Skyping, gaming, friending, tweeting--our society has embraced the
internet as an alternate, and sometimes preferable, social universe.
This is particularly true for young people. According to the Pew
Research Center's Internet and American Life Project (2010), 95% of
18 to 29 year-olds use the internet, more than any other age group.
Among internet users, teens and young adults are more likely than older
adults to use the internet for entertainment purposes, such as gaming,
watching videos, and downloading music; and for social purposes, such as
using social networking sites, blogs, and instant messaging (Pew
Research Center, 2009). While internet use has many beneficial aspects,
it can also be misused and overused, with internet scams and cyber
bullying as examples of the former and internet addiction an example of
the latter. Indeed, internet addiction is under review for inclusion in
the DSM-V and has been conceptualized as an impulse control disorder
(similar to compulsive gambling) involving symptoms such as
preoccupation with going online, spending increasing amounts of time
online, having difficulty cutting back on internet use, and continuing
online activity in the face of negative consequences (Young, 1996).
Despite the conceptualization of excessive internet use as an
addiction, and the increasingly commonplace use of the internet as a
vehicle for socializing and recreating, the relationship between
internet activity (and addiction) and substance use has garnered little
research attention. In one of the few studies in this area, Ko et al.
(2008) examined the co-occurrence of problematic alcohol use and
internet addiction among high school students in Taiwan and found a
significant positive correlation between the two. Further, they found
that those with both problematic behaviors were more often males with
co-occurring problems in the areas of family conflict, family alcohol
use, and deviant behaviors among friends (Ko et al., 2008). Yen, Ko,
Yen, Chen, and Chen (2009) also investigated the relationship between
problematic alcohol use and internet addiction, but among college
students in Taiwan. They also found a significant positive correlation
between the two problem areas, and additionally found both to be
positively correlated with depression. In the only published
investigation of internet and alcohol use using an American sample,
Epstein (2011) sought to examine the association between occurrence of
drinking (lifetime and past month) and computer use (both time spent on
the computer, and self-reported frequency of engaging in various online
activities) in a sample of 13-17 year-olds. Participants who reported
using alcohol in the past month reported significantly more time spent
on the internet engaging in nonacademic tasks than participants who
denied using alcohol in the past month. Moreover, participants reporting
alcohol use at some point in their lifetime reported significantly
greater frequency of internet use for social networking and for
downloading and listening to music (past month use was also associated
with greater frequency of the latter) than participants who denied ever
having used alcohol (Epstein, 2011).
These studies suggest a relationship between drinking and internet
use, and (in the case of the studies conducted in Taiwan) an association
between problematic alcohol use and internet use. However, it is unclear
if the correlation between problematic alcohol use and internet
addiction would generalize to an American population. While the Epstein
study opens the door to investigating this connection in an American
population, the investigation focused on occurrence, duration, or
frequency of use and did not include measures of problematic use (of
either drinking or internet use). In addition, there remains no
empirical information about whether alcohol use is combined with online
activity and, if this is the case, what typifies online drinking. An
article on the Japan Trends website focuses on the rising popularity of
online drinking among young people through "net nomikai" in
which individuals socialize through webcams while drinking (Andrews,
2010). However, the author notes that there is no research or data to
support this "boom." In addition to gleaning insight into
similarities and differences between online drinking and more
traditional alcohol use behavior (e.g., drinking during in-person social
activities), the relevance of online drinking is clear given the
immediacy of online activity and the disinhibiting effect of alcohol.
For example, it is quite possible that problematic online behaviors such
as cyber bullying or sending/posting sexually explicit photos are more
likely when alcohol and internet use are combined. Finally, given that
social anxiety has been identified as a risk factor for both internet
addiction (e.g., Caplan, 2010; Chak & Leung, 2004; Kraut et al.,
1998) and problematic alcohol use (e.g., Crum & Pratt, 2001;
Kushner, Sher, & Beitman, 1990; Morris, Stewart, & Ham, 2005;
Thomas, Randall, & Carrigan, 2003), the question begs as to whether
individuals who combine internet activity (particularly activity that is
social in nature) with alcohol use are higher in social anxiety than
those who do not.
The current study sought to gather data to begin to better
understand the relationship between alcohol use and internet activity.
