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Prevalence and patterns of traditional bullying victimization and cyber-teasing among college population in Spain



Traditional bullying victimization and the growing number of cyber-teasing victims during the last decade is a major public health concern. The objective of this study was to examine the relationship between students’ experiences of traditional bullying victimization and cyber-teasing and the sociodemographic characteristics of a sample composed of college students in Spain.


In the fall of 2014, 543 sixth-grade students from southeast Spain completed an anonymous survey on their experience of both kinds of to ascertain any relationship with sociodemographic characteristics, including gender, nationality, economic problems, family conflicts and alcohol and cannabis use.


A total of 62.2 % of the students reported to having suffered traditional bullying victimization and 52.7 % reported that they had been subject to cyber-teasing. 40.7 % of participants had been victims of traditional bullying victimization and cyber-teasing in the past 12 months. Most (65.7 %) of the victims were at the same time cyber-teasing victims; 77.6 % of cyber-teasing victims were also victimized in a different manner. Traditional bullying victimization was higher among boys than among girls, while female students were more likely to have been subjected to cyber-teasing than male students. The characteristics that most heavily influenced suffering traditional bullying victimization were economic problems, family conflicts and cannabis use.


Our findings confirm overlapping results in the risk factors that influence suffering both traditional bullying victimization and cyber-teasing: there was a strong influence of certain sociodemographic and individual characteristics of the college population, suggesting that specific policies are necessary to improve college students’ environment in Spain.

Peer Review reports


In the recent years, awareness of new young victims of cyber-bullying has increased [1, 2] due to the boom in information and communications technology and the use of social network. The internet and other electronic media offer many benefits to adolescents, even though previous cross-sectional studies have shown that cyber-bullying and cyber-teasing among the school population is associated with mental health problems such as depression, suicidal thoughts and non-fatal suicidal behavior among school students [35].

Cyber-bullying, also known as electronic bullying or online bullying is defined as intentional and repeated harm inflicted on individuals through the use of electronic devices such as mobile phones or computers [6]. Similar to definitions of traditional bullying, cyber-bullying also implies that the offender’s behavior is usually aggressive with intention to harm the victims [7]. Cyber-teasing is a related phenomenon defined as hurtful teasing sent via technology that includes messages resulting in psychological abuse, verbal aggression or social rejection that has received less attention that cyber-bullying [8].

Previous studies have found rates of cyber-bullying among young regular Internet users (between 10 and 17 years old) that range from 4 to 72 % [9, 10], and lower rates among college population compared to adolescent samples ranging from 9 to 20 % [1114]. Cyber-bullying and cyber-teasing are known to cause higher level of depression disorders in victims that traditional ways of bullying among adolescents [15]. Finally, victims frequently suffer additional problems involving aggressiveness, substance use or delinquency [15].

Cyber-bullying can occur in several ways [16], and its effects are intensified by the audience reached or the ability to attack at any time or in any place [7, 15]. Nevertheless, schools still lack of information on the effects and consequences of this problem, which they tend not to distinguish from traditional bullying [7].

According to Olweus [17], bullying is defined as a repeated negative behavior that occurs along time in a relationship characterized by an imbalance of strength and power. However, approximately three decades of studies on bullying victimization have led to different definitions of bullying [18]. Bullying and victimization are two of the most significant health problems among adolescents, with an international prevalence that ranges from 9 to 54 % [19, 20] and from 10 to 25 % among college population [21, 22]. Thus, traditional bullying victimization does not disappear in college; although it is less prevalent than among younger students [15, 19]. A cross-sectional study conducted in 40 countries showed that approximately 25 % of adolescents suffer bullying or victimization with some regularity [23]. Earlier studies also found a high prevalence of traditional bullying victimization among high school and college population [2426].

An analysis of the relationship between traditional bullying victimization and cyber bullying, conducted recently by Schneider and colleagues [25] among high school students found an overlap between both types of victimization (traditional bullying victimization and cyber bullying), whereby 59.7 % of cyber-bullying victims were also school traditional bullying victims, and 36.3 % of school traditional bullying victims were also cyber-bullying victims. Further information about this relation is difficult to find in the literature, especially in the case of college population. Previous studies [9, 27, 28] showed that between 1/3 and 3/4 of school and high school students bullied online were also victims of traditional bullying.

Previous research also documented the high prevalence of drug use among college students [29] especially in the case of marijuana [30, 31] and alcohol [32, 33]. In 2011 in the United States 64 % of college students reported they had drunk alcohol in the month previous to the survey [29]. Different studies among youths [34, 35] have examined the association between substance use and traditional bullying victimization and cyber bullying, finding that bully-victims have a higher risk of drinking alcohol and to substance use compared with their equals that do not suffer any kind of traditional bullying victimization. Finally, in the family context, conflict at home and low socioeconomic status have been traditionally established as risk factors of violence and traditional bullying victimization in adolescents [36 - 39].

