ABSTRACT 
 
 
 
 
Title of Document: ANALOGUE STUDY OF PEER INFLUENCE 
ON RISK-TAKING BEHAVIOR IN OLDER 
ADOLESCENTS 
 
  
 Elizabeth Keats Reynolds, Ph.D., 2011 
 
  
Directed By: Professor Carl W. Lejuez, Department of 
Psychology 
 
 
An accumulation of research has demonstrated that peers play a considerable role in 
older adolescent risk behavior engagement with risk taking often occurring in the 
presence of peers.  Yet, questions remain about how peer influence manifests in the 
immediate context of risk behavior engagement and consequently whether the 
influence of peers has been overestimated.  It is uncertain whether a situational effect 
takes place whereby the presence of the peers and/or their influence in that moment is 
key.  Additionally, it is unclear whether certain adolescents are more or less 
susceptible to peer influence.  To better understand the proposed influence of peers 
during risk-taking behavior, the current experimental study aimed to examine whether 
peers do act in a riskier manner in the presence of peers and further whether peer 
presence alone influences risk behavior or if a direct influence process is necessary.  
Further, the study aimed to examine potential moderators of peer influence.  Utilizing 
  
a behavioral task assessing risk-taking behavior, 183 older adolescents (M age = 
19.16, SD = .57, 63.9% female, 53.0% non-Hispanic White) came to the lab alone 
once and then were randomized to one of three conditions (alone, peers present, peers 
encouraging).  If the target was randomized to peers present or peers encouraging 
conditions, the target brought in two, same-gender close friends for the second 
session.  A repeated measures ANOVA with the within subject factor as the risk task 
score at each session and the between subjects factor as condition revealed a 
significant interaction of session by condition (F(2, 180) = 11.38, p = .001; partial ?2 
= .11) such that at the baseline session there were nominal differences between the 
three conditions but at the experimental session there was a significant increase in 
risk task scores particularly for the encouraging condition.  None of the proposed 
moderators had a significant effect, suggesting that the experimental conditions had 
an equal effect across participants. These findings support the idea that older 
adolescents take more risks when being encouraged by peers but that the presence of 
peers on its own does not lead to more risks than when completing the task alone.  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
ANALOGUE STUDY OF PEER INFLUENCE ON RISK-TAKING BEHAVIOR IN 
OLDER ADOLESCENTS 
  
 
 
 
By 
 
 
Elizabeth Keats Reynolds 
 
 
 
 
 
Dissertation submitted to the Faculty of the Graduate School of the  
University of Maryland, College Park, in partial fulfillment 
of the requirements for the degree of 
Doctor of Philosophy 
2011 
 
 
 
 
 
 
Advisory Committee: 
Professor Carl W. Lejuez, Chair 
Research Assistant Professor Laura MacPherson 
Assistant Professor Andres De Los Reyes 
Distinguished University Professor Nathan Fox 
Professor Monique Mitchell Turner 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
? Copyright by 
Elizabeth Keats Reynolds 
2011 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
Table of Contents 
 
 
Table of Contents.......................................................................................................... ii 
List of Tables ............................................................................................................... iv 
List of Figures ............................................................................................................... v 
Chapter 1: Introduction ................................................................................................. 1 
Proposal Overview.................................................................................................... 1 
Older Adolescence as a Period of Heightened Risk-Taking Behavior ..................... 2 
Alcohol.................................................................................................................. 4 
Illicit Drug Use ..................................................................................................... 5 
Cigarette Smoking ................................................................................................ 6 
Risky Sexual Behavior.......................................................................................... 6 
Factors Associated with Risk-Taking Behavior: Relevance of Peers....................... 7 
Peer Influence in the College Setting........................................................................ 9 
Global Peer Influence Processes............................................................................. 11 
Peer Influence in the Immediate Context of Risk Behavior Engagement .............. 13 
Direct Influence .................................................................................................. 14 
Indirect Influence ................................................................................................ 17 
Factors that May Impact the Salience of Peer Influence ........................................ 21 
Summary of the Literature ...................................................................................... 24 
Current Study .......................................................................................................... 26 
Primary Aim........................................................................................................ 27 
Exploratory Aim ................................................................................................. 27 
Chapter 2: Method ...................................................................................................... 28 
Overall Design ........................................................................................................ 28 
Participants.............................................................................................................. 28 
Procedure ................................................................................................................ 30 
Experimental Conditions .................................................................................... 33 
Room Setup......................................................................................................... 35 
Measures ................................................................................................................. 35 
Balloon Analogue Risk Task .............................................................................. 35 
Demographics ..................................................................................................... 37 
Resistance to Peer Influence ............................................................................... 37 
Risk-Taking Behavior......................................................................................... 38 
Friendship Quality .............................................................................................. 40 
Popularity............................................................................................................ 40 
Data Analysis Plan.................................................................................................. 41 
Chapter 3: Results ....................................................................................................... 42 
Preliminary Analyses .............................................................................................. 42 
Primary Aim............................................................................................................ 43 
Exploratory Aim ..................................................................................................... 44 
Chapter 4: Discussion ................................................................................................. 45 
Main Outcomes....................................................................................................... 45 
Limitations and Future Directions .......................................................................... 53 
 ii 
 
 
Conclusion .............................................................................................................. 55 
Appendices.................................................................................................................. 57 
Moderation.............................................................................................................. 57 
Modeling................................................................................................................. 57 
Norms...................................................................................................................... 58 
Tables.......................................................................................................................... 61 
Figures......................................................................................................................... 65 
References................................................................................................................... 69 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 iii 
 
 
 
List of Tables 
 
 
i. Descriptive statistics and examination of differences between conditions at the 
baseline session?????????????????????????..86 
 
ii. Main repeated measure ANOVA results???????????????...87 
 
iii. Balloon Analogue Risk Task score means for each condition across sessions and 
individual repeated measures ANOVA results???..??????????88 
 
 iv 
 
 
List of Figures 
i. Schematic of the Balloon Analogue Risk Task?????????????.90 
ii. Photo of experimental room set-up??????????????????91 
iii. Balloon Analogue Risk Task performance at the baseline and the experimental 
sessions across condition?????????????????????...92 
 
 v 
 
 
 
Chapter 1: Introduction 
Proposal Overview 
Older adolescence (ages 18-20) is a period of heightened risk-taking behavior.  
In particular, this period has been characterized by the propensity towards 
engagement in risky behaviors (e.g., substance use and abuse, risky sexual behavior, 
reckless driving) that have the potential for serious physical and psychological 
consequences.  An accumulation of research has demonstrated that peers play a 
considerable role in older adolescent risk behavior engagement with risk taking often 
occurring in the presence of peers.  Although extant literature has provided insight 
into the means by which peers may influence risk behavior engagement, there 
continue to be a number of limitations.  As such, questions remain about how peer 
influence manifests in the immediate context of risk behavior engagement and 
consequently whether the influence of peers has been overestimated.  It is uncertain 
whether a situational effect takes place whereby the presence of the peers and/or their 
influence in that moment is the key factor.  Further, if a situational effect is the cause, 
whether it is the presence of peers alone (i.e., indirect influence) or a direct influence 
process (i.e., a peer encourages a behavior) that can lead to increased risk behavior.  
In addition, it is unclear whether certain adolescents are more or less susceptible to 
peer influence.  
To better understand the proposed influence of peers during risk behavior 
engagement, the current experimental study aimed to examine whether peers do act in 
a riskier manner in the presence of peers and whether peer presence alone influences 
 1 
 
 
risk behavior or whether a direct influence process (e.g., peer encouragement of risk 
behavior) is necessary.  Furthermore, the study aimed to examine potential 
moderators of peer influence in order to identify factors that impact vulnerability to 
these peer effects.  A behavioral task assessing risk-taking behavior (i.e., Balloon 
Analogue Risk Task: BART; Lejuez et al., 2002) was used as the outcome measure 
given it provides a well controlled paradigm with an easily quantifiable index of risk 
and as it has been shown to be related to real world risk behavior, indicating its 
validity for the goals of the current study.  Older adolescents came to the lab alone 
once and then were randomized to one of three conditions (alone, peers present, peers 
encouraging).  If the target was randomized to peers present or peers encouraging 
conditions, he/she was asked to bring in two same-gender, close friends for the 
second session (experimental session).  In the alone condition, he/she came back 
without friends.  This design allowed for the examination of differences in risk 
behavior when peers are a) not present; b) in the same room and able to see 
participant behavior on the risk task, but unable to provide any form of advice or 
encouragement in any way; and c) in the same room, able to see participant behavior 
on the risk task, and given the instruction to encourage risk behavior.  In this way, it 
is possible to isolate peer presence and awareness of behavior from the additional 
influence of risk encouragement.  
Older Adolescence as a Period of Heightened Risk-Taking Behavior 
Older adolescence (ages18-20; Clark & Moss, 2010) can be defined as a 
period of exploration of identity and one?s place in the world (Zarrett & Eccles, 
2006).  Older adolescents achieve new levels of independence from their parents, 
 2 
 
 
adopt considerably greater responsibilities, and often make important life decisions 
on their own for the first time.  Although this age period is potentially an exciting 
time of positive change and growth, this period of transition also is marked by 
engagement in a variety of risk-taking behaviors, with the potential for very serious 
life-altering negative consequences.  Research on risk taking has encompassed a 
variety of behaviors including alcohol consumption, tobacco use, risky sexual 
activity, dangerous driving, interpersonal aggression, and delinquent behaviors 
(Boyer, 2006).  In their classic book on the topic, Jessor and Jessor (1977) defined 
risk taking as engagement in ?behavior that is socially defined as a problem, a source 
of concern, or as undesirable by the norms of conventional society and the institutions 
of adult authority, and its occurrence usually elicits some kind of social control 
response? (p.33).  Focusing more explicitly on the consequences of such behavior, 
definitions of risk taking also have taken into consideration the possibility of positive 
outcomes and thus have focused on the balancing of potential for harm or danger to 
the individual with potential achievement or reward (Byrnes, Miller, and Schafer, 
1999; Leigh, 1999).  This latter view is important because it leaves room for the 
influence of a variety of factors that affect an older adolescent?s willingness to take 
risks including the potential gain from risks in terms of positive reinforcement and the 
corresponding opportunity costs for an unwillingness to take risks.  
Rates of participation with most drug use, alcohol use, and unprotected sexual 
activity have been found to peak during older adolescence and then to decline during 
adulthood (Johnston, O?Malley, Bachman, & Schulenberg, 2005).  This decline has 
been linked to the assumption of new roles and responsibilities such as marriage, 
 3 
 
 
parenthood, and employment (Schulenberg, O?Malley, Bachman, Wadsworth, & 
Johnston, 1996).  College typically delays the assumption of many of these adult 
responsibilities and expands the period during which high levels of risk-taking 
behavior such as substance use can be sustained (Schulenberg et al., 1996).  The 
following paragraphs provide an overview of the prevalence of a number of risk-
 taking behaviors (alcohol, illicit drugs, smoking, and risky sexual behavior?of note, 
the behaviors described are not all-inclusive, other important behaviors are dangerous 
driving, interpersonal aggression, and delinquent behaviors) among older adolescents 
in college settings, when possible comparing to non-college attending youth.  
Alcohol  
Individuals entering college show marked increases in alcohol use compared 
to those that live at home or obtain jobs following graduation from high school 
(Johnston, O'Malley, Bachman, & Schulenberg, 2009).  For example, college students 
?binge drink? (i.e., having five or more drinks in a row at least once in the past two 
weeks) significantly more often than other young adults (40% vs. 30%; Johnston et 
al., 2009).  However, in high school, college-bound seniors are less likely to report 
heavy drinking than non-college-bound youth.  This suggests that emerging older 
adolescents in college ?catch up to and pass? their non-college-attending peers 
(Johnston et al., 2009).  Alcohol is considered to be a prominent part of the college 
culture, present at most social functions and part of many peer interactions (Thombs, 
1999).  Excessive drinking among college students is associated with a variety of 
negative consequences that include fatal and nonfatal injuries; alcohol poisoning; 
blackouts; academic failure; violence, including rape and assault; unintended 
 4 
 
 
pregnancy; sexually transmitted diseases, including HIV/AIDS; property damage; and 
vocational and criminal consequences that could jeopardize future job prospects 
(Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994; Wechsler, Lee, Kuo, & 
Lee, 2000; Wechsler et al., 2002).  
Illicit Drug Use 
As with alcohol, the general trajectory for illicit drug use is an increase in 
adolescence, peak in older adolescence/young adulthood, and then decline though 
adulthood (Arnett, 2005; Bachman et al., 2002; Bachman, Wadsworth, O'Malley, & 
Schulenberg, 1997; Chen & Kandel, 1995).  According to the 2004 National Survey 
on Drug Use and Health (SAMHSA, 2004), rates of past month illicit drug use 
climbed steadily for youth from 12 to 17, peaked among 18 to 20 year olds, and 
remained high for those between 21 and 25 before dropping for persons 26 through 
29.  In addition to continuing use, initiation of substances also occurs during this time 
period; for example, one third of new marijuana users start using after age 17 as do 
about 70% of cocaine users (Volkow, 2004).  In terms of variation in use among 
college attendees and non-attendees, illicit drug use has been increasing on college 
campuses since the mid-1990s (Mohler-Kuo, Lee, & Wechsler, 2003).  However, 
college students differ only modestly from their non-college peers in their rate of drug 
use and types of drugs used (Johnston et al., 2005).  The annual prevalence (i.e., use 
of the drug in the past year) for the use of any illicit drug among college students is 
36%, compared to 39% of counterparts not attending college (Johnston et al., 2005), 
and when considering only drugs other than marijuana, rates are 19% for college 
students and 24% for terminal high school graduates.  Thus, illicit drug use among 
 5 
 
 
college students does not appear to exceed rates of use in the general young adult 
population, and for certain drugs, rates appear to be somewhat lower in the college 
student population. 
Cigarette Smoking  
Research shows that smoking initiation typically occurs in the teenage or high 
school years (Johnston et al., 2005).  Although the majority of smokers begin in these 
years, many become dependent on nicotine in the older adolescent/young adult years 
of ages 18?24 (Ellickson, Perlman, & Klein, 2003).  Smoking in this developmental 
period is of concern as the rate of smoking in the 1990?s declined in all age groups 
except ages18-24 (Hebert, 2004).  At present, those aged 18?24 years have the 
highest prevalence of smoking (24.4%) compared with all other age groups 
(American Cancer Society, 2007).  Cigarette smoking occurs less frequently in the 
college student population than in the general young adult population (5.6% vs. 16%; 
Johnston et al., 2005).   
Risky Sexual Behavior  
Adolescents and young adults are more likely than older adults to have 
multiple sex partners, to engage in unprotected sexual intercourse, and to select 
higher risk partners (Bearinger, & Resnick, 2003; CDCP, 2003).  Half of all new HIV 
infections occur among individuals aged 24 years and younger (Futterman, 2005).  
Adolescents and young adults aged 15?25 years have the highest rate of sexually 
transmitted diseases (STDs) of any age group in the United States (CDCP, 2005).  
Emerging evidence indicates a higher prevalence of sexual risk behavior among non-
  6 
 
 
college youth, thought to be due largely to a continuation of patterns of higher risk 
behavior and lower academic performance during high school (Bailey, Fleming, 
Henson, Catalano, & Haggerty, 2008).  Yet, risky sexual behavior is also prevalent 
among college attendees.  For example, many college students do not report using 
condoms consistently (Kiene & Barta, 2003, 2006; Kiene, Barta, Zelenski, & 
Cothran, 2005).  Furthermore, college women are one of the two groups, the other 
being female STD clinic attendees, with the highest prevalence of human 
papillomavirus (HPV) in the United States (Revzina & Diclemente, 2005). 
In sum, older adolescence, the transition period between high school and 
young adulthood, is marked by the formation of identity and transition to new adult-
 type roles.  Yet, it is also a time of increased risk-taking behavior, which can have 
long-term effects.  Attending college may represent a special risk to older adolescents 
for some risk-taking behavior such as drinking. 
Factors Associated with Risk-Taking Behavior: Relevance of Peers 
A number of factors have been found to be related to risk-taking behavior in 
older adolescents (Rolinson, & Scherman, 2003; White, & Jackson, 2004), including 
individual difference variables (e.g., impulsivity and sensation seeking), cognitive 
variables, (e.g., expectancies, perceived risk and benefits), coping strategies (e.g., 
relief from negative feelings), and environmental contexts (e.g., parental influences, 
availability of substances).  Beyond these variables, an accumulation of research has 
demonstrated that peers play a considerable role in older adolescent risk behavior 
engagement (Borsari & Carey, 2001).  As compared to adults, one of the hallmarks of 
adolescent risk taking is that it often occurs in the context of peers (Steinberg, 2009). 
 7 
 
