parental monitoring on traditional victimization and bullying. We expected that although the moral disengagement measure was specific to cyberbullying involvement, there may be a positive association with bullying experiences in traditional contexts. We also expected that parents who monitor their children more online may also keep closer watch on their children’s overall behavior, which might be related to less bullying and victimization in the traditional context. Next, we tested the indirect effect of moral disengagement and parental monitoring on the two outcomes via cyberbullying involvement. We expected moral disengagement to be positively associated with cyberbullying involvement, monitoring to be negatively associated with cyberbullying involvement, and cyberbullying involvement to be positively related to traditional victimization and traditional bullying.

Method

Participants

The 800 participants were elementary and middle school students in the Southwestern United States. Data were collected toward the end of the 2010-2011 school year. Twenty-five percent of the participants were third graders, 19% fourth graders, 20% fifth graders, 14% sixth graders, 11% seventh graders, and 11% eighth graders. Fifty-two percent of the sample were girls. Of the 787 participants for whom race/ethnicity information was reported, 41% of the participants identified as Hispanic, 22% as Caucasian, 17% biracial, and 21% African American or Black, Native American, or Asian American, or Other.

Materials and Procedure

All methods were executed in alignment with protocol approved by a University Institutional Review Board. Parents provided active consent for their children to participate in the study, and children gave their assent to participate. Paper-and-pencil surveys were administered by trained graduate assistants and undergraduates trained by the graduate research assistants at schools during the participants’ regular school days. Time of administration was late April, early May, which is the end of school year, when students were maximally familiar with each other.

Scales were created as part of a large project aimed at discovering more about predictors and consequences of participation in cyberbullying and related constructs among youth. McDonald’s omegas (McDonald, 1999) representative of construct reliability are included for each construct.

Moral disengagement about cyberbullying

The eight moral disengagement items were mostly specific to incidents of cyberbullying, but included some general moral disengagement items as well. The types of moral disengagement (vilifying victims through attribution of blame, reconstruing the conduct/moral justification, obscuring personal causal agency, and misrepresenting the consequences of one’s actions; Bussey et al., 2015) were included in the moral disengagement construct. Participants were asked how much they agreed (1 = strongly disagree, 2 = disagree, 3 = not sure, 4 = agree, 5 = strongly agree) with statements that reflected moral disengagement. Items were as follows: “Cyberbullying annoying classmates is just teaching them a lesson”; “Kids don’t really mind being cyberbullied because it shows others are interested”; “It’s okay to treat someone badly if they behave like a jerk”; “If people give out their passwords to others, they deserve to be cyberbullied”; “It’s okay to get revenge if someone cyberbullies one of your friends”; “It’s okay to spread nasty rumors about someone because it’s not as bad as beating them up”; “Kids who cyberbully other kids because their friends push them to do it should not be blamed for what they do”; and “If kids cyberbully others in school, it’s the teacher’s fault for not stopping it.”

The eight moral disengagement about cyberbullying items were parceled, producing four indicators of the latent construct of moral disengagement about cyberbullying. Item parceling allows for the inclusion of more indicators without introducing modeling problems into the structural equation model (Little, Cunningham, Shahar, & Widaman, 2002). This scale was created by the second author as part of a larger study of cyberbullying involvement. McDonald’s omega was .75. Because the scale was specific to cyberbullying, we refer to it as moral disengagement about cyberbullying to emphasize that the respondent used this cognitive mechanism to disengage from standards of good behavior in cyberspace.

Parental monitoring

Participants were asked questions about how often (1 = never, 2 = monthly, 3 = weekly, 4 = daily) their parents told them how much time they could spend on the Internet, checked on the websites they went to, put filters or blocks on their home computer, and talked with them about right and wrong behaviors using technology. If participants responded that they “don’t know” whether their parents monitor, they were not included in the analyses. These items were included in the model as four indicators of parental monitoring. McDonald’s omega was .74. This child-reported Parental Monitoring Scale was created by the second author as part of a larger study on cyberbullying involvement. We used a child report because we believed that the child’s beliefs, whether accurate or not, would influence their behavior.