Research questions

Recent studies have shown a high prevalence of traditional bullying victimization and cyber-teasing among adolescents and the college population worldwide, including America [16, 32], Asia [2, 40] and Europe [4145]. Nevertheless, the literature that analyzes the associations between traditional bullying victimization and cyber-teasing is limited. Based on the previous literature [15, 16, 19, 21] we hypothesized that prevalence of traditional bullying victimization and cyber-bullying will be lower among our college population sample. The purpose of the current study is to study the prevalence of traditional bullying victimization and cyber-teasing and their association among college students in the southeast of Spain, and to analyze the influence of individual and sociodemographic characteristics on the risk of suffering traditional bullying victimization and cyber-teasing among college population.



The participants were 543 college students studying from 2nd to 6th years in the University of Murcia, with an average age of 22.6 years (SD = 6.01). The distribution of the sample reflected the distribution of the total population studying at the University of Murcia (n = 34,404) during the 2013–2014 course. In our case, 8.1 % were overseas students (with a mean age of 23.7), which is similar to the proportion of foreign students at the University (9.7 %). Approximately a quarter (25.4 %) of the sample was male (with a mean age of 23.5) while the proportion for male students at the University of Murcia as a whole is 32 %.

This bias for gender might be explained in part by the higher proportion of female college students in Spain assuming ≈ 65 % according to the “Instituto Nacional de Estadistica” [46]. But also because the higher proportion of female students in the degree of Medicine in Spain (72 % and approximately 70 % at the University of Murcia [46].

Data collection

A stratified random sample was selected from the university. Participants were medical students from the 2nd grade to 6th years in two courses of the University of Murcia (n = 607), who volunteered to participate in a self-report questionnaire collected from September and December 2014. Of those invited to participate 30 women and 44 men refused for the following reasons: “there is nothing to be gained from the survey” and “I am leaving the University soon”. Questions about traditional bullying victimization and cyber-teasing referred to the previous twelve months. For this reason, the students who were in their first year of university were excluded from the research.

College students and teachers were notified in advance via email including information about the day the survey would take place and they were given the opportunity to pre-view the survey, those who did not want to participate were excused from going to the lesson that day. An interviewer (from the Research staff of University of Murcia) remained in the classroom while the students answered the questionnaire. If participants did not understand a specific question, the interviewer re-read the question in order to make it more clear without leading them in any particular direction. Participants were not paid for taking part in the current research. The survey was voluntary and anonymous.

The surveys were administered in a classroom by 3 interviewers who received approximately 5 h of training on the data collection protocol and questionnaire by the University of Murcia. The interviewers followed a scripted protocol to explain the questionnaire and the study. Surveys were self-administered to groups of approximately 20 people who used paper and pencil to record their responses. The survey questions were written in Spanish and each group session lasted approximately half an hour. The study was approved by the University of Murcia Research Ethics Board. Signed consent forms from participants were obtained prior to the questionnaires being administered.


Variables for the present study were selected according to the previous literature about traditional bullying victimization and cyber-bullying among different ages, in order to detect protective or risk factors linked to both types of victimization such as: foreigner [43], low socioeconomic status [38], family conflicts [37], alcohol or cannabis use [34, 35], and academic grade [25].


Demographic variables included age, gender (coded as 1 for girls and 0 for boys), nationality (coded as 1 for Spanish and 0 for foreigners), and grade. The respective family economic situation consisted of one item: “Currently, does your family have economic difficulties?”. Responses included “Yes” (coded as 1) or “No” (coded as 0). Questions regarding family conflicts were drawn from the Communities that Care survey which has been found to have acceptable psychometric properties [35]: “Do any people in your family often insult or yell at each other?”. Responses included “Yes” (coded as 1) or “No” (coded as 0). At the end of demographic characteristics section, questions from previous research [47] were adapted to measure alcohol and cannabis use by the following questions: “Have you consumed alcohol during the last year?” and “Have you consumed cannabis during the last year?”. Responses included “Yes” (coded as 1) or “No” (coded as 0).

Traditional bullying victimization and cyber-teasing

Students were asked about traditional bullying victimization and cyber-teasing in the past 12 months. Cyber-teasing was measured with the following specific question: “How many times has someone used the Internet, a phone, or any other electronic devices to bully, tease, or threaten you in the past 12 months?”. The questions regarding cyber-teasing were adapted from previous studies [25, 27]. The question regarding traditional bullying victimization was adapted from the “Olweus Bullying Questionnaire” [48, 49], specifically the question was: “How often have you been bullied in the past 12 months?”. Both questions used a 4-point response scale: “I haven’t been cyber-teased in the past 12 months” (coded as 0), “It has only happened once” (coded as 1), “It has happened twice” (coded as 2), and “It has happened more than 2 times” (coded as 3). Respondents who selected affirmative answers (coded as 1, 2 and 3) were classified as subjects that had experienced cyber-teasing or traditional bullying victimization.

