Cyberbullying Victimization as a Predictor of Cyberbullying Perpetration,Body Image Dissatisfaction,Healthy Eating and Dieting Behaviors,and Life Satisfaction

Abstract

Cyberbullying victimization and perpetration continues to be a serious public health, criminal justice, victimology, and educational problem in middle schools in the United States. Adolescents are at a higher risk of experiencing cyberbullying as a victim and/or as a bully given the frequency of their use of the Internet via social networking sites such as Facebook and mobile devices such as cell phones and tablets. To address this important problem, the purpose of this investigation was to examine cyberbullying victimization through communication technology as a predictor of cyberbullying perpetration, body image, healthy eating and dieting behaviors, and life satisfaction of sixth-, seventh-, and eighth-grade-level middle school students. The World Health Organization recruited participants by using a Health Behavior in School-Aged Children (HBSC) survey. In this in-class questionnaire, 6,944 middle school students were asked about their cyberbullying experiences as a victim and as a bully via Internet, email, and mobile communication technologies to obtain their evaluations of their body image, eating and dieting habits, and perceptions of life satisfaction. After controlling for demographic factors such as sex, age, and class level, this study found that cyberbullying victimization was a predictor of cyberbullying perpetration, body image dissatisfaction, dieting behaviors, and life satisfaction. However, this study did not find a correlation between cyberbullying victimization and students’ healthy eating behaviors. This study also discussed each of the findings in the context of previous research findings. In addition, the study provides the strengths, limitations, and future directions for the future examination of cyberbullying victimization in middle schools.

Keywords

bullying Internet and abuse youth violence

Introduction

Cyberbullying is a public health, criminal justice, victimology, and educational problem that continues to affect the well-being of young children in middle schools across the United States. Cyberbullying is a multilayered problem because it negatively affects children’s use of electronic communication (Roberto, Eden, Savage, Ramos-Salazar, & Deiss, 2014a), children’s mental and physical health (Smokowski, Evans, & Cotter, 2014), and children’s exposure to peer-to-peer violence in education (Mason, 2008). These negative effects stimulate the criminal justice system to enact anticyberbullying laws to inform victimology experts about protecting cyberbullying victims (Coburn, Connolly, & Roesch, 2015; Katzer, Fetchenhauer, & Belschak, 2009). Because building intimate relationships with peers with the use of the Internet and mobile devices is valuable to young children and adolescents, this exposure increases young children and adolescents’ risk for cyberbullying perpetration and victimization (Ang, 2015). Several studies have found that cyberbullying victimization has negative health outcomes for young children and adolescents including mental health issues such as anxiety, depression, and suicide ideation (Hinduja & Patchin, 2010; Raskauskas & Stoltz, 2007), physical health issues such as body image issues and substance abuse (Calvete, Orue, & Gámez-Guadix, 2016; Selkie, Kota, Chan, & Moreno, 2015), and relational issues such as experiences of social anxiety and isolation (Ang, Chong, Chye, & Huan, 2012). Thus far, a few studies have examined the correlation between cyberbullying victimization and cyberbullying perpetration (Roberto, Eden, Savage, Ramos-Salazar, & Deiss, 2014b), body image dissatisfaction (Calvete et al., 2016), healthy eating behaviors (DeSmet et al., 2014; Farrow & Fox, 2011), and life satisfaction (DeSmet et al., 2014; Moore, Huebner, & Hills, 2012). To understand potential risk factors that may affect adolescents who are being victimized by cyberbullying behaviors, this study will extend prior correlational research by examining health-related variables such as body image, eating, dieting, and life satisfaction. As previous studies did not control for demographic variables in their middle school samples, this study will control for age, sex, and grade level. Thus, the purpose of this study is to control for demographic variables and examine cyberbullying victimization as a predictor of cyberbullying perpetration, body image dissatisfaction, healthy eating behaviors, and life satisfaction.

Defining Cyberbullying Victimization

Cyberbullying occurs when “an individual or group willfully [uses] information and communication involving electronic technologies to facilitate deliberate and repeated harassment or threat to another individual or group by sending or posting cruel text and/or graphics using technological means” (Mason, 2008, p. 323). Roberto and Eden (2010) defined cyberbullying as “the deliberate and repeated misuse of communication technology by an individual or group to threaten or harm others” (p. 201). Kowalski and Limber (2013) also added the communication channels in which cyberbullying occurs, which include via Internet through email, instant messages, social media (e.g., Facebook), and through text messages in mobile/tablet devices.

Thus far, researchers have included victimization in their conceptualizations of cyberbullying experiences (Kowalski & Limber, 2013; Mason, 2008). In this study, cyberbullying victimization refers to receiving cyberbullying messages by one or more persons (Brown, Demaray, & Secord, 2014). Receiving aggressive messages via electronic communication or social media such as, “you’re ugly,” “you’re stupid,” or “go kill yourself” are examples of cyberbullying victimization. Cyberbullying victimization differs conceptually from traditional victimization in that cyberbullying victimization requires the use of communication technology and/or the Internet to communicate aggressive messages and it can occur inside and/or outside of the boundaries of the school (Mitchell, Ybarra, & Finkelhor, 2007). Cyberbullying victimization also occurs by an anonymous aggressor who may be known or unknown to the victim and who is not physically present in the victim’s environment (Mitchell et al., 2007).

