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Longitudinal predictors of cyberbullying perpetration: Evidence from Korean middle school students
Personality and Individual Differences 89 (2016) 172–176
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Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Longitudinal predictors of cyberbullying perpetration: Evidence from Korean middle school students Sukkyung You a, Sun Ah Lim b,⁎ a b
Hankuk University of Foreign Studies, Republic of Korea Sookmyung Women's University, Republic of Korea
a r t i c l e
i n f o
Article history: Received 30 June 2015 Received in revised form 5 October 2015 Accepted 6 October 2015 Available online 22 October 2015 Keywords: Cyberbullying Perpetration Longitudinal study Adolescent Korean
a b s t r a c t Cyberbullying perpetration is a recent phenomenon that has become an increasingly serious social problem in Korea. This study examined the long-term effects of individual and psychological factors on cyberbullying perpetration from a sample of 3449 middle school students. Logistic regression analyses were employed in order to understand how various factors influence youth cyberbullying perpetration experiences. The findings indicated that longer use of the Internet, more previous bullying and victim experiences, a higher aggression level, and lack of self-control are associated with more cyberbullying perpetration. Implications and future directions are discussed. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction The widespread use of smartphones and the Internet has allowed adolescents to readily access cyberspace. These technological advances have its benefits in providing a means for promoting people's freedom of expression and exchange of information. However, there is also a drawback in the form of cyberbullying, which can include invasion of private information, insulting language use, multiple types of threats, rumors and abuse (Lee & Lee, 2013). Particularly in Korea, the media have reported secondary harm from cyberbullying on adolescents such as depression, suicidal impulse, and suicide. Consequently, cyberbullying is now commonly acknowledged as a social problem among adolescents, suggesting the necessity of related research and preventions (Nam & Kweon, 2013). According to Korea Internet and Security Agency (KISA), 76% of Korean teenagers have experienced cyberbullying, indicating the seriousness of cyberbullying in Korea (KISA, 2012). There are several possible reasons for the prevalence of cyberbullying among Korean teenagers. First, as an environmental factor, growth and widespread use of internet and social networks have been intensified. Along with the diffusion of the Internet, a considerable part of people's social behavior is now taking place in cyberspace as a result of personal media including smartphones, and SNS (Social Networking Service) services such as Twitter and Facebook. Most Korean teenagers following ⁎ Corresponding author at: Graduate School of Education, Sookmyung Women's University, Cheongpa-ro 47-gil 100, Yongsan-gu, Seoul 140-742, Republic of Korea. E-mail address: [email protected] (S.A. Lim).
http://dx.doi.org/10.1016/j.paid.2015.10.019 0191-8869/© 2015 Elsevier Ltd. All rights reserved.
this trend have access to smartphones and the Internet, through which they are easily exposed to cyberbullying (Lee & Lee, 2013). Second, cyberspace provides a certain level of anonymity, non-directness, and virtuality. Anonymity frees adolescents from normative society, conscience, morality, and ethics (Belsey, 2006). Moreover, bullying can be bolder when it happens online; it is a forum where individuals can express their hostile attitude without revealing themselves (Belsey, 2006). Also, the virtual reality of cyberspace deceives the perpetrators into think that they are not doing any physical harm to the victims. Third, the underlying causes of behaviors and characteristics of perpetrators may be related to the stages of development. Gruber and Yurgelun-Todd (2006) found that the brains of adolescents often fail to use consideration and self-control in their decision-making process. One of the most serious characteristics that distinguishes cyberbullying from other types of offline bullying is that bullying through mobile or online media is often done in secret and thus harder for parents or teachers to control (Lee, 2012). Also, the anonymity of cyberspace allows for a certain level of intangible violence or bullying and therefore, capable of harming victims in significant ways. Extant research identified factors for cyberbullying and specifically demographic and behavioral factors have been extensively studied. Some of these factors include family variables such as non-traditional family, school variables such as school violence (Beran & Li, 2007; Hinduja & Patchin, 2007) and previous bullying and victim experiences in school (Hinduja & Patchin, 2012; Kowalski & Limber, 2007; Lee & Jun, 2011), and computer-related variables such as use of the Internet and IT devices (Lee & Kim, 2009; Lee, 2011a; Slonje & Smith, 2008; Snell &
S. You, S.A. Lim / Personality and Individual Differences 89 (2016) 172–176
Englander, 2010; Ybarra & Mitchell, 2004). Research has also identified psychological factors related to cyberbullying including low self-esteem (Lee, 2005; Limber, Kowalski, & Agatston, 2008), low self-control (Lee, 2011b; Lee & Kim, 2009), aggression (Agatson, Kowalski, & Limber, 2008; Ybarra & Mitchell, 2007), emotional regulation (Baroncelli & Ciucci, 2014; Kim, Choi, & Hong, 2007), and sociality (Burton, Florell, & Wygant, 2013; Jun & Lee, 2010). 