Reactions to cyberbullying among high school and university students

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Reactions to cyberbullying among high school and university students Bahadır Eris¸ti, Yavuz Akbulut ∗ Department of Educational Sciences, Faculty of Education, Anadolu University, Turkey

a r t i c l e

i n f o

Article history: Received 27 December 2017 Received in revised form 23 June 2018 Accepted 24 June 2018 Available online xxx Keywords: Cyberbullying Coping Secondary education Post-secondary education

a b s t r a c t In this study, behavioral and emotional reactions to cyberbullying were investigated by surveying 567 undergraduate-level university students and 211 high school students. Among the study participants, 170 of the undergraduates (29.98%) and 120 of the high school students (56.87%) reported that they had recently been cyberbullied. A four-factor scale with 37 items was used to investigate the behavioral cyberbullying reactions of victimized students. The four factors were revenge, countermeasure, negotiation and avoidance. An additional two-factor scale with 11 items was used to classify victimized students’ emotional reactions as either internalizing or externalizing. Explained variance values of both scales were above 50%, and the factors were found to have acceptable internal consistency coefficients. Behavioral and emotional reactions varied according to gender and school level. Computer self-efficacy and internet use were associated with different reaction types. © 2018 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

1. Introduction Developments in emerging communication technologies have created novel interaction opportunities. However, some of these developments have generated a number of user-induced risks, including cyberbullying, a frequently studied topic in recent years. The term cyberbullying derives from school bullying, which is an intentional aggressive behavior enacted by individual(s) against peers who cannot defend themselves (Kowalski, Limber, & Agatston, 2012; Olweus, 1993). Contemporary technologies have transformed bullying patterns, leading researchers to extend the traditional definition to include digital life; that is, this new form of bullying

∗ Corresponding author at: Department of Educational Sciences, Anadolu University, 26470, Eskisehir, Turkey. E-mail addresses: [email protected] (B. Eris¸ti), [email protected] (Y. Akbulut).

transpires through emerging online communication technologies (Beale & Hall, 2007; Beran & Li, 2005). Cyberbullying can be an extension of school bullying but does not necessarily involve a face-to-face confrontation with the bully (Schenk & Fremouw, 2012). Sometimes, victims may not have any clue regarding the identity, location or purpose of the cyberbully. This prevents victims from responding accordingly (Spears, Slee, Owens, & Johnson, 2009). Moreover, perpetrators may have lower self-esteem, which may lead them to act more aggressively than they otherwise would in face-to-face encounters (Aricak et al., 2008), and they may lack empathy for victims, as they are not aware of the negative results their cyberbullying has on their victims (Campbell, Spears, Slee, Butler, & Kift, 2012; Froese-Germain, 2008). Cyberbullying can result in a number of unpleasant, if not damaging, consequences, including anger and sadness (Beran & Li, 2005; Wang, Yang, Yang, Wang,& Lei, 2017), emotional distress (González-Cabrera, Calvete, León-Mejía, Pérez-Sancho.& Peinado, 2017), behavioral problems (Hinduja & Patchin, 2007), lower self-esteem

https://doi.org/10.1016/j.soscij.2018.06.002 0362-3319/© 2018 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

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(Brewer & Kerslake, 2015; Palermiti, Servidio, Bartolo,& Costabile, 2017; Patchin & Hinduja, 2010) and suicidal ideations (Schenk & Fremouw, 2012). Cyberbullying victims, compared to traditional bullying victims, report more social difficulties and higher anxiety (Campbell et al., 2012). Since vulnerability and achievement are negatively related, cyberbullying is likely to affect all aspects of a young victim’s life (Feinberg & Robey, 2008). The ultimate aims of preventive cyberbullying-related measures are to decrease the extent of the behavior, protect individuals from unpleasant/damaging consequences, and increase reporting rates (Finkelhor, Mitchell, & Wolak 2000). In this regard, activities that serve to raise awareness in educational settings may work well (Akbulut, 2014; Tanrikulu, Kinay, & Aricak, 2015). To support such awareness raising activities and eliminate unpleasant/damaging consequences, reactions to cyberbullying and effective coping strategies should be investigated. Empirical studies conducted with adolescents have revealed that some victims removed themselves from online settings, temporarily stayed offline or talked to a friend when exposed to cyberbullying however, they rarely informed an adult about incidences of cyberbullying (Patchin & Hinduja, 2006). Blocking cyberbullies, ignoring them, keeping records of offensive messages, reporting incidences of cyberbullying to authorities, contacting service providers, asking cyberbullies to stop or fighting back were other adolescent reactions that have been reported in the literature (Smith et al., 2008). Young online users in particular have largely been observed to ignore cyberbullies, delete messages or bully back (Dehue, Bolman, & Vollink, 2008). In a college sample, Schenk and Fremouw (2012) listed the reactions they had to cyberbullying as telling someone, avoiding friends, avoiding the internet, getting revenge, engaging in alcohol and drug use, and stopping going to events. Additional reactions to cyberbullying have been listed as revenge/retaliation (Hoff & Mitchell, 2009; Perren et al., 2012; Sticca et al., 2015), seeking support (Aricak et al., 2008; Perren et al., 2012; Sticca et al., 2015), dialogue or forgiveness (Safaria, Tentama, & Suyono, 2016), and disregard or avoidance (Perren et al., 2012; Randa & Reyns, 2014; Safaria et al., 2016; Sticca et al., 2015). In addition to these behavioral reactions, certain studies have focused specifically on emotional responses to cyberbullying, such as anger, frustration, sadness (Hinduja & Patchin, 2007), stress, worry, fear, depression, and embarrassment (Ortega, Elipe, Mora-Merchán, Calmaestra, & Vega, 2009). All these reactions may be classified as either problemfocused or emotion-focused, according to the Coping Theory (Lazarus & Folkman, 1984). More specifically, people tend to employ problem-focused coping strategies when they believe that their current potential resources or support from others can change the situation. In this respect, self-efficacy can be a significant predictor of such strategies (Hoff & Mitchell, 2009; Singh & Bussey, 2011). On the other hand, individuals tend to resort to emotionfocused coping when they believe that they can do little to change an unpleasant situation. Such emotion-focused responses may involve controlling emotional responses in stressful situations through redefining the problem, ignor-

