Workplace hazards and workers’ desires for union representation

This paper investigates “voice” responses to hazardous and otherwise disagreeable working conditions in the form of workers’ expressed desires for union representation. Using data from three surveys conducted between 1977 and 1982, workers exposed to significant health and safety risks on the job are found to be substantially more likely to report a willingness to vote pro-union than are comparable workers not similarly exposed. This finding is obtained using both subjective and objective measures of hazard and both union and nonunion workers. Management resistance, however, appears to play a strong role in determining the actual extent of unionization. Between 41 and 48 percent of workers in hazardous jobs desire union representation but have not achieved it. Support for this research was obtained from the Institute of Industrial Relations, University of California, Berkeley. Computational help was provided by Keun Lee and Glenn Shor. Valuable comments on an earlier draft were obtained from William Dickens.     

The older lesbian

This research uncovers a pattern of support and the existence of a relationship network that runs strongly counter to the stereotyped view that lesbians in later life find themselves friendless and without support. The hypothesis that the older lesbian has developed friendship networks to replace missing or weak kin ties is substantiated. The findings are based on 20 in-depth, structured interviews with lesbians whose ages range from 50 to 73.     

QUALITY CONTROL WITH MULTIVARIATE DATA

The paper examines statistical process control of bivariate and multivariate data, using in particular the multivariate equivalents of the univariate Shewhart chart, CUSUM chart and the Exponentially Weighted Moving Average chart. This illustrates the usefulness of Principal Component methods in statistical process control with multivariate data.     

Asymptotic Expansions for i.i.d. Sums Via Lower-order Convolutions

In this article, we introduce new asymptotic expansions for probability functions of sums of independent and identically distributed random variables. Results are obtained by efficiently employing information provided by lower-order convolutions. In comparison with Edgeworth-type theorems, advantages include improved asymptotic results in the case of symmetric random variables and ease of computation of main error terms and asymptotic crossing points. The first-order estimate can perform quite well against the corresponding renormalized saddlepoint approximation and, pointwise, requires evaluation of only a single convolution integral. While the new expansions are fairly straightforward, the implications are fortuitous and may spur further related work.     

Inference from the Exponentiated Weibull Model with Applications to Real Data

In this paper, a novel Bayesian framework is used to derive the posterior density function, predictive density for a single future response, a bivariate future response, and several future responses from the exponentiated Weibull model (EWM). We study three related types of models, the exponentiated exponential, exponentiated Weibull, and beta generalized exponential, which are all utilized to determine the goodness of fit of two real data sets. The statistical analysis indicates that the EWM best fits both data sets. We determine the predictive means, standard deviations, highest predictive density intervals, and the shape characteristics for a single future response. We also consider a new parameterization method to determine the posterior kernel densities for the parameters. The summary results of the parameters are calculated by using the Markov chain Monte Carlo method.     

The effects of covariate adjustment in generalized linear models

Results from classical linear regression regarding the effects of covariate adjustment, with respect to the issues of confounding, the precision with which an exposure effect can be estimated, and the efficiency of hypothesis tests for no treatment effect in randomized experiments, are often assumed to apply more generally to other types of regression models. In this paper results pertaining to several generalized linear models involving a dichotomous response variable are given, demonstrating that with respect to the issues of confounding and precision, for models having a linear or log link function the results of classical linear regression do generally apply, whereas for other models, including those having a logit, probit, log-log, complementary log-log, or generalized logistic link function, the results of classical linear regression do not always apply. It is also shown, however, that for any link function, covariate adjustment results in improved efficiency of hypothesis tests for no treatment effect in randomized experiments, and hence that the classical linear regression results regarding efficiency do apply for all models having a dichotomous response variable.