SpringerLink
Log in

Peer Influence, Peer Selection and Adolescent Alcohol Use: a Simulation Study Using a Dynamic Network Model of Friendship Ties and Alcohol Use

  • Published:
Prevention Science Aims and scope Submit manuscript

Abstract

While studies suggest that peer influence can in some cases encourage adolescent substance use, recent work demonstrates that peer influence may be on average protective for cigarette smoking, raising questions about whether this effect occurs for other substance use behaviors. Herein, we focus on adolescent drinking, which may follow different social dynamics than smoking. We use a data-calibrated Stochastic Actor-Based (SAB) Model of adolescent friendship tie choice and drinking behavior to explore the impact of manipulating the size of peer influence and selection effects on drinking in two school-based networks. We first fit a SAB Model to data on friendship tie choice and adolescent drinking behavior within two large schools (n = 2178 and n = 976) over three time points using data from the National Longitudinal Study of Adolescent to Adult Health. We then alter the size of the peer influence and selection parameters with all other effects fixed at their estimated values and simulate the social systems forward 1000 times under varying conditions. Whereas peer selection appears to contribute to drinking behavior similarity among adolescents, there is no evidence that it leads to higher levels of drinking at the school level. A stronger peer influence effect lowers the overall level of drinking in both schools. There are many similarities in the patterning of findings between this study of drinking and previous work on smoking, suggesting that peer influence and selection may function similarly with respect to these substances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. The objective function is defined as f(β, x) = ∑ k β k s ik (x), where β k is the log odds multiplier for the kth term in the function of network/behavior s ik (x) and x is the current network/behavioral state. A positive parameter in the objective function implies that the actor prefers options that increase the associated s value, while a negative parameter value implies the avoidance of such changes.

  2. There are three possibilities for changes in adolescent drinking levels—increasing one unit (+1), maintaining the same level (0), or decreasing one unit (−1). In this study, we assume that the utility changes of initiating alcohol use (i.e., 0 vs. +1) and quitting alcohol use (i.e., 0 vs. −1) are the same and thus the peer influence effect in our models is symmetric. In ancillary analyses, we try to decompose the uptake and suppression processes by including separate initiation and quitting effects in the same model. However, model estimation difficulties emerge with unrealistically large parameter estimates and standard errors for the initiation and quitting effects (e.g., >1000). Examining the correlations among the parameter estimates suggests collinearity issues that make it impossible in practice to separate the two effects. Thus, it is not empirically feasible to decompose the suppression and initiation processes in the same model.

  3. The goodness-of-fit for the model is assessed for each school by forward simulation of the model from wave 1 to wave 3 based on the estimated parameters 1000 times and comparing the resulting predicted networks with the actual observed data (see Fig. S1 and Fig. S2 in supplemental materials). Given that the observed network values fall within the range of predicted networks for the distribution of in-degree, out-degree, geodesic distances, and the triadic census, the model does a good job reproducing the network for each school (Lospinoso and Snijders 2011). Furthermore, the number of persons at each level of drinking at each time point in the observed network falls within the range of the simulated networks, and the number of transitions of drinking behavior (i.e., the number of persons beginning drinking and stop** drinking) falls within the range of the predicted networks.

References

  • Adams, J., & Schaefer, D. R. (2016). How initial prevalence moderates network-based smoking change: Estimating contextual effects with stochastic actor oriented models. Journal of Health and Social Behavior, 57, 22–38.

    Article  PubMed  Google Scholar 

  • Bailey, S. L. (1992). Adolescents’ multisubstance use patterns: The role of heavy alcohol and cigarette use. American Journal of Public Health, 82, 1220–1224.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.

    Article  PubMed  Google Scholar 

  • Bearman, P. S., Moody, J., & Stovel, K. (2004). Chains of affection: The structure of adolescent romantic and sexual networks. American Journal of Sociology, 110, 44–91.

    Article  Google Scholar 

  • Burk, W. J., van der Vorst, H., Kerr, M., & Sattin, H. (2012). Alcohol intoxication frequency and friendship dynamics: Selection and socialization in early, mid- and late adolescent peer networks. Journal of Studies on Alcohol & Drugs, 73, 89–98.

    Article  Google Scholar 

  • Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357, 370–379.

    Article  CAS  PubMed  Google Scholar 

  • Christakis, N. A., & Fowler, J. H. (2008). The collective dynamics of smoking in a large social network. The New England Journal of Medicine, 358, 2249–2258.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • de Solla Price, D. J. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27, 292–306.

    Article  Google Scholar 

  • Dee, T. S. (1999). The complementarity of teen smoking and drinking. Journal of Health Economics, 18, 769–793.

    Article  CAS  PubMed  Google Scholar 

  • Dishion, T. J., McCord, J., & Poulin, F. (1999). When interventions harm: Peer groups and problem behavior. American Psychologist, 54, 755–764.

