1) The document examines differences in alcohol use and consequences between undergraduate women living in different on-campus residential environments, including single-sex and mixed-sex residential learning communities (RLCs) and non-RLCs.
2) The results found that women living in single-sex RLCs had the lowest rates of alcohol use, heavy episodic drinking, and alcohol-related consequences compared to those living in other environments.
3) RLCs, particularly single-sex ones, appear to provide an environment that supports lower rates of alcohol use and abuse among undergraduate women.
1. Addictive Behaviors 33 (2008) 987–993
Contents lists available at ScienceDirect
Addictive Behaviors
Heavy episodic drinking and its consequences: The protective
effects of same-sex, residential living-learning communities
for undergraduate women
Carol J. Boyd a,b,⁎, Sean Esteban McCabe a,b, James A.
Cranford b, Michele Morales b,
James E. Lange c, Mark B. Reed c, Julie M. Ketchie c, Marcia
S. Scott d
a Institute for Research on Women and Gender, University of
Michigan, 204 S. State Street, Ann Arbor, MI 48109-1290,
United States
b Substance Abuse Research Center, University of Michigan,
2025 Traverwood Dr., Suite C, Ann Arbor, MI 48105-2194,
United States
c AOD Initiatives Research, San Diego State University
Research Foundation, 6386 Alvarado Ct, Suite 224, San Diego,
CA 92120, United States
d Division of Epidemiology and Prevention Research, National
Institute on Alcohol Abuse and Alcoholism, 5635 Fishers Lane,
Room 2085 MSC 9304,
Bethesda, MD 20892-9304, United States
a r t i c l e i n f o
⁎ Corresponding author. Institute for Research on W
United States. Tel.: +1 734 764 9537; fax: +1 734 764 9
E-mail address: [email protected] (C.J. Boyd).
3. Heavy episodic drinking among college students –which we
define here as 5 or more drinks in a two-hour period for men,
and
4 or more drinks for women – is a well-established concern
among college health experts (Boyd, McCabe, & Morales,
2005).
Researchers have identified several social and environmental
factors associated with this problem, including gender and
living
arrangements. One robust risk factor, demonstrated in numerous
studies, is that college-age males, particularly those in
fraternities, engage in heavy episodic drinking with greater
frequency than their female counterparts; although recent
research
reveals that the sex, gender and/or living arrangement gaps may
be narrowing, especially among high school age students
(Johnston, O'Malley, Bachman, & Schulenberg, 2006; Wechsler,
Lee, Kuo & Lee, 2000; for an international review see Holmila
&
Raitasalo, 2005).
Despite lower rates of heavy drinking, women are particularly
vulnerable to the negative consequences in a college co-
educational setting. It is estimated that alcohol is involved in at
least half of all cases of heterosexual assault among college
students
omen and Gender, School of Nursing and Women's Studies, 204
S. State Street, Ann Arbor, MI 48109-1290
533.
All rights reserved.
,
mailto:[email protected]
http://dx.doi.org/10.1016/j.addbeh.2008.03.005
4. http://www.sciencedirect.com/science/journal/03064603
988 C.J. Boyd et al. / Addictive Behaviors 33 (2008) 987–993
(for reviews see Abbey, 2002;Mohler-Kuo, Dowdall, Koss
&Wechsler, 2004) and the likelihood of sexual assault increases
nine fold
on days in which college women engage in heavy alcohol
consumption (Parks & Fals-Stewart, 2004). Among college
students, the
majority of sexual assaults occur within heterosexual
relationships in which both people are acquainted and a male
student
perpetrates the assault; usually alcohol has been consumed by
one or both people (Abbey, 2002).
1.1. Environmental correlates of college student alcohol use
Of the environmental factors impacting college students' alcohol
consumption, living arrangement has been identified as an
especially important predictor of alcohol use (Boyd, McCabe, &
d'Arcy, 2004; Presley, Meilman & Leichliter, 2002; Weitzman
&
Kawachi, 2000). Research from single-sex institutions finds that
women attending all-women colleges engage in heavy episodic
drinking at significantly lower levels than women attending co-
educational institutions (Wechsler, Lee, Hall, Wagenaar, & Lee,
2002). Students living in fraternity or sorority houses
consistently report heavier levels of alcohol use, higher levels
of intoxication
and more alcohol-involved social activities (for a review, see
Baer, 1994; Cashin, Presley & Meilman, 1998; Glindermann &
Geller,
2003) while students residing in college sponsored, living-
learning communities tend to drink less (Brower, Golde &
Allen, 2003;
5. McCabe et al., 2007). Although these living-learning
communities were not created to address underage drinking,
they were
created to engage students in both curricular and co-curricular
aspects of university/college life.
1.2. Gender and the college living environment
Wechsler et al. (2002) found that nearly twice as many women
attending co-educational institutions could be classified as
frequent heavy episodic drinkers (defined as three ormore
occasions of heavy episodic drinking in the past twoweeks)
thanwomen
attending all-women colleges (21.2% versus 11.9%), suggesting
that interaction with male students may affect the quantity and
frequencyofwomen's alcohol consumption. Young and
colleagues found some support of this association,
usingqualitative data from
undergraduate women classified as “frequent heavy episodic
drinkers.” Using focus-group discussions, these researchers
reported
that female students who tolerate high levels of alcohol
consumption often receive special attention from their male
peers, and are
included as “one of the guys” unlike other less-heavy drinking,
“light-weight” females (Young, Morales, McCabe, Boyd, &
d'Arcy,
2005). However tempting it is to reduce “risk” to gender
differences, there are data supporting that it is not that simple.
1.3. Consequences of collegiate alcohol abuse
There aremultiple primaryand secondaryconsequences related to
collegiate alcohol abuse. In a sampleof 1649undergraduate past
year drinkers, Boyd, McCabe, and d'Arcy (2003) found that 77%
reported at least one negative consequence from their drinking,
6. the
most common being a “hangover” followed by “vomited”, “felt
embarrassed”, “had memory loss” and “missed class” among
others.
Eleven percent reported being sexually harassed and 4%
reported sexually harassing another person. Data from the
College Alcohol
Study (CAS) showthat secondaryconsequencesof heavyepisodic
drinking amongcollege students, includingverbal or physical
assault,
vandalism, and interruptions to sleep or study time among
others (Wechsler, Davenport, Dowdall, Moeykens & Castillo,
1994), are
ubiquitous on college campuses. McCabe, Couper, Cranford,
and Boyd (2006) also found that the majority of undergraduates
in their
sample reported negative, secondary consequences from their
peers' alcohol abuse (McCabe, Couper, Cranford & Boyd,
2006).
1.4. Residential learning communities
Heavy episodic drinking may serve a “community-building
function” on college campuses. In a provocative editorial,
Bruffee
(1999) suggested that collective alcohol consumptionmay serve
to create a kind of community on campuses thatmay otherwise
feel
large and alienating. To address the problem of student
alienation, educators have suggested small, residential learning
communitiesmight help students navigate the first-year
experience, integrate and deepen their learning, and in the case
of women
andminorities, succeed infields inwhich they have traditionally
been under-represented (Hathaway, Sharp&Davis, 2001; Inkelas
&
7. Weisman, 2003).
McCabe et al. (2007) also found that RLC students reported
lower drinking rates and fewer alcohol-related consequences
than
non-RLC students during their first year in college. When
comparing RLC and non-RLC students, McCabe et al. (2007)
reported a
significant “drinking” difference between these groups during
their first semesters on campus. Although in both groups, the
maximum number of drinks (consumed on one occasion)
increased from pre-college to first semester on campus, the
number of
drinks per occasion was larger among non-RLC compared to
RLC students. Of note, however, is that RLC students reported
less
drinking before college than their non-RLC counterparts,
leading McCabe et al. (2007) to conclude that the differences
between
RLC and non-RLC drinking patterns may result from both
selection and initial transition to college socialization effects.
1.5. Hypotheses
Given the aforementioned, we set out to study drinking behavior
and its consequences among undergraduate womenwho live
in one of four types of university living arrangements (same-
andmixed-sex arrangementswithin Residential Learning
Community
(RLC) and non-RLC). We focus on women and their living
arrangements for two reasons: first, because at large co-
educational
colleges women are increasing their heavy use of alcohol
(Wechsler et al., 2002) and second, because sexual assault
among college
8. 989C.J. Boyd et al. / Addictive Behaviors 33 (2008) 987–993
students is one of the negative consequences associated with
college drinking; the assaults most often are perpetrated by
males,
within the context of heterosexual, acquaintance-type
relationships (Rennison, 2002).
Using secondary data from a large, federally funded study, we
were interested in the following: Among female, first-year
undergraduates living in university-sponsored housing: 1) Does
alcohol consumption vary as a function of RLC status (living in
an
RLC versus living in a non-RLC)? 2) Does alcohol consumption
vary as a function of the sex of floor residents (single-sex floors
versus mixed-sex floors)? 3) Do the primary and secondary
consequences of heavy episodic drinking, including being taken
advantage of sexually, vary as a function of RLC status and sex
of floor residents?
2. Methods
2.1. Procedure and recruitment
This on-going, longitudinal study represents a collaborative
relationship between researchers at The University of Michigan
(UM) and San Diego State University (SDSU), with each
Institutional Review Board approving the protocols. Using an
incoming,
2005 population of over 5000 undergraduate students at a large
Midwestern research university, a stratified random sample of
2502 full-time, first-year undergraduate students was selected
from three residential environments that included RLCs and
non-
RLCs. Further, all respondents were asked if the residents on
9. their floor were: all male, all female, or mixed male and female.