Data analysis

Data were analyzed using SPSS Statistics 20 for Windows (SPSS, IBM Inc.) adopting a 95 % (p ≤ 0.05) significance level. The study was conducted in four steps. First, univariate analysis of variance and Chi2 were computed to contrast the distribution by gender and demographic characteristics. Secondly, the prevalence of traditional bullying victimization and/or cyber-teasing among the participants for the whole sample and by gender, as well as Chi2 statistics were calculated in the search for associations by gender and traditional bullying victimization or cyber-teasing. Thirdly, cross-tabulations were used to make a bivariate analysis of both types of victimization and sociodemographic characteristics. Finally, a logistic regression analysis was developed to examine the relationship between traditional bullying victimization, cyber-teasing or both in the previous twelve months (dependent variable) and sociodemographic variables (independent variables), including gender, nationality, grade, economic problems, family conflicts, alcohol use and cannabis use in the last twelve months.


Prevalence and overlap of traditional bullying victimization and cyber-teasing

Table 1 describes the sociodemographic characteristics of the survey college population for full sample and by gender. The prevalence of cyber-teasing and traditional bullying victimization for the whole sample and according to gender is shown in Table 2. Overall, 52.7 % (95 % confidence interval [CI]: 48.3–57.1 %) of students reported cyber-teasing, 62.2 % (95 % CI: 58.0–66.1 %) reported traditional bullying victimization during the previous twelve months, and 40.7 % (95 % CI: 36.8–44.6 %) were victims of both types of victimization (traditional bullying victimization and cyber-teasing) in the last twelve months. Analyzing the overlap between both types of victimization, there was a statistically significant difference: 65.7 % of traditional bullying victims were also victims of cyber-teasing, and 77.6 % of cyber-teasing victims suffered also traditional bullying victimization (χ 2 = 60.789, p <0.001). Categorizing these reports into two, we obtained the following results: 21.3 % suffered traditional bullying victimization only and 12 % victims of cyber-teased only.

Table 1 Sociodemographics characteristics of study sample for full sample and by gender
Table 2 Prevalence of cyber-teasing and traditional bullying victimization during the past twelve months in full sample and by gender

Correlates of traditional bullying victimization and cyber-teasing

Table 3 displays the demographic characteristics of victimization when they are categorized into the following four groups: cyber-teasing victim only, traditional bullying victimization only, cyber-teasing and traditional bullying victimization, and neither. Reports of traditional bullying victimization were higher among boys than among girls (26.8 % vs. 19.5 %, χ 2 = 6.052, p = 0.014) while cyber-teasing was more prevalent among girls than among boys (15.0 % vs. 2.8 %, χ 2 = 10.352, p ≤ 0.001).

Table 3 Sociodemographic correlations of traditional bullying victimization and cyber-teasing of study sample

According to the nationality of the college students, traditional bullying victimization was almost twice more frequent in the case of Spanish students compared with foreign students (22.2 % vs. 11.3 %, χ 2 = 8.052, p = 0.003). As regards the economic situation, cyber-teasing (19.1 % vs. 8.3 %, χ 2 = 11.45, p ≤ 0.001) and traditional bullying victimization (24.4 % vs. 15.3 %, χ 2 = 6.678, p = 0.009) was higher among participants with low financial resources. Family conflicts were also higher among college students who suffered traditional bullying victimization and cyber-teasing (49.0 % vs. 38.7 %, χ 2 = 6.124, p = 0.016). Analyzing the substance use data, 80 % of the college students had drunk alcohol during the last year, and, among these students, traditional bullying victimization was more common than among non-drinkers (23.0 % vs. 14.2 %, χ 2 = 5.831, p = 0.023).

Table 4 present the logistic regressions which regulate the relationship of the different ways of victimization (cyber-teasing victim only, traditional bullying victimization only, cyber-teasing and traditional bullying victimization, or neither of them) for the sociodemographic characteristics. There was a strong relationship between the grade of the students and suffering cyber-teasing only. For example, the college students of 2nd (adjusted odds ratio [AOR] =3.86; 95 % CI: 1.19, 12.48, p-value = 0.004) and 3rd grade (AOR = 3.94; 95 % CI: 1.20–12.94, p-value = 0.006) were almost 4 times more likely to report cyber-teasing than students of 6th grade. Economic problems among the college students were also a risk factor for suffering cyber-teasing only (AOR = 2.46; 95 % CI: 1.29–4.71, p-value = 0.008). Finally, family conflicts were a risk factor for both cyber-teasing and traditional bullying victimization (AOR = 1.62; 95 % CI: 1.10–2.34, p-value ≤ 0.001).

Table 4 Associations between traditional bullying victimization and cyber-teasing and sociodemographic characteristics of study sample

Discussion and conclusions

The aim of the present study was to analyze for the first time in Spain the prevalence and the different characteristics of traditional bullying victimization and cyber-teasing in a university population, and to assess how sociodemographic characteristics might be a protective or risk factor for suffering both types of violence. Data from this research support and extend findings from previous studies about the nature and prevalence of traditional bullying victimization and cyber-teasing. More specifically, we found that over a half of the subjects suffered traditional bullying victimization (62.2 %), cyber-teased (52.7 %), or had suffered both cyber-teasing and traditional bullying victimization (40.7 %) during the past 12 months.

