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How much does your peer group really drink? Examining the relative impact of overestimation, actual group drinking and perceived campus norms on university students' heavy alcohol use
Accepted Manuscript How much does your peer group really drink? Examining the relative impact of overestimation, actual group drinking and perceived campus norms on university students' heavy alcohol use
Tara M. Dumas, Jordan P. Davis, Clayton Neighbors PII: DOI: Reference:
S0306-4603(18)30774-3 https://doi.org/10.1016/j.addbeh.2018.11.041 AB 5813
To appear in:
Addictive Behaviors
Received date: Revised date: Accepted date:
18 July 2018 25 November 2018 26 November 2018
Please cite this article as: Tara M. Dumas, Jordan P. Davis, Clayton Neighbors , How much does your peer group really drink? Examining the relative impact of overestimation, actual group drinking and perceived campus norms on university students' heavy alcohol use. Ab (2018), https://doi.org/10.1016/j.addbeh.2018.11.041
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ACCEPTED MANUSCRIPT Running head: OVERESTIMATION OF DRINKING GROUP NORMS
How Much Does Your Peer Group Really Drink?
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Examining the Relative Impact of Overestimation, Actual Group Drinking and Perceived
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Campus Norms on University Students’ Heavy Alcohol Use
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Tara M. Dumas, PhD
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Jordan P. Davis, PhD
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Clayton Neighbors, PhD
Tara M. Dumas, Department of Psychology, Huron University College at Western University,
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London, ON, Canada
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Jordan P. Davis, University of Southern California, Suzanne Dworak-Peck School of Social
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Work, Department of Children, Youth, and Families Clayton Neighbors, Department of Psychology, University of Houston
Address correspondence to: Tara M. Dumas, Huron University College, 1349 Western Road, London, ON, Canada, N6G 1H3; e-mail address: [email protected].
Declarations of interest: None
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Abstract Introduction. We had 3 aims for this study: (1) to explore the relative impact of perceived drinking group norms versus campus drinking norms on university students’ heavy alcohol use, (2) to examine how students’ overestimation of their drinking group norms predicts individual
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heavy alcohol use, while controlling for actual group drinking, and (3) to test if the interaction
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between overestimation and actual group drinking predicts increased student drinking. Further,
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we adopted a longitudinal design to tease apart within- and between-person effects in the
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aforementioned relationships. Methods. University students (N = 118, Mage, 19.40, SD = 1.49, 60.2% women) were recruited in their peer drinking groups and all group members completed 3
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online surveys in two-month intervals. Overestimation was calculated as the difference between students’ perceptions of their drinking groups’ HED and the actual reported HED of group
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members. Results. As expected, results demonstrated notable overestimation of group HED.
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Further, key results of multilevel growth curve modeling demonstrated that at time points when university students overestimated their drinking groups’ HED more than they usually do (i.e.,
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more than their average), they increase in their own HED. Similar within-person results were not
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found for campus drinking norms or actual group HED and the interaction between overestimation and actual group HED was not significant. Conclusions. Findings emphasize the
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importance of incorporating the peer drinking group as a reference group in personalized normative feedback interventions.
Key Words: university, peer groups, drinking groups, heavy episodic drinking, drinking norms, overestimation
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1. Introduction A wealth of literature suggests that one of the strongest predictors of university students’ alcohol use and related consequences is their perceptions of how much their peers drink (i.e., descriptive norms) (Borsari & Carey, 2003; Dumas, Davis, Maxwell-Smith, & Bell, 2018;
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Neighbors, Lee, Lewis, Fossos, & Larimer, 2007). Research demonstrates that the descriptive
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norms of peers who are more salient (e.g., best friend versus the average university student) have
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a stronger impact on students’ own alcohol use (Mallett, Bachrach, & Turrisi, 2009; Yanovitzky, Stewart, & Lederman, 2006). Typically, university students drink within intact groups of peers
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(Lange, Devos-Comby, Moore, Daniel, & Homer, 2011), and due to their salience, these peer
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drinking groups likely play one of the most powerful roles in influencing alcohol consumption. Yet, surprisingly little is known about the descriptive norms of these groups.
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Prior research has consistently found that university students overestimate the drinking of
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their peers, which is associated with heightened alcohol consumption, heavy drinking, and related consequences (e.g., Perkins, Haines, & Rice, 2005). Further, with the relatively strong
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associations between perceived norms and drinking, researchers have developed intervention
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strategies designed to reduce drinking by correcting normative misperceptions (Lewis & Neighbors, 2006). While findings support the efficacy of comparing students’ own drinking to
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perceived and actual descriptive campus norms (Dotson, Dunn & Bowers, 2015; Martens, Smith & Murphy, 2013; Miller et al., 2013; Reid & Carey, 2015; Walters & Neighbors, 2005), drinking norms of friends is a stronger predictor of university students’ alcohol use than descriptive campus norms (Yanovitzky et al., 2006). For instance, Mallett et al. (2009) found the that relation between descriptive campus norms and students’ own drinking was not significant when perceptions of close friend drinking was a predictor in the same model. Dumas et al. (2018) also demonstrated, cross-sectionally, that descriptive drinking group norms were a strong predictor of negative drinking consequences via young adults’ heavy drinking.
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To date, little consideration has been given to evaluation of normative feedback interventions with more proximal groups. This may be due to findings suggesting that students’ misperceptions of peer alcohol use are smaller for more proximal groups (Borsari & Carey, 2003) and are only sometimes present for best or close friends (Baer & Carney, 1993; Baer &
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Larimer, 1991; Martens, Dams-O'Connor, Duffy-Paiement, & Gibson, 2006). These findings,
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however, are based primarily on students’ estimates of their peers’ alcohol use. For instance,
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individuals who report that they drink less than their friends could be overestimating their friends’ alcohol use; However, it is also possible that they simply have friends that drink more
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than they do. Thus, in order to properly evaluate the extent to which university students
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misperceive the drinking of their close peers, it is essential to recruit these peers to report on their actual drinking.
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Recently, Ecker, Cohen and Buckner (2017) compared university students’ estimates to
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their close friends’ actual, self-reported alcohol problems. Results indicated, that, on average, participants tended to overestimate the severity of their friends’ alcohol use problems and this
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overestimation was positively associated with students’ own drinking. Methodologically, this
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study is a step in the right direction. However, as noted by the authors, this study was crosssectional and relied on data from one friend, thus providing a limited picture of the peer context
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that influences students’ drinking. Additional research is needed that focuses on perceptions of friends’ frequency of alcohol use within the context of their peer drinking groups. Given that university students drink most often within a group of friends (Lange et al., 2011) and these drinking groups possess a powerful set of group norms that guide members’ drinking behavior, we argue that it is important for researchers to examine descriptive norms of students’ drinking groups and the extent to which the misinterpretation of these norms predict members’ alcoholrelated outcomes. Evidence for norm overestimation in this context would provide a compelling
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foundation for using the drinking group as a reference group in normative feedback interventions, even if the magnitude of misperception is smaller than for campus norms. 1.1 The Present Study We conducted a pilot project which represents the first longitudinal study, to our
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knowledge, on university students’ drinking groups. At three points during the academic year,
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students reported on the frequency of their own heavy episodic drinking (HED: defined as 4 or 5
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drinks in a row for women and men, respectively) (Wechsler & Nelson, 2001), their perceptions of HED of their drinking group and of the average student on campus. Overestimation of
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drinking group norms were derived by comparing students’ perceptions of their groups’ HED
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with the actual HED of their group (an average of members’ self-reported heavy drinking), similar to the approach of Ecker et al. (2017). Our longitudinal approach allowed us to
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simultaneously examine average between-person differences in drinking group members’ typical
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overestimation of group norms on their own HED over time and within-person effects – how students’ HED changes as a function of time-specific changes in his or her typical overestimation
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of group norms. Using a multi- level design, we hypothesized that: H1) between- and within-
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person effects of descriptive peer norms on individual HED will be stronger when the reference group is the drinking group rather than the average university student of the same gender; H2)
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After controlling for perceived campus norms and actual group behavior, overestimation of drinking group HED will predict between- and within-person increases in individual HED; and H3) HED will occur most frequently among participants with both heavier drinking groups and larger overestimations of their drinking group norms (between-person effect) and at time points during which participants have both high group HED and high group overestimation scores relative to their average (within-person effect).
