RISK FACTORS ASSOCIATED WITH CYBERBULLYING VICTIMIZATION AND PERPETRATION AMONG TAIWANESE CHILDREN

RISK FACTORS ASSOCIATED WITH CYBERBULLYING VICTIMIZATION AND PERPETRATION AMONG TAIWANESE CHILDREN by MI-TING LIN, BA, MS... RISK FACTORS ASSOCIATED WITH CYBERBULLYING VICTIMIZATION AND

RISK FACTORS ASSOCIATED WITH CYBERBULLYING VICTIMIZATION AND

PERPETRATION AMONG TAIWANESE CHILDREN

by MI-TING LIN, BA, MS

Copyright

by Mi-Ting Lin, BA, MS, PhD

2016

RISK FACTORS ASSOCIATED WITH CYBERBULLYING VICTIMIZATION AND

PERPETRATION AMONG TAIWANESE CHILDREN

by

MI-TING LIN

MS, Soochow University, 2007

BA, National Taipei University, 2003

Presented to the Faculty of The University of Texas

School of Public Health

in Partial Fulfillment

of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

THE UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH

Houston, Texas May, 2016

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RISK FACTORS ASSOCIATED WITH CYBERBULLYING VICTIMIZATION AND

PERPETRATION AMONG TAIWANESE CHILDREN

Mi-Ting Lin, BA, MS, PhD The University of Texas School of Public Health, 2016

Dissertation Chair: Christine Markham, PhD

In Taiwan, a previous study indicates that 18.4% of adolescents were cyberbullying victims, 5.8% were perpetrators, and 11.2% were both The aims of the present study were to determine whether time spent online, risky Internet usage, and parental supervision were risk factors of cyberbullying victimization (study 1), explorethe mechanism underlying the relationship between cyberbullying victimization and perpetration (study 2), and examine the risk factors (time spent online, risky usage, parental supervision, and emotional self-

regulation) of cyberbullying among children, including bullies, victims, and bully-victims (study 3) A 2-wave de-identified secondary data analysis from an elementary school sample (220 5th grade students: Wave 1 and Wave 2, 238 6th grade students: Wave 1 only) in Taiwan was used Logistic regression analysis, mediation analysis, and one-way multivariate analysis of covariance (MANCOVA) were conducted

The results indicated that time spent online and risky usage increased the odds of cyberbullying victimization However, parental supervision failed to predict future

cyberbullying victimization (study 1) The association between cyber victimization and

cyberbullying perpetration was mediated by emotional self-regulation (study 2) Compared to victim-onlys, bully-onlys, and neutrals (i.e., youth who experienced neither bullying

victimization nor perpetration), bully-victims demonstrate higher amounts of time spent on the Internet, higher risky Internet usage, lower parental supervision, and lower emotional self-regulation (study 3) Combined, these studies make a significant contribution to the cyberbullying research These findings may contribute to the development of effective

education interventions in children’s Internet usage to avoid cyberbullying

TABLE OF CONTENTS

List of Tables i 

List of Figures ii 

Background 1 

Introduction 1 

Specific Aims and Hypothesis 4 

Public Health Significance 6 

Human Subjects, Animal Subjects, or Safety Considerations 6 

References 8 

Journal Article 1 11 

Investigating Antecedents of Cyberbullying Victimization: Application of Routine Activity Theory 11 

Computers in Human Behavior 11 

Introduction 11 

Materials and Methods 17 

Results 20 

Discussion 20 

References 26 

Journal Article 2 35 

Linking Cyberbullying Victimization to Subsequent Cyberbullying Perpetration: The Mediating Role of Emotional Self-Regulation 35 

Cyberpsychology, Behavior, and Social Networking 35 

Introduction 35 

Materials and Methods 43 

Results 45 

Discussion 47 

References 52 

Journal Article 3 61 

Risk Factors of Cyberbullying Victims, Bullies, and Bully-Victims in Taiwanese Children 61 

Journal of Early Adolescence 61 

Introduction 61 

Materials and Methods 67 

Results 70 

Discussion 71 

References 78 

LIST OF TABLES

Table 1 Sample characteristics by cyberbullying status (n = 220) 33 

Table 2 The hierarchical binary logistic regression estimates for cyberbullying

victimization 34 

Table 3 Indirect effect and direct effect from cyber victimization to cyberbullying

perpetration 46 

Table 4 Participants demographic characteristics (n = 458) 84 

Table 5 MANCOVA analyses showing associations between risk factors and

participation in cyberbullying 85 

LIST OF FIGURES

Figure 1 The conceptual model of current study 32 Figure 2 Mediation model of this study 43 Figure 3 Results of the mediator model .46 

