Introduction

The extent to which adolescent substance use is influenced by that of their peers has been debated for decades. Although adolescent substance use has declined since 2001, many young people still engage in substance use behaviours that have potentially permanent negative consequences for their physical and psychosocial development (Australian Institute of Health & Welfare, 2022). Especially concerning is the association between substance use and suicidal behaviour, considering both cannabis use and regular tobacco smoking may represent risk factors for the emergence of suicidality in adolescents (Serafini et al., 2012). It is therefore important to research and quantify the effect of different processes on adolescent substance use behaviour. The aim of this study is to synthesise the existing literature on peer influence on adolescent substance use and conduct meta-analyses to determine the magnitude of this effect and its moderators. Although previous systematic reviews have investigated the effect of peer influence on adolescents, these studies did not conduct meta-analyses (e.g., Henneberger et al., 2021; Leung et al., 2014), only assessed actual peer substance use (e.g., Giletta et al., 2021), or were limited to only one substance use behaviour (e.g., smoking in Liu et al., 2017; alcohol in Curcio et al., 2012). Additionally, no existing studies have looked at the moderating effect of perceived vs. actual peer measures, despite evidence suggesting that adolescents tend to be inaccurate when estimating their peers’ substance use behaviour (Trucco, 2020). The current study is significant in meeting this research gap as it aims to quantify the effect of peer influence across multiple substance use behaviours as well as investigating any differences between perceived peer substance use measures and survey data from actual peers.

Theoretical foundations

There are several theories that underpin peer influence effects, and it is important to consider substance use behaviour as having a complex aetiology. Bronfenbrenner’s Bioecological Model of Development (1979) suggests that adolescent development and health behaviour, including substance use behaviour, is shaped by multiple contextual factors arranged as socialisation systems surrounding the adolescent. Individual characteristics are at the centre of this model, and encompass innate factors such as biological sex, genetics, and temperament (e.g., inherited differences in emotional, attentional, and self-regulation processes; Trucco, 2020). These biologically based individual characteristics are considered the basis of susceptibility to the effects of the socialisation contexts.

Allen et al. (2022) reinforced the idea that adolescents are differentially influenced by their peers according to their genetics and traits, such as assertiveness and autonomy. Interestingly, they deemed peer influence processes as reflecting an overall adaptive developmental phenomenon, despite the potential negative effects, and suggested that it is the adolescent subculture promoting substance use behaviour that is problematic, rather than peer influence effects themselves. This was supported by the apparent influence processes being both neutral in valence and strongest for adolescents who were assessed as being the most well-adjusted, contrary to the common assumption that peer influence processes are reflective of poor functioning in the influenced youth (Allen et al., 2022). Bronfenbrenner (1979) elaborates on the socialisation contexts and classifies the concentric circles as the microsystem, mesosystem, exosystem, and macrosystem.

The most proximal system to the individual is the microsystem, which encompasses the immediate socialisation factors that affect the youth directly, including peers and parents. The next circle is the mesosystem, which represents the interactions between the individual’s microsystems, such as the connections between an adolescent’s parents and peers. The mesosystem is encircled by the exosystem, which consists of the larger social systems that operate indirectly with factors within the microsystem but have no direct effect on the individual (e.g., neighbourhood, school board). Lastly, the macrosystem is the outermost socialisation context which has a cascading function on development through the adolescent’s interactions across all settings. The macrosystem includes cultural values, religion, and laws, and, like the exosystem, it operates primarily through the more proximal factors. Bronfenbrenner’s Bioecological Model (1979) is a useful way to conceptualise adolescent substance use, as this behaviour does not occur in a vacuum. Rather, it is both directly and indirectly contributed to by multiple contextual factors with differential salience, with genetic and neurobiological differences affecting the individual’s sensitivity to different contexts (Trucco, 2020).

Another important theory to consider when studying peer influence is Social Learning Theory (Bandura, 1977), which posits that social environments affect behaviour through modelling. It proposes that adolescents develop cognitive representations about various attitudes and behaviours through observing influential social referents, and that these representations are invoked when making their own decisions about engaging in those behaviours. Bandura (1977) suggested that favourable attitudes towards substance use are likely to be reinforced when the adolescent perceives that the role model (a) is rewarded for enacting those behaviours, (b) is similar to the adolescent, and (c) has greater social status. These factors need to be taken into consideration as potential moderators of peer influence effects, as they are likely to contribute to the degree that an adolescent will change their substance use behaviour in accordance with their peer. Integrating Social Learning Theory and Bronfenbrenner’s Bioecological Model is the Social Development Model (Catalano & Hawkins, 1996), which posits that there are four contextual domains: family, school, peers, and religious and community institutions. Adolescents develop social bonds across these domains based on the perceived opportunities and rewards for involvement in either antisocial or prosocial activities. It follows that adolescents who anticipate social reward for prosocial behaviours will have a greater likelihood of engaging in these behaviours, as is true for antisocial actions. Additionally, the Social Development Model suggests that the salience of the socialising agents within the four domains will change as adolescents mature developmentally, with parents representing the main socialisation factor during childhood and early adolescence before shifting to a focus on peers in middle and late adolescence (Trucco, 2020).