ACCEPTED MANUSCRIPT 2. Method 2.1. Recruitment Researchers recruited students from a liberal arts college in Southern Ontario, Canada in their drinking groups (defined as “groups of friends who go to social drinking events such as
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parties and bars together and who usually meet up together to pre-drink before these events”)
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either on route to the campus pub during a student social drinking event (n = 12 groups) or via
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posters around campus (n = 15 groups).1 A more detailed description of recruitment procedures is available elsewhere (Citation removed for blind review, 2018).
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2.2. Participants
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A total of 27 (5 all-male, 10 all-female and 12 mixed-sex) drinking groups participated.2 Group size ranged from 3-7 members (M = 4.37, SD = 1.31). Participants (n = 118) were an
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average age of 19 years (SD = 1.49; range = 17 – 24 years). Participants included more women
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(60.2%) than men and the majority identified as White (85%), followed by Asian (3.7%), East Indian (3.7%) and Hispanic (1%).
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2.3. Procedure
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Participants completed three 30-minute online assessments at two-month intervals (lateNovember, January, and March). For each assessment, consent was obtained from participants
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electronically prior to survey commencement. Assessments included items assessing general demographic information followed by the measures described below. Participants were reimbursed with a $10 e-gift certificate for each assessment completed.
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Because it was important to recruit intact groups and because, in a minority of cases, there are non -drinking members of drinking groups (Demant & Järvinen, 2011), alcohol consumption was not an eligibility criterion for recruitment. Indeed, there was 1 member of 1 drinking group who was abstinent at all three time points. 2 Group membership was largely stable with only one participant leaving her group between the first and second assessment. In three additional groups, Time 3 survey data was missing for all members, thus we do not know whether or not these groups stayed intact.
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2.4. Measures 2.4.1. Heavy Episodic Drinking (HED). Participants reported on the number of days in the past 2 months they engaged in HED (consuming 4/5 drinks in one sitting for women/men, respectively). These criteria have been used extensively in the college drinking literature to
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define heavy episodic or “binge” drinking (Wechsler & Nelson, 2001). To measure the HED of
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HED (i.e., mean group HED, not including the participant).
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participants’ drinking groups, we calculated the average of their group members’ self-reported
2.4.2. Perceived Descriptive HED Norms. Participants estimated the number of days,
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on average, in the past 2 months that their group members and the average student on campus of
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the same gender engaged in HED. These items were used to measure perceived descriptive drinking group and campus norms, respectively.
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2.4.3. Covariates. We measured and controlled for gender (0 = male, 1 = female), age
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(grand-mean centered) and ethnicity (0 = non-white, 1 = white). We dichotomized ethnicity
2.5. Analytic Plan
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because the large majority of participants (85%) were white.
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To test hypotheses, we ran a taxonomy of multi-level growth curve models using the statistical program, HLM 7 (Raudenbush, Bryk, Cheong, Congdon, & Du Toit, 2011). The
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outcome variable for all models was number of HED days in the past 2 months. Because this is a count variable, we estimated hierarchical generalized linear models with Poisson probability distribution and natural log link function and accounted for overdispersion by estimating an overdispersion parameter. Within-person predictors of HED were entered at Level 1 and person mean centered, so that participants’ scores represent deviations from their own average. Between-person predictors were entered at Level 2 and grand mean centered, so we could examine the predictive power of
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participants’ average score relative to others in the study. Further, because participants were nested in drinking groups, we estimated a random Level 3 intercept. We first ran a model, which demonstrated that a linear growth function was most appropriate in modeling change in HED over time. Next, we ran a series of models testing H1.
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We first entered within- and between-person descriptive campus norms as predictors of HED.
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Then, we examined if these relations held once descriptive drinking group norms were entered
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into the model. Finally, a model was estimated to determine if effects varied over time; specifically, if between-person campus and group norms predicted the slope of HED.
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To test H2 and H3, we first calculated students’ overestimation as perceptions of group
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HED minus actual group HED (Ecker et al., 2017). We then ran a second set of multi- level growth curve models with overestimation of group HED as a predictor at the within- and
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between-person level (Model 1). Next, we controlled for actual group HED at both the within-
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and between-person levels (Model 2). In the following step (Model 3), we included perceived campus norms to evaluate unique effects of overestimating drinking group norms relative to
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perceived campus drinking norms. Finally, we allowed overestimation (within and between) to
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vary as a function of group HED (within and between) (Model 4). Cross-level interactions with time were also included so that we could predict the linear slope of HED.
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Attrition rates were 9% at Time 1, 17.7% at Time 2 and 37% at Time 3. Amount of missing data varied across drinking groups; In some groups, all members participated (n = 22, 19 and 9 groups at Time 1, 2 and 3 respectively), in others there was missing data for one or more members (n = 5, 8, and 15 at Time 1, 2, and 3 respectively), and at Time 3, there were 3 groups for which data were missing for all members. To account for this, we used multiple imputation (MI) with k = 50 datasets. MI uses a maximum likelihood procedure, based on relations among all existing data to predict missing values (Little & Rubin, 2002) and produces more plausible parameter estimates than listwise deletion.
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3. Results The number of days in the past two months that that participants perceived the average university student to have engaged in HED ranged from 9.12 (Time 2) to 10.54 (Time 1). The average number of days that participants perceived their drinking groups engaged in HED ranged
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from 6.42 (Time 3) to 7.40 (Time 1). Comparatively, drinking groups, on average, reported 5.07
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(Time 3) to 6.79 (Time 1) days of HED in the past 2 months. Results demonstrate that, on
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average, participants overestimated their drinking groups’ frequency of HED by at least 2 days at each time point (see Table 1).
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Table 2 presents a taxonomy of nested models predicting HED. Model 1 demonstrates a
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significant linear slope, with HED decreasing over time. Further, results revealed a significant
drinking group intercept of HED.
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random slope of HED over time, as well as both a significant between-person and between-
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3.1. Descriptive Drinking Group versus Campus Norms Predicting Individual HED Model 2 (Table 2) demonstrates significant between- and within-person effects of
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descriptive campus norms on individual HED. In line with H1, once descriptive drinking group
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norms were entered into the model (Model 3), the effects of campus norms were no longer significant. Further, both the between- and within-person effects of descriptive drinking group
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norms were significant predictors of HED. In the final model (Model 4), a cross-level interaction between time and descriptive campus norms revealed that only participants with low (-1 SD) descriptive campus norms experienced decreases in HED over time (B = -.02, t = -5.65, p < .01); Participants with high (+1 SD) descriptive campus norms remained high and stable in their HED over the school year (B = .00, t = 0.04, p = .97) (see Figure 1). Deviance tests (i.e., likelihood ratio tests similar to that of R2 difference tests in ordinary least squares regression) were significant for Models 2-4 (see Table 2). This means that for every subsequent model after Model 1, the model fit was significantly improved from the last.
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3.2. Overestimation of Drinking Group Norms Predicting Individual HED Model 1 (Table 3) demonstrates significant between- and within-person effects of overestimation of drinking group norms on individual HED. Consistent with H2, the effect of overestimation remains even after controlling for actual group HED (Model 2) and campus
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norms (Model 3). Further, H3 was not supported. The interaction between overestimation and
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actual drinking group HED was not significant at the within-person or between-person level.
was significantly improved from the last (see Table 3).
