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A real-time examination of context effects on alcohol cognitions
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Rebecca L. Monk and Derek Heim
of Edge Hill University, UK
Author Note
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Rebecca Louise Monk and Derek Heim, Department of Psychology, Edge Hill
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University, St. Helens Road, Ormskirk, Lancashire, L39 4QP, UK. Email:
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monkre@edgehill.ac.uk; derek.heim@edgehill.ac.uk
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Correspondence concerning this article should be addressed to Rebecca Monk,
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Department of Psychology, Edge Hill University, St. Helens Road, Ormskirk,
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Lancashire, L39 4QP, UK. Email: monkre@edgehill.ac.uk. Tel: +44 (0)1695 65 0940
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Word count: 3695
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Running Head: Context effects on alcohol-related expectancies
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“A real-time examination of context effects on alcohol cognitions”
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Background: This research used context aware experiential sampling to investigate
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the effect of contexts on in vivo alcohol-related outcome expectancies. Method: A
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time-stratified random sampling strategy was adopted in order to assess 72 students
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and young professionals at 5-daily intervals over the course of a week using a
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specifically designed smart-phone application. This application recorded
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respondents' present situational and social contexts, alcohol consumption and
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alcohol-related cognitions in real-time. Results: In-vivo social and environmental
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contexts and current alcohol consumption accounted for a significant proportion of
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variance in outcome expectancies. For instance, prompts which occurred whilst
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participants were situated in a pub, bar or club and in a social group of friends were
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associated with heightened outcome expectancies in comparison with other settings.
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Conclusion: Alcohol-related expectancies do not appear to be static but instead
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demonstrate variation across social and environmental contexts. Modern technology
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can be usefully employed to provide a more ecologically valid means of measuring
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such beliefs.
Key Words: Alcohol, Social cognition, Social cognition models, Context,
Expectancies, Smartphone technology, Real-time sampling
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Despite longstanding awareness that people's immediate environments mediate
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behaviour (Bourdieu, 1977; Nyaronga, Greenfield, & McDaniel, 2009; Lott, 1996;
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Rosnow & Rosenthal, 1989), most psychological theories of behaviour and cognitions
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are formulated upon data which are obtained without sufficient consideration of
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contextual influences (Biglan & Hayes, 1996; Biglan, 2001; Hayes, 2004). When
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using social cognition models to explain alcohol consumption this negligence might
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constitute a critical oversight in view of long-documented contextual influences on
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alcohol behaviours (MacAndrew & Edgerton, 1969).
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Research indicates that alcohol-related beliefs predict consumption and, resultantly,
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interventions have been designed to target these beliefs to reduce drinking (c.f. Jones
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et al., 2001). Specifically, outcome expectancies – people’s beliefs about the likely
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consequences of drinking have been found to impact both the quantity and frequency
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of alcohol consumption (c.f. Ham & Hope, 2003; Oei & Morawska, 2004; Reich,
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Below, & Goldman, 2010). Specifically, high positive outcome expectancies appear
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to be associated with recurrent drinking in greater quantities (c.f. for example
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Andersson et al., 2012), whilst higher negative expectancies seem to be associated
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with reduced consumption (c.f. for example Stacy, Widaman, & Marlatt, 1990). While
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it has also been noted for some time that outcome expectancies may vary across
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different contexts (Wall, Mckee, & Hinson, 2000), this body of research has tended
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to rely on single occasion testing and on retrospective self-reports obtained within
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laboratory settings or non-alcohol-related environments (e.g. lecture theatres) without
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adequate consideration of possible contextual influences (Monk & Heim 2013a; in
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press). Accordingly, studies have begun to address these limitations by utilising more
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ecologically aware testing environments such as simulated bars (e.g., Larsen, Engels,
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Wiers, Granic, & Spijkerman, 2012) or wine tasting events (e.g., Kuendig &
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Kuntsche, 2012), and recent findings suggest that social contexts and alcohol-related
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environments are associated with increases in positive expectancies (Monk & Heim,
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2013b; 2013c). While pointing to the importance of social and environmental contexts
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in shaping alcohol-related beliefs, these studies have tended to test participants in
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environments which, to a greater or lesser extent, are removed from real world
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drinking contexts. The current study addresses this by using an experience sampling
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method.
