Kind of a drag: gender, race, and ambivalence in The Birdcage and To Wong Foo, Thanks for Everything! Julie Newmar

This paper examines the ways in which two Hollywood films featuring drag queens, Too Wong Foo, Thanks for Everything! Julie Newmar and The Birdcage, offer a kind of "both/and" look at the complexities of gender, sexuality, race, and culture, simultaneously challenging some institutionalized attitudes (especially heterosexism) while reinforcing others (especially sexism and racism)--making the use of drag as a locus of discovery in both films, at best, ambivalent.     

We're All Number One: Academic Productivity among Schools of Social Work

Abstract

This paper updates, extends and refines a study published in this Journal a decade ago on the academic affiliation of social work authors. The study reported in this article looked at different types of journals and examined how a school's productivity rank varied by type of journal, faculty size and over time.     

Predictors of sexual risk‐taking among new drug users

We studied predictors of HIV‐related sexual risk‐taking among individuals who initiated the use of heroin or metham‐phetamine during the past 5 years. Many studies have linked drug use to HIV risk, but most research has been based on samples of users with long‐established drug careers. We conducted face‐to‐face interviews with 153 adult new drug users in Atlanta, GA. Drug use was not a statistically significant predictor of sexual risk, but gender, age, race, homelessness status, childhood neglect, level of paranoia, and level of (dys)functionality in handling disagreements were associated with frequency of sexual risk‐taking. We discuss the need to incorporate new drug users in HIV/AIDS and other health‐related prevention and intervention programs.     

Properties of two tests for outliers in multivariate data

Mardia's multivariate kurtosis and the generalized distance have desirable properties as multivariate outlier tests. However, extensive critical values have not been published heretofore. A published approximation formula for critical values of the kurtosis is shown to inadequately control the type I error rate, with observed error rates often differing from their intended values by a factor of two or more. Critical values derived from simulations for both tests for up to 25 dimensions and 500 observations are presented. The power curves of both tests are discussed. The generalized distance is the more powerful test when exactly one outlier is present and the contaminant is substantially mean-shifted. However, as the number of outliers increases, the kurtosis becomes the more powerful test. The two tests are compared with respect to power and vulnerability to masking. Recommendations for the use of these tests and interpretation of results are given.     

An Introduction to Practical Sequential Inferences via Single-Arm Binary Response Studies Using the binseqtest R Package

We review sequential designs, including group sequential and two-stage designs, for testing or estimating a single binary parameter. We use this simple case to introduce ideas common to many sequential designs, which in this case can be explained without explicitly using stochastic processes. We focus on methods provided by our newly developed R package, binseqtest, which exactly bound the Type I error rate of tests and exactly maintain proper coverage of confidence intervals. Within this framework, we review some allowable practical adaptations of the sequential design. We explore issues such as the following: How should the design be modified if no assessment was made at one of the planned sequential stopping times? How should the parameter be estimated if the study needs to be stopped early? What reasons for stopping early are allowed? How should inferences be made when the study is stopped for crossing the boundary, but later information is collected about responses of subjects that had enrolled before the decision to stop but had not responded by that time? Answers to these questions are demonstrated using basic methods that are available in our binseqtest R package. Supplementary materials for this article are available online.