A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Network-based social capital and capacity-building programs: an example from Ethiopia

<p>Abstract</p> <p>Introduction</p> <p>Capacity-building programs are vital for healthcare workforce development in low- and middle-income countries. In addition to increasing human capital, participation in such programs may lead to new professional networks and access to social capital. Although network development and social capital generation were not explicit program goals, we took advantage of a natural experiment and studied the social networks that developed in the first year of an executive-education Master of Hospital and Healthcare Administration (MHA) program in Jimma, Ethiopia.</p> <p>Case description</p> <p>We conducted a sociometric network analysis, which included all program participants and supporters (formally affiliated educators and mentors). We studied two networks: the Trainee Network (all 25 trainees) and the Trainee-Supporter Network (25 trainees and 38 supporters). The independent variable of interest was out-degree, the number of program-related connections reported by each respondent. We assessed social capital exchange in terms of resource exchange, both informational and functional. Contingency table analysis for relational data was used to evaluate the relationship between out-degree and informational and functional exchange.</p> <p>Discussion and evaluation</p> <p>Both networks demonstrated growth and inclusion of most or all network members. In the Trainee Network, those with the highest level of out-degree had the highest reports of informational exchange, χ²(1, <it>N </it>= 23) = 123.61, p < 0.01. We did not find a statistically significant relationship between out-degree and functional exchange in this network, χ²(1, <it>N </it>= 23) = 26.11, p > 0.05. In the Trainee-Supporter Network, trainees with the highest level of out-degree had the highest reports of informational exchange, χ²(1, <it>N </it>= 23) = 74.93, p < 0.05. The same pattern held for functional exchange, χ²(1, <it>N </it>= 23) = 81.31, p < 0.01.</p> <p>Conclusions</p> <p>We found substantial and productive development of social networks in the first year of a healthcare management capacity-building program. Environmental constraints, such as limited access to information and communication technologies, or challenges with transportation and logistics, may limit the ability of some participants to engage in the networks fully. This work suggests that intentional social network development may be an important opportunity for capacity-building programs as healthcare systems improve their ability to manage resources and tackle emerging problems.</p

The density of tobacco retailers in both home and school environments and relationship with adolescent smoking behaviours in Scotland.

Background Neighbourhood retailing of tobacco products has been implicated in affecting smoking prevalence rates. Long-term smoking usually begins in adolescence and tobacco control strategies have often focused on regulating ‘child spaces’, such as areas in proximity to schools. This cross-sectional study examines the association between adolescent smoking behaviour and tobacco retail outlet density around home and school environments in Scotland. Methods Data detailing the geographic location of every outlet registered to sell tobacco products in Scotland were acquired from the Scottish Tobacco Retailers Register and used to create a retail outlet density measure for every postcode. This measure was joined to individual responses of the Scottish Schools Adolescent Lifestyle and Substance Use Survey (n=20 446). Using logistic regression models, we explored the association between the density of retailers, around both home and school address, and smoking behaviours. Results Those living in the areas of highest density of retailers around the home environment had 53% higher odds of reporting having ever smoked (95% CI 1.27 to 1.85, p&#60;0.001) and 47% higher odds of reporting current smoking (95% CI 1.13 to 1.91 p&#60;0.01). Conversely, those attending schools in areas of highest retail density had lower odds of having ever smoked (OR 0.66, 95% CI 0.50 to 0.86 p&#60;0.01) and lower odds of current smoking (OR 0.75, 95% CI 0.59 to 0.95, p&#60;0.05). Conclusions The density of tobacco retail outlets in residential neighbourhoods is associated with increased odds of both ever smoked and current smoking among adolescents in Scotland. Policymakers may be advised to focus on reducing the overall density of tobacco outlets, rather than concentrating on ‘child spaces’

The Moderating Role of Context: Relationships between Individual Behaviors and Social Networks

A social context can be viewed as an entity or unit around which a group of individuals organize their activities and interactions. Social contexts take such diverse forms as families, dwelling places, neighborhoods, classrooms, schools, workplaces, voluntary organizations, and sociocultural events or milieus. Understanding social contexts is essential for the study of individual behaviors, social networks, and the relationships between the two. Contexts shape individual behaviors by providing an avenue for non-dyadic conformity and socialization processes. The co-participation within a context affects personal relationships by acting as a focus for tie formation. Where participation in particular contexts confers status, this effect may also lead to differences in popularity within interpersonal networks. Social contexts may further play a moderating role in within-network influence and selection processes, providing circumstances that either amplify or suppress these effects. In this paper we investigate the joint role of co-participation via social contexts and dyadic interaction in shaping and being shaped by individual behaviors with the context of a U.S. high school. Implications for future study of social contexts are suggested

Model Adequacy Checking/Goodness-of-fit Testing for Behavior in Joint Dynamic Network/Behavior Models, with an Extension to Two-mode Networks

The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to reproduce network structure over time, to date there are few indices for assessing the ability of the model to reproduce individuals’ behavior patterns. Drawing on the widely used strategy of assessing model adequacy by comparing index values summarizing features of the observed data to the distribution of those index values on simulated data from the fitted model, we propose four goals that a researcher could reasonably expect of a joint structure/behavior model regarding how well it captures behavior and describe indices for assessing each of these. These reasonably simple and easily implemented indices can be used for assessing model adequacy with any dynamic network models jointly working with networks and behavior, including the stochastic actor-based models implemented within software packages such as RSien version 1.2-24. We demonstrate the use of our indices with an empirical example to show how they can be employed in practical settings, with an additional extension to modeling affiliation dynamics in two-mode networks. Key scripts are provided in the Supplemental Document (which can be found at http://smr.sagepub.com/supplemental/)

Mechanisms through which drug, sex partner, and friendship network characteristics relate to risky needle use among high risk youth and young adults

Drug injector social networks are a primary social space in which risky needle use behaviors associated with HIV transmission occur, but the mechanisms through which these social networks influence risky needle use are unclear. This study investigated two mechanisms, social support and social regulation, through which injection drug users' social networks might relate to risky needle use behaviors. We investigated how these mechanisms work in three types of social networks, namely, drug user, sex partner, and friendship networks. Data are from a study of HIV risk and protective behaviors of youth and young adults, ages 14-43, in the United States who were injection drug users and/or the sexual partners of users (N=277). The three types of networks were constructed based on information respondents provided about their drug use partners, sexual partners, and friends. The networks were characterized by structural (i.e., size and density) and interactional (i.e., multiplexity and closeness) characteristics. We conducted tests for mediation using ordered probit models and multiple linear regression. In the drug networks, social regulation partially meditated the relationship between multiplexity and risky needle use (pSocial networks Injection drug use Social regulation Risk behaviour Youth USA