Research Proposal

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

Demographic Differences in the Prevalence, Co-Occurrence, and Correlates of Adolescent Bullying at School

KELLIE E. CARLYLE, PhD, MPHa

KENNETH J. STEINMAN, PhD, MPH b

ABSTRACT

BACKGROUND: Despite a large literature on bullying, few studies simultaneously examine different dimensions of the phenomenon or consider how they vary by demo-

graphic characteristics. As a result, research findings in this area have been inconsis-

tent. This article focuses on 2 dimensions of bullying behaviors—aggression and

victimization—and 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 in 16 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, several

exceptions as well as considerable variation in the magnitude of demographic differen-

ces. Most youth involved with bullying were either perpetrators or victims, but not

both. For example, only 7.4% of all youths were classified as bully/victims. Substance

use was more strongly associated with aggression, whereas depressive affect was

more strongly associated with victimization.

CONCLUSIONS: Researchers should distinguish different dimensions of bullying and consider how they vary by demographic characteristics. In particular, repeated

aggression 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.

aAssistant 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 ª 2007, American School Health Association d 623

F ew studies in the United States have had samples

large and diverse enough to systematically exam-

ine demographic differences in different dimensions

of adolescent bullying. The term bullying includes

a range of behaviors that are repeated over time 1

(eg, hitting, excluding from group activities, and

spreading 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 explores

how they may vary by age, gender, and ethnicity.

By identifying inconsistent findings in the research

literature and testing them on a large, diverse data

set, this article will help guide future efforts to

understand the universal and particular aspects of

adolescent bullying.

DEMOGRAPHIC DIFFERENCES

Age Research has fairly consistently indicated that

bullying decreases over time for middle and high

school students. 2-5

This downward trend in bullying

is supported by similar national trends in physical

fighting. 6 Still, some studies have found that bully-

ing and victimization increase with age. 7

One

explanation is that, while bullying does tend to

decrease in general from early to late adolescence,

the prevalence rates temporarily peak in the middle

school. Thus, increases may be found based on the

grade levels chosen for comparison. Consistent with

previous research, we hypothesize that the preva-

lence and co-occurrence of both bullying and vic-

timization will decrease overall with age, regardless

of sex, or ethnicity.

Gender Previous 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 influenced by

gender role stereotypes and how aggression itself is

measured (see Underwood et al 11

for a review).

Nonetheless, the general trends in male and female

bullying behaviors are reasonably well supported. As

such, we expect that males will bully and be victim-

ized more than females.

Ethnicity Whereas physical fighting 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 less

well established. Notably, few studies have had sam-

ples large enough to include comparisons of more

than 2 ethnic groups. Some research has suggested

that the bullying-ethnicity relationship may be more

dependent upon specific racial dynamics 1 —dynamics

that may be school or community specific and not

necessarily apply to ethnic groups as an aggregate.

Given the sparse number of studies on the topic, we

assume a null hypothesis: there are no ethnic group

differences.

Correlates Adolescents victimized by bullying can experience

sociopsychological harm 12,13

including higher levels

of depression. 2 Bullying is also associated with other

problem behaviors in general, 7

such as substance

use. 14

It is not clear, however, how these associa-

tions vary across different dimensions of bullying

behaviors. The co-occurrence of aggressive behavior

and substance use, for example, may both reflect

a broad personal orientation toward antisocial

behavior 15

or could be 1 way that adolescents cope

with victimization and peer rejection. 16,17

Guided by

the limited literature in this area, we hypothesize

that perpetration will correlate with substance use

and that victimization will correlate with depression.

Accordingly, this article aims to fill a gap in the liter-

ature by examining a large, ethnically diverse data

set with consistent measures and a systematic statis-

tical approach. Doing so may help reconcile inconsis-

tencies in previous research and guide future

research efforts.

METHOD

Data were collected using the Primary Prevention

Awareness Attitude and Use Survey 18

(PPAAUS).

The PPAAUS was developed by the Safe and Drug

Free 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-12th

graders in Franklin County (metropolitan Colum-

bus), Ohio, every 3 years since 1988. Data for the

present study are based on surveys administered

during fall 2003.

