The contribution of the intervention in social networks and community social work at the local level to social and human development

ABSTRACT

The primary task of community social work is building social networks by reinforcing people's resources and those of the different environmental and social contexts from three dimensions: personal development, social development and organisational development. The new information technologies today establish a relationship of communication with local communities and citizens that promotes proximity to social networks. Social intervention is supported by a set of methods from human geography that can be used as tools to create maps of the territory and the networks for planning, diagnosing and classifying the management of community network intervention. In this discussion we set out to analyse the contribution of the intervention in social networks as a means of achieving a new configuration of social networks at the local level. This information is obtained from semi-structured interviews with social workers and other professionals in the social sphere in municipalities with over 100,000 inhabitants in the Madrid region (Spain). The research results show that intervention in social networks locally multiplies the opportunities to enhance the quality of people's social relationships, thus expanding their social support by strengthening their bonds, their personal network and support systems; secondly, it increases empowerment to facilitate a type of intervention to strengthen human potential and to gain autonomy and full citizenship.     

Use of augmented reality in the usability evaluation of products

Many characteristics of a product are evaluated during the development phase, such as function, aesthetics and manufacturing. The conception phase is supported by drawings and computer graphics, followed by physical prototypes that help evaluating the product features. The augmented reality technology has a great potential to assist the designers in this phase, reducing the development process time and costs and improving the quality of the evaluation. This paper presents a study that intends to investigate the suitability of using augmented reality to evaluate the usability of electronic products.     

Estimation in mixed models through three step minimization

The aim of this article is to present an estimation procedure for both fixed effects and variance components in linear mixed models. This procedure consists of a maximum-likelihood method which we call Three Step Minimization (TSM). The major contribution of this method is that when variances tend to be null standard algorithms behave badly, unlike the TSM method, which uses a grid search algorithm in a compact set. A numerical application with real and simulated data is provided.     

Linear censored regression models with skew scale mixtures of normal distributions

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.     

Interval-censored data with misclassification: a Bayesian approach

Survival data involving silent events are often subject to interval censoring (the event is known to occur within a time interval) and classification errors if a test with no perfect sensitivity and specificity is applied. Considering the nature of this data plays an important role in estimating the time distribution until the occurrence of the event. In this context, we incorporate validation subsets into the parametric proportional hazard model, and show that this additional data, combined with Bayesian inference, compensate the lack of knowledge about test sensitivity and specificity improving the parameter estimates. The proposed model is evaluated through simulation studies, and Bayesian analysis is conducted within a Gibbs sampling procedure. The posterior estimates obtained under validation subset models present lower bias and standard deviation compared to the scenario with no validation subset or the model that assumes perfect sensitivity and specificity. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that have been discussed in the literature.     

Recombinative generalization: an exploratory study in musical reading

The present study aimed to extend the findings of recombinative generalization research in alphabetical reading and spelling to the context of musical reading. One participant was taught to respond discriminatively to six two-note sequences, choosing the corresponding notation on the staff in the presence of each sequence. When novel three- and four-note sequences were presented, she selected the corresponding notation. These results suggest the generality of previous research to the context of musical teaching.     

Constraints and opportunities for civic and political participation: perceptions of young people from immigrant and non-immigrant background in Portugal

Youth civic and political participation (CPP) has been a central concern of research and public policy. This situation has been motivated by growing signs of the disaffection of younger generations, at least regarding conventional forms of participation. Recent theoretical debates stress how forms of CPP are evolving; nevertheless it is obviously important to integrate young people's views in the discussion, particularly taking into account groups at risk of exclusion, such as immigrants. This paper intends to contribute to this discussion by considering the meanings that young people attribute to their civic and political experiences, using data collected with focus groups (N = 94) that address the factors that facilitate and/or inhibit the participation of young people from immigrant (Brazilian and Angolan) and non-immigrant (Portuguese) backgrounds. Data will be analysed according to three main dimensions: (1) participants' sources of knowledge, information and influence; (2) participants' views on civic and political engagement: relevance, resources, personal experiences, trustworthiness and efficacy; and (3) participants' perceptions of excluded groups and proposals to promote inclusion. Results show that the experiences and levels of participation of young people of Brazilian and Angolan origin are influenced by their immigrant background. In addition, they indicate a strong tendency of young people to emphasise constraints over opportunities. They feel like incomplete or in-the-making citizens, and state their claim for rights and opportunities to be heard and to be civic and politically engaged.     

Comparing the performance of normality tests with ROC analysis and confidence intervals

There are several statistical hypothesis tests available for assessing normality assumptions, which is an a priori requirement for most parametric statistical procedures. The usual method for comparing the performances of normality tests is to use Monte Carlo simulations to obtain point estimates for the corresponding powers. The aim of this work is to improve the assessment of 9 normality hypothesis tests. For that purpose, random samples were drawn from several symmetric and asymmetric nonnormal distributions and Monte Carlo simulations were carried out to compute confidence intervals for the power achieved, for each distribution, by two of the most usual normality tests, Kolmogorov–Smirnov with Lilliefors correction and Shapiro–Wilk. In addition, the specificity was computed for each test, again resorting to Monte Carlo simulations, taking samples from standard normal distributions. The analysis was then additionally extended to the Anderson–Darling, Cramer-Von Mises, Pearson chi-square Shapiro–Francia, Jarque–Bera, D'Agostino and uncorrected Kolmogorov–Smirnov tests by determining confidence intervals for the areas under the receiver operating characteristic curves. Simulations were performed to this end, wherein for each sample from a nonnormal distribution an equal-sized sample was taken from a normal distribution. The Shapiro–Wilk test was seen to have the best global performance overall, though in some circumstances the Shapiro–Francia or the D'Agostino tests offered better results. The differences between the tests were not as clear for smaller sample sizes. Also to be noted, the SW and KS tests performed generally quite poorly in distinguishing between samples drawn from normal distributions and t Student distributions.