Cyberbullying involvement

In an initially tested model investigating the factor structure of the cyberbullying and cybervictimization constructs using Full Information Maximum Likelihood (FIML) and a robust estimator, the model fit was poor and the standardized covariance between constructs was .92. Indicators of cyberbullying and cybervictimization were parceled into indicators of “cyberbullying involvement.” Bullying and victimization items that asked about the same form of cyberbullying involvement were parceled. These items were created by the second author as part of a larger study on cyberbullying involvement. The McDonald’s omega for cyberbullying involvement was .81.

Cybervictimization

The six cybervictimization items asked participants to report the frequency (1 = never, 2 = 1-2 times, 3 = 3-5 times, 4 = 5+ times) with which children were harmed by others’ cyberaggression. The cybervictimization items were, “How often have you received mean or scary emails?” “How often have you received mean or nasty text messages?” “How often have you found out someone had sent an embarrassing photo of you via cell phone?” “How often have you had someone pretend to be you and send an email or text message that damaged your reputation or friendships?” “How often have you had someone tell your secrets on the Internet or by cell phone without your permission?” and “How often have you had someone spread a rumor about you on the Internet.” One item about being excluded online was not included due to its effect on model fit when the parceled indicator was included. Items that did not have a corresponding cyberbullying item were not included.

Cyberbullying

Six cyberbullying items assessed the frequency (1 = never, 2 = 1-2 times, 3 = 3-5 times, 4 = 5+ times) with which children harmed others by sending mean or threatening messages or emails. Specifically, the items were, “How often have you sent mean or nasty email messages?” “How often have you sent a mean or nasty text message?” “How often have you sent an embarrassing photo of someone via cell phone?” “How often have you pretended to be someone else on the Internet?” “How often have you told someone else’s secrets online or by cell phone without their permission?” and “How often have you spread a rumor about someone on the Internet?” One item about excluding others online was not included due to its effect on model fit when the parceled indicator was included.

Traditional victimization

Seven traditional victimization indicators (e.g., “How often has another kid hit, kicked, or pushed you in a mean way?” “How often has another kid said he or she would hurt you or beat you up?” and “How often has another chased you to try to hurt you?”) were used to assess participants’ frequency of traditional victimization (1 = never, 2 = once or twice, 3 = a few times, 4 = every week, 5 = most days). These items were adapted from Vernberg’s Victimization of Self scale (Vernberg, Jacobs, & Hershberger, 1999). McDonald’s omega for traditional victimization was .83.

Traditional bullying

Traditional bullying was comprised of seven indicators on which participants rated how often (1 = never, 2 = once or twice, 3 = a few times, 4 = every week, to 5 = most days) they used threats and physical aggression against peers. Some example items are, “How often have you hit, kicked, or pushed another kid in a mean way?” “How often have you told another kid you would hurt them or beat them up?” and “How often have you chased another kid to try to hurt him or her?” These items were adapted from Vernberg’s Victimization of Others scale (Vernberg et al., 1999). The McDonald’s omega for traditional bullying was .80.

Covariates

Gender

Gender was dummy-coded 0 for girls, 1 for boys.

Grade level

Grade Levels 3 to 8 were represented by values 0 to 5.

Missing data on the moral disengagement about cyberbullying variables were 65%; elementary school participants did not receive this scale to decrease the length of the survey. Grade was included as a covariate predicting the outcomes or mediator and outcomes, as appropriate, in all structural equation model analyses. Missing data on the other variables ranged from less than 1% to 9%. Missing data were handled using FIML estimation. FIML supplements the information missing from the model using the observed responses that are available (Little, Jorgensen, Lang, & Moore, 2014). The FIML approach computes a casewise likelihood function with observed variables for each case. Due to sample size retention, in that data are not deleted listwise, accurate standard errors are produced (Schlomer, Bauman, & Card, 2010).

