Psychology & Health

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Direct and interactive effects of parent, friend and schoolmate drinking on alcohol use trajectories Alicia Doyle Lynch, Rebekah Levine Coley, Jacqueline Sims, Caitlin McPherran Lombardi & James R. Mahalik To cite this article: Alicia Doyle Lynch, Rebekah Levine Coley, Jacqueline Sims, Caitlin McPherran Lombardi & James R. Mahalik (2015) Direct and interactive effects of parent, friend and schoolmate drinking on alcohol use trajectories, Psychology & Health, 30:10, 1183-1205, DOI: 10.1080/08870446.2015.1040017 To link to this article: http://dx.doi.org/10.1080/08870446.2015.1040017

Accepted author version posted online: 27 Apr 2015. Published online: 20 May 2015. Submit your article to this journal

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Date: 02 November 2015, At: 11:47

Psychology & Health, 2015 Vol. 30, No. 10, 1183–1205, http://dx.doi.org/10.1080/08870446.2015.1040017

Direct and interactive effects of parent, friend and schoolmate drinking on alcohol use trajectories

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Alicia Doyle Lynch*

, Rebekah Levine Coley, Jacqueline Sims Lombardi and James R. Mahalik

, Caitlin McPherran

Lynch School of Education, Department of Counseling, Developmental, and Educational Psychology, Boston College, Chestnut Hill, MA, USA (Received 3 November 2014; accepted 7 April 2015) Objective: This study considered the unique and interactive roles of social norms from parents, friends and schools in predicting developmental trajectories of adolescent drinking and intoxication. Design and outcome measures: Using data from the National Longitudinal Study of Adolescent Health, which followed adolescents (N = 18,921) for 13 years, we used discrete mixture modelling to identify unique developmental trajectories of drinking and of intoxication. Next, multilevel multinomial regression models examined the role of alcohol-related social norms from parents, friends and schoolmates in the prediction of youths’ trajectory group membership. Results: Results demonstrated that social norms from parents, friends and schoolmates that were favourable towards alcohol use uniquely predicted drinking and intoxication trajectory group membership. Interactions between social norms revealed that schoolmate drinking played an important moderating role, frequently augmenting social norms from parents and friends. The current findings suggest that social norms from multiple sources (parents, friends and schools) work both independently and interactively to predict longitudinal trajectories of adolescent alcohol use. Conclusions: Results highlight the need to identify and understand social messages from multiple developmental contexts in efforts to reduce adolescent alcohol consumption and alcohol-related risk-taking. Keywords: adolescence; alcohol; developmental trajectories; social norms

Alcohol use during adolescence is associated with both immediate and long-term health-related consequences ranging from inhibited decision-making leading to engagement in delinquency and sexual risk behaviours, to the development of patterns of abuse and dependence that persist into adulthood (Grant & Dawson, 1997; Guo et al., 2002). Social norms within family, friend and school contexts are well documented as key predictors of adolescents’ decisions regarding alcohol use (e.g. Kumar, O’Malley, Johnston, Schulenberg, & Bachman, 2002; Lee et al., 2012; Martino, Ellickson, & McCaffrey, 2009; Yarnell, Brown, Pasch, Perry, & Komro, 2013). However, little work has simultaneously compared the concurrent roles of multiple social norm contexts to consider their unique and interactive influences in predicting youth alcohol use. In this *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

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paper, we draw on social learning and norm theories (Bandura, 1977; Cialdini & Trost, 1998; Perkins & Berkowitz, 1986), which suggest that adolescents are exposed to messages from various social contexts regarding the acceptability and desirability of alcohol use, to guide our hypotheses concerning alcohol use risk factors from family, friend and school contexts. The majority of research linking social norms to individual drinking has focused on aggregate measures of alcohol use. That is, researchers tend to average alcohol use across participants to examine a single point in time or longitudinal curve. However, person-centred analyses in the adolescent risk behaviour literature have suggested that alcohol use over time is not adequately described by a single line. This work implies that the longitudinal progression of adolescents’ drinking behaviours is varied and attempting to describe the course of adolescent drinking using a single intercept and slope likely provides an ‘average’ trajectory that is rarely observed in reality. As such, we use a discrete mixture modelling framework to operationalise adolescent alcohol use as a multitrajectory phenomenon based on research suggesting that during adolescence, there are four or five frequently observed longitudinal patterns or ‘trajectories’ of alcohol use (Chassin, Pitts, & Prost, 2002; Hill, White, Chung, Hawkins, & Catalano, 2000). In all, the current study combines and expands upon research that (1) links family, friend and school social norms to adolescent alcohol use and (2) highlights the conceptualisation of lifetime alcohol use as a multitrajectory phenomenon. Using data from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative sample of 7th–12th-grade students (who range in age from 12 to 21 at Wave 1), which has followed youth into their early 30’s through four interviews (N = 18,921), we first used a discrete mixture modelling framework to replicate previously identified trajectories of alcohol consumption and intoxication. Next, we considered the relations between family, friend and school norms and trajectory group membership. Family, friend and school social norms According to Social Learning Theory (Bandura, 1977), individuals learn by observing models (e.g. parents, friends, schoolmates, teachers and media sources) of various behaviours and their consequences. By observing these models, individuals develop cognitive representations that can be called on when making decisions regarding their own behaviours. For example, in social settings where models demonstrate that alcohol consumption is frequent, normative and socially desirable, adolescents may be more likely to develop cognitive representations that are favourable towards alcohol use. Bandura’s theory of modelling aligns with social norm theories (Ajzen & Fishbein, 1980; Cialdini & Trost, 1998; Perkins & Berkowitz, 1986), which further differentiate modelling behaviours into two primary categories of social norms – descriptive and subjective. Descriptive norms are derived from the actual behaviour of others; they guide individuals by providing information about ‘normal’ behaviour in social environments, and constrain behaviour by indicating what actions are considered deviant or off-limits (Cialdini & Trost, 1998). Subjective (or injunctive) norms represent an individual’s beliefs regarding what others will think about their behaviour and influence youth through a desire to conform with important others’ views (Ajzen & Fishbein, 1980). Descriptive norms guide behaviours through example, as ‘individuals are at least marginally aware of the existing