1.1. The present study Research on cyberbullying is still at the beginning stage and most studies are mere reports of prevalence rates and concurrent relationships among factors with cross-sectional data (Hemphill et al., 2012). In Korea, the research on cyberbullying is also a newly emerging topic of interest (Oh, 2010). The current study addresses the gap in the literature by examining the long-term effects of both individual background and psychological variables on later cyberbullying perpetration. The study also examined the transactional pattern of previous offline bullying perpetration and victimization on subsequent cyberbullying using longitudinal data from a sample of Korean students. Current knowledge about those who are both offline and cyberbullying perpetrators is largely based on the correlational relationships using one time point analysis. Based on previous studies, we hypothesized that previous offline bullying experiences (both perpetration and victimization) lead to cyberbullying perpetration. After controlling for individual background factors and previous offline bullying experiences, results indicate that individual psychological factors affect cyberbullying perpetration. That is, adolescents with low self-esteem, low self-control, aggression, low emotional regulation, and a low level of sociality will engage in more cyberbullying perpetration. 2. Method 2.1. Participants The data used in the present study comes from the Korean Children and Youth Panel Survey, a six-year longitudinal study of students' school life experiences conducted by the National Youth Policy Institute, with funding from the national government. Participants were chosen using a stratified multi-stage cluster sampling. The institute stratified 16 administrative districts; then they randomly selected schools in each district in line with the population rate based on proportionate probability sampling, and randomly selected one class per school. This survey was initially completed in 2010, when all of the students were in their second year of middle school. At that time, the mean age of the participants was 13 years. Students then re-took the survey every last quarter of the year until 2011. The analyses in the current study are based only on the first two years of the data (i.e., the second year of middle school to the third year of middle school). Middle school students were selected on the basis that, as a group, they showed the greatest percentage of students with both offline and cyberspace bullying experiences. We examined the longitudinal effects of independent variables (Time point 1) on the next year's cyberbullying aggressor experiences (Time point 2). In the final sample, there were a total of 3449 participants, comprised of 1725 (50%) male and 1724 (50%) female students (range of age: 12 to 14 years old, mean age: 13.78 years, SD: .41 years). The largest portion (30.3%) of the sample reported their father's educational background as having bachelor's degrees, followed by high school diplomas (43.8%), middle school diplomas (7.9%), and post-bachelor's degrees (5.6%). Mothers were slightly less educated with the sample reporting their mother's educational background as having high school diplomas (57.8%), bachelor's degrees (18.3%), middle school diplomas (12.7%), and post-bachelor's degrees (1.5%). Most participants were comprised of middle class (48.5%), followed by upper (31.7%), and lower (16.1%) classes in terms of socio-economic
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level. Family income ranged from 500 dollars to 35,000 dollars per month (mean = 3000 dollars, SD = 2160 dollars). 2.2. Measures 2.2.1. Cyberbullying perpetration Six questions (Cronbach's α = .89) were selected to assess students' cyberbullying perpetration experiences from the Cyber Bullying Inventory (Erdur-Baker & Kavşut, 2007) (e.g., “I send threatening or hurtful comments through e-mail”). The Cyber Bullying Inventory was revised by the National Youth Policy Institute to adapt to Korean adolescents' cyberbullying. 2.2.2. Previous offline bullying/victim experience Eleven items were used to measure students' experiences of or involvement in offline bullying and victimization over the past year (Olweus, 1993). Specifically, five questions assessed students' victim experiences (Cronbach's α = .73). There were six questions designed to capture the individual's involvement with bullying as a perpetrator (Cronbach's α = .73). 2.2.3. Psychological predictors All of the individual psychological variables were measured by a 5-point Likert scale (1 = Not at all to 5 = Always true). Specifically, five items (Cronbach's α = .73) were used for self-esteem (Rosenberg, 1965) (e.g., “I believe that I am a person with good character.”), six items (Cronbach's α = .70) were used for aggression (Weinberger, 1991) (e.g., “I can use physical violence when I become extremely irritated.”), six items (Cronbach's α = .76) were used for lack of self-control (Rohrbeck, Azar, & Wagner, 1991) (e.g., “I can't stay calm when I get angry.”), three items (Cronbach's α = .73) were used for emotional regulation (Goleman, 1995) (e.g., “I can deal with being told no.”), and three items (Cronbach's α = .71) were used for sociality (Armsden & Greenberg, 1989) (e.g., “I get along well with my friends at school.”). Other studies of Korean middle school students also found significant associations with the abovementioned psychological measures and online game addiction (Hong, You, Kim, & No, 2014), self-efficacy (Seon, Hwang, & Jung, 2001), school engagement (Byun & Kim, 2008), and academic stress (Kim & Kim, 2009) providing evidence supporting the validity of the instrument for use with this population. 2.3. Analysis A series of logistic regression models were developed and tested using Mplus (Muthen & Muthen, 2006). The first model contained individual background variables; the second model included previous offline bullying and victim experiences; the third model included psychological variables. Logistic regression models estimated the effect size and statistical significance of a myriad of predictors simultaneously. We determined the unique contribution of each variable in the model while controlling for the effects of the other variables included. 2.4. Missing data The dataset contained missing responses. In order to obtain unbiased estimates of the parameters of interest, despite the incompleteness of the data, this study employed full-information maximum-likelihood estimation. 3. Results 3.1. Descriptive statistics The mean and standard deviation of the variables in this study are illustrated in Table 1. Students in the cyberbullying group showed significantly higher levels of daily Internet use, previous offline bullying
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Table 1 Group statistics for the variables used in the analysis.
