bullying 5.pdf

8/13/2019 bullying 5.pdf

1/8

R E S E A R C H A R T I C L E

Demographic Differences in the

Prevalence, Co-Occurrence, andCorrelates of Adolescent Bullyingat School

K ELLIE E. CARLYLE, PhD, MPH a

K ENNETH J. STEINMAN , PhD, MPH b

ABSTRACT

BACKGROUND: Despite a large literature on bullying, few studies simultaneouslyexamine different dimensions of the phenomenon or consider how they vary by demo-graphic characteristics. As a result, research ndings in this area have been inconsis-tent. This article focuses on 2 dimensions of bullying behaviorsaggression andvictimizationand examines demographic variation in their prevalence, co-occurrence,and association with other health outcomes.

METHODS: School-based surveys were administered to a census of 6th-12th graders in16 school districts across a large metropolitan area in the United States (n = 79,492).

A 2-factor scale assessed repeated experiences with bullying aggression and victimization.RESULTS: Both dimensions of bullying tended to be more common among younger,male, African American and Native American students. There were, however, severalexceptions as well as considerable variation in the magnitude of demographic differen-ces. Most youth involved with bullying were either perpetrators or victims, but notboth. For example, only 7.4% of all youths were classied as bully/victims. Substanceuse was more strongly associated with aggression, whereas depressive affect wasmore strongly associated with victimization.

CONCLUSIONS: Researchers should distinguish different dimensions of bullyingand consider how they vary by demographic characteristics. In particular, repeatedaggression and victimization largely involve different students and may require dis-tinct approaches to prevention.

Keywords: research; bullying; violence.

Citation: Carlyle KE, Steinman KJ. Demographic differences in the prevalence,co-occurrence, and correlates of adolescent bullying at school. J Sch Health. 2007;77: 623-629.

a Assistant Professor, ([email protected]), Hugh Downs School of Human Communication, Arizona State University, PO Box 871205, Tempe, AZ 85287.b Assistant Professor, ([email protected]), School of Public Health, The Ohio State University, 438 Cunz Hall, 1841 Millikin Rd, Columbus, OH 43210.

Address correspondence to: Kellie Carlyle, ([email protected]), Hugh Downs School of Human Communication, Arizona State University, PO Box 871205, Tempe, AZ 85287.

Journal of School Health d November 2007, Vol. 77, No. 9 d 2007, American School Health Association d 623


2/8

F ew studies in the United States have had sampleslarge and diverse enough to systematically exam-ine demographic differences in different dimensionsof adolescent bullying. The term bullying includesa range of behaviors that are repeated over time 1

(eg, hitting, excluding from group activities, andspreading rumors). Adolescents can experience bul-lying as a perpetrator and/or victim. This study

examines the prevalence, co-occurrence, and corre-lates of different dimensions of bullying and exploreshow they may vary by age, gender, and ethnicity.By identifying inconsistent ndings in the researchliterature and testing them on a large, diverse dataset, this article will help guide future efforts tounderstand the universal and particular aspects ofadolescent bullying.

DEMOGRAPHIC DIFFERENCES

AgeResearch has fairly consistently indicated that

bullying decreases over time for middle and highschool students. 2-5 This downward trend in bullyingis supported by similar national trends in physicalghting. 6 Still, some studies have found that bully-ing and victimization increase with age. 7 Oneexplanation is that, while bullying does tend todecrease in general from early to late adolescence,the prevalence rates temporarily peak in the middleschool. Thus, increases may be found based on thegrade levels chosen for comparison. Consistent withprevious research, we hypothesize that the preva-lence and co-occurrence of both bullying and vic-timization will decrease overall with age, regardlessof sex, or ethnicity.

GenderPrevious research suggests that bullying is more

common among males than females. 8-10 However,numerous studies have found no gender differences,and some suggest that results may be inuenced bygender role stereotypes and how aggression itself ismeasured (see Underwood et al 11 for a review).Nonetheless, the general trends in male and female bullying behaviors are reasonably well supported. Assuch, we expect that males will bully and be victim-ized more than females.

EthnicityWhereas physical ghting appears to be more

common among African American (39.7%) and His-panic (36.1%) than white (30.5%) high school stu-dents, 6 ethnic group differences in bullying are lesswell established. Notably, few studies have had sam-ples large enough to include comparisons of morethan 2 ethnic groups. Some research has suggested

that the bullying-ethnicity relationship may be moredependent upon specic racial dynamics 1 dynamicsthat may be school or community specic and notnecessarily apply to ethnic groups as an aggregate.Given the sparse number of studies on the topic, weassume a null hypothesis: there are no ethnic groupdifferences.

