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https://doi.org/10.1177/0886260521997448
Journal of Interpersonal Violence
1 –29
© The Author(s) 2021
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DOI: 10.1177/0886260521997448
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Original Research
Adolescent Peers and
Prevention: Network
Patterns of Sexual
Violence Attitudes and
Bystander Actions
Victoria Banyard,1 Emily A. Waterman,2
Katie M. Edwards,2 and Thomas W. Valente3
Abstract
Peer sexual violence is a significant social problem that affects adolescents
and can lead to negative mental health and developmental consequences.
Peers are a significant source of influence for adolescent behavior. For
example, recent studies show training teens to be bystanders can be an
effective prevention strategy to reduce peer violence and harassment. Peers
can also promote risky behaviors including substance use and violence. The
current study examined how sexual violence-specific risk and protective
attitudes (e.g., denial of peer sexual violence and positive peer prevention
norms) and behaviors (alcohol use and bystander actions to prevent peer
sexual violence) clustered within peer networks cross-sectionally and over
time. Participants were 1,499 7th−10th graders who took surveys during
an academic year and who reported having opportunity to take action as
bystanders to peer sexual violence. Participants took surveys 6 months apart
online in schools. Questions included nomination of best friends to capture
information about peer networks. Social network analyses indicated that
1Rutgers University, New Brunswick, NJ, USA
2University of Nebraska–Lincoln, Lincoln, NE, USA
3University of Southern California, Los Angeles, CA, USA
Corresponding Author:
Victoria Banyard, Rutgers University, 390 George Street, New Brunswick, NJ New Jersey
08901-8554, United States.
Email: Victoria.banyard@rutgers.edu
2 Journal of Interpersonal Violence
there was weak but significant clustering of positive prevention attitudes such
as bystander denial and marginal clustering on reactive bystander behaviors
to address sexual assault. For comparison, alcohol use and academic grades
were analyzed and found to also cluster in networks in these data. These
findings suggest that for early adolescents, peer bystander training may be
influential for some key bystander attitudes and reactive sexual violence
prevention behaviors as individual behaviors are not independent of those
of their friends.
Keywords
bystander, social networks, sexual violence prevention
Introduction
Adolescence is a key developmental period for preventing the initiation of
problematic health behaviors, such as sexual violence and alcohol use, which
can have lasting negative consequences into adulthood (D’Amico et al.,
2020; Hale & Viner, 2016; Peeters et al., 2019). Research consistently docu-
ments the high rates and deleterious outcomes associated with sexual vio-
lence among youth (Basile et al., 2020). Moreover, researchers have begun to
identify risk (e.g., alcohol use) and protective (e.g., readiness to engage in
positive bystander action) factors for experiences of sexual violence (Banyard,
2011; Tharp et al., 2013).
One key source of both risk and protective factors is peer relationships
(Banyard & Edwards, 2016; Fasick, 1984; Foshee et al., 2011; Valente et al.,
2004). During adolescence, teens spend more time with peers, which can
enhance risk by modeling and promoting risky behaviors (Hoorn et al., 2014).
Peers can also promote healthy choices through positive peer deviance, which
occurs when members of high-risk groups display protective factors or low
risk behavior (e.g., receive good grades in school and abstain from alcohol
use). When youth engage in positive peer deviance, they are opposing the
norm and can counteract negative risk factors (Walker et al., 2007). For
example, Foshee et al. (2011) found that youth reported lower levels of physi-
cal dating violence perpetration when they had friend networks that on aver-
age held more prosocial beliefs.
Bystander intervention (third parties who are not victims or perpetrators
but observers who can step in to interrupt risk or who can proactively model
and promote positive social norms) is an important way that peers can influ-
ence one another and seems to be a positive prevention mechanism, particu-
larly in terms of behaviors that model healthy relationship norms (Bush et al.,
2019). Research suggests that youth in high school report high levels of
Banyard et al. 3
opportunity to intervene in instances of peer sexual violence (with ranges
from 12% to 85% depending on the type of situation) (Rothman et al., 2019;
Waterman et al., 2020; Authors Masked), and that adolescents and young
adults are most likely to disclose a victimization to peers, not adults (Campbell
et al., 2015; Orchowski & Gidycz, 2012). Research is needed to better under-
stand younger adolescents (e.g., middle school students) and bystander inter-
vention; most research on bystander intervention has been conducted with
young adults or adolescents in the later years of high school.
Increasingly, prevention researchers are interested in using social network
analysis to understand the ways in which youth peer groups may influence
attitudes and behaviors in positive and negative ways (Valente et al., 2003,
2004). Social network analysis goes beyond examining perceptions of peers’
attitudes and behaviors and objectively examines the extent to which atti-
tudes and behaviors cluster within participant defined networks (Fujimoto &
Valente, 2012; Lakon & Valente, 2012; Petering et al., 2016). Indeed, social
networks can be important for behavior in a number of ways—through the
structure of the network (and how embedded an individual is within the net-
work or across networks), as well as through influence (how mean levels of
network members’ behaviors or attitudes can promote change in individuals
to be more like the group) (Valente, 2005, 2010).
Social network analyses have helped researchers and practitioners design
prevention strategies that capitalize on ways that peers influence one another
(Hunter et al., 2019; Pickering et al., 2018; Valente, 2012). This may enhance
prevention effectiveness as messages are promoted by key peer influencers
(Valente, 2010, 2012). For example, in one study of a tobacco use prevention
program in schools, several different implementation methods were used,
and greater effectiveness was found for identifying peer leaders through
social network analysis and having those peer leaders facilitate prevention
with students in their social networks, then using peer nominated leaders but
randomly assigning students they taught (Valente et al., 2003, 2006). Several
prevention programs reduce sexual violence and harassment by engaging and
training peer bystanders (Coker et al., 2017; Edwards et al., 2019). Bystanders
can act during a situation of sexual violence to prevent or stop it (reactive
bystander behavior). Bystanders can also take action in the absence of sexual
violence to change social norms to be intolerant of sexual violence by using
social media to promote healthy relationship messages or starting prevention
conversations (proactive bystander behavior) (Banyard et al., 2015). Yet, to
date, we know little about the ways in which bystander attitudes and behav-
iors cluster within social networks of adolescents. Do youth think and act like
their close friends? If so, we may want to alter the ways we deliver bystander-
focused prevention programing to focus more on networks.