Individual background variables Father's academic ability Mother's academic ability Family monthly income Internet use time on an average day Mobile phone use time on an average day Achievement Non-traditional family (both parents = 1) Gender (male = 1)a Bullying & victim experience Bullying experiencea Victim experiencea Psychological factors Lack of self-controla Self-esteema Aggressiona Emotional regulation Sociality
Table 2 Effects of individual background and psychological variables on cyberbullying perpetration.
None (N = 2702)
Cyberbullying (N = 1228)
Mean
SD
Mean
SD
Predictors
4.75 4.23 302.17 2.38 1.72 3.14 23.7% 26.3%
1.30 1.10 185.80 1.57 2.93 0.83
4.64 4.18 291.75 2.75 1.73 3.07 76.3% 73.7%
1.30 1.08 173.72 1.69 2.92 0.86
0.13 0.04
0.33 0.20
0.19 0.07
0.39 0.25
2.66 3.28 2.72 3.33 3.78
0.67 0.61 0.71 0.80 0.86
2.85 3.21 2.95 3.33 3.88
0.69 0.67 0.71 0.82 0.82
Step 1: Individual background variables Father's academic ability Mother's academic ability Family monthly income Computer use time on an average day Mobile phone use time on an average day Achievement Non traditional family Gender (male = 1) Step 2: Bullying & victim experience Bullying experience Victim experience Step 3: Psychological factors Lack of self-control Self-esteem Aggression Emotional regulation Sociality
Note. a Group difference at p b .05.
and victim experiences, lack of self-control, and aggression, compared to students who did not report cyberbullying. According to the guidelines of severe non-normality (i.e., skew N 2; kurtosis N 7) proposed by West, Finch, and Curran (1995), the normality assumption for all the variables was well met. Intercorrelations among study variables were generally moderate and below .40. Given that no correlations approached .85, bivariate associations did not indicate problems with multicollinearity (Tabachnick & Fidell, 2001). In addition, collinearity diagnostics indicated no presence of a multicollinearity problem with the current data set. 3.2. Logistic regression analyses The results of the hierarchical logistic regressions of cyberbullying outcomes are shown in Table 2. Initially, the individual background variables were entered into the model. Father's academic ability and Internet use time had significant effects on cyberbullying perpetration. After controlling for differences in individual backgrounds, prior offline bullying and victim experiences were significantly associated with cyberbullying perpetration. The final model confirmed that lack of self-control and aggression had exerted positive effects on the membership of the cyberbullying group. 4. Discussion Cyberbullying is a relatively new form of bullying and the problem is raising many concerns. There are few studies that provide longitudinal data on the predictors of cyberbullying perpetration. This study explored longitudinal factors that influence the development of cyberbullying using data from the Korean Youth Panel Survey on a nationwide sample of Korean adolescents. Specifically, the current study examined prospective associations between each of the grade 8 individual background and psychological factors and grade 9 cyberbullying perpetration. Factors affecting cyberbullying perpetration were Internet use, previous offline bullying and victim experiences in school, lack of selfcontrol, and aggression level. That is, a larger amount of time spent using the Internet, more previous offline bullying and victim experiences in school, lack of self-control, and a higher level of aggression were associated with subsequent cyberbullying. Consistent with previous research, extensive use of Internet emerged as a risk factor for cyberspace perpetration. Adolescents
Cyberbullying
Test χ2 test Model comparison: Δχ2 Cox & Snell's R2
β
Wald's χ2
SE
OR
.85⁎⁎ 1.15 1.00 1.12⁎⁎
−.16 .14 .00 .12 .00 −.08 −.24 .26
.07 .08 .00 .04 .02 .08 .26 .12
5.91 3.56 1.27 9.85 .03 1.01 .85 4.58
.54 .60
.16 .25
11.93 5.91
1.72⁎⁎⁎ 1.83⁎⁎
.32 .06 .27 −.02 .06
.11 .11 .10 .08 .08
8.76 .25 7.11 .05 .60
1.37⁎⁎ 1.05 1.31⁎⁎ .98 1.06
1.00 .92 .78 1.30⁎
Step 1 Step 2 Step 3 28.67⁎⁎⁎ 50.59⁎⁎⁎ 82.64⁎⁎⁎ 21.92⁎⁎⁎ 32.05⁎⁎⁎ .017 .030 .049
Note. OR = odds ratio. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
exposed to the Internet for a longer duration are more likely to have chances for online delinquency and thus are more likely to commit online delinquency. Lee and Ahn (2005) showed that more than 3 h of daily computer use led to a significantly higher level of online delinquency compared to less computer use. In his research on high school students, Lee (2004) studied the relationship between Internet addiction and cyber-delinquency behaviors such as abusive language use, spread of false rumor, and cyber-sexual harassment. He found that the duration of Internet use had a significant influence on cyber-delinquency including verbal violence. These results suggest the need to consider self-control or monitoring by others on Internet use in order to prevent cyberbullying. With advances in technology and media, Internet use has become an integral part of adolescents' daily activities. Therefore, parents can take on more of a leading role in talking to and supervising their children to set rules of computer and mobile phone use as a way of monitoring the content of their activities. Prior traditional bullying experiences in school were also found to be a predictor of cyberbullying. Perpetrators of offline school violence may use mobile phones and the Internet to bully and dominate the victims. In these cases, cyberbullying can be seen as an extended version of school violence. Research has indicated that adolescents who have done harm to others in real life within the past six months are 2.5 times more likely to attack others online; similarly, those who have been victimized in school violence within the past six months are 2.5 times more likely to be victimized in cyberbullying (Hinduja & Patchin, 2012). Previous offline victim experiences in school were also found to be a predictor of cyberbullying. In Kowalski and Limber's (2007) study, most teenagers who admitted to have committed cyberbullying referred to revenge as the reason for their behavior. The victims of school violence successfully resorted to email, instant message, and text message as ways of carrying out their revenge. These previous victims of school violence have potential resentment and aggression toward the perpetrators in their thoughts on revenge, which factors into their intent to use violence on online strangers instead of the perpetrators themselves