Previous studies among adolescent and college students in United States and European countries such as Finland and Sweden observed substantially lower prevalence of traditional bullying victimization or cyber-teasing than we did (e.g. cyber-bullying: approximately 7 % and 5 %) [16, 41, 43]. However, other cross-national studies in Spain among adolescents between 13 and 17 years old [45] and in other countries have found a similar prevalence of traditional bullying victimization or cyber-teasing to that which we observed, ranging from 24 to 40 % over a 6- to 12-month time period [16, 42]. Methodological differences, more specifically the definition and measure of traditional bullying victimization and cyber-teasing and the time frame of this research contributed to the variation in estimated rates [50].

Our results show that prevalence of cyber-teasing was higher than traditional bullying victimization; however, most of the previous studies reported that cyber-teasing was much less prevalent than traditional bullying victimization among school and adolescents students [3, 15, 25, 51]. It should be noted that in the current research teasing was included in the measure for cyber-bullying, but not for victimization, which might explain this finding. In fact, a previous study showed that teasing is one of the most common types of victimization among adolescents’ samples [15, 52].

We found that most cyber-teasing victims also reported that they experienced traditional bullying victimization (and vice versa) during the previous twelve months, suggesting that there is a clear overlap between traditional bullying victimization and suffering cyber-teasing in at any moment. In fact, 65.7 % of traditional bullying victims were also cyber-teasing victims, and 77.6 % of cyber-teasing victims also suffered traditional bullying victimization. This finding is consistent with previous literature about studies in school and high school students [27, 28].

Our results also found differences according to gender, in agreement with previous studies in adolescents [15, 53] and college [19] students, female being more likely than male students to report cyber-teasing. Perhaps this finding can be explained by the fact that female university students are more likely to use their mobile phone [54]. Indeed the increased use of mobile phones in recent years has been linked to suffering cyber victimization among adolescents [55]. Rates of traditional bullying victimization were higher among boys than girls (71 % vs. 59.3 %), while previous studies showed the opposite [56]. However high school girls are more likely to run for conflict resolution strategies [57].

An interest finding was that Spanish students were more often victimized (traditional bullying victimization only or cyber-teasing only) than foreigner students, what is contradictory to previous research from Finnish adolescents [43] or school students in the United States [58]. Perhaps this finding might be explained by the socioeconomic status: exchange college students may be more wealthy than Spanish students, but school and young foreigner students and their families might have a lower socioeconomic background. This difference between foreigner college students and foreigner school students is explained because of the phenomenon of immigration which implicates poverty. In fact, in the current research, economic position was associated with both ways of victimization, as seen in earlier research conducted in children [52] and adolescents [59]. Family conflicts were also associated with traditional bullying victimization and cyber-teasing during the last twelve months, coinciding with previous research developed in schools [36].

Finally, our data support previous research that found high rates of legal and illegal drug use among college students [29, 60]: 80.7 % of our sample population had drunk alcohol and 16.2 % smoked cannabis during the previous twelve months at university. Contrarily to previous research, alcohol or cannabis use were not a predictor of suffering traditional bullying victimization and/or cyber-teasing in this study [26, 61, 62].

Limitations and strengths of the study

This study has a certain number of limitations. Firstly, because we were unable to use a longitudinal design, a cross-sectional study was conducted, so that it was only possible to measure the study variables at a given time and not follow their evolution. Secondly, our time period referred to the last 12 months, whereas most research refers to the past 6 months [48] and the last 2 months [15], which would explain a higher frequency of traditional bullying victimization or cyber-teasing. We decided to use a longer time frame than previous studies among adolescents because we expected a lower prevalence of traditional bullying victimization and cyber-teasing in college students based on previous studies [15, 16, 19]; nevertheless, we found a high prevalence of both types of victimization. Thus our hypothesis was not sustained. Thirdly, we did not have the possibility to analyze psychological and physical consequences of traditional bullying victimization and cyber-teasing in our sample [63]. Moreover, the demographics of our sample were limited by gender distribution (because of the low representation of males, approximately 25 %), potentially limiting the generalizability of our results. However, the prevalence of women students in the current research is similar to previous studies among college population [13]. Despite these limitations, this study addresses gaps in the literature and is one of the first to examine the association among traditional bullying victimization and cyber-teasing, and the risk factors associated to these problems among Spanish college students.