CorrelatesAdolescents victimized by bullying can experience

sociopsychological harm 12,13 including higher levelsof depression. 2 Bullying is also associated with otherproblem behaviors in general, 7 such as substanceuse. 14 It is not clear, however, how these associa-tions vary across different dimensions of bullying behaviors. The co-occurrence of aggressive behaviorand substance use, for example, may both reecta broad personal orientation toward antisocial behavior 15 or could be 1 way that adolescents copewith victimization and peer rejection. 16,17 Guided by

the limited literature in this area, we hypothesizethat perpetration will correlate with substance useand that victimization will correlate with depression.Accordingly, this article aims to ll a gap in the liter-ature by examining a large, ethnically diverse dataset with consistent measures and a systematic statis-tical approach. Doing so may help reconcile inconsis-tencies in previous research and guide futureresearch efforts.

METHOD

Data were collected using the Primary PreventionAwareness Attitude and Use Survey 18 (PPAAUS).The PPAAUS was developed by the Safe and DrugFree Schools Consortium of Franklin County, Ohio,to assess adolescent risk behaviors and their determi-nants to guide public health policy in the county.The PPAAUS has been administered to all 6th-12thgraders in Franklin County (metropolitan Colum- bus), Ohio, every 3 years since 1988. Data for thepresent study are based on surveys administeredduring fall 2003.

Trained teachers and school staff administered thePPAAUS during the second class period in 16 public

school districts (55 high schools and 91 middleschools), 6 private schools, and 36 Catholic schools.Passive parental consent was used, and studentswere given the option of not participating in the sur-vey. Students completed the surveys anonymously,with the only identiers being the respondentsschool building and grade. A readability analysis ofthe instrument items used in this study indicated aFlesch-Kincaid grade level of 6.6. Flesch-Kincaid read-ability tests are widely used and have a high correla-tion with other readability tests. 19 The Institutional

624 d Journal of School Health d November 2007, Vol. 77, No. 9 d 2007, American School Health Association


3/8

Review Board of The Ohio State University approvedthe analyses for the present study.

MeasuresTo study bullying perpetration as well as victimi-

zation, we employed 13 survey items that askedabout frequency of behaviors during the past year.

For each of the constituent items, the originalresponse options included never, once, 2-3times, 4 or more times. Principal componentsanalysis of a random 20% subset of the sampleyielded 2 factors with eigenvalues less than 1 thatexplained 47.6% of the variance in the constituentitems. One factor included 7 items relating to perpe-tration (eg, During the past year at school, howoften have you pushed others around to make themafraid?) and a second factor included 6 itemsrelated to victimization (eg, During the past year atschool, how often has someone verbally attackedyou?). These results were replicated on a secondrandom 20% subset with nearly identical results.Both scales included measures of direct (eg, . . .how often have you threatened to beat someoneup?) as well as indirect (eg, . . . how often haveyou told lies or spread false rumors about some-one?) types of aggression. Analyses offered littlesupport for distinguishing subscales of indirect anddirect aggression. As such, the perpetration and vic-timization scales each included measures of both.The reliability of the scales was acceptable withCronbach a s of .82 and .74 for the perpetration andvictimization scales, respectively. Table 1 presentsthe factor loadings for the constituent items.

Because these scales yielded continuous measuresof aggression and victimization, they were notappropriate for measuring bullying as a repeated pat-tern of behavior. 1 To distinguish the phenomenonfrom lower levels of aggression/victimization, weclassied as bullying any time a youth responded4 or more times to at least one of the constituentitems. Similar measurement approaches have beenused previously. 7,10,20 To examine the validity of thismeasurement strategy, we constructed alternativemeasures of bullying perpetration and victimization by dening as bullies and victims any youths

whose scores fell in the upper quintile of the contin-uous scales. In Results, we discuss how results ofanalyses using the alternative measures differ fromthose of the primary measures.