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(Hay & Evans, 2006). This is also supported by study results that found that 22.5% of the participants chose revenge as the reason for cyberbullying perpetration (Hinduja & Patchin, 2007; Kowalski & Limber, 2007). As cyberbullying triggers less anxiety than face-to-face violence, the victims of school violence are more likely to rely on it as their means of self-defense. Taking revenge is thus a self-protecting strategy for the victims from the embarrassment, sadness, and helplessness (Beattie, 2005). Such an undesirable coping strategy yields other victims, thus forming a vicious circle of perpetrators and victims. In order to break this circle, schools need to take action to help relieve negative experiences of school violence. For example, within the counseling, treatment, or intervention setting for perpetrators of cyberbullying, it is necessary to examine whether he or she has experiences of being victimized in school violence. In addition, it is necessary to determine whether cyberbullying is carried out as a retaliation behavior (target of retaliation and the degree of cyberbullying), and prepare a follow-up intervention measure. Among psychological factors, lack of self-control and high level of aggression were linked to more cyberbullying. Cyberbullying can be an initial behavioral symptom of individual characteristics such as low self-control. It is difficult for impulsive adolescents to control themselves when presented with online bullying opportunities because they lack self-control and thus respond immediately to their emotions, attitudes, and behavior instead of reasonably considering possible consequences (Gottfredson & Hirschi, 1990). Lee (2011a) argued that adolescents with higher self-control restrain their desires and impulses, follow norms and values the society and their parents demand, and engage in less deviant behaviors, whereas adolescents with lack of self-control may cause problems or bully peers on cyberspace because they are ostracized and left out by peers and miss out the opportunity to acquire positive social skills from peers due to their impulsivity. The present study results confirm previous research that suggests that low self-esteem affects cyberbullying and that impulsive teenagers are less able to restrain themselves when given opportunities to bully other people (Gottfredson & Hirschi, 1990; Felson & Staff, 2006). Park (2012) also investigated the effects of adolescent self-control on cyberbullying perpetration and found that it is the foremost predictor of cyberbullying. The inability to control thoughts and behaviors may factor into particular types of adolescent attitudes and behaviors such as aggression. Previous research has noted aggression as one of the major emotions related to school violence and indicated that it is an important predictor of traditional bullying (Bierman, 2004; Kim, 2013). The current study's findings are similar to previous studies (Lee & Oh, 2012; Nam & Kweon, 2013) reporting that aggression is a typical characteristic of cyberbullying perpetrators. Additionally, Ybarra and Mitchell (2007) also showed that adolescent perpetrators of cyberbullying tend to have aggression issues. The effects of aggression on cyberbullying should not only be construed as aggressiveness but it should also be noted that aggression behind perpetration stems from a strong desire for power and domination, self-defense, and rivalry (Olweus, 1994). Aggressive adolescents have a strong desire to show off their power by dominating other students with force and intimidation. Moreover, aggressive students show the characteristics of distortions and deficiencies in not only behavioral characteristics but also cognitive functioning, as shown by the mistaken belief that attacking others will increase their self-esteem (Choi & Chung, 2003). The findings of the current study should be interpreted with consideration of the study limitations. The study relied on students' selfreported data. Although it is arguably a unique source of information regarding cyberspace bullying, it is possible that the levels of bullying perpetration may be under- or overreported. In addition, the current study was limited to individual background and psychological variables, and did not examine other contextual variables. School, family, and peer contexts should be considered in future studies that examine cyberbullying. Despite these limitations, the results of this study
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Contents lists available at ScienceDirect
Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Longitudinal predictors of cyberbullying perpetration: Evidence from Korean middle school students Sukkyung You a, Sun Ah Lim b,⁎ a b
Hankuk University of Foreign Studies, Republic of Korea Sookmyung Women's University, Republic of Korea
a r t i c l e
i n f o
Article history: Received 30 June 2015 Received in revised form 5 October 2015 Accepted 6 October 2015 Available online 22 October 2015 Keywords: Cyberbullying Perpetration Longitudinal study Adolescent Korean