    Article  CAS  PubMed  Google Scholar 

  • Doreian, P. (1989). Network autocorrelation models: Problems and prospects. In D. A. Griffith (Ed.), Spatial statistics: Past, present, future (pp. 369–389). Ann Arbor, MI: Michigan Document Services.

    Google Scholar 

  • Ellickson, P. L., & Hays, R. D. (1991). Antecedents of drinking among young adolescents with different alcohol use histories. Journal of Studies on Alcohol, 52, 398–408.

    Article  CAS  PubMed  Google Scholar 

  • Ennett, S. T., Bauman, K. E., Hussong, A., Faris, R., Foshee, V. A., Cai, L., & DuRant, R. H. (2006). The peer context of adolescent substance use: Findings from social network analysis. Journal of Research on Adolescence, 16, 159–186.

    Article  Google Scholar 

  • Fowler, J. H., & Christakis, N. A. (2008). The dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham heart study. British Medical Journal, 337, a2338.

    Article  PubMed  PubMed Central  Google Scholar 

  • Giletta, M., Scholte, R. H., Prinstein, M. J., Engels, R. C., Rabaglietti, E., & Burk, W. J. (2012). Friendship context matters: Examining the domain specificity of alcohol and depression socialization among adolescents. Journal of Abnormal Child Psychology, 40, 1027–1043.

    Article  PubMed  PubMed Central  Google Scholar 

  • Harris, K. M., Halpern, C. T., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., & Udry, J. R. (2009). The national longitudinal study of adolescent health: Research design. Retrieved from http://www.cpc.unc.edu/projects/AddHealth/design.

  • Hipp, J. R., Wang, C., Butts, C. T., Jose, R., & Lakon, C. M. (2015). Research note: The consequences of different methods for handling missing network data in stochastic actor based models. Social Networks, 41, 56–71.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jackson, J., Henriksen, L., & Foshee, V. A. (1998). The authoritative parenting index: Predicting health risk behaviors among children and adolescents. Health Education & Behavior, 25, 319–337.

    Article  CAS  Google Scholar 

  • Kandel, D. B., & Andrews, K. (1987). Processes of adolescent socialization by parents and peers. International Journal of the Addictions, 22, 319–342.

    Article  CAS  PubMed  Google Scholar 

  • Kandel, D. B., & Logan, J. A. (1984). Patterns of drug use from adolescence to young adulthood: Periods of risk for initiation, continued use, and discontinuation. American Journal of Public Health, 74, 660–666.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Knecht, A. B., Burk, W. J., Weesie, J., & Steglich, C. E. G. (2010). Friendship and alcohol use in early adolescence: A multilevel social network approach. Journal of Research on Adolescence, 21, 475–487.

    Article  Google Scholar 

  • Lakon, C. M., Hipp, J. R., Wang, C., Butts, C. T., & Jose, R. (2015). Simulating dynamic network models and adolescent smoking: The impact of varying peer influence and peer selection. American Journal of Public Health, 105, 2438–2448.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lakon, C. M., Wang, C., Butts, C. T., Jose, R., Timberlake, D. S., & Hipp, J. R. (2014). A dynamic model of adolescent friendship networks, parental influences, and smoking. Journal of Youth and Adolescence, 44, 1767–1786.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lospinoso, J. A., & Snijders, T. A. B. (2011). Goodness of Fit for Social Network Dynamics. Presentation at the Sunbelt XXXI, St. Pete’s Beach, FL, 2011. Retrieved from http://www.stats.ox.ac.uk/~lospinos/pubs/GOFPresentation.pdf.

  • Mathys, C., Burk, W. J., & Cillessen, A. H. N. (2013). Popularity as moderator of peer selection and socialization of adolescent alcohol, marijuana, and tobacco use. Journal of Research on Adolescence, 23, 513–523.

    Article  Google Scholar 

  • Mercken, L., Candel, M., Willems, P., & De Vries, H. (2007). Disentangling social selection and social influence effects on adolescent smoking: The importance of reciprocity in friendships. Addiction, 102, 1483–1492.

    Article  PubMed  Google Scholar 

  • Mercken, L., Steglich, C. E. G., Knibbe, R., & Vries, H. D. (2012). Dynamics of friendship networks and alcohol use in early and mid-adolescence. Journal of Studies on Alcohol & Drugs, 73, 99–110.

    Article  Google Scholar 

  • Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89, 208701.

    Article  CAS  PubMed  Google Scholar 

  • Orlando, M., Tucker, J. S., Ellickson, P. L., & Klein, D. J. (2005). Concurrent use of alcohol and cigarettes from adolescence to young adulthood: An examination of developmental trajectories and outcomes. Substance Use & Misuse, 40, 1051–1069.

    Article  Google Scholar 

  • Osgood, D. W., Ragan, D. T., Wallace, L., Gest, S. D., Feinberg, M. E., & Moody, J. (2013). Peers and the emergence of alcohol use: Influence and selection processes in adolescent friendship networks. Journal of Research on Adolescence, 23, 500–512.