Prevalence of Cyberbullying Victimization and Perpetration in Middle School Students

In the past decade, scholars have examined cyberbullying perpetration and victimization of middle school students in grade levels sixth through eighth. A study found that middle school students are more vulnerable to online bullying victimization through the Internet and through communication technology (e.g., cell phones, tablets) in comparison with elementary or high school students (Williams & Guerra, 2007). Several studies report that cyberbullying victimization rates in middle schools range from 6.6% to 11.5% (Kowalski & Limber, 2007; Patchin & Hinduja, 2013; Rice et al., 2015). Studies also indicate that cyberbullying perpetration rates in middle schools vary by occurrence from 3.9% (within the past month; Patchin & Hinduja, 2013), 4.0% (within the past 2 months; Kowalski & Limber, 2007), and 5% (within the past year; Rice et al., 2015). Moreover, studies report that perpetrator-victim rates in middle schools range from 4.3% to 7% (Kowalski & Limber, 2007; Rice et al., 2015). Interestingly, a study conducted by Brown et al. (2014) focused on the prevalence of cyberbullying victimization of middle school students and they found no differences in grade level nor gender in the experience of cyberbullying victimization. However, the study found that cyberbullying victimization was positively related to experiencing negative social emotional outcomes such as anxiety, depression, and social stress among middle school students (Brown et al., 2014). Therefore, cyberbullying victimization and perpetration continue to be a prevalent public health and educational problem in middle school students.

Cyberbullying Victimization and Cyberbullying Perpetration

Numerous studies have shown that cyberbullying perpetration is positively correlated to cyberbullying victimization. Middle school students who are being cyberbullied by their peers are also likely to experience cyberbullying by others in a given time period (Kowalski & Limber, 2007; Roberto, Eden, Savage, Ramos-Salazar, & Deiss, 2014a). In a longitudinal study, Gámez-Guadix, Gini, and Calvete (2015) found that individuals who were cyberbullied during their adolescent years also reported cyberbullying others for retaliation purposes, and these young individuals were characterized with a “bully-victim status.” Previous studies have confirmed a moderate positive correlation between cyberbullying victimization and cyberbullying perpetration (Gámez-Guadix et al., 2015; Kowalski & Limber, 2007; Li, 2007).

In addition, several criminology studies have established a strong association between the victim–offender overlap among adolescents in physical and virtual environments (Averdijk, Van Gelder, Eisner, & Ribeaud, 2016; Klevens, Duque, & Ramirez, 2002; Lauritsen, Sampson, & Laub, 1991; Novo, Pereira, & Matos, 2015; Turanovic & Pratt, 2013). For instance, criminology reviews have documented that victims of interpersonal violence are more likely to become offenders of others (Jennings, Piquero, & Reingle, 2012; Lauritsen & Laub, 2007). Most recently, a health study by Roberto et al. (2014b) found evidence that cyberbullying perpetration predicted cyberbullying victimization, which suggests that victims are also offenders. Although researchers have established the correlations between demographics, cyberbullying perpetration, and victimization (Kowalski & Limber, 2007; Li, 2007), studies have not adequately controlled for sex, age, and grade level. Therefore, this study will control for these variables in the following hypothesis:

Hypothesis 1: After controlling for demographic variables, cyberbullying victimization experiences will have a positive effect on cyberbullying perpetration.

Cyberbullying Victimization and Body Image Satisfaction

Previous studies have found that one of the reasons for cyberbullying victimization has been adolescents’ self-evaluations of their own body image. Body image appearance refers to the level of satisfaction with one’s own physical or body appearance (Rieves & Cash, 1996). Adolescents in middle school place a high value in their peers’ opinions of their body’s image and this shows when adolescents post photographic images of their bodies using social networking sites such as Facebook and Twitter. In fact, adolescents are motivated to share pictures of their bodies with their mobile or tablet devices to obtain social approval by their peers (Calvete et al., 2016). However, sharing body images through the Internet and through mobile devices places adolescents at a higher risk for cyberbullying victimization.

Studies have shown that adolescents are reporting cyberbullying victimization experiences due to displaying “selfies,” or images of their face and/or body through social media. For example, receiving negative electronic messages about being “fat” or looking “ugly” on a picture can negatively affect an adolescent’s perception of his or her own body image (Frisén, Berne, & Lunde, 2014). In part, body image dissatisfaction has been correlated to being cyberbullied by school peers in online settings (Frisén et al., 2014; Lunde & Frisén, 2011). Victims of cyberbullying report being teased by their peers about their body’s physical appearance through social media and electronic messages, and as a result report having poor body esteem, self-esteem, and weight problems than noncyberbullying victims (Frisén et al., 2014). Unfortunately, being dissatisfied with one’s own body image has negative outcomes such as depressive symptoms (Rawana, Morgan, Nguyen, & Craig, 2010), body shame (Hyde, Mezulis, & Abramson, 2008), and low self-esteem (Menzel et al., 2010). A recent study by Calvete et al. (2016) also found that individuals who reported being cyberbullied in the past also reported being dissatisfied with their body image appearance.

Although prior studies have examined cyberbullying and body image dissatisfaction, no previous work has focused on examining the effect of cyberbullying on body image dissatisfaction in middle school students. Understanding whether cyberbullying victimization serves as a predictor of adolescents’ body image dissatisfaction can inform applied researchers and educational counselors to understand victims’ negative feelings about their body image. Thus, the following hypothesis will be addressed in this study.

