ارتباط بین طرد از مدرسه، بزهکاری و زیرگروه های سایبری وقربانیان F2F: شناسایی و پیش بینی ریسک و انواع تجزیه و تحلیل با استفاده از کلاس پنهان
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|38628||2015||14 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Child Abuse & Neglect, Volume 39, January 2015, Pages 109–122
Abstract This purpose of this paper is to identify risk profiles of youth who are victimized by on- and offline harassment and to explore the consequences of victimization on school outcomes. Latent class analysis is used to explore the overlap and co-occurrence of different clusters of victims and to examine the relationship between class membership and school exclusion and delinquency. Participants were a random sample of youth between the ages of 12 and 18 selected for inclusion to participate in the 2011 National Crime Victimization Survey: School Supplement. The latent class analysis resulted in four categories of victims: approximately 3.1% of students were highly victimized by both bullying and cyberbullying behaviors; 11.6% of youth were classified as being victims of relational bullying, verbal bullying and cyberbullying; a third class of students were victims of relational bullying, verbal bullying and physical bullying but were not cyberbullied (8%); the fourth and final class, characteristic of the majority of students (77.3%), was comprised of non-victims. The inclusion of covariates to the latent class model indicated that gender, grade and race were significant predictors of at least one of the four victim classes. School delinquency measures were included as distal outcomes to test for both overall and pairwise associations between classes. With one exception, the results were indicative of a significant relationship between school delinquency and the victim subtypes. Implications for these findings are discussed.
Introduction The upsurge in school shootings during the past decade has been paralleled by a dramatic increase in research on the criminogenic effects of peer victimization at school (Juvonen and Graham, 2001, Garbarino and DeLara, 2004, Rubinlicht, 2011 and Hong and Espelage, 2012). The impact that bullying has on future criminal behavior and/or delinquency has made it a particularly fruitful area of investigation. While bullying typically conjures up images of physical intimidation, the reality is that youth are victimized in a variety of ways, any or all of which may result in irreparable psychological and/or physical harm (Cullen, Unnever, Hartman, Turner, & Agnew, 2008). In addition to overt acts of hostility, covert forms of harassment including spreading rumors or lies and excluding and/or ignoring other students from school activities are commonplace. Moreover, as a result of today's electronic age, the Internet is supplanting traditional forms of schoolyard bullying however victims characteristics differ according to the type of harm being inflicted (Law, Shapka, Hymel, Olson, & Waterhouse, 2012). The differences in form and function between traditional bullying and cyberbullying begs for consideration of whether this “new” form of harassment (Wade & Beran, 2011) that uses “email, instant messages, cell phones, text messages, photos, videos and social networking websites to humiliate and threaten others” (Grim, 2008, p. 157) is really just a new way to implement the same behavior or rather whether it is indeed a distinct phenomenon. Despite significant advances in our understanding of bully victimization over the past decade, much remains to be understood. An important area of research is to examine potential overlaps in harassment and bullying across all possible environments to gain understanding of the sum total of youth's experiences with victimization (Ybarra, Diener-West, & Leaf, 2007). For example, what victim profiles emerge when multiple indicators of both online and offline harassment are analyzed? Also, what is the association between victim profiles and delinquent behavior at school such as skipping class, absenteeism and/or weapon-carrying and physical fighting? The aim of this paper is to document the co-occurrence of online and face-to-face peer harassment and bullying and its effect on several measures of school alienation, avoidance and delinquency. Latent class analysis is used to identify the nature and form of victimization and bullying in early adolescence, explore the overlap and co-occurrence among different clusters of victims, and to examine the relationship between multiple risk factors for school exclusion, delinquency and membership in each ‘victim’ class.