ing the problem, or focusing on positive aspects of the stressful incident. The literature cites two emotion-focused strategies, namely, internalizing and externalizing. In internalizing strategies, emotions stemming from stressful and unpleasant situations are directed inward and not discussed with others. In contrast, externalizing strategies involve coping with unpleasant emotions by taking them out on other individuals or objects (Raskauskas & Huynh, 2015). Coping strategies can be further classified as internal or external (Bijttebier & Vertommen, 1998), productive or non-productive (Frydenberg, 2008), approach or avoidance (Roth & Cohen, 1986), ineffective coping or improvement in coping (Jacobs, DeHue, Völlink & Lechner, 2014), and aggressive or passive coping (Mahady Wilton, Craig, & Pepler, 2000). Recent cyberbullying studies have classified potential reactions as either maladaptive (e.g., revenge) or adaptive (e.g., social support) (Wright, 2016); behavioral or emotional (Ittel, Mueller, Pfetsch, & Walk, 2014); passive (e.g. avoiding) or risky (e.g., fighting back) (Sittichai & Smith, 2018); and active or passive (Chan & Wong, 2017). A more comprehensive categorization is proposed by Perren et al. (2012), who list potential cyberbullying coping strategies as constructive contact or retaliation (e.g., revenge), ignoring the perpetrator (e.g., avoidance), seeking instrumental or emotional support from peers, and technical countermeasures (e.g., blocking, reporting abuse). Empirical studies have shown that antecedents of coping can vary. In addition to self-efficacy (Aricak et al., 2008; Hoff & Mitchell, 2009), awareness (Smith et al., 2008) or empathy (Lee & Shin, 2017) can influence victims’ reactions. Furthermore, the emotional state of victims, along with the type and degree of aggression (Beran, Rinaldi, Bickham, & Rich, 2012), peers’ attitudes and teachers’ intervention capacity (Christian Elledge et al., 2013), computer skills (Beran & Li, 2005), gender (Dehue et al., 2008; Hinduja & Patchin, 2011; Sittichai & Smith, 2018), age (Chan & Wong, 2017; Sittichai & Smith, 2018; Sourander et al., 2010), self-esteem (Patchin & Hinduja, 2010), beliefs regarding parents’ reactions (Hoff & Mitchell, 2009) or cultural context (Barlett et al., 2014; Hu, Bernardo, Lam & Cheang, 2018; Wright et al., 2018) can serve as predictors of victims’ reactions. An individual’s reactions may also vary in accordance with the type and nature of the aggression (Buckley, Winkel, & Leary, 2004; Davy & Cross, 2004). Moreover, risk research has documented that people have recourse to immediate examples of unpleasant events, and that such experiences with and proximity to risks lead to different degrees of risk perceptions (Schwarz et al., 1991). In other words, changes in reactions may be explained through the degree or intensity of recent experiences, as has been observed in the recent literature (Orel, Campbell, Wozencroft, Leong, & Kimpton, 2017). The current research was therefore conducted with recently victimized students. In the current study, the aim was to develop and classify behavioral and emotional reactions to cyberbullying in a Turkish sample. Peer-reviewed empirical studies conducted in Turkey have focused primarily on the prevalence and predictors of cyberbullying (e.g., Arıcak, 2009; Arslan,