Peer socialisation context

Concentrating on the peer context domain in the Social Development Model, peer influence is thought to operate through direct and indirect socialisation mechanisms. As an overarching concept, peer influence is defined as the social processes by which people change their attitudes and behaviours to conform to that of their friends (Barnett et al., 2022; DeLay et al., 2023; Leung et al., 2014). Peer influence processes can have both positive and negative outcomes, with adolescents describing socialisation pressure to engage in prosocial behaviours as well as antisocial behaviours (Adikini, 2023; Bartolo et al., 2023; Trucco, 2020; Zedaker et al., 2023). De la Haye et al. (2013) suggest that friendships can have a protective effect if friends do not endorse marijuana using behaviours. This finding aligns in with the concept of peer norms, which suggests that the perception of peer approval of a behaviour promotes that behaviour (Trucco, 2020). While the idea of adolescents being convinced to engage in substance use behaviour by coercive and pressuring peers is popular, it is likely that adolescents are more influenced by support and validation than directly by pressuring behaviour (Allen et al., 2022). This is one reason why interventions focused on helping adolescents resist peer pressure are often not effective; only a small part of peer influence may be active persuasion, while most of the effect is contributed to by perception of group norms, social acceptance, and status (Leung et al., 2014). Additionally, it is difficult to disentangle peer selection and peer socialisation processes. Peer selection is consistent with the theory of homophily; i.e., that individuals choose friends who are closely matched to their own attitudes and behaviours (Trucco, 2020). Peer selection processes are also invoked by social identity theory, which states that a fundamental aspect of psychosocial identity development is making judgements about the groups you belong to. Conversely, peer socialisation describes an individual’s decision to modify their attitudes and behaviours to adapt to social norms (Trucco, 2020). This distinction represents a challenge in research on peer influence, as these distinct processes of peer selection (i.e., adolescent’s own substance use behaviour promotes selection of friends who use substances) and peer socialisation (i.e., peer group’s substance use behaviour contributes to the adolescent’s use) appear the same in cross-sectional designs. Longitudinal studies are essential to separate the two processes, but it must also be considered that they can operate together and bi-directionally (Leung et al., 2014). This results in a reinforcing cycle in which substance using adolescents select peers with similar substance use levels, which promotes normative expectations about substance use that influence other adolescents.

Adolescence

Adolescence is a developmental period characterised by significant psychosocial, cognitive, moral, and physical development (Allen et al., 2012). Research suggests that there is a normative increase in deviant behaviour in adolescence, with substance use typically initiated during this period (Veenstra and Laninga-Wijnen, 2022). The World Health Organisation (2021)defines adolescence as the ages between 10 and 19 years, with pubertal onset typically beginning by age 9 to 12 years. The transition from childhood to adolescence is characterised by an increased focus on peer association and acceptance, which is a shift from parents as the primary socialisation factor to peers (Leung et al., 2014). Adolescents are more likely to seek peer approval and internalise the views of their peers, which, when combined with heightened sensitivity to social reward and increased engagement in novel experiences, promotes conforming to perceived group norms.

Aims

The main aim of this study is to quantify the effect that peer influence has on adolescent substance use. The hypothesis is that both actual and perceived peer substance use for alcohol, tobacco, marijuana, and composite substance use will significantly predict change in target adolescents' own substance use over time. Additionally, we predict that perceived substance use will have a greater effect size, as adolescents tend to erroneously overestimate their peers’ substance use behaviour (Helms et al., 2014). Although we predict that peer influence effects will be significant for all substance use behaviours, we hypothesise that alcohol and composite substance use will have the largest average effect sizes. This is because alcohol is the most normalised substance and has the highest proportion of participants partaking, with only 34% of high school students aged 14–17 never having consumed alcohol (Australian Institute of Health & Welfare, 2022; Trucco, 2020). Conversely, the same 2017 survey found that 82% of adolescents aged 12–17 years had never smoked tobacco, and 16% had used cannabis (Australian Institute of Health & Welfare, 2022). This suggests that alcohol and composite substance use will have the largest and most robust effect sizes, predominantly due to the greater proportion of adolescents engaging in that behaviour, with cannabis next and tobacco having the smallest effect. Age is hypothesised to be a moderating factor, with research suggesting both linear and curvilinear effects (Trucco, 2020). Regarding linear effects, we hypothesise that overall substance use will increase with age; however, a curvilinear pattern is hypothesised to emerge, where peer influence peaks in early adolescence (approximately 12–14 years; Giletta et al., 2021). It is hypothesised that time lag between waves in the longitudinal studies will have a moderating effect on peer influence, with shorter time lags being associated with larger effect sizes. Lastly, gender is not expected to be a significant moderating factor. This is not necessarily because there is not a difference in peer influence as a function of gender, rather that opposite effects are commonly found across studies, so we expect that they will mask any real effects that may exist (Leung et al., 2014) (Fig. 1).

Methods Search Procedures

This literature review consisted of five stages: (1) research question development and operational definitions, (2) database search, (3) study screening and evaluation using Covidence, (4) data extraction into IBM SPSS Statistics, and (5) data analysis with R. Eligible studies were identified through electronic searches of the databases Ovid (PsychInfo and PsychArticles) and PubMed. To yield the largest amount of returned results, the literature search used keywords from the following three groups to capture substance use, adolescents and peer influence: ‘drug addiction / drug abuse / substance abuse / substance addiction’ AND ‘adolescents / teenagers / young adults / teen / youth / student / adolescence’ AND ‘peer influence / peer influence on adolescent substance abuse’. The search was restricted to peer reviewed journals published in English between 1972 and 2022. Additional records (n = 20; see Fig. 2) were identified using a ‘snowball’ search of reference lists of included articles and systematic reviews on similar topics. As seen in Fig. 2, these searches yielded 508 unique reports that were first screened for eligibility through examination of titles and abstracts, before evaluating the full texts for suitability.

Selection Criteria

Considering that the aim of this meta-analysis was to quantify the degree that peers influence adolescents’ substance use, only studies that used a prospective longitudinal design were included. While cross-sectional designs are commonly used in studies examining peer influence, it is impossible to differentiate between peer selection and peer influence effects. Concurrent associations between peer and target adolescent substance use in cross-sectional designs could be a result of peer influence processes; however, it is possible that behavioural similarity preceded the peer relationship and contributed to its formation. Longitudinal designs enable the observation of substance use change over time in accordance with peer use, while controlling for past behaviour and potential moderators. Although the processes of peer influence are too complex to establish direct causal relationships, longitudinal survey data can reflect influence as it exists in everyday interactions over time, which provides ecological validity (Giletta et al., 2021). Longitudinal studies that collected data from at least two time points, with both peer and target substance use assessed simultaneously at an initial time point, were included. The decision to take a cross-lagged regression approach whereby effect sizes are computed from three correlation coefficients across any two assessments in a longitudinal study (see Effect Size Calculation), is consistent with previous meta-analyses in peer influence (e.g., Giletta et al., 2021). Only empirical studies collecting quantitative survey data were included, with qualitive methods and reviews excluded. Additionally, studies incorporating interventions or experimental manipulations were excluded, as were studies examining populations in clinical settings. This exclusion criterion was chosen because intervention programs and atypical contexts may have significant effects on peer influence. Studies were included if the outcomes measured substance use as alcohol, tobacco, and/or marijuana, with studies that didn’t differentiate being classified as ‘composite substance use’. The population was limited to participants aged between 10 and 19 years, as this is the age group defined as adolescence (World Health Organisation, 2021). Studies were included if they provided the required statistics to compute three zero-order correlations, standardised estimates from linear regression models adjusting for previous substance use, or path analyses using a longitudinal actor-partner interdependence model (APIM).

