Understanding the Relationship Between Proactive and Reactive Aggression,and Cyberbullying Across United States and Singapore Adolescent Samples

Abstract

This study examined cyberbullying among adolescents across United States and Singapore samples. Specifically, the purpose of the investigation was to study the differential associations between proactive and reactive aggression, and cyberbullying across two cultures. A total of 425 adolescents from the United States (M age = 13 years) and a total of 332 adolescents from Singapore (M age = 14.2 years) participated in the study. Results of the moderator analyses suggested that nationality was not a moderator of the relationship between proactive aggression and cyberbullying, and between reactive aggression and cyberbullying. As expected, findings showed proactive aggression to be positively associated with cyberbullying, after controlling for reactive aggression, across both samples. Likewise, as hypothesized, reactive aggression and cyberbullying was not found to be significant after controlling for proactive aggression across both samples. Implications of these findings were discussed: (a) Proactive aggression is a possible risk factor for both bullying and cyberbullying; (b) proactive and reactive aggression could be argued to be distinct as they have different correlates—only proactive aggression contributed to cyberbullying after controlling for reactive aggression; (c) this research extends previous work and contributes toward cross-cultural work using similar and comparable measures across different samples; and (d) prevention and intervention programs targeted at proactive aggressive adolescents could adopt a two-pronged approach by changing mind sets, and by understanding and adopting a set of rules for Internet etiquette.

Keywords

adolescents cyberbullying proactive aggression reactive aggression cross-cultural

Cyberbullying and Cross-Cultural Studies on Cyberbullying

Cyberbullying refers to the deliberate use of the Internet as a technological medium to intentionally and repeatedly threaten, harm, embarrass, or socially exclude a specific person or group of persons (Patchin & Hinduja, 2006). Other researchers such as Law, Shapka, Domene, and Gagne (2012) also suggested that bullying whether it be traditional face-to-face bullying or cyberbullying should include the component of a power differential between bully and victim. Due to the advancement of technology, bullying has taken on a different and a new form; it has moved from the physical to the virtual. The Internet has become a new and popular platform for social interactions, permitting adolescents to say and do things with some anonymity and with limited oversight by adult monitors. The dearth of social and contextual cues available in cyberspace, and anonymity on the Internet can result in individuals becoming less self-aware and more deindividuated; consequently, individuals bother much less about what others think about his or her behavior (Zimbardo, 1970). Scholars are generally in agreement that there is a greater likelihood of interpersonal misunderstandings occurring in computer-mediated interactions compared to face-to-face interactions (e.g., McKenna & Bargh, 2000).

Kowalski and Limber (2007) studied cyberbullying among 3,767 middle-school students in the United States. They found that 11% were victims of cyberbullying, 7% were cyberbullies/cybervictims, and 4% were cyberbullies at least once over the past couple of months. Approximately half of the victims reported not knowing the perpetrator’s identity. Bullying in cyberspace has extensive and potentially severe consequences such as school refusal, depressive symptoms, and suicide (Raskauskas & Stoltz, 2007). In addition, existing research suggests that characteristics of adolescents who engage in cyberbullying are consistent with the behavioral profiles of adolescents who engage in traditional face-to-face bullying (e.g., Ybarra & Mitchell, 2004). For example, evidence reveals that adolescents who engage in cyberbullying are more likely to concurrently engage in rule-breaking and aggressive behavior (Ybarra & Mitchell, 2007). Even though there has been an increase in the number of empirical studies on the topic of cyberbullying, the field is still in its infancy. Much remains to be studied and understood, specifically, the extent to which correlates of traditional face-to face bullying relate to cyberbullying.

Most published research on cyberbullying among middle and high school students provides reports on data arising from North America, Europe, and Australia (e.g., Calvete, Orue, Estevez, Villardon, & Padilla, 2010). Comparatively, there is a dearth of published studies using samples from Asia in general. Even fewer studies examine different cross-cultural samples using similar and comparable measures. It is important to investigate whether existing empirical evidence is generalizable by studying phenomena using different cross-cultural samples (Arnett, 2008). Given the rapid rise in the use of new media internationally and the possible negative consequences of cyberbullying, research efforts toward a more comprehensive understanding of cyberbullying and its correlates is not only warranted but critical because of the implications it has on the mental health and well-being of adolescents.

