“I Am Becoming More and More Like My Eldest Brother!”: The Relationship Between Older Siblings,Adolescent Gambling Severity,and the Attenuating Role of Parents in a Large-Scale Nationally Representative Survey Study

The present study examined the association between having older siblings who gamble and adolescent at-risk/problem gambling and how parents (i.e., parental knowledge of their whereabouts) and peers might moderate such effects. Data were drawn from the ESPAD^®Italia2012 survey (European School Survey Project on Alcohol and Other Drugs) comprising a nationally representative Italian sample of adolescents. The analysis was carried out on a subsample of 10,063 Italian students aged 15–19 years (average age = 17.10; 55 % girls) who had at least one older sibling and who had gambled at some point in their lives. Respondents’ problem gambling severity, older gambler sibling, gambler peers, parental knowledge, and socio-demographic characteristics were individually assessed. Multinomial logistic regression analyses including two- and three-way interactions were conducted. The odds of being an at-risk/problem gambler were higher among high school students with older siblings that gambled and those with peers who gambled. Higher parental knowledge (of who the adolescent was with and where they were in their leisure time) was associated with lower rates of at-risk/problem gambling. There was also an interaction between gamblers with older siblings and parental knowledge. The combination of having siblings who gambled and a greater level of parental knowledge was associated with lower levels of problem gambling. The present study confirmed the occurrence of social risk processes (older siblings and peers who gambled) and demonstrated that gambling among older siblings and peers represents an important contextual factor for increased at-risk/problem gambling. However, parental knowledge appears to be sufficient to counterbalance the influence of older siblings.     

A Systems-Crisis Approach to Family Treatment

This paper presents a model which integrates a wide range of possible interventions using family treatment and provides a method of differential decision making with regard to the intervention to be used in a given case. The model uses an ecological perspective and draws on concepts from general systems theory and crisis theory. A number of case examples are provided to demonstrate how the model has been applied in a clinical setting.     

Bayesian Kernel Mixtures for Counts

Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.     

A Note On Regions of Given Probability of the Extended Skew-normal Distribution

The construction of regions with assigned probability p and minimum geometric measure has theoretical and practical interests, such as the construction of tolerance regions. Following Azzalini (2001 Azzalini, A. (2001). A note on regions of given probability of the skew-normal distribution. Metron 59(3-4): 27–34. [Google Scholar]) and exploiting the normal approximation of the extended skew-normal distribution when some of its parameters go to infinity, we discuss an approach for the construction of regions with assigned probability p for the bivariate extended skew-normal distribution.     

Bayesian modeling of university first-year students' grades after placement test

University first-year students grades are naturally correlated with the scores obtained at placement tests. Often this characteristic leads the university grades in the first exams to be asymmetrically distributed. Motivated by the analysis of grades of the basic Statistics examination of first-year students, we discuss informative priors for the shape parameter of the skew-normal model, a class of distribution which account for several degree of asymmetry. Our proposed prior leads to closed-form full-conditional posterior distributions, particularly useful in Markov Chain Monte Carlo simulation. A Gibbs sampling algorithm is discussed for the joint vector of parameters and the method is applied to a real data set from the School of Economics, University of Padua, Italy. Our analysis reveals that the correlation between the placement test and the grades of first-year students leads to a measurable positive skewness of the distribution of the university grades.