The main strength of the present study is the fact that it is the first carried out in Spain to examine traditional bullying victimization and cyber-teasing among college students, so that the results and conclusion might serve as a guide for future research in our country to examine and prevent victimization among this collective. Another important strength of the study is that was developed among a college population, whereas most studies worldwide on victimization and cyber-bullying have targeted elementary, middle, and high school students [16, 25, 6466]. Although there is a large number of programs aimed at reducing traditional bullying victimization and cyber-bullying among youth in the United States [9, 67], Asia [2], Europe [68], and specifically Spain [69], it is still not clear what techniques and programs would be effective in order to reduce traditional bullying victimization and cyber-bullying because of the few representative studies focused on this population [16, 70]. Nowadays, different prevention programs are taking place worldwide in order to increase traditional bullying victimization and cyber-bullying knowledge such as the Olweus Bullying Prevention Program [71], the KiVa anti-bullying programme [72] or “zero tolerance” policies [73] demonstrating reductions in both types of victimization for different age groups. However, studies about the patterns and programs on college samples are underdeveloped compared to younger generations. Besides, very often these programs are not relevant because they were developed in schools [74] in spite of that previous studies have shown that cyberbullying in high school may also lead to cyberbullying during college [12, 75]. Moreover, the few specific prevention programs and awareness campaigns created in universities by different developers (counselling office, student organisations [76] or video program [70] were effective and reduced traditional bullying and cyber-bullying rates. Furthermore, prevention and intervention programs should not be limited to school and adolescents samples, and should be extended to college students.

Practice and policy

In summary, the current research increases our knowledge of the association between traditional bullying victimization, cyber-teasing and demographic and behavioral characteristics. These findings have several implications for interventions. Regarding the prevention, there is an association of both types of victimization with other risk behaviors such as substance use and family conflicts (including economic problems). Given that, it could be important to include strategies to prevent bullying based on these patterns. Those strategies could be implemented in college age students, providing counseling services for legal and illegal substance use.

Future research with longitudinal designs is needed in order to investigate associations between traditional bullying victimization and cyber-teasing and subsequent exposure to demographics and substance risk factor exposure, as well as to examine the relationship between cyber-teasing and traditional bullying victimization (and vice versa) among college population, who show similar patterns and risk factors to adolescent samples [23, 35, 38]. Previous studies in this field have shown that traditional bullying victimization and cyber-teasing are an increasing phenomenon and a growing health problem among young people. This study found a strong association between traditional bullying victimization and cyber-teasing (and vice versa), as well as the importance of demographics and individual characteristics as risk factors associated.


  1. 1.

    Park TW, Lee SH, Lee M, Jung YE, Leventhal B, Kim YS, et al. Cyberbullying, problematic internet use, and psychopathologic symptoms among Korean youth. Yonsei Med J. 2014;55(3):826–30.

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Lee MS, Zi-Pei W, Svanström L, Dalal K. Cyber bullying prevention: intervention in Taiwan. PLoS One. 2013;8(5):e64031.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Bannink R, Broeren S, van de Looij-Jansen PM, de Waart FG, Raat H. Cyber and traditional bullying victimization as a risk factor for mental health problems and suicidal ideation in adolescents. PLoS One. 2014;9:e94026.

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Shapira NA, Lessig MC, Goldsmith TD, Szabo ST, Lazoritz M, Gold MS, et al. Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety. 2003;17(4):207–16.

    PubMed  Article  Google Scholar 

  5. 5.

    Suzuki K, Asaga R, Sourander A, Hoven CW, Mandell D. Cyberbullying and adolescent mental health. Int J Adolesc Med Health. 2012;24(1):27–35.

    PubMed  Article  Google Scholar 

  6. 6.

    Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N. Cyberbullying: its nature and impact in secondary school pupils. J Child Psychol Psychiatry Allied Discip. 2008;49(4):376–85.

    Article  Google Scholar 

  7. 7.

    Kiriakidis SP, Kavoura A. Cyberbullying: a review of the literature on harassment through the internet and other electronic means. Fam Community Heal. 2010;33(2):82–93.

    Article  Google Scholar 

  8. 8.

    Madlock PE, Westerman D. Hurtful cyber-teasing and violence who’s laughing out loud? J Interpers Violence. 2011;26(17):3542–60.

    PubMed  Article  Google Scholar 

  9. 9.

    Juvonen J, Gross EF. Extending the school grounds? Bullying experiences in cyberspace. J Sch Health. 2008;78(9):496–505.

    PubMed  Article  Google Scholar 

  10. 10.

    Ybarra ML, Mitchell KJ. Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics. J Child Psychol Psychiatry Allied Discip. 2004;45(7):1308–16.

    Article  Google Scholar 

  11. 11.

    Scherer HL, Snyder JA, Fisher BS. Intimate partner victimization among college students with and without disabilities prevalence of and relationship to emotional well-being. J Interpers Violence. 2016;31(1):49–80.

    PubMed  Article  Google Scholar 

  12. 12.

    Kraft E, Wang J. An exploratory study of the cyberbullying and cyberstalking experiences and factors related to victimization of students at a public liberal arts college. Int J Technoethics. 2010;1(4):74–91.

    Article  Google Scholar 

  13. 13.

    McDonald C, Roberts-Pittman B. Cyberbullying among college students: prevalence and demographic differences. Procedia Soc Behav Sci. 2010;9:2003–9.

    Article  Google Scholar 

  14. 14.

