- Email: [email protected]
Associations between cyberbullying victimization and deviant health risk behaviors
G Model
ARTICLE IN PRESS
SOCSCI-1483; No. of Pages 6
The Social Science Journal xxx (2017) xxx–xxx
Contents lists available at ScienceDirect
The Social Science Journal journal homepage: www.elsevier.com/locate/soscij
Research Note
Associations between cyberbullying victimization and deviant health risk behaviors Roderick Graham ∗ , Frank R. Wood Jr. Department of Sociology and Criminal Justice, Old Dominion University, Norfolk, VA 23529, USA
a r t i c l e
i n f o
Article history: Received 21 January 2018 Received in revised form 20 May 2018 Accepted 21 May 2018 Available online xxx Keywords: Cyberbullying Health risk behaviors Juvenile delinquency Strain theory Bullying
a b s t r a c t The primary aim of this study is to establish associations between cyberbullying victimization and health risk behaviors that have been traditionally linked to juvenile delinquency. These “deviant health risk behaviors” include drug use, alcohol use, and sex with multiple partners. A secondary aim is to compare the effects of cyberbullying on these deviant health risk behaviors to the effects of physical bullying. Models are estimated using the 2015 Youth Risk Behavior Survey conducted by the Center for Disease Control and Prevention (n = 15,624). The findings showed that cyberbullying victimization is positively associated with each deviant health risk behavior predicted. The magnitude of this association increased when respondents reported being both cyberbullied and physically bullied. When comparing the effects of cyberbullying to physical bullying, the findings showed that respondents who were cyberbullied reported higher rates of each deviant health risk behavior. Establishing these associations is important for scholars and education professionals as they point to another pathway through which a young person can adopt delinquent or problematic behavior. Published by Elsevier Inc. on behalf of Western Social Science Association.
1. Introduction Cyberbullying has become commonplace in young people’s lives. The Center for Disease Control reports that in 2015 15.5% of students stated that they had been cyberbullied (Kann, McManus, Harris, Shanklin, & Flint, 2016). Similarly, in that same year, the School Supplement to the National Crime Victimization Survey reports that 11.5% of students had been cyberbullied (Lessne & Yanez, 2016). The prevalence of cyberbullying has urged scholars to understand its impact on young people. Accordingly, scholars have identified an array of consequences linked to cyberbullying victimization. These
∗ Corresponding author. E-mail addresses: [email protected] (R. Graham), [email protected] (F.R. Wood Jr.).
consequences can be divided into two categories. First, studies have linked cyberbullying victimization to a series of adverse psychological outcomes such as feelings of powerlessness and low self-esteem. Second, studies have linked being a victim of cyberbullying to engaging in subsequent deviant behaviors such as aggression and bringing weapons to school. This brief introduces research that establishes associations between cyberbullying victimization and health risk behaviors that have been traditionally linked to juvenile delinquency. Health risk behaviors include lack of sleep, overeating, not eating proper foods, and not getting enough exercise. “Deviant health risk behaviors” include drug use, alcohol use, and sex with multiple partners. As we will discuss below, criminological and sociological work has demonstrated that deviant health risk behaviors co-occur with juvenile delinquency, crime, and deviant subcultures. It is imperative that social scientists explore the linkages
https://doi.org/10.1016/j.soscij.2018.05.005 0362-3319/Published by Elsevier Inc. on behalf of Western Social Science Association.
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
2
between cyberbullying victimization and deviant health risk behaviors as this relationship may present another pathway into juvenile delinquency. 2. Literature review 2.1. Defining cyberbullying Olweus defines bullying as ‘a phenomenon that occurs’ when a person is exposed, repeatedly and over time, to negative actions on the part of one or more other persons, and he or she has difficulty defending himself or herself (Olweus, 1993). The major elements of this phenomenon – repeated behaviors, inflicting harm, the victim being a person or group possessing less power – have been imported into the cyberbullying literature. Cyberbullying definitions add the additional element of the behavior occurring electronically (Brochado, Soares, & Fraga, 2017; Gladden, Vivolo-Kantor, Hamburger, & Lumpkin, 2014; Hinduja & Patchin, 2015; Kim, Song, & Jennings, 2017; Palermiti, Servidio, Bartolo, & Costabile, 2017). 2.2. The effects of cyberbullying The bulk of studies exploring cyberbullying victimization have linked the phenomenon to adverse psychological outcomes. Scholars have identified relationships between cyberbullying and a variety of behaviors including school avoidance (Payne & Hutzell, 2017), low self-esteem (Palermiti et al., 2017; Patchin & Hinduja, 2010), antisocial emotions such as fear and powerlessness (Hoff & Mitchell, 2009), and higher levels of stress and anxiety (Fredstrom, Adams, & Gilman, 2011). A second, but smaller strand of studies links cyberbullying victimization to subsequent deviant behaviors. This includes violent and aggressive behaviors towards others (Calvete, Orue, Estévez, Villardón, & Padilla, 2010) including cyberbullying (Chapin & Coleman, 2017), juvenile delinquency (Hay, Meldrum, & Mann, 2010), and bringing a weapon to school (Keith, 2018). This study will work within this second strand and focus on health risk behaviors that have historically been situated within a context of crime and deviance. 2.3. Health risk behaviors that are also deviant behaviors This study will focus on health risk behaviors that have historically been situated within a context of crime and deviance. Theoretically, deviant behaviors are often copresent. There is a lineage of subcultural theories that argue that groups develop attitudes and beliefs that are conducive to crime and deviance (Cloward & Ohlin, 1966; Cohen, 1971; Sykes & Matza, 1957). Labeling theory argues that individuals adopt a deviant identity and then choose deviant behaviors that conform to that identity (Kroska, Lee, & Carr, 2017; Matza, 2010). Self-control theories argue that individuals with low self-control are more likely to commit deviant or criminal acts that require immediate gratification (Gottfredson & Hirschi, 1990). Some research has shown these co-occurrences empirically, as problem behaviors in youth have been shown to cluster together (Bartlett, Holditch-Davis, & Belyea, 2005).
Some of these co-occurring deviant behaviors are also health risk behaviors. Examples include drug use, alcohol use, and risky sexual behavior. Alcohol use and juvenile delinquency have repeatedly been linked to other deviant behaviors (Felson, Savolainen, Aaltonen, & Moustgaard, 2008), as has drug use and juvenile delinquency (Wilson & Petersilia, 2011). The direct links between risky sexual behavior such as multiple partners or sex without birth control are less established. However, Armour and Haynie (2007) present evidence suggesting that early sexual activity increases the risk of future deviant behavior. Importantly, these behaviors are often co-present within youth who are more likely to engage in deviant or criminal behavior.