To assess whether perpetration and victimizationare differentially related to established correlates of bullying, we constructed measures of depressiveaffect and substance use. Depressive affect wasassessed by 2 variables ( a = .61) reporting the fre-quency of feeling happy and depressed (range = 1almost never to 3 most of the time). The

substance-use variable was constructed by principalcomponents analysis of 3 items measuring the fre-quency of cigarette, alcohol, and marijuana use(range = 1 never used to 6 use about everyday). The factor model explained 73% of the vari-ance in the constituent items, with loadings rangingfrom .84 to .88. Because the distribution of factorscores was highly asymmetrical, we applied a logtransformation to reduce its departure from a normaldistribution (mean = 0.15, SD = 0.33, skewness =0.92, and kurtosis = 0.45 21 ).

ProcedureWe performed a series of cross-tabular analyses to

examine how bullying varied by grade, gender, andethnicity. A series of logistic regression models testedthe association of both dimensions of bullying withsubstance use and depressive affect. In each model,we entered each of the demographic characteristics(ie, grade, gender, and ethnicity) as well as sub-stance use and depressive affect. We then tested all2-way interactions by comparing model v 2 statistics

Table 1. Standardized Factor Loadings from PrincipalComponents Analysis 13 Survey Items *

Factor Loadings

1 2

During the past year at school . . .How often have you told lies or

spread false rumors about someone?0.51 0.152

How often have you pushed othersaround to get something you want?

0.776

How often have you pushed othersaround to make them afraid?

0.821

How often have you threatenedto beat up someone?

0.772

How often have you hit someonewith your fists orbeat up someone?

0.72

How often have you takenmoney or things byforce from people?

0.665

How often have you left someone out of agroup or activity to hurt them?

0.587

How often has someone taken money or

things directly from you usingforce, a weapon, or threats?

0.57

How often have other students spread liesor false rumors about you?

0.677

How often has someone physicallyattacked you?

0.101 0.647

How often has someone verballyattacked you?

0.695

How often has someone left you outof a group or activityto hurt you?

0.729

How often have you feared for yourphysical safety?

0.683

* Factor loadings less than 0.10 are left blank.



4/8

in logistic regression models with the interactionterm (and the main effects) versus models with onlythe main effects. For example, we regressed a yes/no measure of bullying perpetration on grade, gen-der, ethnicity, substance use, and depression in step1 and an interaction term (eg, grade by substanceuse) in step 2. Whenever signicant interactionterms were identied, we then performed stratied

analyses (eg, separate models for males and females)to specify the exact nature of the differences.

Given our large data set and the greater likelihoodof type I errors with repeated statistical tests on thesame variables, p values of less than .001 wereviewed as statistically signicant. 21

RESULTS

This section presents the demographic characteris-tics of the sample, followed by the prevalence andco-occurrence of bullying perpetration and victimiza-tion by grade, gender, and ethnicity.

DemographicsThe 2003 PPAAUS included data from 79,492 stu-

dents representing 81.4% of the enrolled studentpopulation of participating schools and 96.7% ofthose completing the questionnaire. 18 The mostcommon reasons for failing to participate in thestudy included being chronically absent, homeschooled, or otherwise not enrolled in school. Assuch, the data are only representative of studentswho regularly attended schools in Franklin Countyin 2003. The sample was evenly split between males

(49.3%) and females (50.7%) and included white(63.0%), African American (20.6%), Hispanic(2.2%), Asian (3.1%), and Native American (0.7%)youth. In addition, 10.4% of respondents describedthemselves as multiracial, other, or refused torespond. Sixth graders comprised 16.8% of the sam-ple, 7th/8th graders 32.9%, 9th/10th 28.5%, and11th/12th 21.7%.

PrevalenceOverall, 20.1% of youth in the study reported

having been bullied in the past year (Table 2). Vic-

timization was somewhat more common among6th-8th graders and males, although gender differen-ces were only signicant among whites and Asians.Ethnic differences were modest, except for the muchhigher rates among Native American youths. Noother interactions among demographic variableswere detected.

Findings for bullying perpetration presenteda much more complex picture. Overall, 18.8% ofthe youth reported bullying perpetration during thepast year (Table 2). Perpetration was most common

among seventh through ninth graders and amongmales. Both African American and Native Americanyouth reported much higher rates of perpetration,whereas rates among Asian youth were lower.