Hypothesis 2: After controlling for demographic variables, cyberbullying victimization experiences will have a positive effect on body image dissatisfaction.

Cyberbullying Victimization and Healthy Eating Behaviors

Studies have also examined whether cyberbullying victimization is linked to adolescents’ healthy eating behaviors. Healthy eating patterns include eating healthy foods such as fruits and vegetables and unhealthy eating patterns include consuming sugary drinks, sugary snacks, and fast food meals (Iannotti, 2013). Studying adolescents’ healthy eating patterns is valuable because it is positively associated with overall health and well-being (MacNicol, Murray, & Austin, 2003). If adolescents develop healthy eating patterns in middle school, adolescents may continue these eating patterns in adulthood (Currie, Todd, & Thomson, 1994). Thus far, the literature demonstrates a clear association between school bullying and eating patterns in adolescents. For example, Kaltiala-Heino, Rimpelä, Rantanen, and Rimpelä (2000) found that bullying victimization is correlated with eating disorders such as bulimia and anorexia. Also, Libbey, Story, Neumark-Sztainer, and Boutelle (2008) assessed overweight adolescents who were being bullied about their excessive body weight, and they found that these adolescents engaged in unhealthy eating decisions (e.g., eating cookies). The frequency of bullying affected adolescents’ eating thoughts and eating patterns, such that those who were bullied engaged in binge eating behaviors of unhealthy food in comparison with those who were not bullied (Libbey et al., 2008). Similarly, Farrow and Fox (2011) found a correlation between cyberbullying victimization and the eating choices of both boys and girls, but mainly in girls. Finally, in a cross-sectional study, Sampasa-Kanyinga, Roumeliotis, Farrow, and Shi (2014) found that the breakfast skipping behavior of adolescents was positively associated with their cyberbullying victimization experiences.

A reason why these constructs are associated may be that cyberbullying victims who receive aggressive electronic messages may suffer from self-esteem issues (Brewer & Kerslake, 2015) and as a result may alter their eating behaviors to comfort themselves. Although these cross-sectional studies have provided substantial evidence of the correlation among these variables, no study has examined the effect of cyberbullying victimization on healthy eating patterns in middle school students. Thus, to expand the previous correlational findings, the following hypothesis will be examined.

Hypothesis 3: After controlling for demographic variables, cyberbullying victimization experiences will have a negative effect on healthy eating habits.

Cyberbullying Victimization and Dieting Behaviors

Studies are beginning to examine the cyberbullying victimization link with adolescents’ dieting habits. In a national study, Arat (2015) found that cyberbullying victimization was correlated to students’ dieting behaviors such as their green salad intake, potato intake, and carrot intake. Similarly, Sampasa-Kanyinga and Willmore (2015) found a positive correlation between breakfast skipping behavior and cyberbullied experiences in adolescent students. Another study found that cyberbullying victimization was correlated to their dietary lifestyle habits (DeSmet et al., 2014). In particular, cyberbullying victims who internalize peers’ aggressive messages about their overweight or obese physical appearance, may diet to lose weight to cope with this issue (Puhl, Peterson, & Luedicke, 2013). For example, a study by Gonsalves, Hawk, and Goodenow (2013) found that bullied adolescents are at a higher risk of engaging in unhealthy weight control behaviors such as excessive dieting to lose weight, which may lead to eating disorders if continued overtime.

In addition, another study found that bullying victimization was moderately correlated with eating disorders, which may lead to dieting behaviors to lose weight (Carmona-Torres, Cangas, Langer, Aguilar-Parra, & Gallego, 2015). If adolescents receive cyberbullying victimization messages such as, “you’re fat” via Facebook, then this may pressure adolescents to engage in dieting behaviors to manage their weight (Anderson, Bresnahan, & Musatics, 2014). Dieting behavior may promote the initial well-being when adolescents’ body mass index (BMI) scores go down, but it can also become a disordered eating pattern if adolescents engage in binge dieting behaviors, or purging behaviors (Anderson et al., 2014). Although these studies suggest that cyberbullying victimization may influence adolescents’ dieting behavior, no previous study has examined cyberbullying victimization as a predictor of dieting behaviors to lose weight in middle school students. Therefore, this study seeks to address this limitation in the cyberbullying victimization literature by posing the following hypothesis.

Hypothesis 4: After controlling for demographic variables, cyberbullying victimization experiences will have a positive effect on dieting to lose weight.

Cyberbullying Victimization and Life Satisfaction

Cyberbullying victimization has also been shown to be correlated to life satisfaction. Because cyberbullying victimization often leads to negative outcomes such as mental health, substance use, and overweight problems (Merrill & Hanson, 2016), being victimized may negatively affect adolescents’ quality of life. For instance, Moore (2013) examined a total of 843 seventh- and eighth-grade level middle school students and found that students who reported being cyberbullied also indicated having lower life satisfaction with their family, environment, and school in comparison with those who were not victimized. Similarly, Bilic, Flander, and Rafajac (2014) found that children who experienced both traditional bullying and cyberbullying had lower life satisfaction ratings in comparison with children who did not have such experiences. Another study also found that cyberbullying was negatively correlated with psychological well-being and life satisfaction (Navarro, Ruiz-Oliva, Larrañaga, & Yubero, 2015). Most recently, Fullchange and Furlong (2016) found negative effects of cyberbullying victimization in children’s emotional well-being and satisfaction with engaged living. In sum, these studies explain that receiving harmful electronic messages from peers via social media or the Internet may influence adolescents’ evaluations of their life in various domains. However, a limitation of the literature is that most studies have relied on examining the associations of cyberbullying and life satisfaction, and this limits the examination of control variables that may be influencing this relationship. Additional studies are needed to examine whether cyberbullying victimization serves as a negative predictor of life satisfaction in middle school students. To address this limitation, this study will examine the following hypothesis.