2.4. Linking cyberbullying victimization to crime and deviance One theory that links cyberbullying to deviant health risk behaviors is General Strain Theory (GST) (Agnew, 1992). GST posits that deviance can be a result of how an individual copes with life’s stressors or “strains.” The theory argues that there are three categories of strains: (1) not being able to achieve one’s goals, (2) the presentation of noxious or negatively valued stimuli, and (3) the loss of positively valued stimuli. Individuals who are cyberbullied are presented with a strain of the second type. The hateful or insulting communication is a negatively valued stimulus. In an attempt to cope with or remove the negative stimuli, the individual may engage in deviant behaviors. For example, a young person may turn to drugs or alcohol to cope with the emotional toil of being insulted or resort to sexual activity because of the low self-esteem caused by cyberbullying. In a later article, Agnew (2001) further specified GST by describing strain characteristics most likely to lead to crime and deviance. These are strains that are “(1) seen as unjust, (2) seen as high in magnitude, (3) are associated with low social control, and (4) create some pressure or incentive to engage in criminal coping” (Agnew, 2001). In this later article, Agnew specifically identifies bullying as a strain that can lead to crime and deviance. As a result, several studies have linked cyberbullying to deviant behaviors (Hay et al., 2010; Jang, Song, & Kim, 2014; Keith, 2018).
3. The present study While the link between cyberbullying victimization and other types of deviant behaviors is well known, there has been surprisingly little work done linking cyberbullying victimization to behaviors such as drug and alcohol use and risky sexual behavior. The primary aim of this study is to establish associations between cyberbullying victimization and these behaviors. Because cyberbullying and physical bullying are associated with each other (Finkelhor, Shattuck, Turner, Ormrod, & Hamby, 2011; Garnett & BrionMeisels, 2017; Hay et al., 2010; Keith, 2018; Payne & Hutzell, 2017; Resett & Gamez-Guadix, 2017), a secondary aim is to compare the effects of cyberbullying to physical bullying.
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
4. Method 4.1. Participants Data for this study comes from the 2015 Youth Risk Behavior Survey (YRBS) conducted by the Center for Disease Control (CDC). The initial n is 15,624. The YRBS monitors several types of health-risk behaviors including poor diet, lack of exercise, unsafe sexual behaviors, alcohol and other drug use, and tobacco use. The YRBS employs a three-stage cluster sample design to collect a representative sample of 9th through 12th-grade students. The target population consisted of all public and private school students in grades 9 through 12. One of the advantages of using the YRBS is that our results can be compared to several other studies which have also used the YRBS to explore bullying and cyberbullying (Kahle, 2017; Merrill & Hanson, 2016; Peters, Hatzenbuehler, & Davidson, 2017; Romero, Bauman, Ritter, & Anand, 2017; Swahn, Bossarte, Palmier, Yao, & Van Dulmen, 2013).
3
• Alcohol use: “During the past 30 days, on how many days did you have at least one drink of alcohol? 0 days [0]; 1 or 2 days [1]; 3 to 5 days [2]; 6 to 9 days [3]; 10 to 19 days [4]; 20 to 29 days [5]; All 30 days [6].” The control variables included in models are age, gender, race/ethnicity, grade, and sexual orientation. Table 1 shows descriptive statistics for all variables used in the analysis. 4.3. Procedure Multivariate regression analysis was used to identify the effects of cyberbullying on deviant health risk behaviors net of common sociodemographic factors and traditional bullying. Models were run for sexual frequency, alcohol frequency, and marijuana frequency. Weights supplied by the CDC were used for all regression models. 5. Data analysis
4.2. Measures
5.1. Regression models
The primary independent variable in this analysis is cyberbullying:
All models report similar findings (Table 2). First, the effect of physical bullying is not significant. Students who report being physically bullied do not report higher rates of alcohol, drug, or sexual activity when compared to students who report no bullying (the reference group). Second, cyberbullying is significant. For all three models, students who report being cyberbullied also report higher levels of all three deviant health risk behaviors. The effect of being cyberbullied is to increase reported rates of sexual frequency by 0.25, by 0.58 for alcohol frequency, and by 0.35 for marijuana frequency. Third, while physical bullying alone has no effect, respondents who report being both physically bullied and cyberbullied report the highest rates of deviant health risk behaviors. The effect of both types of bullying is associated with an increase of 0.46 for sexual frequency, an increase of 0.64 for alcohol frequency, and an increase of 0.52 for marijuana frequency. The primary aim of this study is to establish associations between cyberbullying victimization and health risk behaviors associated with deviance. To that end, the three models above show consistent associations across three such behaviors. We can see these effects more clearly by producing point estimates for sexual frequency, alcohol, and marijuana usage (Fig. 1). These point estimates are best guesses for respondents in the sample given model parameter estimates. Fig. 1 contextualizes the regression models by grounding values in the measures from the YRBS. We see that the differences between groups based on bullying is consistent and significant, however the magnitude between groups is not particularly large. For example, the difference in marijuana frequency for an average respondent who reports being both bullied and cyberbullied, and an average respondent who reports no bullying is 1.69 to 1.33. This means that both groups fall between the “3 to 9 times” and “10 to 19 times” levels for the survey measure. A secondary aim is to compare the effects of cyberbullying to physical bullying. The above models estimate deviant
• Cyberbullying — “During the past 12 months, have you ever been electronically bullied? (Count being bullied through email, chat rooms, instant messaging, websites, or texting.) Yes or No.” This measure of cyberbullying does not measure all three dimensions of cyberbullying as agreed upon by the literature. However, strains are reacted to based upon the perceptions of those strains. This measure of bullying is subjective, and asked the responded their perception of whether or not they were cyberbullied. • Traditional, or physical bullying — “During the past 12 months, have you ever been bullied on school property? Yes or No.” Bullying victimization and cyberbullying victimization were differentiated by creating a four-category variable that separates respondents into those who were not bullied – 11,649 (75.6%), bullied physically only – 1,506 (9.8%), cyberbullied only – 808 (5.2%), and respondents who reported being both physically bullied and cyberbullied – 1,439 (9.3%). There were three dependent variables in the analysis, multiple sexual partners, marijuana use, and alcohol use. These variables were measured ordinally: • Number of sexual partners: “During the past three months, with how many people did you have sexual intercourse? I have never had sexual intercourse [0]; I have had sexual intercourse; but not during the past 3 months [1]; 1 person [2]; 2 people [3]; 3 people [4]; 4 people [5]; 5 people [6]; 6 or more people [7].” • Marijuana use: “During the past 30 days, how many times did you use marijuana? 0 times [0]; 1 or 2 times [1]; 3 to 9 times [2]; 10 to 19 times [3]; 20 to 39 times [4]; 40 or more times [5].”
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
4 Table 1 Descriptive statistics. Categorical Gender Female Male Total
7,757 (49.6%) 7,867 (50.3%) 15,624
Race White American Indian/Alaska Native Asian African American Hawaiian/Pacific Isl. Hispanic Multiple (Hispanic) Multiple (Non-Hisp.) Total
6,849 (44.9%) 163 (1%) 627 (4.1%) 1,667 (10.9%) 100 (1%) 2,365 (15.5%) 2,756 (18.1%) 739 (5%) 15,266
Grade 9th Grade 10th Grade 11th Grade 12th Grade Total
4,003 (25.9%) 3,938 (25.4% 3,930 (25.4%) 3,601 (23.2%) 15,472
Sexuality Straight Gay/lesbian Bisexual Not sure Total
12,954 (82.9%) 324 (2.2%) 922 (6.3%) 503 (3.4%) 14,703
Bullying Physically bullied only Cyberbullied only Both physical and cyber No bullying Total
1,506 (9.8%) 808 (5.2%) 1,439 (9.3%) 11,649 (75.6%) 15,402
Continuous
No. of sexual partners Marijuana use Alcohol use
Mean
SD
Range
0.88 1.37 1.08
1.28 2.08 2.01
0–7 0–6 0–7
Table 2 Parameter estimates for risky behaviors. Sexual frequency N = 13,265 Estimate (SE)
Alcohol frequency N = 10,582 Estimate (SE)
Marijuana frequency N = 13,811 Estimate (SE)
Intercept Female
0.45 (0.26)*** −0.24 (0.02)***
0.73 (0.05)*** −0.29 (0.04)***
0.75 (0.04)*** −0.42 (0.03)***
Race/Ethnicity1 American Indian Asian African American Hawaiian/Pacific Isl. Hispanic Multiple (Hispanic) Multiple (Non-Hisp.)