Higher order analyses (not shown) found thatgender differences in perpetration varied across dif-ferent ethnic groups, Breslow-Day v 2 (4) = 28.22, p ,.001. Among whites, Hispanics, and Asians, forexample, perpetration was about twice as commonamong males compared with females. For other

groups, however, gender differences were less pro-nounced. Among African Americans, 22.8% offemales reported perpetration compared with 32.5%of males. Gender differences also varied by grade,Breslow-Day v 2 (6) = 31.05, p , .001, such that theywere greatest among older youths. Comparing 8thand 12th graders, for instance, perpetration amongfemales declined from 17.6% to 10.5%, whereasmales only declined from 26.7% to 20.9%. Finally,ethnic group differences in perpetration diminishedmarkedly between 6th and 12th grades. Through9th grade, African American youths were abouttwice as likely as others youths to perpetrate bully-

ing, yet by 12th grade, there was no signicant dif-ference. Native American youth, however, departedfrom this trend, as their rates of perpetrationremained high among youth in older grades. Evenin 12th grade, 33.9% of youths in this groupreported bullying others.

Co-OccurrenceWe examined the co-occurrence of bullying per-

petration and victimization and tested whether their

Table 2. Prevalence of Bullying Aggression and Victimization:Differences by Demographic Group *

n Perpetration Victimization

Overall 78,068 18.80% 20.10%Grade

6 13,077 16.3 22.8

7 12,926 20.0 23.1

8 12,789 22.3 22.5

9 11,730 19.8 18.310 10,541 19.3 18.6

11 9099 16.7 16.7

12 8008 15.6 15.5

GenderMale 37,676 23.3 22.3

Female 39,142 14.3 17.9

EthnicityWhite 49,535 15.5 19.5African American 15,863 27.7 19.6Hispanic 1689 17.4 16.8Asian 2464 11.6 16.5

Native American 570 30.9 27.5

* ns may not sum to overall total because of missing data. Differs from overall column prevalence by p, .001.



5/8

co-occurrence varied across demographic groups.Over two thirds (68.5%) of youths reported neither bullying nor being bullied by others. Being a victimonly (12.7%) was slightly more common than beinga perpetrator only (11.4%), with an additional 7.4%of all youths being classied as bully/victims. Co-occurrence of perpetration and victimization varied by grade, being most common in 8th grade (9.0%)

and least common in 11th (6.3%) and 12th (6.2%)grades. Co-occurrence was also more commonamong Native American (12.8%) and African Amer-ican (9.1%) youth and least common among Asian(5.3%) youth.

These differences, however, were largely associ-ated with the overall prevalence of perpetration andvictimization in each group. When we limited analy-ses to youths who had some exposure to bullying(ie, perpetration and/or victimization), less than onequarter (23.6%), were classied as both bully andvictim. Analyses detected no signicant differences by grade, v 2 (6) = 16.16, p = .013, or ethnicity, v

2(4) =

5.89, p = .21, although males (25.8%) were morelikely than females (20.6%) to be classied as bully/victims, v 2 (1) = 90.00, p , .001.

Correlates of Bullying BehaviorsA series of logistic regression models estimated

how substance use and depressive affect were associ-ated with bullying perpetration and victimization,controlling for age group, ethnicity, and gender. Pre-liminary analysis identied signicant interactioneffects that indicated the need to stratify analysesacross certain demographic variables. Specically,

the association of bullying victimization with depres-sive affect and substance use varied by gender. Bothdepressive affect and substance use were positivelyassociated with victimization, although the effectswere somewhat stronger among females than amongmales (Table 3). For both genders, depressive affecthad larger adjusted odds ratio (AOR) than did sub-stance use.

For bullying perpetration, the association with sub-stance use varied by grade. Table 4 presents results

for logistic regression models at 6th, 8th, and 12thgrades. In each model, the AORs for depressive affectwas much smaller than those for substance use. Nota-

bly, the magnitude of the AORs for substance usediminished with increasing grade levels. The coef-cients for the sixth-grade model should be interpretedwith caution given the questionable model t.

Results Using Alternative Bullying MeasuresTo examine the validity of our measures of bully-

ing, we replicated all analyses using alternativemeasures. For both perpetration and victimization,90% of youths were similarly classied across theprimary and alternative measures. Inconsistent clas-sication of bullying perpetrators was more commonamong males (12.0%), African American, (12.3%)and Native American (15.6%) youth and was lesscommon among sixth graders (8.0%). Inconsistentclassication of bullying victims was not associatedwith grade, ethnicity, or gender. Overall, results ofanalyses using the alternative measure (available fromthe authors) were very similar to those reportedabove.

