In press Social Problems
PEER CONTEXT AND THE CONSEQUENCES OF ADOLESCENT DRINKING
Robert Crosnoe
Chandra Muller
Department of Sociology and Population Research Center
University of Texas at Austin
Kenneth Frank
Department of Counseling, Educational Psychology, and Special Education
Michigan State University
Running Head: Adolescent Drinking
Direct correspondence to the first author at Department of Sociology and Population
Research Center, University of Texas at Austin, 1 University Station A1700, Austin, TX
78712-1088 (crosnoe@mail.la.utexas.edu). The authors acknowledge the support of the
Spencer Foundation and grants from the National Institute of Child Health and Human
Development (R01 HD40428-02, PI: Chandra Muller) and the National Science
Foundation (REC-0126167, Co-PI: Chandra Muller and Pedro Reyes) to the Population
Adolescent Drinking
Research Center, University of Texas at Austin. Opinions reflect those of the author and
not necessarily those of the granting agencies. This research used data from Add Health,
a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan
Harris and funded by a grant P01-HD31921 from the National Institute of Child Health
and Human Development, with cooperative funding from 17 other agencies. Special
acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the
original design of Add Health. Persons interested in obtaining data files from Add Health
should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel
Hill, NC 27516-2524 (www.cpc.unc.edu/addhealth/contract.html). The authors would
like to thank Roz King for helpful comments on an earlier version of this manuscript.
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Adolescent Drinking
Peer Context and the Consequences of Adolescent Drinking
ABSTRACT
Research has focused extensively on adolescent drinking, particularly on peer
influences and other predictors of this behavior. This study shifts the focus by examining
how drinking is associated with other individual trajectories during adolescence and how
these associations vary by peer context. With the National Longitudinal Study of
Adolescent Health, we found that adolescent engagement in drinking predicted declining
academic achievement and escalating emotional distress. These associations, however,
varied by level of drinking in the peer context. In general, person-context mismatches
exacerbated the risk of drinking or weakened the protective nature of not drinking. Both
the micro-level and macro-level peer context (e.g., friends, school drinking rates) were
important for achievement, while the macro-level context was important for emotional
distress. This research suggests new ways of thinking about the role of peers in
adolescent problem behavior.
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Adolescent Drinking
Peer Context and the Consequences of Adolescent Drinking
INTRODUCTION [Heading Level 1]
Recent research has documented the long-term health benefits of moderate
alcohol intake (Mukamal et al. 2003), but these benefits in later life are countered by two
more problematic dynamics. First, through multiple mechanisms, alcohol use can have
severe consequences for family and community life, economic productivity, and public
expenditure. Second, during the early life course, the numerous social and psychological
risks of alcohol use outweigh its eventual health benefits (Elliot, Huizinga, and Ageton
1985; Hawkins, Catalano, and Miller 1992; Schulenberg and Maggs 2002). The intense
empirical focus on the etiology of adolescent alcohol use, especially peer influences, is
related to both dynamics. Given the potential harm of adolescent drinking and given that
adolescence is the foundation for long-term trajectories, understanding the etiology of
adolescent drinking points us to methods for addressing this complex phenomenon
(Chassin, Pitts, and DeLucia 1999; Johnston, O’Malley, and Bachman 1996; Schulenberg
and Maggs 2002). This etiological focus can be enhanced, however, by broader
consideration of the role of drinking in adolescent society.
This study attempts such an investigation by approaching some old problems in
new ways. It does so by applying key concepts of the life course paradigm to
experiences within a specific stage of the life course. First, recognizing the overlap
among different life course trajectories, this study examines how drinking is related to
other primary markers of adolescent functioning. Specifically, is adolescent drinking
associated with declining academic performance and escalating emotional distress? Like
4
Adolescent Drinking
past studies, therefore, this study is concerned with the connections between drinking and
other domains, but, unlike most of the literature, it posits drinking as predictor rather than
outcome. Second, recognizing the context-specific nature of the overlap among life
course trajectories, this study examines whether the potential consequences of adolescent
drinking vary by peer context. Specifically, are the consequences of drinking greater in
cases of mismatch between adolescents and their peer contexts (e.g., drinking in contexts
characterized by low rates of drinking)? Thus, like past research, we consider the
importance of peers, but, unlike much of this literature, we posit the peer context as a
moderator rather than a predictor.
The potential significance of this study is both conceptual and methodological.
Conceptually, we view drinking as one thread in a tapestry of personal attributes and as a
phenomenon that varies qualitatively across social settings. Such an approach promotes
understanding of why drinking is problematic for the individual life course and the larger
society and offers a more nuanced picture of adolescent social life. Methodologically,
this study takes a multi-level view of peer context—micro (e.g., close friends) and macro
(e.g., school culture)—and draws on the National Longitudinal Study of Adolescent
Health (Add Health). This approach allows us to compare two common
conceptualizations of the peer group, use the reports of adolescents and their friends,
address selection issues, and generalize to a national context.
Alcohol Use During Adolescence [Heading Level 2]
As discussed above, adolescent alcohol use is viewed as a major societal problem,
and, as such, it has generated a great deal of attention in both public and scholarly arenas
(Hawkins et al. 1992). Two general themes have emerged from this rich empirical
5
Adolescent Drinking
activity. First, drinking is especially risky and more indicative of general adjustment
problems during this stage because adolescents are psychologically less equipped to drink
responsibly than adults (Newcomb and Bentler 1989; Schulenberg et al. 1999). Second,
during adolescence, drinking is a largely social phenomenon. In this stage of life,
drinking is less stigmatized than other forms of substance use and may be a high-status
activity in some settings. Consequently, adolescents may drink as a way of integrating
themselves into groups and gaining status (Crosnoe 2002; Schulenberg et al. 1999). This
study integrates these two general themes. Although our focus is on one stage of the life
course, we draw on the basic concepts of the life course paradigm to craft our approach.
Adolescent Drinking and Other Domains of Functioning [Heading Level 2]
According to the life course paradigm (Elder 1998), individual lives can be
viewed as consisting of multiple, intertwined trajectories in various domains of
adjustment and functioning. A key challenge of life course research, therefore, is to
examine how different trajectories are intertwined, or connected to each other. For
example, understanding alcohol use requires the examination of how it overlaps with
other individual attributes and behaviors.
In general, past research examining the overlap among domains of adolescent
functioning has treated drinking as an outcome. For example, adolescents are more likely
to use alcohol or other substances when they have problems in personal relationships,
struggle in school, engage in delinquent acts, or suffer emotional problems (Jessor,
Donovan, and Costa 1991; Keefe and Newcomb 1996; Mensch 1988; Schulenberg et al.
1994). Less often has research in this area reversed the focus, examining adolescent
drinking as a predictor. Studies with this focus have revealed, as expected, that
6
Adolescent Drinking
adolescent drinking is a risk factor that increases the likelihood of negative outcomes
during adolescence and later stages of the life course (Chassin et al. 1999; Galambos and
Silbereisen 1987; Garmezy and Masten 1986; Schulenberg and Maggs 2002).