Proactive and Reactive Aggression, and Cyberbullying

The proactive–reactive aggression distinction was first introduced by Dodge and Coie (1987), and this typology focuses on the function (as opposed to form) of aggressive behavior.

Proactive aggression is defined as instrumental aggressive behavior that occurs without apparent provocation or instigation, and is motivated by anticipated rewards and outcomes resulting from aggressive acts. In contrast, reactive aggression is defined as a hostile, impulsive, and angry response that functions as retaliation to a real or perceived threat, provocation, or frustration. Similarly, there is a large body of literature on violence that refers to instrumental and hostile/expressive manifestations of aggression (Anderson & Bushman, 2002; Hickey, 2003), and they are distinguished by the expected goals and motivations of the perpetrator. Instrumental aggression is thought of as a premeditated means of obtaining a particular goal, while hostile/expressive aggression is thought of as unplanned, thoughtless, and primarily perpetrated in reaction to anger-inducing situation. Taken together, it is important to note that reactive/hostile/expressive aggression is performed in response to external social stimuli that are perceived to be aversive, and proactive/instrumental aggression is motivated by internal desires and goals (e.g., domination) that are perceived to be attractive.

Salmivalli, Lagerspetz, Bjorkqvist, Osterman, and Kaukiainen (1996) argued that the definition of school bullying implies that by its nature, bullying behavior is related more to proactive rather than reactive aggression. Therefore, bullying behavior, and by extension cyberbullying behavior, can be considered to be more closely aligned to proactive aggression. In developing this proactive aggression–bullying link further, Fontaine (2007) posited a conceptual framework of instrumental antisocial decision making and behavior in adolescents. Fontaine (2007) argued that evaluative decision making plays an important role in instrumental antisocial behavior such as bullying. Relative to their nonproactive aggressive peers, proactive aggressive adolescents have been shown to evaluate aggression as an acceptable and normative approach to attaining instrumental goals, and expect positive outcomes from their aggressive actions (Dodge & Coie, 1987; Dodge, Lochman, Harnish, Bates, & Pettit, 1997; Hubbard, Dodge, Cillessen, Coie, & Schwartz, 2001).

In a recent study, Law et al. (2012), through exploratory factor analysis, found three methods of cyberbullying: (a) aggressive messaging, which includes sending, receiving, taking part in, or replying to mean messages online; (b) developing hostile websites, which includes creating websites wither alone or with friends in order to embarrass or make fun of people; and (c) posting/commenting about embarrassing photos or videos, which includes viewing or participating in such activities. Interestingly, Law et al. found that for the outcome of hostile website development, only proactive items emerged statistically as important predictors. For the outcome of aggressive messaging, both reactive and proactive items were predictive, and for embarrassing photos, only reactive items were predictive. It is worthwhile to note that in line with the definition of cyberbullying and the criteria of intentionality, repetition, and a power differential, hostile website development probably has the strongest link to cyberbullying. In addition, with respect to the finding on hostile website development, these adolescents appear to have proactive, instrumental reasons for their behavior: They were prepared to invest time in creating a website that could possibly remain permanent, for the intention of hurting others.

Using a large sample of 1,431 school-going adolescents (682 boys and 726 girls) between 12 and 17 years of age from Spain, Calvete et al. (2010) examined cyberbullying in relation to other indicators of aggressive behavior such as proactive, reactive, direct, and indirect behaviors as well as the justification of violence. The authors used multiple regression analysis and this model tested explained 13% of the variance in cyberbullying behavior. Of the various indicators of aggressive behavior, Calvete et al. found that proactive aggression and belief justifying violence were the only two correlates associated with cyberbullying.

Taken together, research thus far indicates that there is sufficient empirical evidence to posit a link between proactive aggression and traditional face-to-face bullying. With the exception of Law et al. (2012) and Calvete et al. (2010), there are few empirical studies at present to substantiate a positive association between proactive aggression and cyberbullying although it should be stated that such an association is both logical and plausible. So far, to the best of our knowledge, there has not been a study examining the relationship between proactive and reactive aggression, and cyberbullying, using different adolescent samples across two cultures.

With respect to potential cultural differences pertaining to proactive and reactive aggression, there has been extremely limited research in this regard (Vitaro & Brendgen, 2005). For the limited studies that have attempted to examine such potential cultural differences, findings appear inconsistent. Little, Jones, Henrich, and Hawley (2003), for example, found that Turkish children reported more reactive aggression but not more proactive aggression than German children. Recent preliminary work using a large Singapore sample comprising more than 1,000 adolescents did not find statistically significant differences across ethnic groups on scores of proactive and reactive aggression (Kom, 2012).

