Transforming the Way We Live Together: A Model to Move Communities From Policy to Implementation

Most cities, counties, and neighborhoods are not designed for an aging population. By providing a range of services to all residents, Lifelong Communities allow individuals to age in place. Although the Lifelong Communities Initiative is based on established guiding principles, little information exists regarding the realities of moving from policy to implementation. The Atlanta Regional Commission conducted a case study in Mableton, Georgia, and found successful implementation requires a combination of support from local citizen groups and government. The Atlanta Regional Commission is replicating these best practices in other communities and providing support to those aspiring to launch or expand Lifelong Communities.     

A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models

The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical characteristics of the network to be encapsulated within an exponential family random graph (ERG) model. For a long time, however, likelihood-based estimation was only feasible for ERG models assuming dyad independence. For more realistic and complex models inference has been based on the pseudo-likelihood. Recent advances in computational methods have made likelihood-based inference practical, and comparison of the different estimators possible.     

Model-based clustering for social networks

Summary.  Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, the latent position cluster model , under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean 'social space', and the actors' locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster. We propose two estimation methods: a two-stage maximum likelihood method and a fully Bayesian method that uses Markov chain Monte Carlo sampling. The former is quicker and simpler, but the latter performs better. We also propose a Bayesian way of determining the number of clusters that are present by using approximate conditional Bayes factors. Our model represents transitivity, homophily by attributes and clustering simultaneously and does not require the number of clusters to be known. The model makes it easy to simulate realistic networks with clustering, which are potentially useful as inputs to models of more complex systems of which the network is part, such as epidemic models of infectious disease. We apply the model to two networks of social relations. A free software package in the R statistical language, latentnet, is available to analyse data by using the model.     

Salience,Valence, Context,and Integration: Conceptualizing the Needs of Sexually and Gender Diverse Youth in P–12 Schools

A framework for conceptualizing the needs of lesbian, gay male, bisexual, transgender, questioning, and other sexually and gender diverse youth is essential for guiding service delivery throughout educational settings. Review and synthesis of the literature reveals that integrating assessment of the salience, valence, and context of issues related to sexual and gender diversity facilitates affirmative practice at the individual student, group, and school-wide levels. The authors describe the components of the salience, valence, context, and integration (SVCI) model with emphasis on practical application. The theoretical and empirical support for each component of the model is also discussed.     

Social Responsibility,Substance Use,and Sexual Risk Behavior in Men Who Have Sex With Men

Considerable public health literature focuses on relationships between problematic human characteristics (e.g., psychopathology) and unhealthy behaviors. A recent movement termed positive psychology emphasizes the advantages of assessing relationships between human strengths (e.g., altruism) and beneficial health behaviors. The present study assessed social responsibility, an orientation to help or protect others even when there is nothing to be gained as an individual, and its relationship to HIV-relevant behaviors. In our sample of 350 men who have sex with men (MSM), social responsibility was negatively correlated with substance use and HIV risk behaviors. Men who had been tested for HIV and knew their HIV status—a behavior that helps men protect their partners but does not protect themselves from the virus—also scored higher in social responsibility. Interventions designed to reduce HIV risk behavior in MSM may benefit from efforts to promote human strengths.     

Respondent-Driven Sampling: An Assessment of Current Methodology

Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure to expand the sample and reduce its dependence on the initial (convenience) sample.The current estimators of population averages make strong assumptions in order to treat the data as a probability sample. We evaluate three critical sensitivities of the estimators: to bias induced by the initial sample, to uncontrollable features of respondent behavior, and to the without-replacement structure of sampling.Our analysis indicates: (1) that the convenience sample of seeds can induce bias, and the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness; (2) that preferential referral behavior by respondents leads to bias; (3) that when a substantial fraction of the target population is sampled the current estimators can have substantial bias.This paper sounds a cautionary note for the users of RDS. While current RDS methodology is powerful and clever, the favorable statistical properties claimed for the current estimates are shown to be heavily dependent on often unrealistic assumptions. We recommend ways to improve the methodology.     

Population constraints on pooled surveys in demographic hazard modeling

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.     

The Culture of High-Risk Alcohol Use Among Club and Intramural Athletes

Abstract

Objective: The purpose of this study was to examine the drinking patterns of club and intramural college athletes and compare their alcohol consumption, perceived norms around the excessive use of alcohol, experience of negative consequences, and employment of protective strategies with those of campus varsity athletes. Participants: A total of 442 undergraduate students attending a private, suburban institution in the Northeast participated in the American College Health Association National College Health Assessment-II Web survey in spring 2011. Thirty-five students identified themselves as varsity athletes, 76 identified as club sport athletes, and 196 students identified themselves as intramural athletes. Methods: Survey responses were analyzed using Statistical Package for the Social Sciences. The Pearson's correlation coefficient and test for independence were applied to identify significant relationships between athlete status and identified variables related to alcohol use. Results: Results indicated that there were significant correlations between athlete status and all variables, to varying degrees. Conclusions: These findings have implications for campus health promotion professionals and athletics program coordinators seeking to address high-risk alcohol use among college athletes.