نتیجه گیری انگلیسی
Results Sample characteristics The sample consists of 5,589 youth between the ages of 12 and 18 (mean = 14.77, s.d. = 1.99). Their highest level of academic achievement was the 9th grade, on average. Males and females comprised 51% and 49% of the sample, respectively. The majority of respondents identified racially as “white” (80%) and the rest as non-white (20%) including African/American or black, Hispanic and/or Asian. Descriptive characteristics Prevalence of cyber and bully victimization Descriptive statistics of the bullying behaviors are provided for the whole sample and by gender in Table 1. Table 1 shows the distinction between direct and indirect forms of bullying. The prevalence of victimization from the 7 types of face-to-face bullying were 18.5% for spreading rumors, 17.7% for verbal (males: 17.1, females: 19.7), 7.9 for physical (males: 9.5, females 7.1), 5.5% for social exclusion (males: 4.5, females 6.5), 5.1 for threatened with harm (males: 5.5, females 5.1), 3.3% for being forced to do things (males: 3.5, females: 3.0) and 2.8% for destroyed property (males: 3.5, females: 2.2). Being threatened or insulted online, via text messaging or via gaming (i.e. verbal cyberbullying) was the most common form of cyberbullying (7.0%; n = 237) followed by non-verbal cyberbullying (subjected to the posting of hurtful information; 3.7%; n = 201), social exclusion (purposefully being excluded from online communications; 1.2%; n = 64) and then outing (having private information, photos or videos on the internet or via phone shared in a hurtful way (1.1%; n = 58). The single measure reported here of being threatened is a constellation of three variables: online threats, threats received from text messaging and on threats received via gaming websites. In the analyses that follow, being threatened by text message was kept as a separate variable but gaming and online threats were consolidated. Consequently, there are 11 victimization variables in Table 1 but 12 victimization variables reported in Table 3. Table 1. Prevalence of bullying and cyberbullying behaviors and chi-square tests of association by gender 1. Prevalence of traditional and cyber victimizations among peers by gender. Traditional Victimization Males Females Total Odds ratio 1. Direct Verbal (i.e. calling someone names) 17.1 19.7 17.7 1.19*** Threatened w/harm 5.2 5.3 5.1 1.02 Physical (i.e. hitting, pushing) 9.5 7.1 7.9 0.728*** Forced to do things 3.5 3.0 3.3 0.853 Social (i.e. excluding someone from group activities) 4.5 6.5 5.5 1.48*** Property (i.e. destroying someone's property) 3.5 2.2 2.8 0.620*** 2. Indirect Spread false rumors 12.8 24.5 18.5 2.21*** Cybervictimization 1. Direct Verbal (i.e. using internet to threaten or insult someone) 5.4 8.8 7.0 1.69*** Social (i.e. excluding someone from online communications) 0.9 1.5 1.2 1.68** Non-verbal (i.e. posting hurtful information) 1.8 5.9 3.7 3.42*** 2. Indirect Outing (i.e. sharing personal information) 0.7 1.5 1.1 2.16*** Table options The results show that bully victimizations differ significantly by gender. The odds of being bullied by rumors or being subjected to the unwanted posting of hurtful information, for example, are 2.11 (p < 0.000) and 3.58 (p < 0.000) times higher for females compared to males (see Table 1). Prevalence of school alienation and aggressive behaviors at school Descriptive statistics of the outcome measures of school delinquency are shown in Table 2. With respect to the social withdrawal and alienation of school activities due to students’ fear of being attacked or harmed, 2% (n = 108) of youth reported staying away from online activities, 1.2% (n = 67) avoided activities at school, 0.7% (n = 43) skipped classes at school and 1% reported staying home from school (n = 50). Regarding aggressive behaviors at school, 4.5% (n = 262) reported being in one or more physical fights and 0.7% (n = 42) reported having carried a gun on school property. Table 2. Outcome measures: school withdrawal and avoidance due to fear of being attacked, and aggressive behaviors at school. Social withdrawal from/avoidance of school n (%) Stay away from online activities 108 (2.0) Avoid activities at school 67 (1.2) Skip classes at school 43 (0.7) Stay home from school 50 (1.0) Aggressive behaviors at school Been in one or more physical fights 262 (4.5) Carried a gun to school 42 (0.7) Table options Several measures of bullying including physical bullying (hitting, pushing, slapping, shoving), coercion (being forced to do something you didn’t want to do) and having property destroyed were tested for their association with the outcome measures of aggressive behaviors at school. The results showed significant relationships existed between fighting on the one hand and physical victimization (chi-square = 493.016, p < 0.000), coercion (chi-square = 58.640, p < 0.000), and having property destroyed (chi-square = 204.186, p < 0.000) on the other. As well, gun-carrying was shown to be significantly associated with both physical victimization (chi-square = 8.047, p = 0.005) and coercion (chi-square = 5.816, p = 0.016). Subtypes of cyber- and traditional-victim classes Latent class analysis was conducted on the 11 victimization variables with one, two, three, four and five classes specified. The model fit statistics are reported in Table 3, which shows how the AIC, BIC, a-BIC and entropy compared across models. The best fitting model was deemed to be the four-class model based on the BIC, a-BIC, and entropy values. In