 
The degree to which an adolescent?s peers use alcohol or illicit drugs has been 
identified as a strong predictor of that adolescent?s own substance use behavior 
(Chassin et al., 2004).  Freshmen whose social networks consist mainly or entirely of 
abstainers are less likely to initiate alcohol use or drink heavily (four/five or more 
drinks per occasion) than students whose social networks consist predominantly of 
drinkers (Reifman, & Watson, 2003; Baer, Kivlahan, & Marlatt, 1995).  This also has 
been shown prospectively from high school: recent work suggests that having fewer 
friends who use substances protects against increases in the frequency of alcohol use, 
heavy episodic drinking, and marijuana use as one transitions out of high school and 
into college (White et al., 2006).  Beyond the beginning of college, a longitudinal 
study of 294 young adults, ages 19 to 25, and both a same- and an opposite-gender 
best friend or romantic partner found similarity across time between both peers and 
the young adult in cigarette use, alcohol use, and binge drinking (Andrews, Tildesley, 
Hops, & Li, 2002).  In prospective analyses, peer use predicted young adult cigarette 
use, binge drinking, and problem use.  Other longitudinal work has demonstrated that 
one of the best predictors of young adults? smoking was whether they had smoking 
friends when they were adolescents (Brook, Whiteman, Czeisler, Shapiro, & Cohen, 
1997).  Examples of peer influence are available beyond substance use as well. Binge 
eating among female college students has been found to cluster within informal social 
groups (Crandall, 1988).  Additionally, adolescents have been found to be more likely 
to be sexually active when their peers are sexually active (DiBlasio & Benda, 1992; 
Udry, 1987).  In fact, this relation has held if there was a belief that friends were 
 8 
 
 
sexually active, regardless of whether or not they actually were (Brooks-Gunn & 
Furstenberg, 1989; Prinstein, Meade, & Cohen, 2003).  
Of particular relevance to the proposed study, research has shown that 
adolescent risk taking is likely to occur in the context of peers.  For example, crash 
rates and fatalities rise dramatically when adolescent drivers are with peer passengers 
(Chen, Baker, Braver, & Li, 2000; Preusser, Ferguson, & Williams, 1998).  Youths 
drive faster and take more risks when carrying peers than when carrying adults as 
passengers (Arnett, Offer, & Fine, 1997; Bingham, Shope, Parow, & Raghunathan, 
2009; Simons-Morton, Lerner, & Singer, 2005), especially if the peers are young men 
(Baxter et al., 1990).  Further, youth are usually accompanied by one or more persons 
when committing crimes that range in seriousness from vandalism and drug use 
(Erickson & Jensen, 1977) to rape and homicide (Zimring, 1998).  Peer context 
influence has also been demonstrated with substance use (Chassin, Hussong, & 
Beltran, 2009). Clapp and colleagues have conducted research on environmental 
predictors and have found that social settings are related to heavy episodic drinking 
among college students (Clapp, Lange, & Shillington, 2003; Clapp, Reed, Holmes, 
Lange, & Voas, 2006).  Furthermore, investigations of initial smoking experiences 
place the occurrence of first-time use in the context of peers (Friedman, Lichtenstein, 
& Biglan, 1985; Lucas & Lloyd, 1999). 
Peer Influence in the College Setting 
In considering the role of peers on older adolescent risk-taking behavior, it is 
important to consider that peers may be an especially important influence among 
those in the college setting.  Starting with matriculation and throughout college, there 
 9 
 
 
is a marked shift in influence from parents to peers.  Early on in adolescence, parents 
have a strong influence on the child?s attitudes and behaviors (Kandel & Andrews, 
1987).  As adolescents get older, they spend less time with their parents and more 
time with friends (Csikszentmihalyi & Larson, 1984), resisting attempts by parents to 
control the selection and involvement of these friends (Smetana & Asquith, 1994).  
Peers become increasingly important and are fairly free of parental oversight and 
control (Brown, Dolcini, & Leventhal, 1997).  This process appears to intensify in 
college where youths face multiple transitions, including changes in their living 
arrangements, academic environments, and friendship networks, while adapting to 
greater independence and responsibility in their personal and academic lives (Pittman, 
& Richmond, 2008).  Matriculating students may be especially vulnerable to the 
influence of peers because of their need to make and maintain new friendships.  
Students entering college seek to establish a peer network that can be a source of 
support and closeness (Paul & Kelleher, 1995).  To develop a peer network on 
campus, students immerse themselves in the social environment (Martin & Hoffman, 
1993).  Within the many social environments on campus, risk-taking behavior 
opportunities are prevalent; for example, alcohol is a prominent part of the college 
culture?it is present at most social functions and part of many peer interactions 
(Thombs, 1999).  
The heightened attention to the views of others can be adaptive, given that 
those who are attuned to peers are more likely to successfully navigate complex 
social scenarios and to form mature relationships (Baumeister & Leary, 1995).  
Adolescents who lack a sense of acceptance experience a greater risk of depression, 
 10 
 
 
anxiety, and future problem behaviors (Capaldi, Dishion, Stoolmiller, & Yoerger, 
2001; Kawachi, & Berkman, 2001).  Furthermore, meta-analyses in adulthood 
indicate that the risks to future mortality resulting from social isolation are high 
(House, Landis, & Umberson, 1988).  As such, available evidence suggests that 
adolescents? preoccupation with their social status is neither needless nor irrational 
(Allen & Brown, 2008).  
In sum, when considering factors associated with risk behavior engagement, 
peers may be a particularly important influence given the pronounced shift in 
influence from parents to peers during college and the prevalence of risk-taking-based 
social opportunities on campus. 
Global Peer Influence Processes 
As previously described, one of the strongest predictors of adolescent risk-
 taking behavior is the risk-taking behavior of close friends (Prinstein & Dodge, 
2008).  On a global level, it has been observed that this similarity in risk-taking 
behavior among peers may result from both socialization and selection effects 
(Kandel, 1996); that is, friends can influence one?s risk behavior and, conversely, that 
one?s risk behavior impacts the selection of friends (Dishion, & Owen, 2002).  Both 
selection and socialization effects have been demonstrated for a wide variety of 
behaviors including delinquency, violence, risky sexual behavior, substance use, and 
weight-related behaviors (see Prinstein, & Dodge, 2008 for review).  In the process of 
selection, individuals choose peers who exhibit similar attitudes and behaviors.  For 
example, Leibsohn (1994) found that entering freshmen sought out new friends with 
whom to drink and use drugs?new friends whose use patterns were similar to those 
 11 
 
 
of their old high school friends.  Selection does not presume that individuals influence 
each other; rather, similar individuals are simply attracted to each other.  Socialization 
effects, on the other hand, refer to the process by which youth?s behavior may be 
affected by their affiliations with other peers.  Initial dissimilarity among affiliates 
will grow into similarity over time through peer influence (Prinstein, & Dodge, 
2008).  
This past body of research on global peer influence processes has offered a 
tremendous contribution by advancing the understanding of why a youth and peers? 
behavior are related to each other.  Yet, remarkably little is known regarding basic 
descriptive aspects of peer influence effects and more specifically, the means by 
which peer influence manifests in the immediate context of risk behavior engagement 
(Allen & Brown, 2008).  As described above, one of the hallmarks of older 
adolescent risk-taking behavior is that it often takes place in a social context, 
specifically, within the presence of peers.  Yet, it is unclear whether risk taking solely 
takes place in a peer context because adolescents spend a considerable amount of 
time with peers (Brown, 2004) or whether there is something inherent about the 
physical presence of peers that leads to increased risk behavior engagement. 
Surprisingly few studies have focused on the specific methods by which peers affect 
individual behavior (Brown, Bakken, Ameringer, & Mahon, 2008) and, as such, more 
information is needed on the means by which peers exert influence in order to assess 
the degree to which individuals? response to peer influence depends on the way it is 
manifested.  Hence in order to better understand social-situational context of risk 
behavior, it is important to conduct more micro-level examinations of peer influence 
 12 
 
 
by researching the role peers can have in the immediate context of risk behavior 
engagement. 
Peer Influence in the Immediate Context of Risk Behavior Engagement 
As previously described, a critical issue that is lacking understanding is what 
is happening in the immediate context of risk behavior engagement.  Focusing on a 
specific risk behavior, Allen and Brown (2008) have identified a number of 
limitations to our current knowledge of why the presence of peers leads to negative 
outcomes when driving.  They posed a number of questions including: is the driver 
simply distracted by the flow of conversation going on among passengers; do teen 
passengers engage in behaviors that interfere with driving more directly, such as 
grabbing the wheel or urging the driver to drive fast; does the sheer presence of teen 
passengers prompt an adolescent to drive differently?  Thus, it has been observed that 
remarkably little is known regarding the nature and quality of peer influence that 
actually takes place, leading to questions about what mode(s) of peer influence can 
impact risk-taking behavior. 
Hence, in order to better understand the proposed potent effects of peer 
influence in the immediate context of risk behavior engagement, it is necessary to 1) 
establish whether youth do inherently act in a more risky manner in the presence of 
peers (not just happenstance that risky behavior occurs in a social context due to 
considerable time spent in peer groups) and then 2) examine in what manner peers 
can exert their influence.  In considering possible means of peer influence in the 
immediate context of risk taking behavior engagement, ?peer pressure? is a 
commonly identified impression.  Although operationalized in different ways, this 
 13 
 
 
term carries the connotation of youth being cajoled or coerced into some behaviors by 
peers (Brown et al., 2008).  Yet, in considering possible means of peer influence in 
the immediate context of risk-taking behavior engagement, it has been suggested that 
peers may influence behavior both directly and indirectly (Borsari & Carey, 2001; 
Graham, Marks, & Hansen, 1991; Oostveen, Knibbe, & De Vries, 1996; Simons-
 Morton et al., 2005).  In contrast, to direct peer influence (i.e., explicit offers or 
pressure to engage in a certain behavior), indirect influence refers to passive, non-
 explicit, or unintended peer influence (e.g., an individual?s perception and 
interpretation of the risk taking and reinforcement patterns of others; Borsari & 
Carey, 2001; Simons-Morton et al., 2005). The following sections provide an 
overview of the available literature on direct and indirect peer influence in the 
immediate context of risk behavior engagement.  
Direct Influence 
Direct peer influence refers to active effort on the part of the peer.  Behaviors 
can range from civil gestures (e.g., offering a drink, cigarette, or marijuana) to overt 
encouragement and orders (e.g., forcing others to drink during drinking games).  
Allen and Brown (2008) have described direct influence in youth driving behavior 
which they refer to as incitement; that is, when a teen is navigating, one car zooms 
past another car with a threatening wave to the driver, and as a result the teen?s 
passengers enthusiastically scream at their driver to catch up and pass him back.  
Studies of direct influence are relatively limited among older adolescents 
(Borsari, & Carey, 2001).  The existing research has shown that direct offers to drink 
are associated with alcohol use and problems (Wood, Read, Palfai, & Stevenson, 
 14 
 
 
2001).  Further, qualitative research has suggested that not drinking at college social 
functions is regarded as an unusual behavior that will elicit several offers to drink 
(Rabow & Duncan-Schill, 1995).  Nondrinking students at parties are repeatedly 
offered drinks, exposed to teasing from friends, and report feelings of inferiority.  In 
terms of other risk behavior, one Australian study indicates that drivers report often 
being incited by youthful passengers (aged 16?24 years) more frequently than by 
adults (Regan & Mistopoulos, 2003).  
In addition to these self-report descriptive studies, some experimental work 
tapping youth's susceptibility to direct peer influence has been conducted.  In 2005, 
Gardner and Steinberg published an experimental study of peer influence on risk 
taking, risk preference, and risky decision-making.  The study recruited 306 
individuals in three age groups: 13?16, 18 ?22, and 24 and older.  The subjects 
completed two questionnaire measures assessing risk preference and risky decision-
 making, and a behavioral task measuring risk taking (called Chicken).  Participants in 
each age group were randomly assigned to complete the measures either alone or with 
two same-gender and -aged peers.  The peers were informed that they could call out 
advice during the behavioral task and questionnaires and that the player was 
instructed that he or she could choose whether to follow the advice of his or her peers.  
The results demonstrated that compared with those who completed the measures by 
themselves, participants who completed the same measures with peers giving advice 
took more risks during the risk-taking game, gave greater weight to the benefits rather 
than the costs of risky activities, and were more likely to select risky courses of action 
in the risky decision-making situations.  Further, the magnitude of the group effect on 
 15 
 
 
risk taking was greater among younger rather than older participants.  The authors 
concluded that relative to adults, younger and older adolescents are more susceptible 
to the influence of their peers in risky situations. 
Recently, Steinberg?s group conducted a follow-up study with a sample of 18 
and 19 year-old undergraduates using a within subject design and a similar behavioral 
task (Albert et al., 2009).  Youth completed the behavioral risk task both alone and 
with peers being allowed to interact without any restrictions.  The results indicated 
that participants were riskier on the task in the peer condition, but only when this 
condition occurred first.  There was no difference in conditions when the task was 
first completed alone.  
These studies have been influential and advanced the understanding of peer 
influence on risk-taking behavior; yet, some limitations are present which may temper 
conclusions.  First, it is unclear what types of interactions took place in the peer 
present condition.  All that is reported is that there was the possibility of 
communication between the peer group during the task and assessments but it is 
unclear whether there was explicit encouragement of risk behavior.  A second factor 
is the payment. Both the target youth and peers received equivalent payment on the 
task based on the target?s performance.  As such, peers might have been collaborating 
with the target to determine the most optimal strategy.  This appears counter to how 
risk taking is thought to take place in the ?real world? where peers may gain the 
benefits from risky behaviors without necessarily experiencing the costs (e.g., peers 
urge on a driver to take some risky action, knowing that they will most likely not 
experience the consequences directly as they do not bear any legal responsibility; 
 16 
 
 
Allen & Brown, 2008).  In the second study, it is difficult to understand the ordering 
effect with particular reference to why the participants who completed the task first 
alone did not increase in risk taking on the second administration in the peer 
condition; replication is needed.  Finally, the behavioral risk task (Chicken) may have 
impacted the results.  While the Chicken task has the advantages of a behavioral task 
over self-report (risk taking that requires participants to make actual decisions about 
how much risk to take in a situation that closely mirrors one faced in everyday life), 
the task is a driving simulation.  Participants may have varied experience with driving 
(particularly due to age differences since one group included participants under the 
legal driving age) thus impacting their behavior on the task.  In sum, these studies 
raise important questions about the nature of the peer effect; specifically, whether 
direct influence of risky behavior (e.g., encouragement) took place.  Thus, focusing 
on the immediate context of engagement in risk-taking behavior, what type of effect 
does/can direct peer influence have? 
Indirect Influence 
Within the immediate context of risk behavior engagement, it may be that 
peer influence does not solely occur through the overt behaviors described above.  
Rather, the simple presence of peers alone may act as a social influence.  For 
example, in the literature on adolescent driving behaviors, young males have reported 
that they would drive in a risky manner when with peers even if their passengers did 
not encourage them to do so (Regan & Mitsopoulos, 2003).   
The premise that the presence of others can provoke behavior change is 
central to Zajonc?s (1965) theory of social facilitation (impact of social presence on 
 17 
 
 
individual performance) and one of the oldest studied in social psychology (Aiello & 
Douthitt, 2001).  Typically, studies on social facilitation investigate the impact of 
social presence (usually the presence of relative strangers) on individual performance, 
specifically focusing on changes in an individual?s performance that occur when a 
person performs in the presence of others versus when alone.  Social facilitation has 
been applied to risk-taking behavior.  For example, research among adults has 
suggested that even just the implied presence of other players increases gambling 
intensity (Rockloff & Dyer, 2007).  In addition, college-age youth have shown more 
aggression when in the presence of others than when alone (Jacquin, Harrison, & 
Alford, 2006).  However, social presence leading to increased risk behavior was not 
supported in a recent fMRI study in which social influence on a monetary betting task 
was examined (Nawa, Nelson, Pine, & Ernst, 2008).  Although there was differential 
neural activation, the behavioral pattern of decision-making and reaction time did not 
differ between the social and non-social conditions. 
Though this collection of social facilitation studies highlights the impact 
social presence can have (or not have) on risk behavior engagement and lends support 
to studying this means of influence, it is important to emphasize that each of these 
studies used relative strangers.  It is expected that the impact of social presence (and 
the process by which influence is exerted) is substantially different depending on who 
the other person might be and how familiar the individual is with the other person 
(Aiello & Douthitt, 2001).  For example, social norms have been found to be most 
relevant for influencing behavior if they are created or evaluated by a salient 
reference group (Brown et al., 2008). Thus, these social facilitation and risk-taking 
 18 
 
 
studies speak to the impact of social presence in general but may provide little insight 
into if the presence of peers, specifically, influences risk behavior engagement.  
Speaking specifically to peer presence, emerging theory and empirical studies 
have suggested that the simple presence of peers may act as an influence on risk-
 taking behavior (Miller & Byrnes, 1997; Morrongiello & Sedore, 2005).  It has been 
proposed that if one?s risk-taking behavior functions as a method for maintaining or 
obtaining reputation and social status, than it would be more likely to occur when a 
peer is present to observe the risk behavior (Jacquin et al., 2006).  Peers are thought 
to provide information regarding which behaviors are accepted, admired, and 
appropriate in a given social context, and therefore what behaviors are likely to lead 
to social acceptance (Borsari & Carey, 2001).  As such, the presence of a peer may 
lead an individual to assess his/her own behavior more closely, in terms of adherence 
to what is considered acceptable behavior (Deaux & Major, 1987).  A second 
hypothesis regarding the impact the presence of peers can have on risk behavior 
engagement has been set forth by Steinberg and colleagues.  They have proposed that 
the presence of peers sensitizes the incentive processing system to respond to cues 
signaling the potential rewards for risky behavior (Chein, Albert, O?Brien, Uckert, & 
Steinberg, 2011; Steinberg, 2007; 2008; 2009).  Accordingly, the presence of peers 
are thought to bias decision making in favor of rewarding, social-processing 
outcomes, rather than rational outcomes.  Although these ideas are interesting, the 
empirical support is somewhat limited. 
A recent fMRI study did model the impact of peer observation on risk taking 
(Chein et al., 2011).  Participants falling into three age groups (14-18, 19-22, and 24-
  19 
 