Trained teachers and school staff administered the

PPAAUS during the second class period in 16 public

school districts (55 high schools and 91 middle

schools), 6 private schools, and 36 Catholic schools.

Passive parental consent was used, and students

were given the option of not participating in the sur-

vey. Students completed the surveys anonymously,

with the only identifiers being the respondent’s

school building and grade. A readability analysis of

the instrument items used in this study indicated a

Flesch-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 ª 2007, American School Health Association

Review Board of The Ohio State University approved

the analyses for the present study.

Measures To study bullying perpetration as well as victimi-

zation, we employed 13 survey items that asked

about frequency of behaviors during the past year.

For each of the constituent items, the original

response options included ‘‘never,’’ ‘‘once,’’ ‘‘2-3

times,’’ ‘‘4 or more times.’’ Principal components

analysis of a random 20% subset of the sample

yielded 2 factors with eigenvalues less than 1 that

explained 47.6% of the variance in the constituent

items. One factor included 7 items relating to perpe-

tration (eg, ‘‘During the past year at school, how

often have you pushed others around to make them

afraid?’’) and a second factor included 6 items

related to victimization (eg, ‘‘During the past year at

school, how often has someone verbally attacked

you?’’). These results were replicated on a second

random 20% subset with nearly identical results.

Both scales included measures of direct (eg, ‘‘ . . . how often have you threatened to beat someone

up?’’) as well as indirect (eg, ‘‘ . . . how often have you told lies or spread false rumors about some-

one?’’) types of aggression. Analyses offered little

support for distinguishing subscales of indirect and

direct aggression. As such, the perpetration and vic-

timization scales each included measures of both.

The reliability of the scales was acceptable with

Cronbach a’s of .82 and .74 for the perpetration and victimization scales, respectively. Table 1 presents

the factor loadings for the constituent items.

Because these scales yielded continuous measures

of aggression and victimization, they were not

appropriate for measuring bullying as a repeated pat-

tern of behavior. 1 To distinguish the phenomenon

from lower levels of aggression/victimization, we

classified as ‘‘bullying’’ any time a youth responded

‘‘4 or more times’’ to at least one of the constituent

items. Similar measurement approaches have been

used previously. 7,10,20

To examine the validity of this

measurement strategy, we constructed alternative

measures of bullying perpetration and victimization

by defining as ‘‘bullies’’ and ‘‘victims’’ any youths

whose scores fell in the upper quintile of the contin-

uous scales. In Results, we discuss how results of

analyses using the alternative measures differ from

those of the primary measures.

To assess whether perpetration and victimization

are differentially related to established correlates of

bullying, we constructed measures of depressive

affect and substance use. Depressive affect was

assessed by 2 variables (a = .61) reporting the fre- quency of feeling happy and depressed (range = 1 ‘‘almost never’’ to 3 ‘‘most of the time’’). The

substance-use variable was constructed by principal

components analysis of 3 items measuring the fre-

quency of cigarette, alcohol, and marijuana use

(range = 1 ‘‘never used’’ to 6 ‘‘use about every day’’). The factor model explained 73% of the vari-

ance in the constituent items, with loadings ranging

from .84 to .88. Because the distribution of factor

scores was highly asymmetrical, we applied a log

transformation to reduce its departure from a normal

distribution (mean = �0.15, SD = 0.33, skewness = 0.92, and kurtosis = �0.4521).

Procedure We performed a series of cross-tabular analyses to

examine how bullying varied by grade, gender, and

ethnicity. A series of logistic regression models tested

the association of both dimensions of bullying with

substance 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 all

2-way interactions by comparing model v2 statistics

Table 1. Standardized Factor Loadings from Principal Components 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 others around to get something you want?

0.776

How often have you pushed others around to make them afraid?

0.821

How often have you threatened to beat up someone?

0.772

How often have you hit someone with your fists or beat up someone?

0.72

How often have you taken money or things by force from people?

0.665

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

0.587

How often has someone taken money or things directly from you using force, a weapon, or threats?

0.57

How often have other students spread lies or false rumors about you?

0.677

How often has someone physically attacked you?