Datawere collected during the students first year at the
university (Fall 2005 andWinter 2006 semesters andwe report
on data
fromWave 1 here); at each wave, students were invited to
participate in the study via a pre-notification letter. The letter
explained
the study and provided directions for taking the survey on the
Web. In Wave 1, the pre-notification letters were sent via
federal
mail and contained a $2.00 bill as a pre-incentive. Respondents
were also entered into a sweepstakes drawing as an additional
incentive that included travel vouchers, iPods, and field passes
to athletic events. Respondents gave their consent to participate
by
checking an “I consent” assent box at the bottom of an online
consent form before they started the web-based survey.
Several strategies were used to increase the validity of the
study. All respondents were informed that a research team,
unaffiliated
with theUM,was contracted to set-up theWeb surveyaswell as
store andmaintain data; further, respondentswere reminded
thatUM
officials, faculty and staff were unable to access any contact
information connected with the data. Students were informed
that
participationwas voluntary and that all responseswould be kept
confidential pursuant to a NIH Certificate of Confidentiality.
TheWeb
survey was maintained on a hosted secure Internet site running
under the secure sockets layer (SSL) protocol to ensure
respondent
data were safely transmitted between the respondent's browser
and the server. Similar web-based protocols have been used by
10. this
investigative team and have been described in detail elsewhere
(Boyd et al., 2004; McCabe et al., 2002).
2.2. Measures
The Residential Community Engagement Survey (RCES) used in
the present study was developed and pilot-tested in 2005. The
RCES includes items from the Monitoring the Future study
(Johnston et al., 2006), the CORE survey (Presley, Meilman &
Cashin,
1996), the College Alcohol Study (Wechsler et al., 2002), and
the Student Life Survey (McCabe et al., 2002). The following
measures
represent the dependent measure outcomes used in the present
study.
2.2.1. Alcohol use
We screened for current alcohol use with the following
question. Alcohol use (lifetime and during the 12 months before
classes
started) was assessed using the following question: “On
howmany occasions (if any) have you had alcohol to drink
(more than just a
few sips) [in your lifetime or during the 12months BEFORE
your first day of classes]? The response choices were: (1) no
occasions, (2)
1–2 occasions, (3) 3–5 occasions, (4) 6–9 occasions, (5) 10–19
occasions, (6) 20–39 occasions, (7) 40 ormore occasions
(M=4.2, SD=2.1
and M=2.2, SD=1.9 for lifetime and past 12-month alcohol use,
respectively). A drink was defined as one beer is 12 oz of beer
at 5%
alcohol, onewine cooler is 12 oz at 5% alcohol, one glass of
wine is 5 oz ofwine at 12% alcohol, and one serving of liquor is
11. 1.5 oz of 80-
proof liquor. If answered affirmatively (answerN than no
occasions), then respondents received additional questions (see
below).
2.2.2. Maximum number of drinks
Current drinking was assessed and respondents were asked: “In
the past 28 days, what is the largest number of drinks you
consumed in a two hour period?” Responses ranged from 0 to 20
drinks (M=3.5, SD=3.5). This variable functions as a control
variable in some analyses.
2.2.3. Heavy episodic drinking
Heavy episodic drinking was assessed by asking questions:
“Over the past two weeks, how many occasions have you had
[FIVE
(male)/FOUR (female)] or more drinks in a row?” Responses
were categorized as either no heavy episodic drinking in the
past two
weeks or at least one heavy drinking episode in the past two
weeks.
2.2.4. Primary consequences
Primary consequence items were adapted from two national
studies of alcohol and other drug use among college students
(Wechsler et al., 2002, 1995; Presley et al., 1996). Students
could endorse as many as 16 negative consequences that they
had
experienced from their drinking (e.g. hangover, nauseated or
vomited, blackout, missed class, hurt or injured, argument or
fight,
12. 990 C.J. Boyd et al. / Addictive Behaviors 33 (2008) 987–993
trouble with police, someone you know said you should cut
down). We coded each item as 0=no, 1=yes and then summed
the
items to create an overall score for each respondent. Although
this means that all consequences are reduced to equal value, this
is
how other studies have operationalized both primary and
secondary consequences.
2.2.5. Secondary consequences
Secondary consequence items were adapted from previous
college-based national studies (Presley et al., 1996; Wechsler et
al.,
1995). Secondary consequences were measured using the
following item: “Please indicate how often during the past 28
days you
have experienced the following as a result of other people's
drinking.” Items included: event spoiled, study disrupted, sleep
disrupted, property stolen or damaged, took care of someone,
found vomit, sexually assaulted, physically assaulted, and
unwanted
sexual advance. We coded each item as 0=no, 1=yes and then
created an overall score for each respondent by summing the
items.
2.2.6. Participants and demographics
A total sample of 1196 first-year students from a large
Midwestern public research university participated during the
Fall
semester (Wave 1), for a response rate of 47.8%. The sample
consisted of 66.5% White, 12.0% Asian, 4.2% Hispanic, 6.3%
African
13. American and 11.0% reported another racial/ethnic category,
with a mean (SD) age of 18.5 (.3) years and was generally
representative of the population of first-year, incoming
students. The modal category for parental incomewas $50,000 to
$99,999,
and 29.5% of women had at least a part-time job.
We examined data from 611 women (51% of the total sample)
who completed Wave 1. Four groups were created: 82 women
(13%) who lived in single-sex RLCs, 212 women (35%) who
lived in mixed-sex RLCs, 147 women (24%) who lived in
single-sex, non-
RLCs and 170 women (28%) who lived in mixed-sex, non-
RLCs. We refer to this 4-level categorical variable as “RLC co-
ed status.”
To assess non-response bias, we conducted a telephone follow-
up survey of 221 randomly selected students who did not
respond to the Wave 1 Web survey. There were no differences
in reasons for non-response between students living in RLCs
and
non-RLCs. There were no statistically significant differences
between responders and non-responders on lifetime frequency
of
alcohol consumption, past 12-month frequency of alcohol
consumption, ormaximumnumber of drinks on one occasion in
the past
28 days (see Cranford et al., 2008 for more details on non-
response analysis).
3. Results
SPSS for Windows 14.0 softwarewas used to conduct all
analyses. We used chi-square tests and analysis of variance to
examine
whether past two week heavy episodic drinking varies as a
14. function of the RLC co-ed status variable.
In a previous report based on data from all males and females in
this sample (McCabe et al., 2007), we found lower levels of
pre-
college drinking among non-RLC compared to RLC students.
Although we did not publish the results for “women only” living
arrangements in McCabe et al. (2007), at that time, we knew
there were differences in pre-college drinking by RLC co-ed
status. A
one-way ANOVA showed a main effect of RLS co-ed status on
pre-college drinking F (3, 552)=3.13, pb .05, and Tukey HSD
post-hoc
comparisons showed that maximum drinks in the 28 days before
college started was higher among the non-RLC, co-ed group
(M=2.6) compared to the RLC single-sex group (M=1.4), pb .05.
These results supported our decision to statistically control for
pre-
college drinking; in this study, we were interested in the
associations between residential environments and alcohol
involvement
among incoming college women, thus, we statistically
controlled for pre-college drinking in all analyses unless
otherwise
indicated. This allowed us to account for selection effects as an
alternative explanation for our results.
In order to assess amount of drinking, one-way analysis of
covariance (ANCOVA) was used to determinewhether the
maximum
number of drinks consumed in a two-hour period in the past 28
days varied as a function of RLC co-ed status (single-sex RLC,
mixed-sex RLC, single-sex non-RLC and mixed-sex non-RLC)
after controlling for the maximum number of drinks consumed
in a
two-hour period in the past 28 days before classes started.
15. Women's drinking behaviors varied as a function of RLC status
and the
sex (single-sex versus mixed-sex) of the floor residents; in fact,
we found a significant effect for RLC co-ed status, F (3,
540)=3.0,
pb .05). As seen in Table 1, women in single-sex (M=2.8) and
mixed-sex RLCs (M=2.9) reported a significantly lower number
of
drinks in a two-hour period (pb .05) than the mixed-sex, non-
RLC women (M=3.6).
Using a chi-square analysis, we examined the prevalence of
heavy episodic drinking (in the past two weeks) across the four
residential groups and found statistically significant differences
between the groups (X2=25.4, df=3, pb .01). We conducted
post-
hoc comparisons between proportions with a modified
Bonferroni correction to maintain the alpha level at .05 (Jaccard
& Becker,
1997). As seen in Table 1, only 15% (n=12) of the single-sex
RLC women reported heavy episodic drinking in the past two
weeks, as
contrasted with 29% (n=60) in the mixed-sex RLC (z=−2.6, pb
.05), 39% (n=72) in the single-sex non-RLC (z=−3.8, pb .05),
and 45%
(n=197) in the mixed-sex, non-RLC (z=−4.7, pb .05). We then
conducted a multiple logistic regression analysis in order to
examine
the association between RLC co-ed status and past 2-weeks
heavy episodic drinking after controlling for pre-college
drinking.
Three dummy variables were constructed to represent the
information in the 4-category RLC co-ed status variable, with
single-sex
RLC women as the reference group. Past 2-weeks binge
drinking was treated as the criterion variable in this analysis.
16. Results
indicated that the odds of past 2-week binge drinking were
significantly higher among single-sex non-RLC women
(OR=3.6, 95%
CI=1.5–8.7) and mixed-sex non-RLC women (OR=3.8, 95%
CI=1.6–9.0) compared to single-sex RLC women, even after
pre-college
drinking was statistically controlled. The odds of past 2-weeks
binge drinking were also higher among co-ed RLC women
(OR=2.0,
95% CI= .8–4.6), but this effect was non-significant (p= .11).