Analytic Plan

In order to test the mediation model and best understand the associations between study constructs, a number of steps were performed. All analyses were performed in R version 3.2.3 using the package lavaan (Rosseel, 2012). All preliminary models before the indirect effects model used lavaan’s Maximum Likelihood Robust estimator due to skewed outcome variables. First, a measurement model of the five study constructs (without covariates) was tested. Second, individual components of the indirect effects model were tested with the inclusion of covariates: (a) the effect of moral disengagement about cyberbullying and parental monitoring on cyberbullying involvement, (b) the direct effects of moral disengagement about cyberbullying and parental monitoring on traditional victimization and traditional bullying, and (c) the association between cyberbullying involvement and traditional victimization and bullying. Last, the indirect effects model was tested with 500 bootstrap samples. Figure 1 depicts this model.

Figure 1.

Hypothesized indirect effects model.

Results

A confirmatory factor analysis (CFA) was first used to examine model fit. The CFA included the constructs moral disengagement, cyberbullying involvement, traditional victimization, traditional bullying, and parental monitoring. The initial robust model fit was satisfactory, χ²(160) = 367.69 (p < .01); root mean square error approximation (RMSEA) = .04 (90% confidence interval [CI] = [.04, .05]); non-normed fit index (NNFI) = .92; comparative fit index (CFI) = .93, scaling correction factor = 1.29 (Little, 2013). All loadings of indicators of constructs were significant at the p < .01 level. Correlations between all indicators from the CFA are available in Table 1. Standardized covariances and standard errors between constructs are available in Table 2.

Table 1.

Correlations Between Indicators From CFA.

Indicator	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19
1. Cyber MD Parcel 1	1.00
2. Cyber MD Parcel 2	.67	1.00
3. Cyber MD Parcel 3	.40	.35	1.00
4. Cyber MD Parcel 4	.45	.38	.40	1.00
5. Cyber Involvement Parcel 1	.15	.20	.11	.12	1.00
6. Cyber Involvement Parcel 2	.17	.17	.11	.16	.63	1.00
7. Cyber Involvement Parcel 3	.01	.06	.11	−.03	.56	.49	1.00
8. Cyber Involvement Parcel 4	.14	.08	.15	.11	.38	.37	.49	1.00
9. Cyber Involvement Parcel 5	.07	.17	.14	−.02	.38	.40	.41	.43	1.00
10. Cyber Involvement Parcel 6	.04	.13	.12	.05	.33	.36	.49	.48	.49	1.00
11. Trad. Victimization 1	.04	−.01	.02	−.08	.09	.08	.13	.17	.11	.09	1.00
12. Trad. Victimization 2	.00	.04	.01	−.04	.23	.20	.28	.27	.19	.21	.63	1.00
13. Trad. Victimization 3	.02	−.01	.05	−.05	.20	.14	.24	.20	.17	.12	.59	.64	1.00
14. Trad. Bullying 1	.23	.23	.12	.11	.13	.13	.06	.16	.09	.13	.22	.16	.12	1.00
15. Trad. Bullying 2	.24	.26	.15	.18	.10	.16	.11	.27	.15	.15	.18	.20	.14	.62	1.00
16. Trad. Bullying 3	.21	.15	.05	.03	.09	.14	.09	.23	.20	.07	.16	.17	.20	.52	.57	1.00
17. Parental Monitoring 1	−.07	−.14	.05	−.02	.08	.01	.06	.12	.06	.07	.06	.05	.10	−.04	.05	.04	1.00
18. Parental Monitoring 2	−.08	−.09	−.05	−.11	.04	.02	.09	.09	.04	.06	.08	.06	.11	.07	.08	.10	.52	1.00
19. Parental Monitoring 3	−.03	−.01	−.02	−.05	.14	.09	.09	.11	.10	.07	.06	.08	.11	.08	.12	.12	.34	.42	1.00
20. Parental Monitoring 4	−.08	−.09	−.01	−.12	.06	.06	.08	.10	.09	.07	.01	.07	.05	.03	.07	.02	.35	.42	.34

Note. Significance of correlations was not available from the lavaan output. CFA = confirmatory factor analysis; Cyber = cyberbullying; MD = moral disengagement; Trad. = traditional.