0.101 0.647

How often has someone verbally attacked you?

0.695

How often has someone left you out of a group or activity to hurt you?

0.729

How often have you feared for your physical safety?

0.683

*Factor loadings less than 0.10 are left blank.

Journal of School Health d November 2007, Vol. 77, No. 9 d ª 2007, American School Health Association d 625

in logistic regression models with the interaction

term (and the main effects) versus models with only

the main effects. For example, we regressed a ‘‘yes/

no’’ measure of bullying perpetration on grade, gen-

der, ethnicity, substance use, and depression in step

1 and an interaction term (eg, grade by substance

use) in step 2. Whenever significant interaction

terms were identified, we then performed stratified

analyses (eg, separate models for males and females)

to specify the exact nature of the differences.

Given our large data set and the greater likelihood

of type I errors with repeated statistical tests on the

same variables, p values of less than .001 were

viewed as statistically significant. 21

RESULTS

This section presents the demographic characteris-

tics of the sample, followed by the prevalence and

co-occurrence of bullying perpetration and victimiza-

tion by grade, gender, and ethnicity.

Demographics The 2003 PPAAUS included data from 79,492 stu-

dents representing 81.4% of the enrolled student

population of participating schools and 96.7% of

those completing the questionnaire. 18

The most

common reasons for failing to participate in the

study included being chronically absent, home

schooled, or otherwise not enrolled in school. As

such, the data are only representative of students

who regularly attended schools in Franklin County

in 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 described

themselves as ‘‘multiracial,’’ ‘‘other,’’ or refused to

respond. Sixth graders comprised 16.8% of the sam-

ple, 7th/8th graders 32.9%, 9th/10th 28.5%, and

11th/12th 21.7%.

Prevalence Overall, 20.1% of youth in the study reported

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

timization was somewhat more common among

6th-8th graders and males, although gender differen-

ces were only significant among whites and Asians.

Ethnic differences were modest, except for the much

higher rates among Native American youths. No

other interactions among demographic variables

were detected.

Findings for bullying perpetration presented

a much more complex picture. Overall, 18.8% of

the youth reported bullying perpetration during the

past year (Table 2). Perpetration was most common

among seventh through ninth graders and among

males. Both African American and Native American

youth reported much higher rates of perpetration,

whereas rates among Asian youth were lower.

Higher order analyses (not shown) found that

gender differences in perpetration varied across dif-

ferent ethnic groups, Breslow-Day v2(4) = 28.22, p , .001. Among whites, Hispanics, and Asians, for

example, perpetration was about twice as common

among males compared with females. For other

groups, however, gender differences were less pro-

nounced. Among African Americans, 22.8% of

females reported perpetration compared with 32.5%

of males. Gender differences also varied by grade,

Breslow-Day v2(6) = 31.05, p , .001, such that they were greatest among older youths. Comparing 8th

and 12th graders, for instance, perpetration among

females declined from 17.6% to 10.5%, whereas

males only declined from 26.7% to 20.9%. Finally,

ethnic group differences in perpetration diminished

markedly between 6th and 12th grades. Through

9th grade, African American youths were about

twice as likely as others youths to perpetrate bully-

ing, yet by 12th grade, there was no significant dif-

ference. Native American youth, however, departed

from this trend, as their rates of perpetration

remained high among youth in older grades. Even

in 12th grade, 33.9% of youths in this group

reported bullying others.

Co-Occurrence We 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 18.3 †

10 10,541 19.3 18.6†

11 9099 16.7† 16.7†

12 8008 15.6 †

15.5 †

Gender Male 37,676 23.3† 22.3†

Female 39,142 14.3† 17.9†

Ethnicity White 49,535 15.5† 19.5 African American 15,863 27.7† 19.6 Hispanic 1689 17.4 16.8 Asian 2464 11.6

† 16.5

†

Native American 570 30.9† 27.5†

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

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

co-occurrence varied across demographic groups.

