Policy Development for the Reduction of Alcohol and Other Drug Problems at an Australian University

Universities, as with other workplaces, experience problems related to alcohol and other drug use by staff and students. There is little published information about the prevalence of these problems among students in the university setting in Australia as compared with the availability of information from the USA. Curtin University of Technology's experience with alcohol-related problems seems to be documented better than most other Australian institutions, and the available evidence indicates that the level of student alcohol consumption and reported alcohol-related problems is cause for concern. However, this is not to say that Curtin's problems may be any greater than those of any other Australian university with alcohol available on the premises. Many years of involvement in health promotion teaching and research, combined with identification of alcohol and drug related problems on campus, were thought to be the primary factors preceding the university's decision to begin the process of formulating comprehensive alcohol and other drug policy for staff and students. The absence of clear and consistent alcohol and other drug policy can lead to 1) undermining of the educational environment, 2) confusion about appropriate behaviour, 3) dealing with drug-related problems by inaction or arbitrary and inappropriate action. Alcohol and other drug policy has two main goals: problem reduction and better management of problems.     

Predictor relative importance and matching regression parameters

Predictor importance in applied regression modeling gives the main operational tools for managers and decision-makers. The paper considers estimation of predictors' importance in regression using measures introduced in works by Gibson and R. Johnson (GJ), then modified by Green, Carroll, and DeSarbo, and developed further by J. Johnson (JJ). These indices of importance are based on the orthonormal decomposition of the data matrix, and the work shows how to improve this approximation. Using predictor importance, the regression coefficients can also be adjusted to reach the best data fit and to be meaningful and interpretable. The results are compared with the robust to multicollinearity, but computationally difficult, Shapley value regression (SVR). They show that the JJ index is good for importance estimation, but the GJ index outperforms it if both predictor importance and coefficients of regression are needed; hence, this index (GJ) can be used in place of the more computationally intensive estimation by SVR. The results can be easily estimated by the considered approach that is very useful in practical regression modeling and analysis, especially for big data.     

Birth Intervals and Childhood Mortality in Rural Bangladesh

This study investigates the relationship between birth intervals and childhood mortality, using longitudinal data from rural Bangladesh known to be of exceptional accuracy and completeness. Results demonstrate significant but very distinctive effects of the previous and subsequent birth intervals on mortality, with the former concentrated in the neonatal period and the latter during early childhood. The impact of short birth intervals on mortality, however, is substantially less than that found in many previous studies of this issue, particularly for the previous birth interval. The findings are discussed in terms of the potential for family planning programs to contribute to improved child survival in settings such as Bangladesh.     

Communicating social value: An experimental study on credible communication and social enterprises

The management of marketing and communication strategies involves a complex mix of different requirements, particularly for social enterprises, which try to fulfill both social and business aims while operating in a resource-constrained context. Although social enterprises are a rising phenomenon, the research on how these businesses communicate their activities remains in its infancy. This study builds on the theory of planned behavior and the source credibility theory, presenting a conceptual framework that distinguishes between high, moderate, and low credibility of a social enterprise's communication, to analyze its effects on potential customers' behavioral intentions. Through an online experiment with 260 subjects, the authors demonstrate that attitude toward a social enterprise, subjective norm, perceived behavioral control, and, ultimately, the intention to support a social enterprise by purchasing its products increases with the social enterprise's message credibility. The authors also present practical implications and avenues for future research on the communication of social enterprises based on the empirical findings.     

Terminating markov renewal processes

Several kinds of terminating Markov Renewal Processes are defined. Of interest in these processes are the time T until termination and the number of transitions N_T until termination. For several kinds of terminating processes, the distribution and moments of T and N_T are obtained along with their covariance. The distributions of associated cumulative processes are also considered. A Markov Renewal model is compared with results of Markov Chains used to model epidemics, and other examples are examined in compartmental modeling and competing risks.     

RDELA—a Delaunay-triangulation-based, location and covariance estimator with high breakdown point

We propose an approach that utilizes the Delaunay triangulation to identify a robust/outlier-free subsample. Given that the data structure of the non-outlying points is convex (e.g. of elliptical shape), this subsample can then be used to give a robust estimation of location and scatter (by applying the classical mean and covariance). The estimators derived from our approach are shown to have a high breakdown point. In addition, we provide a diagnostic plot to expand the initial subset in a data-driven way, further increasing the estimators’ efficiency.     

Statistical studies of infectious disease incidence

Methods for the analysis of data on the incidence of an infectious disease are reviewed, with an emphasis on important objectives that such analyses should address and identifying areas where further work is required. Recent statistical work has adapted methods for constructing estimating functions from martingale theory, methods of data augmentation and methods developed for studying the human immunodeficiency virus–acquired immune deficiency syndrome epidemic. Infectious disease data seem particularly suited to analysis by Markov chain Monte Carlo methods. Epidemic modellers have recently made substantial progress in allowing for community structure and heterogeneity among individuals when studying the requirements for preventing major epidemics. This has stimulated interest in making statistical inferences about crucial parameters from infectious disease data for such community settings.     

Sensitivity of Linear Programs to Related Changes in Multiple Inputs*

In a 1991 paper in Decision Sciences, Camm and Burwell presented smart modeling techniques for obtaining the sensitivity of linear programs to simultaneous common changes in common inputs. This note addresses sensitivity analysis of simultaneous related changes in multiple inputs, and shows how related multi-parametric analysis can be performed with software that has no or only limited parametric programming capability.     

Relative Objective Functions in Multiobjective Decision Support Models*

This paper considers the importance of using the nondominated minimum in forming relative objective functions. Other minima can prevent the decision maker from reaching a preferred decision. They disturb the zero and distort the scale, thereby misguiding the decision maker by falsifying trade-off possibilities. Worse, some of these other minima can inadvertently hide whole regions of preferred solutions. Zeleny's multiple criteria simplex method is used to find the nondominated minimum for the linear case.