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The increasing accessibility of advanced mobile devices (Katz & Aakus, 2002) has
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facilitated the regular, day-to-day assessment of individuals in naturally diverging
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contexts and has opened the field for Ecological Momentary Assessment (EMA) or
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Experience Sampling (Collins, Lapp, Emmons, & Isaac, 1990; Collins et al., 1998;
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Courvoisier, Eid, Lischetzke, & Schreiber, 2010; Killingsworth & Gilbert, 2010;
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Kuntsche & Robert, 2009). The present research used smartphone technology to
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enable participants to provide real-time in vivo reports with a particular focus on
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alcohol-related expectancies. In line with previous research (Monk & Heim, 2013a;
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2013b; Wall et al., 2000; 2001; Wiers et al., 2003), it was predicted that there would
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be an increase in alcohol-related expectancies when assessment occurred within
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alcohol-related environments and in the presence of a social group (in comparison
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with assessments that take place in alcohol neutral environments and in solitary
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contexts).
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Method
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Design
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A within participant design was utilised to investigate the effect of environmental and
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social contexts on participant real-time responses to alcohol expectancy questions.
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Participants
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72 participants comprising students (n = 43) and young professionals (n = 29) who
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were aged 18-34 years (M = 21.73, S.D = 3.64) were recruited for this study from
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universities and businesses in the UK (North West). The majority of the sample were
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White British (88.9%) and 69% of this sample were female. Baseline average AUDIT
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scores were 9.02 (2.07) in the student sample and 8.72 (1.28) in the business sample.
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Measures
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Demographic information and reports regarding personal alcohol consumption
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(AUDIT-C) were recorded at participants’ initial briefings. These were anonymously
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combined with participants’ individual responses using a unique numeric identifier.
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The
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work/lecture, bar/pub/club, restaurant, sporting event, party or other) and social
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contexts (alone, with one friend, with two or more fiends, with family, work
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colleagues or other), whether they were drinking or had had a drink (yes or no), and if
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so what they had been drinking (quantity). Furthermore, all participants answered a
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random selection of items taken from the 34-item Alcohol Outcomes Expectancy
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Questionnaire (Leigh & Stacy, 1993) which covers a range of outcomes, including
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social, sexual and emotional outcomes. However, pilot studies (n = 42) which trialled
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the administration of full and abridged versions of this questionnaire revealed that
smart-phone
application
ascertained
participants’
environment
(home,
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participants were less likely to respond when all items were included. Furthermore, if
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all of the 34 items had been available for random allocation, analyses would be
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limited as any variation observed between contexts may have been the result of
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variation in the expectancy measure presented (e.g. social vs. sexual expectancy
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items). Resultantly, it was only the six social items that were part of the question pool
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(three positive and three negative). In each response session, two positive and two
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negative expectancy items were randomly selected from the question pool and
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separate average scores for positive and negative expectancies were subsequently
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calculated, giving a standardised maximum and minimum score of 1-6.
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Equipment
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A web based smart-phone application designed specifically for this research enabled
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participants to respond to questioning via the use of their own mobile phone – when
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prompted by automated SMS messages. The application was a website built using
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HTML and JavaScript (JavaScript's jQuery mobile library) and answers were tracked
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and stored using Google Analytics. The survey was designed to work on mobile
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phones and native mobile browsers and was web-standards compliant. Each response
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session was individually tracked and involved a personally interactive user experience
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using tree based logic. For example, only those who responded that they consumed
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alcohol were asked about what they had consumed. Participants’ response
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mechanisms were also interactive, determined by the users’ smart-phone - for
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example, Iphone or Android users could indicate their response by pressing or
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‘dragging’ the onscreen response items whilst those without touch screen technology
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responded in a fashion compatible with their phone (e.g., ‘scroll and click’). The
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questions were randomly selected from the database of questions using a computer-
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generated randomisation code. The application was designed to make the user
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interface as
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recommendations (c.f. Palmblad & Tiplady, 2004), there no default answers set..