Hypothesis 5: After controlling for demographic variables, cyberbullying victimization experiences will have a negative effect on life satisfaction.

Method

Participants

The sample included (48% females and 52% males) 6,944 students in 157 participating urban, suburban, and rural American middle schools. Officials of eligible schools were asked whether they would provide consent to administer the survey in middle school classrooms, and out of 314 schools 157 agreed to administer the survey in their classrooms. The administration of surveys was delegated to teachers who agreed to distribute the paper-based survey to the students during class time. With this recruitment approach, the student response rate was 90%. The average age of the participants was 12 years (SD = 1.01). The ethnic background of the participants was composed of 48.5% White or European American, 18.5% Black or African American, 1.5% Native American, 0.6% Native Hawaiian or Pacific Islander, 4.2% Asian American, 19.7% Hispanic American, and 7.0% Other/mixed races. The grade levels of the participants in the middle schools included 2,050 sixth graders, 2,420 seventh graders, and 2,474 eighth graders.

Procedures

Participants were recruited by the 2013 Health Behavior in School-Aged Children (HBSC) survey in collaboration with the World Health Organization (Iannotti, 2013). The middle school representatives read scripts that explained the procedures of this paper–pencil format survey. The student survey took approximately 45 min to complete by hand and a middle school representative (e.g., teacher, counselor, etc.) administered it in classroom settings. Participants were asked to respond to questions about their demographics, cyberbullying victimization experiences, cyberbullying perpetration behaviors, body image, healthy eating behaviors, dieting behaviors, and life satisfaction.

Measures

Cyberbullying victim

The Olweus Victim Questionnaire (Solberg & Olweus, 2003; Wang, Iannotti, & Nansel, 2009) was used to assess middle school students’ perceptions of their own cyberbullying victimization experiences in and out of school settings. The questionnaire included a definition of being a cyberbullied victim to ensure that adolescents understood the term. Sample items of this scale included, “how often have you got bullied using a computer/e-mail?” and “how often have you got bullied using a cell phone?” Items ranged from 1 (I have not been bullied by another student in this way in the past couple of months), 2 (only once or twice), 3 (2 or 3 times a month), 4 (about once a week), and 5 (several times a week). The higher the ratings, the higher the frequency of being cyberbullied by another peer. The alpha reliability for this scale was .92.

Cyberbullying perpetration

The Olweus Bully Questionnaire (Solberg & Olweus, 2003; Wang, Iannotti, & Nansel, 2009) was used to assess middle school students’ perceptions of their own cyberbullying perpetration in and out of school settings. A definition of cyberbullying perpetration was provided to ensure that adolescents understand the term prior to completing the questionnaire. The items included, “how often have you bullied others using a computer/e-mail?” and “how often have you bullied others using a cell phone?” Participants indicated the extent to which they have bullied others using communication technology from 1 (I have not bullied another student in this way in the past couple of months), 2 (only once or twice), 3 (2 or 3 times a month), 4 (about once a week), and 5 (several times a week). The higher the ratings, the higher the frequency of bullying perpetration. The alpha reliability of these set of items was .94.

Body image dissatisfaction

The body image dissatisfaction subscale, which is a part of The Body Investment Scale (BIS; Orbach & Mikulincer, 1998) was used to assess individual’s feelings toward their body image. Six items asked participants to respond to their dissatisfied feelings about their body image, including whether they were frustrated with their appearance, hated their body, and felt anger toward their body. This scale ranged from 1 (strongly disagree) to 7 (strongly agree), and higher scores indicated a higher level of body image dissatisfaction. The alpha reliability for this scale was .85.

Healthy eating behaviors

Two items designed by Iannotti (2013) were used to assess the frequency of healthy eating behaviors. Sample items included, “how often do you eat or drink vegetables?” and “how often do you eat or drink fruits?” Participants indicated their agreement in the scale, 1 (never), 2 (less than once a week), 3 (once a week), 4 (2-4 days a week), 5 (5-6 days a week), 6 (once a day, every day), and 7 (every day more than once). The higher the ratings, the higher the frequency of healthy eating behaviors. The alpha reliability of this scale was .68.

Dieting to lose weight

Dieting to lose weight was assessed with an item developed by Iannotti (2013) to determine whether participants were currently dieting to lose weight. The item asked participants whether they were presently on a diet to lose weight. Participants’ responses ranged from 1 (no, my weight is fine), 2 (no, but I should lose some weight), 3 (no, because I need to put on some weight), and 4 (yes). Items were dichotomized for dieting behavior “yes” and “no.”

Life satisfaction

The Cantril’s (1965) Ladder of Life Satisfaction Scale was used to assess participants’ perceived life satisfaction. This scale is a visual scale that is tailored for middle-aged children and it provides a picture of a ladder. In this ladder, participants were asked to rank their satisfaction with their present life from 0 (worst possible life) to 10 (best possible life). The alpha reliability for this study was .82.