Effect Size Calculation

In Becker’s Eq. (1992), ββ1signifies the standardised regression weight of X1 (i.e., peer substance use at Time 1) predicting Y (i.e., target adolescent’s substance use at Time 2), while controlling for X2 (i.e., target’s substance use at Time 1).

Data Analysis

Of the 27 studies included in the meta-analysis (see Table 1), 16 studies (60%) could extract multiple effect sizes, so a multilevel approach with the robust variance estimation (RVE) technique was used (Hedges et al., 2010; Tipton, 2015). Multiple effect sizes could be calculated either in studies that used multiple time points (i.e., cross-lagged correlation coefficient for correlations between Time 1 to Time 2, Time 2 to Time 3, and Time 1 to Time 3) or multiple substance use behaviours (i.e., separate measures for alcohol and tobacco). This means that these effect sizes are nested within the studies and are not independent, thus violating assumptions of traditional meta-analytic approaches (i.e., random effects and fixed effects models). RVE provides a way to maximise data in a meta-analysis when the assumption of independence is violated by correcting the standard errors (Pustejovsky & Tipton, 2022). Several meta-analyses have found that the RVE method applied a posteriori yields confidence intervals with acceptable Type I error rates even if the form of the dependence is unknown (Fernández-Castilla et al., 2021; Giletta et al., 2021; Pustejovsky & Tipton, 2022). Statistical analyses for the mean effect size and moderating effects were based on RVE, with 95% confidence intervals (CI) and Satterthwaite approximation used to find the effective degrees of freedom in the t-statistics.

The combination of RVE and a four-level random-effects regression model explicitly accounts for the potential dependencies among the multiple effect sizes (Giletta et al., 2021). Level 1, random sampling variance, is also included in traditional random-effects meta-analyses and describes the variation of the observed effect size around the ‘true’ effect size, as a function of sample size. Level 2 represents within-study within-wave variance (e.g., effect sizes for both alcohol and tobacco use measured in the same study from Time 1 to Time 2). Level 3 reflects within-study between-wave variance (e.g., effect sizes for alcohol use measured in the same study from Time 1 to Time 2, Time 2 to Time 3, and Time 1 to Time 3). Lastly, Level 4 is between-study variance (e.g., variation between effect sizes from different studies).

First, the weighted-mean effect size of peer influence effects was estimated from an unconditional four-level random-effects model with RVE correction. Heterogeneity in the effect sizes was examined, with attention to the distribution of the total variance across the four levels. The median sampling variance was used in heterogeneity calculations. Next, each moderator (i.e., substance use behaviour, peer influence measure, mean age at baseline, time lag, and gender) was analysed separately as a predictor in the four-level model. Finally, a multivariate conditional model analysing the effects of the main five moderators simultaneously was conducted. This was to account for possible correlation between moderators (e.g., older adolescents reporting greater substance use) masking effects or yielding illegitimate results.

The meta-analyses were conducted in R for Mac (Version 4.2.2; R Core Team, 2022). The metafor package (Viechtbauer, 2010) was used to estimate multilevel metaregression models, using the restricted maximum likelihood method (REML) with effect sizes weighted by inverse sampling variance (Giletta et al., 2021). RVE correction was applied using the packages clubSandwich (Pustejovsky, 2018) and robumeta (Fisher et al., 2017).

Publication bias was examined in two ways: visual inspection of a funnel plot and moderation analyses of the inverse sampling variance. Funnel plots are a scatterplot with the inverse variance on the y-axis and the effect size on the x-axis. In the absence of publication bias, the studies form a symmetrical funnel shape, with the assumption that studies will scatter centrally around the total overall estimated effect. Asymmetry in the funnel plot suggests that studies with non-significant or negative results were not published, although it could also indicate inadequate screening in the systematic review or reporting bias. As visual inspection might be subjective, it is not considered a reliable method of estimating publication bias (Liu et al., 2017). As a more robust measure, moderation analyses of the inverse sampling variance were conducted. Publication bias is suggested by a significant association between inverse variance and effect sizes, with the assumption that small effect sizes with small inverse variances are less likely to be reported. If publication bias is detected, the validity of the current study’s results is threatened, as the results of the meta-analysis may not represent the reality of peer influence processes.

Results

Sample Characteristics

This systematic review identified 27 independent studies, yielding 99 effect sizes, dating from 1978 to 2022 (Mdn = 2008). Included studies were conducted in five different countries, most in the USA (n = 22), followed by the Netherlands (n = 3), Sweden (n = 1), Australia (n = 1) and China (n = 1). A total number of 28,325 participants (M = 44% boys) were included in the meta-analysis, with sample sizes differing from 126 to 7,108 participants across studies. Participants’ mean age at baseline ranged from 10.50 to 16 years (M = 13.73, SD = 1.41). The average length of time between follow-up assessment in consecutive waves of the longitudinal studies was 15.25 months (SD = 16.45, Mdn = 12 months, range = 6–96 months), with a majority of studies assessing peer influence over a 12-month period (68.8%). Substance use behaviour was classified according to the substance investigated, which comprised alcohol (n = 43), tobacco (n = 26) and marijuana (n = 3). Studies that did not distinguish between different substances (e.g., asked participants how often they used drugs and alcohol), were classified as composite substance use (n = 27). One of the aims of this meta-analysis was to quantify any significant differences between studies that used the target adolescent’s perception of their peers’ substance use and studies that used survey data from actual peers. Across the 99 effect sizes included, 54 used perceived measures of peer substance use (54.5%) and 45 used actual peer measures (45.5%). See Appendix A for a summary of the main descriptive characteristics of the studies included in this review.

