دانلود مقاله ISI انگلیسی شماره 30398
عنوان فارسی مقاله

اثرات منحصر به فرد اجزای مختلف صفت هوش هیجانی در زورگویی سنتی و مزاحمت سایبری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
30398 2014 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Unique effects of different components of trait emotional intelligence in traditional bullying and cyberbullying
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Adolescence, Volume 37, Issue 6, August 2014, Pages 807–815

کلمات کلیدی
هوش هیجانی صفت - قلدری سنتی - مزاحمت سایبری - خودکارآمدی - تنظیم احساسات
پیش نمایش مقاله
پیش نمایش مقاله اثرات منحصر به فرد اجزای مختلف صفت هوش هیجانی در زورگویی سنتی و مزاحمت سایبری

چکیده انگلیسی

This study investigated whether different components of trait emotional intelligence (or trait emotional self-efficacy) were uniquely related to traditional bullying and cyberbullying in a sample of 529 preadolescents (mean age of 12 years and 7 months), while controlling for the other forms of bullying/victimization. Binary logistic regressions showed that the dimension of emotional intelligence concerning the regulation and use of emotions was negatively related both to traditional bullying and cyberbullying; however, this association did not emerge when traditional bullying was controlled for cyberbullying, whilst it still emerged when cyberbullying was controlled for traditional bullying and both forms of victimization. Differently, the dimensions concerning appraisal of own and others' emotions were not deficient in children performing bullying and/or cyberbullying behaviors. Despite high co-occurrence between traditional and electronic bullying, our results suggested that these two forms are distinct phenomena, involving different personality traits. Implications for interventions are discussed.

مقدمه انگلیسی

Emotional correlates of bullying Bullying is a relationship in which an individual, or a group of individuals, intentionally and repeatedly perpetrates aggressive behaviors towards someone unable to defend himself/herself (Olweus, 1993 and Salmivalli, 2010). Traditional forms of bullying consist of physical, verbal and covert forms of aggressive behaviors. Recently, the new phenomenon of cyberbullying has emerged; it is characterized by several offensive acts (e.g., harassment, cyberstalking, spreading of rumors, intimidation, etc.) inflicted through various technological means such as emails, instant messaging, blogs and chat rooms (Menesini et al., 2012, Smith et al., 2008 and Williams and Guerra, 2007). Previous studies found that males were mainly engaged in physical bullying, while females were mainly engaged in indirect bullying; no clear gender difference emerged for cyberbullying (Jolliffe and Farrington, 2006, Slonje and Smith, 2008 and Williams and Guerra, 2007). Several evidences converged in indicating that traditional bullying increases during childhood, having its peak during preadolescence, and decreases later (Brown et al., 2005 and Fitzpatrick et al., 2007); cyberbullying is committed mainly by preadolescents and adolescents who are increasingly using new technologies to engage in personal interaction, but also in harassment against peers (Hinduja and Patchin, 2008 and Smith et al., 2008; Ybarra & Mitchell, 2004). The role of emotions in traditional bullying has emerged from the debate about bullies' social information processing: whereas aggressive children were found to have problems at the initial stages of processing (i.e., encoding and interpretation of social cues), many bullies seemed to quite accurately perceive their social world and to posses more advanced theory of mind skills (i.e., the ability to recognize others' emotions, intentions, beliefs and goals); rather, they displayed a “biased” response evaluation styles, choosing self-oriented goals regardless of the consequences for others (Arsenio and Lemerise, 2001, Camodeca and Goossens, 2005, Crick and Dodge, 1999, Gini, 2006, Sutton et al., 1999a, Sutton et al., 1999b and Sutton et al., 2001). These evidences were supported by several studies on empathy and bullying: affective empathy (the ability to share others' affective states) rather than cognitive empathy (the ability to read and understand others' feelings) was the real bullies' deficiency, especially in males (Caravita et al., 2009, Jolliffe and Farrington, 2006, Jolliffe and Farrington, 2011 and Stavrinides et al., 2010). Bullies were more prone to display a “cold cognition”, a theory of mind formulated in instrumental terms without access to the empathic understanding of others (Sutton et al., 1999b). The intensity of the experienced emotions and the scarce capacity to regulate them (i.e., proneness to anger, emotional lability/negativity, and low levels of fear reactivity and effortful control) are the other emotional processes that influence generation and performing of bullying behaviors, even if bullies seemed skilled as well as other peers in emotional display rules knowledge (Camodeca and Goossens, 2005, Espelage et al., 2001, Garner and Hinton, 2010 and Terranova et al., 2008). Moreover, Lemerise and Arsenio (2000) suggested that children's self-efficacy evaluation in regulating emotion constitutes an important component of the response evaluation process. According to Bandura's theory (Bandura, 2001 and Bandura et al., 2003), we argue that self-efficacy beliefs in regard to emotional skills strongly contribute to social behavior. The present study furthers previous research by examining how bullies self-perceive their emotional skills. Despite of few exceptions (Ang & Goh, 2010), research has disproportionately focused on traditional forms of bullying, while the role of emotions in cyberbullying was scarcely explored: there are several reasons to expect differences in emotional characteristics of cyberbullies considering that these children are removed from the face-to-face interaction with their victims (Dooley, Pyżalski, & Cross, 2009). Bullying and emotional intelligence The term “emotional intelligence” (EI) was used for the first time by Salovey and Mayer (1990) to indicate the subset of social intelligence that involves several emotion-related abilities: appraisal and expression of emotions in self and others, regulation of emotions in self and others, utilization of emotions in problem solving. The components related to perception, appraisal and expression of emotions constitute the basic psychological processes, while the components of reflective regulation and use of emotions are abilities that develop later and emerge as more closely integrated with other skills (Mayer and Salovey, 1997 and Schutte et al., 1998). Over time, different definitions of EI have been advanced and they have included broader non-cognitive aspects, such as competences, skills and personality traits (Bar-On, 1997, Goleman, 1995 and Petrides and Furnham, 2001). Today there is a general consensus in considering “trait EI” and “ability EI” as separate constructs. Trait EI (or trait emotional self-efficacy) refers to personality and consists of self-perceptions and behavior dispositions related to the emotional domain; it is measured by self-report instruments. Ability EI refers to cognitive processes and consists of a series of emotion abilities as they emerge in the maximum-performance test (Mavroveli et al., 2007 and Warwick and Nettelbeck, 2004). Previous studies have revealed a superiority in both trait and ability EI for females and a developmental trend indicating the growth of EI scores with age (Brackett et al., 2004, De Caro and D’Amico, 2008 and Schutte et al., 1998). Quite apart from theoretical definitions of the topic, trait EI seems to play a crucial role in social adjustment (for a review, see Mayer, Roberts, & Barsade, 2008): specifically, children with higher score of trait EI were more likely to reach good academic achievement and to experience positive social relationships, while they were less likely to behave against school rules (i.e., unauthorized absences and exclusion from school; Mavroveli et al., 2009, Mavroveli and Sánchez-Ruiz, 2011 and Petrides et al., 2004). To date, only few studies were implemented to directly investigate trait EI and bullying. Mavroveli and Sánchez-Ruiz (2011) observed a negative association between a total score of EI and both self-reported and peer-reported bullying in a sample of English primary students. Kokkinos and Kipritsi (2012) found that a total score of trait EI was negatively associated with both direct and indirect forms of bullying in a cohort of Greek primary students. Considering several subcomponents of trait EI, Lomas, Stough, Hansen, and Downey (2012) found that the understanding of others' emotions was negatively associated with bullying in a small Australian sample of preadolescents, suggesting that students who failed to understand others' emotions were also unable to understand the consequences of their offensive actions; nevertheless, the low number of participants suggests caution in generalizing the results. The above-mentioned studies are crucial due to having investigated a new field in the research on bullying, showing that self-beliefs about own emotional skills are important correlates of the topic. Nevertheless, they did not simultaneously consider the different forms of bullying (i.e., traditional bullying and cyberbullying) and the different dimensions of trait EI (i.e., emotional appraisal, use and regulation). In our opinion, the importance of considering different dimensions of trait EI is a key issue: self-beliefs about appraisal of others' emotions, appraisal of own emotions, and use and regulation of emotions are interrelated despite distinct aspects of EI (Schutte et al., 1998). As stated by Arsenio and Lemerise (2001), the world of emotions plays a central role in bullying behavior, and a whole understanding of bullying can be achieved if self-beliefs about own emotional processes are in-depth explored.