BACKGROUND Introduction

The development of the Internet is rapidly and extensively diffusing worldwide (Ševčíková, 2015; Wang, Bianchi, & Raley, 2005) In addition, the number of Internet users and the amount of time spent online are continuously increasing among children (Child Trends, 2012; Johnson, 2010; Ybarra & Mitchell, 2004) In fact, Internet use has become the major leisure activity for young adolescents For instance, in the past, young adolescents might go to the stadium to play baseball after school, but now they may go back home

directly and play RPG (Role-Playing Games) online In the United States, 83.3% of children and adolescents lived in households equipped with at least one computer in 2013 Close to 75% of children and adolescents lived in households equipped with Internet access (United States Census Bureau, 2014) In Taiwan, 87.3% of families had a least one computer at home and 84.0% families had access to high speed Internet (Taiwan Network Information Center, 2013) In a report form Research, Development and Evaluation Commission, Executive Yuan (2013), 11.6% of children aged 6-11 years had their own desktop computers at home More than 25% of them had their own tablets and 22% had their own smartphones According to Child Welfare League Foundation of Taiwan (2012), close to 85% of children in fifth and sixth grades use the Internet frequently On weekdays, 21.4% of children used the Internet

On weekends, more than 70% of children used the Internet Furthermore, 21.1% of children spent more than 2 hours on the Internet per day More than 30% of children spent more than

3 hours on the Internet per day

The increased role of Internet use among children has raised dramatic concern about what and how children may be influenced by Internet use Previous studies indicated that children can use the Internet to gain knowledge and acquire information

(Subrahmanyam, Kraut, Greenfield, & Gross, 2000) According to a significant series of studies conducted at Michigan State University (the HomeNetToo project), Jackson and her colleagues investigated the relationship between using the Internet at home and academic performance among children from low-income families (Jackson et al., 2006) Their results found that Internet use can positively predict children’s reading performance and grade point averages (GPAs) after six months and later Furthermore, the more time children spent online, the higher their reading ability However, Internet use is a two-sided coin The Internet can be misused for misbehaviors, such as cyberbullying (Ševčíková, 2015)

Cyberbullying is an increasing public health concern in children and young adolescents (Centers for Disease Control and Prevention, 2014) According to Hinduja and Patchin (2008), cyberbullying refers to intentional and repeated harm inflicted through electronic devices, such as computers and cell phone, in an electronic context (e.g., email, instant message, and text message) For instance, children have used electronic communication technology to threaten, embarrass, or socially exclude others (Mishna, Saini, & Solomon, 2009) The prevalence of cyberbullying victimization worldwide ranges across studies from 10% to 40% (Kowalski, Giumetti, Schroeder, & Lattanner, 2014) The prevalence of

cyberbullying perpetration worldwide ranges from 5% to 36% (Hemphill, Kotevski, & Heerde, 2015; Rice et al., 2015; Schultze-Krumbholz & Scheithauer, 2015) More than 25%

of the children were both (Mishna, Khoury-Kassabri, Gadalla, & Daciuk, 2012) In Taiwan,

18.4% of adolescents were victims of cyberbullying, 5.8% were perpetrators only, and 11.2% were both (Chang, Lee, Chiu, Hsi, Huang, & Pan, 2013)

To date, a major body of research has examined the prevalence, antecedents, and outcomes of cyberbullying, including victims and perpetrators, but several critical gaps in the cyberbullying literature remain First, most previous studies were conducted without

utilizing theory (Kowalski et al., 2014; Tokunaga, 2010) However, theory-based studies can help researchers to identity the potential determinants of cyberbullying and develop related measurements (Kowalski et al., 2014) Namely, the cyberbullying literature needs to develop based on a theoretical foundation Second, the majority of previous studies only focused on two categories of cyberbullying, those who are perpetrators or victims (Mishna et al., 2012) This relationship is based on the premise that victims and perpetrators are different

individuals in the cyberbullying context In fact, research indicates that the experience of bullying victims is related to later cyberbullying misbehaviors (Jang, Song, & Kim, 2014) The children who are both perpetrators and victims have lower self-control, higher offline aggression, and higher peer rejection than perpetrators or victims only (Bayraktar,

Machackova, Dedkova, Cerna, & Ševčíková, 2015) Third, information and communications technology (ICT) is extensively used in Asia Specifically, Internet use among children and youth in Asian countries is increasing dramatically Taiwan is one of the Asian countries that has the most pervasive Internet use (Bhat, Chang, & Ragan, 2013) Greater Internet use may equate to greater cyber-harm The frequency of Internet use could be a predictor of

cyberbullying perpetration and victimization (Dilmaç & Aydoğan, 2010) As such, Internet

use increases children’s online interpersonal interactions and may make children’s

misbehaviors more likely happen (Chou, Condron, & Belland, 2005)