DISCUSSION

Overall, 28.2% of students reported involvementwith some type of bullying behavior, which is consis-

tent with other prevalence reports.22

Our ndings build on previous research by distinguishing differentdimensions of bullying and systematically examiningdifferences by age, gender, and ethnicity. Althoughthe large, diverse nature of the sample permitted ana-lyzing such distinctions, it is important to interpretthe ndings with caution as the sample is taken from1 metropolitan area, which may limit the generaliz-ability. This section highlights a few key ndings, dis-cusses how they relate to the previous literature, andproposes directions for future research.

Table 3. AORs* (with 95% Confidence Intervals) for DepressiveAffect and Substance Use Testing Association with AdolescentBullying Victimization: Models for Males and Females

Males Females

Depressive affect 2.42 (2.31-2.55) 2.78 (2.64-2.93)Substance use 1.57 (1.44-1.72) 1.87 (1.67-2.04)Model fit/calibration, n 32,242 33,744Hosmer-Lemeshow, v 2 (p) 12.90 (.11) 21.11 (.01)Area under the receiver

operating characteristic curve0.65 0.69

* Adjusted for grade and ethnicity.

Table 4. AORs* (with 95% Confidence Intervals) for DepressiveAffect and Substance Use Testing Association with AdolescentBullying Perpetration: Models at 6th, 8th and 12th Grades

Grade

6 8 12

Depressive affect 1.84 (1.66-2.04) 1.62 (1.48-1.77) 1.60 (1.41-1.8Substance use 24.71 (18.73-32.61) 9.68 (8.23-11.39) 6.1 (4.99-7.54Model fit/calibration, n

10,672 10,782 6956

Hosmer-Lemeshow,v

2 (p)31.94 (, .01) 12.41 (.09) 4.43 (.82)

Area under thereceiver operatingcharacteristic curve

0.75 0.75 0.72

* Adjusted for gender and ethnicity.



6/8

Males reported higher levels of both types of bul-lying behaviors. Similarly, males were more likelythan females to be classied as bully/victims. How-ever, while both depressive affect and substance usewere positively associated with victimization, theeffects were somewhat stronger among females thanamong males. One possible explanation for this dif-ference is that females may engage in more psycho-

logical forms of aggression, whereas males use morephysical; the consequences of which may differ.Although the nature of the measures used in thisstudy do not allow for this explanation to be exam-ined here, studies have found that females both useand experience more indirect aggression. 11,12

We detected several persistent ethnic group differ-ences across bullying behaviors. One compellingexplanation for this focus on the minority status ofan ethnic group in particular schools. However, bothAsian and Native American youth represented smallproportions of the student body in each school, yetthey reported very different rates of bullying behav-ior. Similarly, white and African American studentsoften constituted either the majority or a largeminority of students within different schools in thestudy. Yet, the students from these groups alsoreported different levels of bullying behavior. Onepossible explanation relates to previous ndings thatethnic minorities perceive that minorities were morelikely than majority students to experience bully-ing. 23 Consistent with a social constructionist view,Harris 24 found that aggression is affected by culturalfactors.

Previous studies often note that many bullies arealso victims. 7 Several of our ndings suggest that itmay be more appropriate to consider these dimen-sions of bullying as separate phenomena. Overall,only 7.4% of all youths were classied as bully/vic-tims, which is similar to others who have found that between 3.9% and 8.2% are bully/victims. 7,25 More-over, our ndings suggest that bullying perpetrationand victimization represent 2 distinct, if modestlyrelated phenomena, and should be studied as such.

Our analyses of correlates of bullying behaviorsprovide further support for the value of distinguish-ing perpetration from victimization. Substance usewas more strongly associated with aggression than

with victimization (Table 3). This is consistent withprevious ndings 14,17 and problem-behavior the-ory, 15 which suggests that problem behaviors servea functional purpose that aids in achieving a specicgoal such as coping with rejection. Importantly,these are preliminary ndings and future researchshould investigate whether substance use is leadingto bullying behaviors or bullying behaviors are lead-ing to increased substance use. Consistent with pre-vious literature, 12,13 depressive affect was somewhatmore strongly associated with victimization than

with aggression. However, as with substance use,future research should examine the nature of thisrelationship further.