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Savaser, Hallett, & Balci, 2012; Dursun & Akbulut, 2012; Beyazit, S¸ims¸ek, & Ayhan, 2017). Some of the research has also examined interventions to raise awareness (Akbulut, 2014; Nedim-Bal, 2015; Tanrikulu et al., 2015). However, the national literature lacks investigations pertaining to behavioral and emotional reactions to cyberbullying. To fill this gap, a list of reactions was prepared through an extensive literature review, focus-group interviews and expert opinions. After latent variables were validated through exploratory factor analyses, their predictors were examined and are discussed in accordance with the available literature. 2. Methods and procedures 2.1. Research context and participants Researchers have conducted cyberbullying studies in different institutions across Turkey and have been invited by various institutions to give seminars and workshops on this topic. Thus, convenience sampling was performed by collecting data from these institutions. A total of 567 undergraduate students (72.88%) from four universities and 211 students (27.12%) from two high schools in Turkey were surveyed to identify students who were cyberbullied in the last six months. Of the 778 survey respondents, 290 (37.28%) reported that they had been bullied within the last six months. More specifically, 120 of the high school students (56.87%) and 170 of the undergraduates (29.98%) were cyberbullying victims. Victimization among the high school students was found to be more serious (Pearson Chi-Square = 47.555; p < 0.001). Out of the 290 victimized students, 102 were female (35.2%), 154 were male (53.1%), and 34 (11.7%) did not wish to indicate their gender. 2.2. Data collection tools Previous studies that had been performed within the theoretical framework of the present study were used to prepare two separate questionnaires, one for behavioral reactions and one for emotional reactions to cyberbullying (e.g., Ittel et al., 2014; Ortega et al., 2009; Perren et al., 2012; Safaria et al., 2016; Sticca et al., 2015; Wright, 2016). To help enrich available indicators, focus group interviews were conducted with ten graduate-level students in the educational sciences department and 25 undergraduate students in the information technology department. Two experts who had produced international articles on scale development and cyberbullying reviewed the item pool, rated the relevance of each item for target constructs, and revised items for clarity. Pilot implementations of each questionnaire were performed to see whether the items functioned effectively. The draft item pool had 43 items pertaining to behavioral reactions and 12 items pertaining to emotional reactions. Response options regarding a specific reaction ranged from 1 (very untrue of me) to 5 (very true of me). Final versions of the questionnaires following the exploratory factor analyses included 37 items for behavioral reactions (Table 1) and 11 items for emotional reactions (Table 4).

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A personal information form, which was used to address participants’ age, gender, school type (high school or university), daily internet-use duration (5-point Likert scale), and perceived computer self-efficacy (4-point Likert scale), and to determine whether they had been victimized within the last six months, was also developed for this study. In preparing these questions, previous scales developed by researchers were taken into account (e.g, Akbulut & Eristi, 2011). 2.3. Procedure Instructors from the selected schools shared the questionnaire link with students during their information technology courses, which resulted in a response rate of greater than 95%. Data collection took place in March of 2017 and lasted three weeks. The data were then processed through two separate principal components analyses to examine the factor structure of each questionnaire. After the calculation of descriptive statistics, parametric tests (e.g., ANOVA, MANOVA) were conducted, and statistically significant results were supported through effect size values. 3. Findings 3.1. Factor analysis on behavioral reactions The sample size for the factor analysis was determined to be satisfactory (Worthington & Whittaker, 2006). The 43-item draft questionnaire, developed on the basis of 290 participants, had an alpha value of 0.906. Principal components analysis (PCA) with varimax rotation resulted in the identification of eight factors with acceptable eigenvalues (i.e., +1; Kaiser, 1974) and explained 62.04% of the variance. However, in employing the Kaiser Criterion, an overestimation of the number of factors to retain can occur (Field, 2009). In the present study, some factors had items which were not interpretable. Thus, Cattell’s scree test was applied, the results of which revealed four factors and explained an acceptable total variance (i.e., >50%; Henson & Roberts, 2006). Items with very small loadings (<0.32) or cross-loadings (<0.15) were eliminated (Worthington & Whittaker, 2006). Six complex or repetitive items were deleted. The final solution explained 51.557% of the variance with an alpha value of 0.895. Results of the Kaiser–Meyer–Olkin (KMO) confirmed the suitability of the sample size (i.e., 0.878, Hutcheson & Sofroniou, 1999). The results of the Bartlett’s Test of Sphericity were ideal as well (Chi-Square: 5,293.356; df: 666; p < 0.001). In brief, the final draft of the questionnaire included 37 items under four types of reactions to cyberbullying: Revenge, countermeasure, negotiation, and avoidance. Items under each factor were interpretable and had acceptable loadings (i.e., >0.32; Worthington & Whittaker, 2006), and the factors were determined to have high internal consistency coefficients as well (Table 1). All factors had small skewness and kurtosis values (i.e., between 0.03 through 0.65), which suggested a normal distribution (Huck, 2012).

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Table 1 Principle components of the behavioral reactions. Components Revenge (Alpha: 0.908) I look for ways to get even with the perpetrator. I send disturbing messages to the perpetrator. I respond through curse words. I strike back in a setting and time where they cannot identify me. I respond with what they expect and mess with them. I distribute user names of perpetrators in online networks. I respond with a similar message. I say humiliating things. I threaten the perpetrators that I will disclose their personal information online. I respond through sending malware. I make them pay through using different user names and accounts. I say that I disclose their user names in online networks. I say that I can harm them through my technical knowledge. I search for ways to fight back with the perpetrator. I swear. Countermeasure (Alpha: 0.881) I store the perpetration instances as evidence. I say that I will file a complaint to relevant authorities. I take screenshots and store them. I block the perpetrator. I research for ways to protect myself from such situations. I warn my friends. I say that I can find their personal information and inform the competent authorities. I file a complaint to the service or content provider. I change my username and password. I seek support from family or friends.