The present study takes this less common avenue, examining the potential
consequences of adolescent drinking for two key domains of adolescent functioning.
First, academic achievement, a marker of success in a conventional institution, is the
foundation of long-term status attainment. Adolescent drinking may impair academic
performance by disrupting cognitive functioning, downgrading the importance of
academic responsibilities, harming relationships with adults (e.g., teachers, parents) that
influence academic performance, and exposing young people to non-conventional norms
of behavior (Crosnoe 2002; Jessor et al. 1991). Second, emotional distress is a marker of
mental health and is also related to socioemotional adaptation to both intimate and
institutional settings in social life. Adolescent drinking may contribute to emotional
distress by generating conflict in the family, exposing adolescents to stressful situations,
and impairing coping skills (Hussong et al. 2001; Jessor et al. 1991).
Following this, the first goal of this study is to examine whether adolescent
drinking lowers academic achievement and increases emotional distress. In doing so, we
apply a longitudinal framework that addresses the potential bi-directionality of the
associations between alcohol use and these other domains, and we draw on a source of
data that allows these results to be generalized to a national context.
Drinking and the Peer World [Heading Level 2]
In addition to pointing research towards the overlap among various domains of
adolescent functioning, the life course paradigm also emphasizes that such overlap may
7
Adolescent Drinking
vary in magnitude and meaning across social contexts (Elder 1998). In other words,
primary social contexts influence adolescent functioning directly but also moderate the
associations among various individual characteristics.
As described above, the peer context is one of the most commonly studied social
contexts in the literature on adolescent drinking (Costa, Jessor, and Turbin 1999). In
general, such research has focused heavily on the direct influence of peers, reporting a
strong association between peer drinking and adolescent drinking even after taking
selection forces into account (Aseltine 1995; Kandel 1996; Schulenberg et al. 1999). The
intertwining paths of the life course approach just outlined, however, suggest a
reconceptualization of the role of peers that involves moderation rather than direct
influence. Following this, the second goal of this study is to examine whether the
longitudinal associations between adolescent drinking and two domains of functioning
(academic achievement, emotional distress) vary by level of drinking in the peer context.
By taking this approach, we are essentially examining the implications of a
person-context mismatch. A great deal of research suggests that individual functioning is
hampered by such mismatches. For example, student learning is compromised when the
characteristics of students do not align with the structure of educational institutions,
mental health is compromised when the physical attributes of adults are undervalued in
their primary groups, and the socioemotional functioning of young people is impaired
when individuals’ demographic profiles are underrepresented in their immediate social
settings. Such mismatches, which, by definition, identify non-normative circumstances,
increase the likelihood of being stigmatized and of negative self-evaluations and decrease
perceptions of safety, security, and belongingness (Eccles et al. 1993; Johnson, Crosnoe,
8
Adolescent Drinking
and Elder 2001; Ross 1994). Thus, if an individual has some potentially problematic
attribute or engages in some potentially problematic behavior, the negative implications
of this characteristic or behavior may be compounded if that characteristic is also
uncommon in their primary groups.
In this study, the person-context mismatch involves drinkers in peer contexts with
low rates of drinking and non-drinkers in peer contexts with high rates of drinking. In the
former case, we expect that the potential negative consequences of adolescent drinking
will be greater because, in addition to drinking, adolescents will also be out of step with
the normative structure of their primary social setting. In the latter case, we expect that
the potential protection of avoiding risk behaviors will be weaker because such students
could be marginalized within their primary social setting.
In pursuing this goal, we bridge different studies and disciplines by
conceptualizing the peer context on two levels. The micro-level context concerns
adolescents’ inner-circles of friends, the other young people with whom they enjoy
personal connections and sustained interaction. The significance of this level concerns
direct behavioral modeling and the power that comes with emotional involvement
(Crosnoe 2000; Hartup and Stevens 1997). To gauge peer dynamics on this level, we
measure the rate of drinking among close friends. In contrast to these more personal
relationships, the macro-level context encompasses the larger social world of the high
school—the normative and behavioral climate constructed and supported by other
students in the school (Brown 1990; Cleveland and Weibe 2003). To capture this level,
we measure the level of drinking in the school. Thus, our treatment of person-context
mismatch concerns how well students “fit” with their friendship groups and the larger
9
Adolescent Drinking
peer culture of their schools and the power of such mismatches to condition the
developmental significance of drinking.
METHODS [Heading Level 1]
Sample [Heading Level 2]
This study drew on Add Health, an ongoing nationally representative study of
young people (7-12th graders in 1994). Add Health was created with a stratified sampling
design beginning with a representative sample of schools. Using the Quality Education
Database, an exhaustive list of American high schools, as the sampling frame, 80 high
schools were stratified by region, urbanicity (defined by U.S. Census Bureau), sector
(e.g., private, Catholic, public), racial composition, and size and then selected randomly
within strata. Attempts were made to match each of these high schools to one of their
feeder schools, typically a middle school. Feeder schools were selected with a
probability based on their student contribution to the high school, so that the feeder
school that was the origin school of the largest proportion of the high school student body
was most likely to be selected as a study school. Because some high schools were
comprehensive (e.g., contained 7 –12th grades), they were their own feeder schools and
were not matched to an external feeder school. The final sample included 132 high
schools and middle schools from the 80 original strata (see Bearman, Jones, and Udry
1997 for more on the Add Health sampling process).
From September 1994 through April 1995, all available students in these 132
schools (n = 90,118) completed the brief, paper and pencil In-School Survey, designed
primarily as a guide for the later In-Home Interviews and to identify possible subjects for
10
Adolescent Drinking
planned oversamples. A representative sample (n = 20,745 in 132 schools) of this InSchool population, selected evenly across high school – feeder school pairs, then
participated in the In-Home Interview at Wave I (April – December 1995). Wave I,
which included a much broader battery of items as well as information from parents,
school administrators, and the Census, is the core Add Health data set. The Add Health
team attempted to follow up this Wave I sample again from April through August of
1996, although they chose not to follow up the portion of the sample that had graduated
from school after Wave I. Thus, Wave I seniors, as a group, did not participate in Wave
II. With this exclusion, plus the expected attrition, 14,736 adolescents from Wave I also
participated in the Wave II In-Home Interview (Bearman et al. 1997).
The sample for the present study was created by imposing four filters on the InHome sample. Below, we discuss the motivation for each of these selection filters in
detail and also compare the Wave I sample members who were and were not targeted for
exclusion based on the independent application of each selection filter on key study
variables. We then present descriptive statistics that demonstrate how the cumulative, as
opposed to independent, application of these often overlapping selection filters altered the
composition of the study sample.