Weighted-Mean Effect Size and Heterogeneity

For the total sample of 99 effect sizes, cross-lagged effects of peers' substance use on subsequent target adolescents' use controlling for initial similarity ranged from -0.10 for tobacco to 0.36 for composite use (i.e., alcohol and/or tobacco and/or marijuana), which is displayed in Fig. 3. The multivariate meta-regression model with RVE correction to allow for multiple potentially dependent effect sizes from the same study generated a significant weighted mean cross-legged regression coefficient, ββ¯= 0.147 (SE = 0.016, 95% CI [0.115, 0.180], p < 0.001). This result indicates that adolescents changed their substance use behaviour over time in the direction of their peers’ actual or perceived substance use.

Examining heterogeneity in the effect sizes revealed significant variation. The median sampling variance was 0.001 and represented 11.24% of the total variance. The Level 2 variance, 0.003, χ²(1) = 61.91, p < 0.001, represented 28.43% of the total variance, indicating that within-study within-wave variance (i.e., differences in effect sizes between the same wave of a particular study) were larger than expected due to sampling variability alone. The Level 3 variance, 0.001, χ²(1) = 1.76, p = 0.18, represented 11.42% of the total variance, although it was not significant, meaning that within-study between-wave variance (i.e., differences in effect sizes between different waves of a given study) was within the range expected by random variance. The Level 4 variance, 0.005, χ²(1) = 12.84, p < 0.001, represented 48.92% of the total variance, indicating significant between-study variance (i.e., differences in effect sizes between different studies). Heterogeneity at both the within-study within-wave and between-study levels suggests that there are likely moderators of the effects observed, which will be explored in subsequent moderator analyses.

Moderator Analyses Substance Use Behaviour

Moderator analyses were conducted to explain the effect size heterogeneity. First, substance use behaviour was examined to determine whether the specific substance, categorised as alcohol, tobacco, marijuana, or composite substance use in cases that didn’t differentiate between substances, might affect the magnitude of peer influence processes. Moderation analyses revealed significant differences between the substance use behaviours, F(3, 95) = 6.102, p < 0.001 (see Table 2). The average cross-lagged correlation coefficient was significant for alcohol (k = 43, ββ¯= 0.182, SE = 0.021, 95% CI [0.137, 0.227], p < 0.001), tobacco (k = 26, ββ¯= 0.068, SE = 0.023, 95% CI [0.007, 0.128,, p = 0.035), and composite substance use (k = 27, ββ¯= 0.134, SE = 0.013, 95% CI [0.104, 0.164], p < 0.001).

Peer Influence Measure

Substantial research on peer influence has established that, consistent with social norm theories, perceived substance use, acceptance or approval is associated with adopting substance use behaviours (Trucco, 2020). This is especially problematic considering that adolescents tend to overestimate peers’ engagement in substance use, with the size of these misperceptions amounting to large effect sizes (Trucco, 2020). This meta-analysis sought to determine whether there is a significant difference between studies that used perceived measures of peer substance use (i.e., target adolescent’s estimations of their peers’ substance use) and studies that used actual peer measures (i.e., nominated peers record their own substance use). A significant moderation effect was found, F(1, 97) = 12.695, p < 0.001 (see Table 2). Both perceived and actual peer measures were found to be significant, with perceived measures having a larger average effect size (k = 54, ββ¯= 0.179, SE = 0.014, 95% CI [0.148, 0.210], p < 0.001) than actual peer measures (k = 45, ββ¯= 0.095, SE = 0.021, 95% CI [0.050, 0.141], p < 0.001).

Mean Age at Baseline

To examine the moderating effect of participants’ age, a linear and a quadratic model were executed. This was due to evidence suggesting that peer influence effects operate in both a linear manner, with an overall increase from childhood to adolescence, and in a curvilinear way, peaking during mid-adolescence (Trucco, 2020). As shown in Table 2, neither model yielded significant results. This suggests that the strength of peer influence effects does not differ with participant age.

Time Lag

The time lag between assessments was examined following the Lag-as-Moderator Meta-Analysis approach (Card, 2019). This model yields both a linear (i.e., peer influence becoming larger or smaller with longer time spans) and quadratic (i.e., peer influence reaching a maximum effect at a particular lag length) moderation of lag, which is centred on the weighted mean. These analyses revealed no significant effect of time lag for neither linear nor quadratic term (see Table 2).

Multivariate Moderation Model

A multivariate moderation model simultaneously examining all five main moderators (i.e., substance use behaviour, peer substance use measure, time lag, age, and gender) revealed no significant differences.

Publication Bias

Publication bias is a potential threat to all systematic reviews, despite efforts to locate unpublished effect sizes. To evaluate and quantify the impact of publication bias, inverse sampling variance moderation analyses were conducted, and a funnel plot was assessed (see Fig. 4). In the absence of publication bias, the plot should present a symmetrical funnel shape centred on the mean effect size. This means that studies with smaller sample sizes or larger standard errors will scatter widely at the bottom of the plot, while those with larger sample sizes or smaller standard errors have a narrower dispersion. Visually inspecting the funnel plot reveals that the effect sizes (indicated as dots on the plot) largely fall in the inverted funnel shape. For a more robust evaluation, the moderating effect of the inverse sampling variation was calculated, which yielded nonsignificant results (β = 0.000013, SE = 0.00001, 95% CI [-0.00002, 0.00004], p = 0.264). Taken together, the funnel plot and inverse sampling variance moderation suggest that publication bias is not significantly contributing to the results.

Discussion

As hypothesised, the results revealed a significant positive effect (ββ¯= 0.147) of peer influence on adolescent substance use, suggesting that adolescents will change their substance use behaviour in the same direction as their peers. It is important to recognise that this finding also implies that target adolescents with peers using substances at low levels are likely to decrease their substance use behaviour over time (Allen et al., 2022). No significant results emerged to suggest that publication bias is prevalent in this study. In line with the hypotheses, significant differences emerged between the substance use behaviours. As expected, target adolescents significantly changed their alcohol, tobacco, and composite substance use over time in accordance with the level of substance use behaviour of their peers. Alcohol use emerged as having the largest average cross-lagged correlation coefficient (ββ¯= 0.182), which was predicted due to it being the most normalised and prevalent substance use behaviour. The smallest effect size was for tobacco (ββ¯= 0.068), which is likely due to small populations of participants engaging in tobacco smoking behaviours. Only a few studies (k = 3) differentiated marijuana use, with majority of studies that used a composite substance use measure including marijuana. Composite substance use emerged as significant with the same effect size as marijuana (ββ¯= 0.134), although marijuana use was not significantly different. Recent Australian survey results found marijuana to be the most commonly used illicit substance among 12–17 year olds, with 8.2% of 14–17 year olds reporting recently using marijuana in 2019, which was an increase from 7.9% in 2016. It is likely that marijuana use not being significant is due to the small number of studies examining it, and that the composite measure is a better representation of peer influence effects on marijuana use.