Table 2.

Standardized Covariances and Standard Errors Between Constructs From the CFA.

Construct	1	2	3	4
1. Cyberbullying moral disengagement
2. Cyberbullying involvement	.16 (.09)
3. Traditional victimization	−.05 (.08)	.34** (.06)
4. Traditional bullying	.31** (.07)	.25** (.06)	.28** (.06)
5. Parental monitoring	−.15* (.07)	.15** (.06)	.13* (.06)	.12 (.06)

Note. CFA = confirmatory factor analysis.

p < .05. **p < .01.

A first structural equation model was used to analyze the prediction of cyberbullying involvement from moral disengagement about cyberbullying and parental monitoring, controlling for gender and grade. Because they were exogenous variables, gender was included as a dummy-coded variable with 0 = girls and 1 = boys, and grade, an ordinal variable, was included as a continuous predictor (Rosseel, 2016). Robust χ²(200) = 563.22 (p < .05); RMSEA = .05 (90% CI = [.04, .05]); NNFI = .87; CFI = .89, scaling correction factor = 1.23. There was a significant association between moral disengagement about cyberbullying and cyberbullying involvement, β = .33, p < .05 and between parental monitoring and cyberbullying involvement, β = .23, p < .01.

In the second model, the prediction of the hypothesized outcomes, traditional victimization and traditional bullying from moral disengagement about cyberbullying and parental monitoring, was tested. Cyberbullying involvement was not included in this model in order to test for the direct effects between these previously mentioned variables. All reported regressive paths are standardized completely and represent the associations while controlling for gender and grade. Moral disengagement about cyberbullying did not significantly predict traditional victimization, β = −.02, ns, but did predict traditional bullying, β = .34, p < .01. Parental monitoring significantly predicted traditional victimization, β = .13, p < .05, and traditional bullying, β = .17, p < .01. The fit of this regression model was good, robust χ²(95) = 180.79 (p < .01); RMSEA = .03 (90% CI = [.03, .04]); NNFI = .96; CFI = .96, scaling correction factor = 1.06.

A third model tested the association between cyberbullying involvement and the two outcomes, controlling for gender and grade, robust χ²(71) = 259.89 (p < .01); RMSEA = .06 (90% CI = [.05, .06]); NNFI = .87; CFI = .90. Cyberbullying involvement positively and significantly predicted traditional victimization (β = .36, p < .01) and traditional bullying (β = .23, p < .01).

Last, the indirect effects model was tested with 500 bootstrap samples, χ²(194) = 537.33 (p < .01); RMSEA = .05 (90% CI = [.05, .05]); NNFI = .90; CFI = .92. Both direct and indirect effects were included in this model. Indirect effects were established by labeling the effects and multiplying the two pathways for each indirect effect within the model estimation. Figure 2 depicts the standardized estimates of the model parameters and Table 3 contains the unstandardized estimates, standard errors, and confidence intervals from the bootstrapping analysis of the indirect effects model. The intervals were calculated using the bootstrap percentile method. After controlling for the effect of gender and grade level, moral disengagement about cyberbullying predicted cyberbullying involvement, β = .17, 95% bootstrapping CI = [.007, .375]. Moral disengagement about cyberbullying was not directly associated with traditional victimization (β = −.10, 95% bootstrapping CI = [−.321, .100] and was directly associated with traditional bullying (β = .29, 95% bootstrapping CI = [.149, .526]). Parental monitoring was significantly associated with cyberbullying involvement (β = .18, 95% bootstrapping CI = [.065, .302]). Parental monitoring was not directly associated with traditional victimization (β = .06, 95% bootstrapping CI = [−.061, .196]), but was associated with traditional bullying (β = .14, 95% bootstrapping CI =[.009, .314].

Figure 2.