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4. Discussion
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Again, deviance tests demonstrated that for every subsequent model after Model 1, the model fit
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We extended research on descriptive drinking norms by focusing on the drinking group as a salient reference group in comparison to the typical university student. University students
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drink most often within intact drinking groups (Lange et al., 2011), yet minimal research exists
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on their role in drinking. Furthermore, we conducted the first study, to our knowledge, to tease apart between- and within-person effects of descriptive peer norms on university students’
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drinking. Notably, at time points when students’ perceptions of drinking group norms were
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higher than usual (within-person effect), they experienced an increase in the frequency of their own HED. Further, once this effect was entered into our model, the within-person effect of the
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typical university student, the reference group used in most normative feedback interventions, became non-significant. Moreover, to clarify the extent to which findings were due to students’ misperceptions of their drinking groups’ HED, we recruited the whole drinking group and compared actual versus perceived group drinking. Our study is the first to measure these misperceptions within the peer drinking group. Results demonstrate notable overestimation of drinking group HED, which predicted changes in members’ own frequency of HED, even when controlling for actual group
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behavior and campus norms. Results provide a more nuanced understanding of the role of the drinking group in university students’ alcohol consumption. For H1, we hypothesized that the effects of descriptive peer norms on individual heavy drinking will be stronger when the reference group is the drinking group rather than the typical
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university student. In support of H1, results of our main effects models demonstrated significant
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between- and within- person effects of perceptions of the typical university students’ HED on
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students’ own HED, which became non-significant once we accounted for perceptions of drinking group norms. Findings are consistent with past cross-sectional research suggesting that
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perceptions of close friends’ drinking are more strongly related to students’ own drinking as
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compared to perceived campus norms (Mallett et al., 2009; Yanovitzky et al., 2006). Yet, we should not be so quick to dismiss the predictive power of campus drinking norms. By adopting a
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longitudinal design, we gained a more nuanced picture of the relationship between this variable
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and student drinking, which is lost when examining main effects in cross-sectional studies. Even though the within-person effect of perceived campus drinking norms remained non-significant,
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we found a significant cross-level interaction, with between-person descriptive drinking campus
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norms predicting the slope of HED over time. The linear decrease in HED, noted in past research and partly due to the heightened number of drinking events (Frosh Week, Homecoming,
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Halloween) and decreased number of academic deadlines at the beginning of the school year (Tremblay et al., 2010), was only found for students with low descriptive campus norms. Findings suggest that the effect of campus drinking norms is largely between individuals. Thus, individual variation in campus norms may be less important when assessing individual HED. Rather, relative to other university students, those who, on average, tend to perceive stronger campus drinking norms appear at a greater risk of remaining high and stable in their drinking across the school year, even with mounting deadlines and final exams.
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Results further emphasize the salience of drinking group norms in university students’ drinking. Specifically, participants who, on average, tended to perceive their drinking groups participated in more HED tended to engage in more frequent HED themselves. Further, at time points when participants perceived their groups increased in their frequency of HED relative to
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average, they also increased in their own HED. It is important to note that the latter finding
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reflects time-specific deviation, rather than overall time-invariant averages, and suggests that
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fluctuations in students’ perceptions of their groups’ drinking can affect their own HED frequency.
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Further, even though perceptions of HED were lower, and, likely, less inflated, for the
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drinking group as compared to the typical student, overestimation of group HED remained a significant predictor of students’ own drinking, above and beyond campus norms. Consistent
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with H2, students who, on average, overestimated their groups’ HED more than others engaged
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in more frequent HED themselves. Additionally, at time points when students increased in their overestimation of their groups’ HED, they also increased in their own heavy drinking. These
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findings remained significant, even when controlling for how much the group actually drank.
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It is important for future research to examine why university students overestimate the drinking norms of their drinking groups, even though these groups are engaging in alcohol
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consumption together (Lange et al., 2011). One possibility is that when individuals are intoxicated, they are often more noticeable (e.g., uninhibited, loud, aggressive) than when they are sober or drinking moderately. Thus, instances of heavy group drinking may be more salient when participants reflect on how much their group drinks, encouraging overestimation. Relatedly, we have a tendency to attribute others’ behavior as more reflective of stable dispositional characteristics rather than external influences (the fundamental attribution error) (Ross, 1977). Therefore, group members who engage in memorable yet rare instances of HED due to contextual influences such as birthdays or campus-related drinking events (e.g.,
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Homecoming), might be perceived by peers as “heavy drinkers”, thus encouraging overestimation. H3 was not supported. We did not find that the most frequent HED occurred when both group drinking and members’ overestimation was high. In fact, actual group drinking was not a
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particularly robust predictor in our models when also considering students’ perceptions of peer
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drinking. This could mean that students’ perceptions rather than actual group modeling of heavy
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drinking are more important in determining individual behavior. Alternatively, it is possible that our measure of actual group drinking – the frequency of group HED – was not the best choice.
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We focused on HED because it is the most normative type of university drinking and the type
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most tied to negative drinking consequences (Borsari & Carey, 2001). However, future research could adopt a more comprehensive measure of drinking (e.g., frequency of alcohol use and HED,
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maximum number of drinks) to gain a clearer picture of group drinking patterns.
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Our results have important practical implications. They emphasize that perceived campus norms are a good screener for students at risk for more stable HED across the school year. They
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also suggest the importance of targeting students with heightened perceptions of campus
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drinking at the beginning of the school year for interventions to positively affect their HED trajectories over time. Interventions implemented at the beginning of the school year that involve
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a personalized normative feedback component have had success in reducing alcohol use (e.g., for incoming students) (Hustad, Barnett, Borsari, & Jackson, 2010). However, little is known about the impact of these interventions over longer periods, such as across the school year. Most notably, our results suggest that personalized normative feedback interventions would benefit from addressing students’ overestimation of their drinking groups’ norms, rather than campus norms only. For example, intervention participants could provide the contact information of their drinking group members, who would be sent a brief questionnaire on their frequency of alcohol use. This information could then be incorporated into target students’
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personalized feedback on perceived versus actual drinking group norms. Further, given that our within-person findings demonstrate that increases in overestimation of group drinking norms can occur at any point during the academic year, it is likely important for interventions to include booster sessions (Braitman & Henson, 2016; Mason, Benotsch, Way, Kim, & Snipes, 2014) to
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ward off associated increases in individual HED. Thus, intervention participants and their
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drinking group members could be contacted to complete questionnaires on their alcohol use at
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multiple points across the academic year and be sent personalized normative feedback via e-mail or text.
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Implications notwithstanding, this study contains limitations. We improved upon past
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research by conducting a longitudinal study, allowing us to isolate within-person variance and control for all between-person confounds. However, although our time effects were longitudinal,
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the effects of descriptive peer drinking norms were cross-sectional. Thus, we cannot claim
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causality in relations between descriptive drinking norms and individual HED. Further, this was a pilot project with a low n of 27 drinking groups (118 participants). Therefore, to improve
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statistical power, this study should be replicated with a larger sample size. Finally, results may
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not be generalizable to non-university attending young adults. To conclude, the present study demonstrates that, over and above campus descriptive
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norms and the actual drinking behavior of university students’ drinking groups, overestimation of group drinking is a powerful predictor of students’ heavy alcohol consumption. Moreover, overestimation fluctuates over the academic year, with increases accompanied by increases in students’ frequency of HED. Results stress the need for heightened attention on peer drinking groups and students’ perceptions of their group’s drinking in university alcohol interventions and prevention efforts.