To examine primary consequences as a function of living
arrangements, we conducted one-way ANCOVAs with pre-
college
drinking as a covariate. Results showed a statistically
significant effect of RLC co-ed status, F (3, 544)=4.2, pb .01,
with women
Table 1
Prevalence of alcohol involvement and alcohol-related
consequences by RLC co-ed status (N=611)
Single-sex RLC Co-ed RLC Single-sex non-RLC Co-ed non-
RLC F or χ
(n=82) (n=212) (n=147) (n=170)
M or % M or % M or % M or %
Total 13.4% 34.7% 24.1% 27.8%
Max drinks in past 28 days 2.8a 2.9a 3.2a,b 3.8b 3.0*
Heavy episodic drinking (past 2 weeks) 14.6a 29.3b 38.7b,c
44.7c,d 25.4**
17. Primary consequences .7a 1.1a 1.3a,b 1.8b 4.2**
Secondary consequences 2.0a 2.3a 2.3a 2.5a 1.1
Note. Within rows, means and percentages with different
superscripts are significantly different at pb .05.
*pb .05. **pb .01.
991C.J. Boyd et al. / Addictive Behaviors 33 (2008) 987–993
2
living in single-sex RLCs (M= .7) and mixed-sex RLCs (M=1.1)
having a lower mean number of consequences than mixed-sex
non-
RLC women (M=1.8, pb .01) (see Table 1). We used chi-square
tests to examine the association between residential status and
two
specific negative consequences: a) sexual assault after drinking
in past 28 days and b) regretted sex as a result of drinking in
past
28 days. We found group differences, but they were not
statistically significant – probably because of low base rates.
For instance, 5%
(n=3) of respondents in single-sex RLCs reported being taken
advantage of sexually in contrast to 9% (n=14) in mixed-sex
RLCs, 9%
(n=10) in single-sex non-RLCs and 13% (n=18) in mixed-sex
RLCs, χ2 (3)=3.4, p= .3. Only 2% (n=1) of the respondents in
single-sex
RLCs regretted sex (after drinking) while 6% (n=9) in mixed-
sex non-RLCs, 4% (n=4) in single-sex non-RLCs and 7% (n=10)
in
mixed-sex non-RLCs regretted sex after drinking, χ2(3)=3.2, p=
.3.
A one-way ANCOVA of the number of secondary drinking
consequences was conducted, with pre-college drinking as a
covariate. As seen in Table 1, we found that the number of
18. secondary consequences varied as a function of residential
status but the
overall F-ratio was non-significant, F (3, 543)=1.1, ns.
Respondents in the single-sex RLCs had the lowest mean
number of
secondary consequences (M=2.0) and women in the mixed-sex,
non-RLCs had the highest (M=2.5).
4. Discussion
Residential learning communities have been proposed as an
environmental intervention that is protective against heavy
episodic drinking; however, it is impossible to assess the true
impact of RLCs on undergraduate drinking without a
randomized
trial. Perhaps as RLCs become more popular on college
campuses, and thus, RLC living space becomes more limited, a
randomized
trial will be conducted to further test the effects of selection
and socialization.
Findings from this study indicate that women living in RLCs,
whether single or mixed-sex, drank less than their non-RLC
counterparts. By comparison, women living in mixed-sex, non-
RLCs reportedmore drinks in a two-hour period when compared
to
all other residential groups; these non-RLC women – “living
with the guys” –were also more likely to participate in heavy
episodic
drinking. And while single-sex living arrangements appear
protective when compared to mixed-sex arrangements, it is the
RLCs
that appear to confer an added protection as shown by the non-
significant differences between same-sex non-RLCs andmixed-
sex
RLCs residents.
19. Our data lend support to the Brower et al. (2003) findings. They
investigated the impact of collegiate residential learning
communities (RLCs) on alcohol consumption using a random
sample of 6100 first-year students from a large, Midwestern
research
university. Students in the RLCs were significantly less likely
to consume alcohol, and less likely to have had a heavy
drinking
episode in the past two weeks in comparison to students not
living in RLCs (37.7% versus 57.1%). There were no
demographic
differences between students involved in learning communities
(RLC students) and those who were not, although RLC students
were significantly more involved in community service and
volunteer activities, as well as in campus-sponsored activities
and
events.
Not surprisingly, when residents drink less, their floor-mates
are less likely to report secondary consequences and thus,
women
in single-sex living arrangements report fewer primary and
secondary consequences from excessive alcohol consumption
although secondary differences were not statistically
significant. However, we also found that women living in
single-sex, RLCs
reported fewer primary consequences than their peers living in
single-sex, non-RLC environments (M= .7 andM=1.3,
respectively).
It is remarkable that mixed-sex RLC residents reported fewer
consequences (M=1.1) than women residing in single-sex, non-
RLCs
(1.3), a finding that provides additional support for the RLC
environment; it is possible that the RLC provides a protective
factor,
20. independent of the sex composition of the living environment.
We questioned whether women living in mixed-sex, residential
environments, particularly environments with higher drinking
rates, would be more likely to regret having sex (because of
drinking) or to report being taken advantage of sexually (while
drinking). Our data revealed no statistically significant group
differences on these two variables, albeit cell sizes were very
small
and make firm conclusions impossible. However, the raw
numbers were consistent with our other findings: fewer
residents in
single-sex RLCs reported either being taken advantage of
sexually (n=3) or regretting sex after drinking (n=1) when
compared to
mixed-sex RLC residents (14 and 9, respectively), single-sex,
non-RLC residents (10 and 4, respectively) and mixed-sex, non-
RLCs
(18 and 10, respectively).
In previous work, McCabe et al. (2007) noted that RLCs could
deter heavy drinking by providing alternative activities (e.g.,
structured co-curricular) that are less available to non-RLC
students. Our data suggest that RLCs provide structured
activities and
992 C.J. Boyd et al. / Addictive Behaviors 33 (2008) 987–993
increase student engagement; they are protective and create an
environment in which undergraduate women drink less. In turn,
women living in any co-educational arrangement, and
particularly non-RLCs, may increase their alcohol consumption
because
they are with men (who have higher levels of drinking) and
thus, the alcohol is more available.
21. 5. Conclusion
There are several limitations with this study design that require
consideration. The sample was drawn from a single institution
and this limits the generalizability of the findings. In the future,
longitudinal data are needed to characterize the mechanisms by
which women's living arrangements may influence alcohol
involvement (Inkelas & Weisman, 2003) and longitudinal, panel
designs should be considered. Further, because the primary and
secondary consequences measures were dichotomous and did
not
take into account the frequency of each consequence, there may
have been a ceiling effect. As a result, a student whose sleep
was
disturbed once would receive the same score as a student who
was disturbed up to 5 times. This ceiling effect may explain the
finding that mixed-sex RLC women reported fewer negative
consequences than same-sex RLCs.
The present study relied on retrospective recall of pre-college
drinking and the …
Assignment: Ethics in Accounting
Due Week 9 and worth 170 points
Effective financial reporting depends on sound ethical behavior.
Financial scandals in accounting and the businesses world have
resulted in legislation to ensure adequate disclosures and
honesty and integrity in financial reporting. A sound economy
is contingent on truthful and reliable financial reporting.
Instructions:
· Read the following scenario.
· Answer the questions that follow. This will be a 2-3 page
submission in a question and answer format (also in paragraph
form). An introduction and conclusion is not required.
22. · Refer back to your textbook for guidance on how to think
through the scenario.
You have been recently hired as an assistant controller for XYZ
Industries, a large, publically held manufacturing company.
Your immediate supervisor is the controller who also reports
directly to the VP of Finance. The controller has assigned you
the task of preparing the year-end adjusting entries. In the
receivables area, you have prepared an aging accounts
receivable and have applied historical percentages to the
balances of each of the age categories. The analysis indicates
that an appropriate estimated balance for the allowance for
uncollectible accounts is $180,000. The existing balance in the
allowance account prior to any adjusting entry is a $20,000
credit balance.
After showing your analysis to the controller, he tells you to
change the aging category of a large account from over 120
days to current status and to prepare a new invoice to the
customer with a revised date that agrees with the new category.
This will change the required allowance for uncollectible
accounts from $180,000 to $135,000. Tactfully, you ask the
controller for an explanation for the change and he tells you
“We need the extra income, the bottom line is too low.”
Required:
In a 2-3 page paper, discuss the following:
1. Consider what you have learned relative to ethics and
financial reporting. What is the rationale for the
calculations/process used to estimate the $180,000 uncollectible
allowance?
2. How do you think the misstatement of funds will impact the
income statement and balance sheet?
3. What is the ethical dilemma you face? What are the ethical
considerations? Consider your options and responsibilities as
assistant controller.
4. Identify the key internal and external stakeholders. What are
23. the negative impacts that can happen if you do not follow the
instructions of your supervisor?
5. What are the potential consequences if you do comply with
your supervisor’s instructions? Who will be negatively
impacted?
Additional Requirements:
· Use at least one (1) quality academic resource (in addition to
your textbook) for this assignment. Note: Wikipedia and similar
websites do not qualify as academic resources. You have access
to Strayer University’s Online Library
at https://research.strayer.edu and the iCampus University
Library Research page
at https://icampus.strayer.edu/library/research.
Your assignment must follow these formatting requirements:
· Your paper should be double spaced (Arial or Times Roman
12 pt font) and follow general Strayer Writing Standards (SWS)
as they relate to references and citations. Please take a moment
to review the SWS documentation for details (more information
and an example is included in the Strayer Writing Standards
menu link located in your Blackboard).