Indirect Effects Model.

Table 3.

Unstandardized Parameter Estimates, Standard Errors, and Confidence Intervals of Regression Paths and Indirect Paths.

Association	Indirect effects	B	SE	95% CI
Cyberbullying involvement
Grade		.09	.04	[.024, .164]
Gender (0 = girls)		.04	.09	[−.141, .206]
MD from Cyberbullying	a₁	.18	.10	[.007, .375]
Parental monitoring	a₂	.19	.06	[.065, .302]
Traditional victimization
Grade		−.13	.03	[−.186, −.074]
Gender (0 = girls)		.15	.08	[−.011, .303]
Cyberbullying involvement	b₁	.39	.08	[.242, .583]
MD from cyberbullying		−.11	.10	[−.321, .100]
Parental monitoring		.07	.06	[−.061, .196]
Traditional bullying
Grade		.12	.03	[.057, .181]
Gender (0 = girls)		.20	.08	[.052, .356]
Cyberbullying involvement	b₂	.18	.07	[.047, .340]
MD from cyberbullying		.32	.09	[.149, .526]
Parental monitoring		.15	.08	[.009, .314]
Indirect paths
a₁ × b₁		.07	.04	[.004, .173]
a₂ × b₁		.07	.03	[.021, .130]
a₁ × b₂		.03	.02	[.001, .086]
a₂ × b₂		.03	.02	[.006, .073]

Note. CI = confidence interval; MD = Moral Disengagement.

Cyberbullying involvement positively predicted traditional victimization (β = .37, 95% bootstrapping CI = [.242, .583]) and traditional bullying (β = .17, 95% bootstrapping CI = [.047, .340]). The indirect effects of moral disengagement through cyberbullying involvement to traditional victimization and bullying were small, but significantly different from 0 (βs = .06 and .03, 95% bootstrapping CIs = [.004, .173] and [.001, .086], respectively). The indirect effects of parental monitoring through cyberbullying involvement to traditional victimization and bullying were small but significantly different from 0 as well (βs = .07 and .03, 95% bootstrapping CIs = [.021, .130] and [.006, .073], respectively). The covariance between traditional victimization and traditional bullying was positive and significant (ψ = .29, 95% bootstrapping CI = [.161, .440]), as was the association between moral disengagement about cyberbullying and parental monitoring (ψ = −.16, 95% bootstrapping CI = [−.289, −.016]).

Discussion

The aim of this study was to investigate how moral disengagement specific to cyberbullying and child perceptions of parental monitoring impact cyberbullying involvement, and how cyberbullying involvement predicts traditional victimization and bullying. The correlation between cyberbullying and cybervictimization in the current sample was in line with past research which has suggested an overlap of the constructs. Unlike traditional bullying, which shows relatively small percentages of individuals who are both bullies and victims (Haynie et al., 2001) in the cyber context, the correlation between bullying and victimization was substantial. It was in fact so substantial that it led us to collapse these behaviors into one construct—cyberbullying involvement.

Our results showed that youth who endorsed morally disengaged ideas about aggression in the cyber context were more likely to be involved in cyberbullying. Previous research has shown an association between disengaged justifications and traditional bullying (Gini et al., 2014), and this association has been consistent with respect to cyberbullying (Pornari & Wood, 2010), although not in all studies (Perren & Gutzwiller-Helfenfinger, 2012). There was no direct association between moral disengagement about cyberbullying and traditional victimization. This direct association was tested, but was not expected, as youth who use cognitive strategies to justify their enactment of aggression are the ones who morally disengage from the behavior. Those who report morally disengaged attitudes about cyberbullying likely use the same sorts of cognitive strategies to justify traditional bullying involvement. The association between moral disengagement about cyberbullying and cyberbullying involvement, although it included victimization as a component, was not surprising, as the overlap in youth who are both aggressors and victims of cyberbullying is so large. However, we would not necessarily expect a direct association between moral disengagement about cyberbullying and traditional victimization, as the youth who report victimization are at the receiving end of aggression. The indirect effect of moral disengagement on traditional victimization shows how youth who morally disengage from cyberbullying tend to be more involved with cyberbullying, which puts them at risk not only for enactment of traditional bullying, but also for traditional victimization.

