Anxiety and Depression in Cyberbullied College Students: A Retrospective Study

Abstract

Cyberbullying is a worldwide phenomenon and its effects can be severe. To better understand the personal and situational factors in cyberbullying, we approach it from the perspective of the general aggression model. More specifically, we analyze the medium and long-term impact of past experiences of cyberbullying on university students. We also compare their psychological adjustment with peers who have not been cyberbullied by examining the recall of cyberbullying while attending secondary school of 1,593 university students. Participants from a Spanish University (N = 680) and a Bolivian University (N = 913) were invited to participate by filling in an online survey. It included the School Violence Questionnaire-Revised, CUVE-R, to assess school and classroom climate in relation to bullying and cyberbullying, the Beck Depression Inventory, and the State-Trait Anxiety Inventory. Results show that among the participants, 5.1% reported having suffered cyberbullying and 19.3% reported having been a bystander of cyberbullying, with similar percentages between universities. Canonical correlation suggests that variables related to school climate best explain the variability among participants who have and have not been cyberbullied. Those who have been cyberbullied scored significantly higher on anxiety and depression symptoms as well. Being a bystander of cyberbullying was not associated to significant differences on psychological adjustment (i.e., anxiety and depression). Results indicated that experiencing cyberbullying in secondary school is associated to lower psychological adjustment years later as university students. School climate variables contribute more strongly to identifying victims of cyberbullying. These results support the need for psychosocial interventions from a broader perspective, addressing the different dimensions of this phenomenon and its impact on victims.

Keywords

bullying youth violence cultural contexts Internet and abuse

Introduction

Cyberbullying, the use of technology to repeatedly and deliberately threaten, insult, harass, or tease, is a worldwide phenomenon (Ang, 2016; Låftman, Modin, & Östberg, 2013; Rice et al., 2015). Prevalence estimates range from 10% to 40% (Gámez-Guadix, Gini, & Calvete, 2015; Garaigordobil, 2015; Genta et al., 2012; Kowalski, Giumetti, Schroeder, & Lattanner, 2014). Yet, in Latin America, its prevalence is largely unknown (Shaeffer et al., 2007).

Cyberbullying effects can be severe: anxiety, depression, suicidal ideation, and suicide attempts (Alavi, Roberts, Sutton, Axas, & Repetti, 2015; Gini & Espelage, 2014; Hinduja & Patchin, 2010; Rose & Tynes, 2015; Van Geel, Vedder, & Tanilon, 2014; Yen et al., 2014). Even obsessive-compulsive disorders (Stapinski et al., 2014) have been identified in those who have been cyberbullied.

As cyberbullying employs means that do not require face to face interaction, some studies suggest that the effects are less intense (Hase, Goldberg, Smith, Stuck, & Campain, 2015; Kowalski & Limber, 2013). In contrast, other studies indicate that due to the difficulties of escaping such pervasive harassment, the impact on the victim is greater (Cross, Lester, & Barnes, 2015; Melioli, Sirou, Rodgers, & Chabrol, 2015; Wang, Nansel, & Iannotti, 2011). In addition, some findings suggest that those who suffered mixed (cyber and in person) bullying episodes experienced a more severe negative impact (Mitchell, Jones, Turner, Shattuck, & Wolak, 2016).

Another issue in need of additional exploration is the role of bystanders in cyberbullying (Agatston, Kowalski, & Limber, 2007; Conway, Gomez-Garibello, & Talwar, 2014; Cuevas & Marmolejo, 2014; Holfeld, 2014). Here, studies with children have revealed that bystanders who adopt a more active role as defenders of those being cyberbullied showed higher levels of moral sensitivity and emotional contagion, as well as high self-efficacy (Gini, 2006; Pöyhönen, Juvonen, & Salmivalli, 2010; Vannini et al., 2011). Other studies, however, have not succeeded in identifying cognition measures that predict defender behavior (Andreou & Metallidou, 2004; Correia & Dalbert, 2008; Kollerová, Janošová, & Říčan, 2014), although girls seem to adopt the roles of defender and outsider more frequently than boys (Salmivalli, Lagerspetz, Björkqvist, Österman, & Kaukiainen, 1996).

Recent evidence suggest that observers experience negative psychological impact (Conway et al., 2014), but the studies are cross-sectional or focus on the short-term impact (Bayraktar, Machackova, Dedkova, Cerna, & Ševčíková, 2015; Kowalski, Morgan, Drake-Lavelle, & Allison, 2016). Congruently with the moral disengagement construct, which can be understood as an adaptive coping strategy to minimize personal distress when witnessing a peer being harassed (Doramajian & Bukowski, 2015), those who adopt a passive bystander role may experience lower psychological impact than those who have experienced cyberbullying.