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norms and tend to act in accordance with them’ (Cialdini, Reno, & Kallgrean, 1998, p. 1016), while subjective norms guide behaviours through the introduction of formal or social sanctions. In the current analyses, we focus on descriptive norms from parents, friends and schoolmates. In line with prior research that considers the links between descriptive norms and alcohol use (Ali & Dwyer, 2010, Kumar et al., 2002), we operationalise descriptive norms as the normative behaviours surrounding alcohol use that a group (e.g. families, friends and schoolmates) displays. Parental social norms regarding alcohol use may take on a variety of forms – from parents’ modelling of their own alcohol-related behaviours to direct conversations regarding approval/disapproval of alcohol use. Parents’ reports of their own alcohol use have been associated with point-in-time estimates as well as longitudinal changes in adolescent alcohol use. Using a latent growth model approach, Duncan, Gau, Duncan, and Strycker (2011) found that parents’ reports of drinking predicted the longitudinal slope of adolescent alcohol use with higher levels of parental drinking during early adolescence related to increases in adolescent drinking over time (Duncan et al., 2011). Other studies of parental social norms have asked youth to describe the degree to which their parent(s) would approve or disapprove of their alcohol use. These perceptions of parents’ attitudes towards drinking have been associated with both abstinence from alcohol use and with the frequency of alcohol use among individuals who do engage (Mrug & McCay, 2012; Wood, Read, Mitchell, & Brand, 2004). Compared to parental norms, social norms, from friends have received the most attention in the literature with a large body of work documenting links between social norms from friends and individual drinking behaviours (e.g. Halim, Hasking, & Allen, 2012; Lee et al., 2012; Marshal & Chassin, 2000; Urberg, Goldstein, & Toro, 2005). In this literature, social norms are typically assessed by asking individuals to report on their friends’ drinking behaviours or by directly assessing friends’ drinking in a peer nomination format. In one such study, Lee and colleagues (2012) used a growth mixture model framework to identify four latent trajectories of alcohol abuse symptoms during the transition from adolescence into adulthood (ages 21–33). Social norms from friends during adolescence (assessed as individuals’ reports of their 3 or 4 best friends’ drinking from age 10 to 18) were positively associated with membership in a trajectory associated with high, sustained levels of alcohol abuse symptoms versus membership in the most favourable trajectory group, which demonstrated no abuse symptoms over time. School-level social norms in relation to alcohol use have received increasing attention in the literature over the last decade. The majority of work in this area aggregates responses from students within the same school to create measures assessing schoollevel alcohol use (a descriptive norm) and disapproval of alcohol use (a subjective norm) (Ali & Dwyer, 2010; Kumar et al., 2002; Yarnell et al., 2013). Using a nationally representative sample of high school students, Kumar and colleagues (2002) reported findings supporting the importance of school norms in predicting individual alcohol use. Results suggested that individual binge drinking was positively related to schoollevel average alcohol consumption and negatively related to school-level disapproval of alcohol use. When comparing the role of social norms from friends to social norms from schoolmates, Ali and Dwyer (2010) found that both nominated peers and schoolmates influenced youths’ decisions to engage in drinking, but schoolmates’ drinking behaviours additionally predicted the intensity of individual alcohol use.

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Interactions among family, friend and school norms While there is empirical support for the idea that social norms from several sources can concurrently predict individual behaviours, there is also emerging evidence to suggest that social norms from parents, friends and schools may moderate one another to produce protective or even additive effects. For example, norms from friends that favour drinking may be counteracted by norms from parents that discourage adolescents’ alcohol use. On the other hand, if youth experience parallel messages from multiple sources (e.g. social norms from both parents and friends that suggest alcohol use is normative and acceptable), the significance of these norms may be amplified. In other words, exposure to multiple social norms that are in agreement may have a stronger relationship with adolescent alcohol use than exposure to norms whose messages conflict. Using a sample of over 20,000 adolescent students, Mrug and McCay (2012) found that parent and friend social norms that were favourable towards alcohol use predicted increases in adolescent drinking above and beyond the individual effects of parental and friend social norms. Similarly, Urberg and colleagues found additional evidence that social norms from friends and parents may concurrently contribute to adolescent drinking in a sample of high-risk shelter adolescents and a comparison group (e.g. Urberg et al., 2005). There is no research to our knowledge, however, that assesses how broader social forces, such as school-wide norms, may moderate the role of more proximal social forces such social norms from friends and parents. A person-centred approach for measuring adolescent alcohol use The majority of work using a person-centred approach to study adolescent alcohol use has utilised latent trajectory analyses (e.g. latent growth mixture models in MPlus, PROC TRAJ in SAS) to identify distinct, commonly occurring trajectories of drinking behaviour. Following the identification of longitudinal trajectories of alcohol use, several studies have taken the next step of associating predictors and correlates with trajectory group membership, providing external validity supporting the utility of considering alcohol use from a person-centred perspective. While there is a large body of literature offering empirical and theoretical support for considering longitudinal alcohol use as a multitrajectory construct, there has also been recent criticism of this approach (e.g. Bauer & Curran, 2003; Sher, Jackson, & Steinley, 2011). These criticisms of the person-centred approach suggest that while both theory and empirical findings highlight the probability that there are distinct manifestations of alcohol use over the lifetime, it is necessary to avoid reification of these trajectories as concrete, unvarying entities and instead consider these groups as representative of descriptive, gross patterns of alcohol use. Furthermore, results should be interpreted in light of the characteristics of the sample – the population it is intended to represent (e.g. high-risk sample, nationally representative, etc.) and the time period covered (e.g. early to late adolescence, mid-adolescence to early adulthood, etc.). Present study The current study aims to expand prior literature examining the relationship between social norms from family, friends and schools and adolescent alcohol-related behaviours

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using a person-centred approach to assess alcohol use over time. It is hypothesised that social norms that endorse alcohol use will simultaneously predict membership in ‘problematic’ trajectory groups, while social norms suggesting disapproval of alcohol use will predict membership in ‘favourable’ trajectory groups. Further, social norms regarding drinking are hypothesised to moderate one another such that the presence of promotive social norms of alcohol use from multiple contexts will have an additive effect on youth drinking behaviours and that the presence of conflicting social norm information from different contexts may work to counteract one another.