Accordingly, in the present study, I attempt to fill these noteworthy gaps and contribute to the cyberbullying literature in three primary ways This study consists of three independent papers that examine Internet use and cyberbullying among Taiwanese children using secondary data from previously conducted study The papers are described below

Specific Aims and Hypothesis

The first paper, “Investigating Antecedents of Cyberbullying Victimization:

Application of Routine Activity Theory,” involves an analysis of secondary data to examine the determinants of cyberbullying victimization by using Routine Activity Theory (Cohen, & Felson, 1979) The specific aim is:

Aim: To determine whether three risky factors identified in Routine Activity

Theory may influence cyberbullying victimization; that is, (a) exposure motivated offenders, (b) target suitability, and (c) lack of capable guardianship

Hypothesis: These three determinants are significantly related to cyberbullying

victimization

The second paper, “Linking cyberbullying victimization to subsequent

cyberbullying perpetration: The mediating role of emotional self-regulation” involves an analysis of secondary data to examine whether cyberbullying may decrease victims’

emotional self-regulation, which in turn may increase their misbehavior (cyberbullying perpetration) by using General Strain Theory (Agnew, 1992)

Aim: To examine the role of emotional self-regulation as a mechanism underlying

the relationship between cyber victimization and cyberbullying perpetration

Hypothesis 1: Individuals’ experience of cyberbullying victimization is positively

related to cyberbullying perpetration

Hypothesis 2: Individuals’ experience of cyberbullying victimization is negatively

related to emotional self-regulation

Hypothesis 3: Emotional self-regulation is negatively related to individuals’

cyberbullying perpetration

Hypothesis 4: Emotional self-regulation mediates the relationship between

individuals’ cyberbullying victimization and their cyberbullying perpetration

The third paper, “Risk Factors of Cyberbullying Victims, Bullies, and

Bully-Victims in Taiwanese Children” involves an analysis of secondary data to explore the risk factors of cyberbullying by using Routine Activity Theory (Cohen, & Felson, 1979)

Aim: To examine the risk factors (time spent online, risky usage, parental

supervision, and emotional self-regulation) of cyberbullying among children, including victims, bullies, and bully-victims

Hypothesis 1: Bully-victims demonstrate higher amounts of time spent on the

Internet, higher risky Internet usage, lower parental supervision, and lower emotional regulation, compared to other groups

self-Public Health Significance

In the past decade, cyberbullying has been viewed as a worldwide epidemic and considered a highly critical public health problem that can greatly influence children’s health Aside from the contribution to the literature from an empirical viewpoint, this dissertation also provides further insight into the association between cyberbullying and related

determinants among Taiwanese children Previous studies have identified several precursors

of cyberbullying victimization and cyberbullying perpetration However, most the existing cyberbullying literature lacks of a theoretical foundation (Kowalski et al., 2014) In addition, there are few studies that have examined the group of the children who are both

cyberbullying perpetrators and cyberbullying victims Besides, there are limited number of cyberbullying studies in Taiwan (Huang & Chou, 2010) To address the research gaps, the present study uses Routine Activity Theory to explore the precursors of cyberbullying

victimization and cyberbullying perpetration among Taiwanese children Moreover, General Strain Theory is used to exploring the mechanism underlying the relationship between

cyberbullying victimization and cyberbullying among Taiwanese children

Human Subjects, Animal Subjects, or Safety Considerations

The study contains the analysis of previously collected self-reported secondary data from Taiwan No data were linked to personal identification records All data analyzed have been de-identified and therefore pose no risk of identification to study subjects Current approval is HSC-SPH-13-0617 It is determined to qualify for exempt status according to 45

CFR 46.101 (b) on 09/09/2013 All data were saved on an encrypted hard drive and saved in

a locked cabinet

References

Bayraktar, F., Machackova, H., Dedkova, L., Cerna, A., & Ševčíková, A (2015)

Cyberbullying: The discriminant factors among cyberbullies, cybervictims, and

cyberbully-victims in a Czech adolescent sample Journal of interpersonal violence, 30(18), 3192-3216

Bhat, C S., Chang, S H., & Ragan, M A (2013) Cyberbullying in Asia Education about Asia, 18(2), 36-39

Centers for Disease Control and Prevention (2014) Electronic Aggression: Technology and Youth Violence Retrieved from

http://www.cdc.gov/violenceprevention/youthviolence/electronicaggression/

Chang, F., Lee, C., Chiu, C., Hsi, W., Huang, T., & Pan, Y (2013) Relationships among

cyberbullying, school bullying, and mental health in Taiwanese adolescents Journal of School Health, 83(6), 454-462