To better understand the personal and situational factors at play in cyberbullying, the general aggression model may help. As Kowalski et al. (2014) and Kowalski et al. (2016) state, this model focuses upon factors associated with the individual and the situation that influence aggressive behavior. Cyberbullying, like any other behavior, is helped or hindered by contextual variables, and, in this regard, normative beliefs about aggression and moral disengagement have been identified as the strongest predictors of cyberbullying perpetration (Kowalski et al., 2014). Among these variables, school contexts characterized by few rules, high aggressiveness, and so on have been found associated with an increased probability of occurrence of these behaviors (Azeredo, Rinaldi, de Moraes, Levy, & Menezes, 2015; Evans & Smokowski, 2015; Low & Van Ryzin, 2014). Hence, it is not surprising that harassment is often the best predictor of cyberbullying, as it frequently begins face-to-face then extends through technology (Kowalski, Morgan, & Limber, 2012; Melioli et al., 2015).

Individual variables potentially related to victimization have been studied as well, and age has been found associated to these behaviors, with adolescence being an at-risk period for bullying and cyberbullying (Gomez-Garibello, Shariff, McConnell, & Talwar, 2012; Huang & Chou, 2010).

In this study, we are analyzing the medium and long-term impact of past experiences of cyberbullying on university students. We are also comparing their psychological adjustment with peers who have not been cyberbullied. To our knowledge, there are no retrospective studies on cyberbullying with this population. In addition, no studies on bullying or cyberbullying have been conducted with Bolivian students, even though Bolivians make extensive use of the Internet and social networks such as Facebook and Whatsapp (Agencia de Gobierno Electrónico y Tecnologías de Información y Comunicación [AGETIC], 2017), just like Spaniards (European Commission, 2016). Specifically, we hypothesized the following:

Hypothesis 1: Being the victim of cyberbullying will be associated with significantly different ratings in school climate and psychological adjustment (i.e., anxiety and depressive symptoms).

Hypothesis 2: Being bystanders of cyberbullying will have less impact on psychological adjustment than being cyberbullied.

Hypothesis 3: The impact of cyberbullying will be no different between Spanish and Bolivian university students, so the results will generalize to both countries.

Materials and Methods

Participants

The purposeful sample is composed of 1,593 students (79.7% female and 20.3% male, with comparable distribution by gender and University) who are in their first year of college or are preparing to enter. They have finished compulsory secondary education 4 years ago, on average (SD = 3.55). They come from two universities in Spanish-speaking countries, one in Spain (N = 680; 42.7%) and the other in Bolivia (N = 913; 57.3%), with a generalized use of social media and the Internet to which the current authors had access. The average age was 19.7 (SD = 2.5; range = 16-27), and no significant differences were found based on the age of Spanish and Bolivian participants. The analysis of the possible association between belonging to either country and having experienced bullying or not was not significant (χ² = 2.573, p = .109), meaning that both groups were exposed to similar percentages of bullying experiences.

Procedure

The data were collected during the academic years of 2013-2014 and 2014-2015. Contacts with the universities were made via email after seeking the appropriate permissions, ensuring anonymity and confidentiality, and obtaining informed consent. The Ethics Committee of the University of Salamanca approved the study. Professors from both universities volunteered to disseminate the invitation to participate among their first-year students and summary reports were offered to those participants who accepted the invitation to fill-in the online survey. Participants were requested to recall their experience in compulsory secondary education, before college preparatory studies. The response rates averaged 80%, and no significant differences on responses rate by gender were obtained.

Design and Analysis

This is a retrospective study. It follows a descriptive and correlational design, with ex post facto measures. Parametric type descriptive and inferential statistics for continuous variables have been used, after verifying compliance with the parametric requirements. We used multivariate tests to determine possible differences in variables of interest (Garson, 2015). Nonparametric tests (χ²) were used for analysis of categorical variables. In addition, we used discriminant analysis to determine the extent to which the selected variables in this study permit distinguishing between people who say they have been bullied and suffer consequences, compared with those who have not been bullied or do not suffer consequences. An alpha of .05 was set for all the analyses.