4 Journal of Interpersonal Violence
Theoretical Underpinnings
Theories of social norms (Chung & Rimal, 2016; Orchowski, 2019; Perkins
& Berkowitz, 1986) and social learning (Bandura, 2001) have been researched
in relation to prevention and may help explain the importance of peer net-
works for sexual violence prevention. These theories describe how individual
behavior is influenced by observing important role models such as peers and
by perceptions of what a person thinks their peers value and do (Rothman et
al., 2019). Youth and young adults who think their peers accept using vio-
lence and coercion are at risk for interpersonal violence perpetration in the
same way that youth who perceive that their peers are using alcohol are more
likely to use it themselves (Burk et al., 2012; Garthe et al., 2017; Swartout,
2013). Misperceptions of peer norms facilitate risky sexual behaviors like
sexting (Maheux et al., 2020). In the field of juvenile crime, positive social
deviance models describe how having at least one peer who is high achieving
decreases negative outcomes like delinquency (van Dommelen-Gonzalez et
al., 2015). Peer influence also affects physical health including body image
and exercise (Kenny et al., 2017; Montgomery et al., 2019), smoking, alcohol
use (Huang et al., 2014; Jules et al., 2019), and suicide (Jules et al., 2019;
Kenny et al., 2017; Montgomery et al., 2019; Wyman et al., 2019).
Another theory relevant to the current study of youths’ social networks is
diffusion of innovation theory (Rogers, 2002). This theory describes how
changes in ideas and behaviors start with a small group of innovators and
early adopters who are open to innovations (about 16% of a community/
group/population). These individuals then diffuse the new information and
model new behaviors. Several prevention programs for sexual and related
forms of violence among youth (see e.g., Green Dot) are grounded in the idea
that prevention programs enhance diffusion (Cook-Craig et al., 2014; Gidycz
et al., 2011). For example, bystander intervention training started with a focus
on disrupting situations of risk for sexual violence but has since expanded to
a more proactive focus on training youth to promote positive prevention and
healthy relationship norms within their social network (Rothman et al., 2019).
The theory suggests that we will find clustering of sexual violence-related
attitudes and behaviors within networks.
Adolescence and Social Networks of Peers
Developmental research using frameworks like the social development model
(Catalano et al., 1996) documents that social networks are particularly salient
for adolescents but also that their composition changes (Veenstra & Dijkstra,
2011). For example, in childhood and early adolescence, parents and teachers
Banyard et al. 5
have more influence over risky behaviors like substance use initiation, while
later in the teen years, peers become more important influencers. Peers in
adolescence can be a source of stress and promote risky behaviors (Doom et
al., 2017; Authors Masked). Peers can also enhance protective factors and
positive choices (van Rijsewijk et al., 2016).
Research has shown links between peers and risk and protective factors
for sexual violence specifically. For example, positive social norms that are
intolerant of violence are related to decreased perpetration of peer violence
(Banyard et al., 2020a) and also promote proactive bystander helping
(Banyard et al., 2020b). One study of Green Dot’s high school curriculum
found that increased helpful bystander intervention behaviors were a variable
through which rates of violence decreased over time as a result of the pro-
gram (Bush et al., 2019).
Social network analysis is a theoretical perspective and a set of techniques
used to understand relationships between people and other units, such as
organizations or states, how those units interact, and how they affect behav-
iors. A social network approach consists of individuals nominating people
who make up their social network. For example, researchers focused on
youth may map the links between students based on their nominations to
understand the structure of the social network of a group of youth. Social
networks are complex and can be measured in different ways (e.g., research-
ers might ask youth to nominate their best friends and may calculate the size
of someone’s friend network, the number of connections they have to friends
in different networks, or the extent to which individuals within a network
look similar on certain attitudes and behaviors).
To date, these different components and various ways of measuring net-
works have mainly been examined in the sexual violence field in terms of
victimization and perpetration, not attitudes or bystander behaviors related to
sexual violence as in the current study (Katerndahl et al., 2013). These stud-
ies provide a framework for the ways we might examine networks and sexual
violence prevention behaviors including bystander intervention. For exam-
ple, researchers found that measuring network characteristics across com-
munity settings like church and school (e.g., network diversity or the number
of different social roles a person inhabits across social settings) can protect
men from perpetration of sexual assault (Kaczkowski et al., 2017). More
important to bystander intervention, several studies showed that attitudes like
rape myth acceptance or acceptability of partner violence cluster in friend
networks (Sandberg et al., 2018; Swartout, 2013). Swartout’s study found
young college men whose high school male peer networks had strong rape
myth acceptance and hostility toward women showed higher levels of those
attitudes themselves. The finding was not just about how attitudes might
6 Journal of Interpersonal Violence
cluster among friends (e.g., groups of friends have similar attitudes) but also
about how measuring the nature of the networks themselves affected out-
comes. Within the sphere of close friends, young men who were part of close
knit, small, friend networks where a small group knows one another well
overall held fewer of these negative prevention-related attitudes (Swartout,
2013). Attitudes in models of bystander intervention to prevent sexual assault
are not dissimilar from constructs Swartout measured, suggesting that net-
work analysis may help us better understand bystanders (McMahon, 2010).
Current Study
There is an urgent need to take a closer look at the variety of attitudes and
behaviors that may occur in adolescent peer social networks as a platform for
prevention innovations. As such, the current study used social network analy-
sis in a sample of 7th through 10th graders to examine adolescent network
influences on bystander attitudes (i.e., bystander denial of need for preven-
tion and social norms for prevention), proactive behaviors (i.e., modeling
positive prevention behaviors on social media), and reactive behaviors (i.e.,
responding to unwanted touching and sexual photo sharing) specific to sex-
ual violence. We also compared these network associations with participants’
reported academic grades (Frank et al., 2008) and alcohol use (Huang et al.,
2014), two individual outcomes demonstrated in the past to have network
influences. In addition, we use a longitudinal design of two waves to better
understand not just how individuals’ and friends’ attitudes are associated but
how changes in individuals’ and friends’ attitudes are associated. Careful
examination of the role of peers in promoting healthy or unhealthy choices is
critical for improving prevention effectiveness for adolescents.