Consistent with the hypotheses, a significant moderation effect was revealed for perceived vs. actual peer measures, and perceived measures estimated a larger average cross-lagged correlation coefficient (ββ¯= 0.179) than actual peer measures (ββ¯= 0.095). This is consistent with social norm theories, which suggest that adolescents tend to overestimate their peers’ substance use behaviour and that this perception is sufficient to influence target adolescent behaviour (Trucco, 2020). This was reinforced by Helms et al. (2014) who found that adolescents significantly misperceived the substance use of popular peers; however, it contradicts Urberg et al. (2003) who found that adolescents would only conform to peer substance use behaviour if the friendship was positive and reciprocated. This contradiction captures many of the debates about peer influence and lends support to Allen et al.’s (2022) theory that peer influence effects are normative and adaptive; different from the maladaptive effects that can occur from interaction with a problematic subculture or norms. Contrary to the hypothesis, the results showed no linear or quadratic effect of age on peer influence. This finding may be due to the limited variance in age range and variations in the age of puberty, rather than no actual difference. Additionally, no significant effects emerged for time lag or gender. The multivariate moderation model which examined all five moderators simultaneously revealed no significant differences, which was contrary to the univariate analyses for substance use behaviour and peer influence measures. This contradictory result raises questions about the robustness of the moderation analysis results and suggests that peer influence is a complex phenomenon with interacting factors.

Strengths and Limitations

While great effort went into ensuring this study was a comprehensive synthesis and robust meta-analysis, there are significant limitations to consider. Firstly, this study addressed peer influence processes in general, rather than specifically investigating whether the nature of the peer relationship moderates the effect. Considering that the included studies used measures from peers of varying degrees of friendship to the target adolescent, there remains the question of whether the closeness of friendship impacts peer influence processes. Additionally, broader group influence processes may behave differently, with groups of peers exerting an amplifying effect on behaviour. Secondly, the study did not consider differences in the degree to which a peer may actively attempt to be influential. It is suggested that adolescents are more influenced by supportive peers, rather than those who are more coercive and pressuring; however, it may be that coercive peer behaviours have a short-term impact on behaviour in a given situation but aren’t captured by the time lags in the included studies (Allen et al., 2022). Thirdly, while care was taken to collate studies from a variety of nationalities, almost all the records included in the meta-analysis are from cultures that value individualism more highly. A meta-analysis by Liu et al. (2017) found a significant effect of individualism-collectivism, with adolescents from cultures scoring more highly on collectivism being more likely to conform to normative influence from peers. The current review only included one study from a country that scored equal to or above 50 on collectivism, as per Liu et al. (2017) measure, which was Li et al.’s (2017) investigation into tobacco use by Chinese adolescents. This suggests that the results attained in this study may not be representative of peer influence effects in more collectivistic cultures. Additionally, individualism-collectivism is not the only cultural consideration, as peer influence processes may operate differently among adolescents from different countries due to variations in the prevalence of substance use, the availability of substances, laws and policies, social norms, and other cultural factors.

While the results suggested that publication bias was not significantly impacting the results, it is an important consideration in systematic reviews, as many studies that find no significant results or negative results are not published. This ‘file drawer problem’ leads to effect sizes generated in meta-analysis being overestimated. Therefore, it is best practice when conducting a systematic review and meta-analysis to conduct a ‘snowball’ search of reference lists and contact prominent authors on the research topic and request any unpublished studies. Unfortunately, due to the time restraints and scope of this study, previous authors were not contacted, although several unpublished manuscripts were identified and included through reference list searches. It is therefore possible that unpublished data that would contribute to this topic was missed.

Future Research

As social media continues to grow and evolve, future research should look at the processes of online peer influence. Social media has created a new context through which adolescents interact with their peers, as well as perceive peer behaviours and norms (Choukas-Bradley & Nesi, 2020). Additionally, social media is now populated by ‘influencers’ who earn money based on their ability to impact the purchasing decisions of their audience. Influencers present an interesting case, as the influence processes occur exclusively online and often without any direct interaction or reciprocal exchange with users. Research into the impact of social media influencers on adolescents’ decisions to engage in risky behaviours would be worthwhile. Hamilton et al. (2022) noted the potential for online peer influence to be both positive and negative in the context of COVID-19, with images and videos shared by peers practicing safe social distancing practices influencing adolescents to also engage in pro-social health behaviours, while the opposite was also true. Developing a more nuanced understanding of influence processes through social media and identifying the factors that moderate both problematic and productive social media use would be an important area for future research. Additionally, another concerning trend is that of smoking electronic cigarettes. While cigarette smoking is declining overall, with only 5% of Australian secondary school students aged 12–17 reporting being current smokers in 2017, 14% of those students had tried e-cigarettes and vaping e-cigarettes is significantly increasing (Australian Institute of Health & Welfare, 2022). Future research into e-cigarette use in adolescents and whether normative peer influences are greater for vaping than for traditional tobacco smoking would be valuable.

As is common in psychological research, the systematic review yielded few studies in non-Western countries. As mentioned previously, a significant limitation of this study was the overreliance on studies based in Western individualistic cultures, with Li et al.’s (2017) investigation into tobacco use in China being the only non-Western record included. A review by Liu et al. (2017) revealed significant differences in smoking initiation and continuation based on the country’s collectivism-individualism measure, indicating a need for studies with diverse populations. Lastly, future research that conceptualises peer influence as an overall adaptive phenomenon with a neutral valence and investigates the broader subcultures that may be contributing to adolescent substance use would be worthwhile (Allen et al., 2022). With the wealth of research on peer influence and findings that peer influence effects can often overcome targeted interventions, looking past the question of the magnitude of these processes, and instead focusing on the adaptive and maladaptive norms within peer contexts may provide better information about how to prevent adolescents from engaging in substance use behaviours.

