Rurality Index for Small Areas in Spain

An operational definition for “rural area” is pivotal if proposals, policies and decisions aimed at optimising the distribution of resources, closing the gap on inequity between areas and raising standards of living for the least advantaged populations are to be put in place. The concept of rurality, however, is often based on alternative and conflicting definitions, requiring clarification of the underlying theoretical model. Traditionally a geographical area has been classified as rural by taking into account either the number of inhabitants or population density. Two kinds of problem are raised with this approach, however, namely: it is inherently difficult to describe such a complex concept as rurality with a single variable; and reducing the concept down to a rural/urban dichotomy by setting a non-universal cut-off point does not usually provide an accurate account of reality. Within the context of the rural–urban continuum, this paper has devised a rurality index for Spanish municipalities based on the 1991 Population, Housing and Household Survey. The index was built on a principal components factor analysis, giving rise to a single factor that is correlated to the aging of the population, economic dependency, farming, livestock or fishing-related employment, habitability of housing and population density. A score to each municipality was allocated. This index can be regularly updated thus enabling the progress of the concept of rurality to be monitored in our setting over time and then compared with other countries using the same methods.     

Adaptation of a community-based participatory research model to gain community input on identifying indicators of successful parenting

Parenting models are generally based on families in stable homes, rather than in transitional situations such as in foster care, homeless shelters, and other temporary, at-risk residences. Consequently, these models do not recognize the unique challenges of families in transition. This study explored the domains of the Circumplex Model and examined its fit for transitional families using tenets from community-based participatory research. Findings suggest that in addition to the Circumplex Model's components, caregivers with children living in transition believe that managing the scrutiny of external authority systems and countering the negative influences of poverty and racism are two indicators that contribute to parenting success. Obtaining consumer-informed views of parenting not only is an important contributor to standards of practice, but also a promising avenue for future research.     

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.     

Smoothed empirical likelihood confidence intervals for the relative distribution with left‐truncated and right‐censored data

The study of differences among groups is an interesting statistical topic in many applied fields. It is very common in this context to have data that are subject to mechanisms of loss of information, such as censoring and truncation. In the setting of a two‐sample problem with data subject to left truncation and right censoring, we develop an empirical likelihood method to do inference for the relative distribution. We obtain a nonparametric generalization of Wilks' theorem and construct nonparametric pointwise confidence intervals for the relative distribution. Finally, we analyse the coverage probability and length of these confidence intervals through a simulation study and illustrate their use with a real data set on gastric cancer. The Canadian Journal of Statistics 38: 453–473; 2010 © 2010 Statistical Society of Canada     

Being Under Time Pressure: The Case of Workers with Disabilities

This article examines levels of time stress reported by people with and without disabilities. Using data at an individual level from the Time Use Survey for Spain in 2002–2003, we estimate an ordered probit model to investigate the determinants of time stress by disability status. We find that disabled individuals work fewer hours, have more free time and engage in more household labour as compared to the non-disabled. The estimation results show that disabled workers (especially those who are severely or moderately limited in their daily activities) suffer from more stress than their non-disabled counterparts. In addition, longer working hours increase the levels of time stress reported by all individuals, but more intensely so among disabled workers.     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.