Aim 1: We hypothesized that students would name friends with similar
levels of sexual violence prevention attitudes (social norms and denial of
sexual violence as a problem) and bystander behaviors.
Aim 2: Examine the association of changes in individual sexual violence-
specific proactive and reactive behaviors with their friends’ changes in those
behaviors. We hypothesized that an individual’s attitudes would change in a
similar direction over time to their friends’ average change.
Aim 3: Examine the association of individuals’ change over time during
high school in individual academic grades and alcohol use with individuals’
friends’ change over time in academic grades and alcohol use. We hypothesize
a positive association between individuals’ change and friends’ average change,
replicating previous research (thus, demonstrating these data are not unusual).
Banyard et al. 7
Method
Research Design and Setting
These data are part of a larger multiple baseline study to evaluate a youth-led
sexual violence prevention project. Data collection for the current analyses
took place over 1 year: Fall 2017 (W1), Spring 2018 (W2). The average num-
ber of days from W1 to W2 was 180. Two waves were used in order to dem-
onstrate changes in friends’ behavior being associated with changes in
individuals’ behavior. This longitudinal strategy helps us understand tempo-
ral ordering of these changes, getting us closer to casual inference.
Participants
Participants were 1,499 students (grades 7 through 10) from eight schools
(five middle schools and three high schools) in one school district in South
Dakota who completed surveys near the start and end of the academic year.
The percentage of students who were female was 54.7% with an average age
of 13.6 years at W1 (Table 1). A subsample, 10.1%, identified as a sexual
minority (as defined as gay, lesbian, or bisexual). The sample was 82.5%
White, with 16.7% identified as American Indian/Native American, 10.5% as
Hispanic/Latinx, 3.5% as African American, 3.3% as Asian American, and
1.5% as Hawaiian or Pacific Islander (the total exceeds 100% because
respondents could select more than one ethnicity). Students named an aver-
age of 5.4 friends, many of whom were non-participants, thus resulting in an
average in-degree score of 2.96. We invited all students in grades 7 to 10 (n =
4,172) at the beginning of the Fall 2017 semester to enroll in the study; the
Table 1. Demographic Characteristics of the Sample (N = 1,499).
Female 54.70%
Average age 13.6
Sexual minority 10.10%
White 82.50%
African American 3.50%
American Indian/Native American 16.70%
Hawaiian or Pacific Islander 1.50%
Asian American 3.30%
Hispanic/Latinx 10.50%
Out degree 5.4
In degree 2.96
8 Journal of Interpersonal Violence
first survey occurred between October 2017 and December 2017. These
grade levels across middle and high school were the focus of the study
because this is a key age group for the development of peer sexual assault,
because this study was nested within a larger study of prevention program
effects, and because we needed students at baseline who would remain in the
school district during the three-year follow-up of program implementation
and evaluation.
At study initiation, of the 4,172 eligible students1 (as reported by the
school district), the majority (n = 3,257; 78.0%) of youth returned the consent
forms, and of those who returned the forms, the majority (n = 2,667; 81.8%)
of guardians gave permission for their student to take the survey. Of the stu-
dents whose guardians gave consent, most took the survey (n = 2,232; 83.6%).
At Wave 2 (W2), 1,920 students were retained from Wave 1 (W1; 85.6%). Of
these 1,920 students, 1,499 were included in current analyses due to missing
data on one or more of the outcome variables (students who never witnessed
a sexual violence episode and so could not report reactive or proactive behav-
iors were among those dropped).
Procedures
Written parental consent and student assent were required for youth to com-
plete the survey. We used intensive recruitment procedures such that the con-
sent forms were sent to parents in multiple ways (i.e., via their students from
school, mailings, and email), and we called and conducted home visits to
households in which consent forms had not been returned. We also devised
multiple ways in which the consent forms could be returned (e.g., email, text,
and in person).
The survey was administered on computers in school by trained research
staff. All students had unique logins that were created in part so that students
could access the survey only with parental permission. Students received a
small incentive (e.g., fruit snack, pencil) and were entered to win one of 20
US$100 gift cards, which increased to US$150 for W2. Students who missed
the in-school survey (n = 526) were sent a letter in the mail requesting that
they take the survey online; instructions were provided on how to take the
survey online. Return rate of these out-of-school surveys was 2.7% (n = 14).
Attention Checks
We used three questions to identify students who were not paying attention
(henceforth, inattentive responders): “Do you have more than 10 kids?”
“Are you over 9 feet tall?” and “This question is to make sure the survey is
Banyard et al. 9
working OK. Please pick the answer below that says Cat.” Across waves,
2.7%–4.9% answered one or more questions incorrectly; 0.2%–1.4%
answered two or more questions incorrectly, and <0.1–0.4% answered three
questions incorrectly. We defined inattentive responders as participants who
got two or more questions wrong at one or more waves. In the current arti-
cle, there were only two in the subsample used in analyses and, thus, they
were retained.
Participant Attrition Analysis
We conducted a series of chi-square and t-test analyses to understand patterns
in attrition based on demographics and key study variables. We compared
participants who completed Wave 1 and participants who completed Wave 2
to participants who did not complete that subsequent wave. Participants who
completed Wave 2 were more likely to be White, less likely to be Native
American/American Indian, and younger than participants who did not com-
plete Wave 2. Groups did not differ on other measures related to the current
article.
Measures
Demographics.
Youth responded to questions about their age, gender (0 = woman/girl; 1 =
man/boy), and race/ethnicity using two questions: one about choosing a racial
category (White, African American, Native American, Hawaiian or Pacific
Islander, Asian, or Latinx/Hispanic) (0 = non-White and/or Hispanic; 1 =
non-Hispanic White) and the other about sexual orientation (0 = heterosex-
ual; 1 = sexual minority, as defined as gay, lesbian, or bisexual).