 
29), completed a driving stimulation task in which the participants were given the 
goal of reaching the end of a track as quickly as possible to maximize monetary 
rewards (risk taking was defined as not stopping for a yellow light) both alone and 
while being observed by peers.  All participants brought in two same-age, same-
 gender peers, who in the peer condition observed the target participant conduct the 
driving stimulation task.  In order to let the target participant know s/he was being 
observed by the peers while in the scanner in the peer condition, the peers 
communicated with the target via an intercom.  The authors described the allowed 
communication as ?the peers were permitted to speak authentically while informing 
the scanned participant of their presence, demonstrating their ability to observe task 
performance on the monitor, and communicating that they had made predictions 
about the scanned participant?s pending performance. The peers were carefully 
instructed to make these specific points during the interaction and to avoid comments 
that might explicitly or intentionally bias behavior? (p. F3, Chein et al., 2011).  
Results of the study indicated that in the alone condition the three age groups 
performed similarly. Yet, participants in the 14-18 year old age group took 
significantly more risks when observed by peers than when alone.  This was not the 
case for the other two age groups.  
This is one of the first studies to model the influence of familiar peer presence 
on risk behavior engagement; thus it is unique from prior social facilitation work that 
focused on the presence of unknown individuals.  For younger adolescents, this 
familiar peer observation increased risk-taking behavior; however, the presence of 
peers did not have an effect for older adolescents or young adults.  Considering the 
 20 
 
 
novelty of this study, replication of the findings is necessary.  In addition, it is unclear 
what effect the ?communicating that they had made predictions about the scanned 
participant?s pending performance? by peers had on the target?s performance.  
Although the researchers describe this set-up as mere peer presence, this verbal 
communication about predictions may have a different impact than the presence of 
peers alone without any verbal commentary.  Thus, follow-up work is needed to 
better understand the role of peer presence in the immediate context of risk behavior 
engagement, particularly in the older adolescent developmental period during which a 
large influence of peers on an individual?s risk-taking behavior has been observed 
(e.g., Borsari & Carey, 2001).  
Factors that May Impact the Salience of Peer Influence 
In addition to understanding the type of influence peers can have in the 
immediate context of risk behavior engagement (i.e., means by which peers exert 
influence), it is also necessary to take into account potential moderating factors that 
may buffer or exacerbate the effect of such influence.  That is, it may be that some 
adolescents are more susceptible or alternatively more resilient to the influence of 
peers or the type of peer influence.  Knowledge of these differences is particularly 
important for targeted prevention efforts.  Basic research examining moderators of 
peer influence remains relatively rare, yet extant work has suggested several possible 
variables, including demographic factors, characteristics of the target individual, and 
nature of the peer relationship (Prinstein & Dodge, 2008).  
In terms of demographic factors, prior research has suggested a differential 
impact of peer influence for male and female adolescents.  For example, in a recent 
 21 
 
 
study, Prinstein and colleagues (2010) found that gender served as a moderator of 
friend socialization effects such that female adolescents were uniquely susceptible to 
the influence of their best friends? engagement in a health risk behavior.  The 
researchers speculated that this may be due to the fact that female adolescents have 
increased reliance on friendship support as a source of esteem and greater intimacy 
within dyadic friendships as compared to males, making them especially susceptible 
to the influence of peers.  In addition to gender, another demographic variable that 
may moderate peer influence is race/ethnicity.  Research is somewhat sparse and 
equivocal in this area; specifically, race/ethnicity has been found to moderate the 
influence of peers but in different directions.  Some emerging work suggests that 
among entering college students, Caucasians? perceptions of peer behavior exerts a 
stronger influence on drinking behavior than among Latinos (Corbin, Vaughan, & 
Fromme, 2008).  This finding was attributed to cultural differences and the 
importance of family versus peers in the creation of personal values (i.e., Latinos 
maintain strong ties with family and, thus, the influence of family drinking behavior 
outweighs the influence of peers).  Other experimental work has found peer effects on 
risk taking to be greater among non-White than among White adolescents, which was 
ascribed to differential levels of risk behavior engagement (Gardner & Steinberg, 
2005). These equivocal findings could certainly be due to the different groups being 
compared and as such additional work is needed.  
Individual difference factors may also moderate the influence of peers.  One 
such factor is engagement in ?real world? risk behaviors.  Adolescents with greater 
involvement in real world risk-taking behaviors may be more susceptible to pro-risk-
  22 
 
 
taking sources of peer influence (Dishion, Capaldi, Spracklen, & Li, 1995; Dishion, 
Patterson, Stoolmiller, & Skinner, 1991).  Another factor to consider is the trait of 
resistance to peer influence, defined as the degree to which one acts autonomously in 
interactions with his/her peer group (Steinberg & Monahan, 2007).  Adolescents? 
susceptibility to peer influence appears as a major risk factor linked to negative 
outcomes ranging from delinquency and substance abuse to risky sexual behavior 
(Allen, Porter, McFarland, 2006; DiIorio et al., 2001; Hops, Andrews, Duncan, 
Duncan, & Tildesley, 2000; Prinstein, Boergers, & Spirito, 2001).  An individual?s 
ability to resist peer influence may decrease his or her susceptibility to the presence 
and direct encouragement of peers. 
The final category of moderators to consider based on prior empirical and 
conceptual work is the nature of peer relationship, specifically friendship quality and 
popularity.  The quality of peer relationships has been found to affect the strength and 
nature of social influence such that stable, intimate, and supportive peer relationships 
make social influence more potent (Borsari & Carey 2006; Stevens & Prinstein, 2005; 
Urberg, Luo, Pilgrim, & Degirmencioglu, 2003).  Relationships characterized by 
closeness may be especially likely to promote conformity due to individuals? high 
opportunities for discussion regarding behaviors and attitudes (Rose, 2002) as well as 
due to the fact that these intimate relationships are considered to be more important 
than those that are less so (Kobus, 2003).  Another such factor is the status or 
popularity of the peer.  Social psychology theories suggest that individuals tend to 
conform to perceived norms associated with ?role models? of high status (this idea 
dates back to the Asch studies which demonstrated that targets were more likely to 
 23 
 
 
conform to a group of high-status peers than lower-status peers; Bond & Smith, 
1996).  Substantial research suggests that adolescents place a high value on 
reputations and status among peers (Brown, 1990).  Thus, adolescents are thought to 
be especially invested in adopting attitudes and behaviors that may earn them greater 
status among peers (i.e., perceived social rewards).  Cohen and Prinstein (2006) 
demonstrated in an experimental study that adolescents of moderate peer social status 
were likely to comply with the opinions of peers whom they thought were high in 
social status and distance themselves from peers who appeared to be low in status.  In 
sum, a number of potential moderators warrant consideration including demographic 
factors, characteristics of the target individual, and nature of peer relationship 
(Prinstein & Dodge, 2008).  
Summary of the Literature 
There are a number of limitations of the current understanding of peer 
influence that particularly impact the ability to understand the role of peers in the 
immediate context of risk behavior engagement.  First, the peer influence literature 
has been plagued with studies that use cross-sectional, zero-order correlations 
between the behavior of adolescents and the behavior of their peers. This type of 
design and level of analysis may reflect measurement artifacts and friendship 
selection (Jaccard, Blanton, & Dodge, 2005) and, of key relevance to the proposed 
study, provides little insight into understanding how peer influence manifests in the 
immediate context of risk behavior engagement.  Thus, the association between one?s 
own behavior and reports of the behavior of friends cannot be taken as unambiguous 
evidence for peer influence and, further, it provides little information in terms of 
 24 
 
 
influence in the immediate context of risk behavior engagement, if indeed it is 
occurring. 
Also contributing to the lack of understanding on what can take place in the 
immediate context of risk behavior engagement is the extant research on direct peer 
influence.  First, there are a limited number of studies, making it difficult to 
determine which findings are replicable.  In terms of methodology, a number of 
studies have relied on retrospective report of the occurrence and nature of direct 
influence.  Retrospective surveys are subject to self-report bias and social desirability 
factors that could possibly result in a youth denying the influence of peers.  Youth 
may inaccurately represent the nature of their responses to active peer influences.  In 
addition, assessing the frequency of being encouraged to engage in a risk behavior 
does not reveal whether the risk behavior actually takes place.  Although 
experimental studies have begun to address the limitation of understanding how a 
peer may react to direct peer influence, this work has had a number of limitations 
present, including the nature of the task and the way in which the peer influence is 
manipulated. 
Another notable limitation is the fact that peer presence, as indirect influence, 
is a poorly understood phenomenon; the majority of work on peer presence biasing 
youth toward risk behavior engagement has been more theoretical than actually 
supported by empirical findings.  There is a paucity of studies modeling peer 
presence; thus, at a fundamental level it is unclear whether youth do, in fact, 
inherently act in a more risky manner in the presence of peers. Basic empirical 
 25 
 
 
support of peer presence leading to increased risk behavior needs to be established in 
order to move toward understanding the mechanisms underlying this relationship.  
In sum, despite some increasing methodological and analytical sophistication 
of investigations, the ability to make causal conclusions about the influence of peers 
in the immediate context of risk behavior engagement is often limited by designs 
dependent on self-report or clouded by confounds.  It is unclear whether the 
magnitude of peer effects reported in previous research is overestimated.  At a 
fundamental level it is unclear whether youth do, in fact, inherently act in a more 
risky manner in the presence of peers and for whom the influence of peers is most 
salient.  As such, experimental designs in which peer influence can be modeled and 
manipulated in the laboratory are needed to test causal models of peer influence more 
stringently and to eventually examine mechanisms. 
Current Study 
In light of limitations in current questionnaire and experimental work and 
apparent lack of understanding on the effect of peers in the immediate context of risk 
behavior engagement, the current study aimed to understand whether peers do act in a 
riskier manner in the presence of peers and further whether peer presence alone 
influences risk behavior or whether a direct influence process (e.g., peer 
encouragement of risk behavior) is necessary.  Furthermore, the study aimed to 
examine potential moderators of peer influence.  Using a behavioral task of risk-
 taking behavior (the Balloon Analogue Risk Task ? BART; Lejuez et al., 2002) as the 
outcome measure, older adolescents were tested alone once and then were 
randomized to one of three conditions (alone, peers present, peers encouraging).  If 
 26 
 
 
the target was randomized to peers present or peers encouraging conditions, he/she 
was asked to bring in two, same-gender close friends for the experimental session.  In 
the alone condition, he/she came back without friends. This design allowed for the 
examination of differences in risk behavior when peers are a) not present; b) in the 
same room and able to see participant behavior on the risk task, but unable to provide 
any form of advice or encouragement; and c) in the same room, able to see participant 
behavior on the risk task, and given the instruction to encourage risk behavior.  In this 
way, it is possible to isolate peer presence and awareness of behavior from the 
additional influence of risk encouragement. 
Primary Aim 
Using three conditions: alone, peers present, and peers encouraging, the 
current study aimed to examine the influence of peers on older adolescent risky 
behavior as indexed on a behavioral risk task.  Specifically, analyses examined 
whether older adolescent risk-taking behavior is influenced by the presence of peers 
and the encouragement of risk-taking behavior by peers.  Further, analyses examined 
the differences in risk-taking behavior when peers are present versus directly 
encouraging risk behavior.  
Exploratory Aim 
To examine gender, ethnicity, real world risk-taking behavior, resistance to 
peer influence, friendship quality, and peer popularity as potential moderators in the 
primary aim due to their basis in the literature as factors that are likely to affect the 
relationship between peer influence and risk taking. 
 27 
 
 
Chapter 2: Method 
Overall Design 
The study used a repeated measures design (two sessions spaced 1-3 weeks 
apart).  The sample included 183 target 18-20 year olds, and 244 peers (2 for each 
participant in the peer conditions and 0 in the alone condition; total participants = 
427).  All target participants (n = 183) completed an initial session alone and then 
were randomly assigned to one of three experimental conditions: alone (n = 61), peers 
present (n = 61), peers encouraging (n = 61).  Subjects were recruited using local 
advertisements and an online study enrollment system operated by the University of 
Maryland Psychology Department.  The two assessment sessions occurred at the 
Center for Addiction, Personality, and Emotion Research (CAPER) on the University 
of Maryland campus.  Each assessment session lasted approximately 40-60 minutes 
and included the completion of questionnaires and the Balloon Analogue Risk Task 
(BART).  All study procedures were approved by the University of Maryland 
Institutional Review Board.  
Participants 
Participants were recruited primarily through flyers and the online Sona 
System. Flyers were posted in a variety of locations across the University of 
Maryland, College Park campus (e.g., dorms, library, academic buildings, student 
union, and cafeterias).  Subjects also were recruited using the online Sona System, a 
web-based human subject pool management software for universities run by the 
University of Maryland Psychology Department.  The study was advertised on the 
 28 
 
 
paid part of the Sona System and subjects were informed that paid studies did not 
count toward class credit.  
To be eligible for the study, the target participant had to be between 18 and 20 
years of age, a sophomore at the University of Maryland, proficient in English, and 
able to bring in two same-gender, close friends for the experimental session.  The 
peers were selected by the targets who were instructed to bring two close friends of 
the same gender.  Once a participant had been in the study as either a target or a peer 
s/he was not eligible to participate again (thus the sample consists of 427 unique 
participants).  
Of the 188 targets who completed the initial session (baseline), 5 target 
participants were excluded from the present analyses for the following reasons: did 
not come in for the experimental session (denoted by no response to phone or email 
inquires; n = 2), and did not bring in the required type of peers for the second 
assessment (brought a different-gender instead of same-gender peer (n = 1) and 
brought a peer with whom the participant was not friends - indicated on the Network 
of Relationship Inventory as having just met (n = 2)).  Thus, the attrition rate was 
2.66%. 
The resultant sample of 183 targets included participants who were on average 
19.16 years of age (SD = .57), 63.9% female, 53.0% non-Hispanic White, 23.0% 
Black/African-American, 15.3% Asian, 4.4% Hispanic or Latino, 1.1 % American 
Indian or Alaska Native, and 3.3% of more than one race/ethnicity.  The 2 peers of 
each target (n = 244) were randomly assigned as Friend 1 (n = 122) or Friend 2 (n = 
122).  Friend 1 was on average 19.25 years of age (SD = .81), 63.9% female, 55.7% 
 29 
 