Implications for PreventionThe demographic differences illustrated in this

article have important implications for prevention,particularly in the areas of program targeting andmessage tailoring. Targeting is a means of dividinga population based on a variety of dimensions, 26

including sex, age, and ethnicity. For example, ourresults suggest that younger adolescents tend to bevictimized at higher rates. As such, interventionsaimed at coping skills would best be targeted at theseyounger students. Also, Native American studentsexperienced both forms of bullying behaviors ata higher rate than all other ethnicities, which sug-gests that this would be a meaningful subgroup totarget with a culturally relevant intervention.

Similarly, tailoring refers to messages personalized

at the individual level27

and may be based on bullyor victim status, substance use, or other characteris-tics specic to the adolescent. For example, a stu-dents involvement in bullying behaviors can beimmediately assessed in an online survey, the resultsof which determine the subsequent prevention mes-sages the adolescent receives. This type of computer- based tailoring has been used successfully in otherareas of adolescent health 28 and provides a poten-tially rich avenue for future research with its abilityto provide a cost-effective way to target bullies, vic-tims, and the small subset of bully/victims.

Future research should also qualitatively examine

the nature of the ethnic differences (eg, varying cul-tural norms, social contexts, coping skills) to makemore specic recommendations for ethnic targeting.Similarly, future prevention programs should useinteractive technologies to tailor specic risk reduc-tion messages. To date, no programs have beendesigned specically for particular ethnic groups norhas the impact of existing programs adapted for vari-ous ethnic or cultural factors been systematicallyevaluated. 7 Future work by both researchers andpractitioners should address this gap. Such programsshould be modeled after the effective bullying pre-vention programs implemented in the United Statesand other countries (see Olweus 29 for an overview).

Strengths and LimitationsBy analyzing both perpetration and victimization

using a large sample that allowed for multiple com-parisons and more in-depth analysis of interactionsthan has previously been examined, this article wasable to add further renement to the existing litera-ture on bully/victims. Moreover, the sample wasdiverse enough to compare 5 different ethnic groups.



7/8

Our measures of bullying differ from those used byother studies in that we did not nd support for sep-arating direct and indirect measures of aggressionand victimization and we used the threshold of 4 ormore times to qualify as bullying behavior. In manyways, this is a limitation of the literature in generalgiven the lack of an agreed-upon scale for measuring bullying behaviors. 30 Another limitation is that

despite the large sample size, our study is based ona single urban county that may not be representativeof other places in the United States. To the extentthat different cultural and social climates inuencethe prevalence of bullying as well as its determi-nants, 29 our ndings may lack generalizability. Otherstudies, for instance, report that some but not alltypes of adolescent risk behaviors may vary byregion. 31,32 Also, the cross-sectional design of thisstudy precluded our ability to test for the causaleffects of bullying behavior on substance use anddepressive affect.

CONCLUSIONS

This article lls a gap in the literature through itsexamination of the various associations between per-petration and victimization and whether these associ-ations vary by age, gender, and ethnicity. Overall, thendings support the importance of distinguishing bul-lying behaviors and considering how they vary bydemographic characteristics. Future research shouldexamine the social and contextual factors surround-ing these demographic differences and use these nd-ings to further rene prevention programming.

REFERENCES

1. Espelage DL, Swearer SM. Research on school bullying andvictimization: what have we learned and where do we gofrom here? School Psych Rev . 2003;32:365-383.

2. Seals D, Young J. Bullying and victimization: prevalence andrelationship to gender, grade level, ethnicity, self-esteem, anddepression. Adolescence. 2003;38:735-747.

3. Rigby K, Slee PT. Bullying among Australian school children:reported behavior and attitudes toward victims. J Soc Psychol .1994;131:615-627.

4. Landau S, Bjorkqvist K, Lagerspetz KM, et al. The effect ofreligiosity and ethnic origin on direct and indirect aggressionamong males and females: some Israeli ndings. Aggress Behav .2002;28:281-299.

5. Eisenberg ME, Neumark-Sztainer D, Perry CL. Peer harass-ment, school connectedness, and academic achievement. J SchHealth . 2003;73:311-316.

6. Centers for Disease Control and Prevention. Surveillance sum-maries. MMWR Morb Mortal Wkly Rep . 2004;53(SS-2):651-655.

7. Haynie DL, Nansel T, Eitel P, et al. Bullies, victims, and bully/victims: distinct groups of at-risk youth. J Early Adolesc .2001;21:29-49.

