Student Drinking-Related Problems in an Urban Campus: Implications for Research and Prevention

Abstract

Objective: Researchers who study the etiology of college drinking typically employ measures of alcohol-use behaviors as outcomes; however, relatively little is known about the properties of alcohol-related problems (AP). This study aims to develop a single continuous measure of AP. Participants: The sample included 531 undergraduate college students who were surveyed in March 2007. Methods: The Rasch model was employed for dichotomous variables to analyze the measurement properties of 17 AP items. Results: Although issues with respect to differentiation at low levels of problem severity were identified, based on most strategies, the Rasch-based scale was shown to be a valid and reliable measure of AP among college students. Conclusions: Although there may be some need for slight modification and adjustment in the future, the AP scale developed here can potentially serve as a useful measure for etiological and prevention research.     

A shift parameter estimation based on smoothed Kolmogorov–Smirnov

A new procedure of shift parameter estimation in the two-sample location problem is investigated and compared with existing estimators. The proposed procedure smooths the empirical distribution functions of each random sample and replaces empirical distribution functions in the two-sample Kolmogorov–Smirnov method. The smoothed Kolmogorov–Smirnov is minimized with respect to an arbitrary shift variable in order to find an estimate of the shift parameter. The proposed procedure can be considered the smoothed version of a very little known method of shift parameter estimation from Rao-Schuster-Littell (RSL) [Rao et al., Estimation of shift and center of symmetry based on Kolmogorov–Smirnov statistics, Ann. Stat. 3(4) (1975), pp. 862–873]. Their estimator will be discussed and compared with the proposed estimator in this paper. An example and simulation studies have been performed to compare the proposed procedure with existing shift parameter estimators such as Hodges–Lehmann (H–L) and least squares in addition to RSL's estimator. The results show that the proposed estimator has lower mean-squared error as well as higher relative efficiency against RSL's estimator under normal or contaminated normal model assumptions. Moreover, the proposed estimator performs competitively against H–L and least-squares shift estimators. Smoother function and bandwidth selections are also discussed and several alternatives are proposed in the study.     

Change of attitude toward culture: pre- and post-expatriation experiences of managers

Cultural adjustment and intercultural effectiveness strongly influence international managerial issues, such as organizational success in a local firm, assignment completion, cost of expatriation, and repatriation. Therefore, employees assigned to work in another country need to prepare by learning about that country’s culture before expatriation. Much research has been conducted in the last few decades on the role of culture in expatriation. This study examines how the attitude of expatriate managers toward the importance of culture changes between the pre- and post-expatriation periods. This study is structured as a two-step investigation: (1) pre-expatriation cultural preparation and (2) thoughts on the importance of culture after expatriation. The research sample was South Korean expatriate managers working in Turkey or the US. All participants were reached by the trace method, and the interview data were analyzed in QSR NVivo software with content analysis. The results showed that expatriate managers’ attitude toward the importance of culture changed as a result of expatriation. Pre-expatriation, some participants did not prepare for the new culture. However, after the expatriation experience, all agreed that an understanding of the host culture was important during the assignment. An overseas experience allowed participants to understand the significant role played by culture in their adjustment to the new environment.     

Construction of a semi-Markov model for the performance of a football team in the presence of missing data

Using play-by-play data from the very beginning of the professional football league in Turkey, a semi-Markov model is presented for describing the performance of football teams. The official match results of the selected teams during 55 football seasons are used and winning, drawing and losing are considered as Markov states. The semi-Markov model is constructed with transition rates inferred from the official match results. The duration between the last match of a season and the very first match of the following season is much longer than any other duration during the season. Therefore these values are considered as missing values and estimated by using expectation–maximization algorithm. The effect of the sojourn time in a state to the performance of a team is discussed as well as mean sojourn times after losing/winning are estimated. The limiting probabilities of winning, drawing and losing are calculated. Some insights about the performance of the selected teams are presented.     