Instruments

The CUVE-R (Álvarez-García et al., 2010; Álvarez-García, Dobarro, Álvarez, Núñez, & Rodríguez, 2014; Álvarez-García, Núñez, Rodríguez, Álvarez, & Dobarro, 2011) was used to assess the school and classroom climate in relation to bullying and cyberbullying. The questionnaire has 31 items grouped in eight factors and has demonstrated adequate reliability in this study: (a) teacher violence toward students (α = .882); (b) indirect physical violence by students (α = .740); (c) direct physical violence between students (α = .829); (d) verbal violence toward fellow pupils (α = .892); (e) verbal abuse by students of teachers (α = .759); (f) social exclusion (α = .721); (g) disruption in the classroom (α = .906); and (h) violence through information and communication technology (ICT) or cyberbullying (α = .877). Each item is answered on a scale from 1 (never) to 5 (always), and higher scores denote a climate of greater school violence. For the present study, the tense of the items was changed as the students are asked to remember to what extent these behaviors were present when they were doing their secondary education. The data in Table 1 provide support for the internal consistency of the measure. These results are even higher than those obtained with the same measure in previous studies (Álvarez-García et al., 2010; Álvarez-García et al., 2014; Álvarez-García et al., 2011).

Table 1.

Mean, Standard Deviation, and Pearson Correlation Matrix for Continuous Variables (n = 1,593).

	M	SD	F1	F2	F3	F4	F5	F6	F7	F8	STAI-S	STAI-T	BDI
F1	1.82	0.74	(.88; .82; .88)
F2	1.92	0.81	.66**	(.74; .65; .75)
F3	2.01	0.90	.66**	.72**	(.83; .77; .83)
F4	2.46	1.01	.69**	.75**	.71**	(.89; .81; .85)
F5	1.93	0.89	.65**	.71**	.66**	.70**	(.76; .72; .73)
F6	1.85	0.81	.61**	.60**	.62**	.65**	.58**	(.73; .63; .77)
F7	2.58	1.13	.69**	.73**	.69**	.82**	.72**	.58**	(.91; .83; .94)
F8	1.54	0.66	.61**	.65**	.65**	.58**	.60**	.60**	.53**	(.88; .94; .97)
STAI-S	39.39	29.97	.12**	.10**	.08**	.04	.10**	.12**	.02	.16**	(.93; .94; .91)
STAI-T	41.17	29.97	.28**	.24**	.22**	.23**	.23**	.24**	.22**	.25**	.73**	(.89; .92; .85)
BD	6.07	7.03	.26**	.23**	.22**	.24**	.23**	.24**	.23**	.23**	.51**	.56**	(.88; .88; .89)

Note. Global Cronbach’s alphas, followed by reliability indexes for Spanish and Bolivian participants are shown in the diagonal. F1 = violence by teachers toward students; F2 = indirect physical violence by students; F3 = direct physical violence between students; F4 = verbal violence toward fellow pupils; F5 = verbal violence by students to teachers; F6 = social exclusion; F7 = disruption in the classroom; F8 = violence through ICTs; STAI-S = STAI-state percentile score; STAI-T = STAI-trait percentile score; BDI = Beck Depression Inventory; ICT = information and communication technology; STAI = State-Trait Anxiety Inventory.

p < .01.

Next, the Spanish validation (Vázquez & Sanz, 1997) of the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) was used. Each item includes four self-report statements scored on a scale from 0 to 3. The scale designates levels of severity: minimum (0-13), mild (14-19), moderate (20-28), and severe (29-63; Beck, Steer, & Garbin, 1988). A Cronbach’s alpha of .88 was obtained for the present study. That index is comparable with those obtained in previous studies (Vázquez & Sanz, 1997, 1999; Vázquez Morejón, Vázquez-Morejón Jiménez, & Zanin, 2014).

The Spanish validation (Seisdedos, 2002) of the State-Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, & Lushene, 1970) was also used. The measure assesses situation-induced (state) and personality latent (trait) anxiety (20 items; range = 20-80). Cronbach’s alpha were .93 for anxiety state, and .89 for anxiety trait. These indexes are similar to those obtained in previous studies with Spanish participants (Fonseca-Pedrero, Paino, Sierra-Baigrie, Lennos-Girald, & Muniz, 2012; Guillén-Riquelme & Buela-Casal, 2015; Perpiñá-Galvañ, Cabañero-Martínez, & Richart-Martínez, 2013). Table 1 depicts the global results obtained as well as for each country separately.

In addition to these instruments and sociodemographic data, three questions were included: (a) Were you the victim of cyberbullying as a secondary school student? (b) If you were not cyberbullied, have you been bystander of cyberbullying as secondary school student? and (c) Have you overcome the experience or does it still negatively affect you? Being outside the scope of this study, no information on being a bully was gathered.

Results

Data on Cyberbullying

We started by analyzing the percentage of people who say they have suffered cyberbullying. The data show that, while 6.2% of Spanish students say they were a victim, only 4.4% of Bolivian participants report it. This difference is not significant (p = .138); the average percentage for the total sample was 5.1%. As for having been an observer of cyberbullying (bystanders), the total sample is 19.3%, being slightly higher, although not significantly (p = .174) in the case of Spanish students (20.9%) than in the Bolivian students (18.1%).