With regard to bullying research, Spriggs, Iannotti, Nansel, and Haynie (2007) used a nationally representative sample of 6th to 10th graders from the United States, and found that parental communication, social isolation, and classmate relationships were similarly related to bullying across racial/ethnic groups. Cyberbullying is recognized to be a global phenomenon cutting across cultural groups and contexts (Li, Cross, & Smith, 2012). While rates of cyberbullying appear to be higher in the United States compared with that of Japan for example (Aoyama, Utsumi, & Hasegawa, 2012), the authors acknowledged that the reported prevalence rate in Japan could be an underestimate due to possible underreporting or translation issues. In addition, there could also be culture-specific forms of cyberbullying such as “net ijime” in Japan or “kuso” in China (Strohmeier, Aoyama, Gradinger, & Toda, 2013). Collectively, there appears to be mixed findings pertaining to possible cultural differences in proactive and reactive aggression, and in cyberbullying research.

The Present Study and Hypotheses

The purpose of the current study was to extend existing research by examining the differential associations between proactive and reactive aggression and cyberbullying across two cultures. Because of the close alignment posited between proactive aggression and cyberbullying, we expect that proactive aggression will be significantly associated with cyberbullying in both United States and Singapore samples over and above the contribution of reactive aggression. However, with proactive aggression controlled, we expect that reactive aggression will not be significantly associated with cyberbullying in both samples.

Method

Participants

The U.S. sample consisted of 425 adolescents (167 males, 253 females, 5 did not provide information on gender) from five middle schools in Kentucky. The age of the participants ranged from 11 to 16 years (M = 13.00, SD = 0.98). Self-reported ethnic identification for the sample was as follows: 90.6% of the participants were Caucasian, 2.4% African American, 1.6% Hispanic, 1.2% American Indian, 2.3% endorsed Others, and 1.9% did not provide information on ethnicity.

The Singapore sample consisted of 332 adolescents (181 males, 160 females, 5 did not provide information on gender) from four secondary schools in Singapore. The age of the participants ranged from 12 to 17 years (M = 14.20, SD = 1.21). Self-reported ethnic identification for the sample was as follows: 60.1% of the participants were Chinese, 7.9% were Indian, 18.9% were Malay, 13.1% endorsed Others.

Consent and Procedure

The present study was approved by both Institutional Review Boards at Eastern Kentucky University and Division of Psychology, Nanyang Technological University, Singapore. In addition, approval was obtained from the respective Education Ministries and schools in both countries. Participation was strictly voluntary and participants were explicitly informed that they could refuse or discontinue the study at any time without penalty. An identical questionnaire was administered in English to both the United States and Singapore participants in an organized classroom setting. No translation is needed as English is the main language of instruction for schools in Singapore.

Measures

Reactive–Proactive Aggression Questionnaire (RPQ)

The 23-item RPQ (Raine, Dodge, Loeber, Reynolds, & Loeber, 2006) is a measure that assessed reactive and proactive aggression in children and adolescents. The RPQ was initially developed and validated for use with U.S. adolescents, and has strong psychometric properties and validity evidence (Raine et al., 2006). Subsequently, it has been used in research with Singapore adolescents (e.g., Pang, Ang, Kom, Tan, & Chiang, 2013; Seah & Ang, 2008) and it has been demonstrated that RPQ can yield valid scores for this population. Adolescents rated each item as 0 (never), 1 (sometimes), or 2 (often) for frequency of occurrence. In addition to a total aggression score, the scale also yielded two subscale scores, reactive aggression (11 items; for example, “Reacted angrily when provoked by others”) and proactive aggression (12 items; for example, “Hurt others to win a game”). For the purposes of this study, only the reactive and proactive subscales were used. Adequate Cronbach alpha reliability estimates were obtained for reactive aggression (United States: α = .82; Singapore: α = .75) and proactive aggression (United States: α = .80; Singapore: α = .73) subscale scores.