0.21 (0.14) −0.39 (0.06)*** 0.29 (0.03)*** 0.01 (0.16) <0.001 (0.04) 0.22 (0.03)*** 0.27 (0.05)***
0.69 (0.24)** −0.71 (0.10)*** -0.42 (0.06)*** 0.07 (0.28) −0.14 (0.06)* 0.10 (0.06) −0.03 (0.09)
1.09 (0.22)*** −0.66 (0.09)*** 0.45 (0.05)*** 0.18 (0.23) 0.29 (0.06)*** 0.53 (0.05)*** 0.27 (0.08)**
Grade2 10th Grade 11th Grade 12th Grade
0.28 (0.03)*** 0.55 (0.03)*** 0.79 (0.03)***
0.28 (0.05)*** 0.67 (0.05)*** 0.82 (0.05)***
0.41 (0.05)*** 0.76 (0.05)*** 1.00 (0.05)***
Sexual orientation3 Gay/lesbian Bisexual Not sure
0.10 (0.08) 0.19 (0.05)*** 0.03 (0.06)
0.27 (0.15) 0.18 (0.08)* 0.14 (0.11)
0.51 (0.13)*** 0.58 (0.007)*** 0.21 (0.10)*
Bullying victimization4 Physical Cyber Cyber and physical R2
−0.04 (0.04) 0.25 (0.05)*** 0.46 (0.04)*** 0.08
0.11 (0.06) 0.58 (0.08)*** 0.64 (0.06)*** 0.05
−0.09 (0.06) 0.35 (0.07)*** 0.52 (0.06)*** 0.07
Significance codes: ‘***’ = <0.001; ‘**’ = <0.01; ‘*’ = <0.05. Reference categories: 1 — White; 2 — 9th Grade; 3 — Straight; 4 — No bullying.
health-risk behaviors with a dummied bullying variable using students who have never been bullied as the reference group. One way to more clearly differentiate between traditional bullying and cyberbullying is to examine only students who have been bullied and use traditional bully-
ing as the reference group (Table 3). Table 3 shows that when we compare cyberbullied respondents directly to bullied respondents, we see across models a consistent and significant difference, ranging from 0.42 (sexual frequency) to 0.57 (marijuana frequency).
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
5
Fig. 1. Deviant health risk behavior estimates for students grouped by bullying victimization. Table 3 Parameter estimates for risky behaviors using bullied students only.a
Intercept Cyberbullying R2
Sexual frequency N = 3,211 Estimate (SE)
Alcohol frequency N = 2,567 Estimate (SE)
Marijuana frequency N = 3,345 Estimate (SE)
0.44*** 0.42*** 0.08
0.78*** 0.50*** 0.04
0.65*** 0.57*** 0.08
Significance codes: ‘***’ = <0.001; ‘**’ = <0.01; ‘*’ = <0.05 a Estimates shown are controlling for sociodemographic variables.
6. Discussion This study aimed to (1) provide data examining the association between cyberbullying victimization and deviant health-risk behaviors and (2) to compare the effects of cyberbullying to physical bullying. Our results showed a positive association between cyberbullying victimization and all three deviant health risk behaviors. The magnitude of this association increased when respondents reported being both cyberbullied and physically bullied. When comparing the effects of cyberbullying to physical bullying, we also see a consistent effect on respondents who were bullied reporting higher rates of deviant health risk behaviors. In showing that cyberbullying is positively associated with deviant health-risk behaviors, this research is in line with other studies linking cyberbullying victimization to adverse outcomes (Fredstrom et al., 2011; Hoff & Mitchell, 2009; Palermiti et al., 2017; Patchin & Hinduja, 2010; Payne & Hutzell, 2017). This study adds to the extant research by showing how cyberbullying victimization is also associated with deviant health-risk behaviors. Given these findings, scholars and professionals with interest in cyberbullying may need to consider cyberbullying alongside other stressors such as family turmoil and economic inequality when assessing the risk of a young person committing deviant or criminal acts.
We suggest that the causal mechanism linking cyberbullying victimization to these deviant health risk behaviors is that cyberbullying is a strain. Young people cope with strains in a variety of ways, some of them being deviant. Prior studies have made this connection to other deviant behaviors (Hay et al., 2010; Jang et al., 2014; Keith, 2018; Kim et al., 2017), and we are widening the scope of this relationship to include deviant health risk behaviors. The study was designed to establish baseline associations between cyberbullying and deviant health risk behaviors, and differentiate between cyberbullying and physical bullying. We suggest that future research should follow along at least three paths. First, the measure that we used for cyberbullying was narrow. The consensus understanding of cyberbullying is that it is a repeated behavior done to inflict harm on a person or group possessing less power electronically. We were not able to tap into all elements of this definition in this study. Instead, we were able to focus on subjective understandings of cyberbullying. Future research will need to tease apart these dimensions and explore how they interplay with rates of deviant sexual behaviors. A second path will be to explore the process through which being cyberbullied leads to deviant health risk behaviors, which then ultimately results in a wider array of deviant activities including criminal acts and school dropout. General Strain Theory and Labeling Theory pro-
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
6
vide a theoretical rationale for why one would expect cyberbullying to lead to deviance. However empirical research, especially qualitative, is lacking. For instance, consider our finding that while physical bullying alone has no effect, respondents who report being both physically bullied and cyberbullied report the highest rates of deviant health risk behaviors. This interaction effect can be explored in greater detail through statistical measures. However, we suggest that qualitative work is best suited to uncovering and detailing the processes and meanings associated with this correlation. A third path is to explore the interactions between cyberbullying and disadvantaged groups such as racial minorities, sexual minorities, women, and people with disabilities. The logic being that these groups are already in a position of less power, placing them – as per the consensus understanding of cyberbullying – “at-risk.” This is the path upon which the current authors are progressing, and this brief was the initial stages of this research program.
References Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47–88. Armour, S., & Haynie, D. L. (2007). Adolescent sexual debut and later delinquency. Journal of Youth and Adolescence, 36(2), 141–152. http://dx.doi.org/10.1007/s10964-006-9128-4 Agnew, Robert. (2001). Building on the foundation of general strain theory: Specifying the types of strain most likely to lead to crime and delinquency. Journal of Research in Crime and Delinquency, 38(4), 319–391. Bartlett, R., Holditch-Davis, D., & Belyea, M. (2005). Clusters of problem behaviors in adolescents. Research in Nursing and Health, 28(3), 230–239. http://dx.doi.org/10.1002/nur.20078 Brochado, S., Soares, S., & Fraga, S. (2017). A scoping review on studies of cyberbullying prevalence among adolescents. Trauma, Violence, and Abuse, 18(5), 523–531. http://dx.doi.org/10.1177/1524838016641668 Calvete, E., Orue, I., Estévez, A., Villardón, L., & Padilla, P. (2010). Cyberbullying in adolescents: Modalities and aggressors’ profile. Computers in Human Behavior, 26(5), 1128–1135. http://dx.doi.org/10.1016/j.chb.2010.03.017 Chapin, J., & Coleman, G. (2017). The cycle of cyberbullying: Some experience required. The Social Science Journal, 54(3), 314–318. http://dx.doi.org/10.1016/j.soscij.2017.03.004 Cloward, R. A., & Ohlin, L. E. (1966). Delinquency and opportunity: A theory of delinquent gangs. New York, N.Y.; London: Free Press; Collier Macmillan. Cohen, A. K. (1971). Delinquent boys; The culture of the gang. New York: Free Press (Macmillan). Felson, R., Savolainen, J., Aaltonen, M., & Moustgaard, H. (2008). Is the association between alcohol use and delinquency causal or spurious? Criminology, 46(3), 785–808. http://dx.doi.org/10.1111/j.1745-9125.2008.00120.x Finkelhor, D., Shattuck, A., Turner, H. A., Ormrod, R., & Hamby, S. L. (2011). Polyvictimization in developmental context. Journal of Child and Adolescent Trauma, 4(4), 291–300. http://dx.doi.org/10.1080/19361521.2011.610432 Fredstrom, B. K., Adams, R. E., & Gilman, R. (2011). Electronic and school-based victimization: Unique contexts for adjustment difficulties during adolescence. Journal of Youth and Adolescence, 40(4), 405–415. http://dx.doi.org/10.1007/s10964-010-9569-7 Garnett, B. R., & Brion-Meisels, G. (2017). Intersections of victimization among middle and high school youth: Associations between polyvictimization and school climate. Journal of Child and Adolescent Trauma, 10(4), 377–384. http://dx.doi.org/10.1007/s40653-017-0183-7 Gladden, R. M., Vivolo-Kantor, A. M., Hamburger, M. E., & Lumpkin, C. D. (2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements, version 1.0. pp. 116. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention and U.S. Department of Education. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford, Calif: Stanford University Press.
Hay, C., Meldrum, R., & Mann, K. (2010). Traditional bullying, cyber bullying, and deviance: A general strain theory approach. Journal of Contemporary Criminal Justice, 26(2), 130–147. http://dx.doi.org/10.1177/1043986209359557 Hinduja, S., & Patchin, J. W. (2015). Bullying beyond the schoolyard: Preventing and responding to cyberbullying (Second edition). Thousand Oaks, California: Corwin. Hoff, D. L., & Mitchell, S. N. (2009). Cyberbullying: Causes, effects, and remedies. Journal of Educational Administration, 47(5), 652–665. http://dx.doi.org/10.1108/09578230910981107 Jang, H., Song, J., & Kim, R. (2014). Does the offline bully-victimization influence cyberbullying behavior among youths? Application of General Strain Theory. Computers in Human Behavior, 31, 85–93. http://dx.doi.org/10.1016/j.chb.2013.10.007 Kahle, L. (2017). Are sexual minorities more at risk? Bullying victimization among lesbian, gay, bisexual, and questioning youth. Journal of Interpersonal Violence, https://doi.org/10.1177/0886260517718830 Kann, L., McManus, T., Harris, W., Shanklin, S., & Flint, K. (2016). Youth risk behavior surveillance — United States, 2015. Center for Disease Control and Prevention. Retrieved from https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2015/ss6506 updated.pdf Keith, S. (2018). How do traditional bullying and cyberbullying victimization affect fear and coping among students? An application of general strain theory. American Journal of Criminal Justice, 43(1), 67–84. http://dx.doi.org/10.1007/s12103-017-9411-9 Kim, J., Song, H., & Jennings, W. G. (2017). A distinct form of deviance or a variation of bullying? Examining the developmental pathways and motives of cyberbullying compared with traditional bullying in South Korea. Crime and Delinquency, 63(12), 1600–1625. Kroska, A., Lee, J. D., & Carr, N. T. (2017). Juvenile delinquency and selfsentiments: Exploring a labeling theory proposition. Social Science Quarterly, 98(1), 73–88. http://dx.doi.org/10.1111/ssqu.12307 Lessne, D., & Yanez, C. (2016). School crime supplement to the national crime victimization survey.. Retrieved from https://nces.ed.gov/pubs2017/2017015.pdf Matza, D. (2010). Becoming deviant (Rev. ed). New Brunswick, N.J: Transaction Publishers. Merrill, R. M., & Hanson, C. L. (2016). Risk and protective factors associated with being bullied on school property compared with cyberbullied. BMC Public Health, 16(1) http://dx.doi.org/10.1186/s12889-016-2833-3 Olweus, D. (1993). Bullying at school: What we know and what we can do. Oxford, UK; Cambridge, USA: Blackwell. Palermiti, A. L., Servidio, R., Bartolo, M. G., & Costabile, A. (2017). Cyberbullying and self-esteem: An Italian study. Computers in Human Behavior, 69, 136–141. http://dx.doi.org/10.1016/j.chb.2016.12.026 Patchin, J. W., & Hinduja, S. (2010). Cyberbullying and self-esteem. Journal of School Health, 80(12), 614–621. Payne, A. A., & Hutzell, K. L. (2017). Old wine, new bottle? Comparing interpersonal bullying and cyberbullying victimization. Youth and Society, 49(8), 1149–1178. Peters, Z. J., Hatzenbuehler, M. L., & Davidson, L. L. (2017). Examining the intersection of bullying and physical relationship violence among New York City high school students. Journal of Interpersonal Violence, 32(1), 49–75. http://dx.doi.org/10.1177/0886260515585532 Resett, S., & Gamez-Guadix, M. (2017). Traditional bullying and cyberbullying: Differences in emotional problems, and personality. Are cyberbullies more Machiavellians? Journal of Adolescence, 61, 113–116. http://dx.doi.org/10.1016/j.adolescence.2017.09.013 Romero, A., Bauman, S., Ritter, M., & Anand, P. (2017). Examining adolescent suicidal behaviors in relation to gun carrying and bullying. Journal of School Violence, 16(4), 445–458. http://dx.doi.org/10.1080/15388220.2016.1190933 Swahn, M. H., Bossarte, R. M., Palmier, J. B., Yao, H., & Van Dulmen, M. H. M. (2013). Risk factors for multiple forms of violent experiences: Analyses of the 2009 Youth Risk Behavior Survey. Vulnerable Children and Youth Studies, 8(3), 225–236. http://dx.doi.org/10.1080/17450128.2012.722702 Sykes, G. M., & Matza, D. (1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22(6), 8. http://dx.doi.org/10.2307/2089195 Wilson, J. Q., & Petersilia, J. (Eds.). (2011). Crime and public policy. Oxford; New York, NY: Oxford University Press.