Eigenvalue

Variance

Mean

SD

Factor load

Corrected item total r

8.604

18.353

2.79 2.64 2.70 2.70 2.90 3.08 2.84 2.89 3.00

1.42 1.41 1.49 1.46 1.46 1.44 1.31 1.43 1.48

0.76 0.74 0.71 0.69 0.68 0.67 0.66 0.66 0.66

0.69 0.65 0.63 0.61 0.61 0.64 0.58 0.62 0.64

2.67 2.81 2.92 2.91 3.15 3.14

1.52 1.49 1.45 1.40 1.42 1.51

0.64 0.64 0.63 0.57 0.57 0.44

0.58 0.59 0.60 0.56 0.57 0.40

3.67 3.48 3.74 3.84 3.44 3.73 3.46

1.44 1.44 1.42 1.38 1.39 1.40 1.39

0.76 0.76 0.74 0.69 0.69 0.69 0.66

0.71 0.70 0.71 0.59 0.63 0.61 0.61

3.52 3.25 3.05

1.39 1.51 1.40

0.66 0.56 0.54

0.56 0.49 0.48

2.79 3.19 3.14

1.44 1.35 1.45

0.71 0.69 0.68

0.62 0.57 0.58

3.21 2.90 3.30

1.39 1.44 1.34

0.68 0.67 0.66

0.60 0.58 0.60

2.94

1.37

0.49

0.41

2.64 2.50 2.93 2.87

1.46 1.42 1.43 1.46

0.81 0.73 0.61 0.58

0.63 0.47 0.44 0.44

2.95

1.46

0.37

0.26

4.806

Negotiation (Alpha: 0.823) I try to help them give up such behaviors. I respond to understand whether the perpetration is intentional. I tell them how uncomfortable they may feel when such things are done to them or their friends. I tell them such behaviors are useless and dishonorable. I chat with them to understand their rationale for the behavior. I warn the perpetrator regarding the ethical implications of the behavior. I tell them messing with me is just a waste of time.

3.494

Avoidance (Alpha: 0.69) I shut down the computer and walk away. I delete the message or the content. I leave the page or website. I do not react as they may not know me or do the same to many others. I refer them to God.

2.172

15.716

9.222

8.266

n = 290; overall alpha = 0.895; total variance = 51.557%; KMO = 0.878; Bartlett’s Test (Chi-Square: 5293.356; df: 666; p < 0.001) Table 2 Behavioral reaction comparisons between genders. Reaction

Gender

n

Mean

SD

t

df

p

Eta squared

Revenge

Female Male Female Male Female Male Female Male

102 154 102 154 102 154 102 154

2.56 3.09 3.79 3.52 2.96 3.11 2.80 2.62

0.93 0.94 0.93 0.95 1.01 0.96 0.94 0.92

−4.427

254

<0.001

0.072

2.190

254

0.029

0.019

−1.148

254

0.252

0.005

1.490

254

0.137

0.009

Countermeasure Negotiation Avoidance

Scores on each factor significantly differed from one another that had a large effect size (Wilks’ Lambda = 0.707; F3,287 = 39.646; p < 0.001; partial eta squared = 0.293). Pair-

wise comparisons were conducted through Bonferroni, which revealed that the mean score on countermeasure (3.52; SD = 0.98) was significantly higher than the mean

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Table 3 Behavioral reaction comparisons between schools. Reaction

School

n

Mean

SD

t

df

p

Eta squared

Revenge

High school University High school University High school University High school University

120 170 120 170 120 170 120 170

2.89 2.86 3.15 3.78 3.14 3.01 3.14 2.52

0.97 0.95 1.07 0.83 1.01 0.95 1.01 0.85

0.234

288

0.815

0.000

−5.586

288

<0.001

0.098

1.088

288

0.277

0.004

5.624

288

<0.001

0.099

Countermeasure Negotiation Avoidance

scores on negotiation (3.07; SD = 0.97), avoidance (2.78; SD = 0.97) and revenge (2.87; SD = 0.95) (p < 0.001). The mean negotiation score was also higher than the mean revenge (p = 0.021) and avoidance (p < 0.001) scores, but the difference between revenge and avoidance was not statistically significant (p > 0.001).

