Alcohol Consumption Among University Students in North Rhine–Westphalia,Germany—Results From a Multicenter Cross-sectional Study

Abstract

Objective: To assess alcohol use and problem drinking among university students in the German Federal State of North Rhine–Westphalia (NRW) and to examine the associated factors. Method: A multicenter cross-sectional study was conducted in 16 universities in 2006–2007 in NRW by a standardized questionnaire and 3,306 students provided information (response rate of 88%). Problem drinking was measured by the CAGE questionnaire. Results: Alcohol consumption in the last 3 months was reported by >90 % of students. About 80% reported heavy drinking, and 20% displayed problem drinking. Male students, students living in residence halls, and students from sport faculties had a higher risk of heavy drinking and problem drinking. When students were compared across study years, frequency of heavy drinking decreased with higher semesters. Conclusions: Overall, heavy drinking and problem drinking are common among university students in this sample. Intervention programs should be designed for students at a particularly high risk.     

DO LAB EXPERIMENTS MISREPRESENT SOCIAL PREFERENCES? THE CASE OF SELF‐SELECTED STUDENT SAMPLES

Social preference research has received considerable attention among economists in recent years. However, the empirical foundation of social preferences is largely based on laboratory experiments with self‐selected students as participants. This is potentially problematic as students participating in experiments may behave systematically different than nonparticipating students or nonstudents. In this paper we empirically investigate whether laboratory experiments with student samples misrepresent the importance of social preferences. Our first study shows that students who exhibit stronger prosocial inclinations in an unrelated field donation are not more likely to participate in experiments. This suggests that self‐selection of more prosocial students into experiments is not a major issue. Our second study compares the behavior of students and participants recruited from the general population in a trust experiment. In general, we find very similar behavioral patterns for the two groups, but nonstudents make significantly more generous repayments suggesting that results from student samples might be seen as a lower bound for the importance of prosocial behavior.     

Sustainable Development and Capabilities for the Polar Region

The paper develops a sustainable development framework for individual and collective capabilities in mixed subsistence and wage-based economies. We apply this framework to such regions of the Arctic and evaluate interactions and conflicts between two sectors of the mixed economy and between current and future generations of Arctic inhabitants. A recent Arctic Social Indicators Report published by the Arctic Human Development Report (AHDR) Task Force recognizes the importance of the mixed economy in the Arctic and aims to integrate collective assets, as well as individual assets in order to understand the human development in the Arctic. Yet due to its concerns of comparability of social development and data availability across the whole Arctic region (of which some parts do not have the similar population structure), its proposed indicators are not capable of covering the social development of predominantly indigenous regions of the North. We emphasize the importance of tracking collective capabilities, as well as individual capabilities to sustain community development. In addition we suggest that environmental sustainability, which is ignored by the AHDR Task Force, has to be integrated with social development as environmental deterioration significantly influences the social well-being and cultural stability of traditional inhabitants of the Arctic. We critically review the proposed indicators of the AHDR Task Force and make supplementary and alternative suggestions.     

Regularized Gaussian belief propagation

Belief propagation (BP) has been applied in a variety of inference problems as an approximation tool. BP does not necessarily converge in loopy graphs, and even if it does, is not guaranteed to provide exact inference. Even so, BP is useful in many applications due to its computational tractability. In this article, we investigate a regularized BP scheme by focusing on loopy Markov graphs (MGs) induced by a multivariate Gaussian distribution in canonical form. There is a rich literature surrounding BP on Gaussian MGs (labelled Gaussian belief propagation or GaBP), and this is known to experience the same problems as general BP on graphs. GaBP is known to provide the correct marginal means if it converges (this is not guaranteed), but it does not provide the exact marginal precisions. We show that our adjusted BP will always converge, with sufficient tuning, while maintaining the exact marginal means. As a further contribution we show, in an empirical study, that our GaBP variant can accelerate GaBP and compares well with other GaBP-type competitors in terms of convergence speed and accuracy of approximate marginal precisions. These improvements suggest that the principle of regularized BP should be investigated in other inference problems. The selection of the degree of regularization is addressed through the use of two heuristics. A by-product of GaBP is that it can be used to solve linear systems of equations; the same is true for our variant and we make an empirical comparison with the conjugate gradient method.     

Risky investment decisions: How are individuals influenced by their groups?

We investigate the effect of group versus individual decision-making in the context of risky investment decisions in which all subjects are fully informed of the probabilities and payoffs. Although there is full information, the lottery choices pose cognitive challenges so that people may not be sure of their expected utility-maximizing choice. Making such decisions in a group context provides real-time information in which group members can observe others’ choices and revise their own decisions. Our experimental results show that simply observing what others in the group do has a significant impact on behavior. Coupling real-time information with group decisions based on the median value, i.e., majority rule, makes the median investment choice focal, leading people with low values to increase investments and those with high values to decrease investments. Group decisions based on the minimum investment amount produce more asymmetric effects.     

Kernel Estimation of the Spectral Density of Stationary Random Closed Sets

A non‐parametric kernel estimator of the spectral density of stationary random closed sets is studied. Conditions are derived under which this estimator is asymptotically unbiased and mean‐square consistent. For the planar Boolean model with isotropic compact and convex grains, an averaged version of the kernel estimator is compared with the theoretical spectral density.     

Robust binary regression

Robust procedures increase the reliability of the results of a data analysis. We studied such a robust procedure for binary regression models based on the criterion of least absolute deviation. The resulting estimating equation consists in a simple modification of the familiar maximum likelihood equation. This estimator is easy to compute with existing computational procedures and gives a high degree of protection.     

University student enrollment forecasts by analysis structural ratios using ARIMA-methods

Summary Forecasts for the number of students in Germany are conducted by the Kultusministerkonferenz. They use a transition model which does not allow for prediction intervals and therefore lack a measure of uncertainty of the forecast. Since the uncertainty is high for such forecasts, this lack is of importance. In this paper, structural ratios, relating the number of university students to the population of the same age, are analyzed and forescasted using ARIMA-models with outliers. Multiplying these ratios with official population forecasts for Germany provides the future number of students, additionally giving prediction intervals. This number will increase from 1.94 million in 2002 to 2.35 million in 2015. The uncertainty of the forecast is high; the forecast interval in 2015 will range between 1.72 and 2.98 million at a 95% confidence level. Supported by the German Research Foundation (DFG). We are grateful to an anonymous referee for some helpful comments.     

Robust regression through robust covariances

This paper discusses the estimation of regression parameters after summarizing the data by a covariance matrix of the concatenated vector of explanatory variables and response variable. A robust estimate of the covariance matrix leads to a robust regression estimator. An M-estimator at the covariance estimation step is studied in the paper, and the resulting regression estimator is compared to a few previously proposed robust regression estimators.     

A Symposium on Unemployment Statistics

Separate Poisson distributions were evaluated via several hypotheses tests for each of the 16 teams in the National Hockey League using 1973–74 goals for and goals against both home and away. After examining independence of goals for and goals against, they in turn were used to explain order of finish for the 16 teams. The results were quite surprising.