Conclusion

The main aim of this study was to systematically review the large body of literature on peer influence from the past 50 years and quantify the magnitude of peer influence on adolescent substance use behaviour using the best available methodologies. The results revealed a significant positive effect (ββ¯= 0.147), indicating that adolescents whose peers engage in greater or lesser substance use behaviour are significantly likely to alter their own substance use accordingly over time. These findings support the existing literature which identified peer influence processes as contributing to adolescents’ decisions to engage in substance use behaviours. Significant heterogeneity was found between the effect sizes across multiple levels, suggesting that unexamined contextual, individual, and methodological factors may modify peer influence effects. Moderation analyses revealed significant differences between different substance use behaviours and between studies that used perceived vs. actual peer measures; however, when assessed simultaneously no moderators emerged as significant. This suggests that peer influence works through complex processes across substance use behaviours, and that attempting to quantify its magnitude may be innately flawed. Overall, findings from this meta-analysis reveal a significant and robust positive effect for peer influence on adolescent substance use. Definitively establishing the impact of peer influence across multiple substance use behaviours and peer measures of substance use has significant implications for adolescent substance use prevention. This includes the potential of harnessing peer influence as a positive force and the need to target misperceptions of substance use, as well as providing an opportunity to focus on the adolescent subculture and norms that are contributing to risky health behaviour. Research has demonstrated that interventions targeting peer influence are largely ineffective in preventing substance use behaviour in adolescents, potentially due to peer influence representing an overall adaptive developmental phenomenon. Therefore, the emphasis of future studies in preventing dangerous substance use behaviour should look less at peer influence and more at the adolescent subcultures that support problematic behaviour.

Summary

This meta-analysis synthesized existing literature on peer influence's effect on adolescent substance use, employing meta-analyses to determine the magnitude of this effect and its moderators. Previous reviews lacked meta-analyses or focused on single substances. This study addresses this gap by quantifying peer influence across multiple substances (alcohol, tobacco, marijuana, composite) and comparing perceived versus actual peer substance use.

Theoretical Foundations

The study's theoretical framework integrates Bronfenbrenner's Bioecological Model and Social Learning Theory. Bronfenbrenner's model emphasizes multiple contextual factors (microsystem, mesosystem, exosystem, macrosystem) shaping adolescent development, including individual characteristics influencing susceptibility to social contexts. Social Learning Theory highlights modeling and observational learning in shaping attitudes and behaviors, with factors like role model reward, similarity, and status impacting influence. The Social Development Model integrates these perspectives, emphasizing the shifting salience of socializing agents (parents, peers) across adolescence.

Peer Socialization Context

Peer influence, defined as the process of conforming to friends' attitudes and behaviors, operates through direct and indirect mechanisms. It encompasses both positive and negative influences, with peer norms and perceived approval playing key roles. The study acknowledges the difficulty in disentangling peer selection (choosing like-minded friends) and peer socialization (adopting peer behaviors), emphasizing the need for longitudinal designs to address this challenge.

Adolescence

Adolescence, defined as ages 10–19, is characterized by increased peer focus, sensitivity to social reward, and exploration, making adolescents more susceptible to peer influence. The transition from parental to peer influence is a key developmental aspect.

Aims

The study aims to quantify peer influence on adolescent substance use across different substances. It hypothesizes that both perceived and actual peer substance use will predict adolescent substance use, with perceived use having a larger effect. It further hypothesizes that alcohol and composite substance use will show the largest effects, moderated by age and time lag between assessments. Gender is not expected to be a significant moderator.

Methods

The review involved a five-stage process: research question development, database searching (Ovid, PubMed), study screening (Covidence), data extraction (IBM SPSS), and data analysis (R). Inclusion criteria stipulated prospective longitudinal designs, quantitative data, and exclusion of interventions or clinical settings. Effect sizes were calculated using cross-lagged regression, accounting for initial substance use.

Results

The meta-analysis included 27 studies (99 effect sizes), primarily from the USA. A significant weighted mean effect size (β¯ = 0.147) indicated that adolescents' substance use changed in the direction of their peers' use. Heterogeneity analyses revealed significant variation at within-study and between-study levels, suggesting moderators. Moderator analyses showed significant differences across substances (alcohol > composite > tobacco), with perceived peer substance use exhibiting a larger effect than actual peer use. Age and time lag were not significant moderators. Publication bias analyses did not indicate significant bias.

Discussion

The significant positive effect of peer influence supports existing literature, with alcohol showing the strongest effect. The larger effect of perceived peer use aligns with social norm theories. The absence of significant age and time lag effects may reflect limited age range variation or methodological aspects. The study highlights limitations, including the need to investigate peer relationship quality and cultural variations (predominantly Western samples).

Strengths and Limitations

The study's strengths include its comprehensive synthesis and robust methodology. However, it lacks investigation into peer relationship dynamics, the nature of peer influence attempts, and sufficient representation of non-Western cultures. The potential for publication bias, although not statistically significant, remains a consideration.

Future Research

Future research should explore online peer influence, the impact of social media influencers, and e-cigarette use. Moreover, studies with diverse, non-Western populations are needed to address cultural influences on peer influence processes. A shift in focus from quantifying peer influence to understanding the adolescent subcultures promoting risky behaviors is also recommended.

Conclusion

This meta-analysis demonstrates a significant positive effect of peer influence on adolescent substance use. Understanding the complex interplay of factors influencing peer influence and the role of adolescent subcultures is crucial for effective prevention strategies. Focusing on maladaptive norms within peer groups may be more effective than solely targeting peer influence.

Summary

This study synthesizes existing literature and conducts meta-analyses to determine the magnitude of peer influence on adolescent substance use and its moderators. Previous reviews lacked meta-analyses or focused on single substances. This study addresses this gap by analyzing multiple substances and comparing perceived versus actual peer substance use.