The following questions were addressed:
What percent of college student drinkers consume alcohol during
online activities, or use the internet while under the influence of
alcohol (hereafter combined and referred to as online drinking, unless
discussed separately)?
Are there online activities that are more likely to be engaged in
while drinking or under the influence of alcohol (e.g., interactive,
social activities such as instant messaging)? Relatedly, do online
drinkers demonstrate different internet use patterns in general, as
compared to alcohol users who do not engage in online drinking?
What consequences are encountered when people combine drinking with
internet use? The well-publicized case of a Morgan Stanley commodities
trader who made $10 million in risky online trades while intoxicated
(Hosking, 2009), suggests that the immediacy of online activities can
lead to unique consequences for those who use the internet while under
the disinhibiting effects of alcohol.
Given the association between social anxiety and problematic use of
both alcohol and the internet, are individuals who combine alcohol use
and internet activity more socially anxious than those who do not? It
has been found that socially anxious individuals use alcohol with the
expectation that drinking will reduce their anxiety in social situations
(Carrigan & Randall, 2003). Are socially anxious individuals more
likely to engage in interactive internet activities while drinking or
under the influence of alcohol, as compared to their general involvement
in interactive online activities?
Can the relationship between problematic use of both alcohol and
the internet, as reported by Ko and colleagues, be replicated in an
American population?
Finally, given that sex differences have been found for both the
relationship between social anxiety and alcohol (e.g., Morris et al.,
2005) and between problematic alcohol use and internet addiction (Ko et
al., 2008), do males and females differ in terms of the above questions?
METHOD
Participants
Students (128 males, 169 females) at a regional state university in
the southeast United States participated in the IRB-approved study in
exchange for class credit, with an average age of 23.65 years (SD =
6.34). Caucasians (68%) and African-Americans (20%) comprised most of
the sample. The majority were undergraduates (95%) and were divided
equivalently (20-27%) across academic classifications and academic
fields of study. Fifty-seven percent of the participants could be
characterized as traditional college students (i.e., 17-22 years of age,
single, and without children), which is reflective of the university
enrollment as a whole (Armstrong Atlantic State University, 2010).
Participants' reported internet use was typical, however, for a
young adult population in that 89% reported using the internet either
daily or several times daily, with an average of 13.18 (SD = 13.19)
hours online per week estimated by participants. The majority (81%)
reported their favorite location for internet use as their home.
Participants reported that 60% of their online time is spent engaged in
leisure activities (email, social networking, online research, listening
to music, and general web surfing). Because this investigation focused
on online drinking, only current drinkers were eligible to participate
in the study. Drinking status was verified by participant response to
item 1 on the AUDIT (i.e., How often do you have a drink containing
alcohol?) and those few nondrinkers (n = 25) who erroneously volunteered
for the study were excluded from data analyses.
Measures and Procedure
After providing informed consent via an informed consent document
presented to participants at the outset of the survey, the following
measures were administered using the questionnaire administration
program, Survey Monkey:
Internet Use. A 6-item Internet Use Questionnaire was developed by
the author to assess the frequency and locations for internet use.
Participants were asked to report the locations in which they use the
internet (e.g., home, school/work, internet cafe or other public
location), their preferred location, their frequency of internet use,
estimated hours per week of internet use, and their estimated hours per
week of academic/work-related internet use versus leisure use. The
Internet Activities Questionnaire (IAQ) was also developed by the author
to assess self-reported likelihood of engaging in various activities
while online. The items were selected based a literature review of
research on internet use behavior. Participants used a Likert rating
scale from 1 (very unlikely) to 5 (very likely) to indicate how likely
it would be that they would engage in a variety of online activities
(see Table 1 for the list of internet activities included in both the
IAQ and the AIAQ, described below).
Alcohol and Internet Use. Participants were asked two items to
assess if they had ever consumed alcohol while using the internet or had
ever used the internet while under the influence of alcohol. Those who
responded yes to at least one of the items went on to complete the
Alcohol and Internet Activities Questionnaire (AIAQ) and the Alcohol and
Internet Consequences Questionnaire (AICQ), both developed by the
author. The AIAQ asked participants to rate the likelihood of engaging
in the same 16 online activities as in the IAQ, during online drinking.