a b s t r a c t Cyberbullying perpetration is a recent phenomenon that has become an increasingly serious social problem in Korea. This study examined the long-term effects of individual and psychological factors on cyberbullying perpetration from a sample of 3449 middle school students. Logistic regression analyses were employed in order to understand how various factors influence youth cyberbullying perpetration experiences. The findings indicated that longer use of the Internet, more previous bullying and victim experiences, a higher aggression level, and lack of self-control are associated with more cyberbullying perpetration. Implications and future directions are discussed. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction The widespread use of smartphones and the Internet has allowed adolescents to readily access cyberspace. These technological advances have its benefits in providing a means for promoting people's freedom of expression and exchange of information. However, there is also a drawback in the form of cyberbullying, which can include invasion of private information, insulting language use, multiple types of threats, rumors and abuse (Lee & Lee, 2013). Particularly in Korea, the media have reported secondary harm from cyberbullying on adolescents such as depression, suicidal impulse, and suicide. Consequently, cyberbullying is now commonly acknowledged as a social problem among adolescents, suggesting the necessity of related research and preventions (Nam & Kweon, 2013). According to Korea Internet and Security Agency (KISA), 76% of Korean teenagers have experienced cyberbullying, indicating the seriousness of cyberbullying in Korea (KISA, 2012). There are several possible reasons for the prevalence of cyberbullying among Korean teenagers. First, as an environmental factor, growth and widespread use of internet and social networks have been intensified. Along with the diffusion of the Internet, a considerable part of people's social behavior is now taking place in cyberspace as a result of personal media including smartphones, and SNS (Social Networking Service) services such as Twitter and Facebook. Most Korean teenagers following ⁎ Corresponding author at: Graduate School of Education, Sookmyung Women's University, Cheongpa-ro 47-gil 100, Yongsan-gu, Seoul 140-742, Republic of Korea. E-mail address: [email protected] (S.A. Lim).
http://dx.doi.org/10.1016/j.paid.2015.10.019 0191-8869/© 2015 Elsevier Ltd. All rights reserved.
this trend have access to smartphones and the Internet, through which they are easily exposed to cyberbullying (Lee & Lee, 2013). Second, cyberspace provides a certain level of anonymity, non-directness, and virtuality. Anonymity frees adolescents from normative society, conscience, morality, and ethics (Belsey, 2006). Moreover, bullying can be bolder when it happens online; it is a forum where individuals can express their hostile attitude without revealing themselves (Belsey, 2006). Also, the virtual reality of cyberspace deceives the perpetrators into think that they are not doing any physical harm to the victims. Third, the underlying causes of behaviors and characteristics of perpetrators may be related to the stages of development. Gruber and Yurgelun-Todd (2006) found that the brains of adolescents often fail to use consideration and self-control in their decision-making process. One of the most serious characteristics that distinguishes cyberbullying from other types of offline bullying is that bullying through mobile or online media is often done in secret and thus harder for parents or teachers to control (Lee, 2012). Also, the anonymity of cyberspace allows for a certain level of intangible violence or bullying and therefore, capable of harming victims in significant ways. Extant research identified factors for cyberbullying and specifically demographic and behavioral factors have been extensively studied. Some of these factors include family variables such as non-traditional family, school variables such as school violence (Beran & Li, 2007; Hinduja & Patchin, 2007) and previous bullying and victim experiences in school (Hinduja & Patchin, 2012; Kowalski & Limber, 2007; Lee & Jun, 2011), and computer-related variables such as use of the Internet and IT devices (Lee & Kim, 2009; Lee, 2011a; Slonje & Smith, 2008; Snell &
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Englander, 2010; Ybarra & Mitchell, 2004). Research has also identified psychological factors related to cyberbullying including low self-esteem (Lee, 2005; Limber, Kowalski, & Agatston, 2008), low self-control (Lee, 2011b; Lee & Kim, 2009), aggression (Agatson, Kowalski, & Limber, 2008; Ybarra & Mitchell, 2007), emotional regulation (Baroncelli & Ciucci, 2014; Kim, Choi, & Hong, 2007), and sociality (Burton, Florell, & Wygant, 2013; Jun & Lee, 2010). 1.1. The present study Research on cyberbullying is still at the beginning stage and most studies are mere reports of prevalence rates and concurrent relationships among factors with cross-sectional data (Hemphill et al., 2012). In Korea, the research on cyberbullying is also a newly emerging topic of interest (Oh, 2010). The current study addresses the gap in the literature by examining the long-term effects of both individual background and psychological variables on later cyberbullying perpetration. The study also examined the transactional pattern of previous offline bullying perpetration and victimization on subsequent cyberbullying using longitudinal data from a sample of Korean students. Current knowledge about those who are both offline and cyberbullying perpetrators is largely based on the correlational relationships using one time point analysis. Based on previous studies, we hypothesized that previous offline bullying experiences (both perpetration and victimization) lead to cyberbullying perpetration. After controlling for individual background factors and previous offline bullying experiences, results indicate that individual psychological factors affect cyberbullying perpetration. That is, adolescents with low self-esteem, low self-control, aggression, low emotional regulation, and a low level of sociality will engage in more cyberbullying perpetration. 