· Include a cover page containing the title of the assignment, the
student’s name, the professor’s name, the course title, and the
date. The cover page and the reference page are not included in
the required assignment page length.
24. Week 9 Assignment
Professor Name
ACC100
Your Name
Introduction (Optional)
In this section include the purpose of the paper and general
overview of what you will be discussing. Do not copy/paste the
assignment scenario in this section.
1) What is the rationale for the calculations/process used to
estimate the $180,000 uncollectible allowance?
In this section, you want to refer back to chapter 9 in your
textbook and review the process for determining the allowance
for bad debt. What is most important in getting started on this
assignment is to make sure you first understand why companies
use the allowance method for accounting for receivables. The
focus is not on the calculation itself, but the rationale for using
the allowance method in comparison with the direct write-off
method. Why is accounting for uncollectible receivables
necessary? How does the allowance method provide a more
accurate reporting result?
2) How do you think the misstatement of funds will impact the
income statement and balance sheet?
For this question, you want to discuss the potential impact on
the financial statements. The allowance method will provide an
estimate for bad debt expense, as well as estimate the balance
for the allowance contra account to accounts receivable. How
25. will the items affect the income and balance sheet? For
example, how will changing the aging category data affect the
accuracy and reliability of information reported? Will the
balance sheet accounts be accurate if they are over/understated?
How will the over/understatement of expenses, for example,
affect the income statement? Review information from chapter 8
to help you expand on this question.
3) What is the ethical dilemma you face? What are the ethical
considerations? Consider your options and responsibilities as
assistant controller.
For this question, you want to think about certain ethical
considerations such as GAAP violations, lack of integrity,
employee trust, customer trust, etc. How will changing the
numbers noted in the scenario impact the company based on
these ethical considerations? Is the conduct of the controller
appropriate? Why or why not? Please note there are tons of
examples throughout your textbook chapters.
4) Identify the key internal and external stakeholders. What are
the negative impacts that can happen if you do not follow the
instructions of your supervisor?
For this question, you want to discuss the key individuals that
may be affected by the act of the controller if his instructions
are not followed. Who is mostly at risk? What is the
responsibility to the employees, customers, vendors, etc.? What
could reporting a low bottom line mean for the company? Could
the company's financial survival be threatened? What could
happen to the assistant controller if he/she does not comply with
the supervisor’s request?
5) What are the potential consequences if you do comply with
your supervisor’s instructions? Who will be negatively
impacted?
For this question, you want to name potential individuals who
may be negatively impacted if the assistant controller does
26. follow the instructions of his supervisor and how they may be
affected. Again, think about the stakeholders you identified in
question 4. For example, how would this affect the company’s
reputation? Are there other potential ethical and/or criminal
violations?
Sources
There are tons of information in the Strayer library you can
search to assist you with this assignment. Don’t be afraid to
use these tools.
7
27. 689
Adjustment to College in
Students With ADHD
David L. Rabiner
Duke University
Arthur D. Anastopoulos
University of North Carolina at Greensboro
Jane Costello
Rick H. Hoyle
H. Scott Swartzwelder
Duke University
Objective: To examine college adjustment in students reporting
an ADHD diagnosis and the effect of medication treatment
on students’ adjustment. Method: 1,648 first-semester freshmen
attending a public and a private university completed a
Web-based survey to examine their adjustment to college.
Results: Compared with 200 randomly selected control students,
68 students with ADHD reported more academic concerns and
depressive symptoms. This was explained by higher rates of
inattentive symptoms among students with ADHD and was
unrelated to hyperactive-impulsive symptoms. Among students
with ADHD, medication treatment was not related to better
adjustment or diminished ADHD symptoms. The contribution
of inattention to academic concerns and depressive symptoms
remained significant when controlling for personality traits.
Conclusion: Students with ADHD experience greater academic
performance concerns and depressive symptoms during the
transition to college. Medication treatment did not appear to
28. diminish ADHD symptoms nor enhance students’ adjustment.
(J. of Att. Dis. 2008; 11(6) 689-699)
Keywords: ADHD in college students; college adjustment and
ADHD; college adjustment
Although children with ADHD are less likely thantheir peers to
graduate from high school and attend
college (Barkley, Fischer, Edelbrock, & Smallish, 1990),
an increasing number of young adults with ADHD are
enrolling in colleges and universities (DuPaul et al.,
2001; Wolf, 2001). How do students with ADHD adjust
to college life? Does medication treatment for ADHD
positively affect the quality of their academic experience
or psychosocial adjustment to college life? Are difficul-
ties with adjustment to college specifically related to
core ADHD symptoms or to personality characteristics
that may be associated with those symptoms? These are
all questions about which little is known.
The results of prior studies examining adjustment to
college in students with ADHD have been mixed.
Heiligenstein, Guenther, Levy, Savino, and Fulwiler
(1999) reported on students with high rates of ADHD
symptoms who self-referred to a college counseling
center; those with comorbid disorders were excluded.
Compared with students seeking assistance for career con-
cerns, students with ADHD had lower grade point averages
(GPAs) and were more likely to have been on academic
probation. However, differences in self-reported depres-
sion, anxiety, interpersonal relationships, physical health,
or substance use were not found, perhaps because ADHD
students with comorbid disorders were excluded.
Journal of Attention Disorders
30. reported lower levels of self-esteem, a finding that has
also been reported by Dooling-Litfin and Rosen (1997).
Similarly, Grenwald-Mayes (2002) reported that college
students with ADHD described a lower quality of life
than other students. These were older students, however—
older than 24, on average—and thus were not traditional
undergraduates. Finally, Kern, Rasmussen, Byrd, and
Wittschen (1999) suggest that college students with
ADHD may have difficulty obtaining social support
from others, which could interfere with their adjustment
to college. These authors, however, did not directly
examine adjustment to college in students with ADHD
and their sample was restricted to students participating
in an undergraduate psychology course rather than the
wider student body.
It is surprising that these are the only studies pub-
lished to date on adjustment to college in students with
ADHD. In addition to the mixed results reported, the
samples in several of these studies are probably not rep-
resentative of the general population of college students
with ADHD, as one was clinic-based and excluded
students with comorbid disorders, one dealt with com-
muter students, and a third involved older, nontraditional
college students. Given the paucity of data on how
students with ADHD adjust to college life, and the equiv-
ocal findings from these data, an important goal of this
study was to examine college adjustment in a more rep-
resentative sample of students with ADHD.
Because there is an extensive literature documenting
the adverse effect that ADHD has on academic, social, and
psychological functioning (Barkley, 2006), there is cer-
tainly a basis for hypothesizing that students with self-
reported ADHD would report more academic concerns,
reduced satisfaction with their social life, higher levels of
31. depressive symptoms, and higher rates of substance use
(Molina & Pelham, 2003). On the other hand, college
students with ADHD are likely to be a better adjusted sub-
set of the general ADHD population and have experienced
a significant measure of academic success. Thus, we
viewed it as quite possible that they would not show the
same pattern of difficulties that characterize the general
population of individuals with ADHD.
A second issue we examined was the relationship
between medication treatment and students’ adjustment
to college. There is ample evidence that stimulant med-
ications are effective treatments for adolescents and
young adults with ADHD (Greenhill, 2002), suggesting
that medication treatment would promote a more suc-
cessful college transition. However, treatment outcomes
obtained in community settings often fall short of what
occurs in clinical trials (MTA Cooperative Group, 1999),
and college places increased organizational and time
management demands on students, who must cope with
these demands without the support that was previously
provided by parents and teachers who knew them well.
We were thus uncertain whether medication treatment
would be associated with a more positive transition to
college for students with ADHD and are not aware of any
prior studies in which this issue has been examined.
The final issue we explored was the relative contribu-
tion of ADHD symptoms and personality characteristics
to students’ adjustment. Recently, Nigg et al. (2002)
examined the association between ADHD symptoms and
the Big Five personality traits (i.e., extraversion, agree-
ableness, conscientiousness, emotional stability, and
openness to experiences; McCrae & Costa, 1999) among
young adults and found that low conscientiousness and
32. high neuroticism were associated with inattentive symp-
toms, whereas low agreeableness was associated with
hyperactive-impulsive symptoms. Because particular Big
Five traits are also related to various indices of psychoso-
cial adjustment in young adults, including substance use
(Flory, Lynam, Milich, Leukefeld, & Clayton, 2002),
internalizing symptoms (Flory et al., 2002), academic
success in college (Komarraju & Karau, 2005; Ridgell &
Lounsbury, 2004), and perceived quality of social rela-
tionships in college (Lopes, Salovey, & Strauss, 2003), it
is important to test whether ADHD symptoms predict col-
lege adjustment after the association between personality
traits and adjustment is controlled for.
We examined these issues using data collected as part
of a longitudinal study on the nonmedical use and abuse
of ADHD medications among students attending either a
private or public university in the southeast United
States. As part of the initial wave of data collection,
which occurred roughly 10 weeks into students’ first
semester of college, students were asked whether they
were currently diagnosed with ADHD, as well as a vari-
ety of questions pertaining to their psychosocial adjust-
ment. Because the larger study required students to
report on illegal behaviors, all responses were provided
anonymously. This precluded us from being able to inde-
pendently document the validity of students’ self-
reported diagnostic status for ADHD.
690 Journal of Attention Disorders
Methods
Participants
33. Participants were 1,648 freshmen from a public and a
private university located in the southeastern United
States who completed the Web-based survey described
below; the Institutional Review Board at both universi-
ties approved the protocol for this study and all partici-
pating students provided informed consent. The public
university serves predominantly in-state students and has
a female-to-male ratio of more than 2 to 1. The private
university is highly selective, admits a more geographi-
cally diverse student body, and the female-to-male ratio
is nearly equal. Details on the participation rate and
demographic characteristics of the sample are provided
below.