Priority class scheduling: Production scheduling for multi-objective environments

A new scheduling framework (priority class scheduling) is devised which reduces the conflict in the manufacturingmarketing interface. Priority class scheduling rewards manufacturing for making small improvements in efficiency, involves marketing in the setting of production priorities, improves customer service by putting in place realistic production timetables, allows manufacturing to concentrate on improving both quality and efficiency, and improves both the communication and cooperation between the shop floor, the production scheduler, marketing, and customer service. Within a production period, all jobs are classified into priority classes based on both their due dates and importance. Production is scheduled within each production period to maximize throughput subject to the priority class constraints- if any job in priority class i is started in the production period, then all jobs in priority class i - 1 must be completed within the production periods.     

Point‐of‐Dispensing Location and Capacity Optimization via a Decision Support System

Dispensing of mass prophylaxis can be critical to public health during emergency situations and involves complex decisions that must be made in a short period of time. This study presents a model and solution approach for optimizing point‐of‐dispensing (POD) location and capacity decisions. This approach is part of a decision support system designed to help officials prepare for and respond to public health emergencies. The model selects PODs from a candidate set and suggests how to staff each POD so that average travel and waiting times are minimized. A genetic algorithm (GA) quickly solves the problem based on travel and queuing approximations (QAs) and it has the ability to relax soft constraints when the dispensing goals cannot be met. We show that the proposed approach returns solutions comparable with other systems and it is able to evaluate alternative courses of action when the resources are not sufficient to meet the performance targets.     

The meaning of bad news management for managers: an explorative research through metaphors

It is a known reality that managers often encounter bad news, and this study explores the question “What is the perspective of managers on bad news management?” in the context of a developing country with rapidly changing conditions. To answer this question, in-depth interviews were conducted with 32 top and middle managers. The results reveal two root metaphors about bad news management in managers’ minds. Managers focus on the past with the “lesson” metaphor and focus on the future with the “opportunity” metaphor. While managing bad news, they draw meaningful inferences from prior experiences and thus become careful and ready. After managing bad news, they have new opportunities to make their organization more mature, developed, and recovered.     

Androgen deficiency in the adult male

Introduction. The purpose of this study was to investigate the association between severity of lower urinary tract symptoms (LUTS), erectile dysfunction (ED) and metabolic syndrome.

Methods. Our study population included a consecutive series of 190 patients with LUTS (International Prostate Symptom Score-IPSS >7) with or without manifestations of the metabolic syndrome. The diagnoses of diabetes mellitus and hypertension were obtained from the patient's medical history. Data on blood pressure, waist measure, body height and weight were collected and body mass index were calculated. Patients were assessed based on the International Index of Erectile Function (IIEF) for ED and IPSS and IPSS-Quality of Life for LUTS. Blood samples were drawn from fasting patients to determine, fasting blood glucose (FBG), triglycerides, HDL-cholesterol and serum total testosterone levels.

Results. In severe LUTS patient group, IIEF erectile function domain scores were significantly lower than moderate LUTS patient group (p < 0.05). Multiple logistic regression analysis confirmed that presence of ED was the most predictor of severe LUTS. The prevalence of metabolic syndrome was higher in patients with severe LUTS (26%vs. 46%, p = 0.009). The severe form of the LUTS was significantly correlated with waist circumference >102 cm (p < 0.05), blood pressure ≥130/85 mmHg (p < 0.05) and FBG >110 mg/dl (p < 0.01).

Conclusion. Obesity, high plasma level of FBG and hypertension constitute risk factors for the development of severe LUTS. Metabolic syndrome may play a key role in the pathogenesis in both ED and LUTS. Presence of ED is the most predictor of severe LUTS.     

A Methodology to Implement Box-Cox Transformation When No Covariate is Available

Box-Cox transformation is one of the most commonly used methodologies when data do not follow normal distribution. However, its use is restricted since it usually requires the availability of covariates. In this article, the use of a non-informative auxiliary variable is proposed for the implementation of Box-Cox transformation. Simulation studies are conducted to illustrate that the proposed approach is successful in attaining normality under different sample sizes and most of the distributions and in estimating transformation parameter for different sample sizes and mean-variance combinations. Methodology is illustrated on two real-life datasets.