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Methods Sampling and data collection Data for the present study were drawn from the in-home survey sample of the National Longitudinal Study of Adolescent Health (Add Health), a longitudinal survey of a nationally representative school-based sample of adolescents in the US. Add Health began in 1994 and assessed an in-school sample of 11–21-year-old students in grades 7–12 (N = 90,118) and school administrators (N = 143) from 144 schools using a selection procedure that ensured the resulting school sample was representative of the US in regard to region of country, urbanicity, size of school, type of school and racial/ethnic concentrations of the school. As part of the in-school survey, youth completed a peer nomination questionnaire, which asked individuals to identify their 10 closest friends (five female friends and five male friends). If youth identified friends that also participated in Add Health, analysts are able to link an individual’s questionnaire to their friends’ questionnaires. Following completion of the in-school questionnaires, schools were stratified by grade and sex. Approximately 17 students were randomly chosen from each strata within each school, resulting in a sample of N = 20,745 participants who were selected to complete in-home surveys. This in-home subsample was interviewed over four waves in 1995, 1996, 2001/2 and 2007/8, with response rates of 79, 88, 77 and 80%, respectively. In addition, 85% of Wave 1 in-home participants also had a parent who completed questionnaires containing both parent- and youth-specific components. Youth averaged 15.43 (SD = 1.59) years at Wave 1 and 29.10 (SD = 1.76) years at Wave 4. The current analytic sample included all youth participating in the in-home surveys with valid survey weights and school IDs, resulting in a final analytic sample of 18,921 youth from 132 schools. Measures Drinking and intoxication Youth drinking behaviours were measured through self-report at Waves 1 through 4 using audio computer-assisted self-interviewing. In two separate items, youth reported (1) the number of days they drank alcohol and (2) the number of days they were drunk or ‘very, very high’ on alcohol during the past 12 months. Responses were scored on a 7-point Likert scale that ranged from 0 (never) to 6 (every day or almost every day). In order to use a true count variable in the trajectory analysis, values were recoded to indicate the days per month that youth drank alcohol.

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Social norms Social norm variables were derived from parent, friend and schoolmate reports. Social norm variables were coded such that higher scores indicated norms that were more accepting or promoting of drinking activities. Parent Drinking was assessed using a scale ranging from 1 (never) to 6 (nearly every day) and was coded as the highest value of either mother or father drinking at Wave 1. To assess Friend Drinking, we used the peer nomination questionnaire to derive drinking behaviour scores directly from youth’s friends’ surveys. Friends’ responses to the previously described ‘Drinking’ measure, which asked ‘During the past 12 months, on how many days did you drink alcohol?’, were recoded to reflect the number of days per month that friends drank. These scores were then averaged to create a single item assessing friend drinking. Lastly, Schoolmate Drinking was assessed using reports from all youth who participated in the in-school survey. Students’ responses to the question previously described in the ‘Drinking’ measure were aggregated within schools to delineate the average number of days drank per month by students in each school. Covariates A variety of youth, family and school characteristics were included in the present analyses due to their associations with social norms and drinking behaviours in prior literature. Youth characteristics included a continuous measure of age in years at Wave 1, a dichotomous gender variable (1 = male), a series of race/ethnicity dummy indicators (with White used as the omitted referent) and immigrant status (1 = US born). Additionally, youth’s marital status (1 = ever married, 0 = never married) and college experience (1 = attended college, 0 = no college) were assessed at Wave 4 with two dichotomous indicators. Family covariates were derived from both youth and parent reports at Wave 1 and included indicators denoting whether the youth’s biological father and biological mother lived in the household as well as whether the primary caregiver was previously married or never married (with currently married used as the omitted referent). A series of dichotomous variables denoted parents’ highest level of education derived from parent report with high school education serving as the referent group. Family incometo-needs was derived from parental reports of household income and the number of individuals living in the household. The variable was continuously coded to represent the family’s income in relation to the federal poverty line. Lastly, school-level covariates from administrator reports denoted whether the school was public (versus private/ religious) as well as whether the school included high school grades, where students range in age from approximately 14–18 versus only grades 6, 7 and/or 8, where students range in age from approximately 11–13. Regions, denoted as Western, Midwestern, or Northeastern (versus Southern), and urbanicity, denoted as urban or rural (versus suburban), also were assessed. Analytic technique We first identified developmental trajectories of alcohol use for drinking and intoxication using the discrete mixture model add-on (PROC TRAJ) in SAS 9.0 (Nagin, 2005). Once the final model solutions were identified in SAS, trajectory group membership

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was exported to MPlus 6.0. Drinking group membership and intoxication group membership were then modelled separately as multinomial outcomes in multilevel models with students (Level 1) nested within schools (Level 2). Sampling weights were used to account for the complex sampling design of Add Health. Missing data were handled using the maximum likelihood estimation procedure available in MPlus. Results

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Descriptive results Table 1 presents weighted descriptives of the sample. Just under 50% of the participants were males and 27% of youth were racial/ethnic minorities. The vast majority of youth (96%) were US born. Parental educational attainment averaged some training past high school, with 43% of youth having parents with a high school degree or less and 30% of youth having a parent with a bachelor’s degree or better. The vast majority of schools that youth attended were public schools (90%), and just over half (57%) of schools had high school grades (above grade 8). The majority of youth resided in suburban areas (55%). Identifying longitudinal trajectories of alcohol use Commonly occurring trajectories of alcohol use over time were identified separately for drinking and intoxication. For each model, we first identified the appropriate number of trajectory groups using the model BIC and examination of the size of the trajectory groups with the stipulation that each group should contain at least 5% of the sample (Nagin, 2005). Results suggested that the BIC continuously increased with the addition of each trajectory group (BIC3 = −147,360, BIC4 = −137,317, BIC5 = −134,315 and BIC6 = −132,172). However, when the number of groups exceeded five, the size of the smallest group fell below 5%, thus the 5-group solution was selected as our final model. The five-group solution we found also replicates previous analyses of alcohol use behaviour that suggests the presence of four- or five-trajectory groups among adolescent samples (e.g. Chassin et al., 2002; Guo et al., 2002; Tucker, Ellickson, Orlando, Martino, & Klein, 2005). The same procedures were applied to our analyses of the frequency of intoxication. A review of model BICs suggested that the four- or six-group solution (BIC3 = −83,217, BIC4 = −78,429, BIC5 = −76,538 and BIC6 = −76,325) provided the best fitting models. However, the six-group solution had very little representation in the smallest group (3.29%) and had several groups with very large standard errors, suggesting a lack of estimation stability. As such, the four-group solution was selected. After identifying the appropriate number of trajectory groups, the individual trajectory lines were further specified by systematically testing non-linearity to determine whether each line was flat, quadratic, cubic or quartic. Changes in the model BIC, the size of trajectory groups and examination of predicted probabilities of group membership were used as criteria to determine the shape of each trajectory. The final model results for the trajectories of drinking suggested a mix of flat, quadratic, cubic and quartic lines with the smallest trajectory group containing 6.4% of the sample and predicted probabilities ranging from .77 to .98. Final results of the intoxication trajectories

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Table 1. Sample descriptives (N = 18,921).

