CONTACT US: Contact info
|
Citation |
Wang C, Hipp JR, Butts CT, Jose R, Lakon CM. Prev. Sci. 2017; 18(4): 382-393. |
|
Affiliation |
Department of Population Health and Disease Prevention, Program in Public Health, University of California, Irvine, Irvine, CA, 92697, USA. |
|
Copyright |
(Copyright © 2017, Holtzbrinck Springer Nature Publishing Group) |
|
DOI |
|
PMID |
28361198 |
|
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. Language: en |
|
Keywords |
Alcohol use; Forward simulation; Goodness of fit; Peer selection and influence; Stochastic Actor-Based Model |