Chou, C., Condron, L., & Belland, J C (2005) A review of the research on internet

addiction Educational Psychology Review, 17(4), 363-388

Child Trends (2012) Home computer access and Internet use Retrieved from

http://www.childtrendsdatabank.org/?q=node/298

Child Welfare League Foundation of Taiwan (2012) Internet use and related behaviors among children and youth in Taiwan, 2012 Retrieved from http:

http://www.children.org.tw/news/advocacy_detail/354

Cohen, L E., & Felson, M (1979) Social change and crime rate trends: A routine activity

approach American Sociological Review, 44(4), 588-608

Dilmaç, B., & Aydoğan, D (2010) Parental attitudes as a predictor of cyber bullying among

primary school children International Journal of Psychological and Brain Sciences, 5(10), 649-653

Huang, Y Y., & Chou, C (2010) An analysis of multiple factors of cyberbullying among

junior high school students in Taiwan Computers in Human Behavior, 26(6),

1581-1590

Hemphill, S A., Kotevski, A., & Heerde, J A (2015) Longitudinal associations between cyber-bullying perpetration and victimization and problem behavior and mental health

problems in young Australians International journal of public health, 60(2), 227-237

Hinduja, S., & Patchin, J W (2008) Cyberbullying: An exploratory analysis of factors

related to offending and victimization Deviant Behavior, 29(2), 129-156

Jackson, L A., Samona, R., Moomaw, J., Ramsay, L., Murray, C., Smith, A., & Murray, L (2007) What children do on the internet: Domains visited and their relationship to

socio-demographic characteristics and academic performance Cyberpsychology & Behavior, 10(2), 182-190

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cyberbullying behavior among youths? Application of General Strain Theory

Computers in Human Behavior, 31, 85-93

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http://www.ndc.gov.tw/dn.aspx?uid=15601

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bach, J., Plant, A., & Kordic, T (2015) Cyberbullying Perpetration and Victimization

among Middle-School Students American Journal of Public Health, 105(3), 66-72

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and our kids’ health International journal of public health, 60(2), 129-130

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home computer use on children's activities and development The Future of Children, 10(2), 123-144

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research on cyberbullying victimization Computers in Human Behavior, 26(3),

277-287

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Psychiatry, 45(7), 1308-1316

JOURNAL ARTICLE 1 Investigating Antecedents of Cyberbullying Victimization: Application of Routine Activity Theory

Computers in Human Behavior

Introduction

The Internet has a significant impact on children’ cyberbullying behaviors

Although research on aggression has dramatically increased in the last decade to identify and integrate the antecedents and outcomes of bullying (Cook, Williams, Guerra, Kim, & Sadek, 2010; Gini & Pozzoli, 2009; Merrell, Gueldner, Ross, & Isava, 2008; Reijntjes, Kamphuis, Prinzie, & Telch, 2010; Ttofi, Farrington, Lösel, & Loeber, 2011), Internet use has changed the face of bullying behaviors (Smith et al., 2008) As more people utilize the Internet

frequently for school, work, and social use, Internet usage has evolved behaviors from a physical context to an electronic context For example, children aged 12-15 years now spend more time on the Internet (increasing from 14.9 hours to 17.1 hours a week), and

communicating with their friends through instant message, blogs, and social networking sites (SNSs) than ever before (Chiribuca & Răcătău, 2013) As such, Internet use increases

children’s online interpersonal interactions and may make misbehavior (e.g., cyberbullying) more likely to happen (Chou, Condron, & Belland, 2005) According to recent research in the United States, about 15.2% of children and adolescents reported having been cyberbullied (Kowalski & Limber, 2013) Prevalence estimates of cyberbullying victims worldwide were between 10% and 40% (Kowalski, Giumetti, Schroeder, & Lattanner, 2014) Specifically, regarding the prevalence of cyberbullying in Taiwan, an investigation reported that 18.4% of

adolescents were victims of cyberbullying, 5.8% were perpetrators only, and 11.2% were both victim and perpetrator (Chang et al., 2013) These numbers suggest that cyberbullying has become a public health issue in Taiwan Overall, these findings indicate that

cyberbullying is a serious public health problem and a worldwide epidemic

Cyberbullying is generally described as an extension of traditional bullying

Traditional bullying is a subcategory of aggression, referring to negative acts that are

