دانلود مقاله ISI انگلیسی شماره 36831
عنوان فارسی مقاله

الگوهای رفتار زورگویی نوجوانان: فیزیکی، کلامی، محرومیت، شایعه، و سایبری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
36831 2012 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Patterns of adolescent bullying behaviors: Physical, verbal, exclusion, rumor, and cyber
منبع

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

Journal : Journal of School Psychology, Volume 50, Issue 4, August 2012, Pages 521–534

کلمات کلیدی
قلدری - قلدری سایبر - تفاوت های دموگرافیکی - مسائل مربوط - تجزیه و تحلیل کلاس پنهان
پیش نمایش مقاله
پیش نمایش مقاله الگوهای رفتار زورگویی نوجوانان: فیزیکی، کلامی، محرومیت، شایعه، و سایبری

چکیده انگلیسی

Abstract Patterns of engagement in cyber bullying and four types of traditional bullying were examined using latent class analysis (LCA). Demographic differences and externalizing problems were evaluated across latent class membership. Data were obtained from the 2005–2006 Health Behavior in School-aged Survey and the analytic sample included 7,508 U.S. adolescents in grades 6 through 10. LCA models were tested on physical bullying, verbal bullying, social exclusion, spreading rumors, and cyber bullying behaviors. Three latent classes were identified for each gender: All-Types Bullies (10.5% for boys and 4.0% for girls), Verbal/Social Bullies (29.3% for boys and 29.4% for girls), and a Non-Involved class (60.2% for boys and 66.6% for girls). Boys were more likely to be All-Types Bullies than girls. The prevalence rates of All-Types and Verbal/Social Bullies peaked during grades 6 to 8 and grades 7 and 8, respectively. Pairwise comparisons across the three latent classes on externalizing problems were conducted. Overall, the All-Types Bullies were at highest risk of using substances and carrying weapons, the Non-Involved were at lowest risk, and the Verbal/Social Bullies were in the middle. Results also suggest that most cyber bullies belong to a group of highly aggressive adolescents who conduct all types of bullying. This finding does not only improve our understanding of the relation between cyber bullying and traditional bullying, but it also suggests that prevention and intervention efforts could target cyber bullies as a high-risk group for elevated externalizing problems.