    Article  Google Scholar 

  • Pearson, M., Steglich, C. E. G., & Snijders, T. A. B. (2006). Homophily and assimilation among sport-active adolescent substance users. Connect, 27, 47–63.

    Google Scholar 

  • Ripley R. M., Snijders T. A. B., Boda Z., Vörös A., & Preciado P. (2016). Manual for SIENA version 4.0 (version December 16, 2016). Oxford: University of Oxford, Department of Statistics, Nuffield College. Retrieved from http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf.

  • Schaefer, D. R., Adams, J., & Haas, S. A. (2013). Social networks and smoking: Exploring the effects of peer influence and smoker popularity through simulations. Health Education & Behavior, 40, 24S–32S.

    Article  Google Scholar 

  • Shoham, D. A., Tong, L., Lamberson, P. J., Auchincloss, A. H., Zhang, J., Dugas, L., Kaufman, J. S., Cooper, R. S., & Luke, A. (2012). An actor-based model of social network influence on adolescent body size, screen time, and playing sports. PloS One, 7, e39795.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Snijders, T. A. B. (2011). Network dynamics. In J. Scott & P. J. Carrington (Eds.), The SAGE handbook of social network analysis (pp. 501–513). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32, 44–60.

    Article  Google Scholar 

  • Steglich, C. E. G., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329–393.

    Article  Google Scholar 

  • Urberg, K. A., Degirmencioglu, S. M., & Pilgram, C. (1997). Close friend and group influence on adolescent cigarette smoking and alcohol use. Developmental Psychology, 33, 834–844.

    Article  CAS  PubMed  Google Scholar 

  • Valente, T. W., Ritt-Olson, A., Stacy, A., Unger, J. B., Okamoto, J., & Sussman, S. (2007). Peer acceleration: Effects of a social network tailored substance abuse prevention program among high-risk adolescents. Addiction, 102, 1804–1815.

    Article  PubMed  Google Scholar 

  • Wang, C., Butts, C. T., Hipp, J. R., Jose, R., & Lakon, C. M. (2016a). Multiple imputation for missing edge data: A predictive evaluation method with application to add health. Social Networks, 45, 89–98.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, C., Hachen, D. S., & Lizardo, O. (2013). The co-evolution of communication networks and drinking behaviors. AAAI Fall Symposium Series 2013, Social Networks & Social Contagion, 30–36.

  • Wang, C., Hipp, J. R., Butts, C. T., Jose, R., & Lakon, C. M. (2015). Alcohol use among adolescent youth: The role of friendship networks and family factors in a multi-school study. PloS One, 10, e0119965.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, C., Hipp, J. R., Butts, C. T., Jose, R., & Lakon, C. M. (2016b). Coevolution of adolescent friendship networks and smoking and drinking behaviors with consideration of parental influence. Psychology of Addictive Behaviors, 30, 312–324.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Sociology, University of Notre Dame, Notre Dame, IN, 46556, USA

    Cheng Wang

  2. Departments of Criminology, Law and Society and Sociology, University of California, Irvine, Irvine, CA, 92697, USA

    John R. Hipp

  3. Departments of Sociology, Statistics, Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, 92697, USA

    Carter T. Butts

  4. Department of Psychology and Social Behavior, University of California, Irvine, Irvine, CA, 92697, USA

    Rupa Jose

  5. Department of Population Health and Disease Prevention, Program in Public Health, University of California, Irvine, Irvine, CA, 92697, USA

    Cynthia M. Lakon

Authors
  1. Cheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John R. Hipp
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carter T. Butts
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rupa Jose
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Cynthia M. Lakon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng Wang.

Ethics declarations

Funding

This study is funded by Grant no. 1 R21 DA031152-01A1 from National Institute on Drug Abuse (NIDA) Health.

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was reviewed and granted approval under exempt review by the institutional review board at the University of California, Irvine, on April 12, 2013.

Informed Consent

The secondary data used in this study come from early waves of The National Longitudinal Study of Adolescent to Adult Health (Add Health). Add Health participants provided written informed consent for participation in all aspects of Add Health in accordance with the University of North Carolina School of Public Health Institutional Review Board guidelines that are based on the Code of Federal Regulations on the Protection of Human Subjects 45CFR46: http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html. Written informed consent was given by participants (or next of kin/caregiver) for their answers to be used in this study.

Electronic Supplementary Material

ESM 1

(DOCX 468 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Hipp, J.R., Butts, C.T. et al. Peer Influence, Peer Selection and Adolescent Alcohol Use: a Simulation Study Using a Dynamic Network Model of Friendship Ties and Alcohol Use. Prev Sci 18, 382–393 (2017). https://doi.org/10.1007/s11121-017-0773-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11121-017-0773-5

Keywords

Search

Navigation