intuitive/user friendly as
possible and,
in
accordance with
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Procedure
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Following ethical approval, participants were recruited and given a demonstration of
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the response mechanism on their personal mobile phone. In accordance with similar
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EMA procedures (Csikszentmihalyi & Larson, 1992; Wichers et al., 2007) and
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recommendations by Larson and Delespaul (1992), a time-stratified random sampling
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strategy was adopted (c.f. Moberly & Watkins, 2008). Pilot questionnaire data
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examining perceptions of online vs. real-time assessments (Response N = 108)
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indicated that respondents preferred SMS reminders and that five daily prompts were
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deemed the most acceptable number of daily participation requests. Therefore, the
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volunteers received five randomly allocated SMS participation prompts every day for
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one week. No two prompts could occur within 15 minutes (ibid) or outside 0800 -
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2300 hours. Each day of participation was divided into five equal three hour periods
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and one prompt was randomly sent within each period (e.g., once between 0800 and
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1100, once between 11 and 1400 and so on). The exact time a participant was
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prompted at was determined using a random number generator - each 3 hour section
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was split into 15 minute blocks and the generator selected the time that the prompt
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would be sent, making response sessions unpredictable Upon receiving the prompts,
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participants activated the Application by clicking on a link provided in the SMS. The
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questions provided were randomly selected from the question database in order to
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prevent the order effects (Csikszentmihalyi & Larson, 1992).
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Average completion time was recorded at 2 minutes 27 seconds and the overall study
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retention rate was 84.7%. Only relatively few participants completely stopped
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responding and dropped out (n = 8). Furthermore, respondents were removed from
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the sample (n = 3) where the response rate was below 40 percent, based on previous
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research which indicates that low response rates on substance-use-related assessments
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have low reliability (Shiffman, 2009).
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Over the course of the week, there was the potential for participants to respond to 35
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prompted sessions (5 per day for a week). There was no substantial increase in the
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number of missed response sessions as interaction with the application increased,
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suggesting that order effects were limited by the use of this technology. The average
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percentage of failed responses (sessions which were not completed following a
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prompt) was 20% per participant, with the 0800-1100 time-slot eliciting the highest
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number of late or failed responses. The average percentage of late responses (> 15
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minutes post prompt) was 5% per participant and these late responses were excluded
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from subsequent analyses in order to ensure that the results could reasonably be
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asserted to be a representative account of the specific time as opposed to a
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retrospective report (Delespaul, 1995). The study therefore had an average overall
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valid response rate of 75% per participant (26 out of a total possible 35 prompts
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responded to).
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Analytic Strategy
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Multilevel modelling (MLM) is a method of statistical analyses which is capable of
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advanced portioning of variance (Tabachnick & Fidell, 2001). MLM was used as this
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technique can incorporate the natural complex (and related) nature of the data (Heck,
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Thomas, & Tabata, 2010) and look for explained and unexplained variance both
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between and within groups (see Goldstein, 2011). MLM is also able to deal with
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missing data which are to be expected in experiential sampling (Tabachnick & Fidell,
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2001). In the present study variances in outcome expectancies (the dependent
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variable) were modelled at a number of levels: Prompts were nested within days
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which were nested within participants. However, given that data were not recorded at
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the day level (e.g. day, weather etc), it was decided that this level did not warrant
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inclusion within the statistical modelling. Indeed, the day of the week in which
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participants began the research was not consistent in this study (participants chose
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their most personally convenient starting point). This meant that no specific predictors
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required modelling at this level and the lack of information at this level may have
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unduly reduced the overall explanatory power of the model. A series of 2 level
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random intercept multilevel models (prompts within participants) were therefore fitted
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– one for each of the alcohol-related cognitions (positive and negative outcome
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expectancies). MLM therefore allowed analysis of variance at the prompt level
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(context factors) and the person level (individual differences). The resultant
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hierarchical random intercept multilevel model was fitted with predictor variables
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which were justified by correlational analyses (see Table 1). Preliminary analyses
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revealed no evidence of multicollinearity, residuals were normally distributed and
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scatterplots indicated that the assumption of linearity and homoscedasticity were met.