Cyberbullying Questionnaire

A nine-item cyberbullying questionnaire was used to measure cyberbullying behavior in this study (Ang & Goh, 2010). This included items on broadcasting, online actions targeted/directed at the person, and deception (e.g., “I made fun of someone by sending/posting stories, jokes, or pictures about him/her”). Adolescents could indicate on a 5-point scale whether they engaged in these cyberbullying acts “once or twice this school term,” “a few times this school term,” “about once every week,” “about a few times every week,” or if they have “never” bullied others. A total cyberbullying score can be calculated. All items measured the prevalence and frequency of cyberbullying in the current school term, with higher scores indicating greater prevalence and frequency of such acts. The questionnaire was administered toward the end of the school term. In addition, adolescents who did not report any cyberbullying acts were classified as “non-bullies,” those who reported engaging in cyberbullying acts a couple to a few times in the school term were classified as “infrequent bullies,” while those who reported weekly or more cyberbullying acts were classified as “frequent bullies.” Good Cronbach alpha reliability estimates were obtained for both the U.S. sample (α = .91) and the Singapore sample (α = .84).

Ang and Goh (2010) developed and validated the cyberbullying questionnaire with both exploratory and confirmatory factor analysis. In addition, multigroup confirmatory factor analysis was also used to test for invariance across gender. Results of these analyses supported a one-factor, nine-item model. Invariance analyses suggested equivalence of form, factor loadings, and factor variance of this one-factor cyberbullying questionnaire across gender. Card (2013) aptly observed that to date, many cyberbullying researchers have used measures with unknown psychometric properties and recommends that confirmatory factor analysis be used to evaluate a measure’s construct validity. Consequently, the cyberbullying questionnaire (Ang & Goh, 2010) was used in the present study because of its demonstrated psychometric properties.

Data Analytic Plan

First, we tested whether nationality was a moderator of two relationships, between proactive aggression and cyberbullying, and between reactive aggression and cyberbullying. This was performed via two hierarchical multiple regression analyses with cyberbullying as the dependent variable. Both proactive and reactive aggression are continuous variables so these were standardized so they each have a mean of 0 and a standard deviation of 1, prior to creating and entering the respective interaction terms into the equations. The independent variable proactive aggression (and reactive aggression, in a separate analysis) was entered in Step 1 of the analysis. Nationality was a dichotomous variable with values of 1 and 0 representing United States and Singapore, respectively, and this was entered in Step 2 of the analysis. The relevant interaction term was entered in Step 3 of the analysis. The testing, probing, and interpretation of interaction effects followed methods outlined by Aiken and West (1991).

Next, a total of six hierarchical multiple regression analyses were conducted. We investigated whether proactive aggression can account for cyberbullying, controlling for reactive aggression, and whether reactive aggression can account for cyberbullying, controlling for proactive aggression, respectively, in both the United States and Singapore adolescent samples, as well as the combined sample. An additional eight similar supplementary analyses by gender were conducted for both the United States and Singapore samples.

Results

Table 1 contains means, standard deviations, and correlations for all the study variables. Both forms of aggression were statistically and significantly correlated with cyberbullying in the U.S. sample (r = .61 for proactive aggression and cyberbullying; r = .46 for reactive aggression and cyberbullying). Likewise, both proactive aggression (r = .58) and reactive aggression (r = .31) were statistically and significantly related to cyberbullying. The percentages of adolescents in the United States and Singapore involved in cyberbullying were reported to be 17.9% and 16.4%, respectively. For U.S. adolescents, 16.8% were classified as infrequent bullies and 1.1% were classified as frequent bullies. For Singapore adolescents, 15.1% were classified as infrequent bullies and 1.3% were classified as frequent bullies.

Table 1.

Means, Standard Deviations, and Correlations of Study Variables.

	United States		Singapore
Variable	M	SD	M	SD	1	2	3
Proactive aggression	2.62	3.96	2.45	3.32	—	.66**	.61**
Reactive aggression	9.22	4.58	7.96	3.88	.59**	—	.46**
Cyberbullying	11.18	4.69	11.45	4.13	.58**	.31**	—

Note. Correlations for the U.S. sample are printed above the diagonal and correlations for the Singapore sample are printed below the diagonal. The effect sizes associated with the correlations, computed using Cohen’s d, ranged from d = 0.65 to d = 1.76.

p < .01.

Results of the moderator analyses suggested that nationality was not a moderator of the relationship between proactive aggression and cyberbullying, ΔR ² = .00, ΔF(1, 736) = 0.01, ns. The two-way interaction (Proactive Aggression × Nationality) was not statistically significant, β = .01, ns. Likewise, nationality did not moderate the relationship between reactive aggression and cyberbullying, ΔR ² = .01, ΔF(1, 732) = 3.97, ns. The two-way interaction (Reactive Aggression × Nationality) was not statistically significant, β = .11, ns.