intentionally carried out by one or more perpetrators and targeted toward victims who cannot easily defend themselves against the perpetrators over time (Hemphill et al., 2012; Olweus, 1993) Cyberbullying is similar to bullying in that it is an aggressive action with various forms of power imbalance between perpetrators and victims, especially victims who are unable to protect themselves from perpetrators (Dooley, Pyżalski, & Cross, 2009; Olweus, 1993; Vandebosch & Van Cleemput, 2008) Moreover, bullying behaviors include covert (e.g., spreading rumors and social exclusion) or overt (e.g., verbal and physical abuse) acts (Hemphill et al., 2012; Spears, Slee, Owens, & Johnson, 2009) Cyberbullying comprises both covert and overt bullying actions in an electronic context by using electronic devices (e.g., computers and smart phones) Through various forms of electronic media, individuals can bully, exclude, abash, and humiliate their victims, as well as distribute insulting E-mails

or texts to other people (Bossler & Holt, 2009; Li, 2007) As with the negative effects of traditional bullying, cyberbullying has detrimental effects on victims’ emotions (e.g.,

negative mood), psychological well-being (e.g., anxiety and depression), and subsequent behaviors (e.g., suicide and substance abuse) (Chang et al., 2013; Dempsey, Sulkowski, Nichols, & Storch, 2009; Juvonen & Gross, 2008; Sourander et al., 2010; Ybarra & Mitchell,

2007) However, there are some distinctive characteristics of cyberbullying compared to traditional bullying: First, cyberbullying perpetrators can be anonymous and may not need to face their victims In so doing, cyberbullying perpetration can occur in an indirect way Second, cyberbullying perpetrators can bully large numbers of victims with less effort Lastly, cyberbullying perpetrators can reach victims without any geographic impediments at any time during the day or night (Erdur-Baker, 2010; Heirman & Walrave, 2008; Katzer, Fetchenhauer, & Belschak, 2009; Kowalski, Limber, & Agatston, 2008)

Some significant determinants of cyberbullying victimization, including (a)

personal and (b) situational factors, have been identified by prior research (Kowalski, e al., 2014) First, the personal factors include individual characteristics, personality, psychological states, Internet use, and delinquent behaviors Specifically, findings demonstrated that female students are likely to experience more cyberbullying victimization than male students

(Sourander et al., 2010), and early adolescents (i.e., fifth to eighth grade) reported the most cyberbullying victimization compared to different ages (Hinduja & Patchin, 2008)

Furthermore, other research indicated that narcissism, self-esteem, depression, anxiety, technology use, and socioeconomic status are related to cyberbullying victimization Second, the situational factors include parental involvement and school climate (Kowalski, e al., 2014) For instance, parental discussion about online behavior and parental control of

technology are related to less cyberbullying victimization (Cassidy, Brown, & Jackson, 2012; Mesch, 2009) Additionally, youth who perceive the school climate as being unfair,

distrusting, and hostile may have increased vulnerability to cyberbullying victimization (Li, 2006; Mason, 2008)

Although research on cyberbullying has rapidly developed and previous findings have provided important insights, some gaps in understanding still remain Specifically, some findings in previous research lack a theoretical framework to support their outcomes

Consistent with this perspective of the limitation in the existing cyberbullying literature, Kowalski et al (2014) called for future researchers to develop a more solid theoretical

foundation of cyberbullying victimization and perpetration Even though accumulated

research, drawing on general aggression models (e.g., Gullone & Robertson, 2008; Vannucci, Nocentini, Mazzoni, & Menesini, 2012), social information processing (e.g., Camodeca, Goossens, Schuengel, & Terwogt, 2003; Crick & Dodge, 1994), and social cognitive theory (e.g., Toblin, Schwartz, Gorman, Abou-ezzeddine, 2005), has identified the antecedents and consequences of bullying victimization, the routine activity theory (Cohen, & Felson, 1979) may advance our understanding and provide theoretical guidelines of why personal and situational factors are related to cyberbullying victimization Routine activity theory posits that victimization usually occurs when victims are placed in hazardous contexts which cannot protect individuals from offenders and when they are exposed to risky factors Key constructs include: (a) target suitability, referring to a person’s availability as a victim and attractiveness

to the offender, (b) a lack of a capable guardian, referring to the lack of abilities or resources

to protect themselves from victimization, and (c) a motivated offender, referring to a person who intends to participate in perpetration when opportunities are presented (Cohen, &

Felson, 1979; see Figure 1) In short, routine activity theory asserts that victimization and perpetration are not random incidents, but regular patterns if an offender intends to target a suitable person who has no adequate guardianship

