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Prevention Science

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30.03.2017

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

verfasst von: Cheng Wang, John R. Hipp, Carter T. Butts, Rupa Jose, Cynthia M. Lakon

Erschienen in: Prevention Science | Ausgabe 4/2017

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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.

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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.

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.

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 stopping drinking) falls within the range of the predicted networks.

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.CrossRefPubMed

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

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

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.CrossRef

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.CrossRef

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRef

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

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

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.

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.CrossRefPubMed

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRef

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

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.CrossRefPubMedPubMedCentral

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRef

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.CrossRefPubMed

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.CrossRef

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

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.CrossRef

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.CrossRef

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

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.CrossRef

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.CrossRefPubMedPubMedCentral

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.

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.CrossRef

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

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

Titel: Peer Influence, Peer Selection and Adolescent Alcohol Use: a Simulation Study Using a Dynamic Network Model of Friendship Ties and Alcohol Use
verfasst von: Cheng Wang
John R. Hipp
Carter T. Butts
Rupa Jose
Cynthia M. Lakon
Publikationsdatum: 30.03.2017
Verlag: Springer US
Erschienen in: Prevention Science / Ausgabe 4/2017
Print ISSN: 1389-4986
Elektronische ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-017-0773-5

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