نتیجه گیری انگلیسی

Results Preliminary analysis Descriptive statistics of the study variables are reported in Table 1. Skewness and kurtosis of the four bullying scales resulted extremely out of range [−1; +1], indicating that these measures are not normally distributed; for this reason, gender and grade differences were tested using the non-parametric Mann–Whitney U Test. Males presented higher scores on both the traditional bullying scale (U = 43,185.500, p < .001) and the cyberbullying scale (U = 38,103.500, p < .01); students enrolled in 8th grade presented higher scores on both the traditional bullying scale (U = 46,881.500, p < .001) and the cyberbullying scale (U = 40,884.500, p < .001) than those in 6th grade. No gender difference emerged for both traditional victimization (U = 43,185.500, p = .841) and cyber victimization (U = 32,761.500, p = .176), whereas 8th grade students had higher score of both type of victimization (U = 37,742.500, p < .05 for the traditional form; U = 39,983.500, p < .001 for the cyber form). Considering emotional intelligence, gender and grade differences were inspected using the parametric t-Test. Females had higher scores in the EIS I factor (t = 5.244, df = 527 p < .001). No gender differences emerged with regard to EIS II and EIS III (t = 1.761, df = 527 p = .079; t = 1.083, df = 527, p = .279). Students enrolled in 8th grade had higher scores in the EIS I factor (t = −2.427, df = 527, p < .05), and students enrolled in 6th grade had higher scores in the EIS II factor (t = 2.145, df = 527, p < .05). No grade differences emerged with regard to EIS III (t = 1.191, df = 524.275, p = .234). Table 1. Descriptive statistics of the study variables. Reliability M SD Possible range Observed range Skewness Kurtosis Gender differences (p) Grade differences (p) Traditional Bullying α = .71 1.15 .23 1.00–5.00 1.00–3.40 3.84 25.18 .000 (M > F) .000 (8th > 6th) Cyberbullying α = .73 1.05 .16 1.00–5.00 1.00–3.00 6.12 52.47 .005 (M > F) .000 (8th > 6th) Traditional Victimization α = .74 1.25 .33 1.00–5.00 1.00–2.82 2.45 6.91 .841 .028 (8th > 6th) Cyber Victimization α = .81 1.09 .22 1.00–5.00 1.00–3.78 5.86 50.34 .176 .000 (8th > 6th) EIS I α = .73 3.45 .61 1.00–5.00 1.40–5.00 −.18 −.08 .000 (F > M) .016 (8th > 6th) EIS II α = .67 3.50 .80 1.00–5.00 1.50–5.00 −.21 −.45 .079 .032 (6th > 8th) EIS III α = .68 3.70 .63 1.00–5.00 1.14–5.00 −.35 .16 .279 .234 Notes. The last column show p-values of Mann–Whitney U Test and T-test for gender and grade differences, and the direction. Gender: (0 = boys; 1 = girls); Grade: (0 = 6th; 1 = 8th). Table options Bivariate correlations between gender, grade, EIS and the two forms of bullying A correlation analysis, parted by gender, was performed. Spearman's Rho index was chosen because the four bullying scales were not normally distributed. The results are reported in Table 2. In males, traditional bullying was negatively related to EIS III (Rho = −.15, p < .05); similarly, cyberbullying was negatively related to EIS III (Rho = −.16, p < .05). Both traditional victimization (Rho = −.16, p < .05) and cyber victimization (Rho = −.15, p < .05) resulted to be negatively associated to EIS I. In females, traditional bullying was negatively correlated to EIS II (Rho = −.21, p < .001) and to EIS III (Rho = −.13, p < .05); cyberbullying did not show significant correlations with any components of EIS. Traditional victimization was negatively related to EIS III (Rho = −.16, p < .01). The co-occurrence of the four bullying scales in both gender sub-samples must be noted. Table 2. Bivariate correlations – Spearman's Rho – parted by gender (boys are above the main diagonal). (1) (2) (3) (4) (5) (6) (7) (1) – Traditional bullying – .48*** .48*** .33*** −.08 −.08 −.15* (2) – Cyberbullying .37*** – .16* .40*** −.09 −.06 −.16* (3) – Traditional Victimization .49*** .19** – .27*** −.16* −.08 −.12 (4) – Cyber Victimization .27*** .44*** .34*** – −.15* −.12 −.14* (5) – EIS I −.06 −.01 −.07 −.02 – .50*** .58*** (6) – EIS II −.21*** −.09 −.09 −.06 .44*** – .54*** (7) – EIS III −.13* −-.10 −.16** −.09 .46*** .47*** – Notes. EIS I – appraisal of others emotions; EIS II – appraisal of self emotions; EIS III – regulation and use of emotions *p < .05; **p < .01; ***p < .001. Table options Binary logistic regressions We performed regression analyses in order to examine the unique contribution of the three EIS scales in relation to traditional bullying and cyberbullying. Bullying, cyberbullying, traditional victimization and cyber victimization were dummy coded (students involved (1) or not involved (0) in the four phenomena, see Table 3). Basing on previous literature, two different thresholds were adopted for the two forms: considering that repetitiveness of aggressive behavior is a main aspect of traditional bullying, and in line with Solberg and Olweus' (2003) cut-off point, we considered “traditional bullies” those who reported to have acted two or three times a month in at least one of the items making up the scale (similarly we did for traditional victimization). As regards cyberbullying, in view of the actual debate as to whether the repetition of aggressive acts is a criterion for the definition of this phenomenon (e.g., Dooley et al., 2009), and in line with Menesini et al.'s (2011) cut-off point, we considered “cyberbullies” those who declared to have acted only once or twice in at least one of the items making up the scale (similarly we did for cyber victimization). Table 3. Bullying dummy coded. Italics represents the percentage. Presence Absence Traditional bullying 63 (11.91%) 466 (88.09%) Cyberbullying 92 (17.39%) 437 (82.61%) Traditional Victimization 112 (21.17%) 417 (78.83%) Cyber Victimization 130 (24.57%) 399 (75.43%) Table options Traditional bullying (or cyberbullying) was entered as dependent variable, gender and grade were entered in Step 1, the three EIS dimensions were added in Step 2, cyberbullying (or traditional bullying) was entered in Step 3, and the two forms of victimization were added in Step 4; the interaction terms between EIS dimensions and gender and grade were added and tested for statistical significance (variables were centered using the sample mean prior to creating the interaction terms). Since no interactions were found, they were deleted from the final models. As regards traditional bullying (Table 4), the inspection of Step 2 revealed that male gender (B = .92, Exp(B) = 2.52, p < .01), 8th grade (B = 1.01, Exp(B) = 2.75, p < .001) and lower levels of EIS III (B = −.60, Exp(B) = .55, p < .05) were related to traditional