For both the United States and Singapore adolescent samples, cyberbullying was regressed onto proactive aggression, controlling for reactive aggression, and reactive aggression, controlling for proactive aggression, respectively. In each of these analyses, the variable to be controlled was entered in the first block. The results are presented in Table 2. In the first pair of analyses conducted using the U.S. sample, proactive aggression was significantly associated with cyberbullying, controlling for reactive aggression, ΔR ² = .17, ΔF(1, 394) = 106.11, p < .05, while reactive aggression was not significantly associated with cyberbullying after controlling for proactive aggression, ΔR ² = .01, ΔF(1, 394) = 3.67, ns. Proactive aggression (β = .54, p < .01) rather than reactive aggression (β = .10, ns) was responsible for the additional 17% variance explained for cyberbullying over and above reactive aggression. Likewise, in the second pair of analyses conducted using the Singapore sample, proactive aggression was significantly associated with cyberbullying, controlling for reactive aggression, ΔR ² = .24, ΔF(1, 329) = 117.58, p < .05, while reactive aggression was not significantly associated with cyberbullying after controlling for proactive aggression, ΔR ² = .00, ΔF(1, 329) = 0.73, ns. Proactive aggression (β = .60, p < .01) rather than reactive aggression (β = −.05, ns) was responsible for the additional 24% variance explained for cyberbullying over and above reactive aggression. For the combined sample, a similar pattern of results emerged. Proactive aggression was significantly associated with cyberbullying, controlling for reactive aggression, ΔR² = .20, ΔF(1, 726) = 226.66, p < .01, while reactive aggression was not significantly associated with cyberbullying after controlling for proactive aggression, ΔR² = .00, ΔF(1, 726) = 0.59, ns. Proactive aggression (β = .58, p < .01) rather than reactive aggression (β = .03, ns) was responsible for the additional 20% variance explained for cyberbullying over and above reactive aggression.

Table 2.

Predicting Cyberbullying From Proactive and Reactive Aggression.

Variable	β	R ²	ΔR ²	F Change
U.S. sample-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	.10	.21	.21	102.64**
Block 2: Proactive	.54**	.38	.17	106.11**
Reactive with proactive controlled
Block 1: Proactive	.54**	.37	.37	230.84**
Block 2: Reactive	.10	.38	.01	3.67
Singapore sample-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	−.05	.09	.09	34.31**
Block 2: Proactive	.60**	.33	.24	117.58**
Reactive with proactive controlled
Block 1: Proactive	.60	.33	.33	163.43**
Block 2: Reactive	−.05	.33	.00	0.73
Combined sample-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	.03	.15	.15	129.63**
Block 2: Proactive	.58**	.35	.29	226.66**
Reactive with proactive controlled
Block 1: Proactive	.58**	.35	.35	396.17**
Block 2: Reactive	.03	.35	.00	0.59
U.S. sample (Boys)-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	.05	.22	.22	43.11**
Block 2: Proactive	.59**	.39	.17	41.36**
Reactive with proactive controlled
Block 1: Proactive	.59**	.39	.39	96.09**
Block 2: Reactive	.05	.39	.00	0.28
U.S. sample (Girls)-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	.09	.20	.20	57.62**
Block 2: Proactive	.55**	.38	.18	68.66**
Reactive with proactive controlled
Block 1: Proactive	.55**	.38	.38	140.40**
Block 2: Reactive	.09	.38	.00	1.84
Singapore sample (boys)-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	−.03	.10	.10	18.67**
Block 2: Proactive	.57**	.30	.20	45.94**
Reactive with proactive controlled
Block 1: Proactive	.57**	.30	.30	69.91**
Block 2: Reactive	−.03	.30	.00	0.15
Singapore sample (girls)-DV: Cyberbullying
Proactive with reactive controlled
Block 1: Reactive	−.05	.02	.02	3.45
Block 2: Proactive	.39**	.13	.11	20.38**
Reactive with proactive controlled
Block 1: Proactive	.39**	.13	.13	24.06**
Block 2: Reactive	−.05	.13	.00	0.29

Note. DV = Dependent Variable.

p <.05. *p < .01.

