Demographic and Predeparture Factors Associated with Drinking and Alcohol-Related Consequences for College Students Completing Study Abroad Experiences

Abstract

Objective: Study abroad students are at risk for increased and problematic drinking behavior. As few efforts have been made to examine this at-risk population, the authors predicted drinking and alcohol-related consequences abroad from predeparture and site-specific factors. Participants: The sample consisted of 339 students completing study abroad programs. Methods: Participants filled out online measures at predeparture, abroad, and at postreturn. Results: The authors found that drinking and consequences abroad were predicted by a number of factors, including demographics (eg, younger age, male sex, Greek affiliation, white ethnicity), student factors (eg, low GPA, major area of study), study abroad site factors (eg, apartment living abroad, study in Europe), predeparture levels of drinking and consequences, sensation seeking, and goals related to social gathering. Conclusions: Findings can be used to inform campus policies for admission to study abroad programs as well as assist in the development of interventions targeted toward preventing risk for students during abroad experiences.     

Modeling trade policies under alternative market structures

The paper focuses on the importance of assumptions made about market structure and firm behavior in empirical trade policy analysis. The contribution to the relevant literature is 3-fold: first the paper develops two original models which incorporate imperfectly competitive market structures in a spatial modeling framework; then it proposes a procedure to identify the degree of market power in international trading which is consistent with observed prices and traded quantities, and applies it to the banana market; finally, it assesses how analysis of the implications of recent changes in the EU import regimes for bananas (the Economic Partnership Agreements and the December 2009 WTO agreement) is affected by the assumptions made on the prevailing market structure.     

A new rank correlation measure

A new rank correlation measure β _n is proposed, so as to develop a nonparametric test of independence for two variables. β _n is shown to be the symmetrized version of a measure earlier proposed by Borroni and Zenga (Stat Methods Appl 16:289–308, 2007). More specifically, β _n is built so that it can take the opposite sign, without changing its absolute value, when the ranking of one variable is reversed. Further, the meaning of the population equivalent of β _n is discussed. It is pointed out that this latter association measure vanishes not only at independence but, more generally, at indifference, that is when the two variables do not show any “tendency” to positive or negative dependence. The null distribution of β _n needs an independent study: hence, the finite null variance and a table of critical values are determined. Moreover, the asymptotic null distribution of β _n is derived. Finally, the performance of the test based on β _n is evaluated by simulation. β _n is shown to be a good competitor of some classical tests for the same problem.     

A politico-economic model of aging,technology adoption and growth

This paper provides a politico-economic theory that explains how an economy evolves when the longevity of its citizens is jointly determined with the process of economic development. We propose a three-period overlapping generation model where agents’ decisions embrace two dimensions: a private choice about education and a public one on innovation policy. We find that (a) poverty traps can emerge in human capital accumulation, (b) higher life expectancy increases the incentive to innovate for both young and adults, (c) different political configurations can arise depending on endogenous demographic structures and (d) the steady state can entertain both innovation and its absence.     

The Role of Metacognition in Pathological Gambling: A Mediation Model

Pathological gambling involves multitudinous costs related to financial, legal, and public health care aspects, as well as to specific psychological disorders. Despite the overall evidence suggesting that comorbid disorders represent a risk factor for pathological gambling, there is scant evidence on the appropriate treatments for gamblers with such disorders. In this context, metacognitive therapy is an interesting approach because it considers psychological disorders as a result of the activation of perseverative cognitive processes and attentional strategies in response to inner events. Several studies report that metacognition is associated with different psychological problems. This study investigated the relationship among comorbid disorders, metacognition, and pathological gambling. 69 pathological gamblers at the first hospital admission and 58 controls drawn from general population (matched for age, gender, education) completed a battery of self report instruments: Symptom Checklist-90-R, Metacognition Questionnaire 30, South Oaks Gambling Scale. Compared to controls, pathological gamblers showed higher level of comorbid symptomatology and metacognition. Correlation analyses showed that: comorbid symptomatology and metacognition were positively and significantly correlated with pathological gambling; metacognition was positively and significantly associated with comorbid symptomatology. Mediation analysis indicated that dysfunctional metacognitive strategies could have an indirect effect on pathological gambling mediated by concurrent psychological disorders. These findings provide some implications for gambling treatment programs: pathological gamblers should be screened for psychiatric disorders, and metacognitive therapy could be considered a correct treatment of pathological gamblers. Metacognitive therapy might lead to the reduction of the pathological gambling by the diminishing of the concurrent psychological disorders.     

Some measures of kurtosis and their inference on large datasets

AStA Advances in Statistical Analysis - This paper deals with the estimation of kurtosis on large datasets. It aims at overcoming two frequent limitations in applications: first, Pearson's...     

On Bayesian learning via loss functions

We provide a decision theoretic approach to the construction of a learning process in the presence of independent and identically distributed observations. Starting with a probability measure representing beliefs about a key parameter, the approach allows the measure to be updated via the solution to a well defined decision problem. While the learning process encompasses the Bayesian approach, a necessary asymptotic consideration then actually implies the Bayesian learning process is best. This conclusion is due to the requirement of posterior consistency for all models and of having standardized losses between probability distributions. This is shown considering a specific continuous model and a very general class of discrete models.     

Bayesian inference for contingency tables with given marginals

Summary In this paper we introduce a class of prior distributions for contingency tables with given marginals. We are interested in the structrre of concordance/discordance of such tables. There is actually a minor limitation in that the marginals are required to assume only rational values. We do argue, though, that this is not a serious drawback for all applicatory purposes. The posterior and predictive distributions given anM-sample are computed. Examples of Bayesian estimates of some classical indices of concordance are also given. Moreover, we show how to use simulation in order to overcome some difficulties which arise in the computation of the posterior distribution.     

A statistical framework for expression-based molecular classification in cancer

Summary. Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.