Social network nominations.
Youth were asked to list up to seven best friends in grades 7–10 in the dis-
trict, as well as up to three adults in the community who are most trusted. We
chose the best friend wording, given research suggesting youth identified as
best friends have the most influence on behavior (Valente et al., 2013).
Nominations for youth were limited to seven based on practical limitations,
participant burden, and past work showing most people maintain a small
group of close friends (Burt, 1984). If a student entered a best friend’s name
that did not automatically generate a match from the roster, the survey was
programmed such that it would record a text entry of the student nomination,
which was later matched to the roster when possible. Students named an
average of 5.4 friends.
10 Journal of Interpersonal Violence
Bystander behavior.
Reactive bystander behavior. Four questions asked students about behav-
iors in which sexual harassment or sexual violence was about to happen or
had already happened (referred to as reactive bystander behavior or reactive
actionism). These were based upon recent work on high school students as
bystanders to dating violence, sexual harassment and violence, and stalking
(Coker et al., 2011; Edwards et al., 2017). The items included (a) “Saw or
heard about a student grabbing or touching another student sexually (like on
their butt or breasts)”; (b) “Saw or heard about a student sending a naked
photo of another student without that person’s permission”; (c) “Saw or heard
about a student using physical force or alcohol or drugs to make/force another
student to have sex”; and (d) “Saw or heard about a student spreading sexual
rumors about another student.” For the reactive questions, students were first
asked the frequency of times they had witnessed or heard about each situa-
tion. Only students who reported witnessing or knowing about a situation
could receive a score on the corresponding bystander action item. For exam-
ple, students were asked “During the past 6 months, how many times did you
see or hear about a student grabbing or touching another student sexually
(like on their butt or breasts)?” and indicated none through 10 or more times.
Opportunity and its correlates are considered in more detail in a different
publication from these data (Banyard et al., under review). Given the already
large scope of the current article, opportunity was not examined in further
analyses here.
For each of the four reactive behavior items in which participants
responded affirmatively with the opportunity to take action, we asked partici-
pants how they responded. Participants were presented with the following
types of behavior and asked to select all of the things they did in response to
witnessing the experience: (a) “Did nothing/ignored what was happening”;
(b) “Laughed, took a video, or showed that you did not think what was hap-
pening was a big deal”; (c) “Tried to make the situation stop by using distrac-
tion, such as dropping something to make a noise; starting a random
conversation”; (d) “Get help from another teen, parent, and/or adult”; (e)
“Said something or tried to stop the person doing the hurtful behavior”; and
(f) “Said something or tried to help or support the person who was being
hurt.” For each of the response behaviors, students were asked to select one
of the following response options: 0 = 0 times, 1 = 1–2 times, 3 = 3–5 times,
6 = 6–9 times, or 10 = 10 or more times. Scores were computed by setting
everyone at zero and subtracting one for each report of (a) or (b) as indicative
of passive (doing nothing) and active harm that both contribute to contexts
that condone violence and adding a point for each report of (c), (d), (e), or (f).
Banyard et al. 11
Thus, scores ranged from −2 to 4, and because not all students reported see-
ing or hearing about a student touching or grabbing another sexually, there
are more missing data on these measures than the others. We used responses
at the item level for the four reactive behaviors. Of note, our scoring approach
is consistent with the bullying literature in which bystanders can be defend-
ers, assistants, and/or reinforcers (Monks & O’Toole, 2020; Salmivalli, 1999;
Sutton & Smith, 1999).
Proactive bystander behavior. Two proactive bystander behavior ques-
tions were included. The first question asked about how much students
“talked during the past six months with their friends or parents, teacher, min-
ister, elder, etc. about things you all could do that might help stop sexual
assault” and a second question enquired about the “use of social media (like
Facebook, Twitter, etc.) or texting to show that sexual assault is not okay”,
used the following response options for each of the items: 0 = 0 times, 1 = 1–2
times, 3 = 3–5 times, 6 = 6–9 times, or 10 = 10 or more times. All students
potentially had the opportunity to do these, and thus, these items were scored
as collected on the survey as individual continuous variables that did not
require transforming. The Cronbach’s alpha for these two items was .65, and
they were combined into one variable for the current analyses.
Social norms for sexual violence prevention.
Three items were used to assess youth’s perceptions of injunctive norms
related to sexual violence prevention. These items were adapted from earlier
work with middle and high school samples for this study (Edwards et al.,
2017). The three items included: (a) “My friends think that it is important for
adults to talk to students about healthy relationships”; (b) “My friends think
that students should show that it is NOT okay to joke or make fun of people’s
bodies”; and (c) “My friends think that students should talk about how to stop
sexual assault (sexual assault is any sexual thing that happens when someone
doesn’t want it to happen).” Students used a 4-point Likert scale ranging from
1 = strongly disagree to 4 = strongly agree to indicate their perceptions of
injunctive norms. Higher scores across all three items reflected more proso-
cial norm perceptions about prevention behaviors. Composite scores were
created by calculating the mean. Cronbach’s alpha for these items was .69.
Bystander denial.
We used the Denial subscale of the Readiness to Help Scale (D-RHS; Banyard
et al., 2014; Edwards et al., 2017) to assess the extent to which students
rejected the role that they could play in preventing relationship abuse and
sexual assault (e.g., “There is not much need for me to think about relation-
ship abuse and/or sexual assault among middle and high school students.”).
12 Journal of Interpersonal Violence
Response options ranged from 1 = strongly disagree to 4 = strongly agree.
The mean of the items was calculated, so that higher numbers are indicative
of higher denial of responsibility in situations of relationship abuse and sex-
ual assault. In the current study, the Cronbach’s alpha was .59.
Academic grades and alcohol use.