Routine activity theory can provide a theoretical explanation to clarify the

determinants of cyberbullying victimization (Holt, Fitzgerald, Bossler, Chee, & Ng, 2014) For example, research has found that individuals who spend more time online increase their likelihood of cyberbullying victimization (Dilmaç & Aydoğan, 2010) In fact, Internet use can increase exposures to motivated offenders Moreover, female Internet users who are generally seen as more attractive targets often report higher levels of sexual harassment online than male Internet users (Biber, Doverspike, Baznik, Cober, & Ritter, 2002) Measures

of guardianship have focused on computer filtering software and computer skills (Holt et al., 2014); however, these measures ignore other important situational factors (e.g., parental supervision) Furthermore, most research on cyberbullying victimization utilizing routine activity theory has targeted high school or college students as respondents (e.g., Bossler & Holt, 2009; Holt, Fitzgerald, Bossler, Chee, & Ng, 2014; Marcum, Higgins, & Ricketts, 2010) However, the greatest frequency of cyberbullying behavior usually occurs in seventh and eighth grades (i.e., early adolescence) and then significantly decreases after tenth grade (Tokunaga, 2010) Thus, it appears that routine activity theory can be applied to link some important factors to cyberbullying victimization, but future research needs to consider other applicable measures of guardianship (e.g., parental supervision) and theoretically appropriate populations

Accordingly, the main purpose of the current study is to examine the determinants

of cyberbullying victimization in an early adolescent population In doing so, first, in line with routine activity theory, I clarify three risk factors which may influence cyberbullying victimization; that is, (a) exposure to motivated offenders, (b) target suitability, and (c) lack

of capable guardianship Specifically, I anticipate that these three determinants are

significantly related to cyberbullying victimization The current study targets the childhood population with the highest cyberbullying prevalence rates (Tokunaga, 2010), i.e., early adolescents Lastly, previous research suggests that children from different cultures and countries may exhibit different cyberbullying behaviors (Li, 2007) Hence, it is imperative to examine cyberbullying and cyberbullying victimization in a cross-cultural context Taiwan is seen as one representation of collectivism (Ali, Lee, Hsieh, & Krishnan, 2005), and children are taught to increase the harmony of interpersonal relationships, self-regulation, and

tolerance (Wang, & Tamis-Lemonda, 2003) Thus, the current study focuses on children in Taiwan to test my hypotheses: that is, (a) exposure to motivated offenders, (b) target

suitability, and (c) lack of capable guardianship are significantly associated with

cyberbullying literature (Kowalski et al., 2014) Furthermore, I consider appropriate

measures of capable guardianship and assess children’s parental controls as guardians to advance our knowledge of protective factors of cyberbullying Finally, the present study targets Taiwanese children to understand cyberbullying behaviors in different cultural contexts Overall, I believe that the findings of the current study can illuminate the

cyberbullying victimization among Taiwanese children and suggest tentative directions for

parents and educators to reduce cyberbullying through intervention that can increase

children’s protective factors

Materials and Methods

Participants

This study used de-identified secondary data analysis of school-based health surveillance from an elementary school in Taoyuan, Taiwan These health surveillance data were from a 2-wave, prospective study of multi-media use behaviors among 5th grade

students (Wave 1 and Wave 2) and 6th grade students (Wave 1 only) Fifth grade and 6thgrade students in Taiwan are usually 11 to 13 years old In Wave 1, all the 5th and 6th grade students were invited to participate in the survey in March, 2013 In Wave 2, all the 5th grade students were invited to participate in the survey in June, 2013 Students who agreed to participate and provided signed parental consent forms were invited to join the school-based health surveillance study Only the 5th grade students with Wave 1 and Wave 2 data was

analyzed in this study

Measures

All the demographic variables (age and gender) and three elements of routine activity theory (exposure to motivated offenders, target suitability, and lack of capable

guardianship) were assessed at Wave 1 Cyber victimization was assessed at Wave 2

Independent variable 1: Exposure to Motivated Offenders (Wave 1)

Time spent online was used to assess exposure to motivated offenders It was captured by the following two questions: “How much time do you spend online on an

average school day?” and “How much time do you spend online on an average weekend?” (0

= never, 1 = l-30 minutes, 2 = 31-60 minutes, 3 = more than 1 hour to 2 hours, 4 = more than

2 hours to 4 hours, 5 = more than 4 hours to 6 hours, 6 = more than 6 hours) Summary scores were calculated by averaging both responses Higher scores indicate more time spent online which increases the possible exposure to motivated offenders

Independent variable 2: Target Suitability (Wave 1)

Risky online usage was used to assess target suitability It was assessed using three questions utilized in previous studies (Erdur-Baker, 2010; Ybarra, Mitchell, Finkelhor, & Wolak, 2007): “Have you ever asked someone you met on the internet to meet face to face?”,