 
non-Hispanic White, 20.0% Black/African-American, 13.1% Asian, 7.4% Hispanic or 
Latino, 3.3% of more than one race/ethnicity, 16.4% freshman, 66.4% sophomores, 
15.6% juniors, and 1.6% seniors.  Friend 2 was on average 19.37 years of age (SD = 
1.23), 63.9% female, 59.0% non-Hispanic White, 18.9% Black/African-American, 
11.5% Asian, 7.4% Hispanic or Latino, 0.8% Native Hawaiian or Other Pacific 
Islander, 2.5% of more than one race/ethnicity, 15.7% freshman, 67.8% sophomores, 
14.9% juniors, and 1.7% seniors.  The target had known Friend 1 on average 30.64 
months (SD = 45.29) and Friends 2 on average 22.74 months (SD = 30.60).  There 
were no significant demographic differences between Friend 1 and Friend 2. The 
racial distribution of the study sample is largely consistent with the university 
undergraduate student body; however, the sample has a greater proportion of females 
compared to student body (College Portrait, 2009). 
Procedure 
After a brief screen for the previously described inclusion/exclusion criteria, 
target participants came to CAPER for the baseline session.  This baseline session 
began with a thorough explanation of the protocol and required consent.  Once 
consent was obtained, the target participant completed the BART.  The target was 
read the computer task instructions using a visual of the task (see Figure 1) 
accompanied by the directions provided below:  
Throughout the task, you will be presented with 30 balloons, one at a 
time.  For each balloon, you can click on the button labeled ?Click 
Here to Pump up the Balloon? to increase the size of the balloon.  You 
will accumulate 2 cents in a temporary bank for each pump.  At any 
 30 
 
 
point, you can stop pumping up the balloon and click on the button 
labeled ?Collect $$$.?  Clicking this button will start you on the next 
balloon and will transfer the accumulated money from your temporary 
bank to your permanent bank labeled ?Total $$$.?  It is your choice 
to determine how much to pump up the balloon, but be aware that at 
some point the balloon will explode.  The explosion point varies 
across balloons, ranging from the first pump to enough pumps to make 
the balloon fill the entire computer screen. If your balloon explodes 
before you click on ?Collect $$$,? then you move onto the next 
balloon and all money in your temporary bank is lost.  Exploded 
balloons do not affect the money accumulated in your permanent 
bank.  At the end of the experiment, you will be paid the amount of 
money earned on the game.   
Following completion of the BART, the questionnaires were administered 
(see measures section below).  At the end of the baseline session, the target was told 
the outcome of randomization (i.e., whether s/he needed to bring in two friends for 
the experimental session, although not the specific condition), the second session was 
scheduled (one week apart with a maximum of three weeks), and compensation was 
given (between $10-25 based on BART performance).   
The experimental session had three possible scenarios: alone, peers present, or 
peers encouraging to which the targets were randomly assigned.  For the alone 
condition, the target returned for the experimental session alone.  In the peers present 
and peers encouraging conditions, the target returned with two, same-gender, close 
 31 
 
 
friends.  For these two conditions, upon arrival the friends were separated from the 
target, consented, and provided a thorough explanation of the protocol.  Next, the 
BART was completed (see description of conditions below), followed by the 
questionnaires, compensation, and debriefing.  In terms of compensation, for the 
experimental session, the target received between $10-25 based on BART 
performance and an additional $10 as an incentive for completion of the experimental 
session.  The peers were paid between $10-25 based on the target?s riskiness on the 
BART.  The target in both the peers present and peers encouraging conditions were 
given no specific information about peer payment (informed in the debriefing).   For 
the peers in the present condition, they were given no specific information about 
payment beyond the possible range of payment.  Only in the encouraging condition, 
peers were told they would be paid based on how risky the target was on the task 
(more risk by target, more peer payment regardless of the monetary gain of the 
target).  This was done to ensure the peers would be motivated to encourage risk 
behavior and to simulate the real world where peers are not likely to experience the 
consequences of the risk behavior they may encourage in others.  We did not provide 
this information to peers in the present condition because we did not want them to be 
motivated to use subtle nonverbal strategies to influence the target (e.g., laughing or 
nodding affirmatively; Dishion, Spracklen, Andrews, & Patterson, 1996; Piehler & 
Dishion, 2007).  During debriefing the target was informed that the peer payment was 
based on their performance on the task and in the encouraging condition that the peers 
had been instructed to encourage maximal pumps. 
 32 
 
 
Experimental Conditions 
Alone. In the alone condition, the target completed the BART alone.  
Peers Present. In the peers present condition, the peers were in the room with 
the target when the target performed the BART (the peers sat behind the target such 
that they were able to view the computer screen throughout the task; see room setup 
section below).  Prior to joining the target in the experimental room, the peers were 
given a description of the task and instructed to not give any verbal or non-verbal 
feedback. The peers were instructed: 
Your friend is going to complete a computer task and you are going to 
be asked to observe them playing the task.  We ask that you not give 
any verbal or non-verbal feedback to your friend.   Please remain 
silent.  During the computer task, on the screen, your friend will see 30 
balloons, one after another.  For each balloon he/she will click on the 
button labeled ?Click Here to Pump up the Balloon? to increase the 
size of the balloon. He/she will earn 2 cents for each click. At any 
point, he/she can stop pumping up the balloon and click on the button 
labeled ?Collect $$$.? Clicking this button will start him/her on the 
next balloon. At some point the balloon will explode. The explosion 
point varies across balloons, ranging from the first pump to enough 
pumps to make the balloon fill the entire computer screen. Remember, 
we ask that you not give any verbal or nonverbal feedback. Please 
don?t talk.  
 33 
 
 
The target was told that their friends would be present while they completed 
the task.  They were instructed to not talk to them. 
Today you are going to complete the same computer task but this 
time your friends will be present. <BART instructions> Please don?t 
start until I bring your friends in. They are going to observe you 
playing the game. We ask that you not talk to them during the task.  
Peers Encouraging.  In the encouraging condition, the peers received the 
same description of the task yet were told that they want to encourage their friend to 
make as many pumps on each balloon as possible.  They were told that their 
compensation was based on how many pumps their friend makes on the task 
regardless of the monetary gain of the target.  The target was not privy to this 
information.  The peers sat behind the target such that they are able to view the 
computer screen throughout the task (see room setup section below).  The peers were 
instructed following the above outlined description of task: 
Your friend is completing a computer task and you are going to 
be asked to encourage him/her while he/she plays the task.  We 
want you to verbally encourage your friend to make as many 
pumps on the balloons as possible.  Importantly, your 
compensation will be dependent on the number of pumps your 
friend makes on each balloon, even if a balloon pops.  The more 
pumps your friend makes, the more you will get paid.  We ask 
that you not share these instructions with your friend. 
 34 
 
 
If peers asked about whether encouragement would impact their friend?s 
performance they were told that they did not know the best strategy for their friend.  
The target was told that their friends would be present while they completed the task.   
Today you are going to complete the same computer task but this 
time your friends will be present. <BART instructions> Please 
don?t start until I bring your friends in. 
Room Setup 
Each of the conditions took place in a 6 feet by 10 feet experimental room 
(See Figure 2).  The room contained the desk and chair that the target used as well as 
two chairs in which the peers sat in the peer conditions.  The task was completed on 
Dell Optiplex 760.  The room was equipped with two 17 inch monitors.  In the peer 
conditions, one monitor was angled in such a way that the peers had a full view.  
Measures 
Balloon Analogue Risk Task 
Lejuez and colleagues (2002) developed the Balloon Analogue Risk Task 
(BART) to model risk taking in the laboratory.  In a number of studies, BART 
responding has been significantly related to composites of self-reported ?real world? 
risk behaviors (e.g., substance use, delinquency, and safety behaviors; Lejuez et al., 
2002; Lejuez, Aklin, Daughters, Zvolensky, & Kahler, 2007).  This measure is well 
validated in older adolescent and young adult samples.  An independent review by 
Harrison, Young, Butow, Salkeld, and Soloman (2005) of state of the art risk 
measurement strategies identified the BART has having excellent reliability and 
 35 
 
 
validity.  Before starting the BART, the task was thoroughly explained using a visual 
of the task accompanied by task directions.  Once the subject pressed a button 
agreeing that he/she understood the task, the computer screen showed a small 
simulated balloon accompanied by a balloon pump, a reset button labeled ?Collect?, 
and a permanent bank (See Figure 1).  Each click on the pump inflated the balloon 
one degree (about .125? in all directions).  With each pump, 2 cents were accrued in a 
temporary reserve (the number of points in this reserve is never indicated to the 
subject).  When a balloon was pumped past its individual explosion point, a ?pop? 
sound effect emanated from the computer.  When a balloon exploded, all cents in the 
temporary bank were lost and the next un-inflated balloon was shown.  At any point 
during each balloon, the subject could stop pumping the balloon and click the 
?Collect? button.  Clicking this button transferred all cents from the temporary bank 
to the permanent bank (displayed in Total $$$) incrementally cent by cent with a 
?bells? sound-effect playing.  After each balloon explosion or collection, the subject?s 
exposure to that particular balloon ended and a new balloon appeared until 30 
balloons (i.e., trials) had been completed.  The primary dependent measure on the 
BART was the adjusted number of pumps across trials.  This adjusted value, defined 
as the average number of pumps on balloons that did not explode, is preferable to the 
unadjusted average because the number of pumps is necessarily constrained on 
balloons that exploded, thereby limiting between participant variability in the 
unadjusted averages (cf. Lejuez et al., 2002). 
 36 
 
 
Demographics 
The participants completed a basic demographic form regarding personal 
information.  The form included age, gender, race/ethnicity, and education level.  
Race/ethnicity was examined in the analyses as four groups: Non-Hispanic White, 
Black/African American, Asian, and Other (Hispanic or Latino, American Indian or 
Alaska Native, and of more than one race/ethnicity). 
Resistance to Peer Influence 
As described above, there is research suggesting that individuals may vary on 
their susceptibility/resistance to peer influence, which may have implications for 
behavior outcomes (Allen et al., 2006).  As such, the Resistance to Peer influence 
Scale (RPI; Steinberg & Monahan, 2007) was included.  The measure presents 
respondents with a series of 10 pairs of statements and asks them to choose the 
statement that is the best descriptor; that is, which sort of person he/she is most like 
(sample item: ?Some people go along with their friends just to keep their friends 
happy? BUT ?Other people refuse to go along with what their friends want to do, 
even though they know it will make their friends unhappy?).  After indicating the best 
descriptor, the respondent is then asked whether the description is ?Really True for 
me? or ?Sort of True for me.?  Responses are coded on a 4-point scale, ranging from 
?Really True for me? for one descriptor to ?Really True for me? for the other 
descriptor, and averaged.  Higher scores indicate greater resistance to peer influence.  
Examination of the scale?s internal consistency has been conducted in four samples 
(Steinberg & Monahan, 2007).  These analyses indicate that the measure 
 37 
 
 
demonstrates adequate internal consistency (? = .70 - .76), which was replicated in 
this sample (? = .72).  
Risk-Taking Behavior 
Because adolescents with greater risk-taking behaviors might prove more 
susceptible to pro-risk-taking sources of peer influence (Dishion et al., 1995; Dishion 
et al., 1991), self-reported levels of risk-taking behavior were included.  Consistent 
with previous work examining risk behaviors in youth (Aklin, Lejuez, Zvolensky, 
Kahler, & Gwadz, 2005; Lejuez et al., 2007), a modified version of the Youth Risk 
Behavior Surveillance System (YRBSS; Centers for Disease and Control Prevention, 
2001) was used in order to create a risk behavior composite score for the target.  
Previous research by Cooper, Wood, Orcutt, & Albino (2003) has demonstrated that 
covariation among diverse behaviors can be adequately modeled by a single higher 
order factor.  The YRBSS assessed engagement in the following behaviors: a) drunk 
driving (defined as either riding with someone who had been drinking alcohol or 
driving oneself following drinking), b) carrying a weapon (e.g., gun, knife, or club), 
c) been in a physical fight d) smoking cigarettes, e) use of other tobacco products 
(chewing tobacco, snuff, or dip), f) binge drinking (defined as having 5 or more 
alcoholic drinks in a row), g) marijuana use, h) other illicit drug use (cocaine, huffing, 
heroin, methamphetamine, ecstasy), i) sexual intercourse without prevention, and j) 
vomit/laxative use to control weight.  Participants reported on their frequency of 
engagement for these risk behaviors on a Likert-type scale during the past 30 days.  
Four risk behaviors (carrying a weapon, use of other tobacco product, sexual 
intercourse without prevention, and vomit/laxative use) were highly truncated with 
 38 
 
 
over 92.3% of the targets reporting they had not engaged in this behavior.  
Consequently, carrying a weapon, other tobacco product, sexual intercourse without 
prevention and vomit/laxative use were excluded from further analysis as they 
contributed no variability. 
Because of the non-normality of the risk behaviors? distributions, each 
behavior was dichotomized to keep all variables on a relatively equal metric in order 
to combine these items into a single factor (cf. Aklin et al., 2005; Lejuez et al., 2007).  
Five risk behavior variables were truncated with greater than 50% of responses being 
zero (drunk driving, physical fight, smoking cigarettes, marijuana use, and illicit drug 
use) and were subsequently dichotomized as yes/no engaged in the behavior.  For the 
other variable with a less truncated distribution (binge drinking with under 40% 
reporting no engagement in that risk behavior in each case), a median split was used 
to classify each participant as either high or low on the risk-related behavior.  
In the six risk behaviors, results of an iterated principal factor analysis of 
tetrachoric correlations with weighted least squares estimation indicated a dominant 
first factor with an eigenvalue of 3.48, and accounting for 58% of the common 
variance among the items.  Item loadings ranged from .38 (drunk driving) to .90 
(marijuana use), suggesting that all items loaded adequately on this factor.  Items 
were then summed into a risk behavior composite with a scale mean of 1.60 (SD = 
1.63) with a range of 0 to 6.  Internal consistency was adequate (Cronbach?s ? = .70) 
with no item detracting from alpha.  
 39 
 
 
Friendship Quality 
The quality of peer relationships has been found to affect the strength and 
nature of social influence such that stable, intimate, and supportive peer relationships 
make social influence more potent (Borsari & Carey 2006).  Relationships that are 
more intimate are considered to be more important than those that are less so (Kobus, 
2003).  In order to control for this notion that adolescents are more influenced by the 
actions of a quality friend, the Network of Relationships Inventory (Furman & 
Buhrmester, 1985) was used to assess friendship quality.  Each target subject 
completed the questionnaire about each of his/her peers.  The scale has established 
validity and reliability (Furman, 1996; Furman & Buhrmester, 2009).  The instrument 
consists of 30 items designed to assess 10 relationship qualities: reliable alliance, 
intimacy, affection, relative power, conflict, enhancement of worth, instrumental 
help, satisfaction of relationship, companionship, and importance of relationship. 
Two broad factor scores of social support (closeness) and negative interactions 
(discord) for each relationship were created based on the averaging the items on the 
relevant positive and negative scales (Furman & Buhrmester, 1985); higher scores 
indicate greater closeness and discord accordingly.  The target?s impression of the 
two peers was used in analyses (an average of the two reports).  Internal consistency 
for the closeness subscale was ? =.96 and discord was ? =.92.  
Popularity 
Based on the findings that the perceived status of a peer impacts a youth?s 
susceptibility (Cohen & Prinstein, 2006) popularity was included as a measure.  
Popularity was determined by asking the target to rate their friend?s popularity at the 
 40 
 
 
University of Maryland (How popular do you think this friend is at UMD?) on a 4-
 pont Likert scale (Diego, Field, & Sanders, 2003) with higher scores indicating 
greater popularity.  The target?s impression of the two peers was used in analyses (an 
average of the two reports).  
Data Analysis Plan 
Data was entered and analyzed using the statistical package PASW version 
18.  First, the distributional properties of all non-categorical variables were assessed 
to determine whether they met the statistical assumptions for the analyses and to 
check for outliers.  Second, the means and standard deviations of the study variables 
were examined and then ANOVA and Chi-Square were used to compare the three 
conditions on all variables to ensure equivalence of groups at the baseline session.  
To address the primary aim (compare the three conditions in which the BART 
was administered across the two sessions), a repeated measures analysis of variance 
(ANOVA) was conducted, with BART scores for baseline and experimental sessions 
entered as the within-subjects variable and experimental condition (alone, present, 
encouraging) entered as the between-subjects factor.  Following an overall significant 
F test for the interaction of session and condition, the interaction was probed by 1) 
using individual repeated measures ANOVAs to test the impact of the session 
(baseline and experimental) on each of the three condition levels (alone, present, 
encouraging) and 2) testing the conditions (alone, present, encouraging) at each 
session level (baseline and experimental) with individual ANOVAs (IV: condition, 
DVs: baseline session BART score and experimental session BART score 
respectively).  Together these two approaches allow for a comparison within each 
 41 
 
 
condition of the change across time points and a comparison of the conditions against 
each other at both time points.  
To address the exploratory aim examining the potential moderators, a repeated 
measures ANOVA was conducted with the within subject factor as the BART score 
at each session (baseline and experimental) and condition (alone, present, 
encouraging) and each of the proposed moderators as independent variables.  To 
control for Type I error due to multiple comparisons, an error rate of .01 was used.  
Gender, ethnicity, and risk-taking behavior were examined separately as moderators 
with all three conditions.  Resistance to peer influence, friendship closeness, 
friendship discord, and peer popularity were examined separately with the present and 
encouraging conditions (excluding the alone group).    
Chapter 3: Results1
 Preliminary Analyses 
As a preliminary analysis, descriptives of the study variables were examined 
for the whole sample as shown in Table 1.  All variables met the statistical 
assumptions for the analyses (absolute values of less than 2 for skewness and less 
than 4 for kurtosis; Tabachnick & Fidell, 2007) and no outliers were identified.  Next, 
to check for equivalence of groups, the means and standard deviations of the study 
variables at the first assessment were examined with ANOVA and Chi-Square; results 
                                                 