addition, the Lo–Mendell–Rubin adjusted LRT-test and the Vuong–Lo–Mendell test yielded a p-value of 0.087 and 0.086 (not shown), respectively. Accordingly, the 2, 3 and 4 class models each fit the data better than the next highest class. Therefore, all of the fit statistics indicated that 5-class model did not fit the data better than the 4-class model. In addition, the 4-class model provided a more interpretable solution for analysis. Therefore, the 4-class model was deemed to provide the best fit to these data. Table 3. Model Fit Statistics for the n-class models of peer victimization. Akaike (AIC) Bayesian (BIC) Sample size adjusted BIC (a-BIC) Entropy Lo, Mendell, Rubin test n for each class 1-LC 28,693.177 28,773.16 28,735.027 na na C1 = 5,798 2-LC 22,786.01 22,952.641 22,873.199 0.899 5,871.567 C1 = 4,770; C2 = 1,028 3-LC 22,396.149 22,649.429 22,528.676 0.883 412.201 C1 = 4,773; C2 = 816; C3 = 209 4-LC 22,209.327 22,549.256 22,387.193 0.885 210.792 C1 = 4,763; C2 = 662; C3 = 242; C4 = 131 5-LC 22,127.912 22,554.489 22,351.116 0.859 106.393 C1 = 4,507; C2 = 741; C3 = 284; C4 = 163; C5 = 103 Table options The class prevalence and conditional item response probabilities for the four-class model are shown in Table 4. The item response probabilities are plotted in Fig. 2. For each class, the item response probability shows the probability that an individual was victimized by the specific type of behavior. Table 4. Item response probabilities and class prevalence of the 4-class LCM of adolescent victimization. Non-victimized Relational/verbal/physical victim Relational/verbal/cyber victim Highly victimized Verbal 0.043 0.758 0.526 0.979 Rumor spreading 0.049 0.653 0.855 1.000 Threatened w/harm 0.001 0.205 0.182 0.695 Physical 0.001 0.397 0.054 0.811 Coercion 0.007 0.125 0.061 0.356 Social exclusion 0.003 0.213 0.240 0.585 Property destroyed 0.005 0.103 0.032 0.381 Hurtful information posted 0.006 0.018 0.406 0.455 Private information shared 0.000 0.000 0.117 0.208 Threatened online (including gaming) 0.002 0.000 0.195 0.373 Threatened via text 0.004 0.050 0.482 0.508 Online exclusion 0.002 0.014 0.091 0.158 Class prevalence 77.3% (4,353) 8.0% (449) 11.6% (653) 3.1% (177) Table options Conditional response probabilities. Fig. 2. Conditional response probabilities. Figure options The four latent class model yielded an interpretable solution in terms of the division of victimization classes into the following categories (see Table 3). One class represented youth who fell victim to multiple forms and types of bullying and cyberbullying behaviors (Class 4). The conditional probability of being victimized was high across the majority of measures including being threatened both in person (0.695) and via text message (0.508), being physically and (0.811) verbally bullied (0.979), having rumors spread about them (1.00) and having hurtful information posted about them over the Internet (0.455); a second class of youth who had very low probabilities of being cyberbullied (all probabilities were 0.05 or less) but high probabilities of being verbally abused (0.758), including being the object of rumors (0.653), and physically abused (0.397) (Class 2); a third class of youth who similar to Class 2, were abused verbally (Class 3). The distinguishing feature of class 3, however, was the relatively higher likelihood of being cyberbullied: class 3 youth had higher conditional probabilities of both being threated via text message (0.483) and harmed by hurtful information posted (0.406) online; the final class was comprised of youth who were not victimized at all (Class 1). To succinct summary of the classes is as follows: • Non-victimized (77.3%; n = 4,353) (class 1): a class characterized by very low probabilities of being victimized by any of the bullying or cyberbullying behaviors; • Relational/verbal/physical class (8.0%; n = 449) (class 2): a class comprised of victims of traditional bullying from verbal, physical and relational abuse; • Relational/verbal/cyber (11.6%; n = 653) (class 3): a class comprised of victims of verbal/relational bullying and direct forms of cybervictimization (posting harmful information and being threatened via text messages); • Highly victimized class (3.1%; n = 177) (class 4): a class of individuals with high probabilities of being victimized by all forms of bullying and cyberbullying behaviors. Fig. 2 shows the overall pattern of involvement in each class. The pattern is indicative of the semi-ordered nature of item responses in that the probabilities for all items in classes 1, 2 and 4 are highest for classes 1, 2 and 4. Class 3, however, deviates somewhat from this pattern (verbal bullying is higher in class 3 than class 2, rumor spreading is higher in class 2 than class 3, physical bullying is higher in class 3 than class 2, etc.). Demographic characteristics of each class The results of the 4-class model with covariates are reported in Table 5. Demographic covariates included gender (males as referent), age, grade and race (non-white as referent). The model is analogous to a multinomial logistic regression model including covariates on the 4 classes with class 3, the class of non-victims, as the reference group. Fig. 3 shows the predicted probability of class membership by victim characteristics. Table 5. Parameter estimates, Z-scores, and p-values for the multinomial LC regression of sociodemographic characteristics on class membership with non-victimization as the reference class. Logit/OR Z-score p-Value Relational/verbal/cyber