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The MLM was designed to portion variance in outcome expectancies and the
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predicted variance from the null and fitted models were compared in each case. Table
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1 outlines the correlational analyses and the findings of these analyses were used to
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inform the subsequent MLMs. Any variable which significantly correlated with at
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least one of the dependent variables was included.
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Results
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Full random intercept MLMs were calculated, one with positive expectancies as the
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dependent variable and another for negative expectancies. Predictor variables were
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imputed at both levels (as specified in Table 1): Prompt level variables (j social
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context, environmental context, alcohol consumption - yes or no, and number of
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drinks), and individual level predictors (ij age, gender, ethnicity, student/professional
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status and raw (as opposed to therapeutic categories) AUDIT scores were used for
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analyses. In all analyses, binary variables (Gender, 1 = female; Student/Professional
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status, 1 = student ; Ethnicity, 1 = white British; Alcohol Consumption, 1 = yes) were
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dummy coded (for a more easily interpretable outcome), the two categorical
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predictors (environmental and social context) were dummy coded using Home and
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Alone conditions as the respective reference categories (k-1), and the remaining
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variable were left as continuous variables (Positive expectancies, Negative
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expectancies, Age, AUDIT, Number of drinks).
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INSERT TABLE 1 HERE
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How much variance in positive and negative outcome expectancies is found and can
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be subsequently explained at the individual level (variance between participants) and
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the group level (prompt level, variance between prompts/within participants)?
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Empty models (also known as the variance component models - models without
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imputed predictor variables) indicated that there was a significant proportion of
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variance (ICC = 95.55%) to be explained at the prompt (μ0j = 3.68, p < .001) and the
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individual level (ICC = 4.41%, μ0ij = .17, p < .01). The same was also true of
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negative expectancies, with 46.36% (μ0j = .61, p < .001) and 19.74% (μ0ij = .15, p <
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.01) of unexplained variability being identified at the prompt and the individual levels
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respectively. 2* log likelihood statistics (using chi square) and ICC calculations
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revealed that the full positive expectancy model resulted in a significant reduction of
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unexplained variance (χ² (30, n = 61) = 978.06, p < .001), explaining 36.7%.and
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35.3% of the identified variability in positive expectancies at the prompt and
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individual levels respectively. The negative expectancy model also produced
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significant reduction in the amount of unexplained variance (χ² = (9, n = 61) = 575.88,
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p < .001), with 22.95% and 15.38% of variance in negative expectancies being
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explained at the prompt and individual levels respectively.
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Which predictors are significant predictors of variance in expectancies?
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No single individual level predictor was significant within the MLM model of
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negative expectancies. However, for positive expectancies, the only individual level
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predictor that was significant was student/professional status (β0ij = -.23, p < .01),
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such that being a young professional was associated with reduced positive
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expectancies to a significant degree, whilst being a university student was associated
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with an increase in positive expectancies. At the prompt level, having consumed
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alcohol within the last hour of prompting was a significant predictor of both increased
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positive (β0j = -.82, p < .001) and negative expectancies (β0j = -.51, p < .001) whilst
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number of drinks was not a significant predictor of positive expectancies but it did
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negatively predict variance in negative expectancies (β0j = -.09, p < .001). This
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suggests that any level of alcohol consumption may increase both positive and
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negative expectancies. Nonetheless,, whilst the number of drinks did not appear to
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alter positive beliefs (they remained heightened during consumption), negative beliefs
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began to decrease as alcohol consumption increased.
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Both prompt level categorical predictor variables (social and environmental context)
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were also significant predictors of positive and negative outcome expectancies.
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Specifically, responses whilst situated within alcohol-related contexts including bars
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(β0j = -.52, p < .05), parties (β0j = -.91, p < .01) and sporting events (β0j = - .79, p <
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.05) were associated with increased positive expectancies. Similarly, negative
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expectancies were significantly predicted by being in a bar/pub/club (β0j = -.25, p <
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.01), although sporting and party venues did not account for significant variance.