First, our longitudinal design required that adolescents participate in both waves
of the In-Home Interview (71% of the original Wave I In-Home sample). This filter
eliminated all Wave I seniors as well as any adolescent who dropped out of the sample
after initial participation. Because the adolescents who participated in Waves I and II
differed on demographic characteristics (e.g., older, male, more likely to be White, higher
parent education) and on the developmental markers of interest in this study (e.g., higher
11
Adolescent Drinking
achievement, lower emotional distress and alcohol use) compared to the adolescents who
participated in Wave I only, the application of this selection filter biased the sample.
Second, we only included adolescents who had a valid sampling weight (92% of
the Wave I sample). Because Add Health oversampled some groups (e.g., twins, disabled
adolescents), sampling weights, which adjust the sample for this unequal probability of
selection, are required to make the raw data nationally representative. Different weights,
which have been made available with the Add Health data, are necessary for different
combinations of data. Because of our longitudinal framework, we have used the
longitudinal sampling weight. A small portion of the In-Home sample was not assigned
this weight because they attended one of two schools for which weights could not be
calculated or because they were late additions to the special oversamples (refer to
Chantala and Tabor 1999 for more on weighting in Add Health). Our analysis of the
group of excluded adolescents, both for this study and for our prior research with Add
Health, revealed that their exclusion does not substantially alter sample characteristics.
Third, because of the need to use parent-reported data to create some measures,
we included only the adolescents who had at least one parent interviewed at Wave I (85%
of the Wave I sample). The adolescents excluded for this filter did come from more
advantaged backgrounds, as might be expected. They also had lower academic
achievement and higher emotional distress and alcohol use.
Fourth, the Add Health peer network data was a vital resource for this study.
Consequently, we included all adolescents for whom we could construct valid network
alcohol measures, as described above (70% of the Wave I sample). Of the Wave I
adolescents targeted for exclusion by this filter, over four-fifths did not nominate any
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Adolescent Drinking
friends with most of the remainder nominating friends outside the Add Health sample.
This selection filter introduced the most bias to our study sample. These excluded
adolescents, who were generally social isolates or who were part of out-of-school
networks, differed in expected ways from the adolescents who had valid network data
with one important exception. The two groups did not differ on alcohol use.
The application of these filters resulted in a study sample of 7,758 adolescents in
122 schools (out of 20,745 adolescents in 132 schools in Wave I). As we have made
clear above, this study sample did not have the same characteristics as the original Wave
I sample. To characterize the cumulative bias introduced by the systematic application of
these filters, Table 1 includes descriptive statistics for each stage of the selection process.
Because the samples created by the addition of each new filter were not mutually
exclusive, we could not calculate whether differences among them were statistically
significant, but the general trends in this table are informative. The selection process did
bias the study sample towards greater social advantage and social adjustment. This bias
must be remembered in the interpretation of results, but we argue that these selection
criteria were necessary and that this bias was balanced by the benefits of using multilevel, multi-context data from a national sample of adolescents and their friends.
[Table 1 About Here]
Measures [Heading Level 2]
The two dependent variables drew on information from the Wave II In-Home
Interview, the other individual-level variables on information from the Wave I In-Home
Interview (adolescent or parent), and the school-level variables on the reports of school
administrators (at the same time as the Wave I In-Home Interview) or the aggregation of
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Adolescent Drinking
responses from the In-School Survey (which occurred a year before the Wave I In-Home
Interview). Descriptive statistics for all study variables can be found in the Appendix.
Academic Achievement [Heading Level 3]
In both Waves I and II, adolescents reported their grades in four academic
subjects (math, science, English, and social studies) in the past year. These responses,
ranging from 1 (A) to 4 (D or F), were averaged and then converted to a standard four
point grade point scale for each year. This composite was somewhat skewed, with a third
of the sample reporting a grade point average of 3.0 or better.
Adolescent Emotional Distress [Heading Level 3]
The Center for Epidemiologic Studies-Depression (CES-D) scale was used to
measure distress. This scale, which was created to gauge the presence and extent of
depressive symptomatology (e.g., depressed affect, feelings of guilt, hopelessness,
helplessness, and worthlessness), correlates with other distress scales, is high in construct
validity, and has demonstrated reliability across multiple populations (Radloff and Locke
1986). Add Health contained a modified version of the CES-D scale, including 15 of the
20 original items (Resnick et al. 1997). Adolescents were asked how often they had felt
certain things during the past week. Examples included “You felt that you could not
shake off the blues, even with help from your family and your friends” and “You felt
lonely”. Responses ranged from 0 (never or rarely) to 3 (most or all of the time). The
Alcohol Use [Heading Level 3]
Adolescents reported how often they drank alcohol in the past year, with
responses collapsed into a scale ranging from 0 (none) to 7 (everyday or almost
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Adolescent Drinking
everyday). As reported in past research (Chassin et al. 1999), the significance of alcohol
use often takes a non-linear form, with some increases in alcohol use (e.g., from none to
some, crossing the threshold to frequent use) more important than others. Our analyses
revealed the same non-linear pattern. As a result, this continuous scale of alcohol use
would have obscured true effects. To avoid this problem, we could have used a quadratic
or logged term for alcohol use or dummy variables designating different levels of
drinking. Our analytical checks revealed the same pattern of results for all three
approaches, increasing our confidence in our findings on the non-linear effects of alcohol
use on different adolescent outcomes across different peer contexts. Of the three, we
chose the categorical approach and, following past research, broke the alcohol use scale
into three dummy variables. Non-drinkers, the reference category, did not drink alcohol
in the last year. Occasional drinkers drank alcohol more than once but less than once a
month in the past year. Frequent drinkers drank alcohol two or three times a month or
more. We preferred the categorical approach because these designations of alcohol use
are more accessible to general audiences than the quadratic or logged approaches. We
should reiterate, however, that all three approaches produced the same results.
Friends’ Alcohol Use [Heading Level 3]
Each adolescent was asked to list up to five female and five male friends. If a
listed friend was also in Add Health, then we used information from the In-School
Survey on that friend’s alcohol use. Thus, this peer measure does not refer to a
respondents' estimation of the level of alcohol use among friends, but rather to the mean
of the self-reported alcohol use (0 = none in past year, 7 = everyday or almost everyday
in past year) of all listed friends in the sample. As discussed above, using friends’ reports
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Adolescent Drinking
does bias the sample in terms of average characteristics and in meaning. Because of this
application of the peer selection filter, this study essentially focuses on the peer contexts
of adolescents with peers at school and cannot make conclusions about adolescents who
were isolated from peers. We argue, however, that this less representative sample is still
informative. First, it helped us to attend to students, and student functioning, across an
array of social contexts. Second, the use of the network data, based on the direct reports
of friends, had certain advantages. This method reduced the error of ego-based measures
on peer characteristics, which might be inaccurate because adolescents do not have full
information on their friends’ behavior or because they overestimate the extent to which
their friends are similar to them (Kandel 1996; Wilcox and Udry 1986). Of course,
perceptions are often more important than reality in guiding our behavior, and so these
ego-based measures are certainly useful for the study of peer dynamics. Network data,
however, allows for a more accurate description of the use of alcohol in the peer context,
which, beyond perceived alcohol use, helped us to tap the opportunity structure for
adolescents’ own drinking.