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Table 1. Means and Standard Deviations for Number of Days of Heavy Episodic Drinking in the Last 2 Months Perceived
Perceived
Actual
Overestimation of
Campus HED
Drinking
Drinking
Drinking Group
Group HED
Group HED
HED 2.06 (4.01)
10.54 (5.95)
7.40 (4.60)
6.79 (3.58)
2 (Late-January)
9.83 (5.90)
7.40 (5.01)
6.16 (2.98)
3 (Late-March)
9.12 (4.74)
6.42 (3.66)
5.07 (2.91)
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M
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1 (Late-November)
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Time
2.48 (3.99) 2.20 (2.81)
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Table 2. Multilevel Model with Perceptions of Peer HED Predicting Individual HED Model 1
Model 2
Model 3
Model 4
Fixed Effects: b (SE) L1 Predictors of HED Intercept
1.92 (.08)** 1.97 (.08)** 1.78 (.11)**
1.89 (.08)**
Linear Slope
-.13 (.04)**
-.12 (.03)**
.03 (.00)**
.02 (.01)
.02 (.01)*
.03 (.01)**
.03 (.01)**
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WP Descriptive Group Norms L2 Predictors of L1 Intercept
-.28 (.10)**
Age Ethnicity
-.01 (.04)
-.02 (.03)
10 (.08)
.01 (.09)
.03 (.08)
.07 (.01)**
.00 (.02)
-.02 (.02)
.11 (.03)**
.09 (.02)**
.03 (.01)**
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Random Effects:
-.08 (.03)*
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BP Descriptive Group Norms
BP Descriptive Campus Norms
-.17 (.09)
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BP Descriptive Campus Norms
L2 Predictors of Linear Slope
-.17 (.13)
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Gender
BP Descriptive Group Norms
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-.12 (.04)**
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WP Descriptive Campus Norms
-.12 (.04)**
.00 (.01)
Variance Component (SE)
.39 (.06)**
.34 (.05)**
.30 (.05)**
.27 (.05)**
L2 (between-person) Linear Slope
.07 (.02)**
.07 (.02)**
.06 (.02)**
.04 (.02)**
L3 (between-group) Intercept
.09 (.03)**
.12 (.03)**
.03 (.02)
.03 (.02)
--
69.58**
55.16**
11.27**
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Deviance test: 𝜒 2
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L2 (between-person) Intercept
Note: WP = within-person, BP = between-person. Model 1 is an unconditional growth model, with a random intercept and slope at Level 2 and a random intercept at Level 3 . Model 2 adds basic control covariates and the main effects of campus norms. Model 3 adds the main effects of drinking group norms. Model 4 adds the interactions between campus and drinking group norms. * p < .05, ** p < .01.
ACCEPTED MANUSCRIPT 17 Table 3. Multilevel Growth Curve Model with Drinking Group HED and Overestimation of Drinking Group HED Predicting Individual HED Model 1 Model 2 Model 3 Model 4 Fixed Effects: b (SE) L1 Predictors of HED 2.03 (.09)**
1.95 (.09)**
1.82 (.12)**
3.01 (.63)**
Linear Slope
-.15 (.04)**
-.12 (.05)*
-.10 (.06)
-.13 (.03)**
WP Overestimation
.03 (.01)**
.04 (.01)**
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Intercept
.02 (.00)**
.04 (.03)
.04 (.03)
.02 (.01)* .02 (.00)**
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.02 (.01)*
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WP Group HED WP Descriptive Campus Norms
.02 (.01)*
-.24 (.08)**
-.22 (.09)*
-.24 (.12)
-.11 (.05)
Age
-.07 (.04)
-.03 (.04)
-.02 (.04)
-.06 (.02)*
Ethnicity
.02 (.08)
.01 (.08)
.01 (.09)
.01 (.08)
.11 (.02)**
.13 (.02)**
.12 (.04)**
.03 (.02)
.06 (.03)*
.06 (.04)
-.33 (.09)**
.00 (.02)
-.04 (.00)**
WP Overestimation X Group HED
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L2 Predictors of Level 1 Intercept
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Gender
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BP Overestimation BP Group HED
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BP Descriptive Campus Norms
.01 (.01)
BP Overestimation X Group HED
BP Group HED
-.02 (.01)* .01 (.01)
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BP Overestimation
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L2 Predictors of Linear Slope
.01 (.01)
BP Descriptive Campus Norms
.04 (.00)** -.01(.00)
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BP Overestimation X Group HED Random Effects:
Variance Component (SE) L2 (between-person) Intercept
.24 (.05)**
.30 (.05)**
.30 (.05)**
.24 (.05)**
L2 (between-person) Linear Slope
.06 (.02)**
.06 (.02)**
.06 (.02)**
.04 (.02)**
L3 (between-group) Intercept
.15 (.04)**
.05 (.02)*
.06 (.02)**
.09 (.03)**
--
7.88*
14.60**
15.93**
Deviance test: 𝜒 2
Note: WP = within-person, BP = between-person. Model 1 is a main effects model with overestimation of group HED and control covariates. It has a random intercept and slope at Level
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2 and a random intercept at Level 3. Model 2 adds main effects of actual group HED. Model 3 adds the main effects of descriptive campus norms. Model 5 adds the interaction between overestimation and actual group HED. * p < .05, ** p < .01.
ACCEPTED MANUSCRIPT 19 References Baer, J. S., & Carney, M. M. (1993). Biases in the perceptions of the consequences of alcohol use among college students. Journal of studies on alcohol, 54(1), 54-60. Baer, J. S., Stacy, A., & Larimer, M. (1991). Biases in the Perception of Drinking Norms among
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Dumas, T. M., Davis, J. P., Maxwell-Smith, M. A., & Bell, A. (2018). From Drinking Group Norms to Individual Drinking Consequences: A Moderated Mediation Model Examining the Role of Members’ Status, Identification with the Group and with Emerging Adulthood. https://doi.org/10.1080/10826084.2017.1408651. doi:10.1080/10826084.2017.1408651
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ACCEPTED MANUSCRIPT 21 Little, R., J. A., & Rubin, D., B. (2002). Bayes and Multiple Imputation. In Statistical Analysis with Missing Data, Second Edition (pp. 200-220). Mallett, K. A., Bachrach, R. L., & Turrisi, R. (2009). Examining the Unique Influence of Interpersonal and Intrapersonal Drinking Perceptions on Alcohol Consumption Among
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Martens, M. P., Dams-O'Connor, K., Duffy-Paiement, C., & Gibson, J. T. (2006). Perceived
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Mason, M., Benotsch, E. G., Way, T., Kim, H., & Snipes, D. (2014). Text Messaging to Increase
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Readiness to Change Alcohol Use in College Students. The Journal of Primary Prevention, 35(1), 47-52. doi:10.1007/s10935-013-0329-9
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Miller, M. B., Leffingwell, T., Claborn, K., Meier, E., Walters, S., & Neighbors, C. (2013).
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Personalized Feedback Interventions for College Alcohol Misuse: An Update of Walters & Neighbors (2005). Psychol Addict Behav, 27(4), 909-920. doi:10.1037/a0031174
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Neighbors, C., Larimer, M. E., and Lewis, M. A. (2004). Targeting Misperceptions of Descriptive Drinking Norms: Efficacy of a Computer-Delivered Personalized Normative Feedback Intervention. Journal of Consulting and Clinical Psychology, 72(3), 434-447. doi:10.1037/0022-006X.72.3.434
ACCEPTED MANUSCRIPT 22 Neighbors, C., Lee, C. M., Lewis, M. A., Fossos, N., & Larimer, M. E. (2007). Are social norms the best predictor of outcomes among heavy-drinking college students? Journal of Studies on Alcohol and Drugs, 68(4), 556-565. Neighbors, C., Lewis, M. A., Atkins, D. C., Jensen, M. M., Walter, T., Fossos, N., . . . Larimer,
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M. E. (2010). Efficacy of Web-Based Personalized Normative Feedback: A Two-Year
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Perkins, H. W., Haines, M. P., & Rice, R. (2005). Misperceiving the college drinking norm and
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related problems: a nationwide study of exposure to prevention information, perceived
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norms and student alcohol misuse. Journal of studies on alcohol, 66(4), 470-478. Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R., & Du Toit, M. (2011).