Measures
The survey administered to students was intended to
build on current knowledge pertaining to the nonmedical
use and misuse of ADHD medications among college
students. Thus, students who reported using ADHD med-
ication without a prescription or misusing prescribed
ADHD medication were asked a number of questions
about these behaviors, and results pertaining to that
aspect of the study will be reported elsewhere. Below, we
focus on items that directly address college adjustment in
students with ADHD.
ADHD status. Participants were asked whether they
were currently diagnosed with ADHD and classified
based on their response. As noted above, because the
survey was completely anonymous, the accuracy of
students’ self-report diagnostic status could not be inde-
pendently verified via diagnostic interview. We did not
ask whether participants were diagnosed with the inat-
tentive, hyperactive-impulsive, or combined subtype of
34. ADHD because we believed that most students would
not be aware of this information, even if a particular sub-
type had been assigned by the diagnosing clinician.
ADHD symptoms. Because ADHD symptoms in the
Diagnostic and Statistical Manual of Mental Disorders
(4th ed., text revision; DSM-IV-TR) (American
Psychiatric Association, 1994) may not adequately cap-
ture manifestations of ADHD in young adults (Barkley,
Fischer, Smallish, & Fletcher, 2002), we developed
items to measure inattentive and hyperactive-impulsive
symptoms that were directly relevant to college students.
The six-item inattention scale included items inquiring
about attention difficulties related to college academic
tasks, for example, “It is difficult for me to pay attention
during classes,” “I believe that most students in my
courses concentrate better in class than I do,” and “I have
difficulty keeping track of my different school assign-
ments.” Students responded on 5-point scales anchored
by strongly disagree and strongly agree; item responses
were averaged so that higher scores indicate greater self-
reported attention difficulties. Coefficient alpha for the
scale exceeded .90. The complete list of items included
on this scale can be found in the Appendix.
Hyperactive-impulsive symptoms were assessed with
five items selected to reflect the manifestation of such
difficulties in college students, for example, “I feel rest-
less and fidgety during my classes,” “I feel restless and
fidgety when completing schoolwork outside of class,”
and “I am an impulsive person.” As above, students indi-
cated responses ranging from strongly disagree to
strongly agree, and item responses were averaged so that
higher scores indicate greater self-reported attention dif-
ficulties. Coefficient alpha for the scale was .84.
35. Personality factors. Information on personality traits
was assessed using the Ten-Item Personality Inventory
(TIPI; Gosling, Rentfrow, & Swann, 2003). The TIPI
includes two items for each of the Big Five personality
trait domains: extraversion, agreeableness, conscien-
tiousness, emotional stability, and openness to experi-
ences. Each item is a pair of adjectives drawn from
extant adjectival measures of the five-factor model. One
pair in each set represents the negative pole and the other
the positive pole of the personality domain; the nega-
tively worded item was reverse scored and the two items
were summed. Two-week test-retest coefficients are in
the mid .70s. Scores on the five factors correlate highly
with their counterparts on the Big Five Inventory (44
items) and the Revised NEO Personality Inventory
(NEO-PI-R) (240 items). Convergent and discriminant
validity of the TIPI scores are acceptable (Gosling et al.,
2003). Although a more comprehensive assessment of
the Big Five traits would have been preferable, this
short instrument was selected so that the time to com-
plete the survey for students who responded to questions
pertaining to medication abuse and misuse would remain
reasonable.
Academic concerns. This four-item scale assessed
students’ concerns about their early academic perfor-
mance and ability to succeed academically. Two items
were framed positively, for example, “I feel satisfied
with how well I am doing academically,” and two were
framed negatively, for example, “I worry that my grades
will not be as good as I need them to be.” Students
responded to each item on a 5-point strongly disagree to
strongly agree scale based on their feelings for the past
30 days. Positively worded items were reverse scored
and the four items were averaged so that higher scores
36. Rabiner et al. / ADHD and College Adjustment 691
reflect greater academic concerns. Coefficient alpha for
the scale was adequate at .76.
Social dissatisfaction. Four items were created to
assess students’ satisfaction with their friendships and
social life. Two items were framed positively, for
example, “I feel satisfied with the quality of my social life
in college,” and two were framed negatively, for example,
“I feel lonely.” Students responded to each item on a scale
anchored by strongly disagree and strongly agree based
on their feelings for the past 30 days. Positive items were
reverse scored and the four items were averaged so that
higher scores reflect greater dissatisfaction. Coefficient
alpha for the scale was adequate at .76.
Depressive symptoms. Depressive symptoms were
assessed using an eight-item scale derived from the
Center for Epidemiologic Studies Depression Scale
(CES-D; Radloff, 1977). Students were asked how often
during the past 2 weeks they had experienced a variety of
depressive symptoms, for example, “felt sad, blue,
unhappy or down in the dumps,” and “felt that you were
not enjoying the activities you used to,” and responded
on 5-point scales ranging from never to most of the time.
Responses averaged such that a higher score reflects
greater endorsement of depressive symptoms. Coefficient
alpha for the scale was .89.
Alcohol, tobacco, and drug use. Two questions were
asked about alcohol use. First, students were asked, “On
how many occasions (if any) have you had alcohol to
37. drink (more than just a few sips) during the PAST 6
MONTHS?” Consistent with national research, the
response scale was (1) never, (2) 1–2 occasions, (3) 3–5
occasions, (4) 6–9 occasions, (5) 10–19 occasions, (6)
20–39 occasions, and (7) 40+ occasions. Students were
also asked, “What is the greatest number of drinks you
consumed within a 2-hour period during the past 30
days? By a drink, we mean half an ounce of absolute
alcohol (e.g., a 12-ounce can or glass of beer or cooler, a
5-ounce glass of wine, or a drink containing 1 shot of
liquor).” Students who had consumed any alcohol in the
past 30 days were instructed to enter 0.
For drug use, students were asked about their use of
marijuana, cocaine, and a variety of other substances
over the past 6 months and responded using the same
response scale as for alcohol. Finally, students were
asked how many cigarettes they had smoked in the past
30 days and responded on a 7-point scale ranging from
none to more than 2 packs per day.
Procedure
The study was conducted over a 5-week period begin-
ning roughly 9 weeks into the students’ first semester. All
freshmen older than 18 at the private (n = 1,572) and
public (n = 2,033) universities were sent a letter inform-
ing them about a Web-based survey that was being con-
ducted to learn about the possible misuse and abuse of
ADHD medications by college students. Several days
later, students received an e-mail invitation that
explained how to access the survey. Students were
assured that their responses would remain confidential,
that the researchers would not be able to link individual
students with their responses (students accessed the sur-
38. vey using a randomly generated ID number), and that a
Certificate of Confidentiality to protect their privacy had
been obtained. A $10 campus bookstore gift card was
offered as an incentive to participate and students were
informed that they would also be eligible to win one of
10 $100 bookstore gift cards at each campus. Students
who neither responded nor opted out were sent up to
three additional requests to complete the survey at
weekly intervals. Surveys were submitted by 803
students from the private university (51% participation
rate) and 845 students from the public university (42%
participation rate). Across the two schools, the participa-
tion rate was 46%; this figure is consistent with other
college-based studies on this topic (Teter, McCabe,
Cranford, Boyd, & Guthrie, 2005).
Results
Sample Characteristics
The final sample included the 1,648 members of the
freshman class at the two universities who completed
and submitted the survey. Table 1 shows the demo-
graphic characteristics of our sample; the percentages
shown are highly similar to the population of freshmen
at each university.
Sixty-eight students—approximately 4% of those
who participated—reported that they were currently
diagnosed with ADHD. Forty-nine of these students
were from the public university (5.8% of participants)
and 18 were from the private university (2.2% of partic-
ipants); 44 (65%) were female, and 62 (91%) were
Caucasian. The high percentage of females in the ADHD
sample reflects the fact that the reported rate of ADHD
at the public university was more than double the rate at
39. the private university (i.e., 5.8% vs. 2.2%), and females
made up 79% of the public university sample; among the
students who responded, however, approximately 4% of
males and females identified themselves as having
ADHD. In addition to the 68 students who reported a
current ADHD diagnosis, 19 students indicated that
although they were not currently diagnosed with ADHD,
they had been previously diagnosed with ADHD by a
692 Journal of Attention Disorders
health professional. We felt it would be interesting to
compare college adjustment in students previously diag-
nosed with those currently diagnosed and decided to
include this group in several of the analyses reported
below.
Because of the large imbalance in group size between
students identifying themselves as having or not having
ADHD, we identified a sample of 100 students from
each site who had no reported history of either ADHD or
ADHD medication use; these students were randomly
selected from within gender and race groupings to match
the composition of sex and race observed for the popula-
tion of participants at each site. Groups were compared
using analyses of variance with gender, race, and site
included as covariates; when group differences were sig-
nificant, pairwise t tests that controlled for multiple com-
parisons were conducted. Although we had no specific
predictions for whether group differences would be mod-
erated by gender or site, these interactions were tested to
ascertain whether any group effects that emerged were
consistent for males and females as well as for students
attending different types of universities.
40. Inattentive and Hyperactive-Impulsive
Symptoms
Because group assignment was based entirely on
students’ report, we first examined whether the students’
self-reported inattentive and hyperactive-impulsive
symptoms were consistent with their self-reported diag-
nostic status. Results from this analysis can be seen in
the upper portion of Table 2.