    Schenk A, Fremouw W. Prevalence, psychological impact, and coping of cyberbully victims among college students. J Sch Violence. 2012;11(1):21–37.

    Article  Google Scholar 

  15. 15.

    Wang J, Iannotti RJ, Nansel TR. School bullying among adolescents in the United States: physical, verbal, relational, and cyber. J Adolesc Heal. 2009;45(4):368–75.

    Article  Google Scholar 

  16. 16.

    Tokunaga RS. Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput Human Behav. 2010;26(3):277–87.

    Article  Google Scholar 

  17. 17.

    Olweus D. Bully/victim problems among schoolchildren: long-term consequences and an effective intervention program. Mental Disorder and Crime. 1993;2:317–49.

    Google Scholar 

  18. 18.

    Barboza GE, Schiamberg LB, Oehmke J, Korzeniewski SJ, Post LA, Heraux CG. Individual characteristics and the multiple contexts of adolescent bullying: an ecological perspective. J Youth Adolesc. 2009;38(1):101–21.

    PubMed  Article  Google Scholar 

  19. 19.

    Nansel TR, Overpeck M, Pilla RS, Ruan WJ, Simons-Morton B, Scheidt P. Bullying behaviors among US youth: prevalence and association with psychosocial adjustment. JAMA. 2001;285(16):2094–100.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Kim YS, Koh Y-J, Leventhal BL. Prevalence of school bullying in Korean middle school students. Arch Pediatr Adolesc Med. 2004;158(8):737–41.

    PubMed  Article  Google Scholar 

  21. 21.

    Chapell MS, Hasselman SL, Kitchin T, Lomon SN, MacIver KW, Sarullo PL. Bullying in elementary school, high school, and college. Adolescence. 2006;41(164):633–48.

    PubMed  Google Scholar 

  22. 22.

    Paul LA, Walsh K, McCauley JL, Ruggiero KJ, Resnick HS, Kilpatrick DG. College women’s experiences with rape disclosure: a national study. Violence Against Women. 2013;19(4):486–502.

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Craig W, Harel-Fisch Y, Fogel-Grinvald H, Dostaler S, Hetland J, Simons-Morton B, et al. A cross-national profile of bullying and victimization among adolescents in 40 countries. Int J Public Health. 2009;54(2):216–24.

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Humphrey JA, White JW. Women’ s vulnerability to sexual assault from adolescence to young adulthood. J Adolesc Heal. 2000;27(6):419–24.

    CAS  Article  Google Scholar 

  25. 25.

    Schneider SK, Odonnell L, Stueve A, Coulter RW. Cyberbullying, school bullying, and psychological distress: a regional census of high school students. Am J Public Health. 2012;102(1):171–7.

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Rospenda KM, Richman JA, Wolff JM, Burke LA. Bullying victimization among college students: negative consequences for alcohol use. J Addict Dis. 2013;32(4):325–42.

    PubMed  Article  Google Scholar 

  27. 27.

    Kowalski RM, Limber SP. Psychological, physical, and academic correlates of cyberbullying and traditional bullying. J Adolesc Heal. 2013;53(1):13–20.

    Article  Google Scholar 

  28. 28.

    Messias E, Kindrick K, Castro J. School bullying, cyberbullying, or both: correlates of teen suicidality in the 2011 CDC youth risk behavior survey. Compr Psychiatry. 2014;55(5):1063–8.

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future national results on drug use, 1975–2010: Volume I, Secundary school students. Ann Arbor: Institute for Social Research, The University of Michigan; 2011.

    Google Scholar 

  30. 30.

    Berg CJ, Lowe K, Stratton E, Goodwin SB, Grimsley L, Rodd J, et al. Sociodemographic, psychosocial, and health behavior risk factors associated with sexual risk behaviors among southeastern US college students. Open J Prev Med. 2014;4(6):387–95.

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Davis AK, Osborn LA, Leith J, Rosenberg H, Ashrafioun L, Hawley A, et al. Development and evaluation of the marijuana reduction strategies self-efficacy scale. Psychol Addict Behav. 2014;28(2):575–9.

    PubMed  Article  Google Scholar 

  32. 32.

    Gebreslassie M, Feleke A, Melese T. Psychoactive substances use and associated factors among Axum University students, Axum Town, North Ethiopia. BMC Public Health. 2013;13(1):693.

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Wechsler H, Kuo M. College students define binge drinking and estimate its prevalence: results of a national survey. J Am Coll Health. 2000;49(2):57–64.

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Smokowski PR, Kopasz KH. Bullying in school: an overview of types, effects, family characteristics, and intervention strategies. Natl Assoc Soc Work. 2005;17(2):101–10.

    Google Scholar 

  35. 35.

    Carlyle KE, Steinman KJ. Demographic differences in the prevalence, co-occurrence, and correlates of adolescent bullying at school. J Sch Health. 2007;77(9):623–9.

    PubMed  Article  Google Scholar 

  36. 36.

