Research in the area of networks, broadly construed, emphasizes the importance of relationships among interacting units in determining individual demographic choices, health and well-being. DPRC is a leader in the analysis of interconnected outcomes, in large part because of innovations in methods and data collection to analyze linked outcomes.
Connections and interactions among individuals profoundly impact behavior and health. Families are a small network of individuals. When children are young, parents both invest in and influence children; the staying power of these connections influences whether the typical reversal of support, from children to parents, occurs. Children are also profoundly influenced by interactions with peers. While families and peer groups typically comprise a small number of connections, influences across large numbers of people, such as through social networks, are of increasing interest.
Social network analysis has tackled epidemiological studies of pathogen diffusion, peer influence models for the evolution of health-behavior and the spread of medical decision making among health-care providers. Advances in network-based sampling demonstrate that these tools can be leveraged to improve our understanding of diverse health and population problems. The work underway at DPRC is significant, achieving fundamental insights into the effect of social relationships. In the area of network sampling, DPRC and the Carolina Population Center are collaborating to apply these methods in diverse settings, such as the Add Health Parents Study.
DPRC investigators are enthusiastically pursuing approaches to estimating models of interactions among individuals and to using networks as a means of organizing data collection. This has led to innovations in statistical, computational, and sampling methods. For example, research on sampling absent parents is allowing datasets collected on one generation to include important survey information on parents. Network sampling methods that improve implementation of Respondent Driven Sampling, or that implement new network sampling methods, are also providing new ways to collect traditional information about individuals as well as the connections among them.
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