Demographic characteristics

The following demographic variables were used as control variables: sex, age, and grade level. These demographic characteristics were obtained by asking participants demographic questions regarding their sex, age, and middle school grade level.

Analysis

Correlations and descriptive information among the main variables of this study are displayed in Tables 1 and 2. Cyberbullying victimization experiences were positively correlated with cyberbullying, (r = .47, p < .001), body dissatisfaction (r = .13, p < .001), and dieting to lose weight (r = .05, p < .001). Cyberbullying victimization experiences were also negatively correlated with healthy eating (r = −.03, p < .01) and life satisfaction (r = −.05, p < .001). In addition, the tolerance statistics (TS) and variance inflation factor were also examined in the regression analyses to uncover any possible multicollinearity issues among the demographic (sex, age, and class level) independent variables. The lowest tolerance statistic was .25 and the highest variance inflation factor was .70, which showed that multicollinearity was not a main concern in this study given Mertler and Vannatta’s (2002) recommendations.

Table 1.

Reporting Means, Standard Deviations, Skew, and Kurtosis of Variables.

Measure	M	SD	Skew	Kurt
1. Cyber victim	1.14	0.55	4.88	5.82
2. Cyberbully	1.11	0.5	5.62	3.93
3. Body dissatisfaction	2.58	1.08	0.83	0.3
4. Healthy eating	4.82	1.54	−0.36	−0.64
5. Dieting to lose weight	1.92	1.15	0.87	0.78
6. Life satisfaction	7.54	1.99	−0.89	0.67

Table 2.

Reporting Means, Standard Deviations, and Zero-Order Correlation Matrix.

Measure	1	2	3	4	5	6
1. Cyber victim	1
2. Cyberbully	.47**	1
3. Body dissatisfaction	.13**	.08**	1
4. Healthy eating	−.03*	−.05**	−.13**	1
5. Dieting to lose weight	.05**	.04**	.38**	−.03*	1
6. Life satisfaction	−.05**	−.05**	−.34**	.14**	−.13**	1
M	1.15	1.11	2.58	4.82	1.92	7.54
SD	0.56	0.5	1.08	1.54	1.15	1.99

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

Main Analysis and Results

Prior to the main analysis, the prevalence of cyberbullying from the sample of this study was investigated. Of those who reported being cyberbullied, 7.7% reported being bullied via computer using the Internet or via email and 6.7% reported being cyberbullied via cell phone through text messaging or through a mobile application. Of those who reported cyberbullying others, 5.2% reported cyberbullying others using the Internet or via email and 5.7% reported cyberbullying others through text messaging or through a mobile application.

A series of hierarchical multiple regressions were used to analyze Hypotheses 1 to 5. For summaries of the multiple regression findings please see Table 3. The first hypothesis predicted that after controlling for sex, age, and grade level, cyberbullying victimization experiences will positively effect cyberbullying perpetration. The multiple regression analysis revealed a significant model, R² = .24, F(4, 6167) = 492.141, p < .001, after putting the controlling variables in the first block and cyber-victim experiences in the second block. In the first block (R² = .01), sex (β = −.04, t = −3.49, p < .001, pr² = −.04), age (β = .12, t = 5.55, p < .001, pr² = .07), and grade in school (β = −.08, t = −4.05, p < .001, pr² = −.05) were found to be predictors of cyberbullying perpetration. In the second block after accounting for the demographic variables (R² = .24), cyberbullying victimization experiences (β = .48, t = 43.66, p < .001, pr² = .48) positively impacted cyberbullying perpetration. Thus, the first hypothesis was supported.

Table 3.

Results of Multiple Regression Analyses Between the Demographic and Cyberbullied Variable and Dependent Variables.

	Cyberbullying			Body Dissatisfaction			Healthy Eating			Diet to Lose Weight			Life Satisfaction
	t	β	pr ²	t	β	pr ²	t	β	pr ²	t	β	pr ²	t	β	pr ²
Block 1		(R² = .01)			(R² = .03)			(R² = .01)			(R² = .01)			(R² = .01)
Sex	−3.49**	−0.04	−0.04	12.39**	0.16	0.16	2.67*	0.04	0.04	1.98*	0.03	0.03	−3.0*	−0.04	−0.04
Age	5.55**	0.12	−0.07	2.45*	0.05	0.03	−2.68*	−0.06	−0.04	−0.06	−0.01	−0.01	−1.84	−0.04	−0.02
Grade level	−4.06**	−0.09	−0.05	1.21	0.03	0.02	0.55	0.01	0.01	1.22	0.03	0.02	−2.76*	−0.06	−0.04
Block 2		(ΔR² = .24)			(ΔR² = .05)			(ΔR² = .01)			(ΔR² = .01)			(ΔR² = .01)
Cyber victim	43.66**	0.49	0.49	9.68**	0.13	0.13	−1.78	−0.02	−0.02	3.80**	3.79	0.05	−4.05**	−0.05	−0.05

Note. β = standardized beta coefficients.

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

The second hypothesis predicted that after controlling for sex, age, and grade level, cyberbullying victimization experiences will positively effect body image dissatisfaction. The multiple regression analysis revealed a significant model, R² = .05, F(4, 5720) = 68.89, p < .001, after putting the controlling variables in the first block and cyber-victimization experiences in the second block. In the first block (R² = .03), sex (β = .16, t = 12.39, p < .001, pr² = .03) and age (β = .05, t = 2.45, p < .02, pr² = .01) were found to be predictors of body image dissatisfaction, but grade level in school was not found to be a predictor (β = .02, t = 1.21, p > .05, ns). In the second block after accounting for the demographic variables (R² = .04), cyberbullying victimization experiences (β = .12, t = 9.68, p < .001, pr² = .12) positively affected body image dissatisfaction. Thus, the first hypothesis was supported.