We used a number of items from the Youth Risk Behavior Surveillance
Survey (YRBS; Centers for Disease Control and Prevention, 2014; Eaton et
al., 2012), including items measuring academic grades and alcohol. One
question enquired about grades in school during the past 6 months with
response options as follows: 1 = Mostly A’s, 2 = Mostly B’s, 3 = Mostly C’s,
4 = Mostly D’s, and 5 = Mostly F’s; these were recoded because a higher
score reflects better grades. Alcohol use was measured by asking students
“During the past 30 days, on how many days did you have at least one drink
of alcohol?” Any alcohol use was coded 1, while non-use was coded zero.
Analytic Plan
All eight schools within one school district were included in the study, five
middle schools and three high schools. Students were allowed to make friend-
ship nominations to anyone in the school district; hence, some ties span
across schools. To address aim 1, we examined individual and average
friends’ scores at W1 and W2. To address aims 2 and 3, we conducted nine
regression equations predicting changes in a) three proactive behaviors and
prevention attitudes (proactive bystander behaviors, social norms, and
bystander denial), (b) four reactive behaviors (responding to unwanted touch-
ing, photo sharing, sexual violence, and sexual rumors), and (c) two compari-
son behaviors (academic grades and alcohol use). Each model estimated the
outcome at W2 as a function of its lagged variable from W1 to model change,
a common set of demographic variables (sex, age, and ethnicity/race), and
the friends’ averages on each outcome at W1 and W2. Out- and in-degrees
scores were included to act as controls for being in the network. Out-degree
represents the number of friends named by an individual; in-degree repre-
sents the number of times an individual is named by others. In other words,
the association between bystander behavior and network exposure could be a
function of simply having more out- or in-degree nominations; hence, in-
degree and out-degree control for this possibility. We do not apply stochastic
actor-oriented models (SAOMs) to these longitudinal data for several rea-
sons: (a) SAOMs work well for dichotomous outcomes but not for scales; (b)
Ragan et al. (2019) have shown that estimating peer influences through
regression-type models does not provide biased estimates; and (3) SAOMs
Banyard et al. 13
are useful for demonstrating structural tendencies such as reciprocity and
transitivity, but we are not interested in those aspects of these data, as we
wish to focus on whether sexual violence attitudes and behaviors cluster
within friendships (Snijders et al., 2010). We replicated analyses including
school as a clustering variable with no noticeable effect on the results and,
thus, find within-school clustering is not responsible for the associations
reported here. We choose to report the non-clustering results in order to be
able to report beta coefficients, which are intuitive measures of the magnitude
of associations and can be compared within equations.
In the results, we report the conventional p-value cutoffs for statistical
significance or .05 and .01 but also report marginally significant results at the
.10 level for several reasons: the associations are not expected to be strong,
yet they have clinical significance; the sample sizes we analyze are not par-
ticularly large; and we feel this novel line of research warrants reporting all
associations.
Results
Aim 1: Describing Individual and Average Friends’ Scores
Table 2 shows that bystander denial decreased slightly over time from W1 to
W2 by 0.08 with friends’ reported denial also decreasing by 0.07. Taking
action in relation to unwanted touching increased by 0.09, with the friends’
reported averages also increasing by 0.15. Taking action in response to
unwanted photo sharing decreased slightly by 0.03. Actions related to sexual
assault decreased by 0.02 and friends’ reported averages decreased by 0.05.
Finally, bystander action related to responding to the spread of sexual rumors
increased by 0.09, with friends’ reported averages increasing by 0.11.
Students’ reported academic grades declined by 0.18, with friends’ reported
grades declining by 0.20. Alcohol use increased by 0.03, with friends’
reported alcohol use increasing by 0.04. Proactive behaviors and social norms
were unchanged.
For all outcomes, the friends’ average is about the same as the individual
respondent’s, which is to be expected due to homophily in friendship choices
(like associates with like). Individual variances on these measures, however,
are larger than friends’ variances (SD) because individual variances are cal-
culated across the whole sample, whereas friends’ variance (SD) is the aver-
age of the friends’ scores, thus aggregating across people who will generally
have similar characteristics. In other words, the sample variance is calculated
across a more heterogenous group, whereas friends’ average is calculated
across a more homogenous group.
14 Journal of Interpersonal Violence
Table 2. Individual and the Average of their Friends’ Scores on Outcomes
(N = 1,499).
Prevention Attitudes and
Behaviors Individual
Average
Friends Correlation
W1 proactive bystander
behavior
0.36(0.74) 0.37(0.47) 0.05*
W2 proactive bystander
behavior
0.36(0.69) 0.36(0.45) 0.12**
W1 social norms 3.01(0.63) 3.03(0.38) 0.10**
W2 social norms 3.01(0.64) 3.00(0.41) 0.19**
W1 bystander denial 2.10(0.62) 2.10(0.39) 0.10**
W2 bystander denial 2.02(0.59) 2.03(0.38) 0.14**
W1 respond to unwanted
touching
0.55(1.14) 0.57(0.0) 74.05
W2 respond to unwanted
touching
0.64(1.24) 0.72(0.83) 0.10**
W1 respond to photo share 0.22(0.91) 0.25(0.61) 0.10**
W2 respond to photo share 0.19(0.90) 0.26(0.64) 0.07**
W1 respond to sexual assault 0.17(0.69) 0.17(0.45) 0.13**
W2 respond to sexual assault 0.15(0.67) 0.12(0.47) 0.08**
W1 respond to sexual rumors 0.45(1.12) 0.47(0.76) 0.08**
W2 respond to sexual rumors 0.54(1.24) 0.58(0.82) 0.10**
W1 academic grades 4.49(0.80) 4.48(0.59) 0.37**
W2 academic grades 4.31(0.97) 4.29(0.75) 0.45**
W1 alcohol use 0.14(0.35) 0.14(0.23) 0.24**
W2 alcohol use 0.19(0.39) 0.20(0.27) 0.22**
Notes. *p < .05 **p < .01
Aim 2: Examining the Association of Friends' and Individuals'
Sexual Violence-Specific Attitudes and Behaviors
Table 2 also reports the correlations between individual behaviors and their
friends’ averages. They are all weak and statistically significant, with the
exception of W1 (unwanted touching). Table 3 reports the regression analysis
results for prevention attitudes, proactive behaviors, reactive behaviors, aca-
demic grades, and alcohol use. For all models, W1 outcomes were signifi-
cantly associated with W2 ones as expected. For most models, being female
was significantly, positively associated with proactive and reactive bystander
behaviors. For example, being female was significantly associated with
Table 3. Regression Equations for Bystander Attitudes and Behaviors on Network Variables.