8. Olweus D. Bully/victim problems in school: facts and inter-vention. Eur J Psychol Educ . 1995;12:495-510.

9. Schafer M, Werner NE, Crick NR. A comparison of two ap-proaches to the study of negative peer treatment: general vic-timization and bully/victim problems among Germanschoolchildren. Br J Dev Psychol . 2002;20:281-306.

10. Mouttapa M, Valente T, Gallaher P, et al. Social network pre-dictors of bullying and victimization. Adolescence. 2004;39:315-335.

11. Underwood MK, Galen BR, Paquette JA. Top ten challengesfor understanding gender and aggression in children: whycant we all just g et along? Soc Dev . 2001;10:248-266.

12. Crick NR, Casas JF, Nelson DA. Toward a more comprehen-sive understanding of peer maltreatment: studies of relationalvictimization. Curr Dir Psychol Sci . 2002;11:98-102.

13. Crick NR, Grotpeter JK. Relational aggression, gender, andsocial-psychological adjustment. Child Dev . 1995;66:710-722.

14. Berthold KA, Hoover JH. Correlates of bullying and victimiza-tion among intermediate students in the Midwestern USA.Sch Psychol Int . 2000;21:65-78.

15. Jessor R. Problem-behavior theory, psychosocial development,and adolescent problem drinking. Br J Addict . 1987;82:331-342.

16. Cooper ML, Frone MR, Russell M, et al. Drinking to regulatepositive and negative emotions: a motivational model of alco-hol use. J Pers Soc Psychol . 1995;69:990-1005.

17. Rusby JC, Forrester KK, Biglan A, et al. Relationships between peer harassment and adolescent problem behaviors.

J Early Adolesc . 2005;25:453-477.18. Safe and Drug Free Schools Consortium. 2003 primary pre-

vention attitude awareness and use survey website. 2004.Available at: http://www.edcouncil.org/programs/drugfree/ppaaus/web/table_of_contents.htm. Accessed September 1, 2005.

19. Paasche-Orlow M, Taylor H, Brancati F. Readability standardsfor informed consent forms as compared with actual readabil-ity. N Engl J Med . 2003;348:721-726.

20. Eslea M, Menesini E, Morita Y, et al. Friendship and loneli-ness among bullies and victims: data from seven countries. Aggress Behav . 2003;30:71-83.

21. Neter J, Wasserman W, Kutner MH. Applied Linear Statistical Models. 3rd ed. Boston, Mass: Irwin; 1990.

22. US Department of Health and Human Services. U.S. Teens inOur World . Rockville, Md: U.S. Department of Health and Human

Services; 2003.23. Siann G, Callaghan M, Glissov P, et al. Who gets bullied? Theeffect of school, gender and ethnic group. Educ Res. 1994;36:123-134.

24. Harris MB. Ethnicity, gender, and evaluations of aggression. Aggress Behav . 1995;21:343-357.

25. Unnever JD. Bullies, aggressive victims, and victims: are theydistinct groups? Aggress Behav . 2005;31:153-171.

26. Atkin CK. Theory and principles of media health campaigns.In: Rice RE, Atkin CK, eds. Public Communication Campaigns .3rd ed. Thousand Oaks, Calif: Sage, 2001:49-68.

27. Bulger DW, Smith AB. Message tailoring: an essential compo-nent for disease management. Dis Manag Health Outcomes . 1999;5:127-134.

28. Roberto AJ, Zimmerman RS, Carlyle KE, Abner EL. The effectsof a computer-based HIV, STDs, and pregnancy prevention pro-gram on rural adolescents. J Health Comm . 2007;12:53-76.

29. Olweus D. A prole of bullying. Educ Leadersh . 2003;60:12-17.30. Crothers LM, Levinson EM. Assessment of bullying: a review

of methods and instruments. J Couns Dev . 2004;82:496-503.31. Beauvais F. Comparison of drug use rates for reservation

Indian, non-reservation Indian and Anglo youth. Am Indian Alsk Native Ment Health Res . 1992;5:32-37.

32. Johnston LD, OMalley PM, Bachman JG, Schulenberg JE. Monitoring the Future National Survey Results on Drug Use,1975-2004 . Volume I: Secondary School Students (NIH PublicationNo. 05-5727 . Bethesda, Md: National Institute on Drug Abuse;2005.



8/8

bullying 5.pdf

Documents