Dependent variable Days drinking Days intoxicated Youth social norms Parent drinking Friend drinking School social norms Schoolmate drinking Youth covariates Male Age White African-American Hispanic Asian Multiracial and other US born Married Youth attended college Father in household Mother in household Parent previously married Parent never married Parent < 8th grade Parent < high school Parent high school Parent some college Parent college degree or more Family income-to-needs School covariates Public Private School has high school grades Urban area Suburban area Rural area West region Midwest region Northeast region South region

Wave 1

Wave 2

Wave 3

Wave 4

1.07 (3.18) .57 (2.35)

1.41 (3.54) .90 (3.16)

3.90 (5.5) 1.52 (3.26)

4.15 (5.71) 1.15 (2.95)

2.49 (1.46) 1.55 (2.49)

– –

– –

– –

1.42 (.72)

–

–

–

46.17 15.43 (1.59) 72.67 11.29 8.69 2.52 4.83 96.01 – – 75.64 97.02 20.21 4.34 3.44 8.55 31.22 30.41 26.38 2.98 (2.16)

– 16.42 (1.62) – – – – – – – – – – – – – – – – – –

– 21.86 (1.64) – – – – – – – – – – – – – – – – – –

– 29.10 (1.76) – – – – – – 47.90 14.72 – – – – – – – – – –

90.63 9.37 57.03 29.69 55.47 14.84 21.09 21.88 15.63 41.40

– – – – – – – – – –

– – – – – – – – – –

– – – – – – – – – –

Note: Mean (standard deviation) or percentage is reported in each cell.

suggested all trajectories were quartic with the smallest group comprising 5.1% of the sample and predicted probabilities ranging from .78 to .98. Following the identification of the appropriate trajectory group models in SAS, two multinomial variables describing trajectory group membership for drinking and intoxication were created. Figure 1(a) presents the drinking trajectories. The majority of the sample (39%) demonstrated membership in the Abstain trajectory group (Group 1, N = 7379) who consistently reported never drinking across all four waves of the study. The second largest group, the Developmental Average group (Group 2, N = 4919), represented 26%

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Days Drinking per Month

18 16 14 12 10 8 6 4 2 0 14

15

16

17

18

19

20

21

22

23

24

25

26

27

28 29

30

31

Group 1: Abstainers (39.1%)

Group 2: Developmental Average (26%)

Group 3: 2-4 Days/Month (15.5%)

Group 4: Steep Growth (13.1%)

Group 5: Heavy Decliners (6.4%)

Figure 1a. Predicted average scores at each age of the number of days drank alcohol in the past month for the five identified trajectory groups.

12

Days Intoxicatied Per Month

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13

10 8 6 4 2 0 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Age Group 1: Abstainers (65.5%)

Group 2: Developmental Average (23.3%)

Group 3: Steep Growth (5.1%)

Steep Growth & Decline (6.1%)

Figure 1b. Predicted average scores at each age of the number of days intoxicated in the past month for the four identified trajectory groups.

of the sample and was marked by patterns of drinking that aligned with the arithmetic averages of drinking at each Wave of the sample (see Table 1). This group went from never drinking during high school to consuming alcohol approximately 5 days/month by age 31. Sixteen per cent of the sample drank alcohol 2–4 Days/Month (Group 3, N = 2933) throughout the duration of the study. Thirteen per cent of the sample showed

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Steep Growth (Group 4, N = 2479) in the frequency of drinking over time. These individuals consumed alcohol fewer than 2 days/month in high school but demonstrated rapid increases in the frequency of drinking that peaked at approximately 16 days/month at age 31. The smallest group, representing 6% of participants, evidenced a Heavy Decline (Group 5, N = 1211) in drinking from early adolescence through early adulthood. Trajectories for intoxication frequency are presented in Figure 1(b). Similar to the drinking trajectory groups, the majority of the sample (65%) were those belonging to the Abstain (Group 1, N = 12,393) who reported never being intoxicated from early adolescence through early adulthood. Twenty-three per cent of the sample reflected the Developmental Average (Group 2, N = 4409) frequency of intoxication, with patterns of intoxication that aligned with the arithmetic averages of intoxication at each Wave of the sample (see Table 1). Five per cent of participants showed Steep Growth (Group 3, N = 965) in intoxication over time, reporting being intoxicated approximately 1 day/month in early adolescence followed by rapid increases in the frequency of intoxication that reached approximately 12 days/month at age 23. The final intoxication group, representing 6% of the sample, showed Steep Growth and Decline (Group 4, N = 1154) in their intoxication frequency – a pattern marked by a peak rate of intoxication of 10 days/month in high school that quickly declined to almost never being intoxicated by early adulthood. Parent, friend and school social norms and trajectories of adolescent alcohol use Multilevel multinomial models with students (Level 1) nested within schools (Level 2) were used to predict trajectory group membership for drinking and intoxication separately in MPlus 6.0. Sampling weights were used to adjust for Add Health’s complex sampling design and to generate nationally representative estimates. In Tables 2 and 3, we present findings for the drinking and intoxication trajectory models, respectively. The tables include coefficients as well as relative risk ratios (RRRs), which are similar to odds ratios and are derived from the exponentiated coefficients. The RRRs represent the increase in the likelihood of being in a particular trajectory group given a one unit increase in the predictor variable. Holding all other variables in the model constant, the RRR compares (1) the baseline risk of being in a particular trajectory versus the omitted referent group and (2) the revised risk of being in a particular group versus the omitted referent group that is associated with a one unit change in a particular covariate. An RRR of 1 indicates that there is no change in the baseline risk with a one unit change in the predictor, while an RRR greater than 1 indicates that the risk of being in a particular group relative to the omitted referent has increased with the addition of the predictor. An RRR less than 1 indicates that the risk of being in a particular group relative to the omitted reference group has decreased with a one unit increase in the predictor. Drinking trajectories The first multinomial multilevel model predicting drinking trajectory group membership (see Table 2) used the largest trajectory group (Group 1: Abstain) as the omitted reference group. Differences among the remaining trajectory groups were determined by rerunning the analyses using a different omitted referent each time. Results suggested

Model 1a: base model Social norm variables Monthly parent drinking Monthly friend drinking Monthly schoolmate drinking Individual covariates Age Male African-American Asian Hispanic Multiracial or other US born Youth ever married Youth college Family covariates Family income-to-needs Parent < 8th grade Parent < high school Parent some college Parent college or more Parent previously married Parent never married Father in household Mother in household .02 .02 .09 .02 .05 .10 .14 .09 .13 .12 .06 .07 .02 .11 .10 .07 .09 .07 .12 .08 .12