1 An alternative outcome measure on the BART is the number of explosions that take 
place across the 30 balloons. When the current data analyses were conducted with 
explosions as the outcome, the same results were observed as reported for pumps 
adjusted average. 
 42 
 
 
are shown in Table 1.  The three conditions (alone, present, encouraging) did not 
significantly differ on any study variable (ps > .07) at the baseline session.  
Primary Aim 
To investigate the impact of each condition (alone, present, encouraging) on 
BART performance across the two sessions, a repeated measures ANOVA was 
conducted with the within subject factor as the BART score at each session (baseline 
and experimental) and the between subjects factor as condition (alone, present, 
encouraging).  The repeated measures ANOVA revealed a significant main effect of 
session on BART performance (F(1, 180) = 67.37, p = .001; partial ?2 = .27).  This 
main effect of session suggests that the session influences BART performance.  
However, to qualify this main effect, as hypothesized, there was a significant 
interaction of session by condition (F(2, 180) = 11.38, p = .001; partial ?2 = .11).  As 
can be seen in Figure 3, this session by condition interaction indicated that the 
influence of condition on BART score depended on the session (the probing of the 
interaction effect is presented below).  Results of this repeated measures ANOVA are 
presented in Table 2.  
Additional analyses were conducted to further explore the nature of this 
session by condition interaction effect.  First, the effect of experimental session within 
each of the three conditions was tested.  Specifically, when examining simple effects 
with individual repeated measures analyses within each condition, participants had 
significantly higher BART scores at the experimental session than the baseline 
session in the encouraging (F(1, 60) = 63.13, p = .001; partial ?2 = .51), present (F(1, 
60) = 10.70, p = .002; partial ?2 = .15), and alone (F(1, 60) = 6.65, p = .01; partial ?2 
 43 
 
 
= .10) conditions.  That is, all three conditions significantly increased from the 
baseline session to the experimental session (conditional means across sessions and 
results of individual repeated measures ANOVAs are presented in Table 3).  It is 
notable that the effect size was considerably higher in the encouraging condition 
compared to the other two conditions and modestly higher in the present condition 
than in the alone condition. 
Next, to examine the effect of condition at the baseline session and at the 
experimental session, simple effects were analyzed in two separate ANOVA analyses 
using the baseline session BART score and the experimental session BART score as 
dependent variables, respectively.  As expected, no significant condition effect was 
found at the baseline session (F(2, 182) = .62, p = .53).  For the experimental session, 
a significant effect for condition was found (F(2, 182) = 8.36, p = .001).  Post-hoc 
analyses with Tukey?s test indicated that the encouraging condition significantly 
differed from both the alone (p = .001; d = -0.72 medium/large effect size; Cohen, 
1988) and the present (p = .04; d = -0.45 medium effect size) conditions.  The 
difference between the alone condition and the present condition was not significant 
(p = .23; d = -0.29 small effect size).  
Exploratory Aim 
Next, in order to test for moderation, a repeated measures ANOVA was 
conducted with the within subject factor as the BART score at each session (baseline 
and experimental) and condition (alone, present, encouraging) and each of the 
proposed moderators as independent variables. To control for Type I error due to 
multiple comparisons, an error rate of .01 was used. None of the proposed moderators 
 44 
 
 
(ethnicity, gender, risk-taking behavior, resistance to peer influence, friendship 
closeness, friendship discord, and peer popularity) were significant; there were 
neither significant session by moderator two-way interactions nor session by 
condition by moderator three-way interactions2. 
Chapter 4: Discussion 
Main Outcomes 
Older adolescence (ages 18-20) is a time of heightened risk-taking behavior.  
In particular, this period has been characterized by the propensity towards 
engagement in risky behaviors (e.g., substance use and abuse, risky sexual behavior, 
reckless driving) that have the potential for serious physical and psychological 
consequences.  An accumulation of research has demonstrated that peers play a 
considerable role in older adolescent risk behavior engagement with risk taking often 
occurring in the peer social context (Borsari & Carey, 2001; Chassin et al., 2009; 
Chen et al., 2000; Simons-Morton et al., 2005).  Although the extant literature has 
provided insight into the means by which peers may influence risk behavior 
engagement, studies to date possess a number of limitations.  As such, questions have 
remained about the means by which peers can exert influence in the immediate 
context of risk behavior engagement and which factors impact susceptibility/ 
resistance to peer influence.  
To better understand the proposed influence of peers during risk behavior 
engagement, the current study aimed to understand whether older adolescents act in a 
                                                 
2 Please see the appendix for additional moderation analyses.  
 45 
 
 
riskier manner in the presence of peers and whether peer presence alone influences 
risk behavior or whether a direct influence process (e.g., peer encouragement of risk 
behavior) is necessary.  Further, the study aimed to examine potential moderators of 
peer influence.  Using a behavioral risk-taking task as the outcome measure, 183 
adolescents came to the lab alone once and then were randomized to one of three 
conditions (alone, present, encouraging).  When the target was randomized to peers 
present or peers encouraging conditions, the target brought in two same-gender, close 
friends for the experimental session.  In the alone condition, the participant returned 
without friends.  This design allowed for the examination of differences in risk-taking 
behavior when peers were a) not present; b) in the same room and able to see 
participant behavior on the risk task, but unable to provide any form of advice or 
encouragement; and c) in the same room, able to see participant behavior on the risk 
task, and given the instruction to encourage risk behavior.  In this way, it was possible 
to isolate peer presence and awareness of behavior from the additional influence of 
risk encouragement.  
 The primary aim of the study was to examine whether older adolescent risk-
 taking behavior is influenced by the presence of peers and the encouragement of risk 
behavior by peers.  Results from the study demonstrated a significant interaction of 
session and condition.  Specifically, when examining the change from baseline to 
experimental session individually for each of the conditions, all three conditions 
significantly increased from the baseline to experimental session.  Yet, the effect of 
the experimental manipulation was considerably more robust for the encouraging 
condition compared to the other two conditions.  At the baseline session the three 
 46 
 
 
groups (alone, present, encouraging) were not significantly different from one another 
but at the experimental session, the encouraging condition had significantly higher 
risk taking on the BART compared to both the alone and peers present conditions.  
The alone and peers present conditions were not significantly different from each 
other on risk taking at the experimental session. 
 Together these results suggest that while the presence of peers does have some 
impact on risk-taking behavior among older adolescents, this effect is not 
significantly different from those who completed the task in the absence of any peer 
presence.  Having peers encourage risk taking on the task did have a significant 
impact; participants in this condition had a significant increase in risk-taking behavior 
and this significantly differed from both the peers present and alone conditions.  
Thus, an increase in risk-taking behavior was observed when peers explicitly 
encouraged such behavior, clearly identifying the desired and socially rewardable 
outcome.   
It is not surprising that older adolescents were significantly more risky when 
being encouraged by their peers.  This is consistent with a long line of research 
demonstrating that peers have a significant impact on risk behavior engagement 
(Prinstein & Dodge, 2008).  So, while this finding is not unexpected, it does fill in an 
important gap in the literature as it experimentally demonstrates an effect of direct 
peer encouragement on risk-taking behavior.  This is particularly notable as the 
literature on peer influence has been overly reliant on cross-sectional designs which 
provide little insight into understanding what type of influence peers can have an 
impact in the immediate context of risk behavior engagement.  Prior research specific 
 47 
 
 
to direct peer influence has been somewhat limited.  In terms of methodology, a 
number of studies had relied on retrospective report of the occurrence and nature of 
direct influence.  Retrospective surveys are subject to self-report bias and social 
desirability factors that could possibly result in a youth denying the influence of peers 
or inaccurately representing the nature of their responses to active peer influences.  In 
addition, assessing the frequency of being encouraged to engage in a risk behavior 
does not reveal whether the risk behavior actually takes place.  Prior experimental 
work has studied the impact of peer influence more generally (allowing peer input on 
how a task is performed; Gardner & Steinberg, 2005) rather than directly modeling 
encouragement.  Therefore, this study directly addresses limitations in prior work and 
clearly demonstrates that the direct encouragement by peers leads to significantly 
more risk taking.  
The finding that the presence of peers led to significantly more risk taking but 
not significantly more so than in the alone condition was somewhat contrary to what 
has been suggested in the literature.  It has previously been purported that the mere 
presence of peers may be enough to bias increased risk behavior engagement.  Two 
sets of explanations have been put forth to support this claim.  The first is focused on 
the idea that the presence of peers would influence behavior through perceived 
attitudes and behaviors of others and the social rewards perceived to follow from 
initiating those attitudes and behaviors.  For example, adolescents who believe that 
risk behavior will bring them acceptance or approval by peers are more likely to 
engage in that behavior.  The second hypothesis proposed by Steinberg and 
colleagues is that the presence of peers activates the same neural circuitry implicated 
 48 
 
 
in reward processing, and that this impels adolescents toward greater risk behavior 
engagement (Chein et al., 2011; Steinberg, 2007; 2008; 2009). 
A number of potential explanations can be set forth for why in the current 
study the presence of peers did not significantly differ from the alone condition.  
First, due to the novelty of the task, it may be that there was not enough information 
available to the target participant on what was considered to be accepted or admired 
by peers.  This stands in contrast to ?real world? risk behaviors that would have a 
learning history for the target and peers.  Modeling and perceived norms are two such 
implicit ways that an adolescent may be provided information about which behaviors 
are accepted and admired, considered appropriate in a given social context, and 
therefore what behaviors are likely to lead to social acceptance and reinforcement3.  
In the current study, the target did not have any information from the peers about the 
task; that is, they had not seen the peers perform on the task or have information 
about norms on the task.  Thus, due to the novelty of the task the target did not know 
what type of performance would bring them approval by their peers (unlike the 
encouraging condition in which this was explicitly stated by the peers).  
A second explanation, addressing Steinberg?s hypothesis, is that the presence 
of peers (in the absence of direct encouragement) has a diminished impact as 
adolescents age.  In the recently published study conducted by Chein and colleagues 
(2011), the presence of peers did not lead to increased risk-taking behavior in the 19-
 22 age group.  An important caveat to note is that it is somewhat difficult to directly 
apply these findings to the current study as only 9.3% of the current sample falls into 
the younger age categorization (14-18) in the Chein study.  Chein and colleagues 
                                                 
3 Please see the appendix for an additional discussion of modeling and norms. 
 49 
 
 
attribute the lack of peer presence influence in the 19-22 age group to the maturation 
of brain systems that support decision-making, suggesting that the presence of peers 
is not rewarding for this older age group and thus does not lead to increased risk 
behavior.  To support this maturation hypothesis, other work utilizing a self-report 
instrument that assesses susceptibility to peer pressure has found that resistance to 
peer influences increases with age (Steinberg & Monahan, 2007).  Although this is a 
self-report measure of susceptibility and thus would likely vary from behavioral 
assessment, this does lend some support to the idea that the impact of peer influence 
diminishes with age.  Yet, based on the current findings it may be that older 
adolescents are less susceptible to the mere presence of peers but remain susceptible 
to direct encouragement. 
When considering the present findings on peer presence in relation to the 
experimental studies on social facilitation and risk-taking behavior (described in the 
introduction) a few differences are observed.  First, the social facilitation experiments 
used known risk behaviors including gambling and aggression. The one study that 
used a novel task (Nawa et al., 2008) did not find an impact of social presence.  
Second, the difference in findings being a result of an unknown versus known person 
observing the risk taking cannot be ruled out.  Hence, considering the present findings 
in the context of prior work, a number of additional components call for further 
investigation including known versus unknown risk behavior and social presence, 
information on perceived attitudes and behaviors related to the task, and 
developmental period.  
 50 
 
 
The results of the current study show that the alone condition had a significant 
increase in risk taking on the task from the baseline to the experimental condition.  
Considering the equivalence of external factors in the baseline and experimental 
sessions (i.e., no experimental manipulation, same room, came alone to the lab again), 
this suggests that some learning took place on the task.  Prior work has been mixed on 
changes in BART with repeated administration.  One study, administering the BART 
approximately 11.5 days apart to healthy adults, found that risk behavior on the 
BART did not change over time (White, Lejuez, & de Wit, 2008).  Of note this was a 
different version of the BART task consisting of 60 balloon trials with varying levels 
of payoff values.  In another study with undergraduate students, Lejuez and 
colleagues (2003) administered the BART three times on a single test day.  Results of 
this study indicated small but significant increases in risk behavior between 
administrations.  Thus, additional work is needed to better understand the change in 
BART with repeated administration.   
The exploratory aim of the study was to examine gender, ethnicity, risk-taking 
behavior, resistance to peer influence, friendship quality, and peer popularity as 
potential moderators in the primary aim due to their basis in the literature as factors 
that are likely to affect the relationship between peer influence and risk taking.  
Contrary to prior expectations, none of the proposed moderators had a significant 
effect, despite adequate power to detect such effects. This suggests that the 
experimental conditions had an equal effect across participants. A number of potential 
hypotheses can be set forward to explain the lack of moderation.  First, the gold 
standard approach to measuring popularity is sociometric measures of peer status 
 51 
 
 
based on nominations completed by a full sample of adolescents (Prinstein, 2007).  
However, this approach has been exclusively conducted with younger adolescents 
who are in middle or high school (Prinstein, 2007).  Sociometric measures have not 
been applied to college age youth due to a number of issues related to practicality.  
Among college students there is no single setting that can best capture the variety of 
peer relationships and reputations that college students may form within their peer 
network (Prinstein, 2007) and further, with a large undergraduate student body (e.g., 
over 20,000 as in the present study), it would be impossible to have all students on 
one nomination roster.  Examining one specific group (e.g. fraternity or sorority) 
could be confounded by the homogeneity of comparison peers as sociometrics are 
designed to examine reputations relative to a comparison group (i.e., standardized 
scores that indicate preference as compared to other youth on the same nomination 
roster; Zakriski & Prinstein, 2001).  Thus, future research would greatly benefit from 
the development of a means to assess popularity on a college campus.  In terms of 
other measurement issues, with resistance to peer influence and friendship quality, 
one could speculate about measurement artifacts; that is, face validity of the measure 
for resistance to peer influence and lack of range in friendship quality by fault of 
requesting students to bring in two same-gender, close friends.  However, 
examination of the data does not support this conjecture as each measure is normally 
distributed with an adequate range.  In sum, the lack of significant moderation by the 
proposed variables suggests that the experimental manipulations were equally 
consistent for study participants. 
 52 
 
