In this article, we use newly developed statistical methods to examine the generative processes that give rise to widespread patterns in friendship networks. The methods incorporate both traditional demographic measures on individuals (age, sex, and race) and network measures for structural processes operating on individual, dyadic, and triadic levels. We apply the methods to adolescent friendship networks in 59 U.S. schools from the National Longitudinal Survey of Adolescent Health (Add Health). We model friendship formation as a selection process constrained by individuals’ sociality (propensity to make friends), selective mixing in dyads (friendships within race, grade, or sex categories are differentially likely relative to cross-category friendships), and closure in triads (a friend’s friends are more likely to become friends), given local population composition. Blacks are generally the most cohesive racial category, although when whites are in the minority, they display stronger selective mixing than do blacks when blacks are in the minority. Hispanics exhibit disassortative selective mixing under certain circumstances; in other cases, they exhibit assortative mixing but lack the higher-order cohesion common in other groups. Grade levels are always highly cohesive, while females form triangles more than males. We conclude with a discussion of how network analysis may contribute to our understanding of sociodemographic structure and the processes that create it.

/pdf/birds-of-a-feather-or-friend-of-a-friend-using-exponential-3epw4r8nt7.pdf

Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks

Network analysis is an approach to research that is uniquely suited to describing, exploring, and understanding structural and relational aspects of health. It is both a methodological tool and a theoretical paradigm that allows us to pose and answer important ecological questions in public health. In this review we trace the history of network analysis, provide a methodological overview of network techniques, and discuss where and how network analysis has been used in public health. We show how network analysis has its roots in mathematics, statistics, sociology, anthropology, psychology, biology, physics, and computer science. In public health, network analysis has been used to study primarily disease transmission, especially for HIV/AIDS and other sexually transmitted diseases; information transmission, particularly for diffusion of innovations; the role of social support and social capital; the influence of personal and social networks on health behavior; and the interorganizational structure of health systems. We conclude with future directions for network analysis in public health.

https://www.annualreviews.org/doi/pdf/10.1146/annurev.publhealth.28.021406.144132

Network Analysis in Public Health: History, Methods, and Applications

Complex systems abound in public health. Complex systems are made up of heterogeneous elements that interact with one another, have emergent properties that are not explained by understanding the individual elements of the system, persist over time, and adapt to changing circumstances. Public health is starting to use results from systems science studies to shape practice and policy, for example in preparing for global pandemics. However, systems science study designs and analytic methods remain underutilized and are not widely featured in public health curricula or training. In this review we present an argument for the utility of systems science methods in public health, introduce three important systems science methods (system dynamics, network analysis, and agent-based modeling), and provide three case studies in which these methods have been used to answer important public health science questions in the areas of infectious disease, tobacco control, and obesity.

Systems science methods in public health: dynamics, networks, and agents.

This study assessed the hypothesis that popularity in adolescence takes on a twofold role, marking high levels of concurrent adaptation but predicting increases over time in both positive and negative behaviors sanctioned by peer norms. Multimethod, longitudinal data, on a diverse community sample of 185 adolescents (13 to 14 years), addressed these hypotheses. As hypothesized, popular adolescents displayed higher concurrent levels of ego development, secure attachment, and more adaptive interactions with mothers and best friends. Longitudinal analyses supported a popularity-socialization hypothesis, however, in which popular adolescents were more likely to increase behaviors that receive approval in the peer group (e.g., minor levels of drug use and delinquency) and decrease behaviors unlikely to be well received by peers (e.g., hostile behavior with peers).

The two faces of adolescents' success with peers: adolescent popularity, social adaptation, and deviant behavior.

To examine the peer context of adolescent substance use, social network analysis was used to measure three domains of attributes of peer networks: social embeddedness, social status, and social proximity to substance users. The sample was a panel of 5,104 sixth, seventh, and eighth graders in three public school systems surveyed every 6 months for five assessments. Hierarchical generalized linear models showed that adolescents less embedded in the network, with greater status, and with closer social proximity to peer substance users were more likely to use substances. Also, adolescents in less dense networks and networks with higher smoking prevalence were more likely to smoke and use marijuana. Results establish the utility of social network analysis for measuring peer context and indicate that conventionality of relationships—having friends in the network, being liked but not too well liked, and having fewer friends who use substances—is most beneficial.

Marina Piazza

Papers

Peers, schools, and adolescent cigarette smoking