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Hierarchical Linear and Nonlinear Modeling (HLM7). In. Lincolnwood, IL: Scientific
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Reid, A. E., & Carey, K. B. (2015). Interventions to Reduce College Student Drinking: State of
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the Evidence for Mechanisms of Behavior Change. Clin Psychol Rev, 40, 213-224.
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doi:10.1016/j.cpr.2015.06.006 Ross, L. (1977). The Intuitive Psychologist And His Shortcomings: Distortions in the Attribution
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Process. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology (Vol. 10, pp. 173-220): Academic Press. Substance Abuse and Mental Health Services Administration. (2016). Results from the 2015 National Survey on Drug Use and Health: Detailed tables. Retrieved from https://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/NSDUHDetTabs-2015/NSDUHDetTabs-2015.htm#tab6-84b
ACCEPTED MANUSCRIPT 23 Tremblay, P. F., Graham, K., Wells, S., Harris, R., Pulford, R., & Roberts, S. E. (2010). When Do First-Year College Students Drink Most During the Academic Year? An InternetBased Study of Daily and Weekly Drinking. Journal of American College Health, 58(5), 401-411. doi:10.1080/07448480903540465
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Walters, S. T., & Neighbors, C. (2005). Feedback interventions for college alcohol misuse:
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Wechsler, H., & Nelson, T. F. (2001). Binge drinking and the American college students: What's
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five drinks? Psychology of Addictive Behaviors, 15(4), 287-291.
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Yanovitzky, I., Stewart, L. P., & Lederman, L. C. (2006). Social Distance, Perceived Drinking
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by Peers, and Alcohol Use by College Students.
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ACCEPTED MANUSCRIPT 25 Figure 1. Between-Person Descriptive Campus Norms as a Predictor of the Slope of Heavy Episodic Drinking over Time
Low Descriptive Campus Norms (-1 SD) High Descriptive Campus Norms (+1 SD)
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ACCEPTED MANUSCRIPT 26 Highlights
University students overestimated the heavy alcohol use of their peer drinking group
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Overestimation of group drinking was a powerful predictor of students’ own drinking
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Within-person increases in overestimation predicted increases in individual drinking
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Results held even when controlling for how much the group actually drinks
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Results also held when accounting for perceived campus drinking norms
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Tara M. Dumas, Jordan P. Davis, Clayton Neighbors PII: DOI: Reference:
S0306-4603(18)30774-3 https://doi.org/10.1016/j.addbeh.2018.11.041 AB 5813
To appear in:
Addictive Behaviors
Received date: Revised date: Accepted date:
18 July 2018 25 November 2018 26 November 2018
Please cite this article as: Tara M. Dumas, Jordan P. Davis, Clayton Neighbors , How much does your peer group really drink? Examining the relative impact of overestimation, actual group drinking and perceived campus norms on university students' heavy alcohol use. Ab (2018), https://doi.org/10.1016/j.addbeh.2018.11.041
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ACCEPTED MANUSCRIPT Running head: OVERESTIMATION OF DRINKING GROUP NORMS
How Much Does Your Peer Group Really Drink?
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Examining the Relative Impact of Overestimation, Actual Group Drinking and Perceived
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Campus Norms on University Students’ Heavy Alcohol Use
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Tara M. Dumas, PhD
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Jordan P. Davis, PhD
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Clayton Neighbors, PhD
Tara M. Dumas, Department of Psychology, Huron University College at Western University,
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London, ON, Canada
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Jordan P. Davis, University of Southern California, Suzanne Dworak-Peck School of Social
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Work, Department of Children, Youth, and Families Clayton Neighbors, Department of Psychology, University of Houston
Address correspondence to: Tara M. Dumas, Huron University College, 1349 Western Road, London, ON, Canada, N6G 1H3; e-mail address: [email protected].
Declarations of interest: None
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Abstract Introduction. We had 3 aims for this study: (1) to explore the relative impact of perceived drinking group norms versus campus drinking norms on university students’ heavy alcohol use, (2) to examine how students’ overestimation of their drinking group norms predicts individual
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heavy alcohol use, while controlling for actual group drinking, and (3) to test if the interaction
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between overestimation and actual group drinking predicts increased student drinking. Further,
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we adopted a longitudinal design to tease apart within- and between-person effects in the
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aforementioned relationships. Methods. University students (N = 118, Mage, 19.40, SD = 1.49, 60.2% women) were recruited in their peer drinking groups and all group members completed 3
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online surveys in two-month intervals. Overestimation was calculated as the difference between students’ perceptions of their drinking groups’ HED and the actual reported HED of group
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members. Results. As expected, results demonstrated notable overestimation of group HED.
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Further, key results of multilevel growth curve modeling demonstrated that at time points when university students overestimated their drinking groups’ HED more than they usually do (i.e.,
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more than their average), they increase in their own HED. Similar within-person results were not
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found for campus drinking norms or actual group HED and the interaction between overestimation and actual group HED was not significant. Conclusions. Findings emphasize the
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importance of incorporating the peer drinking group as a reference group in personalized normative feedback interventions.
Key Words: university, peer groups, drinking groups, heavy episodic drinking, drinking norms, overestimation
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1. Introduction A wealth of literature suggests that one of the strongest predictors of university students’ alcohol use and related consequences is their perceptions of how much their peers drink (i.e., descriptive norms) (Borsari & Carey, 2003; Dumas, Davis, Maxwell-Smith, & Bell, 2018;
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Neighbors, Lee, Lewis, Fossos, & Larimer, 2007). Research demonstrates that the descriptive
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norms of peers who are more salient (e.g., best friend versus the average university student) have
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a stronger impact on students’ own alcohol use (Mallett, Bachrach, & Turrisi, 2009; Yanovitzky, Stewart, & Lederman, 2006). Typically, university students drink within intact groups of peers
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(Lange, Devos-Comby, Moore, Daniel, & Homer, 2011), and due to their salience, these peer
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drinking groups likely play one of the most powerful roles in influencing alcohol consumption. Yet, surprisingly little is known about the descriptive norms of these groups.
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Prior research has consistently found that university students overestimate the drinking of
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their peers, which is associated with heightened alcohol consumption, heavy drinking, and related consequences (e.g., Perkins, Haines, & Rice, 2005). Further, with the relatively strong
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associations between perceived norms and drinking, researchers have developed intervention
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strategies designed to reduce drinking by correcting normative misperceptions (Lewis & Neighbors, 2006). While findings support the efficacy of comparing students’ own drinking to
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perceived and actual descriptive campus norms (Dotson, Dunn & Bowers, 2015; Martens, Smith & Murphy, 2013; Miller et al., 2013; Reid & Carey, 2015; Walters & Neighbors, 2005), drinking norms of friends is a stronger predictor of university students’ alcohol use than descriptive campus norms (Yanovitzky et al., 2006). For instance, Mallett et al. (2009) found the that relation between descriptive campus norms and students’ own drinking was not significant when perceptions of close friend drinking was a predictor in the same model. Dumas et al. (2018) also demonstrated, cross-sectionally, that descriptive drinking group norms were a strong predictor of negative drinking consequences via young adults’ heavy drinking.
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To date, little consideration has been given to evaluation of normative feedback interventions with more proximal groups. This may be due to findings suggesting that students’ misperceptions of peer alcohol use are smaller for more proximal groups (Borsari & Carey, 2003) and are only sometimes present for best or close friends (Baer & Carney, 1993; Baer &
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Larimer, 1991; Martens, Dams-O'Connor, Duffy-Paiement, & Gibson, 2006). These findings,
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however, are based primarily on students’ estimates of their peers’ alcohol use. For instance,
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individuals who report that they drink less than their friends could be overestimating their friends’ alcohol use; However, it is also possible that they simply have friends that drink more
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than they do. Thus, in order to properly evaluate the extent to which university students
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misperceive the drinking of their close peers, it is essential to recruit these peers to report on their actual drinking.