Identical supplementary statistical analyses by gender were also performed for both samples. Likewise, similar to the results presented for the United States, Singapore, and combined adolescent samples, findings also suggest that proactive aggression also significantly accounted for variance in cyberbullying above and beyond reactive aggression, for both boys and girls in the United States and Singapore samples.

Discussion

This study sought to extend research by investigating the differential associations between proactive and reactive aggression, and cyberbullying across two cultures. Findings indicated that both proactive and reactive aggression were correlated with cyberbullying across cultures, and that percentages of adolescents involved in cyberbullying across the United States and Singapore were relatively comparable. These rates of 17.9% and 16.4% for United States and Singapore respectively fell within the range of prevalence rates obtained across multiple studies as reported by Calvete et al. (2010). Across 13 studies of cyberbullying, Calvete et al. reported that prevalence rates ranged from 9.4% to 35.7%.

In the present study, results indicated that nationality did not moderate the relationship between proactive aggression and cyberbullying, and between reactive aggression and cyberbullying. As expected, we found proactive aggression to be positively associated with cyberbullying, after controlling for reactive aggression, in both the United States and Singapore, and the combined samples. Conversely, as expected, the relation between reactive aggression and cyberbullying was not found to be significant after controlling for proactive aggression across the United States, Singapore, and combined samples. This means that while proactive aggression could uniquely contribute additional variance in predicting the scores in cyberbullying across both the United States and Singapore adolescents, reactive aggression could not.

These present findings extend previous research. Law et al. (2012) found that hostile website development has the strongest connection with cyberbullying; furthermore, the motivation for such hostile website development is proactive (not reactive) in nature. In a different study, Calvete et al. (2010) found a unique link between proactive aggression and cyberbullying in a large sample of Spanish adolescents. In both studies, the link between proactive aggression and cyberbullying has been shown. The present study extended previous work by showing that this proactive aggression–cyberbullying link is fairly robust across two different cultural samples. In addition, in the present research, we controlled for one subtype of aggression while testing for the effects of the other subtype of aggression on cyberbullying. This way, our results show a unique association between proactive aggression and cyberbullying, and it also shows that reactive aggression is not associated with cyberbullying in both of these samples.

There are several issues that warrant further discussion with respect to these present findings. First, the findings from this study provide empirical support to show that the close alignment previously found between proactive aggression and traditional face-to-face bullying (e.g., Fontaine, 2007, Salmivalli et al., 1996) extends to a cyberbullying context across cultures. These findings reinforce the notion that the behavioral profiles and characteristics of offline and online bullies share many similarities (Ybarra & Mitchell, 2004, 2007). This suggests that with the rise of the Internet and the widespread availability and popularity of multiple online platforms for interaction and social networking, adolescents more inclined toward bullying now have additional platforms to carry out these acts. Risk correlates (such as proactive aggression) associated with traditional face-to-face bullying could apply to cyberbullying. Taken together, this suggests that proactive aggression is a possible risk factor for not only bullying but cyberbullying as well.

Second, there has been ongoing debate about the distinctiveness of the proactive–reactive aggression typology. Some researchers argue that both these constructs are not distinct because of the high degree of statistical overlap between proactive and reactive aggression (Dodge, Coie, Pettit, & Price, 1990), and that most aggressive individuals display both proactive and reactive aggression (Brendgen, Vitaro, Boivin, Dionne, & Perusse, 2006). On the other hand, exploratory and confirmatory factor analyses have consistently yielded two factors in line with the proactive–reactive typology (Little et al., 2003). In addition, researchers have found that proactive and reactive aggressions have different correlates providing support for the validity of the proactive–reactive distinction (Vitaro, Brendgen, & Tremblay, 2002). While the present findings are only preliminary and are not conclusive, it does provide some preliminary support showing that proactive and reactive aggression are distinct as they have different correlates; only proactive aggression was associated with cyberbullying scores after controlling for reactive aggression.