Attitudes Reactive Bystander Actions in Response To…
Independent
Variables
Proactivea
(N = 1,426)
Norms
(N = 1,420)
Denial
(N = 1,460)
Touching
(N = 1,208)
Photo Share
(N = 1,208)
Sex Assault
(N = 1,208)
Rumors
(N = 1,208)
Baseline value 0.41** 0.36** 0.34** 0.33** 0.19** 0.13** 0.33**
Female 0.10** 0.14** −0.17** 0.13** 0.06* 0.05*** 0.08**
Age 0.00 0.03 −0.01 −0.01 0.05*** 0.08* 0.02
Sexual minority 0.10** 0.04 −0.04 0.00 0.03 0.01 0.00
African American −0.04 −0.04 −0.02 0.01 0.03 0.04 −0.04
American Indian/
Native American
0.01 0.01 0.00 0.04 0.02 0.01 0.01
Hawaiian Pacific
Islander
0.01 0.02 0.02 −0.04 0.00 0.03 0.01
Asian American 0.00 0.03 0.00 −0.03 −0.03 −0.01 −0.05***
Hispanic/Latinx 0.04 −0.01 0.00 −0.02 0.00 0.00 0.05***
Out degree 0.00 0.00 0.02 0.02 −0.02 −0.02 0.00
In degree −0.01 0.02 −0.03 0.03 0.02 −0.02 0.08**
W1 friend average 0.01 0.04 0.00 0.02 0.00 −0.02 0.02
W2 friend average 0.04 0.11** 0.06* 0.05*** 0.05*** 0.06* 0.05***
Adjusted R20.22 0.21 0.18 0.14 0.05 0.03 0.14
Notes. *** p < .10 * p < .05 ** p <. 01; b Denotes proactive bystander behavior.
16 Journal of Interpersonal Violence
reporting more proactive bystander behavior (β = 0.10, p < 0.01). Bystander
denial had a negative coefficient because this measure has a negative valence,
measuring whether participants think sexual violence is not a problem. Age
was not significantly associated with any proactive behaviors except one reac-
tive measure (sexual violence [β = 0.08, p < 0.01]). Identifying as a sexual
minority was significantly, positively associated with bystander behaviors (β
= 0.10, p < 0.01). Ethnicity was not significantly associated with proactive or
reactive behaviors. Out-degree and in-degree were not associated with proac-
tive or reactive behaviors with the exception that in-degree was positively
associated with actions to respond to sexual rumors (β = 0.08, p < 0.01).
There was no significant association for peer clustering regarding proactive
bystander behaviors. Changes in friends’ average score on the prevention atti-
tudes measures were weakly and positively associated with changes in report
of such attitudes. With regard to social norms and bystander denial, W2
friends’ average was significantly associated with individuals’ social norms (β
= 0.11, p < 0.01) and bystander denial (β = 0.06, p < 0.01), respectively. This
finding indicates homophily or clustering on these attitudes, as adolescents
who increased or decreased these reports had friends who also changed their
reports. For reactive behaviors, the clustering was marginally significant and
varied by situation assessed. Specifically, W2 friends’ average was marginally
associated with the adolescents’ individual respondent’s changes in report for
taking action against unwanted touching (β = 0.05, p < 0.10), photo sharing (β
= 0.05, p < 0.10), and sexual rumors (β = 0.05, p < 0.10) and was significant
for sexual assault (β = 0.06, p < 0.05). Note, however, that for three of these
associations, the probability levels do not meet the standard criteria (p < 0.05)
for accepting a conclusion of statistical significance.
Aim 3: Association of Friends’ Grades and Alcohol Use With
Individual Grades and Alcohol Use
Table 4 reports the results for academic grades and alcohol use. Friends’ aca-
demic grades at baseline (β = 0.05, p < 0.05) and friends’ academic grades at
follow-up (β = 0.15, p < 0.01) were significantly, positively associated with
increases in self-reported academic grades. Similarly, for alcohol use, friends’ use
at baseline (AOR = 2.34, p < 0.01) and friends’ use at follow-up (AOR = 3.04, p
< 0.01) were positively associated with increased likelihood of alcohol use.
Discussion
The purpose of the current study was to examine how sexual violence atti-
tudes and behaviors (i.e., bystander denial, perception of social norms
Banyard et al. 17
Table 4. Regression Equations for Academic Grades and Alcohol Use in Social
Networks.
Independent Variables
Academic Grades
(N = 1,471)
Alcohol Use (AORs)
(N = 1,473)
Baseline value 0.62** 11.7**
Female 0.02 1.07
Age −0.02 1.09
Sexual minority 0.00 0.70
African American 0.02 1.18
American Indian/Native American −0.03*** 0.81
Hawaiian Pacific Islander 0.00 1.10
Asian American 0.00 1.06
Hispanic/Latinx 0.00 1.30
Out degree 0.00 1.02
In degree 0.02 0.96
W1 friend average 0.05* 2.34**
W2 friend average 0.15** 3.04**
Adjusted R20.55
Notes. ***p < .10 *p < .05 **p < .01; AOR= Adjusted Odds Ratio.
supporting prevention, reactive and proactive bystander actions) clustered
within networks of middle and high school students and to compare that clus-
tering to academic grades and alcohol use, which have shown robust cluster-
ing in previous social network analyses (Frank et al., 2008; Huang et al.,
2014). Overall, there was strong homophily on academic grades and alcohol
use, as found in previous research, and weaker evidence of homophily on
reactive bystander behaviors and attitudes (positive social norms that peers
support prevention and bystander denial of peer sexual violence). Homophily
is the rate at which people with the same attributes are connected to one
another, and here, we see homophily on sexual violence indicators. There
was no significant support for homophily for proactive bystander behaviors
(using social media and having prevention-oriented conversations).
