Drinking Plans and Drinking Outcomes: Examining Young Adults' Weekend Drinking Behavior

J. DRUG EDUCATION, Vol. 41(3) 253-270, 2011

DRINKING PLANS AND DRINKING OUTCOMES:

EXAMINING YOUNG ADULTS’ WEEKEND

DRINKING BEHAVIOR*

RYAN S. TRIM, PH.D.

University of California San Diego

JOHN D. CLAPP, PH.D.

MARK B. REED, PH.D.

AUDREY SHILLINGTON, PH.D.

San Diego State University, California

DENNIS THOMBS, PH.D.

University of North Texas, Fort Worth

ABSTRACT

This study examined relationships among drinking intentions, environments,

and outcomes in a random sample of 566 undergraduate college students.

Telephone interviews were conducted with respondents before and after a

single weekend assessing drinking intentions for the coming weekend related

to subsequent drinking behaviors. Latent class analyses found evidence for

four distinct drinking environments distinguished by private/public setting

and presence of few/many intoxicated people. There was evidence that the

drinking environment mediated the relationship between drinking intentions

and heavy episodic drinking in this young adult sample. Future research might

focus on examining person/environment interactions as they relate to heavy

episodic drinking.

*Funding for this study was provided in part by a grant the National Institute on Alcohol

Abuse and Alcoholism to Dr. Clapp (R01 AA01368).

253

� 2011, Baywood Publishing Co., Inc.

doi: 10.2190/DE.41.3.b

http://baywood.com

INTRODUCTION

Despite decades of research and prevention efforts, alcohol misuse among college

students and its attendant problems remains a serious public health concern.

The most recent Monitoring the Future report indicates 85% of all college students

have consumed alcohol at least once in their lifetime, with 40% of college age

drinkers reporting at least one occasion of heavy drinking (five or more drinks in a

single sitting) in the past 2 weeks (Johnston, O’Malley, Bachman, & Schulenberg,

2009). Among the 80,000 alcohol attributable deaths that occur in the United

States each year, about 10,000 occur among people under 25 years of age

(Centers for Disease Control and Prevention [CDC], 2009) and an estimated

1,700 college students die from an alcohol-related cause each year (Hingson,

Zha, & Weitzman, 2009).

The etiology of alcohol misuse among young adults has been conceptual-

ized many ways. Consistent with public health models (e.g., agent-person-

environment) and ecological models, many etiological models include both

psychological factors such as motivations or drinking intentions (Clapp, Lange,

Min, Johnson, Shillington, & Voas, 2003; Lange & Voas, 2001), and environ-

mental factors (Clapp, Min, Shillington, Reed, & Croff, 2008; Clapp, Reed,

Min, Shillington, Croff, Holmes, et al., 2009; O’Mara, Thombs, Wagenaar,

Rossheim, Merves, Wei, et al., 2009). Understanding such person/environment

interactions is necessary to inform prevention planning and policies designed

to regulate harmful drinking and alcohol-related problems. At the drinking

event level, understanding the relationships among person level factors and

environmental characteristics as they relate to heavy drinking represent potential

“leverage points” for prevention efforts (Stokols, 2000).

Recent studies have made advances in what we know about drinking environ-

ments frequented by young adults and how they interact with person-level

variables. Thombs et al. (2009) recently published a field study illustrating that

drinking history, past year alcohol consumption, and age of onset of alcohol

use were related to bar-going behavior, which in turn was related to post-bar

blood alcohol content. Similarly, Clapp and colleagues reported on a series of

field studies that examined both private and public drinking settings (Clapp

et al., 2009; Clapp, Min, et al., 2008) and found that both individual (e.g., intention

to get drunk) and environmental characteristics (e.g., themed drinking events,

temporary bars) contributed to high levels of blood alcohol.

In both the Theory of Reasoned Action (Ajzen & Fishbein, 1980) and the

Theory of Planned Behavior (Ajzen, 1991), behavioral intentions play a sig-

nificant role in the prediction of actual behavior. In the area of college student

drinking, several studies have established an association between intentions to

drink alcohol and alcohol consumption (Collins & Carey, 2007; Hutching, Lac,

& LaBrie, 2008). A similar relationship has also been shown utilizing a sample

of young adult bar patrons (Clapp et al., 2009). An earlier paper by Clapp et al.

254 / TRIM ET AL.