 
Limitations and Future Directions 
The present study has several limitations worth noting.  For the encouraging 
condition, participant interactions were not video or audio-taped nor were peer 
participants provided a script with an encouragement dialogue.  These design 
decisions were made due to concerns with the potential impact these features 
(recordings, script) would have on external validity (Plante, 2010).  That is, in the 
case of video/audio recordings, there was concern that the participants would behave 
in a more socially desirable manner due to knowledge of being observed and 
recorded.  In the case of a script, it was thought that it would come across as artificial 
to the target participants or might limit their interaction because of fear of being 
judged by the staff.  This benefit to external validity comes at the cost of a 
manipulation check and information on the nature of dialogue that took place in the 
peer encouraging condition.  Future work could benefit from an understanding of 
what type of encouragement peers used as well as how the target responded or 
handled this pressure, expanding upon prior work that has coded peer interactions on 
problem-solving tasks (Allen et al., 2006; Dishion, Andrews, & Crosby, 1995).  
Whereas the previously described decisions were made to protect external 
validity, one threat to external validity is that the peers were instructed to and 
benefitted from encouraging risk behavior in the encouraging condition.  This design 
decision was made to facilitate breaking down the various type of peer influence 
(direct versus indirect).  Yet despite the advantages of the laboratory design in 
precision and ability to yield more objective data about peer influence, it is uncertain 
whether the peer motivation in the encouraging condition (i.e., payment for 
 53 
 
 
encouragement) is a valid representation of peer motivation in a naturalistic setting 
where there may be no real benefit for peer encouragement.  Furthermore, this design 
choice means that it is unclear what would happen if peers were present in the room 
and not given any instruction to encourage or discourage risk behavior (but allowed 
to provide commentary).  It may be that, without specific instruction to encourage 
risk, peers would provide encouragement in both directions (toward riskier behavior 
and more conservatism).  With coding of peer interactions, this approach could 
provide information about what types of peer groups gravitate towards risk behavior 
(rather than prescribing all groups to be in the encouragement of risk behavior).  
A related point is that the current study focused solely on encouragement of 
risk behavior.  Yet, based on the strong effect of peer encouragement on risk behavior 
engagement, it is worthwhile to consider whether peers only have such a strong 
influence in the negative direction.  That is, strong peer influence processes should 
make it as likely that a youth would be influenced to engage in risk behavior as they 
would be susceptible to encouragement to not engage in such behavior and/or to 
engage in an alternative prosocial behavior (Allen & Antonishak, 2008).  There are 
innumerable examples of peer influence leading to positive outcomes (e.g., 
involvement in a sport or volunteer activities). As such, future work would benefit 
from an examination of if/how peer influence leads to less risk behavior engagement.  
Finally, older adolescents were chosen for the current study because they 
represent the developmental period in which risk behavior is peaking (Pittman, & 
Richmond, 2008) and in which peers have been shown to play a critical role in risk 
behavior engagement (Borsari & Carey, 2001).  Second year students were selected in 
 54 
 
 
order to limit potential variability due to length of friendship.  It was felt that first 
year students may not have had enough opportunity to develop friendships 
(particularly during the fall semester).  Based on preliminary findings of 
developmental differences in the influence of peer presence on risk behavior 
engagement (Chein et al., 2011), future work would benefit from an expansion of this 
study design to younger adolescents in order to empirically test whether peer presence 
versus encouragement has a differential effect in younger versus older adolescents 
(i.e., opportunity for age groups to be compared).   
Conclusion  
Older adolescence has been characterized in part by the propensity towards 
engagement in risky behaviors (e.g., substance use and abuse, risky sexual behavior, 
reckless driving) that have the potential for serious physical and psychological 
consequences.  The goal of the proposed experimental study was to better understand 
the influence of peers during risk behavior engagement among older adolescents.  
The experimental design of the current study addresses limitations of prior work on 
peer influence in the immediate context of risk behavior engagement and serves as a 
first step to future research.  This study has the potential to advance understanding of 
peer influence in several meaningful ways as it 1) moves beyond retrospective report 
of peer influence, 2) expands upon previous experimental work by addressing the 
nature of the task and the way in which the peer influence is manipulated (direct 
influence rather than collaboration), 3) provides basic testing of peer presence, 4) 
breaks down the ?peer effect? to examine different types of influence (i.e., direct 
versus indirect), and 5) takes a step to understand potential factors that may impact 
 55 
 
 
vulnerability to peer influence.  The methodological strengths of this study, including 
random assignment to peer conditions, provide good reason to feel confident about 
the internal validity of the findings.  Together the results suggest that in older 
adolescence the presence of peers alone does not bias increased risk behavior 
engagement.  Instead, based on the findings that the largest influence on risk behavior 
engagement took place when peers directly identified the valued behavior on the task 
(encouraging condition), it may be that older adolescent risk behavior is largely 
motivated by behavioral reinforcement and perceived social rewards.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 56 
 
 
Appendices 
Moderation 
As an additional analysis, moderation was examined within the three 
conditions separately (moderation of the within-subjects effect within each of the 
conditions, eliminating the between subjects component).  Following the steps 
outlined by Judd and colleagues (2001) for testing moderation in within subject 
designs, the conditions for moderation are satisfied when the independent measure 
significantly predicted scores that represent the difference between the two dependent 
measures.  Accordingly, a BART difference score was calculated by subtracting the 
BART baseline session score from BART experimental session score; then, 
regression was used to test whether the moderator was a significant predictor of the 
difference score.  These analyses were done separately within each condition. Gender, 
ethnicity, and risk-taking behavior were examined as moderators in all three 
conditions.  Resistance to peer influence, friendship closeness, friendship discord 
(both friendship quality variables), and peer popularity were examined for the present 
and encouraging conditions.  To control for Type I error due to multiple comparisons, 
an error rate of .01 was used.  Using this approach, none of the proposed moderators 
were significant. 
Modeling 
Modeling refers to imitation of risk-taking behavior of others such as close 
friends - one?s behavior corresponds to another?s concurrent behavior (Maisto, Carey, 
& Bradizza, 1999).  Laboratory study of modeling became popular in the late 1970s 
 57 
 
 
and early 1980s in alcohol use research.  In general, the modeling research has almost 
exclusively been focused upon alcohol use.  Overall, the modeling research within 
alcohol use indicates that participants exposed to heavy-drinking models consume 
more than students exposed to light drinking models or no models at all.  Borsari, & 
Carey (2001) have identified three characteristics of the model influence on 
participant consumption of alcohol: 1) target youth match the concurrent drinking of a 
confederate, a previous observation of a model does not influence the target youth?s 
subsequent drinking, 2) composition of the group of confederates influences 
participant alcohol use: when two confederates drink at different rates, participants 
model the fast rate of drinking, and 3) the sociability of the confederate (warm or 
cold) during the session influences modeling. Modeling is a means through which 
peers may be indirectly exerting influence in the immediate context of risk behavior 
engagement. 
Norms 
One socialization process that has been studied in depth among college 
populations is normative influences.  Norms are defined as properties of groups that 
characterize where a group is located along an attitudinal or behavior dimension 
(Miller & Prentice, 1996) and have been divided into two types: descriptive and 
injunctive (Cialdini, Reno, & Kallgren, 1990).  Descriptive norms are defined as 
individuals? perceptions about the prevalence of a behavior thus providing 
information about what is normal.  Injunctive norms describe the appropriate course 
of action in a given situation and thus provide information about what ought to be 
done (Cialdini et al., 1990).  Norms are a large part of the literature on college student 
 58 
 
 
alcohol use; specifically, college students have been shown to misperceive their 
peers, assuming more drinking, more heavy drinking, and more comfort with drinking 
than is actually the case (Borsari, & Carey, 2003; Perkins, Haines, & Rice, 2005).  
These misperceived norms have demonstrated relations with heavy drinking and 
alcohol-related problems in college student samples (Perkins & Berkowitz, 1986; 
Larimer, Turner, Mallett, & Geisner, 2004).  
As stated above, norms are a large part of the literature on college student 
alcohol use.  College students have been shown to misperceive their peers, assuming 
more drinking, more heavy drinking, and more comfort with drinking than is actually 
the case (Borsari, & Carey, 2003; Perkins et al., 2005).  Studies have revealed similar 
overestimation for other kinds of drug use (Perkins, Meilman, Leichliter, Cashin, & 
Presley, 1999) and for other health risk behaviors (Gibbons, Helweg-Larsen, & 
Gerrard, 1995). Within the alcohol use literature, these misperceived norms have 
demonstrated relations with heavy drinking and alcohol-related problems in college 
student samples (Perkins & Berkowitz, 1986; Larimer et al., 2004) and have been 
observed among students soon after matriculation into college (Read, Wood, 
Davidoff, McLacken, & Campbell, 2002).  Both elevated descriptive and injunctive 
norms are thought to make excessive alcohol use seem common and acceptable.  
Students may feel pressures to match the drinking behavior they perceive other 
students engaging in (descriptive norm) and approving of (injunctive norm).  
These findings have prompted numerous norms campaigns on campuses that 
involve giving students accurate statistics on what their peers think, feel, and do in 
drinking situations (i.e., alter the perception of norms to which college students 
 59 
 
 
conform).  In a survey of 118 four-year colleges and universities across the US, 
Wechsler and colleagues (2003) found that 57 (48%) had implemented a social norms 
campaign.  However, the success in reducing drinking among college students has 
been mixed (Lewis & Neighbors, 2006; Wechsler et al., 2003).  Prentice (2008) has 
provided a summary of when/how norms interventions work best.  These best 
conditions include heavy drinkers, small, residential campuses, when accompanied by 
personalized feedback, when the message about norms falls within students? latitude 
of acceptance, and when it encompasses a group with which students identify (e.g., 
fraternity members, teammates, friends).  
 60 
 
 
Tables 
 61 
 
 
Table 1 
 
Descriptive Statistics and Examination of Differences between Conditions at the 
Baseline Session 
 
 
 
Total Sample 
Variable 
(n = 183) 
Alone 
(n = 61) 
Present 
(n = 61) 
Encouraging 
(n = 61) 
F  / ?2 p 
  Days Between Sessions  9.62 (3.88) 8.89 (3.56) 10.36 (4.04) 9.60 (3.95) 2.24 .11 
Target Demographic Variables       
  Age (M(SD)) 19.00 (.57) 19.10 (.57) 19.10 (.60) 19.30 (.53) 2.46 .09 
  Gender (% Female) 63.9 62.3 68.9 60.7 1.00 .61 
  Race  (% non-Hispanic White) 53.0 50.8 54.1 54.1 3.30 .97 
  Year in school (% sophomores)  100 100 100 100 - - 
Friend 1 Demographic Variables       
  Age (M(SD)) 19.25 (.81) - 19.20 (.83) 19.31 (.79) .61 .44 
  Gender (% Female) 63.9 - 67.2 60.7 .57 .45 
  Race (% non-Hispanic White)  55.7 - 59.0 52.5 1.39 .85 
  Year in school (% sophomores) 66.4 - 67.2 65.6 3.29 .35 
  Length of Friendship  (M(SD)) 30.64 (45.29)  23.36 (31.47) 38.17 (55.41) 3.27 .07 
Friend 2 Demographic Variables       
  Age M (M(SD)) 19.37 (1.23) - 19.23 (.76) 19.51 (1.57) 1.56 .21 
  Gender (% Female) 63.9 - 67.2 60.7 .57 .45 
  Race  (% non-Hispanic White) 59.0 - 57.4 60.7 6.40 .27 
  Year in school (% sophomores) 67.8 - 68.9 66.7 2.32 .51 
  Length of Friendship  (M(SD)) 22.74 (30.60)  21.68 (26.56) 23.81 (34.43) .14 .71 
BART       
  PumpsAdjAvg (M(SD)) 38.61 (12.40) 37.79 (13.76) 40.08 (12.12) 37.97 (11.27) .64 .53 
Moderators       
  RPI (M(SD)) 2.96 (.42) 2.95 (.44) 2.98 (.43) 2.96 (.39) .12 .89 
  Risk-Taking Behavior (M(SD)) 1.60 (1.63) 1.64 (1.75) 1.29 (1.33) 1.87 (1.76) 1.90 .15 
  Friendship Closeness (M(SD)) 65.11 (14.92) - 62.80 (16.08) 64.95 (14.34) .61 .44 
  Friendship Discord (M(SD)) 9.13 (3.35) - 8.64 (2.77) 8.81 (2.88) .10 .75 
  Popularity (M(SD)) 2.77 (.47) - 2.80 (.42) 2.81 (.39) .01 .91 
 
Note. The reported sample size is for the number of targets (total and in each 
condition). BART = Balloon Analogue Risk Task; PumpsAdjAvg = Pumps Adjusted 
Average; RPI = Resistance to Peer Influence. 
 
 62 
 
 
TTable 2 
Main Repeated Measure ANOVA Results 
 
 df F p ?p
 2
 Session 1 67.37 .001 .27 
Session X Condition 2 11.38 .001 .11 
Within group error 180    
 
Note. Session = baseline and experimental. Condition = alone, present, and 
encouraging. 
 63 
 
 
Table 3 
 
Condition Balloon Analogue Risk Task Score Means across Sessions and Individual 
Repeated Measures ANOVA Results 
 
 
 
Condition 
Baseline 
Session   
(M(SD)) 
Experimental 
Session 
(M(SD)) 
F p ?p2
 Alone 
 37.79 (13.76) 41.08 (13.21) 6.65 .01 .10 
Present  
 40.08 (12.12) 44.77 (11.97) 10.70 .002 .15 
Encouraging 
 37.97 (11.27) 50.15 (11.74) 63.13 .001 .51 
 
 64 
 
 
 
Figures 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 65 
 
 
 
Figure 1. Schematic of the Balloon Analogue Risk Task.  
 
 
 
 
 
 
 
 
 
 
 66 
 
 
 
Figure 2. Photo of experimental room set-up. 
 
 
 
 
 
 
 
 
 
 
 
 
  
 67 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0
 10
 20
 30
 40
 50
 60
 Baseline Session Experimental Session
 Alone
 Present
 EncourageR
 is
 k 
T
 ak
 in
 g:
  P
 um
 ps
  A
 dj
 us
 te
 d 
A
 ve
 ra
 ge
  
 
 
Figure 3. Balloon Analogue Risk Task performance at the baseline and the 
experimental sessions across condition. Error bars represent standard errors. 
 
 
 
 
 
 
 
 
 
 
 
 68 
 
 
References 
 
Aiello, J.R., & Douthitt, E.A. (2001). Social facilitation theory from Triplett to 
electronic performance monitoring. Group Dynamics, 5(3), 163-180. 
doi:10.1037/1089-2699.5.3.163 
Aklin, W.M., Lejuez, C.W., Zvolensky, M.J., Kahler, C.W., & Gwadz, M. (2005). 
Evaluation of a behavioral measure of risk taking propensity with inner-city 
adolescents. Behaviour Research and Therapy, 43(2), 215-228. 
doi:10.1016/j.brat.2003.12.007 
Albert, D., O?Brien, L., DiSorbo, A., Uckert, K., Eagan, D., Chein, J., & Steinberg, L. 
(2009, April). Peer influences on risk taking in young adulthood. Poster 
presented at the 2009 Society for Research in Child Development Biennial 
Meeting, Denver, CO.  
Allen, J.P., & Antonishak, J. (2008). Adolescent peer influences: Beyond the dark 
side. In: M.J. Prinstein, & K.A. Dodge (Eds), Understanding Peer Influence 
in Children and Adolescents (pp. 141-160). New York: Guilford Press. 
Allen, J.P., & Brown, B.B. (2008). Adolescents, peers, and motor vehicles: The 
perfect storm? American Journal of Prevention Medicine, 35(3), S289-S292. 
doi:10.1016/j.amepre.2008.06.017 
Allen, J.P., Porter, M.R., & McFarland, C. (2006). Leaders and followers in 
adolescent close friendships: Susceptibility to peer influence as a predictor of 
risky behavior, friendship instability, and depression. Development and 
Psychopathology, 18, 155?172. doi:10.1017/S0954579406060093 
 69 
 