School Level of Drinking [Heading Level 3]
In the original In-School Survey, all adolescents in each school reported their
level of alcohol use (0 = never in the past year, 6 = nearly everyday in past year). The
mean of all responses within a school was the average level of drinking in that school.
Individual-Level Controls [Heading Level 3]
Analyses controlled for five demographic factors: gender (1 = female), race
(African-American, Hispanic-American, Asian-American, and Other, with non-Hispanic
White as the reference category), parent education (self-reported years of education for
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Adolescent Drinking
mother and father, averaged), family structure (1 = two biological parents living together
with adolescent, 0 = other), and age (in years). We also controlled for number of friends
in order to gauge the differences between having few or many friends who drank
(regardless of level) and for alcohol use of parents (parent report of self and partner use, 0
= never in past year, 6 = nearly everyday, slightly different from student measure), an
important predictor of adolescent drinking and problem behavior (Chassin et al. 1999).
School-Level Controls [Heading Level 3]
School-level analyses controlled for four school-level factors: average parent
education in the school (based on the school mean of parent education, as reported by the
adolescent), minority representation (percent of student body that was non-White), school
size (based on the total enrollment of the school given by school administrator, logged),
and school level (1 = middle school, 0 = high school or comprehensive school).
Plan of Analysis [Heading Level 2]
Our primary goals of this study were to examine the consequences of adolescent
drinking and whether these consequences varied by peer setting, and our analytical
strategy to pursue these goals consisted of three steps for each outcome. First, we
estimated a base model in which the outcome of interest was regressed on all of the
individual-level controls, a prior measure of the outcome, and the alcohol use dummy
variables. This model gauged the degree to which level of alcohol use was associated
with a change in the outcome over one year. Second, we added to this base model the
measure for the rate of drinking among friends and a set of interaction terms between the
adolescent alcohol use dummy variables and the measure for friends’ drinking. This
model gauged the degree to which drinking among friends moderated the associations of
17
Adolescent Drinking
adolescent drinking with the two outcomes. Third, we added to the base model the
school-level controls, the measure for the rate of drinking in school, and interaction terms
for the adolescent alcohol use dummy variables with the school measure. This model
gauged the degree to which the school rate of drinking moderated the associations of
adolescent drinking with the two outcomes.
Add Health data collection was school-based, which means that the adolescents
sampled were not statistically independent observations (e.g., students were likely to be
more similar to those in their school than to those in other schools). Failing to account
for this within-school clustering has negative implications for statistical inference. To
correct for this problem, we included random effects for schools in our models with the
mixed procedure in SAS, a restricted form of multi-level modeling (Bryk and
Raudenbush 1992; Singer 1998). This procedure corrected the design effects of Add
Health. It also allowed us to distinguish the variation in the outcomes that occurred
among students within a school from the variation that occurred among students in
different schools, the estimation of school effects on individual behavior, and the
estimation of cross-level interactions, all of which were important aspects of this study.
RESULTS [Heading Level 1]
Three Types of Adolescent Drinkers [Heading Level 2]
Past research suggests qualitative differences between non-drinkers, occasional
drinkers, and frequent drinkers (Chassin et al. 1999), and preliminary analyses support
this suggestion (Table 2). Beginning with demographic characteristics, frequent drinkers
were older and more likely to be male than other adolescents. The three groups of
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Adolescent Drinking
adolescents did not differ in parent education (an indicator of socioeconomic status), but
the non-drinkers were less likely to be White than others. Next, descriptive statistics also
reveal other key differences. Compared to the other groups, non-drinkers had friends
who drank the least, while frequent drinkers had friends who drank the most. Frequent
drinkers attended schools in which drinking was most common among students.
Moreover, academic achievement decreased at each step between non-drinking and
frequent drinking, while emotional distress increased with each step.
[Table 2 About Here]
Thus, in general, drinkers had different demographic profiles than non-drinkers,
were part of peer contexts in which drinking was more common, and had more
problematic patterns of functioning than drinkers. Among drinkers, however, different
levels of drinking also entailed different characteristics, lifestyles, and environments. To
investigate these descriptive patterns more closely, we turned to multivariate analysis.
Recall that our two general goals were to examine the potential negative consequences of
adolescent drinking and to investigate whether these consequences were context-specific.
In the following sections, we pursue these two goals for two separate outcomes: academic
achievement and emotional distress.
Adolescent Drinking and Academic Trajectories [Heading Level 2]
To begin, Model 1 in Table 3 presents the results relevant to the question of
whether adolescent drinking was associated with declining academic achievement.
Controlling for level of academic achievement in Wave I, occasional and frequent
drinking predicted lower achievement in Wave II (b = -.06, p < .001 for occasional; -.07,
p < .001 for frequent). Switching the reference category for the alcohol use dummy
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Adolescent Drinking
variables revealed that the associations with achievement of occasional and frequent
drinking did not differ significantly from each other. We should also note here that
parents’ alcohol use also did not predict Wave II achievement.
[Table 3 About Here]
Next, Model 2 presents results relevant to the question of whether this association
between drinking and achievement varied by peer context, with the friendship group as
the marker of peer context. Such variation did occur, but only for frequent drinking. The
interaction between friends’ drinking and occasional alcohol use was not significant,
indicating that the decline in academic achievement associated with moderate drinking
was the same whether the adolescent had friends who drank or not. Among frequent
drinkers, however, this decline did vary by the level of drinking among friends (b = -.04,
p < .05). To interpret this interaction, we wrote out multiple equations—varying the
values of frequent alcohol use (1, 0) and friends’ alcohol use (one standard deviation
above and below the mean) while assigning all other variables to their sample means.
Doing so revealed that the academic achievement of frequent drinkers, but not nondrinkers, varied by the level of drinking among their close friends. While non-drinkers
generally had higher achievement (predicted g.p.a. = 2.82), the frequent drinkers who
were members of friendship groups high in drinking had achievement closer to the nondrinkers (2.77) than did the frequent drinkers who were members of friendship groups
low in drinking (2.68).
Finally, Model 3 used the student body as a whole, and not just the friendship
group, as the marker of the peer context. In this case, the peer context measure interacted
with occasional drinking and not frequent drinking (b = .13, p < .01). Writing out the
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Adolescent Drinking
equations for this interaction term as described above, revealed that both non-drinkers
and occasional drinkers drove this interaction. Non-drinkers did slightly better
academically in schools where drinking was less common overall (estimated g.p.a. =
2.78) than in schools where it was more common (2.72), while occasional drinkers did
slightly worse in schools in which drinking was less common (2.67) than in schools in
which it was more common (2.70).
We also performed additional analyses of these three models in which we
controlled for adolescents’ emotional distress and friends’ academic achievement and in
which we combined the friendship group and school models into a single comprehensive
model. These additional analyses, not presented here, generated no different inferences.
