Conflicts inherent in Corporate Codes

Codes of Conduct have enjoyed a long history as guides to human behavior. Corporate Codes of Conduct enjoy significant popularity in the U.S. and U.K. This article examines Codes in terms of their evolution (history), expectations (from possible to impossible), and enforcement (the area of greatest difficulty). The article examines trends in Corporate Codes and defines Codes as one aspect of developing corporate conscience. The article concludes with the view that useful as Codes may be, the inherent problem with Codes has been their separation over time from a moral foundation or philosophical base.     

Street children in Trinidad and Tobago: understanding their lives and experiences

A community-based action research process, over a three-year period, employed interviews and participant observations with five children living on the streets in Port of Spain, the capital of Trinidad and Tobago. Data revealed violence in the family to be a contributing factor to children's presence on the streets: it was also a feature of their lives on the streets. A complex pattern emerged of violence, hostility, and distorted social interactions amidst negative attitudes and disdainful behaviour all around them. It is argued that, although it is not easy, more people – professionals and citizens – would do well to listen to, and understand the lives of, street children in order to be motivated for change.     

On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this performance is very similar to that of a parametric bootstrap correction based on a linear bias function. Three examples with actual data illustrate the application of our bias correction.     

Asymptotically Optimal Nonparametric Empirical Bayes Via Predictive Recursion

An empirical Bayes problem has an unknown prior to be estimated from data. The predictive recursion (PR) algorithm provides fast nonparametric estimation of mixing distributions and is ideally suited for empirical Bayes applications. This article presents a general notion of empirical Bayes asymptotic optimality, and it is shown that PR-based procedures satisfy this property under certain conditions. As an application, the problem of in-season prediction of baseball batting averages is considered. There the PR-based empirical Bayes rule performs well in terms of prediction error and ability to capture the distribution of the latent features.     

Bias reduction for the maximum likelihood estimator of the doubly-truncated Poisson distribution

ABSTRACT

We derive an analytic expression for the bias of the maximum likelihood estimator of the parameter in a doubly-truncated Poisson distribution, which proves highly effective as a means of bias correction. For smaller sample sizes, our method outperforms the alternative of bias correction via the parametric bootstrap. Bias is of little concern in the positive Poisson distribution, the most common form of truncation in the applied literature. Bias appears to be the most severe in the doubly-truncated Poisson distribution, when the mean of the distribution is close to the right (upper) truncation.     

An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures

Estimation of finite mixture models when the mixing distribution support is unknown is an important problem. This article gives a new approach based on a marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet prior model, a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented. By restricting the support to a finite grid, a simulated annealing method is employed to maximize the marginal likelihood and estimate the support. Real and simulated data examples show that this novel stochastic approximation and simulated annealing procedure compares favorably with existing methods.     

Heterogeneity and study size in random-effects meta-analysis

It is well known that heterogeneity between studies in a meta-analysis can be either caused by diversity, for example, variations in populations and interventions, or caused by bias, that is, variations in design quality and conduct of the studies. Heterogeneity that is due to bias is difficult to deal with. On the other hand, heterogeneity that is due to diversity is taken into account by a standard random-effects model. However, such a model generally assumes that heterogeneity does not vary according to study-level variables such as the size of the studies in the meta-analysis and the type of study design used. This paper develops models that allow for this type of variation in heterogeneity and discusses the properties of the resulting methods. The models are fitted using the maximum-likelihood method and by modifying the Paule–Mandel method. Furthermore, a real-world argument is given to support the assumption that the inter-study variance is inversely proportional to study size. Under this assumption, the corresponding random-effects method is shown to be connected with standard fixed-effect meta-analysis in a way that may well appeal to many clinicians. The models and methods that are proposed are applied to data from two large systematic reviews.     

Compassion Satisfaction,Compassion Fatigue,and Burnout: A Replication Study with Victim Advocates

Abstract

Past research has investigated the rates of compassion satisfaction (CS), compassion fatigue (CF), and burnout within health-care providers. Findings indicate higher levels of CS and lower levels of CF predict lower rates of burnout. The current study extended prior research by replicating past research findings, but with victim advocates. This study enrolled a national sample of 142 victim advocates. A hierarchal linear regression was run to test the research questions. In level 1, years of experience was not significant and only accounted for 1.1% of the variance in burnout. When adding CS, the model became significant (p?<?.001) and CS accounted for an additional 51.8% of the variance. When CF was added to the final model, it was significant (p?<?.001), and accounted for an additional 19.3% of the variance. When comparing the findings to past studies, our findings were similar; however, our study did find that CS and CF accounted for significantly more variance than past studies with health-care providers. Therefore, CS and CF might be greater predictors of burnout in victim advocates than in health-care providers. Future studies should aim to create interventions to increase CS and decrease CF, to reduce burnout.