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Recently, Ecker, Cohen and Buckner (2017) compared university students’ estimates to
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their close friends’ actual, self-reported alcohol problems. Results indicated, that, on average, participants tended to overestimate the severity of their friends’ alcohol use problems and this
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overestimation was positively associated with students’ own drinking. Methodologically, this
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study is a step in the right direction. However, as noted by the authors, this study was crosssectional and relied on data from one friend, thus providing a limited picture of the peer context
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that influences students’ drinking. Additional research is needed that focuses on perceptions of friends’ frequency of alcohol use within the context of their peer drinking groups. Given that university students drink most often within a group of friends (Lange et al., 2011) and these drinking groups possess a powerful set of group norms that guide members’ drinking behavior, we argue that it is important for researchers to examine descriptive norms of students’ drinking groups and the extent to which the misinterpretation of these norms predict members’ alcoholrelated outcomes. Evidence for norm overestimation in this context would provide a compelling
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foundation for using the drinking group as a reference group in normative feedback interventions, even if the magnitude of misperception is smaller than for campus norms. 1.1 The Present Study We conducted a pilot project which represents the first longitudinal study, to our
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knowledge, on university students’ drinking groups. At three points during the academic year,
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students reported on the frequency of their own heavy episodic drinking (HED: defined as 4 or 5
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drinks in a row for women and men, respectively) (Wechsler & Nelson, 2001), their perceptions of HED of their drinking group and of the average student on campus. Overestimation of
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drinking group norms were derived by comparing students’ perceptions of their groups’ HED
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with the actual HED of their group (an average of members’ self-reported heavy drinking), similar to the approach of Ecker et al. (2017). Our longitudinal approach allowed us to
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simultaneously examine average between-person differences in drinking group members’ typical
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overestimation of group norms on their own HED over time and within-person effects – how students’ HED changes as a function of time-specific changes in his or her typical overestimation
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of group norms. Using a multi- level design, we hypothesized that: H1) between- and within-
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person effects of descriptive peer norms on individual HED will be stronger when the reference group is the drinking group rather than the average university student of the same gender; H2)
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After controlling for perceived campus norms and actual group behavior, overestimation of drinking group HED will predict between- and within-person increases in individual HED; and H3) HED will occur most frequently among participants with both heavier drinking groups and larger overestimations of their drinking group norms (between-person effect) and at time points during which participants have both high group HED and high group overestimation scores relative to their average (within-person effect).
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parties and bars together and who usually meet up together to pre-drink before these events”)
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either on route to the campus pub during a student social drinking event (n = 12 groups) or via
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posters around campus (n = 15 groups).1 A more detailed description of recruitment procedures is available elsewhere (Citation removed for blind review, 2018).
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2.2. Participants
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A total of 27 (5 all-male, 10 all-female and 12 mixed-sex) drinking groups participated.2 Group size ranged from 3-7 members (M = 4.37, SD = 1.31). Participants (n = 118) were an
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average age of 19 years (SD = 1.49; range = 17 – 24 years). Participants included more women
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(60.2%) than men and the majority identified as White (85%), followed by Asian (3.7%), East Indian (3.7%) and Hispanic (1%).
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2.3. Procedure
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Participants completed three 30-minute online assessments at two-month intervals (lateNovember, January, and March). For each assessment, consent was obtained from participants
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electronically prior to survey commencement. Assessments included items assessing general demographic information followed by the measures described below. Participants were reimbursed with a $10 e-gift certificate for each assessment completed.
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Because it was important to recruit intact groups and because, in a minority of cases, there are non -drinking members of drinking groups (Demant & Järvinen, 2011), alcohol consumption was not an eligibility criterion for recruitment. Indeed, there was 1 member of 1 drinking group who was abstinent at all three time points. 2 Group membership was largely stable with only one participant leaving her group between the first and second assessment. In three additional groups, Time 3 survey data was missing for all members, thus we do not know whether or not these groups stayed intact.
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2.4. Measures 2.4.1. Heavy Episodic Drinking (HED). Participants reported on the number of days in the past 2 months they engaged in HED (consuming 4/5 drinks in one sitting for women/men, respectively). These criteria have been used extensively in the college drinking literature to
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define heavy episodic or “binge” drinking (Wechsler & Nelson, 2001). To measure the HED of
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HED (i.e., mean group HED, not including the participant).
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participants’ drinking groups, we calculated the average of their group members’ self-reported
2.4.2. Perceived Descriptive HED Norms. Participants estimated the number of days,
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on average, in the past 2 months that their group members and the average student on campus of
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the same gender engaged in HED. These items were used to measure perceived descriptive drinking group and campus norms, respectively.
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2.4.3. Covariates. We measured and controlled for gender (0 = male, 1 = female), age
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(grand-mean centered) and ethnicity (0 = non-white, 1 = white). We dichotomized ethnicity
2.5. Analytic Plan
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because the large majority of participants (85%) were white.
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To test hypotheses, we ran a taxonomy of multi-level growth curve models using the statistical program, HLM 7 (Raudenbush, Bryk, Cheong, Congdon, & Du Toit, 2011). The
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outcome variable for all models was number of HED days in the past 2 months. Because this is a count variable, we estimated hierarchical generalized linear models with Poisson probability distribution and natural log link function and accounted for overdispersion by estimating an overdispersion parameter. Within-person predictors of HED were entered at Level 1 and person mean centered, so that participants’ scores represent deviations from their own average. Between-person predictors were entered at Level 2 and grand mean centered, so we could examine the predictive power of
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participants’ average score relative to others in the study. Further, because participants were nested in drinking groups, we estimated a random Level 3 intercept. We first ran a model, which demonstrated that a linear growth function was most appropriate in modeling change in HED over time. Next, we ran a series of models testing H1.
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We first entered within- and between-person descriptive campus norms as predictors of HED.
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Then, we examined if these relations held once descriptive drinking group norms were entered
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into the model. Finally, a model was estimated to determine if effects varied over time; specifically, if between-person campus and group norms predicted the slope of HED.
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To test H2 and H3, we first calculated students’ overestimation as perceptions of group
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HED minus actual group HED (Ecker et al., 2017). We then ran a second set of multi- level growth curve models with overestimation of group HED as a predictor at the within- and
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between-person level (Model 1). Next, we controlled for actual group HED at both the within-
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and between-person levels (Model 2). In the following step (Model 3), we included perceived campus norms to evaluate unique effects of overestimating drinking group norms relative to
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perceived campus drinking norms. Finally, we allowed overestimation (within and between) to
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vary as a function of group HED (within and between) (Model 4). Cross-level interactions with time were also included so that we could predict the linear slope of HED.
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Attrition rates were 9% at Time 1, 17.7% at Time 2 and 37% at Time 3. Amount of missing data varied across drinking groups; In some groups, all members participated (n = 22, 19 and 9 groups at Time 1, 2 and 3 respectively), in others there was missing data for one or more members (n = 5, 8, and 15 at Time 1, 2, and 3 respectively), and at Time 3, there were 3 groups for which data were missing for all members. To account for this, we used multiple imputation (MI) with k = 50 datasets. MI uses a maximum likelihood procedure, based on relations among all existing data to predict missing values (Little & Rubin, 2002) and produces more plausible parameter estimates than listwise deletion.
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3. Results The number of days in the past two months that that participants perceived the average university student to have engaged in HED ranged from 9.12 (Time 2) to 10.54 (Time 1). The average number of days that participants perceived their drinking groups engaged in HED ranged
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from 6.42 (Time 3) to 7.40 (Time 1). Comparatively, drinking groups, on average, reported 5.07
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(Time 3) to 6.79 (Time 1) days of HED in the past 2 months. Results demonstrate that, on
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average, participants overestimated their drinking groups’ frequency of HED by at least 2 days at each time point (see Table 1).