The third hypothesis predicted that after controlling for sex, age, and grade level, cyberbullying victimization experiences will negatively effect healthy eating behaviors. The multiple regression analysis revealed a significant model, R² = .06, F(4, 5926) = 6.71, p < .001, after putting the controlling variables in the first block and cyber-victim experiences in the second block. In the first block (R² = .01), sex (β = .03, t = 2.67, p < .01, pr² = .03) and age (β = −.06, t = −2.68, p < .01, pr² = −.03) were found to be predictors of healthy eating behaviors, but grade level in school was not found to be a predictor (β = .01, t = .54, p > .05, ns). In the second block after accounting for the demographic variables (R² = .01), cyberbullying victimization experiences (β = −.02, t = −1.78, p > .05, ns) did not impact healthy eating behaviors. Thus, the third hypothesis was not supported.

The fourth hypothesis predicted that after controlling for sex, age, and grade level, cyberbullying victimization experiences will positively effect dieting to lose weight. The multiple regression analysis revealed a significant model, R² = .06, F(4, 6239) = 5.47, p < .001, after putting the controlling variables in the first block and cyber-victim experiences in the second block. In the first block (R² = .01), sex (β = .02, t = 1.97, p < .05, pr² = .02) was found to be predictor of dieting to lose weight, but age (β = −.01, t = 1.22, p > .05, ns) and grade level in school (β = .02, t = 1.22, p > .05, ns) were not found to be predictors of dieting to lose weight. In the second block after accounting for the demographic variables (R² = .01), cyberbullying victimization experiences (β = .04, t = 3.79, p < .001, pr² = .04) positively predicted dieting to lose weight. Thus, the fourth hypothesis was supported.

The fifth hypothesis predicted that after controlling for sex, age, and grade level, cyberbullying victimization experiences will negatively effect life satisfaction. The multiple regression analysis revealed a significant model, R² = .01, F(4, 6245) = 19.13, p < .001, after putting the controlling variables in the first block and cyber-victim experiences in the second block. In the first block (R² = .01), sex (β = –.03, t = −2.99, p < .01, pr² = −.03) and grade in school (β = −.05, t = −2.75, p < .01, pr² = −.03) were found to be predictors of cyberbullying perpetration. However, age (β = −.07, t = −1.83, p > .05, ns) was not a significant predictor of life satisfaction. In the second block after accounting for the demographic variables (R² = .01), being cyberbullied (β = −.05, t = −4.05, p < .001, pr² = −.05) negatively effected life satisfaction. Thus, the fifth hypothesis was supported.

Discussion

The main purpose of this study was to examine cyberbullying victimization as a predictor of cyberbullying perception and health-related variables. The first significant finding revealed that cyberbullying victimization experiences were moderately associated to cyberbullying perpetration and there was evidence that cyberbullying victimization served as a strong predictor after controlling for the demographic variables. This finding is consistent to Gámez-Guadix et al. (2015) longitudinal study, supporting the idea that cyberbullied students also report cyberbullying other adolescents in middle schools regardless of students’ demographic characteristics (e.g., sex, age). Similarly, this finding is consistent across previous correlational findings that have established a moderate link between cyberbullying perpetration and cyberbullying victimization (Kowalski & Limber, 2007; Li, 2007; Roberto et al., 2014b). As previously suggested, this study’s finding suggests that cyberbullying victimization is a moderate predictor of cyberbullying perpetration regardless of sex, age, and grade level.

Diversity differences in this study also suggest that these factors explain cyberbullying victimization and perpetration rates. For instance, post hoc analyses revealed that females reported higher cyberbullying victimization rates than did the males, and males reported higher cyberbullying perpetration rates than females did. Because of these sex differences, controlling for sex provides valuable evidence that cyberbullying victimization remains a predictor of cyberbullying perpetration. The argumentative skill deficiency model explains that middle school students might not have developed effective conflict management skills, which leads them to retaliate against others by cyberbullying others, when being cyberbullied (Roberto, 1999; Roberto et al., 2014a). Another explanation may be that given that adolescents spend more time on the Internet and use mobile/tablet devices, adolescents may be more exposed to sending and receiving aggressive messages, which can explain why students can be cyberbullies and cyber victims at the same time regardless of grade level (Ang, 2015; Kowalski & Limber, 2007).

Cyberbullying victimization was also found to have an effect on body image dissatisfaction in middle school students. Previously, Frisén et al. (2014) found that being victimized by school peers over the Internet is linked to victim’s low perceptions of his or her own body image. A possible bidirectional relationship might exist between cyberbullying victimization and body image dissatisfaction. For instance, if an adolescent is dissatisfied with his or her body, and posts about this dissatisfaction online, then peers may make negative virtual remarks about this adolescent’s appearance. Another reason for this phenomenon is explained by self-presentation theory that suggests that adolescents are likely to post images of their body through social networking sites such as Facebook and Twitter to seek others’ approval, which exposes them to higher levels of peer criticism about their body image (Dredge, Gleeson, & de la Piedad Garcia, 2014; Lunde & Frisén, 2011).

