Developing Life Skills through Physical Activity: A Teaching Personal and Social Responsibility Model Approach

Ethnographic methods were used to study a weekly after-school physical activity program over an eight-month period. Based on Hellison’s Teaching Personal and Social Responsibility (TPSR) model, the program sought to foster positive life skills amongst youth. The study investigated how the developed program influenced this life skills education experience. Several themes were identified from the data revolving around culture, life skills, pedagogy, and lessons learned. Data suggests that the positive environment developed within the program positively influenced youths’ life skill education experience. The topic of ethnicity as it relates to the experience of marginalized youth in physical activity settings is also discussed. This study supports TPSR literature and suggests that effort to establish caring relationships and empower youth contribute to the establishment of a positive atmosphere where life skills education can occur. Beyond this, practical tools were developed through this study to help others deliver life skill education.

Using a Bayesian model for bankruptcy prediction : a comparative approach

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors' concerns with respect to assessing failure risk.