Theoretical Foundations

Peer influence on adolescent substance use is examined through several theoretical lenses. Bronfenbrenner's Bioecological Model highlights multiple contextual factors influencing adolescent development, including individual characteristics (genetics, temperament) and social systems (microsystem, mesosystem, exosystem, macrosystem). Social Learning Theory emphasizes modeling and observational learning, where adolescents learn attitudes and behaviors by observing role models. The Social Development Model integrates these perspectives, emphasizing the interplay of family, school, peer, and community influences on adolescent behavior.

Peer Socialization Context

Peer influence operates through direct and indirect mechanisms. While peer pressure is often cited, support and validation may be more influential. Distinguishing between peer selection (choosing like-minded friends) and peer socialization (adopting friends' behaviors) is crucial, yet challenging in cross-sectional studies. Longitudinal designs offer a stronger approach.

Adolescence

Adolescence is characterized by significant developmental changes, including increased peer influence and sensitivity to social rewards. This period witnesses a shift from parental to peer influence, potentially increasing susceptibility to normative peer behaviors.

Aims

The study aims to quantify peer influence on adolescent substance use, hypothesizing that both perceived and actual peer substance use will predict changes in adolescents' own substance use. It further predicts larger effect sizes for perceived use and for alcohol and composite substance use due to higher prevalence. Age and time lag are hypothesized as moderators.

Methods

The study used a five-stage process: research question development, database searches (Ovid, PubMed), study screening (Cvidence), data extraction (IBM SPSS), and data analysis (R). Inclusion criteria focused on prospective longitudinal studies with at least two time points, quantitative data, and measurements of alcohol, tobacco, marijuana, or composite substance use in adolescents aged 10-19.

Selection Criteria

Only prospective longitudinal studies were included to differentiate between peer selection and peer influence. Cross-sectional studies, interventions, clinical settings, and qualitative data were excluded. Studies had to provide sufficient data for effect size calculation.

Effect Size Calculation

Effect sizes were calculated using a cross-lagged regression approach, analyzing correlations between peer substance use at Time 1 and target adolescent's use at Time 2, controlling for initial similarity (Time 1).

Data Analysis

A multilevel meta-analysis with robust variance estimation (RVE) was employed to account for dependencies among multiple effect sizes within studies. A four-level random-effects model addressed sampling variance, within-study within-wave variance, within-study between-wave variance, and between-study variance. Moderation analyses explored the effects of substance type, peer influence measure (perceived vs. actual), age, time lag, and gender. Publication bias was assessed using funnel plots and inverse variance moderation analysis.

Results

The meta-analysis included 27 studies (99 effect sizes). A significant positive effect of peer influence was found (β = 0.147), indicating that adolescents' substance use changes in the direction of their peers' use. Significant heterogeneity was observed. Moderation analyses revealed significant effects for substance type (alcohol > composite > tobacco) and peer influence measure (perceived > actual). Age, time lag, and gender were non-significant moderators. Publication bias was deemed insignificant.

Discussion

The significant positive effect of peer influence supports existing literature. The stronger effect of perceived peer use highlights the role of social norms. The lack of significant moderation by age, time lag, and gender requires further investigation. Limitations include the focus on general peer influence rather than specific relationship dynamics, the limited consideration of the degree of peer influence attempts, and the overrepresentation of Western individualistic cultures.

Strengths and Limitations

Strengths include the comprehensive literature search and use of robust statistical methods. Limitations include the lack of focus on relationship type, the lack of exploration into the influence intensity, and the underrepresentation of non-Western cultures. The potential for publication bias, though assessed, remains a consideration.

Future Research

Future research should explore online peer influence, including the impact of social media influencers, and the influence of e-cigarette use. Studies incorporating diverse cultural perspectives and investigation into the adaptive and maladaptive aspects of peer norms are also needed.

Conclusion

This meta-analysis demonstrates a significant positive effect of peer influence on adolescent substance use. Understanding the nuances of peer influence, including social norms, and cultural contexts, is crucial for developing effective prevention strategies. Future efforts should focus less on the magnitude of peer influence and more on addressing the underlying social and cultural norms that promote risky behaviors.

Summary

This study examines how much peers influence adolescent substance use. While substance use among teens has decreased since 2001, it remains a concern, especially its link to suicidal thoughts. This research reviews existing studies and uses meta-analysis to determine the strength of peer influence and what factors might change that strength. Previous reviews didn't do meta-analyses or only looked at one type of substance. This study is unique because it examines multiple substances and compares perceived peer behavior to actual peer behavior.

Theoretical Foundations

Several theories explain peer influence on substance use. Bronfenbrenner's Bioecological Model shows how multiple factors—from genetics to culture—affect a teen's behavior. Individual characteristics like genetics and temperament make some teens more susceptible to peer influence than others. Allen et al. (2022) suggest peer influence is a normal part of development, even if it leads to negative outcomes. The problem is not peer influence itself but rather the teen subculture promoting substance use. Bronfenbrenner's model uses nested circles (microsystem, mesosystem, exosystem, macrosystem) to show how these factors interact. Social Learning Theory (Bandura, 1977) adds that teens learn by watching others, especially those they see as similar or higher status. The Social Development Model combines these ideas, highlighting the shifting importance of family and peers in a teen’s life.

Peer Socialization Context

Peer influence involves changing attitudes and behaviors to match friends. It can be positive or negative. Studies show friends can either protect against substance use or encourage it. The perceived approval of a behavior is a key factor. While pressure is a factor, support and validation are probably more influential. Separating peer selection (choosing friends with similar habits) from peer socialization (changing habits to fit the group) is challenging, especially with cross-sectional studies. Longitudinal studies are needed to see how these processes play out over time.

Adolescence

Adolescence is a time of major change. Teens increasingly look to peers for acceptance, combined with a higher sensitivity to social rewards and a drive to try new things. This makes them more likely to follow perceived group norms.

Aims

This study aims to measure peer influence on teen substance use. It hypothesizes that both perceived and actual peer substance use will predict a teen's own substance use. Perceived use is expected to have a stronger effect because teens often overestimate their peers' substance use. Alcohol and composite substance use are expected to show the largest effects due to higher prevalence. Age is expected to moderate the effect, with a peak in early adolescence. The time between data collection points in the studies is also expected to have an effect. Gender is not expected to be a significant factor.