(2003) modeled the relationships among intentions to get drunk, drinking environ-

ment, and drinking outcomes and found that environmental factors such as

the presence of illicit drugs, the playing of drinking games, and perceiving

many people at an event were intoxicated mediated intentions and behaviors.

Additionally, the results of a recent study examining drinking behavior in a sample

of college women showed factors such as injunctive and descriptive norms,

peers approve of drinking and peers drink more heavily, as well as pressures

to drink mediated the relationship between pre-college drinking intentions and

heavy episodic drinking (HED) in college (Testa, Kearns-Bodkin, & Livingston,

2009). The authors concluded intentions to drink among these students might

have influenced the selection of social environments that support and facilitate

heavy drinking (i.e., peers who condone heavy drinking and who drink heavily

themselves).

Despite these advances, relatively little is understood concerning how drinking

intentions relate to the selection of drinking environments, and ultimately drinking

behavior given the limitations of the studies cited above. For example, the study

conducted by Clapp et al. (2003) was limited in that it relied on cross-sectional

data and relatively simplistic indicators of environment and drinking intentions.

These simplistic indicators included dichotomous responses on environmental

characteristics including the presence of food, whether illicit drugs such as

marijuana were available, and the availability of beer or hard alcohol. Moreover,

the generalizability of the results of Testa et al. (2009) is limited given their

sample only included female college students. This study addresses these gaps by

employing pre- and post-drinking measures of specific drinking events, a more

comprehensive measure of drinking intentions, and the inclusion of both male and

female participants. For the initial analyses, classification of a priori groups

that vary on intent to get drunk and self-reported HED provides insight into

the impact of consistency (or lack thereof) of planned behaviors on individual

and environmental characteristics. Specifically, the study examines the following

research questions:

1. Do drinking intentions predict drinking environment characteristics and

does this vary as a function of self-reported drinking?

2. How do demographics, intentions, and heavy drinking relate to different

classes of drinking environments?

3. Do drinking environments mediate the relation between drinking inten-

tions and heavy drinking?

METHODS

Design and Sample

To answer the above research questions we conducted an event-level study

in which drinking intentions were measured prior to specific drinking episodes,

with drinking outcomes being measured a day or two after the same drinking

DRINKING PLANS AND DRINKING OUTCOMES / 255

event. This approach was designed to overcome problems associated with

retrospective surveys (Clapp et al., 2009), including inaccurate attribution of

drinking intentions for a past drinking event (i.e., post-hoc attributions of drinking

intentions to drinking outcomes) and faulty recall due to a long period of time

between drinking event and follow-up assessment. This study was approved by

the San Diego State University Institutional Review Board.

In the spring of 2005, we collected survey data from a random sample of

30 individuals enrolled at a 4-year undergraduate institution each week for a

total of 19 weeks. The sample was generated from university records. Demo-

graphically, the sample was representative of the campus population from which

it was drawn (95% CI, ± 3%); however, the sample did contain more heavy

drinkers than previous studies from this same population (Clapp, Shillington,

& Segars, 2000). Compared to the demographic make-up of the host university,

the sample was very similar with respect to gender (53% female), and the majority

were white, non-Hispanic (62.6%). The mean age of the respondents was 21.0

years (SD = 1.71), and 63% of the sample reported at least one instance of

heavy episodic drinking (5 or more drinks) in the past 2 weeks. The mean age of

undergraduates at the university the year the data were collected was 22.5 years

and males represented 40.1% of the student population that year.

Each week, 25 respondents were interviewed by telephone two times within

a 5-day period. Data were collected on Wednesdays (pre-weekend survey) and

Mondays (post-weekend survey). Respondents were assigned a unique identifica-

tion number and all contact information for the respondents was destroyed after

data collection. To test for the possibility that respondents might alter their

behavior due to the pre-event interview (i.e., reactivity), five respondents were

interviewed on Mondays only and asked about their past weekend drinking. We

compared drinking patterns of these two samples and found no testing effect

related to the pre-weekend survey. A total of 566 matched pre/post interviews

and 146 post-weekend only interviews were completed. The study had a refusal

rate of 20.6%; 79.4% of qualified students contacted agreed to participate.