Compared to the replicated significant clustering for use of alcohol, the
more varied and less significant network clustering of sexual violence pre-
vention behaviors may be related to how visible these different types of
actions are and how available they are to peers to be observed. Alcohol is
commonly consumed with friends and is a behavior that may be more conta-
gious because of observation and proximity. Reactive bystander behavior is
18 Journal of Interpersonal Violence
more complex and may be something that happens in smaller groups (2–3
peers), or when a bystander is alone without peers, which would suggest that
there is less chance to observe the behavior and less strong effects of net-
works. Research with adults suggests that one-third of sexually violent events
occur in the presence of a bystander (Lukacena et al., 2019; Planty, 2002),
and adolescents report high levels of opportunity to help across the contin-
uum of peer sexual violence (Waterman et al., 2020). Bystanders are present
in almost two-thirds of emotional and physical dating violence victimization,
again in mainly adult samples (Black et al., 2008; Hamby et al., 2016). More
research focused specifically on adolescents is needed. Furthermore, research
is needed to better understand the extent to which positive bystanders are tak-
ing action alone versus in a situation where other peer bystanders are observ-
ing their helping. Thus, even if teens are taking preventive actions, there may
be less opportunity for their peers to see them modeling these helpful behav-
iors (part of the mechanism through which diffusion in networks happens).
Future research may find more robust peer effects for different forms of peer
violence (e.g., bullying).
The Role of Development
As dating and intimate relationships increase as youth transition from middle
to high school (Orpinas et al., 2013) and parental monitoring decreases
(Bachman et al., 1997), youth may be presented with increasing opportunities
for exposure to their friends’ attitudes and behaviors specific to sexual vio-
lence. Indeed, the broader developmental literature finds that the nature and
salience of peer relationships and their influence change during adolescence
and are particularly strong later in this developmental phase (Doom et al,
2017). Although research with college samples has found homophily on sex-
ual violence-related attitudes (Swartout, 2013), these may be less pronounced
in the younger sample studied here. Furthermore, it is possible that bystander
behaviors, even with increasing age, do not cluster within networks. They
may cluster situationally in a moment if there is opportunity, such as when
many people are present and one person steps in to help and others pitch in,
but less overall in specific best friend networks. Indeed, research on “diffu-
sion of responsibility” (Darley & Latane, 1968) suggests that peers may elect
not to help when others are present, and thus, it may be understood that inac-
tion clusters more in friend networks than actions, as measured here. As an
extension, this may also be the case if a bystander sees someone else inter-
vene, though this specific scenario is less studied. Having a friend who steps
in to be an active bystander may make others in the peer network feel less
responsible for taking action themselves and lowers the mean actions of those
Banyard et al. 19
in network and lessens the effect size in our analyses compared to the robust
clustering of things like grades and alcohol use. More research is needed to
better unpack these findings. The current study was able to examine changes
in individual attitudes and behaviors in relationship to changes in friends’
views across baseline data. Our findings explore more developmental or nat-
ural situational variation in these relationships since the data were collected
prior to any prevention strategy implementation.
Reactive Bystander Behaviors and Attitudes Cluster in Networks
On the other hand, there were some marginally significant findings for reac-
tive bystander behaviors. Consistent with theories of social norms and social
learning theory, youth who see their friends engaging in these types of behav-
iors are themselves more likely to engage in them, which is a promising find-
ing. Interestingly, proactive bystander behaviors did not cluster within
networks. This was surprising, given that these are largely public behaviors
such as having conversations with friends and social media posts about the
unacceptability of sexual violence. However, these were also relatively infre-
quent behaviors in our sample. Again, sample selection with half of students
being in middle school may be a factor in these findings as peer conversations
about healthy dating relationships and sexual behavior may emerge more
often among older adolescents and emerging adults (Tolman & McClelland,
2011; Waterman et al., 2018).
The indicators of bystander attitudes measured in this study did, in fact,
cluster within networks. More specifically, as adolescents’ friends’ ratings of
bystander denial and social norms changed so too did those of individuals.
Although these findings might seem to contradict the findings regarding
bystander behavior, it is likely that friends’ attitudes about sexual violence
are more apparent to others than youths’ actual bystander behavior, which
may not be as observable for reasons described above.
Consistent with previous research, alcohol use and grades did cluster in
friend groups. Clustering or homophily, the tendency for adolescents to share
attitudes and behaviors, may be stronger for observable and more objective
measures such as academic performance or alcohol use than for sexual vic-
timization attitudes and behaviors. Again, because sexual violence attitudes
and behaviors are sensitive in nature, they may be less strongly diffused
through peer networks (though we did find some of them were related to
networks over time). It also may be that older youth, who are more engaged
in intimate relationships and may have more frequent contact with instances
of peer sexual violence, show more diffusion in networks (see e.g., the diffu-
sion of attitudes like rape myths found by Swartout, 2013, in a college
20 Journal of Interpersonal Violence
sample). Bystander behavior may be more a function of individual character-
istics such as household influences (i.e., parental monitoring), especially for
younger youth. Risk behaviors, such as alcohol use, are observable and more
likely to spread through peer networks.
Implications
Sexual violence prevention needs to be multifaceted and should focus on
individual as well as peer networks. The current findings do suggest that
reactive bystander-focused prevention for adolescents that focuses on atti-
tudes and skills for stepping in when risk for sexual violence is present might
benefit from being conducted in the context of peer groups rather than just
broad classroom-based curricula. This is exemplified by Coaching Boys into
Men, which trains youth who are on the same sports team and who thus may
be more likely to consider one another as social network members and friends
than the more variable group of students who are assigned to a classroom
together in school (Miller et al., 2016). Given that alcohol use followed a
similar pattern, de-siloing and developing prevention strategies that target
both of these content areas using a peer network approach may be helpful.