    Farrington DP, Ttofi MM. Bullying as a predictor of offending, violence and later life outcomes. Crim Behav Ment Heal. 2011;21(2):90–8.

    Article  Google Scholar 

  37. 37.

    Herrenkohl TI, Maguin E, Hill KG, Hawkins JD, Abbott RD, Catalano RF. Developmental risk factors for youth violence. J Adolesc Health. 2000;26(3):176–86.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Toumbourou JW, Hemphill SA, Tresidder J, Humphreys C, Edwards J, Murray D. Mental health promotion and socio-economic disadvantage: lessons from substance abuse, violence and crime prevention and child health. Health Promot J Austr. 2007;18(3):184–90.

    CAS  PubMed  Google Scholar 

  39. 39.

    Allan EJ, Madden M. The nature and extent of college student hazing. Int J Adolesc Med Health. 2012;24(1):83–90.

    PubMed  Article  Google Scholar 

  40. 40.

    Bhang SY, Yoo HK, Kim JH, Kim B, Lee YS, Ahn D, et al. Victims of bullying among Korean adolescents: prevalence and association with psychopathology evaluated using the adolescent mental health and problem behavior screening questionnaire-II standardization study data. J Korean Acad Child Adolesc Psychiatry. 2012;23(1):23–30.

    Article  Google Scholar 

  41. 41.

    Brolin Låftman S, Modin B, Östberg V. Cyberbullying and subjective health: a large-scale study of students in Stockholm, Sweden. Child Youth Serv Rev. 2013;35(1):112–9.

    Article  Google Scholar 

  42. 42.

    Helweg‐Larsen K, Schütt N, Larsen HB. Predictors and protective factors for adolescent Internet victimization: results from a 2008 nationwide Danish youth survey. Acta Paediatr. 2012;101(5):533–9.

    PubMed  Article  Google Scholar 

  43. 43.

    Sourander A, Brunstein Klomek A, Ikonen M, Lindroos J, Luntamo T, Koskelainen M, et al. Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study. Arch Gen Psychiatry. 2010;67(7):720–8.

    PubMed  Article  Google Scholar 

  44. 44.

    Menesini E, Nocentini A, Palladino BE, Frisén A, Berne S, Ortega-Ruiz R, et al. Cyberbullying definition among adolescents: a comparison across six European countries. Cyberpsychol Behav Soc Netw. 2012;15(9):455–63.

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Gámez-Guadix M, Orue I, Smith PK, Calvete E. Longitudinal and reciprocal relations of cyberbullying with depression, substance use, and problematic internet use among adolescents. J Adolescent Health. 2013;53(4):446–52.

    Article  Google Scholar 

  46. 46.

    Instituto Nacional de Estadística (INE) (2014).[06 January 2016].

  47. 47.

    McCabe SE, Morales M, Cranford JA, Delva J, McPherson MD, Boyd CJ. Race/ethnicity and gender differences in drug use and abuse among college students. J Ethn Subst Abuse. 2007;6(2):75–95.

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Olweus D. No The revised Olweus bully/victim questionnaire. University of Bergen, Norway: Research Centre for Health Promotion; 2007.

  49. 49.

    Ybarra ML, Diener-West M, Leaf PJ. Examining the overlap in internet harassment and school bullying: implications for school. Int J Adolesc Med Health. 2007;41(6):42–50.

    Article  Google Scholar 

  50. 50.

    Langos C. Cyberbullying: the challenge to define. Cyberpsychol Behav Soc Netw. 2012;15(6):285–9.

    PubMed  Article  Google Scholar 

  51. 51.

    Sampasa-Kanyinga H, Roumeliotis P, Xu H. Associations between cyberbullying and school bullying victimization and suicidal ideation, plans and attempts among Canadian schoolchildren. PLoS One. 2014;9(7):e102145.

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Jansen PW, Verlinden M, Dommisse-van Berkel A, Mieloo C, van der Ende J, Veenstra R, et al. Prevalence of bullying and victimization among children in early elementary school: Do family and school neighbourhood socioeconomic status matter? BMC Public Health. 2012;12(1):494–504.

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Gradinger P, Strohmeier D, Spiel C. Traditional bullying and cyberbullying. Psychol. 2010;217(4):205–13.

    Google Scholar 

  54. 54.

    Madden M, Lenhart A, Duggan M, Cortesi S, Gasser U. Teens and technology. Washington, DC: Pew Internet & American Life Project; 2013.

    Google Scholar 

  55. 55.

    Werner NE, Bumpus MF, Rock D. Involvement in internet aggression during early adolescence. J Youth Adolesc. 2010;39(6):607–19.

    PubMed  Article  Google Scholar 

  56. 56.

    Frank E, Carrera JS, Stratton T, Bickel J, Nora LM. Experiences of belittlement and harassment and their correlates among medical students in the United States: longitudinal survey. BMJ. 2006;333(7570):682–8.

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Feldman SS, Gowen LK. Conflict negotiation tactics in romantic relationships in high school students. J Youth Adolesc. 1998;27(6):691–717.