Next, we identified the normal versus clinical values in variables selected for this study. Thus, in terms of percentages of depression found in the sample, a total of 6.7% have clinical scores (i.e., greater than 18) on the BDI. There were no significant differences (p = .466) with 6.2% of Spanish participants showing clinical scores, compared with 7.1% of the Bolivian participants.

Regarding rates of depression found depending on having been cyberbullied or not, the data indicated that 9.8% of those who were victims exhibit clinical depressive symptoms today, compared with 6.4% of those with these symptoms but who did not suffer cyberbullying. Again, the differences were not significant (p = .236). The scores on the STAI were recoded into percentiles according to the norms, and scores equal or superior to the 75th percentile (i.e., high) were calculated. The data show that 17.5% of participants scored high on anxiety state and 20.5% scored high in trait anxiety. By countries, the percentages of elevated anxiety were higher in Spanish participants (19.4% and 29.6%, respectively) than in the Bolivian participants (16.1% and 13.5%, respectively). The only differences in percentage that are significant were the percentages of trait anxiety (p < .001). On the contrary, 26.8% of victims of cyberbullying had STAI-state scores above the 75th percentile, compared with 16.7% of those who obtained scores on the STAI-state above the 75th percentile but were not the victim of cyberbullying. These differences were significant (p = .018). The differences in the percentage of people with high trait anxiety (over 75th percentile) and being or not a victim of cyberbullying were even higher (35.4% vs. 19.3%) and significant (p < .001).

Third, we analyzed the possible presence of differences in the variables of interest depending on the origin of the participants. The analysis revealed the existence of significant differences considering all the variables together, Wilks’s λ = 0.445, F(11, 1528) = 173.44, p < .001, $η_{p}^{2}$ = .56. Table 2 presents the results obtained. It can be seen how the Spanish participants obtained significantly higher scores on all variables except for anxiety-state dimension. The following are small, medium, and large effects for $η_{p}^{2}$ , respectively, .01, .06, and .14 (Stevens, 1996), which reveals that the greatest effects are obtained in most of the variables measured by the CUVE-R questionnaire.

Table 2.

Descriptive Statistics and Significance of Differences (ANOVA Test) on Selected Variables by Country.

Variables	Country	M	SD	F	$η_{p}^{2}$
Factor 1				528.932**	.250
	Spain	2.25	0.68
	Bolivia	1.50	0.61
Factor 2				345.479**	.178
	Spain	2.32	0.77
	Bolivia	1.63	0.70
Factor 3				276.183**	.148
	Spain	2.41	0.89
	Bolivia	1.71	0.79
Factor 4				959.216**	.376
	Spain	3.18	0.80
	Bolivia	1.92	0.80
Factor 5				318.383**	.167
	Spain	2.35	0.89
	Bolivia	1.61	0.76
Factor 6				143.450**	.083
	Spain	2.12	0.82
	Bolivia	1.65	0.75
Factor 7				1,222.079**	.434
	Spain	3.44	0.87
	Bolivia	1.93	0.84
Factor 8				78.038**	.047
	Spain	1.71	0.70
	Bolivia	1.42	0.60
STAI-S				4.498*	.003
	Spain	37.55	31.37
	Bolivia	40.76	28.82
STAI-T				50.953**	.031
	Spain	47.28	32.03
	Bolivia	36.61	27.48
BDI				23.719**	.015
	Spain	7.05	6.93
	Bolivia	5.33	7.03

Note. Factor 1 = teacher violence toward students; Factor 2 = indirect physical violence by students; Factor 3 = direct physical violence between students; Factor 4 = verbal violence toward fellow pupils; Factor 5 = verbal abuse by students of teachers; Factor 6 = social exclusion; Factor 7 = disruption in the classroom; Factor 8 = violence through ICTs; STAI-S = STAI-state percentile score; STAI-T = STAI-trait percentile score; BDI = Beck Depression Inventory; ICT = information and communication technology; STAI = State-Trait Anxiety Inventory.

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

Variables That Help Predict Cyberbullying Victimization

We used MANOVA test to obtain the canonical correlation to determine a set of variables that best explain the variability among participants who have been cyberbullied or not. The overall multivariate test of the entire model was significant, Pillais = .052; F(11, 1528) = 7.57; p < .001. The squared canonical correlation was .052, which means that 5.2% of the variability in the super dependent variable (composed of the 11 dependent variables) is accounted for by the independent variable (having been bullied or not).