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
ARTICLE IN PRESS
SOCSCI-1483; No. of Pages 6
The Social Science Journal xxx (2017) xxx–xxx
Contents lists available at ScienceDirect
The Social Science Journal journal homepage: www.elsevier.com/locate/soscij
Research Note
Associations between cyberbullying victimization and deviant health risk behaviors Roderick Graham ∗ , Frank R. Wood Jr. Department of Sociology and Criminal Justice, Old Dominion University, Norfolk, VA 23529, USA
a r t i c l e
i n f o
Article history: Received 21 January 2018 Received in revised form 20 May 2018 Accepted 21 May 2018 Available online xxx Keywords: Cyberbullying Health risk behaviors Juvenile delinquency Strain theory Bullying
a b s t r a c t The primary aim of this study is to establish associations between cyberbullying victimization and health risk behaviors that have been traditionally linked to juvenile delinquency. These “deviant health risk behaviors” include drug use, alcohol use, and sex with multiple partners. A secondary aim is to compare the effects of cyberbullying on these deviant health risk behaviors to the effects of physical bullying. Models are estimated using the 2015 Youth Risk Behavior Survey conducted by the Center for Disease Control and Prevention (n = 15,624). The findings showed that cyberbullying victimization is positively associated with each deviant health risk behavior predicted. The magnitude of this association increased when respondents reported being both cyberbullied and physically bullied. When comparing the effects of cyberbullying to physical bullying, the findings showed that respondents who were cyberbullied reported higher rates of each deviant health risk behavior. Establishing these associations is important for scholars and education professionals as they point to another pathway through which a young person can adopt delinquent or problematic behavior. Published by Elsevier Inc. on behalf of Western Social Science Association.
1. Introduction Cyberbullying has become commonplace in young people’s lives. The Center for Disease Control reports that in 2015 15.5% of students stated that they had been cyberbullied (Kann, McManus, Harris, Shanklin, & Flint, 2016). Similarly, in that same year, the School Supplement to the National Crime Victimization Survey reports that 11.5% of students had been cyberbullied (Lessne & Yanez, 2016). The prevalence of cyberbullying has urged scholars to understand its impact on young people. Accordingly, scholars have identified an array of consequences linked to cyberbullying victimization. These
∗ Corresponding author. E-mail addresses: [email protected] (R. Graham), [email protected] (F.R. Wood Jr.).
consequences can be divided into two categories. First, studies have linked cyberbullying victimization to a series of adverse psychological outcomes such as feelings of powerlessness and low self-esteem. Second, studies have linked being a victim of cyberbullying to engaging in subsequent deviant behaviors such as aggression and bringing weapons to school. This brief introduces research that establishes associations between cyberbullying victimization and health risk behaviors that have been traditionally linked to juvenile delinquency. Health risk behaviors include lack of sleep, overeating, not eating proper foods, and not getting enough exercise. “Deviant health risk behaviors” include drug use, alcohol use, and sex with multiple partners. As we will discuss below, criminological and sociological work has demonstrated that deviant health risk behaviors co-occur with juvenile delinquency, crime, and deviant subcultures. It is imperative that social scientists explore the linkages
https://doi.org/10.1016/j.soscij.2018.05.005 0362-3319/Published by Elsevier Inc. on behalf of Western Social Science Association.
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
2
between cyberbullying victimization and deviant health risk behaviors as this relationship may present another pathway into juvenile delinquency. 2. Literature review 2.1. Defining cyberbullying Olweus defines bullying as ‘a phenomenon that occurs’ when a person is exposed, repeatedly and over time, to negative actions on the part of one or more other persons, and he or she has difficulty defending himself or herself (Olweus, 1993). The major elements of this phenomenon – repeated behaviors, inflicting harm, the victim being a person or group possessing less power – have been imported into the cyberbullying literature. Cyberbullying definitions add the additional element of the behavior occurring electronically (Brochado, Soares, & Fraga, 2017; Gladden, Vivolo-Kantor, Hamburger, & Lumpkin, 2014; Hinduja & Patchin, 2015; Kim, Song, & Jennings, 2017; Palermiti, Servidio, Bartolo, & Costabile, 2017). 2.2. The effects of cyberbullying The bulk of studies exploring cyberbullying victimization have linked the phenomenon to adverse psychological outcomes. Scholars have identified relationships between cyberbullying and a variety of behaviors including school avoidance (Payne & Hutzell, 2017), low self-esteem (Palermiti et al., 2017; Patchin & Hinduja, 2010), antisocial emotions such as fear and powerlessness (Hoff & Mitchell, 2009), and higher levels of stress and anxiety (Fredstrom, Adams, & Gilman, 2011). A second, but smaller strand of studies links cyberbullying victimization to subsequent deviant behaviors. This includes violent and aggressive behaviors towards others (Calvete, Orue, Estévez, Villardón, & Padilla, 2010) including cyberbullying (Chapin & Coleman, 2017), juvenile delinquency (Hay, Meldrum, & Mann, 2010), and bringing a weapon to school (Keith, 2018). This study will work within this second strand and focus on health risk behaviors that have historically been situated within a context of crime and deviance. 2.3. Health risk behaviors that are also deviant behaviors This study will focus on health risk behaviors that have historically been situated within a context of crime and deviance. Theoretically, deviant behaviors are often copresent. There is a lineage of subcultural theories that argue that groups develop attitudes and beliefs that are conducive to crime and deviance (Cloward & Ohlin, 1966; Cohen, 1971; Sykes & Matza, 1957). Labeling theory argues that individuals adopt a deviant identity and then choose deviant behaviors that conform to that identity (Kroska, Lee, & Carr, 2017; Matza, 2010). Self-control theories argue that individuals with low self-control are more likely to commit deviant or criminal acts that require immediate gratification (Gottfredson & Hirschi, 1990). Some research has shown these co-occurrences empirically, as problem behaviors in youth have been shown to cluster together (Bartlett, Holditch-Davis, & Belyea, 2005).
Some of these co-occurring deviant behaviors are also health risk behaviors. Examples include drug use, alcohol use, and risky sexual behavior. Alcohol use and juvenile delinquency have repeatedly been linked to other deviant behaviors (Felson, Savolainen, Aaltonen, & Moustgaard, 2008), as has drug use and juvenile delinquency (Wilson & Petersilia, 2011). The direct links between risky sexual behavior such as multiple partners or sex without birth control are less established. However, Armour and Haynie (2007) present evidence suggesting that early sexual activity increases the risk of future deviant behavior. Importantly, these behaviors are often co-present within youth who are more likely to engage in deviant or criminal behavior.
2.4. Linking cyberbullying victimization to crime and deviance One theory that links cyberbullying to deviant health risk behaviors is General Strain Theory (GST) (Agnew, 1992). GST posits that deviance can be a result of how an individual copes with life’s stressors or “strains.” The theory argues that there are three categories of strains: (1) not being able to achieve one’s goals, (2) the presentation of noxious or negatively valued stimuli, and (3) the loss of positively valued stimuli. Individuals who are cyberbullied are presented with a strain of the second type. The hateful or insulting communication is a negatively valued stimulus. In an attempt to cope with or remove the negative stimuli, the individual may engage in deviant behaviors. For example, a young person may turn to drugs or alcohol to cope with the emotional toil of being insulted or resort to sexual activity because of the low self-esteem caused by cyberbullying. In a later article, Agnew (2001) further specified GST by describing strain characteristics most likely to lead to crime and deviance. These are strains that are “(1) seen as unjust, (2) seen as high in magnitude, (3) are associated with low social control, and (4) create some pressure or incentive to engage in criminal coping” (Agnew, 2001). In this later article, Agnew specifically identifies bullying as a strain that can lead to crime and deviance. As a result, several studies have linked cyberbullying to deviant behaviors (Hay et al., 2010; Jang, Song, & Kim, 2014; Keith, 2018).
