Fatigue effects of noise on aeroplane mechanics

Subjective fatigue and reaction time performance were measured in a group of 24 aeroplane mechanics, during 1 week of high noise exposure and 1 week of low noise exposure. Subjective ratings were collected before and after each work day. On the last day of each week subjects also completed a reaction time task before and after work. The mechanics felt more sleepy and less energetic during the high noise week. This effect was most evident towards the end of the day and there was a build-up of the effect during the week. Reaction times were prolonged during the high noise week. Possible confounding factors are discussed and found to be less likely explanations of the effects.     

An extension of the Dirichlet prior for the analysis of longitudinal multinomial data

Studies producing longitudinal multinomial data arise in several subject areas. This article suggests a Bayesian approach to the analysis of such data. Rather than infusing a latent model structure, we develop a prior distribution for the multinomial parameters which reflects the longitudinal nature of the observations. This distribution is constructed by modifying the prior that posits independent Dirichlet distributions for the multinomial parameters across time. Posterior analysis, which is implemented using Monte Carlo methods, can then be used to assess the temporal behaviour of the multinomial parameters underlying the observed data. We test this methodology on simulated data, opinion polling data, and data from a study concerning the development of moral reasoning.     

On the Value of derivative evaluations and random walk suppression in Markov Chain Monte Carlo algorithms

Two strategies that can potentially improve Markov Chain Monte Carlo algorithms are to use derivative evaluations of the target density, and to suppress random walk behaviour in the chain. The use of one or both of these strategies has been investigated in a few specific applications, but neither is used routinely. We undertake a broader evaluation of these techniques, with a view to assessing their utility for routine use. In addition to comparing different algorithms, we also compare two different ways in which the algorithms can be applied to a multivariate target distribution. Specifically, the univariate version of an algorithm can be applied repeatedly to one-dimensional conditional distributions, or the multivariate version can be applied directly to the target distribution.     

Sample size implications when biases are modelled rather than ignored

Summary.  Realistic statistical modelling of observational data often suggests a statistical model which is not fully identified, owing to potential biases that are not under the control of study investigators. Bayesian inference can be implemented with such a model, ideally with the most precise prior knowledge that can be ascertained. However, as a consequence of the non-identifiability, inference cannot be made arbitrarily accurate by choosing the sample size to be sufficiently large. In turn, this has consequences for sample size determination. The paper presents a sample size criterion that is based on a quantification of how much Bayesian learning can arise in a given non-identified model. A global perspective is adopted, whereby choosing larger sample sizes for some studies necessarily implies that some other potentially worthwhile studies cannot be undertaken. This suggests that smaller sample sizes should be selected with non-identified models, as larger sample sizes constitute a squandering of resources in making estimator variances very small compared with their biases. Particularly, consider two investigators planning the same study, one of whom admits to the potential biases at hand and consequently uses a non-identified model, whereas the other pretends that there are no biases, leading to an identified but less realistic model. It is seen that the former investigator always selects a smaller sample size than the latter, with the difference being quite marked in some illustrative cases.     

About the contributors

This paper first maps the distribution of indigenous gambling in cultures around the world. On the basis of extensive ethnographic and historical evidence, it is concluded that gambling is not a universal phenomenon; prior to the era of European colonisation, non-gambling societies appear to have covered large areas of the globe. The pattern of gambling and non-gambling peoples and nations invites speculation and investigation. The second part of the paper reviews and critically discusses statistical cross-cultural studies that have aimed to uncover factors that promote or restrain the playing of games of chance and the practice of gambling. Some of these factors, which allow us to predict to a certain extent the presence and intensity of gambling in societies, are: the presence of commercially used money, social inequality, societal complexity, and the presence of certain kinds of competitive inter-tribal relations.     

The child welfare service's assessments in custody cases that involve minors

The percentage of breakdowns in marriages, cohabitations and civil partnerships is increasing in the Western world, resulting in questions of child custody. In Norway and other Western countries, there is little knowledge of the support system's work in child custody cases. This article focuses on the assessments Norwegian child welfare service employees conduct in 37 cases about child custody. We explain why they sometimes conduct investigations of reports and at other times do not.

‘Street-level bureaucracy’ is the theoretical reference framework for interpreting the interviews. Assessments and decisions of the child welfare service in custody cases vary from one service to another. It is relatively difficult to predict whether the notifications will be investigated or dropped. Decisions are the result of a jigsaw puzzle of risk assessment, interpretations of legislation, cooperative procedures and an understanding of the county social welfare board. Notifications concerning violence and notifications from family counselling offices stand out since generally they result in investigations.

Child welfare services' handling of notifications regarding custody questions is challenging as these cases are in the grey zone between the Children Act and the Child Welfare Act. Child welfare workers want clearer guidelines for the cases on which they are to work. However, new rules cannot replace the professional and ethical assessments the child welfare service workers have to make in each individual case.     

In Defense of a Utilitarian Business Ethic

In this article, I suggest and support a utilitarian approach to business ethics. Utilitarianism is already widely used as a business ethic approach, although it is not well developed in the literature. Utilitarianism provides a guiding framework of decision making rooted in social benefit which helps direct business toward more ethical behavior. It is the basis for much of our discussion regarding the failures of Enron, Worldcom, and even the subprime mess and Wall Street Meltdown. In short, the negative social consequences are constantly referred to as proof of the wrongness of these actions and events, and the positive social consequences of bailouts and other plans are used as ethical support for those plans to right the wrongs. I believe the main cause of the neglect of the utilitarian approach is because of misguided criticisms. Here, I defend utilitarianism as a basis for business ethics against many criticisms found in the business ethics literature, showing that a business ethics approach relying on John Stuart Mill's utilitarianism supports principles like justice, is not biased against the minority, and is more reasonable than other views such as a Kantian view when dealing with workers and making other decisions in business. I also explain utilitarian moral motivation and use satisficing theory to attempt to defend utilitarian business ethics from questions raised regarding utilitarian calculus.     

Does change process healthiness reduce the negative effects of organizational change on the psychosocial work environment?

Abstract

This study aimed to investigate whether the detrimental effects of organizational change on the psychosocial work environment are reduced by the “healthiness” of change processes. This includes the management's awareness that the change may be experienced differently by various individuals and groups (diversity); availability of the manager during the process; the degree to which conflicts are resolved constructively; and the degree to which the new roles to be taken on are clarified. Two studies are presented. Using a randomized sample of the Norwegian working population (N = 2389), the first study showed that there were both direct and indirect positive relationships between organizational change and stress, with job demands (but not control and support) as a mediator. In the second study a healthy change process index (HCPI) was developed from dimensions of healthy change that had emerged in an earlier qualitative study. Using data from seven Norwegian enterprises undergoing change (N = 561), this study showed that the healthiness of the change process was related negatively to stress and positively to Control and Support, but not to Demands. Overall, these findings support the idea that a healthy process may not reduce the additional demands produced by organizational change. However, a healthy process may still be able to reduce the experience of stress and facilitate coping with stress and associated increased demands through enhancing the psychosocial work environment.     

Multivariate survival mixed models for genetic analysis of longevity traits

A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the baseline hazard function will be assumed to be piece-wise constant. The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed. Some key model control techniques are discussed in a supplementary online material.