Thus, in terms of changes in academic performance over time, the key distinction
seems to have been between drinking and not drinking. Those who drank, no matter
what the level, experienced declines in their achievement level across a one-year period,
although the context of their alcohol use moderated its academic risk status. In general,
occasional and frequent drinking coincided with academic decline, but this decline was
slightly worse (in other words, the risk was greater) among those who drank on a regular
basis outside of a peer context, either the friendship group or the student body as a whole,
engaged in drinking. This finding suggests that person-context mismatches add to the
negative consequences of adolescent drinking.
Of course, these associations were not great in magnitude. For example, the
association with achievement for both levels of drinking represented only about 10% of a
standard deviation in achievement, or the difference of a tenth of a letter grade. Still,
what we measured here was not an association between adolescent drinking and
21
Adolescent Drinking
academic achievement but an association between drinking and a one-year change in
achievement. The persistence of these associations despite this more conservative
framework and the control for a host of individual and social factors suggests that, while
modest, they are meaningful. The fact that we have controlled for previous grades
implies that peer dynamics affect change in behaviors, and, therefore, these small effects
can accumulate over time.
Adolescent Drinking and Emotional Distress [Heading Level 2]
To adapt our two general research goals to the second adolescent outcome, we
asked whether increasing alcohol use was associated with escalating emotional distress
and whether this association varied by peer context. Table 4 presents the results of three
multi-level models relevant to these questions.
[Table 4 About Here]
Model 1 in Table 4 indicates that drinking in Wave I was a risk factor for
emotional distress in Wave II (b = .04, p < .001 for occasional; .05, p < .001 for
frequent). Switching the reference category for the alcohol dummy variables revealed no
significant differences between the two categories of drinkers in emotional distress.
Model 1 in Table 4 also reveals that drinking among parents was not significantly
associated with changes in emotional distress over time.
Model 2 in Table 4 includes interactions between the alcohol use dummy
variables and the friendship group markers of the peer context. Unlike for academic
achievement, the rate of drinking among close friends did not moderate the associations
between adolescent drinking and emotional distress. Model 3 in Table 4 replaces the
friendship group marker and interactions with the school marker of peer context. Level
22
Adolescent Drinking
of drinking interacted significantly with the average rate of alcohol use in the student
body (b = -.05, p < .05 for occasional; b = -.08, p < .01 for frequent). These interactions
did not change when controlling for friends’ level of emotional distress and adolescents’
own academic achievement or when estimated simultaneously with the friendship group
markers and interactions (not shown).
To interpret these interaction terms, we again wrote out multiple equations—
varying the values of occasional and frequent alcohol use (1, 0) and school level of
alcohol use (one standard deviation above the mean and below the mean) while assigning
all other variables in the model to their sample means. In both cases, the significant
interaction term was driven by the non-drinkers. Emotional distress was generally higher
for occasional and frequent drinkers (predicted distress = .60), but the emotional distress
of non-drinkers, although always lower, differed by the rate of alcohol use in their
schools. Specifically, non drinkers were more emotionally distressed when attending
schools characterized by high levels of alcohol use (.58) than when attending schools low
in alcohol use (.53).
Thus, drinkers, of all levels, were at a greater risk of increasing emotional distress
over a one-year period. Nevertheless, like academic achievement, this risk was
moderated by drinking norms in the peer context. Unlike academic achievement,
however, this moderation largely occurred because of person-context mismatches
involving non-drinkers rather than drinkers. Also unlike academic achievement, macrolevel peer contexts, as opposed to micro-level, were more likely to moderate the
associations between adolescent drinking and emotional distress.
23
Adolescent Drinking
CONCLUSION [Heading Level 1]
The issue of underage alcohol use has been studied extensively, but the ways in
which it has been studied have been less extensive. We know a great deal about who
drinks, the reasons for drinking, and how personal relationships influence drinking but far
less about the social psychological implications of drinking and how such implications
vary across interpersonal contexts. Guided by the life course paradigm, this study
explored these under-studied phenomena by asking why drinking is problematic and how
this problematic nature depends on the peer context in which it occurs.
To begin, we explored the consequences of adolescent drinking. Like past
studies, we found that adolescent drinking was associated with declining academic
performance and escalating emotional distress. These findings help to fill in the
widespread assumptions about the dangers of early drinking. While it may indeed be part
of growing up, adolescent drinking is problematic because it is related to other individual
trajectories. The implications of this overlap can be seen in the short-term—undermining
school success, risking mental health problems. Even though this study did not
investigate the long-term consequences of adolescent drinking, we argue that these
findings hint at how adolescent drinking may disrupt the transition to adulthood and,
therefore, have lasting consequences. For example, problems in school or emotional
difficulties could decrease the likelihood of attending college, with far-reaching effects.
Because the adult life course is, in many ways, predicated on what occurs during
adolescence, we need to understand adolescent drinking and its implications for postadolescence experiences. Future research can do more to explore the long-term
consequences of adolescent drinking than we were able to do here by utilizing more
24
Adolescent Drinking
extensive longitudinal data, modeling growth curves across multiple time points, and
exploring the degree to which different behavioral trajectories intertwine over time and
across life stages. Because our models revealed strong lagged effects for achievement
and distress (e.g., prior achievement strongly predicting later achievement) during a
relatively short time span, modeling associations among trajectories across broader
periods of time will likely reveal how different experiences develop over time in
opposition to or in complement with each other and, ultimately, which of these
experiences has the most lasting effect.
We also investigated how the consequences of adolescent drinking varied by peer
context. Like past studies, we found that adolescents who associated with peers who
drank had more problematic trajectories during this time period. Unlike past studies, we
also explored whether peer factors moderated the association between adolescent
drinking and other behaviors and, in doing so, uncovered a complex pattern of risk. In
general, mismatches between adolescents and their peer contexts were problematic. For
academic achievement, drinkers generally had lower achievement than non-drinkers, but
this was especially true when they were members of friendship groups in which drinking
was uncommon. For emotional distress, non-drinkers typically had lower distress, but
this was less true when they were part of friendship groups or attended schools in which
drinking was normative. Thus, the burden of being out of step with peers appeared to add
to the risk of drinking and subtract from the protection of not drinking.
These results suggest that the peer orientation of young people and their
sensitivity to social conformity complicate our understating of the nature of risky health
behavior. Drinking is generally problematic, but even more so when it defies prevailing
25
Adolescent Drinking
norms. Not drinking is generally positive during this stage, but less so when drinking is
valued. The social meaning of drinking, which varies from context to context, matters.
This conclusion is closely related to the work of Terri Moffit, Avshalom Capsi, and
colleagues (see Moffitt 1997) on adolescent problem behavior. According to their life
course perspective, the substance use of some adolescents is indicative of
psychopathology and, as such, likely leads to major problems in the long term. In the
short term, however, these youth attain a social status in schools that leads other, less
troubled adolescents to mimic their behavior. These latter adolescents typically age out
of their early problem behavior. Clearly, these two patterns of drinking have different
meanings and, therefore, different consequences. As echoed by our study, the
circumstances in which adolescent drinking occurs are required to decipher the meaning
of this behavior.