2. Method 2.1. Participants The data used in the present study comes from the Korean Children and Youth Panel Survey, a six-year longitudinal study of students' school life experiences conducted by the National Youth Policy Institute, with funding from the national government. Participants were chosen using a stratified multi-stage cluster sampling. The institute stratified 16 administrative districts; then they randomly selected schools in each district in line with the population rate based on proportionate probability sampling, and randomly selected one class per school. This survey was initially completed in 2010, when all of the students were in their second year of middle school. At that time, the mean age of the participants was 13 years. Students then re-took the survey every last quarter of the year until 2011. The analyses in the current study are based only on the first two years of the data (i.e., the second year of middle school to the third year of middle school). Middle school students were selected on the basis that, as a group, they showed the greatest percentage of students with both offline and cyberspace bullying experiences. We examined the longitudinal effects of independent variables (Time point 1) on the next year's cyberbullying aggressor experiences (Time point 2). In the final sample, there were a total of 3449 participants, comprised of 1725 (50%) male and 1724 (50%) female students (range of age: 12 to 14 years old, mean age: 13.78 years, SD: .41 years). The largest portion (30.3%) of the sample reported their father's educational background as having bachelor's degrees, followed by high school diplomas (43.8%), middle school diplomas (7.9%), and post-bachelor's degrees (5.6%). Mothers were slightly less educated with the sample reporting their mother's educational background as having high school diplomas (57.8%), bachelor's degrees (18.3%), middle school diplomas (12.7%), and post-bachelor's degrees (1.5%). Most participants were comprised of middle class (48.5%), followed by upper (31.7%), and lower (16.1%) classes in terms of socio-economic
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level. Family income ranged from 500 dollars to 35,000 dollars per month (mean = 3000 dollars, SD = 2160 dollars). 2.2. Measures 2.2.1. Cyberbullying perpetration Six questions (Cronbach's α = .89) were selected to assess students' cyberbullying perpetration experiences from the Cyber Bullying Inventory (Erdur-Baker & Kavşut, 2007) (e.g., “I send threatening or hurtful comments through e-mail”). The Cyber Bullying Inventory was revised by the National Youth Policy Institute to adapt to Korean adolescents' cyberbullying. 2.2.2. Previous offline bullying/victim experience Eleven items were used to measure students' experiences of or involvement in offline bullying and victimization over the past year (Olweus, 1993). Specifically, five questions assessed students' victim experiences (Cronbach's α = .73). There were six questions designed to capture the individual's involvement with bullying as a perpetrator (Cronbach's α = .73). 2.2.3. Psychological predictors All of the individual psychological variables were measured by a 5-point Likert scale (1 = Not at all to 5 = Always true). Specifically, five items (Cronbach's α = .73) were used for self-esteem (Rosenberg, 1965) (e.g., “I believe that I am a person with good character.”), six items (Cronbach's α = .70) were used for aggression (Weinberger, 1991) (e.g., “I can use physical violence when I become extremely irritated.”), six items (Cronbach's α = .76) were used for lack of self-control (Rohrbeck, Azar, & Wagner, 1991) (e.g., “I can't stay calm when I get angry.”), three items (Cronbach's α = .73) were used for emotional regulation (Goleman, 1995) (e.g., “I can deal with being told no.”), and three items (Cronbach's α = .71) were used for sociality (Armsden & Greenberg, 1989) (e.g., “I get along well with my friends at school.”). Other studies of Korean middle school students also found significant associations with the abovementioned psychological measures and online game addiction (Hong, You, Kim, & No, 2014), self-efficacy (Seon, Hwang, & Jung, 2001), school engagement (Byun & Kim, 2008), and academic stress (Kim & Kim, 2009) providing evidence supporting the validity of the instrument for use with this population. 2.3. Analysis A series of logistic regression models were developed and tested using Mplus (Muthen & Muthen, 2006). The first model contained individual background variables; the second model included previous offline bullying and victim experiences; the third model included psychological variables. Logistic regression models estimated the effect size and statistical significance of a myriad of predictors simultaneously. We determined the unique contribution of each variable in the model while controlling for the effects of the other variables included. 2.4. Missing data The dataset contained missing responses. In order to obtain unbiased estimates of the parameters of interest, despite the incompleteness of the data, this study employed full-information maximum-likelihood estimation. 3. Results 3.1. Descriptive statistics The mean and standard deviation of the variables in this study are illustrated in Table 1. Students in the cyberbullying group showed significantly higher levels of daily Internet use, previous offline bullying
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Table 1 Group statistics for the variables used in the analysis.