By including the same activities on the IAQ and the AIAQ, comparisons
could be drawn between the likelihood of engaging in each online
activity when drinking or intoxicated, versus general likelihood of
engaging in the activity. On this measure, only participants who ever
engaged in that particular online activity were asked to rate their
likelihood of engaging in the activity while drinking or intoxicated,
from 1 (very unlikely) to 5 (very likely). The AICQ was used to assess
whether participants had ever experienced 11 possible consequences
related to online drinking (see Table 2). Since research has not
explored online drinking consequences, items were selected based on a
review of measures of general alcohol use consequences (such as the
Drinker's Inventory of Consequences; Miller, Tonigan, &
Longabaugh, 1995) and a literature review of problematic
internet-related behaviors. Participants indicated "yes" or
"no" to each item and total scores reflect the total number of
consequences reported by participants.
Internet Addiction Test. The IAT was developed by Young (1996,
1998) based on DSM-IV-IV criteria for impulse control disorders such as
compulsive gambling. Participants are asked how often they have
experienced 20 situations related to their internet use, capturing the
effect of internet use on their relationships, emotions, sleeping, and
daily activities, among others. Scores range from 20-100, with scores
greater than 40 indicating frequent problems and scores greater than 70
indicating significant problems related to internet use. Factor analysis
of the IAT has yielded six subscales, with good internal consistency.
Concurrent validity has also been established (Widyanto & McMurran,
2004).
Alcohol Outcome Expectancy Scale. The AOES was administered to
assess the outcome expectancies that participants have for their alcohol
use. The measure was developed by Leigh and Stacy (1993) and asks
participants to rate the likelihood that 34 different outcomes would
occur to them if they were to consume alcohol. The outcomes include
negative outcome categories (negative social, emotional, physical, and
cognitive/performance effects), and positive outcome categories (social
facilitation, fun, sex, and tension reduction). The subscales and the
positive and negative dimensions have been found to have good internal
consistency (Leigh & Stacy, 1993). While both positive and negative
expectancies have been found to be associated with drinking behavior,
endorsement of stronger positive expectancies has been found to account
for greater variability in alcohol use (Leigh & Stacy, 1993). A
particular interest in this investigation was the expectation that
alcohol will facilitate social interaction and reduce tension.
Social Anxiety and Distress Scale. The SAD is a well-established
measure of anxiety in social situations. It consists of 28 true-false
items and yields a total score with excellent internal consistency and
good one-month test-retest reliability (Watson & Friend, 1969).
There is no established cut-off score (higher scores indicate greater
social anxiety), although past research has yielded mean scores and
standard deviations for college males and females.
Alcohol Use Disorders Identification Test. The AUDIT is an
established screening measure for alcohol problems and was developed in
collaboration with the World Health Organization to provide a gender and
culture-neutral screening measure for the early detection of alcohol
problems (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). It
contains ten items that assess both quantity and frequency of alcohol
use, as well as dependence symptoms and the occurrence of problems
related to alcohol use. The AUDIT yields a total score of up to 40, with
a criterion of 8 typically used to identify individuals with potential
alcohol problems.
Demographic Questionnaire. A questionnaire was used to gather
demographic information on participants, including their age, sex, race,
class standing, major program of study, marital status, and residence
status.
Preliminary Analyses
As research on alcohol use during online activity is a new area of
investigation, several questionnaires were developed by the author due
to an absence of established measures of the constructs of interest.
Specifically, the IAQ assessed reported likelihood of engaging in a
variety of internet activities when online, the AIAQ assessed reported
likelihood of engaging in those same activities during online drinking,
and the AICQ assessed whether a variety of consequences had ever
occurred as a result of online drinking. Activities surveyed by the IAQ
and AIAQ are listed in Table 1 and consequences that were included in
the AICQ are listed in Table 2. The Cronbach's Alphas for the IAQ,
AIAQ, and AICQ are presented in Table 3, along with preliminary
validation in the form of correlations with other measures used in the
study that were expected to yield associations with the constructs of
interest. As can be seen in the Table, internal reliability is
marginally acceptable for the IAQ and AIAQ, but good for the AICQ.