3. The present study While the link between cyberbullying victimization and other types of deviant behaviors is well known, there has been surprisingly little work done linking cyberbullying victimization to behaviors such as drug and alcohol use and risky sexual behavior. The primary aim of this study is to establish associations between cyberbullying victimization and these behaviors. Because cyberbullying and physical bullying are associated with each other (Finkelhor, Shattuck, Turner, Ormrod, & Hamby, 2011; Garnett & BrionMeisels, 2017; Hay et al., 2010; Keith, 2018; Payne & Hutzell, 2017; Resett & Gamez-Guadix, 2017), a secondary aim is to compare the effects of cyberbullying to physical bullying.
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
4. Method 4.1. Participants Data for this study comes from the 2015 Youth Risk Behavior Survey (YRBS) conducted by the Center for Disease Control (CDC). The initial n is 15,624. The YRBS monitors several types of health-risk behaviors including poor diet, lack of exercise, unsafe sexual behaviors, alcohol and other drug use, and tobacco use. The YRBS employs a three-stage cluster sample design to collect a representative sample of 9th through 12th-grade students. The target population consisted of all public and private school students in grades 9 through 12. One of the advantages of using the YRBS is that our results can be compared to several other studies which have also used the YRBS to explore bullying and cyberbullying (Kahle, 2017; Merrill & Hanson, 2016; Peters, Hatzenbuehler, & Davidson, 2017; Romero, Bauman, Ritter, & Anand, 2017; Swahn, Bossarte, Palmier, Yao, & Van Dulmen, 2013).
3
• Alcohol use: “During the past 30 days, on how many days did you have at least one drink of alcohol? 0 days [0]; 1 or 2 days [1]; 3 to 5 days [2]; 6 to 9 days [3]; 10 to 19 days [4]; 20 to 29 days [5]; All 30 days [6].” The control variables included in models are age, gender, race/ethnicity, grade, and sexual orientation. Table 1 shows descriptive statistics for all variables used in the analysis. 4.3. Procedure Multivariate regression analysis was used to identify the effects of cyberbullying on deviant health risk behaviors net of common sociodemographic factors and traditional bullying. Models were run for sexual frequency, alcohol frequency, and marijuana frequency. Weights supplied by the CDC were used for all regression models. 5. Data analysis
4.2. Measures
5.1. Regression models
The primary independent variable in this analysis is cyberbullying:
All models report similar findings (Table 2). First, the effect of physical bullying is not significant. Students who report being physically bullied do not report higher rates of alcohol, drug, or sexual activity when compared to students who report no bullying (the reference group). Second, cyberbullying is significant. For all three models, students who report being cyberbullied also report higher levels of all three deviant health risk behaviors. The effect of being cyberbullied is to increase reported rates of sexual frequency by 0.25, by 0.58 for alcohol frequency, and by 0.35 for marijuana frequency. Third, while physical bullying alone has no effect, respondents who report being both physically bullied and cyberbullied report the highest rates of deviant health risk behaviors. The effect of both types of bullying is associated with an increase of 0.46 for sexual frequency, an increase of 0.64 for alcohol frequency, and an increase of 0.52 for marijuana frequency. The primary aim of this study is to establish associations between cyberbullying victimization and health risk behaviors associated with deviance. To that end, the three models above show consistent associations across three such behaviors. We can see these effects more clearly by producing point estimates for sexual frequency, alcohol, and marijuana usage (Fig. 1). These point estimates are best guesses for respondents in the sample given model parameter estimates. Fig. 1 contextualizes the regression models by grounding values in the measures from the YRBS. We see that the differences between groups based on bullying is consistent and significant, however the magnitude between groups is not particularly large. For example, the difference in marijuana frequency for an average respondent who reports being both bullied and cyberbullied, and an average respondent who reports no bullying is 1.69 to 1.33. This means that both groups fall between the “3 to 9 times” and “10 to 19 times” levels for the survey measure. A secondary aim is to compare the effects of cyberbullying to physical bullying. The above models estimate deviant
• Cyberbullying — “During the past 12 months, have you ever been electronically bullied? (Count being bullied through email, chat rooms, instant messaging, websites, or texting.) Yes or No.” This measure of cyberbullying does not measure all three dimensions of cyberbullying as agreed upon by the literature. However, strains are reacted to based upon the perceptions of those strains. This measure of bullying is subjective, and asked the responded their perception of whether or not they were cyberbullied. • Traditional, or physical bullying — “During the past 12 months, have you ever been bullied on school property? Yes or No.” Bullying victimization and cyberbullying victimization were differentiated by creating a four-category variable that separates respondents into those who were not bullied – 11,649 (75.6%), bullied physically only – 1,506 (9.8%), cyberbullied only – 808 (5.2%), and respondents who reported being both physically bullied and cyberbullied – 1,439 (9.3%). There were three dependent variables in the analysis, multiple sexual partners, marijuana use, and alcohol use. These variables were measured ordinally: • Number of sexual partners: “During the past three months, with how many people did you have sexual intercourse? I have never had sexual intercourse [0]; I have had sexual intercourse; but not during the past 3 months [1]; 1 person [2]; 2 people [3]; 3 people [4]; 4 people [5]; 5 people [6]; 6 or more people [7].” • Marijuana use: “During the past 30 days, how many times did you use marijuana? 0 times [0]; 1 or 2 times [1]; 3 to 9 times [2]; 10 to 19 times [3]; 20 to 39 times [4]; 40 or more times [5].”
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
4 Table 1 Descriptive statistics. Categorical Gender Female Male Total
7,757 (49.6%) 7,867 (50.3%) 15,624
Race White American Indian/Alaska Native Asian African American Hawaiian/Pacific Isl. Hispanic Multiple (Hispanic) Multiple (Non-Hisp.) Total
6,849 (44.9%) 163 (1%) 627 (4.1%) 1,667 (10.9%) 100 (1%) 2,365 (15.5%) 2,756 (18.1%) 739 (5%) 15,266
Grade 9th Grade 10th Grade 11th Grade 12th Grade Total
4,003 (25.9%) 3,938 (25.4% 3,930 (25.4%) 3,601 (23.2%) 15,472
Sexuality Straight Gay/lesbian Bisexual Not sure Total
12,954 (82.9%) 324 (2.2%) 922 (6.3%) 503 (3.4%) 14,703
Bullying Physically bullied only Cyberbullied only Both physical and cyber No bullying Total
1,506 (9.8%) 808 (5.2%) 1,439 (9.3%) 11,649 (75.6%) 15,402
Continuous
No. of sexual partners Marijuana use Alcohol use
Mean
SD
Range
0.88 1.37 1.08
1.28 2.08 2.01
0–7 0–6 0–7
Table 2 Parameter estimates for risky behaviors. Sexual frequency N = 13,265 Estimate (SE)
Alcohol frequency N = 10,582 Estimate (SE)
Marijuana frequency N = 13,811 Estimate (SE)
Intercept Female
0.45 (0.26)*** −0.24 (0.02)***
0.73 (0.05)*** −0.29 (0.04)***
0.75 (0.04)*** −0.42 (0.03)***
Race/Ethnicity1 American Indian Asian African American Hawaiian/Pacific Isl. Hispanic Multiple (Hispanic) Multiple (Non-Hisp.)