Only students who provided complete data on drinking intentions, environ-

ments, and drinking outcomes were included in the current study (n = 375; 66% of

total sample). Regarding the targeted weekend of interest, 26% reported drinking

on both Friday and Saturday nights, 48% only drank on Friday night, and 26%

only drank on Saturday night. To avoid confounds due to non-independence of

observations, only data from Friday were used for subjects who reported drinking

on both nights (since Saturday drinking would likely be biased by the level of

Friday drinking for these individuals).

Measures

An original interview schedule was developed for this study. Given the intent of

the study was to further examine the relationships among drinking intentions,

256 / TRIM ET AL.

drinking environments, and drinking behavior, we included a series of questions

measuring contexts of student drinking. Several of these context items originated

from the College Alcohol Risk Assessment Guide (Ryan, Colthurst, & Segars,

1997) and have been refined in our prior work (Clapp et al., 2000, 2003; Clapp,

Min, Shillington, Reed, Lange, & Holmes, 2006; Clapp, Voas, & Segars, 2001).

Clapp et al. (2003) established the predictive validity of several of these original

items in an earlier study.

The pre-drinking event survey included demographic items and drinking

plan items (intentions on level of intoxication, place of planned drinking, social

purpose, and planned transportation) for the coming Friday and Saturday nights.

Finally, we included the Johnston et al. (2009) measure of heavy episodic drinking

(“In the past two weeks on how many occasions did you consume five or more

drinks at a single sitting?”). This variable was recoded into a yes/no variable

for our final analyses.

Our primary drinking intention item (included in the pre-event survey and

used in the Clapp et al., 2003, study) was: If you decide to drink (asked separately

for Friday and Saturday nights), do you think you will:

1. drink without feeling any effects from alcohol;

2. drink only enough to feel relaxed;

3. drink enough to feel a little buzzed;

4. drink enough to feel a little drunk; or

5. drink enough to feel very drunk?

(Those reporting either of the latter two choices were coded as positive for

intention to get drunk).

The variables measuring place of planned (pre-event interview) and actual

drinking (post-event drinking) included the following options:

1. in a bar;

2. in a restaurant;

3. in a fraternity or sorority house;

4. in an apartment or residence hall;

5. in a private apartment or house; or

6. some other place (specify).

For analytic purposes these categories were recoded into public (bar, restaurant,

other public places such as beach) and private (apartment, fraternity/sorority,

house, residence hall room) settings for the actual place of drinking.

The post-drinking event measure included items assessing (separate items

for each night) whether the respondent drank alcohol, and if so the total number

of drinks consumed (a drink was defined as 1.5 ounces of spirits, a 12-ounce

beer or malt beverage, a mixed drink containing one shot of spirits, or a 6-ounce

glass of wine). In addition, we asked a series of yes/no items related to the


environmental characteristics of the event. Specifically we used the following

items in our analyses:

1. Was the event open to the public?

2. Was the event advertised?

3. Were most people standing?

4. Were most people at the event the same gender?

5. Were many people intoxicated?

6. Were people getting rowdy or out of hand?

7. Were illegal drugs available?

8. Were people playing drinking games?

9. Was food available?

In addition, we collected information concerning the availability of alcohol at

each event. Overall, beer was available at 85.4% of the events, wine or wine

coolers at 75% of the events, mixed drinks at 74.5% of the events, and shot

of spirits at 84.8% of the events.

The demographic variables included continuous measures of age and GPA,

as well as dichotomous measures of gender (male/female), ethnicity (Caucasian/

other), Greek affiliation (yes/no), being in a committed relationship (including

married; yes/no), and any heavy episodic drinking (HED) in the past 2 weeks

(yes/no). The predictor variable of most interest was self-report of intended

effects of drinking (drunk/not drunk) obtained prior to the event and HED

(5+ drinks) at the location of interest.

Data Analytic Plan

A series of analyses were conducted to answer the three primary research

questions of the study. In order to evaluate whether drinking intentions predicted

drinking environment characteristics as a function of self-reported drinking, four

a priori groups were created based on whether the respondent endorsed an

intention to get drunk that night and whether he/she consumed five or more

drinks (considered heavy episodic drinking). Thus we had groups reflecting:

1. no intent to get drunk/drank less than five drinks;

2. no intent to get drunk/drank more than five drinks;

3. intent to get drunk/drank fewer than five drinks; and

4. intent to get drunk/drank five or more drinks.

Group differences on both background demographics and drinking environ-

ment characteristics for the four intention-drinking groups were assessed using

a conservative Welch ANOVA F-test to account for unequal group variances.