However, with regard to the IAQ and AIAQ, it has been noted that
internal consistency in the .6 to .7 range is acceptable for exploratory
studies (Garson, 2011), which accurately describes the nature of this
investigation. Total scores for the IAQ and AIAQ were not a focus of
this investigation and were not used in the principal analyses. However,
IAQ and AIAQ total scores were examined relative to other potentially
related measures for the purpose of exploring construct validity.
Expected correlations between the newly developed measures and more
established measures of related constructs were found, providing
preliminary support for their validity. For example, greater likelihood
of engaging in a variety of online activities on the IAQ was
significantly positively associated with scores on the IAT, whereas
greater likelihood of engaging in various online activities while
drinking and reports of greater consequences of online drinking were
significantly positively associated with both IAT scores and AUDIT
scores.
In order to verify the internal consistency of more established
measures used in the primary analyses, Cronbach's Alphas for these
measures and subscales (e.g., tension reduction scale of the AOES) were
also computed. Internal consistency was high in all cases, ranging from
.79 for the AUDIT to .93 for the SAD.
Primary Analyses
What percent of college student drinkers consume alcohol during
online activities, or use the internet while under the influence of
alcohol? Forty percent of participants (47% males, 36% females) reported
using alcohol while engaged in online activity and 52% (59% males; 47%
females) reported going online while under the influence of alcohol.
Perhaps not surprisingly, online drinkers had significantly higher AUDIT
scores (M = 9.27, SD = 5.21) than online abstainers (M = 4.93, SD =
4.08), [t(295) = -7.87, p < .01].
Are there online activities that are more likely to be engaged in
while drinking or under the influence of alcohol? As shown in Table 1,
the online activities most often engaged in when participants use
alcohol are social or entertainment-based. Independent samples t-tests
comparing males and females indicated that males were significantly more
likely to go online to watch or download music or videos, use regular or
interactive cybersex sites, or play interactive games during online
drinking, whereas females were significantly more likely to use social
networking sites (all p's < .01). These sex differences were
similar to those evidenced by participants when asked about their
general (non-alcohol related) online activities.
Do online drinkers demonstrate different internet use patterns in
general, as compared to alcohol users who do not engage in online
drinking? With regard to general online activities, individuals who
engage in online drinking were found to be significantly more likely
than alcohol users who abstain from online drinking to spend more time
online and more time engaged in online leisure activities (see Table 4).
As can be seen in the Table, they were also significantly more likely to
report spending their online time engaged in general web surfing and
entertainment-oriented activities, including shopping and visiting
cybersex sites.
Are there unique consequences associated with mixing alcohol and
internet use? Participants acknowledged experiencing a range of
consequences as a result of online drinking. As shown in Table 2, the
most common consequence was spending too much time online and neglecting
other tasks. However, approximately 30-40% of participants indicated
that they said, wrote, or did something they later regretted; got into
an argument; felt more comfortable/less anxious during the online
activity; and gave out too much information about themselves as a result
of their alcohol use.
Can the relationship between problematic use of both alcohol and
the internet, as reported by Ko and colleagues, be replicated in an
American population? Using the recommended IAT criterion score of 40,
20% of our sample evidenced a problematic pattern of internet use (23%
males, 17% females). Using the recommended AUDIT criterion score of 8,
40% of our sample responded to the measure in such a way as to indicate
potential problems with alcohol use (53% males, 31% females). A
significant positive correlation was found between IAT and AUDIT scores
(r = .22, p < .001). However, when computed for males and females
separately, it was discovered that the relationship was only present for
female participants (males: r = .09, p = .31; females: r = .29, p <
.001). Finally, participants who reported consuming alcohol during
online activity (M = 30.98, SD = 14.3) had significantly higher IAT
scores than participants who denied drinking during online activity (M =
27.71, SD = 11.89), [t(292) = 2.13, p < .05]. Similarly, participants
who reported going online when under the influence of alcohol (M =
30.95, SD = 13.9) had significantly higher IAT scores than participants
who denied online intoxication (M = 26.94, SD = 11.63), [t(292) = 2.68,
p < .01]. This pattern was similar for male and female participants.