    Article  Google Scholar 

  58. 58.

    Foster H, Brooks-Gunn J. Neighborhood, family and individual influences on school physical victimization. J Youth Adolesc. 2013;42(10):1596–610.

    PubMed  Article  Google Scholar 

  59. 59.

    Magklara K, Skapinakis P, Gkatsa T, Bellos S, Araya R, Stylianidis S, et al. Bullying behaviour in schools, socioeconomic position and psychiatric morbidity: a cross-sectional study in late adolescents in Greece. Child Adolesc Psychiatry Ment Health. 2012;6(8):1–13.

    Google Scholar 

  60. 60.

    McCabe SE, Teter CJ. Drug use related problems among nonmedical users of prescription stimulants: a web-based survey of college students from a Midwestern university. Drug Alcohol Depend. 2007;91(1):69–76.

    PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Gilreath TD, Astor RA, Estrada JN, Benbenishty R, Unger JB. School victimization and substance use among adolescents in California. Prev Sci. 2014;15(6):897–906.

    PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Nowotny KM, Graves JL. Substance use and intimate partner violence victimization among White, African American, and Latina women. J Interpers Violence. 2013;28(17):3301–18.

    PubMed  Article  Google Scholar 

  63. 63.

    Reijntjes A, Kamphuis JH, Prinzie P, Telch MJ. Peer victimization and internalizing problems in children: a meta-analysis of longitudinal studies. Child Abus Negl. 2010;34(4):244–52.

    Article  Google Scholar 

  64. 64.

    Della Cioppa V, O’Neil A, Craig W. Learning from traditional bullying interventions: a review of research on cyberbullying and best practice. Aggress Violent Beh. 2015;23:61–8.

    Article  Google Scholar 

  65. 65.

    Kowalski RM, Giumetti GW, Schroeder AN, Lattanner MR. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol Bull. 2014;140(4):1073–137.

    PubMed  Article  Google Scholar 

  66. 66.

    Thomas HJ, Connor JP, Scott JG. Integrating traditional bullying and cyberbullying: challenges of definition and measurement in adolescents. A review. Educ Psychol Rev. 2014;27(1):135–52.

    Article  Google Scholar 

  67. 67.

    Polanin JR, Espelage DL, Pigott TD. A meta-analysis of school-based bullying prevention programs’ effects on bystander intervention behavior. School Psychol Rev. 2012;41(1):47–65.

    Google Scholar 

  68. 68.

    Slonje R, Smith PK, Frisén A. The nature of cyberbullying, and strategies for prevention. Comput Hum Behav. 2013;29(1):26–32.

    Article  Google Scholar 

  69. 69.

    Casas JA, Del Rey R, Ortega-Ruiz R. Bullying and cyberbullying: convergent and divergent predictor variables. Comput Hum Behav. 2013;29(3):580–7.

    Article  Google Scholar 

  70. 70.

    Doane AN, Kelley ML, Pearson MR. Reducing cyberbullying: A theory of reasoned action‐based video prevention program for college students. Aggress Behav. 2015. In press.

  71. 71.

    Olweus D, Limber SP. Olweus bullying prevention program. 2007.

    Google Scholar 

  72. 72.

    Salmivalli C, Kärnä A, Poskiparta E. Counteracting bullying in Finland: the KiVa program and its effects on different forms of being bullied. Int J Behav Dev. 2011;35(5):405–11.

    Article  Google Scholar 

  73. 73.

    American Psychological Association Zero Tolerance Task Force. Are zero tolerance policies effective in the schools?: an evidentiary review and recommendations. Am Psychol. 2008;63(9):852–62.

    Article  Google Scholar 

  74. 74.

    Foody M, Samara M, Carlbring P. A review of cyberbullying and suggestions for online psychological therapy. Internet Interv. 2015;2(3):235–42.

    Article  Google Scholar 

  75. 75.

    Zalaquett CP, Chatters SJ. Cyberbullying in college: frequency, characteristics, and practical implications. SAGE Open. 2014;4(1):1–8.

    Article  Google Scholar 

  76. 76.

    Crosslin K, Golman M. “Maybe you don’t want to face it” college students’ perspectives on cyberbullying. Comput Hum Behav. 2014;41:14–20.

    Article  Google Scholar 

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Thanks to the Department of Social and Health Sciences, University of Murcia, and especially to the students who participated in the study.

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Correspondence to Francisco Caravaca Sánchez.

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The authors declare that they have no competing interests.

Authors’ contributions

FCS and ALM designed, planned, and oversaw all scientific aspects of the study. MFR oversaw program coordination and data acquisition. JNZ was involved in devising the analytic plan and in study design. ALRC and FCS were responsible for database management. ORF participated in the revision of the manuscript. All authors read and approved the final manuscript.

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Caravaca Sánchez, F., Falcón Romero, M., Navarro-Zaragoza, J. et al. Prevalence and patterns of traditional bullying victimization and cyber-teasing among college population in Spain. BMC Public Health 16, 176 (2016).

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