For those outcomes with a significant omnibus Welch F-Test, the Games-Howell

post-hoc comparison procedure was used to account for unequal group sizes

and variances.

258 / TRIM ET AL.

To address the second research question, latent classes of college drinking

environments were identified using latent class analysis (LCA) on the nine binary

indicators used in the previous analyses, and several model fit statistics were

examined to assess the optimal number of extracted classes. Data simulations

have found that the bootstrapped parametric likelihood ratio test (BLRT) typically

outperformed other useful indexes such as Bayesian Information Criterion (BIC)

and Lo-Mendell-Rubin (LMR) adjusted LRT when identifying the correct number

of classes across mixture modeling approaches (Nylund, Asparouhov, & Muthen,

2007). These measures of relative model fit, and other informative indices such

as classification precision (entropy), were used to help determine the best-fitting

class solution for college drinking environments. The characteristics of each class

in the best-fitting solution were used to create a descriptive label for each drinking

environment subgroup. Finally, differences in demographics, intention to get

drunk, and heavy episodic drinking were estimated for individuals who reported

being in each of the drinking environment classes identified in the LCA.

The third aim of this article was to replicate and extend the Clapp et al. (2003)

study which found that drinking environment mediated the effect of drinking

motivations on college student drinking through traditional mediation testing

(Baron & Kenny, 1986) within an SEM framework. The current study seeks to

evaluate a similar mediation pathway from intention to get drunk to drinking

environment to heavy episodic drinking in college students using a product-of-

coefficients test known as the distribution of the product (MacKinnon, Warsi, &

Dwyer, 1995) in Mplus 5.1 (Muthen & Muthen, 1998-2006). Since the asym-

metric distribution of the product is not normally distributed, the bias-corrected

bootstrap test (with 1000 re-samples) was used to provide a more accurate estimate

of the range of potential values for the mediated effects; simulation studies

have recommended the use of this approach based on increased power to detect

“true” mediation effects (Fritz & MacKinnon, 2007). The values in the bootstrap

sample at the 2.5th and 97.5th percentile reflect the lower and upper limits of

the 95% confidence interval; mediation can be said to occur if this confidence

interval does not contain zero.

RESULTS

Examining Individual and Environmental Differences

across Subgroups Defined by Drinking Intentions

and Subsequent Heavy Episodic Drinking

For the first research question, 378 respondents were classified into one of

four groups based on:

1. their intention to get drunk that night; and

2. whether they engaged in heavy episodic drinking, i.e., consumed five or

more drinks that same night.


The largest group consisted of 169 (44.7%) respondents who reported no intent

to get drunk and drank less than five drinks (Group 1), followed by 89 (23.5%)

respondents who reported no intent to get drunk, but consumed five or more

drinks (Group 2); 35 (9.3%) who intended to get drunk, but had less than five

drinks (Group 3); and 82 (22.5%) who intended to get drunk and had five or

more drinks (Group 4).

Table 1 illustrates the demographic differences across these groups. There was

a significant effect of group on gender (Welch F(3, 122.8) = 14.05, p < .001), age

(Welch F(3, 122.6) = 2.72, p < .05), relationship status (Welch F(3, 124.9) = 5.59,

p < .01), and recent heavy drinking (i.e., 5+ drinks at least on occasion in past

2 weeks; F(3, 124.9) = 19.41, p < .001). The Games-Howell post-hoc procedure

was used for the three variables with a significant omnibus test to account for

unequal group sizes and variances: Group 1 had a lower proportion of males than

both Groups 2 and 4 (31% vs. 58% and 68%); Group 1 was older than Group 3

(21.1 vs. 20.2 years); Group 1 had a higher proportion of subjects in a com-

mitted relationship compared to Group 4 (49% vs. 25%); and Group 1 had less

students with recent heavy drinking than Groups 2/3/4 (51% vs. 76%/82%/91%);

and Group 4 had a higher rate of recent heavy drinking compared to Group 2

(91% vs. 76%).