Are individuals who combine alcohol use and internet activity more
socially anxious than those who do not? The mean score for participants
on the SAD was 8.07 (SD = 7.04), which is similar to the scores (M =
9.1, SD = 8) for university students reported by the measure developers
(Watson & Friend, 1969). As expected, SAD scores were significantly
positively correlated with IAT scores (r = .284, p < .001) for
participants as a whole, and when examining scores separately for males
(r = .342, p < .001) and females (r = .253, p = .001). Contrary to
expectations, there was no correlation between AUDIT and SAD scores (r =
.037, p > .05). T-tests revealed that participants who reported using
the internet while drinking [t(295) = -.169, p > .05] or intoxicated
[t(295) = .01, p > .05] reported similar social anxiety and avoidance
levels on the SAD as compared to participants who denied such
involvement.
Are socially anxious individuals more likely to engage in
particular internet activities (e.g., interactive activities) while
drinking or under the influence of alcohol? Correlation analyses were
run to determine if SAD scores were associated with a greater likelihood
of drinking during particular types of online activities as reported on
the AIAQ. Results indicated that SAD scores were not significantly
associated with likelihood of involvement in most types of online
activities while drinking. An exception was reported likelihood of
online gambling while drinking, which was significantly positively
associated with SAD scores (r = .316, p < .05) for participants as a
whole, and drinking during role play gaming, which was significantly
positively associated with SAD scores for female participants (r = .392,
p < .05). Finally, hierarchical linear regression was used to examine
whether social anxiety and avoidance, alcohol outcome expectancies, or
the interaction between these variables, might predict likelihood of
engaging in various online activities while drinking. Because males and
females differed in their reported likelihood of engaging in many online
activities (in general, and while drinking), participant sex was first
entered in the regression model. In a second step, SAD, AOEQ tension
reduction, and AOEQ social facilitation scores were entered in a
stepwise (forward) manner, along with interaction terms combining SAD x
AOEQ tension reduction scores and SAD x AOEQ social facilitation scores.
Results indicated that social anxiety, alcohol outcome expectancies, and
their combination failed to predict reported likelihood of engaging in
the various online activities when drinking (regression results
available on request for the 16 online activities assessed on the AIAQ).
DISCUSSION
Just over half of current drinkers (55%), acknowledged either using
the internet while drinking or going online while under the influence of
alcohol. Thus, combining drinking and online activity is a commonplace,
though not universal, phenomenon among the college students in our
sample. Participants reported they were most likely to drink while using
the internet for entertainment or social purposes. In this way, the
online world appears to mirror the offline world in terms of the
settings (e.g., social networking sites) and activities in which alcohol
use is most common. However, consequences can still arise in these
situations. Over half of participants reported their alcohol use led
them to spend too much time online and to neglect something important as
a result. Over a third of participants acknowledged saying (posting)
something they later regretted or getting into an argument as a result
of combining alcohol and online activity, while a third of females
reported giving out too much information about themselves due to online
drinking. Because many people go online when they are alone (and
unmonitored), and since online behaviors can occur so quickly (e.g.,
spending money with a few key strokes or clicks of the mouse), the
disinhibiting effects of alcohol can be particularly problematic when
combined with online activity. Indeed, participants who reported online
drinking had significantly higher IAT scores than their sober web
surfing peers. Given the correlational nature of this study, the
direction of the relationship between online drinking and problematic
(e.g., compulsive, excessive) internet use is unclear. It is possible
that online drinking leads to increased risk for the development of
problematic internet behaviors, such as spending too much time and
neglecting other activities when online. Individuals with problems
related to their internet use may also be more inclined to drink during
online activity, as a means of assuaging feelings of guilt or as a way
of heightening pleasure from the activity. The top three most frequently
identified consequences of online drinking by participants (spending too
much time online, neglecting something important, and enjoying the
online activity more) are consistent with both possibilities. Further
research is needed to better understand the nature of the relationship
found here between online drinking and internet addiction.
Online drinkers also reported different general internet use
patterns than online abstainers. They reported spending more time
online, particularly engaged in leisure activities such as those that
could be considered escapist in nature (listening to music and videos,
shopping, general web surfing, and visiting cybersex sites). While an
acquiescence response bias could account for this difference, the lack
of a relationship between online drinking and social anxiety, as well as
lower reported likelihood of engaging in some forms of online activity
(e.g., online research) are not consistent with this interpretation. A
third variable, such as depression or sensation seeking, could account
for the greater likelihood of some drinkers using the internet more
often, and for particular means, even when not combining alcohol and
internet use.