.10 .03 .02

−.05 .44 −.69 −.51 −.13 −.24 .28 −.51 .07

.09 −.41 −.28 .05 .19 .14 .03 −.07 −.24

−.11 .17 −.90 −.75 .07 −.05 .34 −.28 −.07 .06 −.31 −.16 .14 .01 .17 .25 .13 .02

1.09**4 .66** .76* 1.054 1.21*4 1.15+ 1.034 .935 .79+35

.16 .12 .26

.95*345 1.55**345 .50**45 .60**4 .884 .79+4 1.32*5 .60**34 1.07

1.11**345 1.03345 1.02345

.02 .15 .12 .07 .11 .10 .16 .08 .11

.03 .07 .09 .13 .12 .12 .18 .07 .10

.02 .02 .09

SE

1.06**4 .73* .85 1.15*4 1.014 1.19+ 1.28 1.14 1.022

.90**25 1.19*245 .41**4 .47** 1.074 .954 1.40+5 .76**24 .93

1.17**245 1.13**245 1.30**2

RRR

Coef

RRR

Coef

SE

Group 3: 2–4 days/month (N = 2933)

Group 2: developmental average (N = 4919)

.13 −.32 −.45 .32 .43 .23 .46 .07 −.04

−.12 1.18 −1.24 −1.16 −.49 −.56 .61 −.93 −.04

.23 .08 .17

Coef

.02 .17 .11 .08 .10 .10 .18 .09 .16

.03 .07 .11 .35 .11 .21 .18 .08 .11

.02 .02 .11

SE

1.14**235 .73+ .64** 1.38**23 1.54**23 1.26* 1.58*2 1.07 .96

.89**25 3.25**235 .29**23 .31**2 .61**235 .57*23 1.84**5 .39**235 .96

1.26**23 1.08**235 1.1925

RRR

Group 4: steep growth (N = 2479)

.08 −.03 −.19 .11 .00 .23 .36 .33 .33

.09 .89 −1.01 −.96 −.02 −.23 1.18 −.52 −.18

.25 .17 .40

Coef

.02 .20 .16 .12 .15 .11 .19 .12 .19

.03 .09 .13 .32 .13 .22 .21 .10 .15

.03 .02 .10

SE

(Continued)

1.08**4 .97 .83 1.12 1.004 1.26* 1.43+ 1.39*2 1.39+5

1.09**234 2.44**234 .36**2 .38** .984 .79 3.25**234 .59**4 .84

1.28**23 1.19**234 1.49**24

RRR

Group 5: heavy decline (N = 1211)

Table 2. Summary of coefficients and standard errors for model predicting days drinking per month trajectory group membership with Group 1 (Abstain, N = 7379) omitted (N = 18,921).

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(Continued).

−.16 −.05 −.21 −.02 −.11 .01 −.05

.99 1.00 1.00

.02 .03 .03

.01 −.05 −.09

.16 .14 .26

−.09 −.02 −.29 .25 −.06 .14 .03

.02 .03 .03

.02 .02 .10

.17 .13 .09 .13 .11 .11 .11

Notes: Coef – Coefficient; SE – standard error; RRR – relative risk ratio. + p < .10; *p < .05; **p < .01. Within each row, superscripts indicate differences within groups at the p < .05 level.

1.10**345 1.03345 1.0135

.85 .95 .81+35 .983 .9035 1.0135 .9535

.02 .02 .09

.18 .11 .10 .13 .11 .11 .14

SE

1.01 .95+4 .92**4

1.18**245 1.14**345 1.30*2

.91 .98 .75**2 1.2825 .9424 1.152 1.032

RRR

Coef

RRR

Coef

SE

Group 3: 2–4 days/month (N = 2933)

Group 2: developmental average (N = 4919)

Model 1b: Social norm interactions Main effect terms Parent drinking .09 Friend drinking .03 Schoolmate drinking .01 Interaction terms Parent × friend drinking −.01 Parent × school drinking .00 Friend × school drinking .00

School controls Public Rural Urban Has high school grades West Midwest Northeast

Table 2.

.00 .03 .00

.23 .08 .13

−.22 −.02 −.10 .15 .34 .25 .13

Coef

.02 .03 .04

.02 .02 .18

.21 .14 .11 .14 .12 .14 .15

SE

1.00 1.033 1.003

1.25**23 1.08**235 1.14

.80 .98 .90 1.16 1.40*3 1.28+ 1.14

RRR

Group 4: steep growth (N = 2479)

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.01 −.05 −.02

.24 .18 .38

−.16 −.06 .02 .22 .24 .35 .26

Coef

.02 .04 .05

.03 .02 .11

.17 .10 .09 .11 .09 .11 .11

SE

1.01 .96 .98

1.28**23 1.20**234 1.46**2

.85 .94 1.022 1.253 1.27*24 1.42**2 1.30*2

RRR

Group 5: heavy decline (N = 1211)

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Table 3. Summary of coefficients and standard errors for model predicting days intoxicated per month trajectory group membership with Group 1 (Abstain, N = 12,393) omitted (N = 18,921). Group 2: developmental average (N = 4409)

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Coef Model 1a: base model Social norm variables Monthly parent drinking Monthly friend drinking Monthly schoolmate drinking Individual covariates Age Male African-American Asian Hispanic Multiracial or other US born Youth ever married Youth college Family covariates Family income-to-needs Parent < 8th grade Parent < high school Parent some college Parent college or more Parent previously married Parent never married Father in household Mother in household School controls Public Rural Urban Has high school grades West Midwest Northeast

SE

RRR

.13 .02 1.14** .09 .02 1.09**34 .17 .06 1.18**4 −.06 .61 −1.38 −.93 −.28 −.21 .53 −.55 −.11

Group 3: steep growth (N = 965) Coef

SE

RRR

.12 .03 1.13** .13 .02 1.14**2 .27 .13 1.31*

.02 .94*4 −.07 .03 .94+4 34 .06 1.83** 1.22 .11 3.40**24 .08 .25**34 −1.01 .18 .36**2 .14 .39** −.86 .79 .42 .09 .75**4 −.20 .18 .82 .11 .81+4 −.26 .21 .77 .09 1.69**3 1.51 .39 4.54**2 3 .08 .58** −1.24 .12 .29**24 .08 .90 −.47 .18 .62* 1.09**4 .64 .79 1.254 1.174 1.17 1.28 1.10 1.10

.05 −.14 −.15 .16 .22 .27 .16 −.10 −.15

.01 .12 .09 .06 .07 .08 .13 .09 .12

1.06** .87 .87+ 1.17*4 1.25**4 1.31** 1.18 .914 .864

.08 −.45 −.24 .22 .15 .15 .25 .10 .09

.02 .30 .19 .14 .15 .17 .25 .15 .19

−.12 .02 −.21 .02 .09 .20 .00

.10 .06 .07 .08 .08 .07 .10

.89 1.02 .81** 1.02 1.09 1.22** 1.00

−.26 −.20 −.17 −.26 .08 .10 −.19

.24 .77 .14 .82 .16 .85 .18 .77 .15 1.08 .16 1.10 .19 .83

Model 1b: social norm interactions Main effect terms Parent drinking .13 Friend drinking .08 Schoolmate drinking .17 Interaction terms Parent × friend drinking .03 Friend × school drinking .03 Parent × school drinking −.06

.02 1.14** .02 1.09**34 .06 1.18**4

.12 .03 1.13** .13 .02 1.14**2 .25 .14 1.28+

.01 1.03* .03 1.034 .03 .95+

.02 .02 1.02 .02 .04 1.02 .01 .05 1.01

Notes: Coef – Coefficient; SE – standard error; RRR – relative risk ratio. + p < .10; *p < .05; **p < .01. Within each row, superscripts indicate differences within groups at the p < .05 level.