The standardized canonical coefficients suggest that the variables that are carrying the weight of this discriminant function were Factor 8, violence through ICT (r_c = 1.117); Factor 4, verbal violence toward fellow pupils (r_c = 1.095); and Factor 7, disruption in the classroom (r_c = −.557). Thus, high scores on Factors 8 and 4, together with low scores on Factor 7, were the variables on which the groups of people, cyberbullied or not, were really differing. This means that, whereas those who were victims of cyberbullying perceived elevated violence through ICT, verbal violence toward fellow pupils, and high disruption in the classroom, those who were not victims did not recall a school climate prone to violence, or violence through technologies. Using the raw discriminant function coefficients, we created the observed supervariable (canonical variate) and we ran the MANOVA test on the created variable. The analysis revealed that there were significant differences between the groups on that observed variable depending on if they had been cyberbullied or not, F(1, 1538) = 83.766; p < .001; $η_{p}^{2}$ = .052. Those who had been cyberbullied scored significantly higher (M = 3.16; SD = 1.41) than those who had not been cyberbullied (M = 2.10; SD = 0.98).

The univariate F tests showed that there are significant differences on each of the dependent variables. Those who claimed to have been cyberbullied scored significantly higher on each of the dependent variables (see Table 3). Yet, the effects were small for the different variables.

Table 3.

Descriptive Statistics and Significance of Differences (ANOVA Test) on Selected Variables by Having Been Cyberbullied or Not.

Variables	Factor	n	M	SD	SE	F	$η_{p}^{2}$
Factor 1						15.038**	.009
	No	1,511	1.81	0.73	0.02
	Yes	82	2.13	0.81	0.09
Factor 2						24.122**	.015
	No	1,511	1.90	0.79	0.02
	Yes	82	2.35	1.04	0.12
Factor3						16.175**	.010
	No	1,511	1.98	0.89	0.02
	Yes	82	2.39	1.08	0.12
Factor 4						40.408**	.025
	No	1,511	2.42	0.99	0.03
	Yes	82	3.14	1.12	0.13
Factor 5						20.564**	.015
	No	1,511	1.90	0.88	0.02
	Yes	82	2.36	1.07	0.12
Factor 6						22.210**	.014
	No	1,511	1.83	0.80	0.02
	Yes	82	2.26	0.96	0.11
Factor 7						12.048**	.008
	No	1,511	2.56	1.13	0.03
	Yes	82	3.00	1.18	0.13
Factor 8						50.122**	.031
	No	1,511	1.52	0.64	0.02
	Yes	82	2.04	0.87	0.10
STAI-S						9.184**	.006
	No	1,511	38.86	29.87	0.78
	Yes	82	49.13	30.22	3.34
STAI-T						13.140**	.008
	No	1,511	40.53	29.77	0.78
	Yes	82	52.80	31.46	3.47
BDI						17.178**	.011
	No	1,511	5.90	6.96	0.18
	Yes	82	9.19	7.77	0.86

p < .01.

To further analyze differences between those who were not cyberbullied, compared with cyberbully victims, the same procedure was used to determine which variables help explain variability among participants who have not been cyberbullied versus those who claimed to have overcome that episode versus those who indicate still having some negative impact. The overall multivariate test of the entire model was significant, Pillais = .075, F(22, 3056) = 4.96, p < .001. The first squared canonical correlation was .066, which means that 6.6% of the variability in the super dependent variable (composed by the 11 dependent variables) is accounted for in the independent variable (the three groups). The standardized canonical coefficients suggest that the variables that are carrying the weight of this discriminant function are, again, Factor 4 (r_c = 1.258), Factor 8 (r_c = .712), and Factor 7 (r_c = −.605). After creating the observed canonical variable, MANOVA test was significant, F(2, 1537) = 54.051; p < .001; $η_{p}^{2}$ = .066, and those who had not been cyberbullied scored significantly lower (M = 1.78; SD = 0.95) than those who had been cyberbullied but have overcome the experience (M = 2.34; SD = 1.13). Those who claimed not having overcome the experience scored much higher (M = 3.53; SD = 1.44). Post hoc test (Duncan) revealed that the three groups were significantly different from each other (p < .05).

The univariate F tests showed that there are significant differences on each of the dependent variables (Table 4). Those who claimed to have been cyberbullied scored significantly higher on each of the dependent variables. The post hoc analysis (Duncan) revealed that the group that had not experienced cyberbullying obtained significantly lower scores in all the variables than the group that says they had not overcome the experience. The effects are somewhat larger than in the previous analysis, summarized in Table 3 above, suggesting that for some of the victims of this interpersonal violence, the experience of having been cyberbullied in the past is still affecting their lives.

Table 4.

Descriptive Statistics and Significance of Differences (ANOVA Test) on Selected Variables by Cyberbullied or Not and Its Impact.