While online drinking was associated with increased internet
addiction scores for both males and females in our sample, the
association between problematic alcohol use and internet addiction that
has been identified by others (Ko et al., 2008; Yen et al., 2009) was
only found for female participants in this study. When a sex difference
has been found in Asian samples, it has supported a stronger association
between alcohol and internet addiction for males. Ko et al. found that
co-occurring alcohol problems and internet addiction were associated
with other variables that suggested a pattern of family disruption and
conduct problems. It may be that in an American college population, the
risk pattern is different. Although social anxiety did not appear to
play a significant role in online drinking in this study, other
variables (e.g., depression) that may be relevant for understanding the
relationship between alcohol use and internet addiction were not
assessed. American college student females may be using both alcohol and
internet activity to help cope with aversive emotions, to a point that
both become excessive or problematic. Further research is needed to
explore mechanisms underlying the development of problematic patterns of
alcohol use and internet use for female college students.
Interestingly, although social anxiety was associated with internet
addiction in this study, it added little to understanding online
drinking. It could be that, unlike face-to-face interactions, online
communication provides enough of a buffer for those with social anxiety
that drinking during online activity is not needed. Future research
might test this directly by using an experimental design in which
participants high versus low in social anxiety are asked to consume
alcohol during a "taste rating task" prior to engaging in
either in vivo or internet-based social interactions. Future research
might also ask participants to monitor their alcohol use during online
activities in order to address a relative limitation of this research
design, which is a reliance on participants' retrospective reports
on their online drinking behavior and an absence of information about
the amount of alcohol consumed during online drinking.
Another limitation of this study is that the participant sample may
not represent American college students more generally. However,
national data suggest that our sample is in line with changes in college
enrollment and that our participants reflect in many ways this changing
face of today's college student (e.g., older, more likely to have
children; Kennedy & Ishler, 2008). Despite these limitations, this
study advances the alcohol literature by examining online drinking
rates, behaviors, and consequences, along with the association between
alcohol and internet use problems. As internet activity becomes
increasingly integrated in our lives, it is important to understand how
other behaviors, such as drinking, are manifested in, alter, or are
altered by our online existence. While correlational and exploratory in
nature, this investigation presents initial descriptive information
about drinking and online activity, and reveals some associations that
can be further explored by longitudinal and experimental research in
this area.
Acknowledgements: Thank you to Forrest Files and Shrinidhi
Subramaniam for assistance with measure development and data collection.
Thank you to Vann Scott and Bradley Sturz for providing feedback and
editing assistance with this manuscript.
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Wendy L. Wolfe
Armstrong Atlantic State University
Author info: Correspondence should be sent to: Wendy L. Wolfe,
Department of Psychology, Armstrong Atlantic State University, 11935
Abercorn St., Savannah, GA 31419. Phone: 912-344-2955; Email:
wendy.wolfe@armstrong.edu.
TABLE 1 Likelihood of Engaging in Various Internet Activities
During Online Drinking
Activity n M SD
Social networking sites 135 3.71 1.27
Watch/downloading video 149 3.48 1.31
Listen to/downloading music 151 3.38 1.44
General web surfing 151 3.21 1.38
Interactive chat 99 2.75 1.51
Reading/Responding to Email 148 2.59 1.45
Cybersex websites 82 2.51 1.39
Reading/Posting to Blogs 101 2.33 1.40
Gaming 72 2.13 1.15
Interactive Gaming 59 2.12 1.35
Shopping 116 2.05 1.28
Research 143 1.95 1.19
Interactive Cybersex 38 1.95 1.37
Dating Websites 38 1.84 1.24
Roleplay Gaming 46 1.78 1.26
Gambling 32 1.44 .91
Note. Only participants who reported using the internet while
drinking or intoxicated are reflected in the table. For each
activity, ratings are only included for participants who reported
ever engaging in that particular online activity. Ratings were
assigned using a 1 (Very Unlikely) to 5 (Very Likely) scale in
response to the question, "How likely are you to engage in the
following internet activities while drinking alcohol or while
under the influence of alcohol?".