Over two thirds (68.5%) of youths reported neither

bullying nor being bullied by others. Being a victim

only (12.7%) was slightly more common than being

a perpetrator only (11.4%), with an additional 7.4%

of all youths being classified 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 common

among 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 and

victimization in each group. When we limited analy-

ses to youths who had some exposure to bullying

(ie, perpetration and/or victimization), less than one

quarter (23.6%), were classified as both bully and

victim. Analyses detected no significant differences

by grade, v2(6) = 16.16, p = .013, or ethnicity, v 2 (4) =

5.89, p = .21, although males (25.8%) were more likely than females (20.6%) to be classified as bully/

victims, v2(1) = 90.00, p , .001.

Correlates of Bullying Behaviors A 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 identified significant interaction

effects that indicated the need to stratify analyses

across certain demographic variables. Specifically,

the association of bullying victimization with depres-

sive affect and substance use varied by gender. Both

depressive affect and substance use were positively

associated with victimization, although the effects

were somewhat stronger among females than among

males (Table 3). For both genders, depressive affect

had 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 12th

grades. In each model, the AORs for depressive affect

was much smaller than those for substance use. Nota-

bly, the magnitude of the AORs for substance use

diminished with increasing grade levels. The coeffi-

cients for the sixth-grade model should be interpreted

with caution given the questionable model fit.

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

ing, we replicated all analyses using alternative

measures. For both perpetration and victimization,

90% of youths were similarly classified across the

primary and alternative measures. Inconsistent clas-

sification of bullying perpetrators was more common

among males (12.0%), African American, (12.3%)

and Native American (15.6%) youth and was less

common among sixth graders (8.0%). Inconsistent

classification of bullying victims was not associated

with grade, ethnicity, or gender. Overall, results of

analyses using the alternative measure (available from

the authors) were very similar to those reported

above.

DISCUSSION

Overall, 28.2% of students reported involvement

with some type of bullying behavior, which is consis-

tent with other prevalence reports. 22

Our findings

build on previous research by distinguishing different

dimensions of bullying and systematically examining

differences by age, gender, and ethnicity. Although

the large, diverse nature of the sample permitted ana-

lyzing such distinctions, it is important to interpret

the findings with caution as the sample is taken from

1 metropolitan area, which may limit the generaliz-

ability. This section highlights a few key findings, dis-

cusses how they relate to the previous literature, and

proposes directions for future research.

Table 3. AORs* (with 95% Confidence Intervals) for Depressive Affect and Substance Use Testing Association with Adolescent Bullying 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 33,744 Hosmer-Lemeshow, v2 (p) 12.90 (.11) 21.11 (.01) Area under the receiver operating characteristic curve

0.65 0.69

*Adjusted for grade and ethnicity.

Table 4. AORs* (with 95% Confidence Intervals) for Depressive Affect and Substance Use Testing Association with Adolescent Bullying 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 (1.41-1.81) Substance use 24.71 (18.73-32.61) 9.68 (8.23-11.39) 6.1 (4.99-7.54) Model fit/ calibration, n

10,672 10,782 6956

Hosmer-Lemeshow, v2 (p)

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

Area under the receiver operating characteristic curve

0.75 0.75 0.72

*Adjusted for gender and ethnicity.

Journal of School Health d November 2007, Vol. 77, No. 9 d ª 2007, American School Health Association d 627

Males reported higher levels of both types of bul-

lying behaviors. Similarly, males were more likely

than females to be classified as bully/victims. How-

ever, while both depressive affect and substance use

were positively associated with victimization, the

effects were somewhat stronger among females than

among males. One possible explanation for this dif-

ference is that females may engage in more psycho-

logical forms of aggression, whereas males use more

physical; the consequences of which may differ.

Although the nature of the measures used in this

study do not allow for this explanation to be exam-

ined here, studies have found that females both use

and experience more indirect aggression. 11,12

We detected several persistent ethnic group differ-

ences across bullying behaviors. One compelling

explanation for this focus on the minority status of

an ethnic group in particular schools. However, both

Asian and Native American youth represented small

proportions of the student body in each school, yet

they reported very different rates of bullying behav-

ior. Similarly, white and African American students

often constituted either the majority or a large

minority of students within different schools in the

study. Yet, the students from these groups also

reported different levels of bullying behavior. One

possible explanation relates to previous findings that

ethnic minorities perceive that minorities were more

likely than majority students to experience bully-

ing. 23

Consistent with a social constructionist view,

Harris 24

found that aggression is affected by cultural

factors.

