The Impact of Neighboring on Changes in Sense of Community Over Time: A Latent Transition Analysis

Sense of community (SOC) is associated with the quality of community life and the building of social capital. While its linkage to informal social behavior, such as neighboring, is inherent in discussions regarding theory, empirical evidence remains scarce. Moreover, the degree to which neighboring behavior influences SOC over time is largely unknown. Using a latent transition analysis, the effect of neighboring on SOC was investigated over a 5-year span from 2006 to 2011 among a sample of adults (n?=?165) in Arizona. Initially, a latent class analysis identified two SOC subgroups: Low SOC and High SOC. The likelihood of shifts in SOC class membership over 5 years was generally stable, with most individuals staying in the same group (82.3% Low SOC; 92.4% High SOC). Neighboring behavior and socio-demographic covariates impacted the likelihood that individuals changed classes, with 25.3% of Low SOC individuals transitioning to High SOC in 2011 and 55.4% of High SOC individuals moving to Low SOC in 2011. Specifically, having an income greater than $60,000 and visiting with neighbors lessened the likelihood of being in the Low SOC class in 2006; and length of residence and exchanging favors with neighbors lessened the likelihood of being in the Low SOC class in 2011. These findings have implications for both community design and community development practice. Design and development interventions that promote greater social interaction may help build and sustain SOC over time.

     

Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness

Understanding the risk of biological invasions associated with particular transport pathways and source regions is critical for implementing effective biosecurity management. This may require both a model for physical connectedness between regions, and a measure of environmental similarity, so as to quantify the potential for a species to be transported from a given region and to survive at a destination region. We present an analysis of integrated biosecurity risk into Australia, based on flights and shipping data from each global geopolitical region, and an adaptation of the “range bagging” method to determine environmental matching between regions. Here, we describe global patterns of environmental matching and highlight those regions with many physical connections. We classify patterns of global invasion risk (high to low) into Australian states and territories. We validate our analysis by comparison with global presence data for 844 phytophagous insect pest species, and produce a list of high‐risk species not previously known to be present in Australia. We determined that, of the insect pest species used for validation, the species most likely to be present in Australia were those also present in geopolitical regions with high transport connectivity to Australia, and those regions that were geographically close, and had similar environments.     

Gender and the Politics of Death: Female Representation,Political and Developmental Context,and Population Health in a Cross-National Panel

There is considerable speculation that female political empowerment could improve population health. Yet, evidence to date is limited, and explanations for why political empowerment would matter and the conditions under which this might be enhanced or muted are not well understood. In this article, we draw on theoretical work on the politics of representation to frame an investigation of whether increases in the percentage of females in a country’s parliament influence mortality rates. We further examine whether the relationship is conditioned by extent of democracy and economic and social development. Through multivariate longitudinal regression, we analyze four indicators of mortality in 155 countries spanning 1990 to 2014 with controls for initial country conditions, time-stable structural predispositions to higher mortality, and a number of time-varying potential confounders. Results indicate that a high level of female representation—30 % or greater in our models—has large negative associations with mortality, that these are particularly strong in lesser developed and weak democratic contexts, that high female political representation effectively offsets liabilities associated with low development, and that the relationships are robust to various operationalizations of social development. In the end, our research provides a particularly thorough accounting of the relationship between female political representation and population health, particularly by specifying the conditions under which female representation is most salient. In doing so, the research suggests important links between issues of female empowerment, political context, and developmental trajectories of countries more generally.     

Suicide Attempts by Adolescent Latinas: An Exploratory Study of Individual and Family Correlates

Epidemiological data show high rates of suicide attempts among adolescent Latinas. Few studies have addressed the psychosocial, cultural and family correlates of suicide attempts among this age group of a rapidly growing population. The authors studied 31 adolescent Hispanic females who were receiving mental health services; 14 girls had attempted suicide in the previous five years and 17 had never attempted suicide. The two groups of girls did not differ significantly with respect to demographic profiles, levels of depression, family type, acculturation, or self-esteem. However, as hypothesized, the mutuality between girls and their mothers was lower among suicide attempters. Maladaptive coping skills of withdrawal and wishful thinking were more commonly used by attempters, and non-attempters used emotional regulation and problem-solving more frequently. Findings are discussed within the context of the empirical and theoretical literature and implications for practice are considered.     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

Local dependence estimation using semiparametric archimedean copulas

The authors define a new semiparametric Archimedean copula family which has a flexible dependence structure. The generator of the family is a local interpolation of existing generators. It has locally‐defined dependence parameters. The authors present a penalized constrained least‐squares method to estimate and smooth these parameters. They illustrate the flexibility of their dependence model in a bi‐variate survival example.