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Being at a friend or family member’s house was also a significant predictor of
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increased positive (β0j = -1.10, p < .001) and negative expectancies (β0j = -.67, p <
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.001). Being at work was also a significant predictor of positive (β0j = .61, p < .01)
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and negative expectancies (β0j = -.28, p < .05). Here, being outside of work was
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associated with an increase in positive expectancies, and a decrease in negative
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expectancies. Being at home during responses was the reference category for both
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expectancy types and this context also appeared to be associated with decreased
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positive and negative expectancies..
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The social context sub-categories also varied to a statistically significant degree.
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Prompts that occurred whilst participants were with 1 friend (β0j = -1.78, p < .001:
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β0j = -.74, p < .001), 2 or more friends (β0j = -1.75, p < .001: β0j = -.84, p < .001) or
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family members (β0j = -1.10, p < .001: β0j = -.79, p < .001) were significant
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predictors associated with increases in positive and negative expectancies
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respectively. However, being with work colleagues predicted significant decreases in
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positive expectancies (β0j = .72, p < .05) and increases in negative expectancies (β0j
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= -.43, p < .001). Being alone during responses was the reference category for both
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expectancies categories, meaning that this context also appears to be associated with
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decreased expectancies. The ‘other’ response for social context was also a significant
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predictor of positive expectancies (β0j = 2.44, p < .01) but the large standard error
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here (.92) suggests a high degree of variability in participants’ responses in this
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category, perhaps due to the diversity of contexts captured by this response. Any
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attempt to interpret this finding without any further contextual information would
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therefore be unwise.
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Discussion
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With the aim of conducting an ecologically valid assessment of the impact social and
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environmental contexts have on outcome expectancies, this study used smart-phone
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technology to conduct context aware experiential sampling. Social and environmental
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contexts, specifically, being in a pub, bar or club, were significant predictors of both
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increased positive and negative outcome expectancies. The same pattern was observed
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for social contexts including being with a friend, with two or more friends and with
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family members. Being at work or at home, and being with work colleagues or alone
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was associated with a reverse pattern of results, whereby these contexts were
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associated with decreased expectancies. In accordance with previous lab (e.g., Wall et
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al., 2000; 2001) and field research (e.g., LaBrie et al., 2011), these findings provide
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real-time support for the assertion that alcohol-related environmental contexts are
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associated with changes in cognition – specifically, changes in the anticipated
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consequences of alcohol consumption. It was particularly interesting to note that,
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against expectations, negative as well as positive expectancies increased in alcohol-
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related environments and in social group contexts. In studies of problem and non-
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problem drinkers, alcohol-related cues (their favourite alcoholic drink) have been
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shown to create both positive and negative expectations and physiological arousal
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(Cooney, Gillespie, Baker, & Kaplan, 1987). These results suggest that in vivo
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contextual cues can trigger both positive and negative beliefs (c.f. Wall et al., 2000;
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Wiers et al., 2003) and underline the current findings that both positive and negative
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expectancies increased when participants were in social groups and alcohol-related
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environments. The importance of the relationship between social and environmental
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contexts and the decision to drink or exercise restraint is also affirmed by the current
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findings (Andersson et al., 2013; Lau-Barraco & Linden, 2014). It has been suggested
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that interventions need to be able to target the context-dependent nature of substance
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use and associated beliefs in order to be successful (Biglan & Hayes, 1996; Davies,
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1997). The current research may therefore offer insights towards the improvement of
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therapeutic practice, by increasing our ability to target the contextual-varying beliefs
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which are associated with alcohol consumption. Any level of alcohol consumption
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alcohol within the last hour was also associated with increases in both positive and
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negative expectancies respectively. However, number of drinks was only a significant
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predicator of decreased negative expectancies. Therefore, whilst positive expectancies
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appear to remain heightened regardless of the level of alcohol consumed, greater
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levels of consumption may be associated with subsequent decreases in negative
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beliefs. This suggests that real-time alcohol consumption is associated with a
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reduction of the invivo cognitions which are related to restraint (c.f. Baldwin, Oei, &
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Young, 1993). Conversely, consumption appears to increase the positive beliefs which
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are associated with drinking (c.f. Reich et al., 2010).