3.2. Predictors of behavioral reactions 3.2.1. Gender Two-way MANOVA on gender and school type (high school vs. university) revealed that there was not an interaction effect between gender and school type with regard to behavioral reactions (Wilks’ Lambda = 0.970; F4,249 = 1.950; p = 0.103). Thus, separate parametric analyses were conducted for gender and school type to identify the extent of behavioral reactions for both variables. A two-way mixed-design ANOVA was used to see the influence of gender on reaction type. The main effect for reaction type was significant (F3,762 = 64.880; p < 0.001; partial eta squared = 0.203), but the main effect for gender was not (F1,254 = 0.525; p = 0.47; partial eta squared = 0.002). Mean differences across revenge, countermeasure, negotiation and avoidance were presented above in Section 3.1. The interaction effect of gender by reaction type was statistically significant (F3,762 = 7.924; p < 0.001; partial eta squared = 0.045) that required simple main-effect analyses (Huck, 2012). Descriptive statistics and comparisons are presented in Table 2. The analyses revealed that males had higher scores on revenge, whereas females had higher scores on countermeasure. Mean scores on negotiation and avoidance were similar across genders.

hand, differences in terms of revenge and negotiation were not significant. 3.2.3. Technology habits Analysis of the relationship between cyberbullying reactions and internet and technology habits revealed that internet-use duration correlated positively with revenge (r = 0.128; p = 0.03) and negotiation (r = 0.13; p = 0.028). In contrast, computer self-efficacy correlated positively with countermeasure (r = 0.128; p = 0.03) and negatively with avoidance (r = −0.247; p < 0.001). 3.3. Exploratory factor analysis on emotional reactions Similar to the analyses on behavioral reactions, a PCA with varimax rotation was conducted. The analysis revealed two emotional reaction components, which explained 59.88% of the total variance. Only one item with cross-loadings on different factors was eliminated. Results of the KMO confirmed the suitability of the sample size (i.e., 0.827) and the results of the Bartlett’s Test were good (ChiSquare: 1,564.058; df: 55; p < 0.001). The final version of the questionnaire had 11 items under two types of emotional reactions to cyberbullying: internalizing and externalizing. Factor loadings were strong, items under each factor were interpretable, and the factors had acceptable internal consistency coefficients (Table 4). Both factors had small skewness and kurtosis (i.e., 0.12 to −0.88). The mean score on externalizing reactions (3.21; SD = 1.16) was higher than that of internalizing reactions (2.53; SD = 1.09) and had a large effect size (t289 = 7.396; p < 0.001; eta squared = 0.159). 3.4. Predictors of emotional reactions

3.2.2. School type (high school vs. university) A two-way mixed-design ANOVA was used to see the influence of school type on each behavioral reaction. The main effect for the reaction type (F3,864 = 34.735; p < 0.001; partial eta squared = 0.108) and the interaction effect between school type and reaction was significant (F3,864 = 27.096; p < 0.001; partial eta squared = 0.086) whereas the main effect for school type was not significant (F1,288 = 0.237; p = 0.627). That is, individual reaction types differed between high school and undergraduate students. Descriptive statistics and comparisons are provided in Table 3. While high school students had significantly higher scores in terms of avoidance, university students had higher scores in terms of countermeasure. Both differences demonstrated a medium effect size. On the other

3.4.1. Gender and school type The two-way MANOVA, where gender and school (high school vs. university) were the independent variables and emotional reactions were the dependent variables, revealed that gender (Wilks’ Lambda = 0.951; F2,251 = 6.504; p = 0.002; partial eta squared = 0.049) and school type had a significant impact on emotional reaction (Wilks’ Lambda = 0.908; F2,251 = 12.670; p < 0.001; partial eta squared = 0.092). The interaction effect of gender by school type was significant as well (Wilks’ Lambda = 0.956; F2,251 = 5.771; p = 0.004; partial eta squared = 0.044). Due to this mutual effect, the mean scores according to gender and school type are discussed together in the current analyses. The related descriptive statistics are presented in Table 5.

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Table 4 Principle components of emotional reactions. Components Internalizing (Alpha: 0.893) Scared Panic Crying Anxious Embarrassed Worried Excited Guilty Externalizing (Alpha: 0.702) Want revenge Become aggressive Show anger

Eigenvalue

Variance

Mean

SD

Factor load

Corrected item total r

4.632

42.11

2.62 2.63 2.18 2.86 2.38 2.83 2.69 2.07

1.49 1.45 1.47 1.44 1.50 1.39 1.39 1.41

0.85 0.84 0.80 0.79 0.79 0.69 0.66 0.62

0.78 0.77 0.71 0.72 0.70 0.59 0.57 0.52

1.955

17.774

2.99 2.98 3.67

1.55 1.48 1.37

0.84 0.81 0.70

0.59 0.59 0.39

n = 290; overall alpha = 0.819; total variance = 59.884%; KMO = 0.827; Bartlett’s Test (Chi-Square: 1564.058; df: 55; p < 0.001). Table 5 Emotional reactions with regard to school type and gender. Reaction