Individual background variables Father's academic ability Mother's academic ability Family monthly income Internet use time on an average day Mobile phone use time on an average day Achievement Non-traditional family (both parents = 1) Gender (male = 1)a Bullying & victim experience Bullying experiencea Victim experiencea Psychological factors Lack of self-controla Self-esteema Aggressiona Emotional regulation Sociality
Table 2 Effects of individual background and psychological variables on cyberbullying perpetration.
None (N = 2702)
Cyberbullying (N = 1228)
Mean
SD
Mean
SD
Predictors
4.75 4.23 302.17 2.38 1.72 3.14 23.7% 26.3%
1.30 1.10 185.80 1.57 2.93 0.83
4.64 4.18 291.75 2.75 1.73 3.07 76.3% 73.7%
1.30 1.08 173.72 1.69 2.92 0.86
0.13 0.04
0.33 0.20
0.19 0.07
0.39 0.25
2.66 3.28 2.72 3.33 3.78
0.67 0.61 0.71 0.80 0.86
2.85 3.21 2.95 3.33 3.88
0.69 0.67 0.71 0.82 0.82
Step 1: Individual background variables Father's academic ability Mother's academic ability Family monthly income Computer use time on an average day Mobile phone use time on an average day Achievement Non traditional family Gender (male = 1) Step 2: Bullying & victim experience Bullying experience Victim experience Step 3: Psychological factors Lack of self-control Self-esteem Aggression Emotional regulation Sociality
Note. a Group difference at p b .05.
and victim experiences, lack of self-control, and aggression, compared to students who did not report cyberbullying. According to the guidelines of severe non-normality (i.e., skew N 2; kurtosis N 7) proposed by West, Finch, and Curran (1995), the normality assumption for all the variables was well met. Intercorrelations among study variables were generally moderate and below .40. Given that no correlations approached .85, bivariate associations did not indicate problems with multicollinearity (Tabachnick & Fidell, 2001). In addition, collinearity diagnostics indicated no presence of a multicollinearity problem with the current data set. 3.2. Logistic regression analyses The results of the hierarchical logistic regressions of cyberbullying outcomes are shown in Table 2. Initially, the individual background variables were entered into the model. Father's academic ability and Internet use time had significant effects on cyberbullying perpetration. After controlling for differences in individual backgrounds, prior offline bullying and victim experiences were significantly associated with cyberbullying perpetration. The final model confirmed that lack of self-control and aggression had exerted positive effects on the membership of the cyberbullying group. 4. Discussion Cyberbullying is a relatively new form of bullying and the problem is raising many concerns. There are few studies that provide longitudinal data on the predictors of cyberbullying perpetration. This study explored longitudinal factors that influence the development of cyberbullying using data from the Korean Youth Panel Survey on a nationwide sample of Korean adolescents. Specifically, the current study examined prospective associations between each of the grade 8 individual background and psychological factors and grade 9 cyberbullying perpetration. Factors affecting cyberbullying perpetration were Internet use, previous offline bullying and victim experiences in school, lack of selfcontrol, and aggression level. That is, a larger amount of time spent using the Internet, more previous offline bullying and victim experiences in school, lack of self-control, and a higher level of aggression were associated with subsequent cyberbullying. Consistent with previous research, extensive use of Internet emerged as a risk factor for cyberspace perpetration. Adolescents
Cyberbullying
Test χ2 test Model comparison: Δχ2 Cox & Snell's R2
β
Wald's χ2
SE
OR
.85⁎⁎ 1.15 1.00 1.12⁎⁎
−.16 .14 .00 .12 .00 −.08 −.24 .26
.07 .08 .00 .04 .02 .08 .26 .12
5.91 3.56 1.27 9.85 .03 1.01 .85 4.58
.54 .60
.16 .25
11.93 5.91
1.72⁎⁎⁎ 1.83⁎⁎
.32 .06 .27 −.02 .06
.11 .11 .10 .08 .08
8.76 .25 7.11 .05 .60
1.37⁎⁎ 1.05 1.31⁎⁎ .98 1.06
1.00 .92 .78 1.30⁎
Step 1 Step 2 Step 3 28.67⁎⁎⁎ 50.59⁎⁎⁎ 82.64⁎⁎⁎ 21.92⁎⁎⁎ 32.05⁎⁎⁎ .017 .030 .049
Note. OR = odds ratio. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
exposed to the Internet for a longer duration are more likely to have chances for online delinquency and thus are more likely to commit online delinquency. Lee and Ahn (2005) showed that more than 3 h of daily computer use led to a significantly higher level of online delinquency compared to less computer use. In his research on high school students, Lee (2004) studied the relationship between Internet addiction and cyber-delinquency behaviors such as abusive language use, spread of false rumor, and cyber-sexual harassment. He found that the duration of Internet use had a significant influence on cyber-delinquency including verbal violence. These results suggest the need to consider self-control or monitoring by others on Internet use in order to prevent cyberbullying. With advances in technology and media, Internet use has become an integral part of adolescents' daily activities. Therefore, parents can take on more of a leading role in talking to and supervising their children to set rules of computer and mobile phone use as a way of monitoring the content of their activities. Prior traditional bullying experiences in school were also found to be a predictor of cyberbullying. Perpetrators of offline school violence may use mobile phones and the Internet to bully and dominate the victims. In these cases, cyberbullying can be seen