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Table 2 presents a taxonomy of nested models predicting HED. Model 1 demonstrates a
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significant linear slope, with HED decreasing over time. Further, results revealed a significant
drinking group intercept of HED.
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random slope of HED over time, as well as both a significant between-person and between-
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3.1. Descriptive Drinking Group versus Campus Norms Predicting Individual HED Model 2 (Table 2) demonstrates significant between- and within-person effects of
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descriptive campus norms on individual HED. In line with H1, once descriptive drinking group
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norms were entered into the model (Model 3), the effects of campus norms were no longer significant. Further, both the between- and within-person effects of descriptive drinking group
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norms were significant predictors of HED. In the final model (Model 4), a cross-level interaction between time and descriptive campus norms revealed that only participants with low (-1 SD) descriptive campus norms experienced decreases in HED over time (B = -.02, t = -5.65, p < .01); Participants with high (+1 SD) descriptive campus norms remained high and stable in their HED over the school year (B = .00, t = 0.04, p = .97) (see Figure 1). Deviance tests (i.e., likelihood ratio tests similar to that of R2 difference tests in ordinary least squares regression) were significant for Models 2-4 (see Table 2). This means that for every subsequent model after Model 1, the model fit was significantly improved from the last.
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3.2. Overestimation of Drinking Group Norms Predicting Individual HED Model 1 (Table 3) demonstrates significant between- and within-person effects of overestimation of drinking group norms on individual HED. Consistent with H2, the effect of overestimation remains even after controlling for actual group HED (Model 2) and campus
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norms (Model 3). Further, H3 was not supported. The interaction between overestimation and
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actual drinking group HED was not significant at the within-person or between-person level.
was significantly improved from the last (see Table 3).
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4. Discussion
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Again, deviance tests demonstrated that for every subsequent model after Model 1, the model fit
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We extended research on descriptive drinking norms by focusing on the drinking group as a salient reference group in comparison to the typical university student. University students
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drink most often within intact drinking groups (Lange et al., 2011), yet minimal research exists
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on their role in drinking. Furthermore, we conducted the first study, to our knowledge, to tease apart between- and within-person effects of descriptive peer norms on university students’
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drinking. Notably, at time points when students’ perceptions of drinking group norms were
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higher than usual (within-person effect), they experienced an increase in the frequency of their own HED. Further, once this effect was entered into our model, the within-person effect of the
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typical university student, the reference group used in most normative feedback interventions, became non-significant. Moreover, to clarify the extent to which findings were due to students’ misperceptions of their drinking groups’ HED, we recruited the whole drinking group and compared actual versus perceived group drinking. Our study is the first to measure these misperceptions within the peer drinking group. Results demonstrate notable overestimation of drinking group HED, which predicted changes in members’ own frequency of HED, even when controlling for actual group
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behavior and campus norms. Results provide a more nuanced understanding of the role of the drinking group in university students’ alcohol consumption. For H1, we hypothesized that the effects of descriptive peer norms on individual heavy drinking will be stronger when the reference group is the drinking group rather than the typical
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university student. In support of H1, results of our main effects models demonstrated significant
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between- and within- person effects of perceptions of the typical university students’ HED on
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students’ own HED, which became non-significant once we accounted for perceptions of drinking group norms. Findings are consistent with past cross-sectional research suggesting that
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perceptions of close friends’ drinking are more strongly related to students’ own drinking as
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compared to perceived campus norms (Mallett et al., 2009; Yanovitzky et al., 2006). Yet, we should not be so quick to dismiss the predictive power of campus drinking norms. By adopting a
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longitudinal design, we gained a more nuanced picture of the relationship between this variable
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and student drinking, which is lost when examining main effects in cross-sectional studies. Even though the within-person effect of perceived campus drinking norms remained non-significant,
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we found a significant cross-level interaction, with between-person descriptive drinking campus
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norms predicting the slope of HED over time. The linear decrease in HED, noted in past research and partly due to the heightened number of drinking events (Frosh Week, Homecoming,
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Halloween) and decreased number of academic deadlines at the beginning of the school year (Tremblay et al., 2010), was only found for students with low descriptive campus norms. Findings suggest that the effect of campus drinking norms is largely between individuals. Thus, individual variation in campus norms may be less important when assessing individual HED. Rather, relative to other university students, those who, on average, tend to perceive stronger campus drinking norms appear at a greater risk of remaining high and stable in their drinking across the school year, even with mounting deadlines and final exams.
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Results further emphasize the salience of drinking group norms in university students’ drinking. Specifically, participants who, on average, tended to perceive their drinking groups participated in more HED tended to engage in more frequent HED themselves. Further, at time points when participants perceived their groups increased in their frequency of HED relative to
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average, they also increased in their own HED. It is important to note that the latter finding
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reflects time-specific deviation, rather than overall time-invariant averages, and suggests that
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fluctuations in students’ perceptions of their groups’ drinking can affect their own HED frequency.
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Further, even though perceptions of HED were lower, and, likely, less inflated, for the
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drinking group as compared to the typical student, overestimation of group HED remained a significant predictor of students’ own drinking, above and beyond campus norms. Consistent
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with H2, students who, on average, overestimated their groups’ HED more than others engaged
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in more frequent HED themselves. Additionally, at time points when students increased in their overestimation of their groups’ HED, they also increased in their own heavy drinking. These
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findings remained significant, even when controlling for how much the group actually drank.
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It is important for future research to examine why university students overestimate the drinking norms of their drinking groups, even though these groups are engaging in alcohol
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consumption together (Lange et al., 2011). One possibility is that when individuals are intoxicated, they are often more noticeable (e.g., uninhibited, loud, aggressive) than when they are sober or drinking moderately. Thus, instances of heavy group drinking may be more salient when participants reflect on how much their group drinks, encouraging overestimation. Relatedly, we have a tendency to attribute others’ behavior as more reflective of stable dispositional characteristics rather than external influences (the fundamental attribution error) (Ross, 1977). Therefore, group members who engage in memorable yet rare instances of HED due to contextual influences such as birthdays or campus-related drinking events (e.g.,
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Homecoming), might be perceived by peers as “heavy drinkers”, thus encouraging overestimation. H3 was not supported. We did not find that the most frequent HED occurred when both group drinking and members’ overestimation was high. In fact, actual group drinking was not a
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particularly robust predictor in our models when also considering students’ perceptions of peer
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drinking. This could mean that students’ perceptions rather than actual group modeling of heavy
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drinking are more important in determining individual behavior. Alternatively, it is possible that our measure of actual group drinking – the frequency of group HED – was not the best choice.
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We focused on HED because it is the most normative type of university drinking and the type
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most tied to negative drinking consequences (Borsari & Carey, 2001). However, future research could adopt a more comprehensive measure of drinking (e.g., frequency of alcohol use and HED,
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maximum number of drinks) to gain a clearer picture of group drinking patterns.
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Our results have important practical implications. They emphasize that perceived campus norms are a good screener for students at risk for more stable HED across the school year. They
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also suggest the importance of targeting students with heightened perceptions of campus
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drinking at the beginning of the school year for interventions to positively affect their HED trajectories over time. Interventions implemented at the beginning of the school year that involve
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a personalized normative feedback component have had success in reducing alcohol use (e.g., for incoming students) (Hustad, Barnett, Borsari, & Jackson, 2010). However, little is known about the impact of these interventions over longer periods, such as across the school year. Most notably, our results suggest that personalized normative feedback interventions would benefit from addressing students’ overestimation of their drinking groups’ norms, rather than campus norms only. For example, intervention participants could provide the contact information of their drinking group members, who would be sent a brief questionnaire on their frequency of alcohol use. This information could then be incorporated into target students’
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personalized feedback on perceived versus actual drinking group norms. Further, given that our within-person findings demonstrate that increases in overestimation of group drinking norms can occur at any point during the academic year, it is likely important for interventions to include booster sessions (Braitman & Henson, 2016; Mason, Benotsch, Way, Kim, & Snipes, 2014) to
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ward off associated increases in individual HED. Thus, intervention participants and their
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drinking group members could be contacted to complete questionnaires on their alcohol use at
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multiple points across the academic year and be sent personalized normative feedback via e-mail or text.