Variables	Factor	n	M	SD	SE	F	$η_{p}^{2}$
Factor 1						8.412**	.010
	Not cyberbullied	1,511	1.81	0.73	0.02
	No current impact	51	2.05	0.84	0.12
	Negative impact	31	2.27	0.75	0.13
Factor 2						13.510**	.017
	Not cyberbullied	1,511	1.90	0.79	0.02
	No current impact	51	2.23	1.11	0.16
	Negative impact	31	2.54	0.88	0.16
Factor 3						9.167**	.011
	Not cyberbullied	1,511	1.98	0.88	0.02
	No current impact	51	2.28	1.05	0.15
	Negative impact	31	2.58	1.12	0.20
Factor 4						27.180**	.033
	Not cyberbullied	1,511	2.42	0.99	0.03
	No current impact	51	2.82	1.12	0.16
	Negative impact	31	3.66	1.11	0.17
Factor 5						12.228**	.016
	Not cyberbullied	1,511	1.90	0.88	0.02
	No current impact	51	2.25	1.17	0.16
	Negative impact	31	2.53	0.88	0.16
Factor 6						11.620**	.014
	Not cyberbullied	1,511	1.83	0.80	0.02
	No current impact	51	2.19	0.96	0.14
	Negative impact	31	2.38	0.97	0.18
Factor 7						7.934**	.010
	Not cyberbullied	1,511	2.56	1.13	0.03
	No current impact	51	2.81	1.24	0.18
	Negative impact	31	3.31	1.00	0.19
Factor 8						28.195**	.034
	Not cyberbullied	1,511	1.52	0.64	0.02
	No current impact	51	1.90	0.88	0.12
	Negative impact	31	2.27	0.84	0.15
STAI-S						5.245**	.005
	Not cyberbullied	1,511	38.86	29.87	0.77
	No current impact	51	46.20	30.42	4.26
	Negative impact	31	53.97	29.75	5.34
STAI-T						12.442**	.015
	Not cyberbullied	1,511	40.53	29.77	0.77
	No current impact	51	44.06	30.94	4.33
	Negative impact	31	67.19	27.07	4.86
BDI						10.298**	.013
	Not cyberbullied	1,511	5.90	6.96	0.18
	No current impact	51	7.95	7.00	0.98
	Negative impact	31	11.21	8.63	1.55

p < .01.

Predictors of Being Bystander of Cyberbullying

Finally, we analyzed the potential variables that help explain being a bystander of cyberbullying. The overall multivariate test of the entire model was significant, Pillais = .038, F(11, 1528) = 5.48, p < .001. The squared canonical correlation was = .038, which means that 3.8% of the variability in the super dependent variable (composed by the 11 dependent variables) is accounted for in the independent variable (the three groups). The standardized canonical coefficients suggest that the variables that are carrying the weight of this discriminant function are Factor 3, direct physical violence between students (r_c = .735); Factor 1, teacher violence toward students (r_c = −.661); Factor 6, social exclusion (r_c = .431); and Factor 8, violence through ICT (r_c = .425). After creating the observed canonical variable, MANOVA test was significant, F(1, 1538) = 60.659, p < .001, $η_{p}^{2}$ = .038, and those who had observed someone else being cyberbullied scored significantly higher (M = 1.89; SD = 1.12) than those who had not (M = 1.39; SD = 0.97). Those who claimed having negative impact scored much higher (M = 3.64; SD = 1.47) than the previously mentioned observers and nonobservers groups.

The univariate F tests showed that there are significant differences only on the CUVE-R factors (Table 5). Those who claimed to have observed bullying scored significantly higher on each of the dependent variables. Again, the effects are somewhat larger than in the previous analysis (see Table 4).

Table 5.

Descriptive Statistics and Significance of Differences (ANOVA Test) on Selected Variables by Having Been a Bystander of Cyberbullying or Not.

Variables	Factor	n	M	SD	SE	F	$η_{p}^{2}$
Factor 1						5.044*	.003
	No	1,286	1.80	0.74	0.02
	Yes	307	1.91	0.76	0.04
Factor 2						16.815**	.010
	No	1,286	1.88	0.80	0.02
	Yes	307	2.09	0.85	0.05
Factor 3						36.198**	.022
	No	1,286	1.94	0.88	0.02
	Yes	307	2.28	0.96	0.05
Factor 4						19.730**	.012
	No	1,286	2.40	1.02	0.03
	Yes	307	2.69	0.97	0.06
Factor 5						16.021**	.010
	No	1,286	1.88	0.88	0.03
	Yes	307	2.11	0.92	0.05
Factor 6						26.337**	.016
	No	1,286	1.80	0.79	0.02
	Yes	307	2.06	0.87	0.05
Factor 7						16.519**	.010
	No	1,286	2.52	1.14	0.03
	Yes	307	2.81	1.09	0.06
Factor 8						30.174**	.019
	No	1,286	1.50	0.63	0.02
	Yes	307	1.73	0.75	0.04
STAI-S						0.977	.001
	No	1,286	39.75	30.06	0.84
	Yes	307	37.88	29.57	1.69
STAI-T						0.000	<.001
	No	1,286	41.17	30.34	0.85
	Yes	307	41.13	28.39	1.62
BDI						1.176	.001
	No	1,286	5.96	7.20	0.20
	Yes	307	6.51	6.30	0.36