The significance of the social meaning of drinking, which our findings illustrate,
brings up three related issues. First, we took a social approach to the interaction between
adolescent behavior and the peer context, focusing on the developmental risks of personcontext mismatches. Yet, a more psychological phenomenon could also be at work here.
Young people who drink despite prevailing norms against drinking may have underlying
dispositions (e.g., alcoholism, severe maladjustment) that are strong enough to break
social norms (Shedler and Block 1990). For example, the literature on social settings of
drinking, typically not involving adolescents or peers, suggests that solitary, private
drinking is more indicative of psychopathology than social, public drinking (Mayer et al.
1998). In all likelihood, both social and psychological (or biological) mechanisms are at
work here. We have not been able to tease them apart, although controlling for prior
26
Adolescent Drinking
adjustment should partly do this. Future research could help to shed light on this process
by examining the factors that select young people into person-context mismatches.
Second, by necessity, our selection process eliminated adolescents without
friends. By doing so, however, we likely eliminated an intriguing group: socially isolated
drinkers. In many ways, these young people represent the most problematic adolescent
population. Given the social nature of drinking, those who drink outside of social groups
are more likely to have the internal dispositions to drink discussed above. Yet, the focus
of this study was person-context mismatches, not solitary vs. social drinking. We were
interested in those adolescents who had friends but were out of step with their friends, a
focus which sheds light on interpersonal and developmental processes just as it informs
knowledge on drinking. Again, future research can build on what we have done here by
comparing these two related but different phenomena.
Third, our analyses revealed a clear pattern in which the association between
drinking and achievement was closely related to both intimate and larger peer contexts
and the association between drinking and emotional distress was only related to the larger
peer culture of the school. This pattern suggests that mismatches between adolescents
and their close friends interfere with their day to day behavior, but that consequences of
mismatches between adolescents and their general social settings are also manifested in
emotional maladjustment. The mechanisms behind these two different scenarios (e.g.,
the micro-context of behavioral trajectories, the macro-context of psychological
trajectories) need to be studied more extensively. This basic pattern, however,
demonstrates the value of taking multiple perspectives on the peer world.
27
Adolescent Drinking
The findings of this study can be extended and deepened in multiple ways beyond
those suggestions we have already made. Drinking is one social problem with important
health consequences, yet there are others, such as drug use and cigarette smoking, that
may also be responsive to social context. Furthermore, in studying social context, we
have focused on two levels of interpersonal context (e.g., the friendship group, the peer
culture of the school), but more refined measures of peer group and other within-school
environments are likely to better inform the processes that contribute to adolescent
development. Of course, social structural, as opposed to interpersonal, contexts may also
play a role in shaping adolescent behaviors. For example, the trends we have observed
could vary by race, ethnicity, or gender. Finally, the findings on the school peer group,
which suggest that the type of school that an adolescent attends can have behavioral
implications, hints at potential institutional effects that need to be further explored. The
school is the logical starting point for adolescents, but other institutions during this
unique stage of life (e.g., workplace, church) may also shed light on drinking behavior.
This study contributes important new information about the contextual nature of
adolescent life. Drinking is an individual risk behavior, but one that occurs within a
social environment. Health-related risk behaviors, and the complications associated with
them, are major problems facing adolescents, their families, their schools, and the other
institutions that serve them, but to understand the complex patterns in these behaviors we
need to explore more fully the settings in which they occur, are maintained, or are
discouraged. This study takes a first step in that direction. This lesson, no doubt, also
applies to other social problems at other stages of the life course.
28
Adolescent Drinking
Table 1. Descriptive Statistics for Each Stage of the Sample Selection Process
Means
Wave I
Filter 1
Filter 1-2
Filter 1-3
Filter 1-4
Female
.51
.51
.51
.51
.53
White
.50
.51
.52
.54
.56
Age (years)
16.16
15.82
15.82
15.75
15.72
Parent education
5.41
5.44
5.45
5.45
5.55
Wave I alcohol use
1.09
1.01
1.01
1.00
1.00
Wave I academic achievement
2.75
2.76
2.77
2.78
2.82
Wave I emotional distress
.49
.49
.48
.47
.47
Wave I number of friends
2.98
3.07
3.04
3.02
3.26
n
20,745
14,736
13,568
11,927
7,758
Note: Filter 1 excluded adolescents in the Wave I In-Home sample who did not participate in the
Wave II In-Home Interview, Filter 2 those who did not have a valid sampling weight, Filter 3 those
who did not have a parent interviewed, and Filter 4 those who did not have valid friendship
information.
29
Adolescent Drinking
Table 2. Descriptive Statistics by Drinking Status
Three Levels of Alcohol Use
None
Occasional
Frequent
Sociodemographic Characteristics
Gender (female)
White
Parent education
Age (years)
Peer Context
Friends’ alcohol use
School level of alcohol use
53%a
52%b
5.57
(2.24)
15.38c
(1.56)
56%a
61%a
5.57
(2.16)
16.00b
(1.37)
46%b
64%a
5.50
(2.17)
16.36a
(1.40)
.80c
(1.00)
.84c
(.34)
1.30b
(1.15)
1.00b
(.32)
1.81a
(1.26)
1.06a
(.30)
Adolescent Behavior
Academic achievement (Wave II)
2.92a
2.77b
2.64c
(.73)
(.76)
(.75)
Emotional distress (Wave II)
.42c
.51b
.57a
(.36)
(.39)
(.24)
n
4,212
2,277
1,214
Note: Standard deviations presented in parentheses below means. Statistics with different
subscripts differ significantly (p < .05), as determined by one-way ANOVA for means and
cross-tabulations for frequencies. A represents the highest (or most positive) mean or
frequency with B and C representing statistics in descending order. The statistical tests for the
school-level factors are to be interpreted with caution because, unlike the multivariate analyses
presented later, they do not control for the multi-level nature of the data. Occasional drinkers
drank alcohol more than once a year but less than once a month. Frequent drinkers drank
alcohol more than once a month.