School

Gender

n

Mean

SD

Internalizing

High school

Female Male Total Female Male Total Female Male Total

43 43 86 59 111 170 102 154 256

2.96 2.81 2.89 2.60 1.90 2.14 2.75 2.15 2.39

1.06 1.24 1.15 0.97 0.78 0.91 1.02 1.01 1.06

Female Male Total Female Male Total Female Male Total

43 43 86 59 111 170 102 154 256

3.22 3.03 3.13 2.94 3.46 3.28 3.06 3.34 3.23

1.28 1.24 1.26 1.00 1.15 1.13 1.13 1.19 1.17

University

Total

Externalizing High school University

Total

As summarized in Section 3.3, the mean score on externalizing was higher than the mean score on internalizing. A significant gender difference was observed in terms of internalizing (F1,252 = 10.767; p = 0.001; partialeta squared = 0.041) as compared to externalizing (F1,252 = 1.069; p = 0.302). That is, the females had a higher mean score in internalizing. The effect of school type had the same pattern in terms of internalizing (F1,252 = 24.05; p < 0.001; partialeta squared = 0.087), yet the difference in terms of externalizing was not significant (F1,252 = 0.223; p = 0.637). More specifically, the mean score of the high school students on internalizing was higher than that of the university students. The interaction effects of gender by school type were significant for both internalizing (F1,252 = 4.758; p = 0.003; partial eta squared = 0.019) and externalizing (F1,252 = 5.187; p = 0.024; partial eta squared = 0.02) reactions. In brief, internalizing and externalizing appeared to demonstrate different patterns across genders and schools. To better understand the dynamics of this triple interaction across emotional reactions (internalizing vs. externalizing), genders (male vs. female) and schools (high school vs. university), a three-way mixed design ANOVA was

Fig. 1. The illustration of the interaction effect (School * gender * emotional reaction).

conducted. As expected, the interaction effect of emotional reaction, gender, and school type was significant (Wilks’ Lambda = 0.956; F1,252 = 11.499; p = 0.001; partial eta squared = 0.044). This effect is illustrated in Fig. 1. The simple main effect analyses used to interpret this

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interaction effect revealed that the extent of internalizing across all participants decreased from high school to university (t288 = 7.994; p < 0.001; eta squared = 0.182); however, this decrease was statistically significant for males (t152 = 5.495; p < 0.001; eta squared = 0.166) but nonsignificant for females (t100 = 1.742; p = 0.085). On the other hand, the degree of externalizing remained stable in females (t100 = 1.244; p = 0.216) but increased in males (t152 = 2.026; p = 0.044; eta squared = 0.026) when they proceeded to university. In other words, changes in internalizing and externalizing were experienced by males but not by females. 3.4.2. Technology habits Analysis of the relationship between emotional reactions and internet and technology habits revealed that internet-use duration correlated positively with externalizing (r = 0.152; p = 0.01). On the other hand, computer self-efficacy correlated negatively with internalizing (r = −0.347; p < 0.001). 3.5. Relationship between reactions The correlation matrix of interrelationships across behavioral and emotional reactions is provided in Table 6. Internalizing correlated positively with avoidance and negotiation, while externalizing correlated positively with revenge, countermeasure, and negotiation. The strongest coefficients related to these correlations were those found in the relationship between externalizing and revenge (R squared = 38.9) and between avoidance and internalizing (R squared = 30.5). 4. Concluding remarks 4.1. Behavioral reactions The behavioral reactions examined in the present study were revenge, countermeasure, negotiation, and avoidance. A revenge reaction is considered a maladaptive (Wright, 2016) but common reaction to cyberbullying, where the victim is motivated to return harm to the aggressor (Hoff & Mitchell, 2009; Perren et al., 2012; Sticca et al., 2015). Studies involving traditional bullying victims also reveal that victims tend to turn their perpetrators into victims of the same behavior that they themselves had endured (König, Gollwitzer, & Steffgen, 2010). The emotional distress and anger stemming from the victimization experience is likely to lead to adolescents taking revenge, which in effect means that if the victims felt threatened, deceived or humiliated, their attempt to punish back the aggressor would be an expected behavior. The same proved to be the case in this study, where it was found that revenge was a reaction to cyberbullying. As observed in the relevant items on the questionnaire, most revenge behaviors could be regarded as instances of cyberbullying. That is, the items under the revenge factor basically constitute the cyberbullying of others. Partially in line with the literature, countermeasure reactions included those items involving social, legal and technical reactions (Perren et al., 2012). In addition, collect-