Many traditional bullying prevention and intervention efforts have included a component aimed at increasing empathy and awareness of victims’ distress (Kärnä et al., 2011; Polanin, Espelage, & Pigott, 2012). Because self-regulatory cognitions link moral thoughts to enacted behavior (Bandura, 2001), interventions that address such cognitions would be worth exploring. Not only may influencing cognitions about cyberbullying impact cyberbullying behavior, but it may also activate responses from online bystanders who have the opportunity to ignore or intervene in cases of cyberbullying, and support victims of cyberaggression. Moral disengagement about cyberbullying had a direct effect on traditional bullying; it was indirectly associated with both traditional victimization and traditional bullying. Traditional and cyberbullying have been shown to be distinct forms of aggression (Law et al., 2012), but bullying online may overflow into real life contexts, just as bullying in traditional contexts may follow youth into cyber contexts. This suggests that interventions designed to decrease bullying in a particular context, especially if they target social-cognitive mediators of bullying such as moral disengagement, may be successful in affecting bullying in the other context as well (Palladino, Nocenti, & Menesini, 2016; Salmivalli, Kärnä, & Poskiparta, 2011). Prevention and intervention programming may not need to be specific to cyberbullying or separate from traditional bullying programs to effectively reduce cyberbullying involvement.

It has been suggested that ecologically informed prevention and intervention programs that do not specifically target bullying in only one context, but instead include evidence-based components shown to decrease bullying involvement more universally, may be effective in reducing both forms of bullying involvement (Palladino et al., 2016; Pearce, Cross, Monks, Waters, & Falconer, 2011). Interventions that successfully impact bullying and victimization in both traditional and cyber contexts may prove especially successful due to the finding that bullying in the traditional context is predicted by bullying in the cyber context demonstrated in this and other studies.

Contrary to what was expected, those who reported a perception of more parental monitoring also reported more cyberbullying involvement. There was also an indirect and direct effect from parental monitoring to traditional bullying involvement. We anticipated a negative association between parental monitoring and cyberbullying involvement, but believe that the finding of a positive association between these constructs may be because of the concurrent nature of the data. It is important to note that the studies on the association between parents’ monitoring of online behavior and cyberbullying involvement were concurrent, and cannot conclusively determine the direction of effects. The literature documents associations between parental monitoring and cyberbullying involvement, but it is also possible that children’s cyberbullying involvement predicts parents’ monitoring of their children’s online behavior. When youth are involved in cyberbullying, it may be that their parents recognize their children’s misbehavior, risky behavior, or victimization and take steps to intervene and teach about safe interactions with technology while perhaps limiting their children’s technology use or access. Youth who are involved with cyberbullying may also be aware of their parents’ monitoring of online behavior because it impedes their ability to engage in cyberbullying successfully. Youth who cyberbully or are victimized online or via text message may be sensitive to their parents’ monitoring of their behavior because they believe their parents would disapprove of their behavior or would worry about their victimization. Many youth have reported a fear that their parents would not allow them to access tools needed to interact in the cyber environment as a reason not to report cybervictimization (Bauman, 2010; Cassidy, Jackson, & Brown, 2009). Youth who are uninvolved with cyberbullying may not need the monitoring as much or may not perceive their parents monitoring to be intrusive because they use the Internet and devices in appropriate ways.

Strengths and Limitations

This is, to our knowledge, the first study to investigate moral disengagement specific to cyberbullying. The traditional conception of moral disengagement in association with cyberbullying has been a component in previous research (Almeida, Correia, Marinho, & Garcia, 2012; Bauman, 2010; Pornari & Wood, 2010); however, the current study’s results suggest that just as traditional moral disengagement has been found to have effects on traditional peer aggression, cyberbullying moral disengagement can serve as a cognitive predictor of cyberbullying involvement.