30
Adolescent Drinking
Table 3. Results of Individual-Level and Multi-Level Models for Academic Achievement
Model 1
Model 2
Model 3
Individual-Level Factors
Gender (female)
.09***
.09***
.09***
(.01)
(.01)
(.01)
African-American
-.15***
-.15***
-.14***
(.02)
(.02)
(.02)
Hispanic-American
-.08**
-.08**
-.07**
(.03)
(.03)
(.03)
Asian-American
.02
.02
.03
(.04)
(.04)
(.04)
Other
-.07
-.07
-.06
(.04)
(.04)
(.04)
Parent education
.03***
.03***
.03***
(.00)
(.00)
(.00)
Family structure (two-parent)
.07***
.07***
.07***
(.01)
(.01)
(.01)
Age (in years)
.01*
.01*
.01
(.01)
(.01)
(.01)
Number of friends
.00
.00
.00
(.00)
(.00)
(.00)
Parental alcohol use
.01
.01
.01
(.01)
(.01)
(.01)
Prior academic achievement
.60***
.60***
.60***
(.01)
(.01)
(.01)
Occasional alcohol use
-.06***
-.08***
-.18***
(.02)
(.02)
(.04)
Frequent alcohol use
-.07***
-.14***
-.13*
(.02)
(.03)
(.06)
Friends’ alcohol use
---.00
--(.01)
Individual-Level Interactions
Occasional * friends’ alcohol use
--.01
--(.01)
Frequent * friends’ alcohol use
--.04*
--(.02)
31
Adolescent Drinking
Table 3 (continued)
Model 1
Model 2
Model 3
School-Level Factors
Mean parent education
---
---
Minority representation
---
---
School size (log)
---
---
Middle school
---
---
School level of alcohol use
---
---
.01
(.02)
-.00
(.00)
-.03
(.02)
-.09*
(.04)
-.07
(.05)
Cross-Level Interaction Terms
Occasional * school level of alcohol use
---
---
Frequent * school level of alcohol use
---
---
.13**
(.04)
.06
(.06)
n = 6,818
*** p < .001, ** p < .01, * p < .05
Note: Unstandardized coefficients presented with standard errors in parentheses.
32
Adolescent Drinking
Table 4. Results of Individual-Level and Multi-Level Models for Emotional Distress
Model 1
Model 2
Model 3
Individual-Level Factors
Gender (female)
.06***
.06***
.06***
(.01)
(.01)
(.01)
African-American
.01
.01
.01
(.01)
(.01)
(.01)
Hispanic-American
.05**
.05**
.05**
(.01)
(.01)
(.02)
Asian-American
.07**
.07**
.06**
(.02)
(.02)
(.02)
Other
.05*
.05*
.04*
(.02)
(.02)
(.02)
Parent education
-.01***
-.01***
-.01***
(.00)
(.00)
(.00)
Family structure (two-parent)
-.04***
-.04***
-.04***
(.01)
(.01)
(.01)
Age (in years)
.01*
.01*
.01
(.02)
(.02)
(.00)
Number of friends
.00
.00
.00
(.00)
(.00)
(.00)
Parental alcohol use
-.01
-.01
-.01
(.00)
(.00)
(.00)
Prior emotional distress
.53***
.53***
.53***
(.01)
(.01)
(.01)
Occasional alcohol use
.04***
.04***
.08***
(.01)
(.01)
(.02)
Frequent alcohol use
.05***
.06***
.12***
(.01)
(.02)
(.03)
Friends’ alcohol use
---.00
--(.01)
Individual-Level Interaction Terms
Occasional * friends’ alcohol use
---.00
--(.01)
Frequent * friends’ alcohol use
---.01
--(.01)
33
Adolescent Drinking
Table 4 (continued)
Model 1
Model 2
Model 3
School-Level Factors
Mean parent education
---
---
Minority representation
---
---
School size (log)
---
---
Middle school
---
---
School level of alcohol use
---
---
.00
(.01)
.00
(.00)
-.01
(.01)
.02
(.02)
.06*
(.02)
Cross-Level Interaction Terms
Occasional * school level of alcohol use
---
---
Frequent * school level of alcohol use
---
---
-.05*
(.02)
-.08*
(.03)
n = 7,338
*** p < .001, ** p < .01, * p < .05
Note: Unstandardized coefficients presented with standard errors in parentheses.
34
Adolescent Drinking
Appendix. Descriptive Statistics for Study Variables
Mean
(SD)
Key Study Variables
Adolescent alcohol use
None
--Occasional
--Frequent
--Academic Achievement
Wave I
2.82
(.76)
Wave II
2.83
(.75)
Emotional distress
Wave I
.47
(.38)
Wave II
.49
(.39)
Friends’ alcohol use
1.12
(1.15)
School level of alcohol use
.92
(.34)
Individual-Level Characteristics
Gender (female)
--Race
Non-Hispanic White
--African-American
--Hispanic-American
--Asian-American
--Other
--Family structure (two-parent)
--Parent education
5.55
(2.90)
Age (years)
15.72
(1.53)
Number of friends
3.26
(2.72)
Parental alcohol use
-.01
(.80)
Frequency
55%
30%
15%
-----
---------
53%
56%
20%
15%
6%
3%
56%
---------
35
Adolescent Drinking
Appendix (continued)
Mean
(SD)
School-Level Characteristics
Mean parent education
Minority representation
School size
Middle school
n
4.65
(.72)
46.50
(33.58)
1032.46
(784.32)
--7,758
Frequency
------24%
7,758
36
Adolescent Drinking
REFERENCES [Heading Level 1]
Aseltine, Robert. 1995. “A Reconsideration of Parental and Peer influences on
Adolescent Deviance.” Journal of Health and Social Behavior 3:103-121.
Bearman, Peter, Jo Jones, and J. Richard Udry. 1997. “The National Longitudinal Study
of Adolescent Health: Research Design.” Carolina Population Center, University of
North Carolina at Chapel Hill. WWW document: http://www.cpc.unc.edu/
projects/addhealth/design.html. Retrieved September, 15, 2002.
Brown, B. Bradford. 1990. “Peer Groups and Peer Cultures.” Pp. 171-196 in At the
Threshold, edited by Shirley Feldman and Glen Elliott. Cambridge, MA: Harvard.
Bryk, Anthony S. and Stephen W. Raudenbush. 1992. Hierarchical Linear Models:
Applications and Data Analysis Methods. Newbury Park, CA: Sage.
Chantala, Kim and Joyce Tabor. 1999. “Strategies to Perform a Design-Based Analysis
Using the AddHealth Data.” Carolina Population Center, University of North
Carolina at Chapel Hill. WWW document: http://www.cpc.unc.edu/projects/
addhealth/strategies.html. Retrieved December 1, 2002.
Chassin, Laurie, Steven C. Pitts, and Christian DeLucia. 1999. “The Relation of
Adolescent Substance Use to Young Adult Autonomy, Positive Activity Involvement,
and Perceived Competence.” Development and Psychopathology 11:915-932.
Cleveland, Hobart H. and Richard P. Wiebe. 2003. “The Moderation of Adolescent-toPeer Similarity in Tobacco Use by School Levels of Substance Use. Child
Development 74:279-91.
Coleman, James. 1961. The Adolescent Society. New York: Free Press of Glencoe.
37
Adolescent Drinking
Costa, Frances M., Richard Jessor, and Mark S. Turbin. 1999. “Transition into
Adolescent Problem Drinking: The Role of Psychosocial Risk and Protective
Factors.” Journal of Studies on Alcohol 60:280-490.
Crosnoe, Robert. 2000. “Friendships in Childhood and Adolescence: The Life Course and
New Directions.” Social Psychology Quarterly 63:377-391.