Group 4: steep growth and decline (N = 1154) Coef SE

RRR

.19 .03 1.21** .15 .02 1.16**2 .37 .10 1.45**2 .04 .79 −.81 −.87 .23 .24 .71 −.32 −.37

.04 .08 .14 .19 .15 .16 .23 .12 .19

1.0423 2.19**23 .44**2 .42** 1.262 1.272 2.04** .72*3 .69+

.00 .07 −.05 −.16 −.39 .11 .17 .36 .44

.03 .21 .13 .11 .19 .11 .21 .12 .15

1.003 1.07 .95 .8523 .68*23 1.11 1.19 1.44**2 1.55**2

−.30 .04 −.43 −.04 −.05 .12 −.15

.16 .74+ .15 1.04 .13 .65** .15 .96 .13 .95 .14 1.13 .15 .86

.18 .03 1.19** .16 .02 1.18**2 .39 .10 1.48**2 .04 .02 1.04* −.04 .03 .96*2 .01 .04 1.01

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that several covariates uniquely predicted trajectory group membership. Notably, females were consistently more likely than males to be in the least problematic, Abstain (Group 1) trajectory than any other trajectory and less likely than males to be in the 2–4 Days/Month (Group 3) and the problematic Steep Growth (Group 4) group than any other group. African-American and Asian-American youth were more likely to be in the Abstain group than White youth as were youth who were married by Wave 4. Family income-to-needs consistently predicted a lower probability of being in the Abstain group and a higher probability of being in the problematic Steep Growth (Group 4) trajectory versus any other group, suggesting an association between higher family income and unfavourable patterns of drinking. The primary variables of interest, the parent, friend and school social norms demonstrated several notable patterns in the prediction of drinking trajectory group membership. Figure 2(a) displays the changes in the predicted probability of drinking trajectory group membership associated with changes in social norms from parents, friends and schoolmates. Youth who experienced social norms from parents and friends that endorsed alcohol use were less likely to be in the Abstain group than any other group. In particular, parent drinking seemed to confer an increased risk for the most problematic patterns of alcohol use. A one unit increase in parent drinking raised the likelihood of being in both the Steep Growth (Group 4) and Heavy Decline (Group 5) trajectories, which were marked by excessive adolescent or early adult alcohol use, by 25 and 28%, respectively. To a lesser degree, parent drinking also increased the risk that youth would be in the less problematic Developmental Average (Group 2; RRR = 1.09, p < .01) or 2–4 Days/Month (Group 3, RRR = 1.15, p < .01) trajectories versus the Abstain group. Parent drinking similarly increased the likelihood of being in the Steep Growth (Group 4) and Heavy Decline (Group 5) trajectories, both of which display periods of excessive alcohol use, versus the 2–4 Days/Month (Group 3) and Developmental Average (Group 2) trajectories, which are both marked by drinking no more than 2–6 days/month. 0.4

Probability of Group Membership

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0.35 0.3

Trajectory 1: Abstain

0.25 Trajectory 2: Developmental Average

0.2

Trajectory 3: 2-4 Days/Month 0.15 Trajectory 4: Steep Growth 0.1 Trajectory 5: Heavy Decline

0.05 0

Baseline

1 SD Increase 1 SD Increase 1 SD Increase Friend Parent Schoolmate Drinking Drinking Drinking

Figure 2a. Predicted probabilities of drinking trajectory group membership in relation to one standard deviation unit increases in parent, friend and schoolmate drinking.

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0.7

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0.5

Membership

Probability of Trajectory Group

0.6

Trajectory 1: Abstain 0.4 Trajectory 2: Developmental Average

0.3

Trajectory 3: Steep Growth 0.2 Trajectory 4: Steep Growth & Decline 0.1

0 Baseline

1 SD Increase 1 SD Increase 1 SD Increase Friend Parent Schoolmate Drinking Drinking Drinking

Figure 2b. Predicted probabilities of intoxication trajectory group membership in relation to one standard deviation unit increases in parent, friend and schoolmate drinking.

Replicating findings from our measure of parent drinking, the presence of friend social norms that endorse alcohol use meant youth were less likely to be in the Abstain (Group 1) versus any other group. A more nuanced look at results further suggested that social norms from friends were most influential in predicting early alcohol use. Specifically, friend drinking was associated with the greatest risk for being in the Heavy Decline (Group 5) group, which is defined by frequent drinking during early adolescence that rapidly transitions to lower levels of use (less than 4 day/month) during early adulthood. Similarly, friend drinking also predicted a higher probability of membership in the 2–4 Days/Month (Group 3) (versus Developmental Average (Group 2) and Steep Growth (Group 4), which, like the Heavy Decline group, is defined by early onset alcohol use. School social norms made fewer distinctions among the trajectory groups but did follow a similar pattern to the friend social norms in predicting early onset of alcohol use with school drinking predicting an increased probability of being in the 2–4 Days/ Month (Group 3) and Heavy Decline (Group 5) versus the Abstain (Group 1) or Developmental Average (Group 2) trajectories. In fact, a one unit increase in schoolmate drinking increased the youth’s risk of being in the Heavy Decline (Group 5) by 49% and the risk of being in the 2–4 Days/Month (Group 3) by 30% versus the Abstain (Group 1) trajectory. Intoxication trajectories Our second model predicting intoxication trajectory group membership (see Table 3) used the largest trajectory group (Abstain (Group 1)), which comprised 66% of the sample, as the omitted reference group. By rerunning the analyses and changing the omitted

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reference group each time, we were able to identify significant differences among all other groups. Findings in relation to the covariates included in the model followed similar patterns to those identified in the model predicting drinking trajectories. Females (vs. males) and youth who were married by Wave 4 were more likely to be in the Abstain (Group 1) than any other trajectory. African-American youth were more likely than White youth to be in the Abstain (Group 1) trajectory. Youth born in the US (vs. immigrant youth) were much less likely to be in the Abstain (Group 1) trajectory than any other group. In regard to social norms, Figure 2(b) displays the changes in the predicted probability of drinking trajectory group membership associated with changes in social norms from parents, friends and schoolmates. Parent drinking was associated with a lower likelihood of being in the Abstain (Group 1) trajectory, but did not further distinguish among membership in the remaining three trajectory groups. Friend drinking also was associated with a decreased likelihood of being in the Abstain (Group 1). In addition, youth whose friends displayed social norms endorsing alcohol use demonstrated an increased risk of being in the Developmental Average (Group 2) trajectory and the Steep Growth and Decline (Group 4) trajectory, which is marked by frequent intoxication during early adolescence that quickly tapers off in young adulthood. Finally, schoolmate drinking also predicted intoxication trajectory membership, with higher levels of schoolmate drinking predicting a decreased likelihood of being in the Abstain (Group 1). Similar to our findings in regard to friend norms, schoolmate drinking was associated with an increased risk of being in the Steep Growth and Decline (Group 4) trajectory versus the Developmental Average (Group 2) trajectory, suggesting a link between schoolmate drinking and rates of intoxication that sharply increase throughout adolescence and decline in early adulthood.