Third, the pattern of findings in the present study was similar for both the United States and Singapore adolescent samples, and the combined sample. This extends previous research largely conducted with North American, European, or Australian samples, and contributes toward cross-cultural work using similar and comparable measures across different samples. Arnett (2008) argued convincingly that there is a need for a broader psychology; while knowledge obtained from studies conducted with largely Caucasian samples has helped further our understanding of the phenomena of cyberbullying, it is equally important to contribute to the generalizability of empirical evidence and the process of theory building by studying similar phenomena in ethnically diverse, non-Caucasian samples. How might researchers make sense of these findings since no cultural differences were found across both the United States and Singapore adolescent samples? Cinnirella and Green’s (2007) innovative study could provide some insights. Using an experimental design, Cinnirella and Green studied the effects of communication type (face-to-face and computer-mediated communication) and culture (participants from individualist versus collectivist cultures) on social conformity. The authors found an interaction between communication type and culture in which cultural differences were shown only in the face-to-face condition and absent from the computer-mediated condition. Therefore, Cinnirella and Green’s experimental findings suggest that cultural differences in social behavior may indeed be negated or operate differently when communication is computer-mediated. Taken together, though inconclusive, it may be possible that the nature of the Internet might be to flatten certain cross-cultural differences.

Finally, these findings have implications with reference to cyberbullying prevention and intervention work with youths. In our study, we found a unique association between proactive aggression (and not reactive aggression) and cyberbullying across both the United States and Singapore samples. Previous research has also shown that proactive aggressive adolescents view bullying as an acceptable way to attain instrumental goals, and expect positive outcomes from their actions (e.g., Dodge et al., 1997; Hubbard et al., 2001). Prevention and intervention programs targeted at proactive aggressive adolescents could adopt a two-pronged approach: by changing mind sets and by understanding and adopting a set of rules for Internet etiquette. Changing mind sets about the acceptability of aggression is not easy, but research has shown that such beliefs and cognitions can be changed (Guerra & Slaby, 1990). In such programs, adolescents learn that bullying and cyberbullying are not legitimate responses, and that such actions hurt the victims and victims do not deserve to be hurt. Shea (1994) suggested attempting to change norms governing computer-mediated communication, through the promotion of Netiquette, a set of guidelines for Internet etiquette. Netiquette should include the explicit teaching that beliefs supporting the use of aggression are not acceptable and not justifiable. At the same time, Netiquette should establish appropriate prosocial norms in online communities.

Some limitations of this study warrant comment. Our study is a cross-sectional study and no definitive claims can be made regarding directionality and causality. We found that proactive aggression could predict cyberbullying above and beyond the variance contributed by reactive aggression. It could be possible that cyberbullying is antecedent to and contributed to proactive aggression, but such issues of directionality can only be examined in a study utilizing a longitudinal design. However, it would be equally important to point out that regardless of issues of directionality, this finding of a close link between proactive (as opposed to reactive) aggression and cyberbullying across both the United States and Singapore adolescent samples is noteworthy and should be further studied. In addition, the data for this study were based on adolescents’ self-reports that may underestimate levels of aggression and this may occur due to social desirability. It should be noted that although the adolescents from both samples completed the questionnaires anonymously, the possible and potential influence of social desirability cannot be completely ruled out. Future studies in the area of cyberbullying could consider peer reports for example, in addition to self-reports. Incorporating a multi-informat, multi-method strategy would also avoid problems associated with shared method variance. Even though the sample size was adequate in this study, it would be helpful to collect more data for future work so as to facilitate the investigation of other correlates closely associated with cyberbullying.

These limitations notwithstanding, the current study extended previous research by investigating differential correlates of cyberbullying across the United States and Singapore adolescent, school-going samples. Findings suggested that proactive rather than reactive aggression contributed unique variance to cyberbullying. These findings contribute to and build upon an existing body of theoretical and empirical work in the areas of aggression as well as bullying and cyberbullying. Furthermore, these results can also contribute toward cyberbullying prevention and intervention work with youths.

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) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Rebecca P. Ang, PhD, is an associate professor at Psychological Studies Academic Group, National Institute of Education, Nanyang Technological University, Singapore. Her research interests include developmental psychopathology especially aggression, bullying, and cyberbullying behavior. She is also interested in prevention and intervention work with children and adolescents at risk for externalizing behavior problems.

Vivien S. Huan, PhD, is an associate professor at Psychological Studies Academic Group, National Institute of Education, Nanyang Technological University, Singapore. Her research interests include juvenile delinquency, deviancy, serious and violent youth offenders, managing aggressive and disruptive students, and conflict resolution.

Dan Florell, PhD, is an assistant professor at Eastern Kentucky University, USA. He has a range of research interests centering around the adolescent population and their use of technology, with a specific focus on bullying and cyberbullying behaviors.

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