Crosnoe, Robert. 2002. “Academic and Health-Related Trajectories in High School: The
Intersection of Gender and Athletics.” Journal of Health and Social Behavior 43:317335.
Eccles, Jacquelynne S., Carol Midgely, Allan Wigfield, Christy M. Buchanan, David
Reuman, Douglas MacIver. 1993. “Development During Adolescence: The Impact
of Stage-Environment Fit on Young Adolescents' Experiences in Schools and in
Families.” American Psychologist 48:90-101.
Elder, Glen H. Jr. 1998. “Life Course and Human Development.” Pp. 939-991 in
Handbook of Child Psychology, edited by William Damon. New York: Wiley.
Elliot, Delbert, David Huizinga, and Suzanne Ageton. 1985. Explaining Delinquency and
Substance Use. Beverly Hills: Sage.
Galambos, Nancy L. and Ranier K. Silbereisen, 1987. “Substance Use in West German
Youth: A Longitudinal Study of Adolescents' Use of Alcohol and Tobacco.” Journal
of Adolescent Research 2:161-174.
Garmezy, Neil and Ann Masten. 1986. “Stress, Competence, and Resilience: Common
Frontiers for Therapist and Psychopathologist.” Behavior Therapy 17:500-521.
Hartup, Willard and Nan Stevens. 1997. “Friendships and Adaptation in the Life Course.”
Psychological Bulletin 121:355-370.
38
Adolescent Drinking
Hawkins, J. David, Richard Catalano, and Janet Miller. 1992. “Risk and Protective
Factors for Alcohol and Other Drug Problems in Adolescence and Early Adulthood:
Implications for Substance Use Prevention.” Psychological Bulletin 112:64-105.
Hussong, Andrea M., Richard E. Hicks, Suzanne A. Levy, and Patrick J. Curran. 2001.
“Specifying the Relations Between Affect and Heavy Alcohol Use Among Young
Adults.” Journal of Abnormal Psychology 110:449-461.
Jessor, Richard, John Edward Donovan, and Frances Marie Costa. 1991. Beyond
Adolescence: Problem Behavior and Young Adult Development. Cambridge:
Cambridge University.
Johnson, Monica K., Robert Crosnoe, and Glen H. Elder, Jr. 2001. “Student Attachment
and Academic Engagement: The Role of Ethnicity.” Sociology of Education 74:31840.
Johnston, Lloyd D., Patrick M. O’Malley, and Jerald G. Bachman. 1996. National Survey
Results on Drug Use from the Monitoring the Future Study, 1975-1995, vol. 2,
Secondary School Students. National Institute of Health Publication No. 96-4139.
Washington D.C.: Government Printing Office.
Kandel, Denise. 1996. “The Parental and Peer Contexts of Adolescent Deviance: An
Algebra of Interpersonal Influences.” Journal of Drug Issues 26:289-315.
Keefe, Keunho and Michael D. Newcomb. 1996. “Demographic and Psychosocial Risk
for Alcohol Use: Ethnic Differences.” Journal of Studies on Alcohol 57:521-534.
Mayer, Randall R., Jean L. Forster, David M. Murray, and Alexander C. Wagenaar.
1998. “Social Settings and Situations of Underage Drinking.” Journal of Studies on
Alcohol 59:207-215.
39
Adolescent Drinking
Mensch, Barbara S. 1988. “Dropping Out of High School and Drug Involvement.”
Sociology of Education 61:95-113.
Moffitt, Terrie E. 1997. “Adolescent-Limited and Life Course Persistent Offending: A
Complementary Pair of Developmental Theories.” Pp. 11-54 in Developmental
Theories of Crime and Delinquency: Advances in Criminological Theory, edited by
Terence P. Thornberry. New Brunswick, NJ: Transaction.
Mukamal, K.J., Kate M. Conigrave, Murray A. Mittleman, Carlos A. Camargo Jr., Meir
J. Stampfer, Walter C. Willett, Eric B. Rimm. 2003. “Roles of Drinking Pattern and
Type of Alcohol Consumed in Coronary Heart Disease in Men.” New England
Journal of Medicine 348:109-118.
Newcomb, Michael D. and Peter M. Bentler. 1989. “Substance Use and Abuse Among
Children and Teenagers.” American Psychologist 44:242-248.
Radloff, Lenore S. and Locke, Ben Z. 1986. “The Community Mental Health Assessment
Survey and the CES-D Scale.” Pp. 177-189 in Community Surveys of Psychiatric
Disorders, edited by Myrna Weissman, Jerome Meyers, and Catherine Ross. New
Brunswick, NJ: Rutgers.
Resnick, Michael D., Peter S. Bearman, Robert W. Blum, Karl E. Bauman, Kathleen M.
Harris, Jo Jones, Joyce Tabor, Trish Beuhring, Renee E. Sieving, Marcia Shew,
Marjorie Ireland, Linda H. Bearinger, J. Richard Udry 1997. “Protecting Adolescents
from Harm: Findings from the National Longitudinal Study of Adolescent Health.”
Journal of the American Medical Association 278:823-832.
Ross, Catherine E. 1994. “Overweight and Depression.” Journal of Health and Social
Behavior 33:63-78.
40
Adolescent Drinking
Scheier, Lawrence M., Gilbert Botvin, and Eli Baker. 1997. “Risk and Protective Factors
As Predictors of Adolescent Alcohol Involvement and Transitions in Alcohol Use: A
Prospective Analysis.” Journal of Studies on Alcohol 58:652-667.
Schulenberg, John and Jennifer Maggs. 2002. “A Developmental Perspective on Alcohol
Use and Heavy Drinking during Adolescence and the Transition to Young
Adulthood.” Journal of Studies on Alcohol 63:54-70.
Schulenberg, John, Jennifer Maggs, Ted Dielman, Sharon Leech, Deborah Kloska, Jean
Shope, and Virginia Laetz. 1999. “On Peer Influences to Get Drunk: A Panel Study
of Young Adolescents.” Merrill-Palmer Quarterly 45:108-142.
Schulenberg, John, Jerald G. Bachman, Patrick M. O’Malley, and Lloyd D. Johnston.
1994. “High School Educational Success and Subsequent Substance Use: A Panel
Analysis Following Adolescents to Young Adulthood.” Journal of Health and Social
Behavior 35:45-62.
Shedler, Jonathan and Jack Block. 1990. “Adolescent Drug Use and Psychological
Health: A Longitudinal Inquiry.” American Psychologist 45:612-630.
Singer, Judith D. 1998. “Using SAS Proc Mixed to Fit Multilevel Models, Hierarchical
Models, and Individual Growth Models.” Journal of Educational and Behavioral
Statistics 24:323-355.
Wilcox, Steven and J. Richard Udry. 1986. “Autism and Accuracy in Adolescent
Perceptions of Friends' Sexual Attitudes and Behavior.” Journal of Applied Social
Psychology 16:361-374.
41