Previous studies often note that many bullies are

also victims. 7 Several of our findings suggest that it

may be more appropriate to consider these dimen-

sions of bullying as separate phenomena. Overall,

only 7.4% of all youths were classified 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 findings suggest that bullying perpetration

and victimization represent 2 distinct, if modestly

related phenomena, and should be studied as such.

Our analyses of correlates of bullying behaviors

provide further support for the value of distinguish-

ing perpetration from victimization. Substance use

was more strongly associated with aggression than

with victimization (Table 3). This is consistent with

previous findings 14,17

and problem-behavior the-

ory, 15

which suggests that problem behaviors serve

a functional purpose that aids in achieving a specific

goal such as coping with rejection. Importantly,

these are preliminary findings and future research

should investigate whether substance use is leading

to bullying behaviors or bullying behaviors are lead-

ing to increased substance use. Consistent with pre-

vious literature, 12,13

depressive affect was somewhat

more strongly associated with victimization than

with aggression. However, as with substance use,

future research should examine the nature of this

relationship further.

Implications for Prevention The demographic differences illustrated in this

article have important implications for prevention,

particularly in the areas of program targeting and

message tailoring. Targeting is a means of dividing

a population based on a variety of dimensions, 26

including sex, age, and ethnicity. For example, our

results suggest that younger adolescents tend to be

victimized at higher rates. As such, interventions

aimed at coping skills would best be targeted at these

younger students. Also, Native American students

experienced both forms of bullying behaviors at

a higher rate than all other ethnicities, which sug-

gests that this would be a meaningful subgroup to

target with a culturally relevant intervention.

Similarly, tailoring refers to messages personalized

at the individual level 27

and may be based on bully

or victim status, substance use, or other characteris-

tics specific to the adolescent. For example, a stu-

dent’s involvement in bullying behaviors can be

immediately assessed in an online survey, the results

of which determine the subsequent prevention mes-

sages the adolescent receives. This type of computer-

based tailoring has been used successfully in other

areas of adolescent health 28

and provides a poten-

tially rich avenue for future research with its ability

to 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 make

more specific recommendations for ethnic targeting.

Similarly, future prevention programs should use

interactive technologies to tailor specific risk reduc-

tion messages. To date, no programs have been

designed specifically for particular ethnic groups nor

has the impact of existing programs adapted for vari-

ous ethnic or cultural factors been systematically

evaluated. 7

Future work by both researchers and

practitioners should address this gap. Such programs

should be modeled after the effective bullying pre-

vention programs implemented in the United States

and other countries (see Olweus 29

for an overview).

Strengths and Limitations By analyzing both perpetration and victimization

using a large sample that allowed for multiple com-

parisons and more in-depth analysis of interactions

than has previously been examined, this article was

able to add further refinement to the existing litera-

ture on bully/victims. Moreover, the sample was

diverse enough to compare 5 different ethnic groups.

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

Our measures of bullying differ from those used by

other studies in that we did not find support for sep-

arating direct and indirect measures of aggression

and victimization and we used the threshold of 4 or

more times to qualify as bullying behavior. In many

ways, this is a limitation of the literature in general

given 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 on

a single urban county that may not be representative

of other places in the United States. To the extent

that different cultural and social climates influence

the prevalence of bullying as well as its determi-

nants, 29

our findings may lack generalizability. Other

studies, for instance, report that some but not all

types of adolescent risk behaviors may vary by

region. 31,32

Also, the cross-sectional design of this

study precluded our ability to test for the causal

effects of bullying behavior on substance use and

depressive affect.

CONCLUSIONS

This article fills a gap in the literature through its

examination of the various associations between per-

petration and victimization and whether these associ-

ations vary by age, gender, and ethnicity. Overall, the

findings support the importance of distinguishing bul-

lying behaviors and considering how they vary by

demographic characteristics. Future research should

examine the social and contextual factors surround-

ing these demographic differences and use these find-

ings to further refine prevention programming.

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