bullying. However, when cyberbullying was added in Step 3, the association between traditional bullying and EIS III reduced to non-significance (B = −.41, Exp(B) = .66, p > .05). The final model (Step 4) showed that male gender (B = .87, Exp(B) = 2.39, p < .01), 8th grade (B = .83, Exp(B) = 2.28, p < .05), the involvement in cyberbullying (B = 1.68, Exp(B) = 5.34, p < .001) and in traditional victimization (B = 1.62, Exp(B) = 5.07, p < .001) were related to traditional bullying. No interaction terms emerged. Table 4. Binary logistic regressions on traditional bullying. B Exp(B) Step 1 Step χ2 = 23.63***; df = 2; Nagelkerke ΔR2 = .08 Gender .98*** 2.66 Model χ2 = 23.63***; df = 2; Nagelkerke R2 = .08 Grade .97*** 2.65 Step 2 Gender .92** 2.52 Step χ2 = 9.73*; df = 3; Nagelkerke ΔR2 = .04 Grade 1.01*** 2.75 Model χ2 = 33.36***; df = 5; Nagelkerke R2 = .12 EIS I −.16 .85 EIS II .03 1.03 EIS III −.60* .55 Step 3 Gender .78* 2.17 Step χ2 = 23.06***; df = 1; Nagelkerke ΔR2 = .08 Grade .66* 1.93 Model χ2 = 56.42***; df = 6; Nagelkerke R2 = .20 EIS I −.13 .88 EIS II −.02 .98 EIS III −.41 .66 Cyberbullying 1.51*** 4.51 Step 4 Gender .87** 2.39 Step χ2 = 24.65***; df = 2; Nagelkerke ΔR2 = .07 Grade .83* 2.28 Model χ2 = 81.06***; df = 8; Nagelkerke R2 = .27 EIS I −.08 .92 EIS II −.18 .83 EIS III −.29 .75 Cyberbullying 1.68*** 5.34 Traditional victimization 1.62*** 5.07 Cyber victimization −.23 .80 Notes. * = p < .05, ** = p < .01, *** = p < .001. Table options Considering cyberbullying (Table 5), the inspection of Step 2 revealed that once again male gender (B = .60, Exp(B) = 1.83, p < .05), 8th grade (B = 1.33, Exp(B) = 3.80, p < .001) and lower levels of EIS III (B = −.66, Exp(B) = .52, p < .01) were related to this form of bullying. When traditional bullying was added in Step 3, the role of lower levels of EIS III in predicting cyberbullying still emerged (B = −.57, Exp(B) = .57, p < .05). The final model (Step 4) showed that male gender (B = .67, Exp(B) = 1.95, p < .05), 8th grade (B = .89, Exp(B) = 2.43, p < .01), lower levels of EIS III (B = −.65, Exp(B) = .52, p < .05), the involvement in traditional bullying (B = 1.64, Exp(B) = 5.17, p < .001) and in cyber victimization (B = 2.08, Exp(B) = 7.99, p < .001) were associated to cyberbullying. No interaction terms emerged. Table 5. Binary logistic regressions on cyberbullying. B Exp(B) Step 1 Step χ2 = 34.37***; df = 2; Nagelkerke ΔR2 = .10 Gender .65** 1.91 Model χ2 = 34.37***; df = 2; Nagelkerke R2 = .10 Grade 1.28*** 3.60 Step 2 Gender .60* 1.83 Step χ2 = 12.72**; df = 3; Nagelkerke ΔR2 = .04 Grade 1.33*** 3.80 Model χ2 = 47.09***; df = 5; Nagelkerke R2 = .14 EIS I −.16 .86 EIS II .11 1.11 EIS III −.66** .52 Step 3 Gender .42 1.52 Step χ2 = 23.42***; df = 1; Nagelkerke ΔR2 = .07 Grade 1.18*** 3.25 Model χ2 = 70.51***; df = 6; Nagelkerke R2 = .21 EIS I −.12 .88 EIS II .10 1.10 EIS III −.57* .57 Traditional bullying 1.51*** 4.50 Step 4 Gender .67* 1.95 Step χ2 = 56.35***; df = 2; Nagelkerke ΔR2 = .14 Grade .89** 2.43 Model χ2 = 126.854***; df = 8; Nagelkerke R2 = .35 EIS I .00 1.00 EIS II .18 1.20 EIS III −.65* .52 Traditional bullying 1.64*** 5.17 Traditional victimization −.60 .55 Cyber victimization 2.08*** 7.99 Notes. * = p < .05, ** = p < .01, *** = p < .001.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.