Table 2 shows the drinking location characteristics endorsed by each intention/

drinking group. There was a significant effect of group on the following qualities:

“most people standing” (F(3, 125.1) = 7.87, p < .001), “many people intoxicated”

(F(3, 125.2) = 30.80, p < .001), “rowdy or people getting a little out of hand”

(F(3, 116.7) = 9.68, p < .001), “illegal drugs available” (F(3, 108.7) = 7.37,

p < .001), “drinking games being played” (F(3, 119.3) = 9.72, p < .01), and “food

available” (F(3, 123.5) = 2.93, p < .05). In general, Groups 1 and 3 (no recent

heavy drinking) reported being in an environment with lower rates of “most

people standing,” “many people intoxicated,” “rowdy people,” and “drinking

games played” compared to those who consumed five or more drinks. Respon-

dents reporting “illegal drugs available” were highest in Groups 3 and 4 (those

groups intending to get drunk). Group 1 had a significantly higher rate of being

in a place with “food available” compared to Group 4.

Identifying Group Differences Across

Drinking Environment Subgroups

The second approach focused on first identifying latent classes of college

drinking environments based on nine binary indicators used in the previous

analyses. Results from a range of 2- to 5-class solutions were examined to

determine the best-fitting class solution. The bootstrapped parametric likelihood

ratio test (BLRT), when comparing a model with 4 classes to a model with

3 classes, had a p-value of < .001 and the Lo-Mendell-Rubin LRT had p = .01.

When comparing a model with 5 classes to a model with 4 classes, the BLRT was

260 / TRIM ET AL.

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262 / TRIM ET AL.

not replicated and the Lo-Mendell-Rubin LRT had p = 0.38. Compared to the

5-class model, the BIC was lower for the 4-class model (4692 vs. 4723), and

both entropy (a measure of classification uncertainty where higher values are

preferred; 0.81 vs. 0.79) and the posterior probabilities for each class (84-96%

vs. 80-93%) were higher in the 4-class model. Taken together, these indices

suggest that four classes were sufficient in identifying unobserved heterogeneity

of drinking locations and five classes were not needed.

Results from the 4-class model are shown in Table 3. Through examination

of the class characteristics, these groups are best classified based on the public/

private status of the venue and presence of many intoxicated people. For naming

purposes, latent classes with a high proportion of respondents reporting “many

people were intoxicated” are labeled as “heavy.” Thus, we had the following

four classes:

1. Private/Light;

2. Public/Light;

3. Private/Heavy;

4. Public/Heavy.

The majority of respondents in the two “heavy” drinking classes (94%) charac-

terized the location as having “many people intoxicated” compared to 21-22% of

respondents endorsing this quality in “lighter” classes. Almost all respondents

agreed with the description of “most people standing” for Private/Heavy locations

compared to ~20% for the Private/Light and Public/Light classes. Respondents

in the Private/Light class endorsed the highest rates of “most people same gender”

at 30% compared to Private/Heavy class where this was rarely endorsed (8%).

The location with the highest probability of illegal drugs (19%) and drinking

games (50%) was Private/Heavy. Food was available at most locations with

few intoxicated people (79-91%) compared to rates of 35-44% at locations

with many intoxicated people (heavy locations).

Table 4 illustrates the demographic differences of respondents who reported

drinking in each type of environment. There was a significant effect of drinking

environment on gender (F(3, 160.8) = 2.65, p < .05), age (F(3, 168.4) = 5.77,

p < .01), ethnicity (F(3, 158.5) = 2.99, p < .05), relationship status (F(3, 157.5) =

11.68, p < .001), intent to get drunk (F(3, 163.4) = 7.84, p < .001), and heavy

drinking at the location (F(3, 161.2) = 27.16, p < .001).

Respondents who reported going to heavy locations were less likely to be in

a committed relationship compared to those in lighter locations (28/25% vs.

57/52%). Respondents in Public/Heavy venues were less likely to be Caucasian

(49%), compared to those who were in other types of drinking environments

(63-68%). Those who were in Public/Light were, on average, slightly older

(21.7 years) compared to individuals who were in private venues. Private/Heavy

venues had the highest proportion of males (57%), but this was not significantly

higher than the rates of males at public venues. Compared to locations with fewer


Tab

le3

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nt

Cla

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1

(n=

12

1)

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ht

Cla

ss

2

(n=

46

)

Pu

blic

/Lig

ht

Cla

ss

3

(n=

93

)

Pu

blic

/H

eavy

Cla

ss

4

(n=

11

5)

Pri

vate

/H

eavy

Ad

vert

ised

Mo

st

peo

ple

sta

nd

ing

Mo

st

peo

ple

sam

eg

en

der

Man

yp

eo

ple

into

xic

ate

d

Peo

ple

row

dy

Ille

gald

rug

savaila

ble

Dri

nkin

gg

am

es

pla

yed

Fo

od

availa

ble

.00

.19

.30

.21

.02

.12

.05

.79

.24

.22

.13

.22

.09

.07

.04

.91

.37

.99

.08

.94

.25

.13

.13

.35

.00

.70

.17

.94

.17

.19

.50

.44

264 / TRIM ET AL.

