Characteristics of smokeless tobacco use among high school football players as related to type of smokeless tobacco and period of use.

The present study was conducted to assess differences in the behavioral and demographic characteristics of snuff (dip) users as compared to users of chewing tobacco. High School football players (1116) were surveyed concerning their use and perceptions of smokeless tobacco. Adolescent athletes who tried smokeless tobacco were more likely to be white, to use cigarettes, alcohol, and cigars and to have family users than those who never tried. Initial use was highest before the age of fourteen years and was influenced by friends, curiosity and family. Dippers tended to initiate use because of friends, while chewers started because of family users. Users of both dip and chew started primarily because of curiosity. Users of both were more likely to consume greater amounts to alcohol and cigarettes and to smoke cigars and pipes. It appears that the longer smokeless tobacco is consumed, the more likely both dip and chew will be used. Users of smokeless tobacco for more than two years tended to consume more of the product each week, used it for more hours/day, initiated use at an earlier age, and used it more often at school and work than those using it for less than two years. Use of cigars/pipes, consumption of alcohol, and quantity of cigarette consumption increased significantly with longer duration of smokeless tobacco use. Intervention and prevention programs would be helped by understanding differences between users of various smokeless tobacco products and differences related to the duration of use. In addition, further analyses of smokeless tobacco users should study chewers, dippers, and users of both separately.     

Conditioning effect of prior reputation on perception of corporate giving

Prior corporate reputation, one responsibility of a public relations department, affects public perceptions toward corporate philanthropic messages and ultimately affects public attitudes toward the company. Using an experiment that emulates the sort of news consumption individuals normally undertake, participants inferred corporate charitable giving as a mutually beneficial activity when a company had a good reputation (H1a). Participants inferred corporate charitable giving as a self-interested activity when the company had a bad reputation (H1b). Also, public inference (suspicion) successfully mediated corporate prior reputation on public attitude toward the company (H2). Participants showed positive attitude toward the company when they inferred the company had an altruistic motive for charitable giving (H3a). However, participants showed negative attitude toward the company when they inferred the company had a self-interested motive for charitable giving (H3b).     

Statistical software packages for PCs — A market survey

The increase of statistical software applications for PCs is caused by decreasing hardware costs and dramatically enhanced PC performance. Whereas in the past the domain of statistical computing has been reserved to mainframe solutions, a great number of new software packages for PCs have come out in the last five years. Therefore, the producers of established mainframe software were also forced to offer PC-based solutions. By limiting a market analysis to products with a medium set of well known statistical methods, the immense number of available products is reduced to about fifty systems. We ordered an evaluation copy of these systems to test the numerical quality, the system speed, and the performance of several procedures. Seventeen packages were made available for an extensive examination. This paper will (1) discuss the problems and the solutions of obtaining a complete and correct datamatrix that describes the entire market and (2) present the results of a comparative market analysis.     

Analytical approximations for iterated bootstrap confidence intervals

Standard algorithms for the construction of iterated bootstrap confidence intervals are computationally very demanding, requiring nested levels of bootstrap resampling. We propose an alternative approach to constructing double bootstrap confidence intervals that involves replacing the inner level of resampling by an analytical approximation. This approximation is based on saddlepoint methods and a tail probability approximation of DiCiccio and Martin (1991). Our technique significantly reduces the computational expense of iterated bootstrap calculations. A formal algorithm for the construction of our approximate iterated bootstrap confidence intervals is presented, and some crucial practical issues arising in its implementation are discussed. Our procedure is illustrated in the case of constructing confidence intervals for ratios of means using both real and simulated data. We repeat an experiment of Schenker (1985) involving the construction of bootstrap confidence intervals for a variance and demonstrate that our technique makes feasible the construction of accurate bootstrap confidence intervals in that context. Finally, we investigate the use of our technique in a more complex setting, that of constructing confidence intervals for a correlation coefficient.     

TOWARD A SOCIOLOGY OF CULT FILMS: Reading Rocky Horror

This article provides a sociological reading of cult films, in particular, The Rocky Horror Picture Show. Cult films are secular documents, celebrated as sacred texts by audiences and used as shared foci to collectively create rituals and belief systems. They differ from popular re-releases, fad films, films with cult qualities, and critical cult films in that they involve typical people in atypical situations, sympathetic deviance, challenges to traditional authority, reflections of societal strains, and paradoxical and interpretable resolutions. Examination of the Rocky Horror text and the cult activities that occur during its viewing, reveal it as a paradoxical indictment and validation of traditional societal arrangements.     

Improved Monte Carlo inference for models with additive error

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.