v non-victim Female 1.326/3.77 5.107 <0.000 Age 0.088/1.09 1.225 0.221 Grade −0.013/0.987 −0.151 0.880 White 0.375/1.45 1.937 0.053 Relational/verbal/physical vs. non-victim Female −0.501/0.606 −2.976 0.003 Age −0.136/0.873 −1.557 0.119 Grade −0.216/0.801 −2.716 0.007 White 0.029/1.03 0.173 0.863 Highly victimized vs. non-victim Female 1.321/3.75 4.391 <0.000 Age −0.136/0.873 −0.869 0.385 Grade 0.108/1.11 0.703 0.482 White −2.850/0.058 2.395 0.017 Intercepts C#1 11.523 3.894 <0.000 C#2 22.913 6.131 <0.000 C#3 27.457 7.914 <0.000 Table options Demographic characteristics and victim classes. Fig. 3. Demographic characteristics and victim classes. Figure options Sociodemographic correlates of victimization subtypes Compared to males, females were more likely to be in relational/verbal/cyber bullied class (OR = 3.77; p < 0.000) and the highly victimized class (OR = 3.75 p < 0.000) but less likely to be in the relational/verbal/physical bullied class (OR = .606; p = 0.003). White students were significantly less likely to be highly victimized compared to non-white students (OR = 0.058; p = 0.017). There was a trend for there to be higher proportions of whites to be victims of relational/verbal/cyber bullying compared to not being bullied for white students, but the race difference only approached statistical significance at the p = 0.10 level (OR = 1.45; p = 0.053). Educational attainment was significantly associated with membership in the relational/verbal/physical bullied class but not in either of the relational/verbal/cyber nor highly victimized classes. As students’ grade level increases, the likelihood of being in the relational/verbal/physical bullied class (compared to class of non-victims) decreases (OR = 0.801; p = 0.007). No significant age differences were found with respect to being in one of the three victimized classes compared to being in the non-victimized class. Associations of victimization with school avoidance and aggression The means of the four avoidance behaviors and 2 antisocial behaviors were tested: (1) for their overall association with each of the four latent classes; and (2) for mean differences across all possible combinations of paired comparisons (in total there are View the MathML source42=6 total comparisons). Table 6 presents the results of only one comparison, that is the comparison of classes 1, 2 and 3 versus class 4, or no victimization, relational/verbal/physical victimization, relational/verbal/cyber victimization classes versus highly victimized. Table 6. Equality tests of means across classes using posterior probabilities of most likely class membership (comparison class = highly victimized). Non-victims Relational/verbal/physical Relational/verbal/cyber Social withdrawal/avoidance Stay away from online activities 23.272 (0.000) 12.65 (0.000) 12.13 (0.000) Avoid activities at school 18.935 (0.000) 7.140 (0.008) 16.11 (0.000) Skip classes at school 11.043 (0.001) 4.503 (0.034) 7.596 (0.006) Stay home from school 17.255 (0.000) 7.932 (0.005) 11.94 (0.001) Aggressive behaviors at school Been in one or more physical fights 21.846 (0.000) 0.013 (0.908) 15.713 (0.000) Carried a gun to school 4.683 (0.030) 2.219 (0.136) 2.770 (0.096) Table options All three mean comparisons revealed that class membership is strongly related to avoidance and withdrawal behaviors at school (Table 6). Both the overall test and all paired comparisons for each of the four avoidance behaviors were statistically significant (p < 0.000 for all). As shown by Table 6, the highly victimized class showed the strongest association with all measures of school avoidance, followed by the relational/verbal/cyber class and finally the relational/verbal/physical bullied class. Likewise, the overall test for physical fighting at school was very statistically significant across all classes (chi-square = 102.807; p < 0.000). The overall test for carrying a gun to school, however, was insignificant. Upon closer inspection, this result was attributed to only one of three mean differences (1 vs. 4, 2 vs. 4 and 3 vs. 4) being statistically significant across this measure. More specifically, the results were indicative of a significant difference between the highly victimized and non-victimized classes (chi-square = 4.683, p = 0.030) but not between the highly victimized class on the one hand and the relational/verbal/cyber or relational/verbal/cyber classes on the other. The implication is that there is no difference in the prevalence of gun carrying across victim classes. Hence the analysis did not detect a victim class that might be predictive of gun carrying behavior. Fig. 4 shows the predicted probability of engaging in each behavior by class membership. As shown, the predicted probability of engaging in four of the school delinquency behaviors is highest for highly victimized youth. The predicted probability of avoiding school activities because of fear is 0.132, skipping classes because of fear is 0.108, staying home because of fear is 0.126, and carrying a gun to school is 0.042. The two exceptions to this pattern are as follows. Youth who are victims of relational/verbal/physical bullying are more likely to be involved in one or more fights (predicted probability = 0.276) and youth who are victims of relational/verbal/cyber abuse are more likely to stay away from online activities for fear of being attacked or harmed (predicted probability = 0.049). Delinquency characteristics and victim classes. Fig. 4. Delinquency characteristics and victim classes.