“Have you ever disclosed your personal information to unknown people over the internet, including password and username?” and “Have you ever accepted an invitation to meet in person someone you met over the Internet?” (0 = Never, 1 = 1 time, 2 = 2-4 times, 3 = several times) A summary score was calculated by averaging all responses Higher scores indicate higher target suitability

Independent variable 3: Lack of Capable Guardianship (Wave 1)

Three questions were used to assess the lack of capable guardianship (parental supervision of the Internet use): “Do you have to get your parents’ permission before using the Internet?”, “Do your parents have any rules regarding the amount time you could spend online?”, and “Do your parents have any restriction regarding your online activities?” (0 =

no, 1 = yes) Summary scores were calculated by an average of all responses Lower scores indicate having lower lack of capable guardianship

Dependent variable: Cyber victimization (Wave 2)

Cyber victimization was measured with the eight-item General Online

Victimization Subscale (Tynes, Rose, & Williams, 2010; Cronbach’s alpha = 84), answered

on a 6-point Likert-type scale The response options ranged from 0 = never to 5 = every day

A sample item would be: “People have said negative things (like rumors or name calling) about how I look, act, or dress online.” A total score of cyber victimization was created by summing up scores from the eight questions The total score was dichotomized to represent being a cyberbullying victim (the total score is equal to 1 or greater than 1) and non-

cyberbullying victim (the total score is equal to zero)

Control variables: Socio-Demographic variables (Wave 1)

Demographic variables included gender (1 = male, 2 = female) and age

Analysis Plan

Descriptive statistics were conducted to determine the prevalence of cyber

victimization and to provide a description of demographic characteristics (age and gender) Then, a logistic regression analysis was performed to test the hypothesis as to whether exposure to motivated offenders, target suitability, and lack of capable guardianship were significantly related to cyberbullying victimization Control variables (gender and age) were entered in the first step In the next step, all independent variables (exposure to motivated offenders, target suitability, and lack of capable guardianship) were entered in one step to measure how well cyber victimization at Wave 2 could be predicted from the three elements

of routine activity theory measured in Wave 1 after controlling for demographics All descriptive statistics and regression were conducted in IBM SPSS Statistics 22

Results

Descriptive statistics are presented in Table 1 The mean age of participants was 11.40 years (SD = 49) The majority of participants were males (58.2%) Almost 25% of students reported cyberbullying victimization (24.6%) To explore whether three

determinants identified in Routine Activity Theory may influence cyberbullying

victimization, Table 2 presents the hierarchical binary logistic regression estimates for cyberbullying victimization The model controlled for the participants’ age and gender The results indicated that two precursors of routine activities theory affected the odds of

cyberbullying victimization First, greater time spent online (exposure to motivated

offenders) in 5th grade increased the odds of cyberbullying victimization in 6th grade (OR =

1.30, 95% CI = 1.01-1.65, p < 05) Specifically, the odds of cyberbullying victimization

increased by 30% by any increase of 1 unit in time spent online Second, greater risky online usage (target suitability) in 5th grade increased the odds of cyberbullying victimization in 6th

grade (OR = 3.37, 95% CI = 1.07-10.61, p < 05) In other words, the odds of cyberbullying

victimization increased by 3.37 times for each one-unit increase in risky online usage However, parental supervision (capable guardianship) in 5th grade did not affect the odds of cyberbullying victimization in 6th grade (OR = 69, 95% CI = 25-1.90, p > 05)

Discussion

The purpose of the present study was to shed light into what factors result in cyberbullying victimization among Taiwanese children To extend our understanding of cyberbullying victimization, I built on routine activity theory to demonstrate whether (a)

exposure to motivated offenders (i.e., time spent online), (b) target suitability (i.e., risky online usage), and (c) lack of capable guardianship (i.e., parental supervision of the Internet use) have impacts on cyberbullying victimization Specifically, I predicted that exposure to motivated offenders, target suitability, and lack of capable guardianship were positively related to cyberbullying victimization

The results generally support the hypotheses The key findings in the current study were that time spent online and risky online usage were significant antecedents of

cyberbullying victimization Specifically, children who spent more time online in 5th grade reported higher cyberbullying victimization in 6th grade compared to those who spent less time online Additionally, children who demonstrated higher risky online usage in 5th grade reported higher likelihood of cyberbullying victimization in 6th grade than those who

demonstrate lower risky online usage However, the results showed that parental supervision

of the Internet in 5th grade was not associated with greater likelihood of cyberbullying victimization in 6th grade I discuss theoretical and practical implications of these findings in the proceeding sections