TABLE 2 Consequences of Combining Alcohol and Online Activity
%
Reporting % %
Consequence Yes Males Females
Spent too much time online 61 54 67
Neglected something important 53 52 54
Found activity more enjoyable 51 61 42
Said/wrote something later 44 44 44
regretted
Felt more comfortable being myself 41 46 36
Got into an argument 38 34 42
Felt less anxious when online 30 32 28
Did something I later regretted 29 27 31
Gave too much info about self 29 24 33
Spent too much money 23 24 22
Drank more than intended 14 18 11
Note. Above reflects percentage of online drinkers who reported
the consequence has ever occurred to them as a result of their
use of the internet while drinking or intoxicated.
TABLE 3 Internal Consistency and Concurrent Validity of New
Measures
Cronbach's Correlation with
Measures Alpha Measures
Internet Use Questionnaire Not
--online frequency assessed IAT (r = .15 *)
--weekly hrs online IAT (r = .30 ***)
IAQ .63 IAT (r = .42 ***)
AIAQ .66 AUDIT (r = .26 ***),
IAT (r = .31 ***)
AICQ .77 AUDIT (r = .34 ***),
IAT (r = .41 ***)
Note: Other than Internet Use Questionnaire, analyses above were
based on total scores for the measures. * p < .05, *** p < .001. IAQ
= Internet Activities Questionnaire, AIAQ = Alcohol and Internet
Activities Questionnaire, AICQ = Alcohol and Internet Consequences
Questionnaire, IAT = Internet Addiction Test, AUDIT = Alcohol Use
Disorders Identification Test.
TABLE 4 Differences in General Reported Internet Use Between
Online Drinkers and Online Abstainers
Drinkers: Abstainers:
Internet Use/Activity M (SD) M (SD)
Weekly hours online 15.68 (15.73) 10.17 (8.4)
Weekly hours online leisure 9.72 (13.25) 5.57 (5.82)
Weekly hours online non- 5.96 (6.03) 5.1 (5.89)
leisure
Reading/Responding to Email 3.8 (1.23) 3.7 (1.26)
Reading/Posting to Blogs 2.22 (1.35) 1.96 (1.27)
Interactive chat 2.07 (1.42) 1.74 (1.17)
Social networking 3.56 (1.5) 3.65 (1.53)
Gambling 1.13 (.45) 1.11 (.49)
Shopping 2.49 (1.2) 2.2 (1.08)
Gaming 1.8 (1.14) 1.67 (1.05)
Interactive Gaming 1.54 (1.07) 1.45 (.98)
Roleplay Gaming 1.34 (.9) 1.24 (.72)
Cybersex 1.79 (1.13) 1.5 (.92)
Interactive Cybersex 1.17 (.54) 1.15 (.6)
Dating websites 1.21 (.6) 1.19 (.73)
Watching or downloading 3.27 (1.24) 2.65 (1.37)
video
Listening to or downloading 3.6 (1.29) 3.28 (1.35)
music
Research 3.7 (1.07) 3.88 (1.02)
General web surfing 3.82 (1.07) 3.49 (1.19)
Internet Use/Activity T-test
Weekly hours online t(295)=-3.66 **
Weekly hours online leisure t(295)=-3.38 **
Weekly hours online non- t(295)= -1.24
leisure
Reading/Responding to Email t(294)= -.67
Reading/Posting to Blogs t(294)= -1.70
Interactive chat t(293)= -2.14 *
Social networking t(293)= .27
Gambling t(293)= -.34
Shopping t(293)= -2.16 *
Gaming t(294)= -1.02
Interactive Gaming t(295)= -.76
Roleplay Gaming t(291)= -.98
Cybersex t(292)= -2.32 *
Interactive Cybersex t(292)= -.29
Dating websites t(291)= -.31
Watching or downloading t(292)=-4.02 **
video
Listening to or downloading t(294)= -2.05 *
music
Research t(292)= 1.42
General web surfing t(294)= -2.55 *
Note. * p < .05, ** p < .01. Participants who reported using the
internet when drinking or when under the influence of alcohol
comprise the "Drinkers" group above. Comparing these two groups
separately to alcohol users who denied online drinking yielded
similar findings as above, with additional significant group
differences for participants who reported drinking while online
(compared to online abstainers), who were more likely to post to or
read discussion boards/blogs (p<.05) and to engage in roleplay
gaming (p<.05), but less likely to spend time doing online research
(p<.01).