مقدمه انگلیسی

1. Introduction Bullying can be defined as a type of aggressive behavior which is intentional, repeated, and usually involves imbalance of power between the bully and the victim (Olweus, 1993). It is a widespread school problem that is linked to various psychological adjustment and academic problems among school-aged children and adolescents (Gini, 2008, Nansel et al., 2004 and Schwartz et al., 2005). In school, bullying behaviors may take various forms (Crick and Grotpeter, 1995 and Olweus, 1993), including physical bullying (e.g., hitting, pushing, and kicking), verbal bullying (e.g., calling mean names in a hurtful way), social exclusion (e.g., ignoring or leaving out others on purpose), and spreading rumors (e.g., telling lies about others). Recently, cyber bullying has emerged as a new type of bullying which involves bullying others using electronic devices such as cell phones and computers (Li, 2007, Raskauskas and Stoltz, 2007, Slonje and Smith, 2008 and Williams and Guerra, 2007). A recent national study showed that each of the above bullying subtypes is common among U.S. youth, ranging from 13.6% for cyber bullying to 53.6% for verbal bullying (Wang, Iannotti, & Nansel, 2009). 1.1. Traditional and cyber forms of bullying As cyber bullying emerges as a new form of bullying, researchers have sought to understand the association between traditional bullying and cyber bullying. According to Juvonen and Gross (2008), cyber bullying is an extension of traditional bullying, with the location of bullying extended from school to the cyber space. Similarly, Raskauskas and Stoltz (2007) reported that students' roles in traditional bullying predicted the same role in cyber bullying. However, whereas the majority of studies generally suggest a positive correlation between traditional and cyber bullying (Gradinger et al., 2009, Smith et al., 2008 and Ybarra et al., 2007), most prior studies did not differentiate between various subtypes of traditional bullying and have combined physical bullying, verbal bullying, social exclusion, and spreading rumors into a single traditional bullying construct (e.g., Li, 2007 and Raskauskas and Stoltz, 2007). As such, it remains unclear whether the relation between traditional bullying and cyber bullying is common to all subtypes of traditional bullying or is driven by certain subtypes of traditional bullying. For example, given that both cell phones and computers are often used as means of communication, it may be that the relation between traditional and cyber bullying is largely accounted for by verbal bullying, social exclusion, and spreading rumors but not physical bullying. Few previous studies, if any, have examined this question. This gap in the literature is an important one to address because understanding which subtypes of traditional bullying are linked to or co-occur with cyber bullying can help teachers, school counselors, psychologists, and parents evaluate the degree of seriousness of adolescents' bullying behaviors (e.g., how likely are they to also engage in physical bullying) when they observe the presence of cyber bullying. Accordingly, this information can inform prevention and intervention efforts targeting adolescent bullying. To address this gap, the current study applies a latent class analysis (LCA) to examine how five subtypes of bullying, including cyber bullying, co-occur in the same person. LCA is a person-based latent variable approach in which latent classes or groups can be identified based on participants’ observed response to multiple categorical variables (Andersen and McCutcheon, 2003, Magidson and Vermunt, 2004 and Nylund et al., 2007). As such, LCA is an appropriate method to examine patterns of involvement in multiple subtypes of bullying and identify groups of individuals who are likely to endorse a particular pattern of bullying involvement. Using an LCA model, Wang, Iannotti, Luk, and Nansel (2010) examined how different subtypes of victimization occurred in the same person and extracted three latent classes, including (a) a latent class of “all-types victims” who were victims of all types of bullying, (b) a latent class of “verbal/social victims” who were marked by victimization by verbal bullying, social exclusion, and spreading rumors, and (c) a latent class of “non-victims” who had minimal probabilities of being victimized by any bullying behavior. The all-types victims consisted of 9.7% of boys and 6.2% of girls, whereas the verbal/social victims consisted of 28.1% of boys and 35.1% of girls. Moreover, a graded relation was found between the three latent classes of victimization on their level of depression and frequency of medically attended injuries and medicine use. However, the prior study focused on the co-occurrence of subtypes of victimization (i.e., being targets of bullying behaviors) and did not consider the co-occurrence of subtypes of bullying (i.e., conducting bullying behaviors). As such, it remains unclear how various subtypes of bullying co-occur in the same student or whether such co-occurrence is linked to correlates of bullying. If a similar pattern can be found for bullying perpetration in which there is a group of bullies who engage in all traditional and cyber forms of bullying, the identification of such a highly aggressive group, as well as the demographic characteristics associated with it, could guide more targeted prevention and intervention efforts. Thus, the first goal of the current study was to examine how cyber bullying and four subtypes of traditional bullying, including physical bullying, verbal bullying, social exclusion, and spreading rumors, co-occurs in the same person. 