As expected, compared with those in the representa-
tive sample, students currently diagnosed with ADHD
reported significantly higher rates of inattention and
hyperactivity-impulsivity; the effect size for these differ-
ences was large in both cases. Students with ADHD also
tended to report higher rates of attention difficulties than
students who were previously diagnosed, but this differ-
ence was only marginally significant (i.e., p < .07).
Students who were previously diagnosed with ADHD
also reported higher rates of inattentive and hyperactive-
impulsive symptoms than students in the representative
sample. Interactions of group with gender and site did
not approach significance.
College Adjustment in Students With ADHD
The mean ratings for academic concerns, depressive
symptoms, and social satisfaction are also shown in
Table 2. Compared with the representative sample of
students, those currently diagnosed with ADHD reported
more concerns about their academic performance as well
as higher rates of depressive symptoms; the effect size
would be considered small to moderate. For depressive
symptoms, the group effect was qualified by a significant
41. Group × Site interaction. This interaction reflected the
fact that although mean depressive symptoms were sub-
stantially higher in the public university students with
ADHD than in students from the representative sample
(3.17 vs. 2.50), this was not the case for students attend-
ing the private university (2.28 vs. 2.50). Although
students previously diagnosed with ADHD appeared to
report greater academic concerns than students who
were never diagnosed, this difference was not signifi-
cant. They did, however, report higher levels of depres-
sive symptoms. Students’ report of their social
satisfaction was comparable for all groups.
Rabiner et al. / ADHD and College Adjustment 693
Table 1
Percentage Distributions of Sample/Population
Characteristics
Private Public Total Sample
Characteristic n = 803 n = 845 n = 1648
Male 49 22 35
Female 51 79 65
White 68 78 70
African American 6 16 11
Asian 21 2 10
Hispanic 5 3 4
Note: Entries indicate the percentage of participants in each
demo-
graphic group.
Table 2
42. Group Means and Standard Deviations
(in parentheses) for ADHD Symptoms, Academic
Concerns, Depressive Symptoms, and Social
Satisfaction
Current ADHD Prior ADHD Never ADHD
Outcome n = 68 n = 19 n = 200 d
Inattention 3.35a 3.18a 2.36b .96
(1.19) (1.05) (.96)
Hyperactivity 3.27a 3.27a 2.47b .92
(.80) (.88) (.74)
Academic 2.60a 2.75b 3.06b .48
(.99) (.87) (.96)
Depression 2.80a 3.07a 2.49b .37
(.85) (.98) (.85)
Social 3.82a 3.92a 3.86a —
(.83) (1.04) (.87)
Note: Within each row, means that share a superscript do not
signifi-
cantly differ, p < .01. The final column, d, represents the effect
size of
the difference between the Current ADHD and Never ADHD
groups.
Alcohol, Drug, and Cigarette Use
43. A series of logistic regression analyses was conducted
to determine whether students with ADHD were more
likely than peers to drink, smoke, or use marijuana.
Although students were also asked about the use of other
substances such as cocaine, ecstasy, inhalants, and so on,
the small number of students reporting use of the sub-
stances precluded statistical analysis. As with the other
adjustment measures, gender, site, and race were included
as predictors in the model.
Students with either current or past ADHD were not
more likely than others to report consuming alcohol dur-
ing the past 6 months, and among those who reported
drinking in the past 30 days, the maximum number of
drinks consumed during a 2-hour period was highly sim-
ilar across groups. The percentage of students in each
group reporting marijuana use was also highly similar.
Students with current and prior ADHD were, however,
between 2.5 and 3.5 times as likely to have smoked cig-
arettes during the past 30 days.
Does Medication Treatment Enhance
Adjustment in Students With ADHD?
Of the 68 students reporting a current ADHD diagno-
sis, 47 indicated that they were being treated with medica-
tion, whereas 21 reported no current medication treatment.
To determine whether medication treatment was associ-
ated with better adjustment, we compared these groups on
their academic concerns, depressive symptoms, and social
satisfaction; we also tested for group differences in inat-
tentive and hyperactive-impulsive symptoms, for which
medication treatment would be most expected to be help-
ful. As above, gender, site, and race were included as con-
trol variables. The results of these comparisons are shown
44. in Table 3, where it is evident that the reports of students
in each group were remarkably consistent, and none of the
differences approached significance. We also examined
whether medication treatment was associated with the like-
lihood of consuming alcohol, marijuana, or cocaine during
the prior 6 months or of smoking cigarettes in the past 30
days. The only difference to approach significance was
that students who reported having ADHD and being
treated with medication tended to be more likely to have
used marijuana in the prior 6 months (36% vs. 19%;
X2 = 2.77, p < .10).
The Contribution of ADHD Symptoms and
Personality Traits to College Students’ Adjustment
As noted above, Nigg et al. (2002) recently demon-
strated that ADHD symptoms are associated with several
Big Five personality domains, which in turn are known
to be related to several indices of adjustment in young
adults (Axelrod, Widiger, Trull, & Corbitt, 1997; Blais,
1997; Flory et al., 2002; Komarraju & Karau, 2005;
Lopes et al., 2003; Ridgell & Lounsbury, 2004). Thus,
we felt it would be important to examine whether ADHD
symptoms contribute to students’ adjustment after con-
trolling for differences related to personality domains.
Prior to examining this question, we sought to repli-
cate Nigg et al.’s (2002) findings pertaining to the rela-
tionship between ADHD symptoms and Big Five
personality domains. The correlation between these vari-
ables is presented in Table 4, which also presents the
relationship between personality traits and the adjust-
ment measures we examined. Although these analyses
relied on dimensional scores for ADHD symptoms rather
than discrete categories, we used the same representative
45. sample rather than including all participants, to be con-
sistent with analyses reported above.
As seen in Table 4, small to moderate correlations
with the personality domains were found for both inat-
tentive and hyperactive-impulsive symptoms. Consistent
with Nigg et al.’s (2002) report, inattentive symptoms
showed a moderate negative correlation with conscien-
tiousness and smaller negative associations with both
emotional stability and agreeableness, whereas hyperactive-
impulsive symptoms were negatively correlated with
conscientiousness and agreeableness. Unlike their
report, modest but significant associations between
hyperactive-impulsive symptoms and the remaining Big
Five traits were also evident. As can be seen, there were
also a number of significant correlations between per-
sonality domains and the different adjustment measures.
Of note is that conscientiousness and emotional stability
showed a moderate and significant negative correlation
694 Journal of Attention Disorders
Table 3
Means and Standard Deviations (in parentheses)
Difference for ADHD Symptoms, Academic
Concerns, Depressive Symptoms, and Social
Satisfaction for Students With ADHD Based on
Medication Treatment Status
Medication Treatment No Medication Treatment
Outcome n = 46 n = 21
Inattention 3.32 (1.14) 3.39 (1.32)
46. Hyperactivity 3.06 (.88) 3.34 (0.97)
Academics 2.60 (1.04) 2.68 (0.97)
Depression 2.88 (0.91) 3.06 (0.73)
Social 3.93 (0.85) 3.55 (0.77)
Note: Means could range from 1 to 5, with higher values
indicating
more of the outcome.
with all three adjustment measures. Relationships
between the other Big Five domains and the adjustment
outcomes were also found but were less consistent and
generally smaller in magnitude.
To test whether ADHD symptoms contributed to
students’ adjustment after controlling for personality
characteristics, we conducted a series of hierarchical
multiple regressions in which gender, race, and site were
entered as the first step, personality trait scores were
entered as the second step, and inattentive and hyperac-
tive-impulsive symptoms were entered as the final step.
All independent variables were …
Psychological Predictors of Young Adults’
Use of Social Networking Sites
Kathryn Wilson, Post.Grad.Dip., Stephanie Fornasier,
Post.Grad.Dip., and Katherine M. White, Ph.D.
Abstract
Young people are increasingly using social networking sites
47. (SNSs) like MySpace and Facebook to engage with
others. The use of SNSs can have both positive and negative
effects on the individual; however, few studies identify
the types of people who frequent these Internet sites. This study
sought to predict young adults’ use of SNSs and
addictive tendency toward the use of SNSs from their
personality characteristics and levels of self-esteem. Uni-
versity students (N¼201), aged 17 to 24 years, reported their
use of SNSs and addictive tendencies for SNSs use
and completed the NEO Five-Factor Personality Inventory1 and
the Coopersmith Self-Esteem Inventory.2 Multiple
regression analyses revealed that, as a group, the personality
and self-esteem factors significantly predicted both
level of SNS use and addictive tendency but did not explain a
large amount of variance in either outcome measure.
The findings indicated that extroverted and unconscientious
individuals reported higher levels of both SNS use
and addictive tendencies. Future research should attempt to
identify which other psychosocial characteristics
explain young people’s level of use and propensity for addictive
tendencies for these popular Internet sites.
Introduction
The proliferation of social networking sites (SNSs) hascreated a
phenomenon that engages millions of Internet
users around the world, especially young people.3,4 Given
the popularity of these sites and their importance in young
people’s lives to facilitate communication and relationships, it
is important to understand the factors influencing SNS use,
especially at higher levels, and to identify those who may
be prone to developing addictive tendencies toward new
communication technologies.5 As with other communication
technologies,6,7 a useful starting point may be to examine the
role of personality traits and self-esteem on young people’s
SNS use.