as an extended version of school violence. Research has indicated that adolescents who have done harm to others in real life within the past six months are 2.5 times more likely to attack others online; similarly, those who have been victimized in school violence within the past six months are 2.5 times more likely to be victimized in cyberbullying (Hinduja & Patchin, 2012). Previous offline victim experiences in school were also found to be a predictor of cyberbullying. In Kowalski and Limber's (2007) study, most teenagers who admitted to have committed cyberbullying referred to revenge as the reason for their behavior. The victims of school violence successfully resorted to email, instant message, and text message as ways of carrying out their revenge. These previous victims of school violence have potential resentment and aggression toward the perpetrators in their thoughts on revenge, which factors into their intent to use violence on online strangers instead of the perpetrators themselves
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(Hay & Evans, 2006). This is also supported by study results that found that 22.5% of the participants chose revenge as the reason for cyberbullying perpetration (Hinduja & Patchin, 2007; Kowalski & Limber, 2007). As cyberbullying triggers less anxiety than face-to-face violence, the victims of school violence are more likely to rely on it as their means of self-defense. Taking revenge is thus a self-protecting strategy for the victims from the embarrassment, sadness, and helplessness (Beattie, 2005). Such an undesirable coping strategy yields other victims, thus forming a vicious circle of perpetrators and victims. In order to break this circle, schools need to take action to help relieve negative experiences of school violence. For example, within the counseling, treatment, or intervention setting for perpetrators of cyberbullying, it is necessary to examine whether he or she has experiences of being victimized in school violence. In addition, it is necessary to determine whether cyberbullying is carried out as a retaliation behavior (target of retaliation and the degree of cyberbullying), and prepare a follow-up intervention measure. Among psychological factors, lack of self-control and high level of aggression were linked to more cyberbullying. Cyberbullying can be an initial behavioral symptom of individual characteristics such as low self-control. It is difficult for impulsive adolescents to control themselves when presented with online bullying opportunities because they lack self-control and thus respond immediately to their emotions, attitudes, and behavior instead of reasonably considering possible consequences (Gottfredson & Hirschi, 1990). Lee (2011a) argued that adolescents with higher self-control restrain their desires and impulses, follow norms and values the society and their parents demand, and engage in less deviant behaviors, whereas adolescents with lack of self-control may cause problems or bully peers on cyberspace because they are ostracized and left out by peers and miss out the opportunity to acquire positive social skills from peers due to their impulsivity. The present study results confirm previous research that suggests that low self-esteem affects cyberbullying and that impulsive teenagers are less able to restrain themselves when given opportunities to bully other people (Gottfredson & Hirschi, 1990; Felson & Staff, 2006). Park (2012) also investigated the effects of adolescent self-control on cyberbullying perpetration and found that it is the foremost predictor of cyberbullying. The inability to control thoughts and behaviors may factor into particular types of adolescent attitudes and behaviors such as aggression. Previous research has noted aggression as one of the major emotions related to school violence and indicated that it is an important predictor of traditional bullying (Bierman, 2004; Kim, 2013). The current study's findings are similar to previous studies (Lee & Oh, 2012; Nam & Kweon, 2013) reporting that aggression is a typical characteristic of cyberbullying perpetrators. Additionally, Ybarra and Mitchell (2007) also showed that adolescent perpetrators of cyberbullying tend to have aggression issues. The effects of aggression on cyberbullying should not only be construed as aggressiveness but it should also be noted that aggression behind perpetration stems from a strong desire for power and domination, self-defense, and rivalry (Olweus, 1994). Aggressive adolescents have a strong desire to show off their power by dominating other students with force and intimidation. Moreover, aggressive students show the characteristics of distortions and deficiencies in not only behavioral characteristics but also cognitive functioning, as shown by the mistaken belief that attacking others will increase their self-esteem (Choi & Chung, 2003). The findings of the current study should be interpreted with consideration of the study limitations. The study relied on students' selfreported data. Although it is arguably a unique source of information regarding cyberspace bullying, it is possible that the levels of bullying perpetration may be under- or overreported. In addition, the current study was limited to individual background and psychological variables, and did not examine other contextual variables. School, family, and peer contexts should be considered in future studies that examine cyberbullying. Despite these limitations, the results of this study
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