1.2. Demographic characteristics: gender, grade, and race/ethnicity Numerous studies have examined demographic differences in adolescent bullying behaviors in the United States. When traditional bullying is conceptualized as a single construct, researchers have found that boys are more likely to be bullies, and bullying seems to peak in middle school (Goldbaum, Craig, Pepler, & Connolly, 2007). When specific subtypes of bullying were taken into consideration, studies have shown that boys are more likely to be involved in physical or verbal bullying than girls, whereas girls may be more likely to be bullying others socially or relationally than boys (Bjorkqvist, 1994 and Owens et al., 2000). With regard to cyber bullying, some studies found that boys were more likely to be cyber bullies (Aricak et al., 2008 and Li, 2006), whereas other studies did not find any gender difference (Slonje and Smith, 2008 and Williams and Guerra, 2007). Among the few studies that have examined grade differences in specific subtypes of bullying, a recent study showed that physical bullying and cyber bullying peaked in middle school and declined in high school, whereas verbal bullying peaked in middle school and remained relatively elevated during high school (Williams & Guerra, 2007). In contrast to studies on gender and grade differences, relatively few and inconsistent findings have been reported with respect to racial/ethnic differences in overall bullying or specific subtypes of bullying. For instance, a higher prevalence of bullying was found among African American adolescents than Hispanic adolescents in metropolitan Los Angeles (Juvonen, Graham, & Schuster, 2003). In a national survey, Nansel and colleagues (Nansel et al., 2001) reported that Hispanic adolescents were more likely to engage in bullying than Caucasian adolescents. However, more recent national data suggest that African American adolescents were more likely than Caucasian adolescents to be physical, verbal, and cyber bullies (Wang et al., 2009). Moreover, there are other studies which showed no racial/ethnic differences in bullying behaviors (Seals and Young, 2003 and Spriggs et al., 2007). 1.3. Bullying and externalizing problem behaviors Multiple cross-sectional and longitudinal studies have shown that bullies are more likely to engage in externalizing behaviors, such as substance use and violent behaviors (Barker et al., 2008, Gini and Pozzoli, 2009, Niemelä et al., 2011, Sourander et al., 2007 and Stein et al., 2007), yet few studies have distinguished between different subtypes of bullying behaviors. Among the few studies that examined bullying subtypes, a recent study has shown that physical, verbal, and cyber bullies are more likely to use alcohol (Peleg-Oren, Cardenas, Comerford, & Galea, in press). In a cross national study, Nansel et al. (2004) found that involvement in bullying is positively associated with carrying weapon in all six different countries included in their study. However, none of the above studies have examined the extent to which co-occurrence of multiple types of bullying is related to substance use and carrying weapon. Thus, the third purpose of the current study was to examine the association between the latent class membership extracted in the LCA with substance use and weapon carrying. 1.4. Gaps in the literature and the current study This study is designed to address several limitations of prior research. First, most previous studies either examined overall bullying or only some subtypes of bullying without considering co-occurrence of multiple subtypes of bullying. Specifically, it is unclear whether the traditional-cyber bullying relation is driven by some or all subtypes of traditional bullying. Second, many existing studies were conducted in local or regional samples, thereby limiting their ability to generalize the results on demographic differences across various subtypes of bullying to the general population. Third, although a positive association between bullying and externalizing behaviors has been documented in prior research, some of these studies only included boys (Niemelä et al., 2011 and Stein et al., 2007) and most did not differentiate between subtypes of bullying (e.g., Barker et al., 2008 and Sourander et al., 2007). Thus, it is of interest to test whether co-occurrence of subtypes of bullying is related to externalizing problems. Adolescent substance use and weapon carrying are two externalizing problems that might further interfere with the learning environment in schools, and as such were included in the present study. Using a nationally representative sample, three research questions were examined in the current study. First, we tested patterns of co-occurrence in students' involvement in physical bullying, verbal bullying, social exclusion, rumor spreading, and cyber bullying using LCA models. Second, we examined whether there were notable demographic differences across the extracted latent classes. Third, we investigated whether a graded relation in substance use and weapon carrying was observable across the extracted latent classes. Based on a similar study on patterns of co-occurrence of peer victimization (Wang et al., 2010), we hypothesized that a three-class solution would best fit the data, including one class of highly aggressive students who engaged in all subtypes of bullying. Moreover, we expected to find gender, grade, and race/ethnicity differences across the extracted latent classes. Finally, we predicted that involvement in more subtypes of bullying would be associated with higher levels of substance use and weapon carrying.

نتیجه گیری انگلیسی

3. Results 3.1. Handling of missing data There were 7508 adolescents who completed the HBSC 2005–2006 survey with the bully/victim items in the HBSC 2005–2006 survey. In the analyses of LCA with covariates, 33 adolescents were excluded, due to missing values on demographic variables. A chi-square statistic was calculated for each of the five bullying variables, and there was no significant difference between the 33 adolescents with missing data and the analytic sample of adolescents with complete data at an alpha of .05. With full information maximum likelihood estimation, the analytic sample consisting of 7475 adolescents who had complete data on all demographic variables was used in the LCA with covariates model. To increase generalizability to a national population, the sample of 7508 adolescents was used in the initial analyses identifying the optimal number of classes, but the reduced sample size of 7475 was used for the analysis of LCA with covariates.

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