48. Researchers have confirmed repeatedly that the five-factor
model of personality adequately accounts for and explains
personality by taking the approach that personality consists
of five traits: openness to experience (pursuing and appreci-
ating all types of experience), conscientiousness (control,
regulation, and direction of goals and impulses), extroversion
(amount and intensity of interpersonal interactions), agree-
ableness (the type of interactions a person prefers to have with
others), and neuroticism (degree of emotional adjustment and
instability).8 Self-esteem is the subjective evaluation a person
makes and maintains about himself or herself and the extent
of belief in their capability, worth, and significance, which is
conveyed through their attitudes and verbal behavior.2 Due
to the recent introduction of SNSs, research investigating the
intrapersonal characteristics of people who access these sites
is limited. However, research investigating the personality
and self-esteem of people who access the wider Internet, as
well as use other technological innovations to connect with
others (such as mobile phones), has been growing.
For instance, it was found that extroversion was negatively
related to higher levels of Internet use among undergraduate
students, suggesting that introverts had more spare time or
were attracted to the Internet’s online appeal.9 Agreeableness
was also negatively related to higher levels of use, suggesting
that those who do not get along with others spend their time
on the Internet, as there are few demands for agreeable be-
havior. Lower scores on conscientiousness were also associ-
ated with high Internet use, perhaps due to the Internet’s
limited rules and unstructured policies. Similarly, other re-
searchers revealed that introversion predicted general Inter-
net use but also found neuroticism and openness to be
predictors of time spent online.10
49. Other studies have distinguished between the various
ways the Internet can be used and intrapersonal characteris-
tics. For example, it was found that introverted and highly
neurotic females frequently utilize the social services avail-
able on the Internet.11 These researchers suggested that in-
troverted and neurotic females may feel protected and safe
when using the Internet to socially interact with others be-
cause it is essentially an anonymous, virtual environment.
School of Psychology and Counselling, Queensland University
of Technology, Queensland, Australia.
CYBERPSYCHOLOGY, BEHAVIOR, AND SOCIAL
NETWORKING
Volume 13, Number 2, 2010
ª Mary Ann Liebert, Inc.
DOI: 10.1089=cyber.2009.0094
173
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52. functions provided by the Internet,12 suggesting that young
adults using SNS might score high on openness to experience
given that SNSs are a new innovation.
One study found that extroverted people used the Internet
for instrumental purposes, such as researching, and extroverts
rejected the use of the Internet for social purposes because
they preferred social contact in more traditional contexts.13 A
separate study also supported the finding that extroverts
reject the Internet as a medium by which to communicate with
others by identifying that extroverted young people, as
opposed to introverts, preferred communicating with others
offline.14 In addition, two studies found that neurotic people
reported being comfortable and feeling a sense of belonging
when interacting with others via the Internet.15,13
A number of studies have investigated the intrapersonal
characteristic of self-esteem as a possible predictor of Internet
use. For example, one study investigated the communication
choices participants made (face-to-face, e-mail, letter, or tele-
phone) and self-esteem and found that participants with low
self-esteem showed a greater preference for e-mail commu-
nication.16 Similarly, another study found that young people
with low levels of self-esteem reported a high level of instant
messaging use.7 These findings indicate that the Internet has
the potential to relieve some of the social anxiety that people
with low self-esteem often experience in more traditional in-
terpersonal situations.17
Like all Internet applications, overuse of SNSs can lead to
an array of social, psychological, physical, and other problems
for young people.18 Griffiths19 speculated that the structural
and design characteristics of a Web site seem to be particu-
larly addictive. For example, he states that an interactive ex-
perience can satisfy the user’s personal needs and therefore
reinforce the behavior. This argument implies that SNSs can
53. potentially encourage addictive tendency because these In-
ternet sites promote interactivity.
Although numerous definitions of Internet addiction exist,
many of them refer to the substance-dependence criteria of
the Diagnostic and Statistical Manual Fourth Edition (DSM-IV)
to define it.19 This definition purports that Internet addic-
tion, like other technological addictions, consists of six central
components: salience, mood modification, tolerance, with-
drawal symptoms, conflict, and relapse.19,20 In line with this
definition, some researchers revealed that the symptoms of
withdrawal (negative physiological or psychological re-
sponse to not engaging in the behavior), loss of control (en-
gaging in the behavior more than intended), and salience (the
activity dominating thoughts or behavior) were indicative of
mobile phone addiction.5 Therefore, in order to adequately
assess not only level of SNS use but addictive tendencies to-
ward their use, the scale developed by Walsh et al.5 (see also
Ehrenberg et al.7) has been adapted for use in the present
study as a measure of SNS addictive tendencies.
Hardie and Tee21 found that high levels of emotional
loneliness, social anxiety, neuroticism, and low levels of ex-
troversion contributed to Internet addiction. These results are
perhaps unsurprising given introverts often avoid large social
occasions and prefer to spend time in solitary activity, thus
making them prone to Internet addiction.11 Likewise, it is not
entirely unexpected that highly neurotic people would be
prone to Internet addiction, as these people often feel they
are misinterpreted in face-to-face social situations and there-
fore might prefer online interactions where they feel less
restrained.13 With similar results to other studies,22,23 Arm-
strong et al.24 found that low self-esteem predicted addictive
Internet use as measured by the Internet Related Problem
Scale (IRPS). Similar to Walsh et al.’s5 measure of addictive
54. tendencies, the IRPS captures some of the DSM-IV criteria for
substance-dependence, such as withdrawal, craving, and
tolerance. In addition, Ehrenberg et al.7 found that low levels
of self-esteem predicted young adults’ instant messaging
addictive tendencies.
Drawing on previous research examining related commu-
nication technologies, this study aims to test the role of per-
sonality and self-esteem in the context of young adults
and their level of SNS use. In addition, the study aims to
investigate whether these intrapersonal characteristics predict
young adults’ addictive tendencies toward the use of SNSs.
Materials and Methods
Participants
A total of 201 (46 males, 153 females) currently enrolled
students at a major Australian university were recruited for
this study (ethics approval number 0800000159). Inclusion
criteria required participants to be between 17 to 24 years old
(M¼19.07, SD¼1.86) and to have a personal page on an SNS
site. On average, participants reported using their SNS 4 days
per week (M¼4.49, SD¼2.06) and reported logging on to
their SNS nearly 10 times per week (M¼9.97, SD¼10.21).
Measures
NEO Five-Factor Inventory (FFI). The 60-item NEO-FFI1
measured participants’ level of agreement (1, strongly disagree,
to 5, strongly agree) for statements on five 12-item scales:
Neuroticism (a¼0.85), Extroversion (a¼0.78), Openness
(a¼0.69), Agreeableness (a¼0.75), and Conscientiousness
(a¼0.84).
55. Coopersmith Self-Esteem Inventory (SEI). The 25-item
SEI2 assessed participants’ evaluative attitudes toward
themselves (like me or unlike me) in areas of academic, social,
family, and personal experience (a¼0.85).
Time spent using SNSs. Participants reported the aver-
age number of hours per week they spend using their SNS.
Addictive tendencies scale. Based on previous re-
search,5,7 the addictive tendencies scale (a¼0.76) comprised
three items measuring level of salience (‘‘One of the first things
I do each morning is log onto a social networking Internet site
[e.g., MySpace or Facebook]’’), loss of control (‘‘I find it hard
to
control my use of a social networking site [e.g., MySpace or
Facebook]’’), and withdrawal (I feel lost when I cannot access
my social networking site [e.g., MySpace or Facebook]’’).
Results
Multiple regression analysis for time spent
using social networking sites
Given its substantial positive skew, an inverse transfor-
mation was applied to the dependent variable of time spent
using an SNS. A standard multiple regression was performed
174 WILSON ET AL.
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to examine the impact of the personality factors (openness to
experience, conscientiousness, extroversion, agreeableness,
neuroticism) and self-esteem on the amount of time (in hours)
participants spent using an SNS per week (see Table 1). As a
group, these variables significantly predicted participants’
SNS use, F(6, 192)¼3.14, p < 0.01, accounting for 8.9% of the
variance. The significant predictors were conscientiousness
and extroversion. Participants scoring lower on conscien-
tiousness and higher on extroversion reported spending more
time using an SNS.
Multiple regression analysis for social
networking site addictive tendencies
A standard multiple regression was performed also ex-
amining the impact of personality factors and self-esteem on
participants’ SNS addictive tendencies (see Table 1). As a
group, these variables significantly predicted participants’
SNS addictive tendencies scores, F(6, 194)¼2.99, p < 0.01,
accounting for 8.5% of the variance. The significant predictors
were conscientiousness and extroversion. Participants scor-
ing lower on conscientiousness and higher on extroversion
reported stronger addictive tendencies towards using SNSs.
Discussion
The aim of the present study was to explore whether the
personality and self-esteem of young adults can predict time
spent using SNSs and addictive tendencies toward the use of
59. SNSs. Participants scoring higher on extroversion spent more
time using an SNS, a finding inconsistent with previous re-
searchers who have typically concluded that extroverts do
not view the Internet as a suitable replacement for face-to-face
interactions.13–15 The findings of the present study suggest,
however, that SNSs may offer to the wider Internet some-
thing unique that makes them more appealing to extroverts.
For example, because extroverts tend to require a high level of
stimulation and a large social network,8 the numerous func-
tional abilities and unlimited contact with friends may be
specifically attracting their attention. Based on the concep-
tualization of Griffiths20 that the Internet is a tool assisting
people to access their objects of interest, it is likely that ex-
troverts may become overly involved with the opportunity to
connect with=reach out to others, as well as present and
display information about themselves via SNSs. Participants
scoring low on conscientiousness also were found to spend an
increased amount of time using an SNS, in line with previous
findings that unconscientious people are frequent users of the
general Internet.9 It may be that students who score low on
conscientiousness use SNS to occupy their time while pro-
crastinating about completing other tasks, such as study.