An alternative reason for this finding is that cyberbullying victimization has been correlated with having poor self-esteem and having poor self-evaluations of their own body image (Patchin & Hinduja, 2010; Tokunaga, 2010). Because cyberbullying victimization by Internet and/or mobile devices leads to negative mental states, such as depression, low self-esteem, and anxiety (Rawana et al., 2010), it may also lead to feelings of poor body image. Interestingly, sex and age differences were apparent in students’ levels of poor body image when controlling for these factors. Future studies may need to investigate additional factors that can explain why females are less satisfied with their bodies than males after being victimized. Also, studies may need to explore why age differences play a role in being dissatisfied with one’s body image. Thus, future studies still need to develop theoretical causal links between cyberbullying victimization and the evaluations of students’ body image.

Interestingly, cyberbullying victimization was not associated with middle school students’ healthy eating behaviors. Eating healthy food such as fruits and vegetables was not affected by students’ cyberbullying victimization experiences. This finding is not consistent with Kaltiala-Heino et al.’s (2000) study that found a correlation between cyberbullying and healthy eating patterns in adolescents. Previously, Farrow and Fox (2011) also found a correlation between cyberbullying victimization and the healthy eating choices of adolescents. One reason for not finding a correlation among these two variables might have been that some middle school students prefer to not eat healthy foods such as fruits and vegetables at that age level (Story, Neumark-Sztainer, & French, 2002), which might have affected the findings of this study. Some studies suggest that middle school students do not always make the healthiest food choices (Sun, Schutz, & Maffeis, 2004) and cyberbullying victimization might not have influenced their decision-making process of eating healthier meals. However, future studies may continue to explore whether cyberbullying victimization affects high school or college students’ eating behaviors.

However, cyberbullying victimization positively predicted dieting to lose weight across middle school students after controlling for the demographic variables. Dieting emphasized adolescents’ decision to diet to lose weight to improve their body’s image, which may be to eat less food, or to modify their diet so that they lose weight. The finding of this study is consistent with Arat’s (2015) study that found that cyberbullying victimization was correlated with young students’ dieting behaviors. Similarly, Sampasa-Kanyinga and Willmore (2015) found that cyberbullied students were more likely to skip breakfast to lose weight in comparison with those who were not cyberbullied. Although dieting behaviors may be healthy in the short term, these behaviors can become disordered eating patterns in the long term (Loth et al., 2015). One reason why cyberbullying victimization may be a predictor of dieting to lose weight is that receiving aggressive messages such as “you’re fat” or “you’re ugly” can deeply affect students’ psyche, which can affect their decisions to diet to lose weight so that they feel better about themselves (Anderson et al., 2014; DeSmet et al., 2014). Also, other studies suggest that cyberbullied students are more likely to engage in eating disorders such as anorexia and bulimia (Carmona-Torres et al., 2015).

Furthermore, studies report that sex differences occur in dieting behavior. Post hoc analyses revealed that victimized females exhibited dieting behavior than the males did, which is consistent with a previous study by Salafia and Lemer (2012) that found that females in middle school experience disordered eating habits to lose weight in comparison with males. However, cyberbullying alone may not be the sole predictor of eating behaviors; additional factors may also explain why adolescents are exhibiting dieting behaviors such as poor self-esteem and depression (Rasmus, Anna-Lisa, Mauri, Riittakerttu, & Kaj, 2010). Therefore, future studies should continue to investigate this phenomenon controlling for factors such as sex, self-esteem, and depression.

Finally, being cyberbullied had a negative impact on students’ life satisfaction. This finding is stable across the literature; for instance, Moore (2013) also found that cyberbullied middle school students were less satisfied with their family, environment, and school life. In addition, Navarro and colleagues (2015) also found a negative correlation between cyberbullying victimization and life satisfaction of middle school students. Because being cyberbullied leads to negative outcomes such as social anxiety, isolation, depression, and suicide ideation (Ang et al., 2012; Hinduja & Patchin, 2010), this reduces the amount of perceived life satisfaction of middle school students. Studies have shown that cyberbullying victimization negatively affects students’ quality of life both inside the school and outside of the school boundaries into their personal family and social life (Ang et al., 2012).

In addition, a bidirectional explanation can also explain the relationship between these constructs. For instance, if adolescent students are experiencing low life satisfaction, this can lead students to spend more time on social media websites or on their mobile devices to obtain peer approval, which may increase students’ exposure to cyberbullying victimization (Vigil & Wu, 2015). When examining the demographic factors, sex differences emerged in life satisfaction; specifically, victimized males reported being less satisfied with their life in comparison with victimized females. This finding suggests that victimized males may need additional support after being victimized. Future studies may need to continue to examine sex differences in cyberbullying victimization, and control for this variable. Thus, studies should continue to examine the negative impacts of cyberbullying victimization on students’ reports of life satisfaction after controlling for demographic variables.

Strengths of the Study

This cyberbullying victimization study had several strengths that will be discussed. First, because this study included a large national sample of middle school students, it reduced the amount of variance. Second, this sample was ethnically diverse, which represents a diverse population across the United States. Third, this is one of the few studies that has controlled for demographic variables (sex, age, and grade level) in the examination of cyberbullying victimization as a predictor of the variables examined in this study. Because differences exist in these diverse factors, cyberbullying victimization studies may continue to control for sex, age, and grade level. Fourth, this is one of the first studies to examine cyberbullying victimization as a predictor of body image dissatisfaction and healthy eating behaviors in middle school students.