This study benefitted from its consideration of traditional and cyberbullying involvement as latent constructs of continuously measured variables. Many previous studies classified participants into groups (e.g., cyberbully, cybervictim, cyberbully/victim, uninvolved) using various statistical criteria for doing so. However, given the very low rates of these behaviors, those methods may be classifying persons with very low levels of involvement in one of these groups. Our approach allowed for examination of the latent associations between constructs which controls for measurement error. Furthermore, structural equation modeling allowed us to test our model in its entirely, rather than one part of the indirect effects process at a time.

Despite these strengths, there are limitations to be discussed. First, complete data were not available for all participants. Although one of the most robust methods was used to deal with the missing data, complete data from all participants would have been preferred. In the case that in reality, the association between moral disengagement about cyberbullying and another variable was moderated by age, the estimates would be biased. We encourage replications of these findings with a complete sample, and for the moderating effect of grade to be investigated. For this reason and others, we encourage future research to investigate potential differences between the preadolescents in elementary (Grades 3-5) and early adolescents (in Grades 6-8). Given the developmental differences in these groups, such as an expected increase in cyberbullying involvement, an increase in social forms of bullying and victimization, and a decrease in overt forms of bullying and victimization, such analyses might be useful in future research.

In addition, traditional and cyberbullying have been described in previous research to have multiple forms, such as physical, verbal, relational, direct, and indirect forms. The effects of specific forms of bullying and victimization in the traditional and online contexts were not investigated in the current analysis, and are an important consideration to be addressed in future work.

Due the cross-sectional design of our study, the results cannot be interpreted as causal. It is not possible to test whether the cognitions preceded cyberbullying involvement, and whether cyberbullying involvement led to traditional victimization and bullying and more parental monitoring. Longitudinal and experimental work could begin to test the direction of effects, which is an important next step in cyberbullying research as a whole, as much of it utilizes concurrent data and therefore temporal effects can be inferred, but not confirmed.

Although our sample included an ethnically diverse group of children and early adolescents, the results may not generalize to other populations of youth or to other regions or countries. Furthermore, participants who responded “don’t know” to the questions about parental monitoring were not included in the current analyses. Therefore, the results of these analyses only pertain to participants who feel competent to report on their parents’ behavior in regard to their parents monitoring them and talking to them about their behavior online. An interesting line of future research would be to investigate differences between youth who are aware of their parents monitoring and those who are unsure of it, and children who perceive that their parents do not or rarely monitor them and those who are unsure of it to see if there is an association between perception of parents’ monitoring and actual behavior.

Conclusion and Implication

This study made important contributions to our understanding of the relations between cognitions about cyberbullying, cyberbullying involvement, and how cognitions about cyberbullying can indirectly impact outcomes in other contexts. While studies designed to establish associations between predictors and outcomes of bullying involvement within context are important, attention to bullying involvement in the cyber and traditional contexts may better represent the plight of victimized youth, who now, even from a young age, have access to and use cyber means of communication and networking.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding was provided by the National Science Foundation Grant No. DGE-1143953 to the first author and National Science Foundation Award Number 1019196 to the second author.

Notes

Author Biographies

Diana J. Meter is a postdoctoral research associate in the School of Behavioral and Brain Sciences at The University of Texas at Dallas. Her research focuses on peer victimization and bullying among children and adolescents. Her work aims to investigate the effects of involvement in peer victimization and bullying in different roles and contexts, and how peer victimization and bullying involvement can be affected by and also affect adjustment, peer relationships, and parent-child relationships.

Sheri Bauman is a professor of counseling at the University of Arizona. She is a former teacher and school counselor who has had a clinical practice as a psychologist. She currently serves on the editorial board of the Journal of School Psychology and is a former editor of the Journal for Specialists in Group Work. Her research focuses on peer victimization and bullying in students with special needs. Ultimately, she would like to apply the results of her research to the development of effective prevention and intervention programs for general and special education students.

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