Openness to experience did not have any impact on SNS
use, which is inconsistent with previous research investigat-
ing Internet use.12 It may be that SNS may no longer be as
‘‘new’’ an experience for some young people, so has lost some
of its appeal for those eager to experience even newer activ-
ities. Agreeableness did not predict SNS use either, a find-
ing inconsistent with Landers and Lounsbury,9 who found
disagreeable people to spend more time on the Internet.
However, these researchers did not differentiate between the
different uses of the Internet, suggesting that while dis-
agreeable people may use the Internet more often, they are
not necessarily using it to engage with other people socially
60. and may be using it for more functional purposes such as
business-related interactions.
Neuroticism was not associated with increased levels of
SNS use. Similar to agreeableness, previous research
has demonstrated neuroticism to be significantly associated
with time spent on the Internet.11–13,15 However, Tuten and
Bosnjak12 found that neuroticism was only a predictor of us-
ing the Internet for the specific purpose of seeking information
(rather than socializing). It is likely that because of their in-
secure and anxious nature,8 neurotic young adults may not
like the idea of posting photos and information about them-
selves on an SNS and instead prefer to use the Internet for
other functions. Self-esteem did not influence SNS use, a
finding inconsistent with previous findings that people with
low self-esteem prefer online social interaction over face-to-
face exchanges.16,17 As one researcher found, self-esteem can
either increase or decrease depending on the tone of feedback
received on people’s virtual profiles;25 therefore, it is possible
that both people with high levels and people with low levels
of self-esteem seek to use SNSs to confirm or as a means of
increasing their feelings of self-worth in the hope of positive
feedback from other users.
Table 1. Multiple Regression Analysis for Variables Predicting
Time Spent Using
a Social Networking Site (SNS) and SNS Addictive Tendencies
Variable R R2 b t p
Prediction of time spent using a SNS
Openness to experience 0.30 0.09 �0.11 �10.47 0.14
Conscientiousness �0.18 �20.40 0.02
Extroversion 0.27 30.34 0.00
Agreeableness 0.02 00.23 0.82
Neuroticism 0.16 10.66 0.10
61. Self-esteem 0.05 00.47 0.64
Prediction of SNS addictive tendencies
Openness to experience 0.29 0.09 �0.06 �00.82 0.41
Conscientiousness �0.15 �20.02 0.05
Extroversion 0.28 30.45 0.00
Agreeableness 0.04 00.49 0.62
Neuroticism 0.14 10.46 0.15
Self-esteem �0.09 �00.87 0.39
SOCIAL NETWORKING SITE USE 175
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The personality traits and self-esteem explained slightly
less variance in addictive tendencies than they did in time
spent using an SNS. Similar predictors influenced people’s
addictive tendencies as for their level of use. Extroversion
was found to be associated with addictive tendencies, sug-
gesting that extroverts may become overly reliant on SNSs
because the interactive experience meets their need for stim-
ulation and social interaction. Low scores on conscien-
tiousness significantly predicted addictive tendencies. It is
plausible that unconscientious young adults demonstrate
64. addictive tendencies toward the use of SNS because un-
conscientious people tend to lack self-control.26 There was no
relationship between openness to experience, agreeableness,
neuroticism, or self-esteem and self-reported addictive ten-
dencies.
In general, the intrapersonal characteristics accounted for
only a small proportion of variance in both behavioral out-
comes, suggesting that there are other factors underlying
people’s SNS usage, especially given evidence of other po-
tential influences (e.g., motivation to communicate).27 Some
important practical applications, however, can be gained
from the study’s findings. For example, knowing that extro-
version and unconscientiousness are predictors of SNS use
and addictive tendencies may mean that Web developers can
modify the features of their SNS to further attract people with
these types of personalities. For example, SNS sites could be
designed to display more stimulating and interactive features
(e.g., webcams) that would appeal to extroverts or include
more time-consuming games to appeal to those who are less
conscientious. Importantly, the present findings can be used
to inform psychologists, counselors, and educators within
schools and universities of the types of young people who are
likely to use SNSs at higher levels or develop a behavioral
addiction toward the use of SNS. For example, as a preven-
tative measure to combat the overuse of the Internet, which
often leads to sedentary behavior, schools and universities
could encourage extroverts to interact and socialize with
others more offline, while unconscientious young people can
be taught better time management and organizational skills to
avoid procrastination via SNS use.
This study is one of the first to identify the intrapersonal
characteristics of people who frequent SNSs, as well as those
people who are likely to demonstrate SNS addictive tenden-
cies, using a population that typically access SNSs. Despite
65. these strengths, this study had several limitations. The gen-
eralization of the study’s results is limited because the sample
was predominately female and solely university students.
Further, the scale reliability for addictive tendencies was
slightly lower than expected, and item examination suggests
that the scale may reflect impulse control difficulties in respect
to SNS use rather than addictive tendencies per se. Future
research should include more participants from a range of
ages and locations and a more balanced gender ratio. In ad-
dition, longitudinal studies could be conducted to establish
when, and for what reasons, frequent users and those with
addictive tendencies reduce the time they spend using the
sites and how any addictive tendencies are managed. More
research is needed to establish valid and reliable measures of
the addictive tendencies construct. Furthermore, to yield
more accurate measures of use, future research should utilize
objective measures (e.g., diary records or a computer program
that records use unobtrusively).
Although personality and self-esteem were significant
predictors of both time spent using SNSs and addictive
tendencies, they did not explain a large amount of variance,
indicating that other factors account for young adults’ use of
SNSs, which should be examined in future. For example, the
SNS functions of posting information and photos about
oneself may attract the attention of narcissistic people.28
Also, sensation seekers have characteristics in common with
extroverts, such as the need for stimulation and a large
number of friends.29 In summary, although personality and
self-esteem explained only a small amount of variance in both
SNS use and addictive tendencies, extroversion emerged as
a positive predictor and conscientiousness as a negative
predictor, of both time spent using SNSs and SNS addictive
tendencies. However, despite these findings and their prac-
tical implications, it is imperative for researchers to con-
66. tinue to identify the psychosocial factors that influence some
young adults to use at high levels and potentially to develop
addictive tendencies toward this worldwide Internet phe-
nomenon.
Acknowledgments
The authors thank Shari Walsh for assistance in the design
of the study and Eric Livingston for assistance in data col-
lection.
Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Dr. Katherine M. White
School of Psychology and Counselling
Queensland University of Technology
Victoria Park Rd, Kelvin Grove 4059
Brisbane Queensland
Australia
E-mail: [email protected]
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Addictive Behaviors 33 (2008) 987–993
Contents lists available at ScienceDirect
Addictive Behaviors
Heavy episodic drinking and its consequences: The protective
effects of same-sex, residential living-learning communities
for undergraduate women
80. fraternities, engage in heavy episodic drinking with greater
frequency than their female counterparts; although recent
research
reveals that the sex, gender and/or living arrangement gaps may
be narrowing, especially among high school age students
(Johnston, O'Malley, Bachman, & Schulenberg, 2006; Wechsler,
Lee, Kuo & Lee, 2000; for an international review see Holmila
&
Raitasalo, 2005).
Despite lower rates of heavy drinking, women are particularly
vulnerable to the negative consequences in a college co-
educational setting. It is estimated that alcohol is involved in at
least half of all cases of heterosexual assault among college
students
omen and Gender, School of Nursing and Women's Studies, 204
S. State Street, Ann Arbor, MI 48109-1290
533.
All rights reserved.
,
mailto:[email protected]
http://dx.doi.org/10.1016/j.addbeh.2008.03.005
http://www.sciencedirect.com/science/journal/03064603
988 C.J. Boyd et al. / Addictive Behaviors 33 (2008) 987–993
(for reviews see Abbey, 2002; Mohler-Kuo, Dowdall, Koss &
Wechsler, 2004) and the likelihood of sexual assault increases
nine fold
on days in which college women engage in heavy alcohol
consumption (Parks & Fals-Stewart, 2004). Among college
students, the
majority of sexual assaults occur within heterosexual
relationships in which both people are acquainted and a male
![Addictive Behaviors 33 (2008) 987–993
Contents lists available at ScienceDirect
Addictive Behaviors
Heavy episodic drinking and its consequences: The protective
effects of same-sex, residential living-learning communities
for undergraduate women
Carol J. Boyd a,b,⁎, Sean Esteban McCabe a,b, James A.
Cranford b, Michele Morales b,
James E. Lange c, Mark B. Reed c, Julie M. Ketchie c, Marcia
S. Scott d
a Institute for Research on Women and Gender, University of
Michigan, 204 S. State Street, Ann Arbor, MI 48109-1290,
United States
b Substance Abuse Research Center, University of Michigan,
2025 Traverwood Dr., Suite C, Ann Arbor, MI 48105-2194,
United States
c AOD Initiatives Research, San Diego State University
Research Foundation, 6386 Alvarado Ct, Suite 224, San Diego,
CA 92120, United States
d Division of Epidemiology and Prevention Research, National
Institute on Alcohol Abuse and Alcoholism, 5635 Fishers Lane,
Room 2085 MSC 9304,
Bethesda, MD 20892-9304, United States
a r t i c l e i n f o
⁎ Corresponding author. Institute for Research on W
United States. Tel.: +1 734 764 9537; fax: +1 734 764 9
E-mail address: [email protected] (C.J. Boyd).](https://image.slidesharecdn.com/addictivebehaviors332008987993contentslistsavaila-221203134444-5656f101/85/Addictive-Behaviors-33-2008-987-993Contents-lists-availa-docx-1-320.jpg)
