This application addresses broad Challenge Area (01) Behavior, Behavioral Change, and Prevention and specific Challenge Topic 01-AA-103* Capturing Social Network Information for Groups at High Risk for Negative Health Behaviors Research on social network influences on adolescent health behavior has focused primarily on assessments of peer influence and selection within friendship networks. Although friendships are important, and perhaps the most important adolescent relationship, they may not necessarily represent the relationship that put all adolescents at risk for negative health behaviors. For example, it may be that some adolescents are more strongly influenced to try negative health behaviors (smoking, drinking) by their desired romantic partners or those they perceive as popular rather than friends. While several findings in the field of adolescent health behavior are well-established, considerable basic science regarding the collection of network data remains to be done. This study proposes to investigate how social network data may be used to identify adolescents at risk for negative health behaviors such as smoking, alcohol use, or drug use by comparing several aspects of survey data collection. These comparisons are necessary to establish the best methods for measuring social influence and hence designing effective interventions. To conduct this research we will collect new longitudinal data among a cohort of 10th grade students in five southern California high schools. While evidence indicates that network effects are strong and persistent, there remain several questions regarding how social network data should be collected among in-school adolescents. This often occurs for example because the network questions are rarely the focus of adolescent health research, the behaviors are. At least five unanswered questions within the field of social network influences will be investigated. These issues are the: (1) type of network that is most strongly associated with peer influence and/or selection;(2) relevant boundary for asking network questions, (3) extent to which tie strength matters, (4) the correspondence between online and offline networks, and (5) the intersection of social networks with social identity and group/team affiliations. The first dimensions, network type and boundary specification will be systematically varied with survey administration to measure up to 25 different networks in this population. The new data and networks will be used to assess social network risk factors for tobacco, alcohol, and substance use at two time points within one academic year. Baseline data will be used to identify at-risk youth using specific well-established network indicators based on the following categorization schemes: non-using students who are: (a) popular students embedded within using networks;(b) embedded in using friendship networks;and (c) bridges or marginals connected to using segments in their social networks. Analyses will then determine the stability and predictability of high-risk categorizations given variations in network type, network boundary, and tie strength. This study varies survey question wording and methodology to determine the most salient networks for estimating peer influence and selection on adolescent substance use. Data are collected among 10th grade students at two points in time in one academic year and also linked to adolescents'online profiles on social networking sites. The stability and predictability of at risk categorizations based on network and behavior are estimated.
This study varies survey question wording and methodology to determine the most salient networks for estimating peer influence and selection on adolescent substance use. Data are collected among 10th grade students at two points in time in one academic year and also linked to adolescents'online profiles on social networking sites. The stability and predictability of at risk categorizations based on network and behavior are estimated.
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