 
American Cancer Society. (2007). Cancer prevention & early detection facts and 
figures. Atlanta, GA: American Cancer Society. 
Andrews, J.A., Tildesley, E., Hops, H., & Li, F. (2002). The influence of peers on 
young adult substance use. Health Psychology, 21, 349?357. 
doi:10.1037//0278-6133.21.4.349 
Arnett, J.J. (2005). The developmental context of substance use in emerging 
adulthood. Journal of Drug Issues, 35, 235?253. Retrieved from 
http://www2.criminology.fsu.edu/~jdi/ 
Arnett, J.J., Offer, D., & Fine, M.A. (1997). Reckless driving in adolescence: ?state? 
and ?trait? factors. Accident Analysis and Prevention, 29, 57? 63. 
doi:10.1016/S0001-4575(97)87007-8 
Bachman, J.G., Wadsworth, K.N., O'Malley, P.M., & Schulenberg, J. (1997). 
Marriage, divorce, and parenthood during the transition to young adulthood: 
Impacts on drug use and abuse. In J. Schulenberg, & J. Maggs (Eds.), Health 
risks and developmental transitions during adolescence (pp. 246-269). NY: 
Cambridge University Press. 
Bachman, J.G., O'Malley, P.M., Schulenberg, J.E., Johnston, L.D., Bryant, A.L., & 
Merline, A.C. (2002). The decline of substance use in young adulthood: 
Changes in social activities, roles, and beliefs. Mahwah, NJ: Lawrence 
Erlbaum Associates Publishers. 
Bailey, J.A., Fleming, C.B., Henson, J.N., Catalano, R.F., & Haggerty, K.P. (2008). 
Sexual risk behavior 6 months post-high school: Associations with college 
 70 
 
 
attendance, living with a parent, and prior risk behavior. Journal of Adolescent 
Health, 42, 573-579. doi:10.1016/j.jadohealth.2007.11.138 
Baer, J.S., Kivlahan, D.R., & Marlatt, G.A. (1995). High-risk drinking across the 
transition from high school to college. Alcoholism: Clinical and Experimental 
Research, 19, 54-61. doi:10.1111/j.1530-0277.1995.tb01472.x 
Baumeister, R.F., & Leary, M.R. (1995). The need to belong: Desire for interpersonal 
attachments as a fundamental human motivation. Psychological Bulletin, 117, 
497-529. doi:10.1037//0033-2909.117.3.497 
Baxter, J.S., Manstead, A.S.R., Stradling, S.G., Campbell, K.A., Reason, J.T., & 
Parker, D. (1990). Social facilitation and driver behaviour. British Journal of 
Psychology, 81, 351? 360. Retrieved from 
http://www.bps.org.uk/publications/journals/journaltitles/bjp.cfm 
Bearinger, L.H., & Resnick, M.D. (2003). Dual method use in adolescents: A review 
and framework for research on use of STD and pregnancy protection, Journal 
of Adolescent Health, 32, 340-349. doi:10.1016/S1054-139X(03)00018-1  
Bingham, C.R., Shope, J.T., Parow, J.E., & Raghunathan, T.E. (2009). Crash types: 
Markers of increased risk of alcohol-involved crashes among teen drivers. 
Journal of Studies on Alcohol, 70, 528-535. Retrieved from 
http://www.jsad.com/ 
Bond, R., & Smith, P. (1996). Culture and conformity: A meta-analysis of studies 
using Asch?s (1952b, 1956) line judgment task. Psychological Bulletin, 119, 
111-137. doi:10.1037//0033-2909.119.1.111 
 71 
 
 
Borsari, B., & Carey, K.B. (2001). Peer influence on college drinking: A review of 
the research. Journal of Substance Abuse, 13, 391-424. doi:10.1016/S0899-
 3289(01)00098-0 
Borsari, B., & Carey, K.B. (2003). Descriptive and injunctive norms in college 
drinking: A meta-analytic integration. Journal of Studies on Alcohol, 64, 331-
 341. Retrieved from http://www.jsad.com/ 
Borsari, B., & Carey, K.B. (2006). Influence of the quality of peer relationships on 
college drinking. Drug and Alcohol Review, 25, 361-370. 
doi:10.1080/09595230600741339 
Boyer, T.W. (2006). The development of risk-taking: A multi-perspective review, 
Developmental Review, 26, 291?345. doi:10.1016/j.dr.2006.05.002 
Brook, J.S., Whiteman, M., Czeisler, L.J., Shapiro, J., & Cohen, P. (1997). Cigarette 
smoking in young adults: Childhood and adolescent personality, familial, and 
peer antecedents. Journal of Genetic Psychology, 158, 172?188. 
doi:10.1080/00221329709596660 
Brooks-Gunn, J., & Furstenberg, F., Jr (1989). Adolescent sexual behavior. American 
Psychologist, 44, 249?257. doi:10.1037//0003-066X.44.2.249 
Brown, B. B. (1990). Peer groups and peer cultures. In S.S. Feldman & G.R. Elliott 
(Eds.), At the threshold: The developing adolescent (pp. 171 ? 196). 
Cambridge, MA: Harvard University Press. 
Brown, B. (2004). Adolescents? relationships with peers. In R. Lerner & L. Steinberg 
(Eds.), Handbook of adolescent psychology, 2nd edition (pp. 363?394). New 
York: Wiley. 
 72 
 
 
Brown, B.B., Bakken, J.P., Ameringer, S.W., & Mahon, S.D. (2008).  A 
comprehensive conceptualization of the peer influence process in 
adolescence.  In M.J. Prinstein & K.A. Dodge (Eds.), Understanding Peer 
Influence in Children and Adolescents (pp. 17-44).  New York: Guildford 
Publications.  
Brown, B.B., Dolcini, M.M., & Leventhal, A. (1997). Transformations in peer 
relationships at adolescence: implications for health-related behavior. In: J. 
Schulenberg, J.L. Maggs, & K. Hurrelman (Eds.), Health risks and 
developmental transitions during adolescence (pp. 161-189). New York: 
Cambridge University Press. 
Byrnes, J.P., Miller, D.C., & Schafer, W.D. (1999). Gender differences in risk taking: 
A meta-analysis. Psychological Bulletin, 125, 367-383. doi:10.1037/0033-
 2909.125.3.367 
Capaldi, D.M., Dishion, T.J., Stoolmiller, M., & Yoerger, K. (2001). Aggression 
toward female partners by at-risk young men: the contribution of male 
adolescent friendships. Developmental Psychology, 37, 61?73. 
doi:10.1037//0012-1649.37.1.61 
Centers for Disease Control and Prevention (CDCP). (2001). National school-based 
Youth Risk Behavior Survey. Public-use data documentation. Atlanta, GA: 
Author. 
Centers for Disease Control and Prevention (CDCP). (2003). Sexually transmitted 
disease surveillance, 2002, U.S. Department of Health and Human Services, 
Atlanta, GA. 
 73 
 
 
Centers for Disease Control and Prevention (CDCP). (2005). Sexually transmitted 
disease surveillance, 2004. Atlanta, GA: US Department of Health and 
Human Services, Centers for Disease Control and Prevention. 
Chassin, L., Hussong, A., Barrera, M. Jr., Molina, B.S.G., Trim, R., & Ritter, J. 
(2004). Adolescent substance use. In R.M. Lerner, & L. Steinberg (Eds.). 
Handbook of adolescent psychology, 2nd Edition (pp. 199?230). Hoboken, 
NJ: Wiley.
 Chassin, L., Hussong, A., & Beltran, I. (2009). Adolescent substance use. In: R. 
Lerner, & L. Steinberg (Eds.), Handbook of Adolescent Psychology, 3rd 
Edition (pp. 723-763). Hoboken, NJ: John Wiley & Sons Inc. 
Chen, L., Baker, S.P., Braver, E.R., & Li, G. (2000). Carrying passengers as a risk 
factor for crashes fatal to 16- and 17-year-old drivers. JAMA: The Journal of 
the American Medical Association, 283, 1578?1582. 
doi:10.1001/jama.283.12.1578 
Chen, K., & Kandel, D.B. (1995). The natural history of drug use from adolescence to 
the mid-thirties in a general population sample. American Journal of Public 
Health, 85, 41-47. doi:10.2105/AJPH.85.1.41 
Chein, J., Albert, D., O?Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase 
adolescent risk taking by enhancing activity in the brain?s reward circuitry. 
Developmental Science, 14(2), F1?F10. doi:10.1111/j.1467-
 7687.2010.01035.x 
Cialdini, R.B., & Reno, R.R., & Kallgren, C.A. (1990). A focus theory of normative 
conduct: Recycling the concept of norms to reduce littering in public places. 
 74 
 
 
Journal of Personality and Social Psychology, 58, 1015-1026. 
doi:10.1037//0022-3514.58.6.1015 
Clapp, J.D., Lange, J.W., & Shillington, A.M. (2003). Two Studies Examining 
Environmental Predictors of Heavy Drinking by College Students. Prevention 
Science, 4, 99-108. doi:10.1023/A:1022974215675 
Clapp, D., Reed, M.B., Holmes, M.R., Lange, J.E., & Voas, R.B. (2006). Drunk in 
public, drunk in private: The relationship between college students' drinking 
environments and alcohol consumption. American Journal of Drug and 
Alcohol Abuse, 32, 275?285. doi:10.1080/00952990500481205 
Clark, D.B., & Moss, H.B. (2010). Providing Alcohol-Related Screening and Brief 
Interventions to Adolescents through Health Care Systems: Obstacles and 
Solutions. Public Library of Science ? Medicine, 7(3), e1000214. 
doi:10.1371/journal.pmed.1000214 
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edition). 
Hillsdale, NJ: Erlbaum. 
Cohen, G.L., & Prinstein, M.J. (2006). Peer contagion of aggression and health-risk 
behavior among adolescent males: An experimental investigation of effects on 
public conduct and private attitudes. Child Development, 77, 967-983. 
doi:10.1111/j.1467-8624.2006.00913.x 
College Portrait, University of Maryland ? College Park. (2009). Retrieved from 
http://www.collegeportraits.org/MD/UMCP/characteristics 
Cooper, M. L., Wood, P. K., Orcutt, H. K., & Albino, A. (2003). Personality and the 
predisposition to engage in risky or problem behaviors during adolescence. 
 75 
 
 
Journal of Personality and Social Psychology, 84, 390?410. 
doi:10.1037=0022-3514.84.2.390
 Corbin, W.R., Vaughan, E.L., & Fromme, K. (2008). Ethnic differences and the 
closing of the sex gap in alcohol use among college-bound students. 
Psychology of Addictive Behaviors, 22, 240-248. doi:10.1037/0893-
 164X.22.2.240 
Crandall, C.S. (1988). Social contagion of binge eating. Journal of Personality and 
Social Psychology, 55, 588-598. doi:10.1037/0022-3514.55.4.588 
Csikszentmihalyi, M. & Larson, R. (1984). Being adolescent. New York: Basic 
Books. 
Deaux, K., & Major, B. (1987). Putting gender into context: An interactive model of 
gender-related behavior. Psychological Review, 94, 369-389. 
doi:10.1037/0033-295X.94.3.369 
DiBlasio, F., & Benda, B. (1992). Gender differences in theories of adolescent sexual 
activity. Sex Roles, 27, 221?240. doi:10.1007/BF00289926 
DiIorio, C., Dudley, W.N., Kelly, M., Soet, J.E., Mbwara, J., & Potter, J.S. (2001). 
Social cognitive correlates of sexual experience and condom use among 13- 
through 15-year-old adolescents. Journal of Adolescent Health, 29, 208-216. 
doi:10.1016/S1054-139X(00)00200-7 
Diego, M.A., Field, T.M., & Sanders, C.E. (2003). Academic performance, 
popularity, and depression predict adolescent substance use. Adolescence, 
38(149), 35-42.  
Dishion, T.J., Andrews, D.W., & Crosby, L. (1995). Antisocial boys and their friends 
 76 
 
 
in early adolescence: Relationship characteristics, quality, and interactional 
process. Child Development, 66, 139-151. doi:10.2307/1131196 
Dishion, T.J., Capaldi, D., Spracklen, K.M., & Li, F. (1995). Peer ecology of male 
adolescent drug use. Development and Psychopathology, 7, 803 ? 824. 
doi:10.1017/S0954579400006854 
Dishion, T.J., & Owen, L.D. (2002). A longitudinal analysis of friendships and 
substance use: Bidirectional influence from adolescence to adulthood. 
Developmental Psychology, 38, 480?491. doi:10.1037//0012-1649.38.4.480 
Dishion, T.J., Patterson, G.R., Stoolmiller, M., & Skinner, M.L. (1991). Family, 
school, and behavioral antecedents to early adolescent involvement with 
antisocial peers. Developmental Psychology, 27, 172 ? 180. 
doi:10.1037//0012-1649.27.1.172 
Dishion, T.J., Spracklen, K.M., Andrews, D.W., & Patterson, G.R. (1996). Deviancy 
training in male adolescents? friendships. Behavior Therapy, 27, 373 ? 390. 
doi:10.1016/S0005-7894(96)80023-2 
Ellickson, P.L., Perlman, M., & Klein, D.J. (2003). Explaining racial/ethnic 
differences in smoking during the transition to adulthood. Addictive 
Behaviors, 28, 915?931. doi:10.1016/S0306-4603(01)00285-4 
Erickson, M., & Jensen, G.F. (1977). Delinquency is still a group behavior! Toward 
revitalizing the group premise of the sociology of deviance. Journal of 
Criminal Law and Criminology, 68, 262?273. doi:10.2307/1142849 
Friedman, L.S., Lichtenstein, E. & Biglan, A. (1985) Smoking onset among teens: An 
empirical analysis of initial situations. Addictive Behaviors, 10, 1?13. 
 77 
 
 
doi:10.1016/0306-4603(85)90048-6 
Furman, W. (1996). The measurement of friendship perceptions: Conceptual and 
methodological issues. In: W.M. Bukowski, A.F. Newcomb, & W.W. Hartup 
(Eds.), The Company they Keep: Friendship in Childhood and Adolescence 
(pp. 41-65). New York, NY, US: Cambridge University Press. 
Furman, W., & Buhrmester, D. (1985). Children?s perceptions of the personal 
relationships in their social networks. Developmental Psychology, 21, 1016-
 1024. doi:10.1037//0012-1649.21.6.1016 
Furman, W., & Buhrmester, D. (2009).The Network of Relationships Inventory: 
Behavioral Systems. International Journal of Behavioral Development, 33, 
470-478. doi:10.1177/0165025409342634 
Futterman, D.C. (2005). HIV in adolescents and young adults: Half of all new 
infections in the United States. Topics in HIV Medicine, 13, 101?105. 
Retrieved from http://www.iasusa.org/pub/ 
Gardner, M., & Steinberg, L. (2005). Peer influence on risk-taking, risk preference, 
and risky decision-making in adolescence and adulthood: An experimental 
study. Developmental Psychology, 41, 625-635. doi:10.1037/0012-
 1649.41.4.625 
Graham, J., Marks, G., & Hansen, W. (1991). Social influence processes affecting 
adolescent substance use. Journal of Applied Psychology, 76, 291-298. 
doi:10.1037//0021-9010.76.2.291 
Harrison, J. D., Young, J. M., Butow, P., Salkeld, G., & Soloman, M. J. (2005). Is it 
worth the risk? A systematic review of instruments that measure risk 
 78 
 
 
propensity for use in the health setting. Social Science & Medicine, 60, 1385?
 1396. doi:10.1016/j.socscimed.2004.07.006 
Hebert, R. (2004). What?s new in nicotine and tobacco research? Nicotine and 
Tobacco Research, 6, S279-S283. doi:10.1080/14622200412331320671
 Hops, H., Andrews, J.A., Duncan, S.C., Duncan, T.E., Tildesley, E. (2000). 
Adolescent drug use development: A social interactional and contextual 
perspective. In: A.J. Sameroff, M. Lewis, & S.M. Miller (Eds.), Handbook of 
developmental psychopathology 2nd ed. (pp. 589-605).  Dordrecht, 
Netherlands: Kluwer Academic Publishers.
 House, J.S., Landis, K.R., & Umberson, D. (1988). Social relationships and health. 
Science, 241, 540 ?545. doi:10.1126/science.3399889 
Jaccard, J., Blanton, H., & Dodge, T. (2005). Peer influences on risk behavior: An 
analysis of the effects of a close friend. Developmental Psychology, 41, 135-
 147. doi:10.1037/0012-1649.41.1.135 
Jacquin, K.M., Harrison, M.L., & Alford, S.M. (2006). Gender and peer presence 
influence responses to aggressive provocation. American Journal of Forensic 
Psychology, 24(3), 29-44. Retrieved from 
http://www.forensicpsychology.org/journal.htm 
Jessor, R., & Jessor, S.L. (1977). Problem behavior and psychological development: 
A longitudinal study of youth. New York: Academic Press. 
Johnston, L.D., O?Malley, P.M., Bachman, J.G., & Schulenberg, J.E. (2005). 
Monitoring the future: National survey results on drug use, 1975-2004. 
 79 
 
 
Volume I: Secondary school students. NIH Publication 05-5727. Bethesda, 
MD: National Institute on Drug Abuse.  
Johnston, L.D., O'Malley, P.M., Bachman, J.G., & Schulenberg, J.E. (2009). 
Monitoring the Future national survey results on drug use, 1975-2008. 
Volume II: College students and adults ages 19-50. NIH Publication No. 09-
 7403. Bethesda, MD: National Institute on Drug Abuse, 306 pp. 
Judd, C.M., Kenny, D.A., & McClelland, G.H. (2001). Estimating and testing 
mediation and moderation in within-participant designs. Psychological 
Methods, 6, 115-134. doi:10.1037//1082-989X.6.2.115 
Kandel, D.B. (1996). The parental and peer contexts of adolescent deviance: An 
algebra of interpersonal influences. Journal of Drug Issues, 26, 289?315. 
Retrieved from http://www2.criminology.fsu.edu/~jdi/ 
Kandel, D.B., & Andrews, K. (1987). Processes of adolescent socialization by parents 
and peers. International Journal of Addictions, 22, 319?342. 
doi:10.3109/10826088709027433 
Kawachi, I.B., & Berkman, L.F. (2001). Social ties and mental health. Journal of 
Urban Health, 78, 458?67. doi:10.1093/jurban/78.3.458 
Kiene, S.M., & Barta, W. (2003). Personal narrative as a medium for STD/HIV 
intervention: A preliminary study. Journal of Applied Social Psychology, 33, 
2327?2340. doi:10.1111/j.1559-1816.2003.tb01887.x 
Kiene, S.M., & Barta, W.D. (2006). A brief individualized computer-delivered sexual 
risk reduction intervention increases HIV/AIDS preventive behavior. Journal 
of Adolescent Health, 39, 404 ?410. doi:10.1016/j.jadohealth.2005.12.029 
 80 
 