Tab

le4

.Late

nt

Cla

sses

ofD

rin

kin

gE

nvir

on

men

tsb

yD

em

og

rap

hic

Ch

ara

cte

ristics

(pro

po

rtio

ns)

Cla

ss

1

(n=

12

1)

Pri

vate

/Lig

ht

Cla

ss

2

(n=

46

)

Pu

blic

/Lig

ht

Cla

ss

3

(n=

93

)

Pu

blic

/H

eavy

Cla

ss

4

(n=

11

5)

Pri

vate

/H

eavy

%m

ale

*

Ag

e*

%C

au

casia

n

GP

Ala

st

sem

este

r

%in

frat/

so

rori

ty

%in

co

mm

itte

dre

latio

nsh

ip*

%w

ith

HE

Din

past

2w

eeks

Inte

nt

tog

et

dru

nk*

5+

dri

nks

at

locatio

n*

.45

21.0

a

.68

a

3.2 .1

5

.57

a,b

.71

.20

a,b

.23

a,b

.35

21.7

a,b

.63

3.1 .0

9

.52

c,d

.57

.17

c

.22

c,d

.43

21.2 .4

9a,b

3.5 .1

6

.28

a,c

.67

.37

a

.58

a,c

.57

20.6

b

.68

b

3.4 .1

6

.25

b,d

.78

.44

b,c

.69

b,d

*In

dic

ate

sa

sig

nific

an

t(p

<.0

5)

om

nib

us

gro

up

diffe

ren

ce

usin

gth

eW

elc

hte

st

toacco

un

tfo

ru

neq

ual

gro

up

vari

an

ces.

With

inth

ese

row

s,

gro

up

ssh

ari

ng

aco

effic

ien

tw

ere

sig

nific

an

tly

diffe

ren

t(p

<.0

5)

usin

gG

am

es-H

ow

ell

po

st-

ho

cp

roced

ure

toacco

un

tfo

ru

neq

ual

gro

up

siz

es

an

dvari

an

ces.


intoxicated people, respondents at locations with many intoxicated people

reported higher rates of both intending to get drunk (44/37% vs. 20/17%) and

heavy episodic drinking (23/22% vs. 58/69%) while in that environment.

Testing for Mediating Effects of Drinking Environment

on the Relation Between Drinking Intentions

and Heavy Episodic Drinking

Mplus 5.1 software was used to evaluate the indirect effect of intention to

get drunk (dichotomous) on heavy episodic drinking (dichotomous) through

its impact on drinking environment (ordinal). Based on previous analyses, the

4-level latent class solution of drinking environment was retained as an ordinal

mediator variable reflecting an increase in contextual drinking risk ranging from

Private/Light to Private/Heavy. Note that similar results were found when this

4-level variable was collapsed into a dichotomous variable based on few versus

many people intoxicated. For a model with no additional predictors, the indirect

effect was significant (b = .258, se = .064, p < .001) and the 95% confidence

interval obtained from the bias-corrected bootstrap test did not include zero

(0.147, 0.396). When the set of seven covariates from the prior analyses were

controlled for, the indirect effect remained significant (b = .225, se = .077, p < .01;

95% CI = 0.078-0.390); note also that the direct paths from intention to get drunk

to drinking environment (b = .548, se = .124, p < .001), drinking environment

to recent heavy drinking (b = .470, se = .063, p < .001), and intention to get drunk

to recent heavy drinking (b = .668, se = .139, p < .001) were all significant even

after controlling for the effects of the covariates. These results suggest that

the effect of drinking intentions on recent heavy drinking is mediated through

qualities of the drinking environment.

DISCUSSION

This study builds on the extant research literature on person and environment

relationships related to drinking behavior. In summary, we found:

1. drinking intentions were inconsistent with drinking outcomes for about

one-third of this college sample;

2. drinking environments can be statistically classified on pubic/private and

light/heavy dimensions; and

3. drinking intentions are mediated by drinking environments.