Theoretical and Practical Implications

The results of the current study contribute to the cyberbullying literature by

corroborating and extending previous findings in several ways First, the significant findings

of time spent online and risky online usage as antecedents of cyberbullying victims might extend the integrated cyberbullying model and crime opportunity theory (i.e., routine activity theory) Despite past research that has focused on associations between personal factors (e.g , gender and age), situational factors (e.g., school climate), and cyberbullying

victimization, relatively little research has investigated these significant factors by applying a solid theoretical base In a recent meta-analytic research, Kowalski et al (2014) utilized the general aggression model to identify several factors (e.g., traditional victimization, traditional bullying, school safety, and anger) that may result in cyberbullying victimization The

present study applied routine activity theory examining whether there were other antecedents which lead to cyberbullying victimization to advance our understanding of cyberbullying victimization Consistent with this theoretical rationale and prior research, my findings demonstrate that when children spend more time online or engage in more risky online behaviors, their likelihoods of being cyberbullying victims will increase (Chang et al., 2015; Erdur-Baker, 2010) These finding are important because they suggest that increased

exposure to motivated offenders (e.g., the time children spent online) and target suitability (e.g., the display of risky online behaviors) will cause high risks of cyberbullying

victimization Unexpectedly, I obtained non-significant findings regarding parental

supervision of the Internet This is similar to Kowalski et al.’s (2014) findings; they found parent control of technology was not related to cyberbullying victimization One of the possible reasons for this is that parental behaviors (e.g., Internet supervision) may be

underestimated or overestimated by children’s self-report (Liau, Khoo, & Ang, 2008) For example, Lenhart (2005) found that 62% of parents reported supervision of children’s

Internet usage, but only 33% of children believed their parents had checked up on the sites that they had visited Thus, the measure of parental behaviors from children’s perspective may be biased (e.g., social desirability bias) which in turn would influence my results

Overall, the current study clarifies what risk factors (i.e., time spent online and risky online usage) have a significant impact on cyberbullying victimization

My findings also have critical practical implications for parents and educators First, although my findings indicate that the more time children spend online, the more exposed they are to being cyberbullying victims, it is not likely to be a reasonable or practical solution to decrease children’ s time spent online because the Internet usage still provides lots

of benefits (e.g., education) for them Therefore, it is important to educate children on ethical, polite, safe, and responsible interactions in an electronic context, particularly how to protect themselves in a cyber-environment Second, my findings suggest that participating in online risk behaviors (e.g., providing passwords or personal information to friends) increases the likelihood of cyberbullying victimization Thus, it is necessary for parents and educators to enhance children’s knowledge and safety skills to reduce their risky usage of the Internet Third, my findings also propose a training intervention targeting parents’ supervision of children’s online usage Specifically, through training, parents can increase related

knowledge, correct children’s risky online behaviors, and educate children on online safety behaviors Overall, it is imperative to implement evidence-based cyberbullying prevention programs that educate students on how to avoid cyberbullying and how to respond to

cyberbullying to decrease the likelihood of cyberbullying

Limitations

I acknowledge some limitations of the present study First, this study uses reported data, and all participants were asked to respond to items based on their perception and experience It might be possible that children may not truly evaluate their own behaviors

self-or parents’ behaviself-ors (Liau et al., 2008) Social desirability bias may influence participants’ responses That is, cyberbullying victimization might be underestimated or overestimated in the present study Future studies may employ alternative methods for data collection For example, researchers can recruit parents or teachers to assess children’s cyberbullying

victimization Second, the measures of cyberbullying victimization vary from study to study

As such, variance in the prevalence of cyberbullying victimization may exist when

comparing prevalence rates across studies and countries Finally, generalizing the results from our study might be limited by the sample I examine All the participants were recruited

in Taiwan Taiwan is seen as one representation of collectivism (Ali, Lee, Hsieh, &

Krishnan, 2005), and it may demonstrate different patterns of cyberbullying in a

cross-cultural context Moreover, all participants were 5th grade students in the present study Caution must be taken in generalizing the results to students in the nonparticipating grades

Conclusion

Overall, findings from the current study provide a significant and programmatic contribution to the existing literature on cyberbullying Specifically, the present study is unique in examining the risk factors of cyberbullying victimization among Taiwanese

children My findings indicate that the time children spend online and risky online usage will significantly influence the likelihood of cyberbullying victimization In conclusion, this study provides valuable and useful information to parents and educators planning to implement cyberbullying prevention programs, as well as to researchers interested in identifying what risk factors result in cyberbullying victimization These findings may contribute to the

development of effective education interventions in children’s Internet usage to avoid cyberbullying

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