 
Kiene, S.M., Barta, W.D., Zelenski, J.M., & Cothran, D.L. (2005). Why are you 
bringing up condoms now? The effect of message content on framing effects 
of condom use messages. Health Psychology, 24, 321?326. doi:10.1037/0278-
 6133.24.3.321 
Kobus, K. (2003). Peers and adolescent smoking. Addiction, 98, 37?55. 
doi:10.1046/j.1360-0443.98.s1.4.x 
Larimer, M.E., Turner, A.P., Mallett, K.A., & Geisner, I.M. (2004). Predicting 
drinking behavior and alcohol-related problems among fraternity and sorority 
members: Examining the role of descriptive and injunctive norms. Psychology 
of Addictive Behaviors, 18, 203?212. doi:10.1037/0893-164X.18.3.203 
Leigh, B.C. (1999). Peril, chance, adventure: Concepts of risk, alcohol use and risky 
behavior in young adults. Addiction, 94, 371-383. doi:10.1046/j.1360-
 0443.1999.9433717.x 
Leibsohn, J. (1994). The relationship between drug and alcohol use and peer group 
associations of college freshmen as they transition from high school. Journal 
of Drug Education, 24, 177?192. doi:10.2190/DVYX-PUX7-KA7T-1X4R 
Lejuez, C.W., Aklin, W.M., Daughters, S.B., Zvolensky, M.J., Kahler, C.W., & 
Gwadz, M. (2007). Reliability and validity of the youth version of the Balloon 
Analogue Risk Task (BART-Y) in the assessment of risk-taking behavior 
among inner-city adolescents. Journal of Clinical Child and Adolescent 
Psychology, 36, 106-111. doi:10.1207/s15374424jccp3601_11 
Lejuez, C.W., Aklin, W.M., Jones, H.A., Richards, J.R., Strong, D.R., Kahler, C.W., 
& Read, J.P. (2003). The Balloon Analogue Risk Task differentiates smokers 
 81 
 
 
and nonsmokers. Experimental and Clinical Psychopharmacology, 11, 26-33. 
doi:10.1037//1064-1297.11.1.26 
Lejuez, C.W., Read, J.P., Kahler, C.W., Richards, J.B., Ramsey, S.E., Stuart, G.L., 
Strong, D.R., & Brown, R.A. (2002). Evaluation of a behavioral measure of 
risk-taking: The Balloon Analogue Risk Task (BART). Journal of Experimental 
Psychology: Applied, 8, 75-84. doi:10.1037//1076-898X.8.2.75 
Lewis, M.A., & Neighbors, C. (2006). Social norms approaches using descriptive 
drinking norms education: A review of the research. Journal of American 
College Health, 54, 213-218. doi:10.3200/JACH.54.4.213-218 
Lucas, K., & Lloyd, B. (1999) Starting smoking: girls? explanations of the influence 
of peers. Journal of Adolescence, 22, 647?655. doi:10.1006/jado.1999.0260 
Maisto, S.A., Carey, K.B., & Bradizza, C.M. (1999). Social learning theory. In K.E. 
Leonard, & H.T. Blane (Eds.), Psychological theories of drinking and 
alcoholism, 2nd edition (pp. 106-163). New York: Guilford Press. 
Martin, C.M., & Hoffman, M.A. (1993). Alcohol expectancies, living environment, 
peer influence, and gender: A model of college-student drinking. Journal of 
College Student Development, 34, 206?211. 
Miller, D.C., & Byrnes, J.P. (1997). Role of contextual and personal factors in 
children's risk-taking. Developmental Psychology, 33, 814-823. 
doi:10.1037/0012-1649.33.5.814 
Miller, D.T., & Prentice, D.A. (1996). The construction of social norms and 
standards. In F.T. Higgins, & A.W. Kruglanski (Eds.), Social psychology: 
handbook of basic principles (pp. 799-829). New York: Guilford Press. 
 82 
 
 
Mohler-Kuo, M., Lee, J.E., & Wechsler, H. (2003). Trends in marijuana and other 
illicit drug use among college students: Results from 4 Harvard School of 
Public Health College Alcohol Study Surveys: 1993-2001. Journal of 
American College Health, 52, 17-24. doi:10.1080/07448480309595719 
Morrongiello, B.A., & Sedore, L. (2005). The influence of child attributes and social-
 situational context on school-age children's risk-taking behaviors that can lead 
to injury. Journal of Applied Developmental Psychology, 26, 347-361. 
doi:10.1016/j.appdev.2005.02.003 
Nawa, N.E., Nelson, E.E., Pine, D.S., & Ernst, M. (2008). Do you make a difference? 
Social context in a betting task. Social Cognitive & Affective Neuroscience, 3, 
367-376. doi:10.1093/scan/nsn032
 Oostveen, T., Knibbe, R.A., & De Vries, H. (1996). Social influences on young 
adults' alcohol consumption; norms, modeling, pressure, socializing and 
conformity. Addictive Behaviors, 21, 187?197. doi:10.1016/0306-
 4603(95)00052-6 
Paul, E.L., & Kelleher, M. (1995). Precollege concerns about losing and making 
friends in college. Journal of College Student Development, 36, 513?521. 
Perkins, H.W., & Berkowitz, A.A. (1986). Using student alcohol surveys: Notes on 
clinical and educational Program applications. Journal of Alcohol and Drug 
Education, 31, 44-51. Retrieved from http://vlex.com/source/journal-alcohol-
 drug-education-3082  
Perkins, H.W., Haines, M.P., & Rice, R.M. (2005). Misperceiving the college 
drinking norm and related problems: A nationwide study of exposure to 
 83 
 
 
prevention information, perceived norms, and student alcohol misuse. Journal 
of Studies on Alcohol, 66, 470-478. Retrieved from http://www.jsad.com/ 
Piehler, T.F., & Dishion, T.J. (2007). Interpersonal dynamics within adolescent 
friendships: Dyadic mutuality, deviant talk, and patterns of antisocial 
behavior. Child Development, 78, 1611-1624. doi:10.1111/j.1467-
 8624.2007.01086.x
 Pittman, L.D., & Richmond, A. (2008). University belonging and friendship quality 
during the transition to college: Links to self perceptions and psychological 
symptoms. Journal of Experimental Education, 76, 343-361. 
doi:10.3200/JEXE.76.4.343-362 
Plante, T.G. (2010). Contemporary Clinical Psychology. Hoboken, NJ: John Wiley & 
Sons Inc. 
Prentice, D.A. (2008). Mobilizing and weakening peer influence as mechanisms for 
changing behavior. In M.J. Prinstein &. K.A. Dodge (Eds.), Understanding 
Peer Influence in Children and Adolescents (pp. 161-180). New York: 
Guilford. 
Preusser, D.F., Ferguson, S.A., & Williams, A.F. (1998). The effect of teenage 
passengers on the fatal crash risk of teenage drivers. Accident Analysis and 
Prevention, 30, 217?22. doi:10.1016/S0001-4575(97)00081-X 
Prinstein, M. J. (2007). Assessment of adolescents? preference- and reputation-based 
popularity using sociometric experts.  Merrill-Palmer Quarterly, 53, 243-261. 
doi:10.1353/mpq.2007.0013
  84 
 
 
Prinstein, M.J., Boergers, J., & Spirito, A. (2001). Adolescents' and their friends' 
health-risk behavior: Factors that alter or add to peer influence. Journal of 
Pediatric Psychology, 26, 287-298. 
Prinstein, M.J. & Dodge, K.A. (2008). Current issues in peer influence research. In 
M.J. Prinstein, & K.A. Dodge (Eds.), Understanding peer influence in 
children and adolescents (pp. 3-13).  New York: Guilford. 
Prinstein, M.J., Heilbron, N., Guerry, J.D., Franklin, J.C., Rancourt, D., Simon, V., & 
Spirito, A. (2010). Peer influence and nonsuicidal self injury: Longitudinal 
results in community and clinically-referred adolescent samples. Journal of 
Abnormal Child Psychology, 38, 669-682. doi:10.1007/s10802-010-9423-0 
Prinstein, M., Meade, C., & Cohen, G. (2003). Adolescent oral sex, peer popularity, 
and perceptions of best friends? sexual behavior. Journal of Pediatric 
Psychology, 28, 243?249. doi:10.1093/jpepsy/jsg012 
Rabow, J., & Duncan-Schill, M. (1995). Drinking among college students. Journal of 
Alcohol and Drug Education, 40, 52-64. Retrieved from 
http://vlex.com/source/journal-alcohol-drug-education-3082 
Read, J.P., Wood, M.D., Davidoff, O.J., McLacken, J., & Campbell, J.F. (2002). 
Making the transition from high school to college: The role of alcohol-related 
social influence factors in students? drinking. Substance Abuse, 23, 53?65. 
doi:10.1080/08897070209511474 
Regan, M.A., & Mistopoulos, E. (2003). Understanding passenger influences on 
driver behaviour: implications for road safety and recommendations for 
 85 
 
 
countermeasure development. Victoria, Australia: Accident Research Centre, 
Monash University. Report  #180. 
Reifman, A., & Watson, K. (2003). Binge drinking during the first semester of 
college. Journal of American College Health, 52, 73?81. 
doi:10.1080/07448480309595727 
Revzina, N.V., & Diclemente, R.J. (2005). Prevalence and incidence of human 
papillomavirus infection in women in the USA: a systematic review. 
International Journal of STD & AIDS, 16, 528?537. 
doi:10.1258/0956462054679214 
Rockloff, M.J., & Dyer, V. (2007). An experiment on the social facilitation of 
gambling behavior. Journal of Gambling Studies, 23, 1-12. 
doi:10.1007/s10899-006-9042-4
 Rolinson, M.R., & Scherman, A.S. (2003). College student risk-taking from three 
perspectives. Adolescence, 38, 689-704.  
Schulenberg, J.E., O'Malley, P.M., Bachman, J.G., Wadsworth, K.N., & Johnston, 
L.D. (1996). Getting drunk and growing up: Trajectories of frequent binge 
drinking during the transition to young adulthood. Journal of Studies on 
Alcohol, 57(3), 289-304. Retrieved from http://www.jsad.com/ 
Simons-Morton, B., Lerner, N., & Singer, J. (2005). The observed effects of teenage 
passengers on the risky driving behavior of teenage drivers. Accident Analysis 
and Prevention, 37, 973-982. doi:10.1016/j.aap.2005.04.014 
 86 
 
 
Smetana, J.G., & Asquith, P. (1994). Adolescents? and parents? conceptions of 
parental authority and personal autonomy. Child Development, 65, 1147?
 1162. doi:10.2307/1131311 
Steinberg, L. (2007). Risk taking in adolescence: New perspectives from brain and 
behavioral science. Current Directions in Psychological Science, 16, 55-59. 
doi:10.1111/j.1467-8721.2007.00475.x 
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. 
Developmental Review, 28, 78-106. doi:10.1016/j.dr.2007.08.002 
Steinberg, L. (2009). Adolescent development and juvenile justice. Annual Review of 
Clinical Psychology, 5, 47-73. doi:10.1146/annurev.clinpsy.032408.153603 
Steinberg, L. & Monahan, K.C. (2007). Age differences in resistance to peer 
influence. Developmental Psychology, 43, 1531?1543. doi:10.1037/0012-
 1649.43.6.1531 
Stevens, E. A. & Prinstein, M. J. (2005). Peer contagion of depressogenic 
attributional styles among adolescents: A longitudinal study. Journal of 
Abnormal Child Psychology, 33, 25-37. doi:10.1007/s10802-005-0931-2 
Substance Abuse and Mental Health Services Administration (SAMHSA). Results 
from the 2004 National Survey on Drug Use and Health: National Findings. 
Retrieved from 
http://www.oas.samhsa.gov/NSDUH/2k4NSDUH/2k4results/2k4results.htm#f
 ig7.3.  
Tabachnick, B.G., & Fidell, L.S. (2007). Using Multivariate Statistics (5th Ed.). 
Boston, MA: Pearson. 
 87 
 
 
Thombs, D.L. (1999). An introduction to addictive behaviors, 2nd Edition. New York: 
Guilford Press. 
Udry, J. (1987). Hormonal and social determinants of adolescent sexual initiation. In 
J. Bancroft (Ed). Adolescence and puberty (pp.70-87). New York: Oxford 
University Press. 
Urberg, K.A., Luo, Q., Pilgrim, C., & Degirmencioglu, S.M. (2003). A two-stage 
model of peer influence in adolescent substance use: Individual and 
relationship-specific differences in susceptibility to influence. Addictive 
Behaviors, 28, 1243-1256. doi:10.1016/S0306-4603(02)00256-3 
Volkow, N.D. (2004). Exploring the why's of adolescent drug abuse. NIDA Notes, 
19(3), 1?2. Retrieved from 
http://www.nida.nih.gov/NIDA_Notes/nnv23index.html 
Wechsler, H., Davenport, A., Dowdall, G.W., Moeykens, B., & Castillo, S. (1994). 
Health and behavioral consequences of binge drinking in college. A national 
survey of students at 140 campuses. JAMA: The Journal of the American 
Medical Association, 272, 1672-1677. doi:10.1001/jama.272.21.1672 
Wechsler, H., Lee, J., Kuo, M., & Lee, H. (2000). College binge drinking in the 
1990s: A continuing problem?Results of the Harvard School of Public 
Health 1999 College Alcohol Study. Journal of American College Health, 48, 
199?210. doi:10.1080/07448480009599305 
Wechsler, H., Lee, J.E., Kuo, M., Seibring, M., Nelson, T.F., & Lee, H. (2002). 
Trends in college binge drinking during a period of increased prevention 
efforts. Findings from 4 Harvard School of Public Health College Alcohol 
 88 
 
 
Study Surveys: 1933-2001. Journal of American College Health, 50, 203-217. 
doi:10.1080/07448480209595713 
Wechsler, H., Nelson, T.F., Lee, J.E., Seibring, M., Lewis, C., & Keeling, R.P. 
(2003). Perception and reality: A national evaluation of social norms 
marketing interventions to reduce college students' heavy alcohol use. Journal 
of Studies on Alcohol, 64, 484-494. 
White, H.R., & Jackson, K. (2004). Social and psychological influences on emerging 
adult drinking behavior. Alcohol Research & Health, 28, 182-190. 
White, T.L., Lejuez, C.W., & de Wit, H. (2008). Test-retest characteristics of the 
Balloon Analogue Risk Task (BART). Experimental and Clinical 
Psychopharmacology, 16, 565-570. doi:10.1037/a0014083 
White, H.R., McMorris, B.J., Catalano, R.F., Fleming, C.B., Haggerty, K.P., & 
Abbott, R.D. (2006). Increases in alcohol and marijuana use during the 
transition out of high school into emerging adulthood: The effects of leaving 
home, going to college, and high school protective factors. Journal of Studies 
on Alcohol, 67, 810-822.  
Wood, M.D., Read, J., Palfai, T., & Stevenson, J.F. (2001). Social influence processes 
and college student drinking: The mediation role of alcohol outcome 
expectancies. Journal of Studies on Alcohol, 62, 32-43. Retrieved from 
http://www.jsad.com/ 
Zajonc, R.B. (1965). Social Facilitation. Science, 149, 269-274. 
doi:10.1126/science.149.3681.269 
 89 
 
 
Zakriski, A. L., & Prinstein, M. J. (2001). Sociometric status of child inpatients in 
clinical and normative peer groups: Is peer status in a clinical setting a useful 
measure of adjustment? Journal of Applied Developmental Psychology, 22(2), 
1?17. doi:10.1016/S0193-3973(01)00074-0 
Zarrett, N., & Eccles, J. (2006). The passage to adulthood: Challenges of late 
adolescence. New Directions for Youth Development, 2006(111), 13-28. 
doi:10.1002/yd.179 
Zimring, F.E. (1998). American youth violence. Oxford, England: Oxford University 
Press. 
 
 
 
 90