Note. Factor 1 = violence by teachers toward students; Factor 2 = indirect physical violence by students; Factor 3 = direct physical violence between students; Factor 4 = verbal violence toward fellow pupils; Factor 5 = verbal violence by students to teachers; Factor 6 = social exclusion; Factor 7 = disruption in the classroom; Factor 8 = violence through ICTs; STAI-S = STAI-state percentile score; STAI-T = STAI-trait percentile score; BDI = Beck Depression Inventory; ICT = information and communication technology; STAI = State-Trait Anxiety Inventory.

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

Discussion

The purpose of this study was to look back on the direct (victim) and indirect (bystander) experience of cyberbullying and the associated personal and situational factors, in a sample of freshmen and entering students. This research broadens the focus of existing research because it analyzes past experiences. Data in the present study suggest that 6.2% of participants have experienced cyberbullying. To this is added the 19.7% who say that have observed cyberbullying. These data generalize to both countries of the study. These percentages are lower than those obtained in Spanish studies with secondary school students (Garaigordobil, 2015; Garaigordobil & Aliri, 2013; Ortega et al., 2012), and in other cultures (Gan et al., 2014; Hemphill et al., 2012; Mishna, Khoury-Kassabri, Gadalla, & Daciuk, 2012; Schneider, O’Donnell, Stueve, & Coulter, 2012). These data are also smaller than those obtained in the Global School–Based Student Health Survey (GSHS; Organización Panamericana de la Salud/Organización Mundial de la Salud [OPS/OMS], 2013), a large scale school-based survey according to which the percentage of students who were bullied on one or more days during the past 30 days was 30.2% for students aged 13 to 15 years, the age of interest in the current study. As one of the effects of cyberbullying is school failure, and therefore withdraw from school (Hemphill et al., 2012; Rose & Tynes, 2015), and given that this study was carried out with college students, this failure may explain the differences in percentages, as victims of cyberbullying may not have succeeded in their transition from high school to college.

This study makes several additional contributions. First, our study suggests that there is significant overlap between bullying, cyberbullying, and other types of school violence. The medium-high size significant and positive correlations obtained between factors of the CUVE-R support this fact. These results agree with various authors who utilized similar analysis procedures, that is, correlational analyses (Cuadrado-Gordillo & Fernández-Antelo, 2016; Del Rey, Elipe, & Ortega-Ruiz, 2012; Riebel, Jäger, & Fischer, 2009), but differ from Kubiszewski, Fontaine, Potard, and Auzoult (2015) where no overlap was found in self-reported victimization. Differences in the way information was obtained may help explain these differences. This stresses the relevance of gathering multisource information when these phenomenon are concerned.

Our results are also consistent with the general aggression model (Kowalski et al., 2014) that situates cyberbullying in climates prone to justify violence. In other words, cyberbullying exists and flourishes and grows (in both amount and severity) in contexts and environments (and, in this case, in classrooms) where it is allowed to and where structures and sanctions against it are not exercised. For example, from the GSHS data (OPS/OMS, 2013), we see that 33.0% students were in a physical fight 1 or more times in the previous 12 months and 48.5% of students were seriously injured 1 or more times in the same period. These percentages are aligned with the obtained 30.2% of bullying in school settings. The percentages are also much higher than those obtained in other Latin American countries based on the same methodology, with percentages near 20% for Costa Rica (OPS/OMS, 2009), but smaller than percentages obtained in other regions such as Peru, with percentages near 45% (OPS/OMS, 2011). Normalization of violence could help explain why it could be more acceptable for some cultures and, thus, less noticeable. Our findings on significantly higher scores in the CUVE by Spanish students seem to support this effect.

In accordance with these data, our findings suggest that the experience of cyberbullying is associated to verbal violence by peers. As several studies have demonstrated, the exposure to violence leads to more aggression-supporting beliefs and in turn to greater aggression, as well to avoidant coping (Boxer et al., 2008). As Gådin, Weiner, and Ahlgren (2013) state, schools need to be aware of the normalization processes of violence and harassment that takes place to promote healthy educational environments. In this regard, some programs aimed at increasing socio-emotional skills have proven to be useful to reduce direct and relational aggressive behaviors (Murrieta, Ruvalcaba, Caballo, & Lorenzo, 2014).