Methods

The study used five steps: (1) defining the research question and terms; (2) searching databases (Ovid and PubMed); (3) screening studies using Covidence; (4) extracting data into SPSS; and (5) analyzing data with R. The search used keywords related to substance use, adolescents, and peer influence. It included studies published in English between 1972 and 2022. Additional studies were found through reference lists.

Selection Criteria

Only prospective longitudinal studies were included to separate peer selection from peer influence. Studies needed at least two data collection points, measuring both peer and teen substance use. Only studies using quantitative survey data were included, excluding qualitative studies, interventions, and clinical settings. Substances included alcohol, tobacco, marijuana, and a composite category. Participants had to be aged 10-19. Studies had to provide data suitable for analysis.

Effect Size Calculation and Data Analysis

Effect sizes were calculated using a statistical method appropriate for longitudinal data. A multilevel approach with robust variance estimation was used because multiple effect sizes were often available from a single study. This approach handles the statistical dependencies among these effect sizes. The analysis first calculated the overall effect of peer influence. Then it explored how several factors (substance type, type of peer measure, age, time lag, and gender) affected the size of that influence. Finally, publication bias was checked.

Results: Sample Characteristics and Weighted-Mean Effect Size

27 studies were included, totaling 99 effect sizes and 28,325 participants. Most studies were from the USA. The overall effect size showed a significant positive relationship between peer substance use and a teen's own substance use. There was significant variation in effect sizes (heterogeneity).

Moderator Analyses

Analyses examining how various factors changed the size of the peer influence effect showed that: the type of substance used (alcohol having the strongest effect), whether peer use was perceived or actual (perceived use having a stronger effect), age, time lag between measurements and gender did not significantly affect the size of peer influence. The multivariate model including all five moderators did not yield significant results, which differed from some univariate analyses.

Publication Bias

Examination of a funnel plot and statistical analysis suggested publication bias was not a major problem.

Discussion

The results confirm that peer influence significantly affects adolescent substance use. Alcohol had the strongest effect, followed by composite substance use and tobacco. Perceived peer substance use had a stronger effect than actual peer substance use. Age, time lag, and gender did not significantly moderate the effect. The lack of significant findings in the multivariate model suggests the complexities of peer influence.

Strengths and Limitations

The study is a comprehensive meta-analysis but has limitations. It didn't examine how the type of peer relationship affects influence (e.g., close friends vs. acquaintances). It also didn't differentiate the degree of active influence attempts by peers. The study's focus on Western, individualistic cultures limits generalizability to other cultures. While publication bias was addressed, some potentially relevant unpublished studies may have been missed.

Future Research

Future research should explore online peer influence, including the role of social media influencers and the evolving context of e-cigarette use. More research is needed in non-Western cultures to better understand how cultural factors influence peer dynamics and substance use. Future studies should also consider peer influence as a complex interplay between adaptive developmental processes and maladaptive subcultural norms.

Conclusion

This meta-analysis confirms the significant impact of peer influence on adolescent substance use. Understanding this influence, including both its positive and negative aspects, is crucial for developing effective prevention strategies. However, the findings also suggest the complexity of peer influence effects and the potential limitations of simply focusing on the magnitude of this influence. Future efforts should focus more on understanding the subcultural norms that drive harmful behaviors.

Summary

This study looked at how friends influence teens' choices about using alcohol, tobacco, and marijuana. It found that teens tend to copy their friends' behavior. The study also showed that what teens think their friends do matters even more than what their friends actually do.

Theoretical Foundations

Scientists use different ideas to explain how friends influence each other. One idea is that teens are influenced by the people and things around them, like family and school. Another idea is that teens learn by watching others, and if they see friends enjoying something, they might want to try it too. These ideas suggest that many things work together to make teens try drugs or not.

Peer Socialization Context

Friends influence each other directly and indirectly. If friends don't use drugs, that can protect others from using too. Teens often think their friends use drugs more than they actually do. This belief can be powerful. It’s also hard to tell if teens become friends because they already have similar habits or if friends influence each other's habits.

Adolescence

Being a teenager is a big change. Teens start to care more about what their friends think. They also like trying new things and wanting to fit in. All of this makes them more likely to follow what their friends do.

Aims

The study aimed to see how much friends influence teens' drug use. It expected that what teens think their friends do would matter most. It also thought alcohol would be the biggest influence, followed by marijuana, and then tobacco. The scientists wanted to find out if age made a difference in this influence.

Methods

Scientists searched through many research papers to find studies that followed teens over time. They only used studies that measured teens' drug use and what they thought their friends were doing. They used math to combine the results from all these studies.

Selection Criteria

The study chose only studies that followed groups of teens for a period of time, to better understand if friends influence drug use. This is better than just taking a snapshot of what's happening at one time. They looked only at studies that tracked the use of alcohol, tobacco, marijuana, or a combination of these.

Effect Size Calculation

The scientists used a specific way to measure how much friends influenced teen drug use.

Data Analysis

The scientists used special math to combine the results from many studies. They looked for things that might change the results, such as the type of drug, age of teens, and how long the study lasted.

Results

The study found that friends do influence teens' drug use. Teens tended to do what their friends did, especially for alcohol. What teens thought their friends did had an even stronger effect. Age didn't seem to matter much.

Discussion

The results showed that friends have a big influence on teens' drug use, particularly for alcohol. What teens believe their friends are doing is a stronger influence than what friends are actually doing.

Strengths and Limitations

The study was big and used good methods. However, it didn't look at how close the friendships were, or how much friends tried to pressure each other. It also mostly used information from Western countries, so the results may not be the same everywhere. There is also always the possibility that studies showing no effect were not included.

Future Research

Future studies could look at how social media affects teens' drug use and how vaping is affecting teens. More studies should also include teens from more countries and cultures. It might be more helpful to study the ideas and beliefs of groups of teens than just focusing on how much friends influence them directly.

Conclusion

Friends strongly influence teens' drug use. This is especially true for alcohol. To help teens, we need to understand what ideas and beliefs are popular among teens, and try to change those ideas, rather than just focus on friendship pressures.