Interestingly, as noted above, our results suggest drinking intentions do not

necessarily lead to the initially planned drinking outcomes. That is, it is fairly

common for college students to drink more (23.5%) or less than they intended

(9.3%). Young women were predictably over-represented among those who

intended to consume less than five drinks and subsequently reported drinking less

266 / TRIM ET AL.

than five drinks at the assessed event. Numerous other studies have found women

are less likely to engage in heavy episodic drinking (e.g., Johnston et al., 2009;

Wechsler, Lee, Kuo, Seibring, Nelson, & Lee, 2002). These gender patterns held

across the latent classes, with the exception of women being over-represented in

Public/Heavy settings. In previous studies (Clapp, Ketchie, Reed, Shillington,

Lange, & Holmes, 2008a, Clapp, Min, et al., 2008) we found that themed drinking

events (e.g., toga parties, etc.) resulted in heavy drinking among women, and

future research is needed to examine aspects of environments that may have

gender-specific effects. Also consistent with the literature on drinking events,

those with a history of heavy episodic drinking were more likely to drink five

or more drinks when they intended not to do so (Thombs et al., 2009).

The environmental characteristics associated with the drinking intention/

drinking sub-groups were very consistent with previous field research studies

(Clapp et al., 2000, 2006; Clapp, Min, et al., 2008). Specifically, having many

people intoxicated, rowdiness, drinking games being played, and illicit drugs

available were all associated with the heavier reported drinking classes. Similarly,

when the public versus private nature of the environment was combined with

reports of having “many people intoxicated” to define classes, reports of having

“many people intoxicated” were associated with heavier drinking, regardless

of public or private venue. In contrast, the presence of “drinking games” and

the availability of “illegal drugs” were most likely in private settings in which

respondents tended to report heavy drinking regardless of their drinking inten-

tions. Thus, these environmental characteristics tended to be present in settings

for those who intended to drink heavily and did, as well as for those who did not

intend to drink heavily but reported consuming five or more drinks. Consistent

with this finding and previous research (Clapp et al., 2003), our meditational

analysis indicated that drinking environment proved to mediate drinking inten-

tions and drinking outcomes (heavy episodic drinking).

Together, these results suggest the importance of considering both individual

level and environmental factors when developing both etiological models of

college drinking and prevention approaches. Consistent with this, numerous

researchers and governmental recommendations have encouraged comprehensive

“person and environment” or ecological models (Clapp et al., 2001; DeJong &

Langford, 2002; DeJong, Vince-Whitman, Colthurst, Cretella, Gilbreath, Rosati,

et al., 1998; Office of the Surgeon General, 2007; Task Force of the National

Advisory Council on Alcohol Abuse and Alcoholism, 2002) when considering

prevention approaches to college drinking problems. Although this study and

others like it have begun to examine such person/environment interactions,

the mechanisms underlying such relationships require further study to facilitate

prevention efforts. Understanding, for instance, why the perception of “many

intoxicated people” at events is a consistent indicator of drinking events in both

observational (Clapp, Min, et al., 2008) and self-report studies (Clapp et al., 2006)

might have implications for responsible host training. Further future research is


needed to explore and develop prevention approaches that target specific factors

at both the person and the environmental level in a systematic way, especially in

light of the current findings that drinking environments mediate the effect of

planned intentions on drinking outcome. For instance, social host policies might

be combined with enforcement, media campaigns, party host training, and a

normative feedback approach to comprehensively intervene at both the individual

and environmental level of college drinking.

Compared to previous cross-sectional studies, the present study had several

strengths including a random sample of college age drinkers, collection of pre- and

post-drinking event data, and use of contemporary approaches to both latent

class and mediation analyses. Like most studies examining college or young

adult drinking, we relied on self-reports of drinking and drinking environments,

which is a limitation; however, the brief recall period between drinking events

and the interview might improve the reliability of self-reports. Although the

study might have limited external validity due to its limited geographic area and a

slightly older sample of students than found in other college drinking research,

the types of situations reported on by our sample are relatively typical for college

students. Thus, we believe the present study has good ecological validity.

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John Clapp, Ph.D.

Center for Alcohol and Drug Studies

6386 Alvarado Ct., Suite 224

San Diego, CA 92120

e-mail: [email protected]

270 / TRIM ET AL.

Drinking Plans and Drinking Outcomes: Examining Young Adults' Weekend Drinking Behavior

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