0.21 (0.14) −0.39 (0.06)*** 0.29 (0.03)*** 0.01 (0.16) <0.001 (0.04) 0.22 (0.03)*** 0.27 (0.05)***
0.69 (0.24)** −0.71 (0.10)*** -0.42 (0.06)*** 0.07 (0.28) −0.14 (0.06)* 0.10 (0.06) −0.03 (0.09)
1.09 (0.22)*** −0.66 (0.09)*** 0.45 (0.05)*** 0.18 (0.23) 0.29 (0.06)*** 0.53 (0.05)*** 0.27 (0.08)**
Grade2 10th Grade 11th Grade 12th Grade
0.28 (0.03)*** 0.55 (0.03)*** 0.79 (0.03)***
0.28 (0.05)*** 0.67 (0.05)*** 0.82 (0.05)***
0.41 (0.05)*** 0.76 (0.05)*** 1.00 (0.05)***
Sexual orientation3 Gay/lesbian Bisexual Not sure
0.10 (0.08) 0.19 (0.05)*** 0.03 (0.06)
0.27 (0.15) 0.18 (0.08)* 0.14 (0.11)
0.51 (0.13)*** 0.58 (0.007)*** 0.21 (0.10)*
Bullying victimization4 Physical Cyber Cyber and physical R2
−0.04 (0.04) 0.25 (0.05)*** 0.46 (0.04)*** 0.08
0.11 (0.06) 0.58 (0.08)*** 0.64 (0.06)*** 0.05
−0.09 (0.06) 0.35 (0.07)*** 0.52 (0.06)*** 0.07
Significance codes: ‘***’ = <0.001; ‘**’ = <0.01; ‘*’ = <0.05. Reference categories: 1 — White; 2 — 9th Grade; 3 — Straight; 4 — No bullying.
health-risk behaviors with a dummied bullying variable using students who have never been bullied as the reference group. One way to more clearly differentiate between traditional bullying and cyberbullying is to examine only students who have been bullied and use traditional bully-
ing as the reference group (Table 3). Table 3 shows that when we compare cyberbullied respondents directly to bullied respondents, we see across models a consistent and significant difference, ranging from 0.42 (sexual frequency) to 0.57 (marijuana frequency).
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
5
Fig. 1. Deviant health risk behavior estimates for students grouped by bullying victimization. Table 3 Parameter estimates for risky behaviors using bullied students only.a
Intercept Cyberbullying R2
Sexual frequency N = 3,211 Estimate (SE)
Alcohol frequency N = 2,567 Estimate (SE)
Marijuana frequency N = 3,345 Estimate (SE)
0.44*** 0.42*** 0.08
0.78*** 0.50*** 0.04
0.65*** 0.57*** 0.08
Significance codes: ‘***’ = <0.001; ‘**’ = <0.01; ‘*’ = <0.05 a Estimates shown are controlling for sociodemographic variables.
6. Discussion This study aimed to (1) provide data examining the association between cyberbullying victimization and deviant health-risk behaviors and (2) to compare the effects of cyberbullying to physical bullying. Our results showed a positive association between cyberbullying victimization and all three deviant health risk behaviors. The magnitude of this association increased when respondents reported being both cyberbullied and physically bullied. When comparing the effects of cyberbullying to physical bullying, we also see a consistent effect on respondents who were bullied reporting higher rates of deviant health risk behaviors. In showing that cyberbullying is positively associated with deviant health-risk behaviors, this research is in line with other studies linking cyberbullying victimization to adverse outcomes (Fredstrom et al., 2011; Hoff & Mitchell, 2009; Palermiti et al., 2017; Patchin & Hinduja, 2010; Payne & Hutzell, 2017). This study adds to the extant research by showing how cyberbullying victimization is also associated with deviant health-risk behaviors. Given these findings, scholars and professionals with interest in cyberbullying may need to consider cyberbullying alongside other stressors such as family turmoil and economic inequality when assessing the risk of a young person committing deviant or criminal acts.
We suggest that the causal mechanism linking cyberbullying victimization to these deviant health risk behaviors is that cyberbullying is a strain. Young people cope with strains in a variety of ways, some of them being deviant. Prior studies have made this connection to other deviant behaviors (Hay et al., 2010; Jang et al., 2014; Keith, 2018; Kim et al., 2017), and we are widening the scope of this relationship to include deviant health risk behaviors. The study was designed to establish baseline associations between cyberbullying and deviant health risk behaviors, and differentiate between cyberbullying and physical bullying. We suggest that future research should follow along at least three paths. First, the measure that we used for cyberbullying was narrow. The consensus understanding of cyberbullying is that it is a repeated behavior done to inflict harm on a person or group possessing less power electronically. We were not able to tap into all elements of this definition in this study. Instead, we were able to focus on subjective understandings of cyberbullying. Future research will need to tease apart these dimensions and explore how they interplay with rates of deviant sexual behaviors. A second path will be to explore the process through which being cyberbullied leads to deviant health risk behaviors, which then ultimately results in a wider array of deviant activities including criminal acts and school dropout. General Strain Theory and Labeling Theory pro-
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005
G Model SOCSCI-1483; No. of Pages 6
ARTICLE IN PRESS R. Graham, F.R. Wood Jr. / The Social Science Journal xxx (2017) xxx–xxx
6
vide a theoretical rationale for why one would expect cyberbullying to lead to deviance. However empirical research, especially qualitative, is lacking. For instance, consider our finding that while physical bullying alone has no effect, respondents who report being both physically bullied and cyberbullied report the highest rates of deviant health risk behaviors. This interaction effect can be explored in greater detail through statistical measures. However, we suggest that qualitative work is best suited to uncovering and detailing the processes and meanings associated with this correlation. A third path is to explore the interactions between cyberbullying and disadvantaged groups such as racial minorities, sexual minorities, women, and people with disabilities. The logic being that these groups are already in a position of less power, placing them – as per the consensus understanding of cyberbullying – “at-risk.” This is the path upon which the current authors are progressing, and this brief was the initial stages of this research program.
References Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47–88. Armour, S., & Haynie, D. L. (2007). Adolescent sexual debut and later delinquency. Journal of Youth and Adolescence, 36(2), 141–152. http://dx.doi.org/10.1007/s10964-006-9128-4 Agnew, Robert. (2001). Building on the foundation of general strain theory: Specifying the types of strain most likely to lead to crime and delinquency. Journal of Research in Crime and Delinquency, 38(4), 319–391. Bartlett, R., Holditch-Davis, D., & Belyea, M. (2005). Clusters of problem behaviors in adolescents. Research in Nursing and Health, 28(3), 230–239. http://dx.doi.org/10.1002/nur.20078 Brochado, S., Soares, S., & Fraga, S. (2017). A scoping review on studies of cyberbullying prevalence among adolescents. Trauma, Violence, and Abuse, 18(5), 523–531. http://dx.doi.org/10.1177/1524838016641668 Calvete, E., Orue, I., Estévez, A., Villardón, L., & Padilla, P. (2010). Cyberbullying in adolescents: Modalities and aggressors’ profile. Computers in Human Behavior, 26(5), 1128–1135. http://dx.doi.org/10.1016/j.chb.2010.03.017 Chapin, J., & Coleman, G. (2017). The cycle of cyberbullying: Some experience required. The Social Science Journal, 54(3), 314–318. http://dx.doi.org/10.1016/j.soscij.2017.03.004 Cloward, R. A., & Ohlin, L. E. (1966). Delinquency and opportunity: A theory of delinquent gangs. New York, N.Y.; London: Free Press; Collier Macmillan. Cohen, A. K. (1971). Delinquent boys; The culture of the gang. New York: Free Press (Macmillan). Felson, R., Savolainen, J., Aaltonen, M., & Moustgaard, H. (2008). Is the association between alcohol use and delinquency causal or spurious? Criminology, 46(3), 785–808. http://dx.doi.org/10.1111/j.1745-9125.2008.00120.x Finkelhor, D., Shattuck, A., Turner, H. A., Ormrod, R., & Hamby, S. L. (2011). Polyvictimization in developmental context. Journal of Child and Adolescent Trauma, 4(4), 291–300. http://dx.doi.org/10.1080/19361521.2011.610432 Fredstrom, B. K., Adams, R. E., & Gilman, R. (2011). Electronic and school-based victimization: Unique contexts for adjustment difficulties during adolescence. Journal of Youth and Adolescence, 40(4), 405–415. http://dx.doi.org/10.1007/s10964-010-9569-7 Garnett, B. R., & Brion-Meisels, G. (2017). Intersections of victimization among middle and high school youth: Associations between polyvictimization and school climate. Journal of Child and Adolescent Trauma, 10(4), 377–384. http://dx.doi.org/10.1007/s40653-017-0183-7 Gladden, R. M., Vivolo-Kantor, A. M., Hamburger, M. E., & Lumpkin, C. D. (2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements, version 1.0. pp. 116. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention and U.S. Department of Education. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford, Calif: Stanford University Press.
Hay, C., Meldrum, R., & Mann, K. (2010). Traditional bullying, cyber bullying, and deviance: A general strain theory approach. Journal of Contemporary Criminal Justice, 26(2), 130–147. http://dx.doi.org/10.1177/1043986209359557 Hinduja, S., & Patchin, J. W. (2015). Bullying beyond the schoolyard: Preventing and responding to cyberbullying (Second edition). Thousand Oaks, California: Corwin. Hoff, D. L., & Mitchell, S. N. (2009). Cyberbullying: Causes, effects, and remedies. Journal of Educational Administration, 47(5), 652–665. http://dx.doi.org/10.1108/09578230910981107 Jang, H., Song, J., & Kim, R. (2014). Does the offline bully-victimization influence cyberbullying behavior among youths? Application of General Strain Theory. Computers in Human Behavior, 31, 85–93. http://dx.doi.org/10.1016/j.chb.2013.10.007 Kahle, L. (2017). Are sexual minorities more at risk? Bullying victimization among lesbian, gay, bisexual, and questioning youth. Journal of Interpersonal Violence, https://doi.org/10.1177/0886260517718830 Kann, L., McManus, T., Harris, W., Shanklin, S., & Flint, K. (2016). Youth risk behavior surveillance — United States, 2015. Center for Disease Control and Prevention. Retrieved from https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2015/ss6506 updated.pdf Keith, S. (2018). How do traditional bullying and cyberbullying victimization affect fear and coping among students? An application of general strain theory. American Journal of Criminal Justice, 43(1), 67–84. http://dx.doi.org/10.1007/s12103-017-9411-9 Kim, J., Song, H., & Jennings, W. G. (2017). A distinct form of deviance or a variation of bullying? Examining the developmental pathways and motives of cyberbullying compared with traditional bullying in South Korea. Crime and Delinquency, 63(12), 1600–1625. Kroska, A., Lee, J. D., & Carr, N. T. (2017). Juvenile delinquency and selfsentiments: Exploring a labeling theory proposition. Social Science Quarterly, 98(1), 73–88. http://dx.doi.org/10.1111/ssqu.12307 Lessne, D., & Yanez, C. (2016). School crime supplement to the national crime victimization survey.. Retrieved from https://nces.ed.gov/pubs2017/2017015.pdf Matza, D. (2010). Becoming deviant (Rev. ed). New Brunswick, N.J: Transaction Publishers. Merrill, R. M., & Hanson, C. L. (2016). Risk and protective factors associated with being bullied on school property compared with cyberbullied. BMC Public Health, 16(1) http://dx.doi.org/10.1186/s12889-016-2833-3 Olweus, D. (1993). Bullying at school: What we know and what we can do. Oxford, UK; Cambridge, USA: Blackwell. Palermiti, A. L., Servidio, R., Bartolo, M. G., & Costabile, A. (2017). Cyberbullying and self-esteem: An Italian study. Computers in Human Behavior, 69, 136–141. http://dx.doi.org/10.1016/j.chb.2016.12.026 Patchin, J. W., & Hinduja, S. (2010). Cyberbullying and self-esteem. Journal of School Health, 80(12), 614–621. Payne, A. A., & Hutzell, K. L. (2017). Old wine, new bottle? Comparing interpersonal bullying and cyberbullying victimization. Youth and Society, 49(8), 1149–1178. Peters, Z. J., Hatzenbuehler, M. L., & Davidson, L. L. (2017). Examining the intersection of bullying and physical relationship violence among New York City high school students. Journal of Interpersonal Violence, 32(1), 49–75. http://dx.doi.org/10.1177/0886260515585532 Resett, S., & Gamez-Guadix, M. (2017). Traditional bullying and cyberbullying: Differences in emotional problems, and personality. Are cyberbullies more Machiavellians? Journal of Adolescence, 61, 113–116. http://dx.doi.org/10.1016/j.adolescence.2017.09.013 Romero, A., Bauman, S., Ritter, M., & Anand, P. (2017). Examining adolescent suicidal behaviors in relation to gun carrying and bullying. Journal of School Violence, 16(4), 445–458. http://dx.doi.org/10.1080/15388220.2016.1190933 Swahn, M. H., Bossarte, R. M., Palmier, J. B., Yao, H., & Van Dulmen, M. H. M. (2013). Risk factors for multiple forms of violent experiences: Analyses of the 2009 Youth Risk Behavior Survey. Vulnerable Children and Youth Studies, 8(3), 225–236. http://dx.doi.org/10.1080/17450128.2012.722702 Sykes, G. M., & Matza, D. (1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22(6), 8. http://dx.doi.org/10.2307/2089195 Wilson, J. Q., & Petersilia, J. (Eds.). (2011). Crime and public policy. Oxford; New York, NY: Oxford University Press.
Please cite this article in press as: Graham, R., & Wood Jr., F.R. Associations between cyberbullying victimization and deviant health risk behaviors. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.05.005