Career Counseling in a Volatile Job Market: Tiedeman's Perspective Revisited

This article explores implications of Tiedeman's original theory for career counselors. Some components of the theory seem to be compatible with existing volatile job market conditions. Notions of career path recycling, development in reverse, nonlinear progress, and parallel streams in career development are explored. Suggestions are made for counseling interventions and future research using this perspective.     

The allocation of time to social and leisure activities as social indicators for the integration of aged ethnic minorities

One way in which a better understanding of social indicators to be employed in assessing the degree of social integration of aged ethnic minorities is to determine their time-use patterns for various daily activities. This paper reports on a research project undertaken in a planned community of La Villeneuve, France, which examines (a) the social context in which various activities took place, and (b) the duration and frequency of the activities. This was achieved through the combined techniques of time-budget analysis, social network mapping, and participant observations. Our data provides another dimension appropriate for social indicators research.     

Effects on students of teacher training in use of a drug education curriculum

The study reported here examines the effects of differential teacher training in use of a drug education curriculum on students' knowledge levels, problem-solving and coping skills, attitudes towards planned decision making, behavioral intentions and tobacco and alcohol use. An experimental design was used to compare the effects of exposure to teachers who had either intensive staff development, in-service training or no training. The findings indicate a statistically significant difference between groups of students on the intention to drink alcohol. Students whose teachers had intensive staff development were less likely to intend to take a drink if offered than students whose teachers had in-service or no training. Possible reasons for a lack of significant results on other variables are the confounding influences of school, classroom and teacher effects.     

UNBIASED ESTIMATION OF PARAMETRIC FUNCTIONS IN SAMPLING FROM TWO ONE-TRUNCATION PARAMETER FAMILIES

We consider the problem of minimum variance unbiased estimation of a U-estimable function of two unknown truncation parameters based on independent random samples from two one-truncation parameter families. In particular, we obtain the UMVU estimator of the probability that Y > X.     

Supersaturated design including an orthogonal base

Recently, many supersaturated designs have been proposed. A supersaturated design is a fractional factorial design in which the number of factors is greater than the number of experimental runs. The main thrust of the previous studies has been to generate more columns while avoiding large values of squared inner products among all design columns. These designs would be appropriate if the probability for each factor being active is uniformly distributed. When factors can be partitioned into two groups, namely, with high and low probabilities of each factor being active, it is desirable to maintain orthogonality among columns to be assigned to the factors in the high-probability group. We discuss a supersaturated design including an orthogonal base which is suitable for this common situation. Mathematical results on the existence of the supersaturated designs are shown, and the construction of supersaturated designs is presented. We next discuss some properties of the proposed supersaturated designs based on the squared inner products.     

A tolerance interval approach for assessment of agreement in method comparison studies with repeated measurements

This paper generalizes the tolerance interval approach for assessing agreement between two methods of continuous measurement for repeated measurement data—a common scenario in applications. The repeated measurements may be longitudinal or they may be replicates of the same underlying measurement. Our approach is to first model the data using a mixed model and then construct a relevant asymptotic tolerance interval (or band) for the distribution of appropriately defined differences. We present the methodology in the general context of a mixed model that can incorporate covariates, heteroscedasticity and serial correlation in the errors. Simulation for the no-covariate case shows good small-sample performance of the proposed methodology. For the longitudinal data, we also describe an extension for the case when the observed time profiles are modelled nonparametrically through penalized splines. Two real data applications are presented.     

Editorial

Helmut Anheier is Assistant Professor of Sociology at Rutgers University, New Brunswick, New Jersey; Research Associate, Institute for Policy Studies, Johns Hopkins University, Baltimore, Maryland, USA.     

A Bayesian method of sample size determination with practical applications

Summary.  The problem motivating the paper is the determination of sample size in clinical trials under normal likelihoods and at the substantive testing stage of a financial audit where normality is not an appropriate assumption. A combination of analytical and simulation-based techniques within the Bayesian framework is proposed. The framework accommodates two different prior distributions: one is the general purpose fitting prior distribution that is used in Bayesian analysis and the other is the expert subjective prior distribution, the sampling prior which is believed to generate the parameter values which in turn generate the data. We obtain many theoretical results and one key result is that typical non-informative prior distributions lead to very small sample sizes. In contrast, a very informative prior distribution may either lead to a very small or a very large sample size depending on the location of the centre of the prior distribution and the hypothesized value of the parameter. The methods that are developed are quite general and can be applied to other sample size determination problems. Some numerical illustrations which bring out many other aspects of the optimum sample size are given.