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Whilst AUDIT scores did correlate with positive expectancies, being a university
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student was the only demographic variable which, on its own, was a significant
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predictor of increased positive outcome expectancies. Therefore, whilst the majority
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of expectancy research relies on student samples, using a non-student sample with a
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comparable age may produce different results (lower average expectancy scores).
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Indeed, age was a not a significant predictor in the study which may suggest that there
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are aspects of the student experience which create deviations in expectancies in
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comparison to those of the same age who are not students. This pattern of results is in
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line with suggestions that there is a ‘culture of drinking’ at University which
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moderates students’’ expectancies (Borsari & Carey, 2001). Future research may
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therefore benefit from greater inclusion of non-student participants.
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As responses that did not occur within 15 minutes of the participation prompt were
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discarded, the current findings can be reasonably believed to be representative of real-
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time cognitions. This removes the problems noted in previous EMA research (c.f.
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Kuntesche & Labhart, 2012) where a lack of signal or power may delay prompts, thus
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increasing the reliance on the participant’s memory and potentially limiting response
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reliability. Nevertheless, it remains possible that a lack of signal or power of
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respondents’ mobiles may have resulted in some data loss in the current research,
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although the high response rate for this study suggests that this is likely to have been
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minimal. It must also be noted that whilst the participation window of 0800-2300 was
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selected in order to maximise responses, future research may be improved by
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exploring the feasibility of responses beyond 2300. This would allow assessments of
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late night/early morning drinking practices and may further elucidate complex
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cognitive processes. Furthermore, it should be noted that participants’ intoxication
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levels may have impaired/hindered responses (cf. Fromme, Katz, & D’Amico, 1997;
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Hindmarch, Kerr, & Sherwood, 1991 LaBrie et al., 2011). While such effects may
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mirror real-life situations, a degree of caution is nonetheless advised when
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considering the current findings.
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In conclusion the present research confirms concerns about the abundant previous
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research which is conducted with participants who are assessed alone, in non alcohol-
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related environments and are sober during the completion of their questionnaires. In
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particular, the results of the current investigation indicate that responses which were
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recorded in solitary contexts and when in alcohol-neutral environments (such as at
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work or at home) were associated with lower expectancies. As specified, alcohol
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consumption was also associated with changes in responses. These results therefore
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suggest that previous research in this field may have captured responses which do not
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necessarily equate to cognitions in real-life situations. Here, the use of smart-phone
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technology to conduct real-time, context aware experiential sampling appears to offer
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a viable solution to this issue. Findings from this research may also provide a
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promising avenue to pursue for the development of context-sensitive interventions.
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Table 1
Bivariate correlations between mean alcohol-related cognitions and all predicator
variables.^
1. Positive Expect.
2. Negative Expect.
3. Environmental
Context
4. Social Context
5. Student/ Young
Professional (YP = 0)
6.Ethnic (Non White British
=0)
7.Gender (Male = 0)
8. Age
9. AUDIT
10.Consumed Alcohol (No=
0)
11. Number drinks
consumed
1
.71**
.67**
2
3
4
5
.48**
-
.59**
-.09**
.50**
-.10**
.02
.59**
.09**
.17*
-
-.04*
.04*
.05*
.10**
-
.09**
-.04
.00
.50**
.04
.08**
.04*
.26**
.01
.05*
.05*
.66**
.01
.14**
-.02
.31**
-.07**
.70**
.00
.09
.50**
.28**
.63**
.63**
.30**
501
** p < .01 * p < .05
502
503
504
505
^
6
7
8
9
10
.49**
.27**
.12**
.37**
-.22**
-.04
.22**
-.23**
.06**
.01
-
.07**
.04
.03
.04
.04
It may be noted that a number of these correlations are significant but are not
sufficient to be deemed strong (r = .07). However, these weak effects may be an issue
of sample size, whereby the ability to detect effects is only improved when sample
sizes are increased (Cohen, 1992).
506
507
508
509
23
11
-