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ing and storing the evidence of unpleasant online behaviors (Kowalski et al., 2012) and seeking support (Aricak et al., 2008; Perren et al., 2012; Sticca et al., 2015) were grouped under this factor. The reaction of negotiation involved communicating with the perpetrator to cease the aggressive behavior (Jones, Mitchell & Turner, 2015; Weinstein et al., 2016). According to Perren et al. (2012) negotiation can also be regarded as constructive contact behavior. Although this behavior was not frequently observed in the literature, it was an interpretable factor in the current measure, which could be attributed to either individual differences of the participants (Malti & Latzko, 2010) or cultural differences (Hu et al., 2018; Ortega, Elipe, & Mora-Merchán, 2012) and therefore requires further cross-cultural work to understand the construct. The final reaction, avoidance, or disregard was observed frequently in the literature (Perren et al., 2012; Randa & Reyns, 2014; Safaria et al., 2016; Sticca et al., 2015). Ignoring the action and deleting the message (Staksrud & Livingstone, 2009), leaving the current website (Price & Dalgleish, 2010), or not responding (Sleglova & Cerna, 2011) are all examples of such reactions. This avoidance can be explained either through the victims’ lack of knowledge on what to do (Hoff & Mitchell, 2009) or fear of parents’ reactions to the incident (Mishna, Saini, & Solomon, 2009). Gender was observed as a predictor of behavioral reactions, and males and females demonstrated different reactions to instances of cyberbullying. More specifically, males had a higher mean score in terms of revenge whereas females had higher mean scores in terms of countermeasure. This finding could be due to the tendency of females to avoid aggression (Geen, 2001; Graham 2001) and opt for indirect ways of coping with unpleasant incidents (Archer, 2004; Archer, & Coyne, 2005). Similar to gender, school type had varying influences on reactions. High school students had higher avoidance scores, whereas university students had higher countermeasure scores. Previous studies have shown that characteristics of the physical or social environment may result in fear and protective behaviors (May, Rader, & Goodrum, 2010). The higher computer selfefficacy of university students could further explain these differences. Technology habits and internet use correlated with different behavioral reactions, as indicated in the literature (Beran & Li, 2005; Erdur-Baker, 2010). The gradual decline in deviant behaviors that begins after middle school could also account for the differences in reactions by age (Smith, Madsen, & Moody, 1999). That is, the age of victims may have an influence on risk and vulnerability perceptions (Randa & Reyns, 2014). 4.2. Emotional reactions It has been frequently reported that cyberbullying perpetration can lead to psychological, emotional or psychosomatic problems in victims (Patchin & Hinduja, 2006). More serious consequences, such as trauma and suicidal ideations, have been reported as well (Hinduja & Patchin, 2010; Schenk & Fremouw, 2012). Emotional reactions to cyberbullying, like anger, vulnerability, sadness, fear, frustration, crying, or self-blame, have been reported in different studies (Beran & Li, 2005; Hinduja & Patchin,

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8 Table 6 Correlations across reactions. Reaction

Revenge

Countermeasure

Negotiation

Avoidance

Internalizing

Countermeasure Negotiation Avoidance Internalizing Externalizing

0.263*** 0.337*** 0.066 0.068 0.623***

– 0.299*** 0.05 0.026 0.297***

– 0.286*** 0.270*** 0.140*

– 0.552*** −0.058

– 0.035

* ***

p < 0.05. p < 0.001.

2007; Hoff & Mitchell, 2009; Ortega et al., 2009; Schenk & Fremouw, 2012). In accordance with such studies, emotional reaction to cyberbullying has been classified as either internalizing or externalizing. While the former includes items pertaining to fear, panic, anxiety, embarrassment or guilt, the latter involves emotions like seeking revenge or getting aggressive or angry. The gender difference was quite clear in that females were much more likely than males to internalize their emotions. The same pattern was observed between high school students and undergraduates, with the former being more apt to internalize. These differences were expected, as the literature clearly points to variations in emotional response according to gender and grade level (Ortega et al., 2009). The interaction between gender, school type, and reaction type was quite interesting, insofar as the extent of internalizing decreased from high school to university, with the decrease being significant among males. On the other hand, the degree of externalizing remained stable across females but increased across males when they proceeded to university. This finding could be peculiar to the strict gender roles experienced in the current context. While males switched from internalizing to externalizing when they proceeded to higher education, the degree to which females demonstrated internalizing and externalizing reactions remained stable. The relationship between technology habits and emotional reactions was significant. That is, when computer self-efficacy increased, internalizing decreased. It could be argued that increased self-efficacy helped victims to cope with cyberbullying instances more actively. However, internet-use duration and externalizing were correlated, which suggests that effective use of information and communication technologies could contribute to effective coping, whereas excessive use could be problematic. Moreover, these findings suggest that activities directed at promoting technology skills in youth should go hand in hand with awareness raising activities to cultivate healthy emotion regulation. Finally, the correlation matrix on behavioral and emotional reactions revealed that the best predictor of avoidance was internalizing, while the best predictor of revenge was externalizing. Ignoring and deleting the cyberbullying content (Staksrud & Livingstone, 2009), leaving the current webpage (Price & Dalgleish, 2010) or not responding to an aggressor (Sleglova & Cerna, 2011) are passive or internalizing reactions, whereas revenge behaviors are concrete, active, and externalizing reactions (Hinduja & Patchin, 2009).

4.3. Limitations and suggestions for further research The current study developed two questionnaires to address behavioral and emotional reactions to cyberbullying. Before selecting the factors to be examined in exploring the types and prevalence of cyberbullying reactions across different cultures and groups, confirmatory factor analyses in different contexts should be performed. Furthermore, additional variables of interest, like individual differences, socioeconomic status, education level, parents’ attitudes, school policies and teacher attitudes, should be examined to see their influence on reaction types Students who had been recently victimized were the focus of the current study. However, the nature of those unique victimization instances was not investigated. Thus, future studies should address the prevalence and nature of victimization in parallel with the specific reaction type. Results from such analyses would help education professionals to organize awareness raising activities, as previous research has demonstrated the usefulness of such sensitivity training (e.g., Akbulut, 2014). Additional research could investigate the contribution of such awareness raising activities to the transformations in reaction types. This study did not delve into the effectiveness of alternative coping strategies. However, previous studies have shown that different coping strategies have different degrees of effectiveness (Machackova, Cerna, Sevcikova, Dedkova, & Daneback, 2013). Thus, in addition to the prevalence of victimization and reaction types, the individual contribution of each reaction type to stopping the behavior or helping the victim should be investigated. Convenience sampling was employed in the current study. The online data collection method might have affected the reliability of the responses (Akbulut, 2015). Thus, the confirmation of current scales based on sufficiently large samples from different high schools and universities could increase their usability in future research. It is critically important that future research apply multivariate analyses to identify the predictors of each behavioral and emotional reaction. That is, univariate analyses focusing on only a single background variable (e.g., gender, school type, age) seem inadequate considering that several compound effects of multiple background variables were observed in the current study. Accordingly, the development of structural equation models that are capable of identifying the interrelationships among multiple theorysupported variables would be useful in further research.

