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

Multi-level, cross-sectional study of workplace social capital and smoking among Japanese employees

<p>Abstract</p> <p>Background</p> <p>Social capital is hypothesized to be relevant to health promotion, and the association between community social capital and cigarette smoking has been examined. Individual-level social capital has been found to be associated with smoking cessation, but evidence remains sparse on the contextual effect of social capital and smoking. Further, evidence remains sparse on the association between smoking and social capital in the workplace, where people are spending an increasing portion of their daily lives. We examined the association between workplace social capital and smoking status among Japanese private sector employees.</p> <p>Methods</p> <p>We employed a two-stage stratified random sampling procedure. Of the total of 1,800 subjects in 60 companies, 1,171 (men/women; 834/337) employees (65.1%) were identified from 46 companies in Okayama in 2007. Workplace social capital was assessed in two dimensions; trust and reciprocity. Company-level social capital was based on inquiring about employee perceptions of trust and reciprocity among co-workers, and then aggregating their responses in order to calculate the proportion of workers reporting mistrust and lack of reciprocity. Multilevel logistic regression analysis was conducted using Markov Chain Monte Carlo methods to explore whether individual- and company-level social capital was associated with smoking. Odds ratios (ORs) and 95% credible intervals (CIs) for current smoking were obtained.</p> <p>Results</p> <p>Overall, 33.3% of the subjects smoked currently. There was no relationship between individual-level mistrust of others and smoking status. By contrast, one-standard deviation change in company-level mistrust was associated with higher odds of smoking (OR: 1.25, 95% CI: 1.06-1.46) even after controlling for individual-level mistrust, sex, age, occupation, educational attainment, alcohol use, physical activity, body mass index, and chronic diseases. No clear associations were found between lack of reciprocity and smoking both at the individual- and company-level.</p> <p>Conclusions</p> <p>Company-level mistrust is associated with higher likelihood of smoking among Japanese employees, while individual perceptions of mistrust were not associated. The link between lack of reciprocity and smoking was not supported either at the individual- or company-level. Further studies are warranted to examine the possible link between company-level trust and smoking cessation in the Japanese workplace.</p

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/)