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Implications notwithstanding, this study contains limitations. We improved upon past
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research by conducting a longitudinal study, allowing us to isolate within-person variance and control for all between-person confounds. However, although our time effects were longitudinal,
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the effects of descriptive peer drinking norms were cross-sectional. Thus, we cannot claim
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causality in relations between descriptive drinking norms and individual HED. Further, this was a pilot project with a low n of 27 drinking groups (118 participants). Therefore, to improve
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statistical power, this study should be replicated with a larger sample size. Finally, results may
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not be generalizable to non-university attending young adults. To conclude, the present study demonstrates that, over and above campus descriptive
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norms and the actual drinking behavior of university students’ drinking groups, overestimation of group drinking is a powerful predictor of students’ heavy alcohol consumption. Moreover, overestimation fluctuates over the academic year, with increases accompanied by increases in students’ frequency of HED. Results stress the need for heightened attention on peer drinking groups and students’ perceptions of their group’s drinking in university alcohol interventions and prevention efforts.
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Table 1. Means and Standard Deviations for Number of Days of Heavy Episodic Drinking in the Last 2 Months Perceived
Perceived
Actual
Overestimation of
Campus HED
Drinking
Drinking
Drinking Group
Group HED
Group HED
HED 2.06 (4.01)
10.54 (5.95)
7.40 (4.60)
6.79 (3.58)
2 (Late-January)
9.83 (5.90)
7.40 (5.01)
6.16 (2.98)
3 (Late-March)
9.12 (4.74)
6.42 (3.66)
5.07 (2.91)
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M
AN
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1 (Late-November)
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Time
2.48 (3.99) 2.20 (2.81)
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Table 2. Multilevel Model with Perceptions of Peer HED Predicting Individual HED Model 1
Model 2
Model 3
Model 4
Fixed Effects: b (SE) L1 Predictors of HED Intercept
1.92 (.08)** 1.97 (.08)** 1.78 (.11)**
1.89 (.08)**
Linear Slope
-.13 (.04)**
-.12 (.03)**
.03 (.00)**
.02 (.01)
.02 (.01)*
.03 (.01)**
.03 (.01)**
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WP Descriptive Group Norms L2 Predictors of L1 Intercept
-.28 (.10)**
Age Ethnicity
-.01 (.04)
-.02 (.03)
10 (.08)
.01 (.09)
.03 (.08)
.07 (.01)**
.00 (.02)
-.02 (.02)
.11 (.03)**
.09 (.02)**
.03 (.01)**
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Random Effects:
-.08 (.03)*
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BP Descriptive Group Norms
BP Descriptive Campus Norms
-.17 (.09)
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BP Descriptive Campus Norms
L2 Predictors of Linear Slope
-.17 (.13)
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Gender
BP Descriptive Group Norms
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-.12 (.04)**
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WP Descriptive Campus Norms
-.12 (.04)**
.00 (.01)
Variance Component (SE)
.39 (.06)**
.34 (.05)**
.30 (.05)**
.27 (.05)**
L2 (between-person) Linear Slope
.07 (.02)**
.07 (.02)**
.06 (.02)**
.04 (.02)**
L3 (between-group) Intercept
.09 (.03)**
.12 (.03)**
.03 (.02)
.03 (.02)
--
69.58**
55.16**
11.27**
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Deviance test: 𝜒 2
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L2 (between-person) Intercept
Note: WP = within-person, BP = between-person. Model 1 is an unconditional growth model, with a random intercept and slope at Level 2 and a random intercept at Level 3 . Model 2 adds basic control covariates and the main effects of campus norms. Model 3 adds the main effects of drinking group norms. Model 4 adds the interactions between campus and drinking group norms. * p < .05, ** p < .01.
ACCEPTED MANUSCRIPT 17 Table 3. Multilevel Growth Curve Model with Drinking Group HED and Overestimation of Drinking Group HED Predicting Individual HED Model 1 Model 2 Model 3 Model 4 Fixed Effects: b (SE) L1 Predictors of HED 2.03 (.09)**
1.95 (.09)**
1.82 (.12)**
3.01 (.63)**
Linear Slope
-.15 (.04)**
-.12 (.05)*
-.10 (.06)
-.13 (.03)**
WP Overestimation
.03 (.01)**
.04 (.01)**
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Intercept
.02 (.00)**
.04 (.03)
.04 (.03)
.02 (.01)* .02 (.00)**
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.02 (.01)*
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WP Group HED WP Descriptive Campus Norms
.02 (.01)*
-.24 (.08)**
-.22 (.09)*
-.24 (.12)
-.11 (.05)
Age
-.07 (.04)
-.03 (.04)
-.02 (.04)
-.06 (.02)*
Ethnicity
.02 (.08)
.01 (.08)
.01 (.09)
.01 (.08)
.11 (.02)**
.13 (.02)**
.12 (.04)**
.03 (.02)
.06 (.03)*
.06 (.04)
-.33 (.09)**
.00 (.02)
-.04 (.00)**
WP Overestimation X Group HED
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L2 Predictors of Level 1 Intercept
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Gender
M
BP Overestimation BP Group HED
ED
BP Descriptive Campus Norms
.01 (.01)
BP Overestimation X Group HED
BP Group HED
-.02 (.01)* .01 (.01)
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BP Overestimation
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L2 Predictors of Linear Slope
.01 (.01)
BP Descriptive Campus Norms
.04 (.00)** -.01(.00)
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BP Overestimation X Group HED Random Effects:
Variance Component (SE) L2 (between-person) Intercept
.24 (.05)**
.30 (.05)**
.30 (.05)**
.24 (.05)**
L2 (between-person) Linear Slope
.06 (.02)**
.06 (.02)**
.06 (.02)**
.04 (.02)**
L3 (between-group) Intercept
.15 (.04)**
.05 (.02)*
.06 (.02)**
.09 (.03)**
--
7.88*
14.60**
15.93**
Deviance test: 𝜒 2
Note: WP = within-person, BP = between-person. Model 1 is a main effects model with overestimation of group HED and control covariates. It has a random intercept and slope at Level
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ED
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2 and a random intercept at Level 3. Model 2 adds main effects of actual group HED. Model 3 adds the main effects of descriptive campus norms. Model 5 adds the interaction between overestimation and actual group HED. * p < .05, ** p < .01.
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ACCEPTED MANUSCRIPT 23 Tremblay, P. F., Graham, K., Wells, S., Harris, R., Pulford, R., & Roberts, S. E. (2010). When Do First-Year College Students Drink Most During the Academic Year? An InternetBased Study of Daily and Weekly Drinking. Journal of American College Health, 58(5), 401-411. doi:10.1080/07448480903540465
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ACCEPTED MANUSCRIPT 25 Figure 1. Between-Person Descriptive Campus Norms as a Predictor of the Slope of Heavy Episodic Drinking over Time
Low Descriptive Campus Norms (-1 SD) High Descriptive Campus Norms (+1 SD)
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ACCEPTED MANUSCRIPT 26 Highlights
University students overestimated the heavy alcohol use of their peer drinking group
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Overestimation of group drinking was a powerful predictor of students’ own drinking
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Within-person increases in overestimation predicted increases in individual drinking
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Results held even when controlling for how much the group actually drinks
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Results also held when accounting for perceived campus drinking norms
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