Research on tobacco use prevention highlights how social network analysis
can be used to diffuse key prevention messages within specific youth net-
works (Valente et al., 2003). Recent work highlights how prevention practi-
tioners might identify peer leaders and their social networks for prevention
diffusion (Edwards et al., 2020; Valente & Pumpuang, 2007). On the other
hand, more proactive bystander actions may require a different set of preven-
tion strategies to activate. This work reinforces studies that suggest that for
adolescents, peer education for prevention, which solely relies on teachers
and other adults as prevention program facilitators, may be more effective
(Kelly, 2004; McMahon et al., 2014; Weisz & Black, 2010; Wiist & Snider,
1991; Wyman et al., 2010; Zambuto et al., 2020).
Limitations
There are limitations to the current study. Given the silence that surrounds
this topic and in some cases, the lower prevalence of sexual victimization
compared to other peer violence like bullying, there can be considerable
missing data when students are not in a situation where they have the chance
to help (Waterman et al., 2020). The current sample included younger teens
who may not yet be experimenting with dating or sexual initiation. Even
among adolescents who are sexually active, discussions about these issues
and topics like sexual violence may take place more often in smaller friend
groups of one or two closest friends rather than a fuller social network node.
Banyard et al. 21
Furthermore, there is variability in networks related to exposure, all of which
affects sample sizes and power. It is unclear how missing network data affect
these results; since nearly half of the nominations were to non-participants
(2.44 of the 5.4 nominations made), the network exposure terms are calcu-
lated only from the participants. If non-participants have higher sexual vio-
lence rates, then the exposure terms are underestimated and coefficients for
those reporting proactive and reactive behaviors underestimated. Replicating
exposure effects on alcohol use and academic grades, however, gives us con-
fidence that the coefficients are reasonably accurate estimates of the associa-
tions. Another limitation with regard to missing data is that we use somewhat
different samples in the same analyses, indicating comparisons across models
should be done with caution. Further research with larger samples is needed.
Other limitations include concerns about measurement and sampling.
Several measures showed low reliability, which is consistent with calls in the
field for ongoing measurement development (McMahon et al., 2017).
Measurement of bystander behaviors is still a relatively new field, and future
research should continue to develop new questions to better capture youths’
experiences. In the current study, the nomination form for social network
analysis used the term “best friend,” while the social norms measure indi-
cated “friends.” Consistency in future research may be warranted. We did not
measure other sexual violence-related attitudes, such as rape myths, which
may cluster within networks, and we know rape myths are key predictors of
sexual violence perpetration (Edwards et al., 2011) and bystander non-action
(McMahon, 2010). Furthermore, there may be a problem in the current study
with sample selection. The younger adolescent sample in the current study
(grades 7–10), half of whom were in middle school, may have less access to
peers’ views related to sexual relationships and limited exposure to actual
instances of sexual violence in particular. These behaviors may become more
apparent in older high school samples. Our sample was also largely White
and from a more rural part of the United States. Although there were a num-
ber of Native American youth in our sample, it is possible that the findings
from the current study will not generalize to samples of youth who are more
diverse (e.g., African American youth). Finally, we did not examine bystander
behaviors associated with other forms of youth violence, like bullying, which
may be more observable to youth.
Conclusion
In sum, the current study sheds light on the extent to which sexual violence
attitudes and behaviors cluster within the social networks of middle and high
school youth. Some evidence was found for homophily specific to sexual
violence attitudes and bystander behaviors, which suggests that these
22 Journal of Interpersonal Violence
behaviors operate similarly within social networks to more observable behav-
iors like alcohol use, though with smaller effect sizes. Future research is
needed to further understand these nuanced and complex relationships, which
could inform the extent to which bystander-focused prevention programs are
implemented within the context of social networks.
Acknowledgments
We owe a great deal of gratitude to our school and community partners and project
staff. Without these individuals, this project would not have been possible.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: Funding for this study was provided by
the U. S. Centers for Disease Control and Prevention’s (CDC), National Center for
Injury Prevention and Control, Grant #U01-CEO02838. The findings and conclusions
in this manuscript are those of the authors and do not necessarily represent the official
position of the CDC.
Notes
1. Of note is that 4,172 is the number that was provided by the school district,
but when we collected data and students could name anyone, the total district
population was 3,685. In other words, there were 497 students listed by the
district whom no one named as a friend and so are probably not currently enrolled
in any of the schools.
ORCID iDs
Victoria Banyard https://orcid.org/0000-0002-9645-5055
Katie Edwards https://orcid.org/0000-0003-1888-7386
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Author Biographies
Victoria Banyard is a professor at the School of Social Work at Rutgers, the State
University of New Jersey and associate director of the Center on Violence Against
Women and Children. Her research focuses on evaluating sexual and relationship
violence prevention program, understanding how bystanders can be mobilized to help
prevention violence, and better understanding resilience among survivors and com-
munities who have experienced violence. She has published over 150 articles and
book chapters on these topics and led numerous grant-funded research studies.
Emily A. Waterman is an assistant professor at Bennington College. An applied
developmental scientist, the aim of Waterman’s research program is to both prevent
sexual and dating violence among adolescents and young adults and to improve out-
comes for victims of sexual and dating violence. Waterman conducts research on risk
and protective factors for sexual and dating violence as well as outcome evaluations
of preventative interventions. Waterman has been supported by the National Institutes
of Health and Centers for Disease Control.
Katie M. Edwards is associate professor of Educational Psychology and faculty at
the Nebraska Center for Research on Children, Youth, Families, and Schools at the
University of Nebraska Lincoln. Dr Edwards’ interdisciplinary program of research
focuses broadly on better understanding the causes and consequences of interpersonal
violence, primarily intimate partner violence and sexual assault among youth. She has
published over 120 papers and book chapters on these topics and has funding from
CDC, NIH, DOJ, and NSF.
Tom Valente is a professor in the Department of Preventive Medicine, Keck School
of Medicine, at the University of Southern California. He has authored 3 books and
over 200 articles and chapters on social networks, behavior change, and program
evaluation. Valente uses social network analysis, health communication, and mathe-
matical models to implement and evaluate health promotion programs designed to
prevent tobacco and substance abuse, unintended fertility, and STD/HIV infections.
He has received several awards for research and mentoring and is currently working
on specifications for analyzing network models of diffusion and contagion with the R
package netdiffuseR.
