Stress: the perceptions of social work lectures in Britain

Summary Stress has been a focus of attention for several years amongvarious professional groups, including social work. However,only limited attention has been given to stress experiencedby academics and even less to social work lecturers. This paperexamines job satisfaction, felt pressure and stress amongstsocial work lecturers in the United Kingdom. It looks at demands,supports and constraints, psychological well-being, the impactof the department and the institution, and changes that mightalleviate stress. The results obtained are compared, where possible,with studies of social workers and, in particular, academicsin general. A 60-item postal questionnaire was sent to all social work lecturersin the United Kingdom; 322 responses were received. Respondentsenjoyed undertaking a wide variety of tasks and apparently receivedvarying amounts of support from their managers. The social worklecturers experienced high levels of job satisfaction and stressscores were similar to those of the general United Kingdom population.But a significant proportion - one quarter - of social worklecturers were suffering from borderline levels of anxiety anddepression. Overall, social work lecturers' responses were morefavourable than the responses of other academics and socialworkers. In common with other research on stress, this studysuggests that pressure and stress should not be seen primarilyas individual problems, but as a product of interactions atteam, department and institutional levels, and at the home/workinterface.     

Generalized model for IGC growth in aluminum alloys

A generalized brick wall model is developed to describe intergranular corrosion in Equi-axed AA7178-T6 and Wingskin AA7178-T6 aluminum alloys. The intergranular corrosion rate is highly related to grain size and shape. High strength aluminum alloys are often elongated and anisotropic, with the fastest nominal IGC growth rate in the longitudinal direction (L) or long transverse direction (T) and the slowest in the short transverse direction (S). We propose a three-way intersection model and use it to simulate the corrosion kinetics for each direction. With a proper combination of model parameters, the generalized IGC model provides a good fit to experimental data developed by the foil penetration technique.     

Describing the Dynamics of Attention to TV Commercials: A Hierarchical Bayes Analysis of the Time to Zap an Ad

This paper provides insights into the dynamics of attention to TV commercials via an analysis of the length of time that commercials are viewed before being 'zapped'. The model, which incorporates a flexible baseline hazard rate and captures unobserved heterogeneity across both consumers and commercials using a hierarchical Bayes approach, is estimated on two datasets in which commercial viewing is captured by a passive online device that continually monitors a household's TV viewing. Consistent with previous findings in psychology about the nature of attentional engagement in TV viewing, baseline hazard rates are found to be non-monotonic. In addition, the data show considerable ad-to-ad and household-to-household heterogeneity in zapping behavior. While one of the datasets contains some information on characteristics of the ads, these data do not reveal any firm links between the ad heterogeneity and the ad characteristics. A number of methodological and computational issues arise in the hierarchical Bayes analysis.     

Some tests for detection of faulty inspection

Some heuristic tests for detection of existence of errors in inspection are proposed. Some of these tests are only effective if the sampling fraction ((sample size)/(lot size)) is rather large, and in all cases their application predicates special experiments to provide the requisite data. Feasibility of these experiments will vary according to specific circumstances.     

Sampling from the von Mises distribution via a comparison of random numbers

An algorithm is described for generating von Mises variates using Forsythe's method. The performance is analysed with respect to speed, and compactness. Conclusions are drawn as to when it is preferred to Best and Fisher's envelope rejection method.     

Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion

Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AIC_C, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, AIC_C can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AIC_C avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other 'classical' approaches (such as generalized cross-validation (GCV) or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AIC_C-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.     

Estimation of the number of studies with positive trends when studies with negative trends are present

A two-point estimator is proposed for the proportion of studies with positive trends among a collection of studies, some of which may demonstrate negative trends. The proposed estimator is the y-intercept of the secant line joining the points (a, F?(a)) and (b, F?(b)), where F?(p) is the empirical distribution function of p-values from one-tailed tests for positive trend derived from the individual studies. Although this estimator is negatively biased for any choice of the points 0 ≤ a < b ≤ 1, the bias is less than that of the previously proposed one-point estimator defined by setting b = 1. The bias of the two-point estimator is smallest when a and b approach the inflection point of the true distribution function, E [F?(p)]. The utility of the two-point estimator is demonstrated by using it to estimate the number of male-mouse liver carcinogens among carcinogenicity studies conducted by the National Toxicology Program.