Interactions among parent, friend and school social norms The second set of hypotheses that we tested focused on the possibility that parent, friend and school social norms may moderate one another with concordant social norms potentially amplifying one another’s effects and discordant norms serving to counteract each other. These hypotheses were tested by adding interaction terms between each of the social norms (Parent × Friend; Parent × School; and Friend × School) to the models. Drinking Results demonstrated that interactions between friend and schoolmate drinking and parent and schoolmate drinking predicted membership in the problematic Steep Growth (Group 4) trajectory, which is marked by a rapid acceleration in drinking frequency that begins around age 15, versus the 2–4 Drinks/Month (Group 3) trajectory, which depicts drinking 2–4 days/month from early adolescence through early adulthood. As demonstrated in Figure 3(a), these interactions suggest that when frequent drinking among friends or parents is coupled with frequent drinking among schoolmates, the risk for membership in the Steep Growth trajectory is further exacerbated.

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Friend Drinking 1 SD Above Mean

Trajectory 4

0.35

Friend Drinking 1 SD Below Mean Parent Drinking 1 SD Above Mean

0.3

Parent Drinking 1 SD Below Mean

0.25

0.2 Schoolmate Drinking 1 SD Below Mean

Schoolmate Drinking 1 SD Above Mean

Figure 3a. The interactions between friend and schoolmate drinking and parent and schoolmate drinking and the relative risk of membership in the Steep Growth (Group 4).

0.14

0.12

0.1

Trajectory 4

Probability of Membership in

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Probability of Membership in

0.4

Friend Drinking 1 SD Above Mean 0.08 Friend Drinking 1 SD Below Mean

0.06

0.04

0.02

0

Parent Drinking 1 SD Below Mean

Parent Drinking 1 SD Above Mean

Figure 3b. The interaction between friend and parent drinking and parent and the relative risk of membership in the Steep Growth and Decline (Group 4) trajectory.

Intoxication Interactions between parent and friend drinking and friend and schoolmate drinking demonstrated similar patterns. Concurrent social norms from parents, friends and schoolmates increased the risk of membership in the problematic Steep Growth and Decline (Group 4) trajectory, which is marked by very high rates of intoxication during

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the high school years that rapidly decrease during early adulthood. In particular, concurrent social norms from parents and friends favouring alcohol use increased the probability of membership in the Steep Growth and Decline (Group 4) and Developmental Average (Group 2) trajectories versus the Abstain (Group 1), while concurrent social norms from schoolmates and friends increased youth’s risk of membership in the Steep Growth and Decline (Group 4) trajectory versus the Abstain (Group 1) and Developmental Average (Group 2) trajectories (see Figure 3(b)).

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Discussion The current study expands on two important areas of alcohol research – research linking social norms and youth alcohol use, and research demonstrating the need to consider longitudinal patterns of alcohol use from a person-centred perspective. After identifying distinct longitudinal patterns of youth drinking and intoxication, we found significant relationships between parent, friend and school social norms and youth trajectory group membership. Social norms that were favourable towards alcohol use predicted membership in more problematic trajectories and a lower probability of abstaining from alcohol use. An examination of interactions among friend, parent and schoolmate social norms further suggested that schoolmate drinking played an important moderating role, serving to augment social norms from friends and parents. Using a person-centred approach to study longitudinal alcohol use Existing research that has used a person-centred approach to identify longitudinal trajectories of alcohol use varies greatly in regard to methodology. The research to date contains the results of trajectory analyses that differ in terms of (1) sample size, (2) alcohol use construct measured (e.g. any drinking versus binge drinking), (3) age range of participants, (4) participant characteristics (e.g. at-risk sample, normative sample, etc.) and (5) frequency of measurement. In spite of these varying methodologies, a reoccurring four-group solution typically emerges. With few exceptions, a review of papers documenting trajectories of alcohol consumption and binge drinking that use a similar age range to that of the current study (i.e. beginning in early adolescence (age 13 or earlier) and continuing into late adolescence (age 18 or later)) highlights a fourtrajectory solution with (1) a low, abstain group, (2) a moderate alcohol use group that remains stable over time, (3) a group that begins low but increases over time and (4) a group that begins high and decreases over time (e.g. Chassin et al., 2002; Guo et al., 2002; Tucker et al., 2005). A handful of studies has also identified a fifth commonly occurring group which represents individuals who begin as frequent/severe drinkers at the intercept and remain high for the duration of the time period being studied. The drinking and intoxication trajectories identified in the present analyses replicate findings from prior research in this area and also add the additional benefit of analysing a large, nationally representative sample that was followed for more than a decade. Given the age of the sample, which ranges from 11 to 31, we were able to construct trajectories of alcohol use across almost two decades – from early adolescence through the transition to adulthood and beyond. The fact that trajectory group membership was uniquely predicted by the majority of our covariates, as well as parent, friend and school social norms supports the validity of the person-centred approach and the need

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to continue to develop our understanding of the benefits of a discrete mixture modelling approach for understanding alcohol use over time. One trajectory group that we did not observe, but has been occasionally identified in other studies of this kind, is a group that consistently demonstrates very frequent alcohol consumption across a period of several years. This is likely due to the age span of our sample, which reflects a period wherein long-term patterns of high-frequency drinking have less time to become established.