Limitations and Future Directions

There are limitations that will also be discussed along with future directions. First, this study included self-reported survey data, which relied on children’s perceptions. Future studies should adopt the use of other-reported survey data, such as friends or relatives’ reports of participants’ perceived health behaviors. Second, this study was a cross-sectional study, which focused on collected data at one point in time. Future studies may adopt the use of longitudinal data of the same participants from sixth grade through eighth grade. This will allow researchers to determine whether cyberbullying victimization remains a strong predictor of the variables investigated in this study across the middle school years. Conducting longitudinal design methods can also enable researchers to detect the development of the possible effects of cyberbullying victimization overtime such as body image dissatisfaction, dieting to lose weight, and reduced life satisfaction.

Third, because online and social media sites often have age restrictions, not all participants in this sample may have equal access to these platforms because the average age of the sample was 12. This age restriction may exclude participants who did not have access to social media platforms such as Facebook, which requires users to be 13 years old. Also, participants living in economically disadvantaged situations may have limited access to online forums. Future studies may include questions addressing the accessibility of online and social media platforms to determine the types of social media websites that are accessible to younger children who are younger than 13 years of age. Also, studies may control for economical disadvantages that children may face to determine whether this limits children’s access to online platforms. Fourth, this study did not adopt the use of a specific theoretical framework because previous scholarship had only emphasized the correlations of the variables investigated in this study. The findings from this study may build theoretical models of the variables with the inclusion of additional variables such as social self-efficacy from Bandura’s (2001) social cognitive theory to examine whether social self-efficacy regulates the attitudes and behaviors of cyberbullying victims. Future studies could also assess relevant theories such as self-concept theory (Baumeister, 1999) to examine how cyberbullying victims’ body dissatisfaction and dieting behavior influence their self-concept.

Policy Implications for Intervention

Findings of this study inform recommendations for potential policy implications to manage cyberbullying victimization affairs in middle schools. Because of the negative effects of cyberbullying victimization, policy makers can encourage the development of cyberbullying victimization policies in middle school districts to avoid legal ramifications from the state and to address students’ negative body image and dieting patterns. Developing anticyberbullying policies at the district level can encourage school administrators and counselors to integrate intervention programs to prevent cyberbullying victimization behavior by supporting victims and providing consequences for cyberbullying behaviors (Franek, 2006). Interventions that focus on educating counselors, teachers, and parents about cyberbullying victimization policies can be effective in helping stakeholders respond appropriately to cyberbullying victimization episodes (Beale & Hal, 2007; Bhat, 2008). For example, if an educator becomes a witness of cyberbullying perpetration behavior, he or she can refer to the policy on the consequences of such behavior, and may penalize the behavior appropriately (e.g., suspending the adolescent) before it continues to affect other victimized children.

In addition, given the establishment of the victim–offender link, interventions may target victims of cyberbullying. By educating victims about the strategies to cope with cyberbullying and alerting them of the negative consequences of cyberbullying behavior, practitioners can prevent the “victim-offender” cycle in peer relationships (Lauritsen & Laub, 2007). For example, the KiVA program, a peer-support program of cyberbullying victims has been shown to be effective at reducing both cyberbullying and cyber-victimization rates in middle schools (Karna et al., 2013).

To address students’ body image and dieting issues, anticyberbullying policies should recommend mental and behavioral health assessments, and support mechanisms. Administrators, teachers, or counselors who detect cyberbullying victimization can assess victims’ and bullies’ mental and behavioral well-being to address these risk factors. These student assessments can examine the possible symptoms of being victimized (e.g., body image issues) to offer counseling support to victims of cyberbullying. Intervention programs that emphasize both reporting and response mechanisms through intervention teams in schools can enable victims and peer bystanders to report cyberbullying victimization occurrences through textual alert systems, and provide response mechanisms to enable educators, counselors, and providers the means to address cyberbullying victimization as it emerges (Sabella, 2012). Future intervention research may continue to assess policy implementations in middle schools (sixth-, seventh-, and eighth-grade levels) to determine the effectiveness of anticyberbullying policies in students, teachers, administrators, and counselors to halt future victimization patterns.

Conclusion

In conclusion, the focus of this investigation was to examine cyberbullying victimization as a predictor of cyberbullying perpetration, body image dissatisfaction, healthy eating behaviors, and life satisfaction in middle school students. After controlling for the demographic factors, this study found that cyberbullying victimization predicted cyberbullying perpetration, which suggests that those who cyberbully others may also be cyberbullied by others. Another finding of this study was that cyberbullying victimization experiences positively predicted body image dissatisfaction. However, after controlling for the demographic factors, cyberbullying victimization did not predict healthy related eating behaviors. However, cyberbullying victimization did predict students’ dieting behaviors to lose weight after controlling for the demographic variables. Finally, this study found that cyberbullying victimization negatively affected students’ perceived life satisfaction after controlling for the demographic factors. Therefore, this investigation underscores the value of examining how cyberbullying victimization affects middle school students’ cyberbullying perpetration, body image dissatisfaction, and life satisfaction.

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 Biography

Leslie Ramos Salazar (PhD, Arizona State University) is an Assistant Professor of Business Communication and Decision Management at the College of Business at West Texas A&M University. Her research interests include health communication, business communication, conflict management, and interpersonal communication.

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