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4.4. Conclusion This study examined a number of different variables to gain a better understanding of both behavioral and emotional reactions to cyberbullying. Some of the predictors or correlates of these reactions have been explored as well. While the current classification of behavioral and emotional reactions may serve the aims of future survey studies, the addition of new items and predictors would help to improve the understanding of the nature and correlates of different reactions. In addition to the need to confirm the current indicators in different contexts, it would be beneficial to conduct qualitative in-depth analyses to match each reaction type with specific victimization experiences and to propose interventions to cultivate students’ awareness in different contexts. Contemporary literature shows that components of coping have been heavily addressed, whereas the entire coping process has not yet been adequately studied (Raskauskas & Huynh, 2015). Thus, new model and intervention proposals directed at reducing the unpleasant/damaging victimization consequences may better serve the goal of mitigating these consequences than descriptive investigations. Funding This work was supported by the Anadolu University Research Fund (Grant Number 1705E422). Acknowledgements We thank the Editors and anonymous reviewers for their contributive comments. Preliminary findings have been presented as an abstract in AECT 2017 International Convention. References Akbulut, Y. (2014). Effect of case-based video support on cyberbullying awareness. Australian Educational Computing, 29(1). Retrieved June 16, 2018 from http://journal.acce.edu.au/index.php/AEC/article/view/35 Akbulut, Y. (2015). Predictors of inconsistent responding in web surveys. Internet Research, 25(1), 131–147. http://dx.doi.org/10.1108/IntR-01-2014-0017 Akbulut, Y., & Eristi, B. (2011). Cyberbullying and victimisation among Turkish university students. Australasian Journal of Educational Technology, 27(7), 1155–1170. Retrieved June 23, 2018 from https://ajet.org.au/index.php/AJET/article/download/910/187 Archer, J. (2004). Sex differences in aggression in real world settings: A meta-analytic review. Review of General Psychology, 8, 291–322. http://dx.doi.org/10.1037/1089-2680.8.4.291 Archer, J., & Coyne, S. M. (2005). An integrated review of indirect, relational, and social aggression. Personality and Social Psychology Review, 9(3), 212–230. http://dx.doi.org/10.1207/s15327957pspr0903 2 Arıcak, O. T. (2009). Psychiatric symptomatology as a predictor of cyberbullying among university students. Eurasian Journal of Educational Research (EJER), 8(34), 167–184. Aricak, T., Siyahhan, S., Uzunhasanoglu, A., Saribeyoglu, S., Ciplak, S., Yilmaz, N., & Memmedov, C. (2008). Cyberbullying among Turkish Adolescents. Cyberpsychology & Behavior, 11(3), 253–261. http://dx.doi.org/10.1089/cpb.2007.0016 Arslan, S., Savaser, S., Hallett, V., & Balci, S. (2012). Cyberbullying among primary school students in Turkey: Self-reported prevalence and associations with home and school life. Cyberpsychology, Behavior, and Social Networking, 15(10), 527–533. http://dx.doi.org/10.1089/cyber.2012.0207 Barlett, C. P., Douglas, A., Gentile, D. A., Anderson, C. A., Suzuki, K., Sakamoto, A., . . . & Katsura, R. (2014). Cross-cultural dif-

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ˇ cíková, A., Wright, M. F., Yanagida, T., Macháˇcková, H., Dˇedková, L., Sevˇ Aoyama, I., . . . & Lei, L. (2018). Face-to-face and cyber victimization among adolescents in six countries: The interaction between attributions and coping strategies. Journal of Child & Adolescent Trauma, 1–14. http://dx.doi.org/10.1007/s40653-018-0210-3

Bahadır Eris¸ti is an associate professor in the Department of Educational Sciences at Anadolu University, Turkey. He has a BA, MA and PhD in Curriculum and Instruction. His research interests are value education, effective instructional strategies and program development. Yavuz Akbulut is a professor in the Department of Educational Sciences at Anadolu University, Turkey. He has an MA in computer assisted language teaching and a PhD in instructional design and technology. He conducts research on cyberpsychology and learning, multitasking and online gaming behaviors.

Please cite this article in press as: Eris¸ti, B., & Akbulut, Y. Reactions to cyberbullying among high school and university students. The Social Science Journal (2017), https://doi.org/10.1016/j.soscij.2018.06.002