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Social norms and adolescent alcohol use While prior research has examined the role of parent, friend and school social norms separately, our study is the first to our knowledge to consider the relative contributions from each of these essential contexts simultaneously. Our findings replicate prior work suggesting the importance of all three sources of social norms and offer further evidence that messages from parents, friends and schools all play a unique role in predicting adolescent alcohol use. Social norms that indicate favourable attitudes towards alcohol use increase the likelihood of adolescent drinking and decrease the probability of abstaining from alcohol use from adolescence through early adulthood. In relation to drinking, social norms predicted changes in the probabilities of trajectory group membership for almost all trajectory groups. Although there were not notable overall patterns in the differential roles that parent, friend and schoolmate drinking played in predicting trajectory group membership, there was some indication that friend and schoolmate drinking was associated with increased risk for membership in trajectories marked by the highest rates of drinking and intoxication. It is also notable that in our models predicting intoxication trajectory group membership, the RRRs associated with schoolmate drinking were consistently larger than those associated with friend or parent drinking, suggesting schoolmate drinking to be the strongest predictor of intoxication trajectory group membership. It is important to consider the current findings in relation to the wide degree of variation in the content of social norms across contexts. Youths’ experiences with friend, parent and schoolmate drinking are likely to be qualitatively different. Drinking behaviours within these three contexts vary in regard to the type of alcohol use represented (e.g. drinking to intoxication, alcohol abuse and addiction, and consuming a single drink with dinner) and the duration and frequency of exposure to drinking behaviours. Observations of lifetime patterns of alcohol use suggest that rates of drinking and intoxication peak during adolescence (Baer, 1993). As such, friend and schoolmate drinking patterns may reflect more frequent consumption of alcohol in larger amounts than parents’ drinking. On the other hand, adolescents’ experiences with parent drinking may include extreme experiences such as parental alcohol abuse and addiction, ‘social drinking’ with peers, or occasional consumption of small amounts of alcohol. These differences in the content of social norms may provide insight into the tendency for friend and schoolmate norms to be associated with membership in trajectories that reflected the highest rates of drinking among the sample. The current data do not include information regarding the wide variation in alcohol-related experiences that youth are likely exposed to. Adolescents’ experiences with parent, friend and schoolmate norms are also likely to vary in regard to the duration and frequency of youth’s opportunities to observe

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drinking within each of these contexts. For example, youths’ exposure to parental social norms surrounding alcohol use is likely to be both long in duration and frequent. Adolescents live with their parents (our measure of parental social norms reflected alcohol use of a residential parent), and in most cases will have had the opportunity to view parental alcohol-related norms over the course of many years. Alternately, adolescent friendships are known to be fluid and unstable especially during early adolescence, where friendship configurations have been observed to change on a monthly basis (Chan & Poulin, 2009). Given the instability in adolescent friendships, it is surprising that friend drinking played a role similar to parent drinking in the prediction of trajectory group membership. However, the transition to adolescence is often associated with a shift towards utilising peers, rather than parents and family, as the primary source of social interaction (Brown & Larson, 2009). As such, it is possible that although exposure to social norms from friends may be frequently changing and variegated, these norms may have a powerful and salient effect on adolescent drinking. Variation in youths’ opportunities to observe social norms among various contexts may also guide our understanding of why schoolmate norms demonstrated stronger effects in the prediction of intoxication trajectory group membership than friend or parent social norms. Although adolescents may attend school with their fellow students for several years, alcohol use typically occurs outside of the school setting, reducing the likelihood that youth have an opportunity to form a complete understanding of schoolmates’ drinking preferences. The majority of adolescents’ exposure to schoolmate drinking norms may come in the form of gossip and rumours, which may tend to exaggerate norms and focus primarily on very notable, problematic episodes of drinking. School social norms also appeared to play an important moderating role, augmenting the effects of friend and parent drinking. When coupled with friend and parent social norms favouring alcohol use, schoolmate drinking increased the risk for membership in the problematic Steep Growth (Group 4) drinking trajectory versus the 2–4 Days/Month (Group 3) drinking trajectory. It is surprising that schoolmate norms exacerbate risk for membership in this particular group, which suggests an acceleration in alcohol use that begins towards the end of high school. However, research suggests that in early adolescence, youth tend towards the formation of ‘cliques’ which become more permeable towards later adolescence (Smetana, Campione-Barr, & Metzger, 2006). As such, adolescents’ tendency to expand their friendship networks as they progress through high school may mean that schoolmate norms enjoy increased importance. Limitations Although the current analyses have several notable strengths including the inclusion of social norms from multiple sources, the use of discrete mixture modelling framework, and a nationally representative sample, there are also important limitations. First, we were only able to capture social norms at a single time point and these norms were used to predict longitudinal trajectories of alcohol use. Given the rapid social, cognitive and biological development that occurs during adolescence, it is possible that the links between social norms and adolescent drinking vary throughout development. The ability to track the relationship between time-varying social norms and adolescent alcohol use would provide important insights into the immediacy, strength and duration of influences from various social norm contexts.

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It is also important to note that all the social norms we tested could be classified as descriptive norms, which describe normative behaviours among various social norm sources. As such, we were not able to test the relative importance of adolescents’ perceptions of parent, friend and schoolmate drinking, which are typically described as subjective norms. For example, a friend’s actual drinking behaviour (which we assessed) may represent a stark contrast to a youth’s perception of his/her friend’s drinking. It is possible that youths’ perceptions of parent, friend and schoolmate drinking are more central predictors of individual alcohol use than actual parent, friend and schoolmate drinking. Finally, the current study is not immune to the complex issues of selection and selection-generated biases that arise when examining social norm contexts. Our measure of friend drinking is particularly vulnerable to this threat given that we are not able to differentiate between variation due to selection into friendship groups based on similarity and variation due to actual peer influences. In addition, given the strong body of literature documenting the relationship between genes, environmental factors and alcohol use (e.g. Moffitt, Caspi, & Rutter, 2005; Rutter & Silberg, 2002), it is likely that the observed relationships between parental norms and adolescent drinking may reflect both environmental as well as genetic influences. Conclusions The current results have implications in both research and applied settings. Although discrete mixture modelling methods have been the subject of recent criticism, the frequency with which our covariates and social norm variables distinguished among trajectory group membership suggests the continued utility of a discrete mixture modelling framework for examining longitudinal patterns of alcohol use. Our results further emphasise that social norms from parents, friends and schoolmates may have both direct and interactive effects in the prediction of adolescent drinking behaviour, highlighting the importance of understanding adolescents’ alcohol-related decision-making as a complex process that is likely tied to a conceptualisation of ‘appropriate’ or ‘desirable’ drinking behaviour that is derived from multiple sources. These findings provide useful information for parents and school administrators alike, suggesting reducing adolescent drinking likely requires a multipronged approach involving cooperation among families and schools. Disclosure statement No potential conflict of interest was reported by the authors.

Funding This work was supported by W.T. Grant Foundation [grant number 10909].

ORCID Alicia Doyle Lynch http://orcid.org/ORCID=0000-0003-2093-4057 Jacqueline Sims http://orcid.org/0000-0001-6709-058X

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