Although few in number, the people who claim to have been a victim of cyberbullying and, especially, who show that this negatively impacts their present life score higher not only in the perception in different components of school violence but also in anxious and depressive symptoms. The evidence that levels of anxiety and depression are higher in these participants than those in the general population (American Psychiatric Association [APA], 1994/1995) justifies the need for further research along these lines. In this regard, the utilization of a comprehensive assessment instrument of school violence, namely, the CUVE-R, responds to one of the demands of the scientific community on the need to provide a clear and comprehensive definition of bullying and cyberbullying (Vivolo-Kantor, Martell, Holland, & Westby, 2014).

Another interesting contribution relates to the analysis of the impact of cyberbullying in bystanders. Unlike cyberbullying, the impact of this experience is not reflected in differences in anxiety or depression symptoms, which agrees with the moral disengagement approach. Yet, when comparing the scores of those in a bystander role with those who have not being cyberbullied and those who have been cyberbullied and have overcome or not the negative event, it is noteworthy that bystanders score higher than those who have not been cyberbullied, but lower than those who have been victims. This intermediate position may reflect the fact that diverse roles, such as defenders or supporters, could be included in this group. As we mentioned earlier, whereas defenders may show higher emotional contagion (Gini, 2006; Pöyhönen et al., 2010; Vannini et al., 2011), those who adopt a passive or avoidant role (Boxer et al., 2008; Doramajian & Bukowski, 2015) may feel less psychological impact. As protecting oneself from this stressful event may be a useful survival strategy in violent environments where violence is normalized, interventions aimed at increasing moral sensitivity and emotional contagion are advisable if we want to promote cooperative learning environments and increase the success of school-based anti-bullying programs (Hymel, McClure, Miller, Shumka, & Trach, 2015).

While these contributions are novel, some shortcomings must be noted. First, the results are limited by the retrospective nature of the study, as the participants might not have remembered or reported their experiences accurately. Second, the selection procedure of the sample limits generalizing the data to other participants and contexts. Third, the study analyzes only some of the contextual or situational variables of interest from the general aggression model. In consequence, further studies should investigate additional factors (e.g., type of secondary schools, types of studies, and types and diversity of students) that may increase the likelihood of developing harassment and cyberbullying situations. For example, some studies suggest that at university levels, being a minority, due to personal or situational characteristics, increases the risks for those students to be cyberbullied. Some of these characteristics relate to having a disability (Kowalski et al., 2016), having a low cumulative grade point average (GPA; Özçınar & Aldağ, 2012), or having a nonheterosexual orientation (Wensley & Campbell, 2012).

A fourth shortcoming relates to the cross-sectional and correlational nature of the study. Thus, while it is not possible to establish cause–effect relationships, findings suggest that anxiety and depression rates are higher, especially regarding trait anxiety, in people who have suffered cyberbullying. Further studies should elucidate the relationships between personal vulnerabilities and harassment.

In short, as ICTs become ever more available, their improper use will become more frequent. As seen in the present study, the impact of these experiences is extended in time because the ratings are about situations that occurred in secondary school. These results coincide with recent meta-analyses of the long-term outcomes for former bullies and victims that provide “convincing evidence that being involved in such problems is not just a harmless and passing school problem” (Olweus, 2013, p. 770) but something that has serious internalized and externalized adjustment and public health consequences that also entail great costs to society.

Conclusion

There is an overlap between bullying, cyberbullying, and the broader school climate for justifying violence between peers and in teacher–student interactions. Thus, the use of the general aggression model is advisable when studying this particular type of violence. Further affecting school climate, being a bystander of cyberbullying seems to be associated to a moral disengagement cognitive strategy, protecting psychological well-being as a consequence.

Cyberbullying has a persistent negative impact: Participants who were cyberbullied in the past currently score significantly higher on anxiety and depression. Although time has passed, the effects of cyberbullying seem to follow victims, even in those who succeeded in their transition from school to University.

Footnotes

Ethical Approval

All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Cristina Jenaro is full professor and director of the Department of Personality, Assessment and Psychological Treatment at the Faculty of Psychology, University of Salamanca, Spain.

Noelia Flores is professor and academic secretary of the Department of Personality, Assessment and Psychological Treatment at the Faculty of Psychology, University of Salamanca, Spain.

Cinthia Patricia Frías is associated professor of the Universidad Católica Boliviana–Cochabamba, Bolivia, and PhD student at the Faculty of Psychology, University of Salamanca, Spain.

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