Social Psychological Processes that Facilitate Sexual Assault within the Fraternity Party Subculture

Rape on the college campus has gained increasing amounts of attention in higher education, mainstream news, and public policy. The prevalence rates of rape are especially high among students who frequent campus parties, such as those hosted by fraternities. Researchers have described this increased risk by focusing on individual attitudes and behaviors of fraternity members or on the organizational norms and practices within the fraternity party subculture. To incorporate these studies into a single theoretical framework, this essay uses a social psychological approach to connect individual‐level attitudes, (sub)culture, and behavior. I describe and apply identity theory and affect control theory, two structural symbolic interactionist theories, to explain why certain men are drawn to high‐risk fraternities and how membership reinforces hostility toward and abuse of women. In doing so, I suggest several avenues for future research that would increase social psychological understanding of the heightened prevalence of sexual victimization within the fraternity party subculture.     

Sexual Beliefs and Sexual Functioning: The Mediating Role of Cognitive Distraction

Dysfunctional sexual beliefs are vulnerability factors for sexual dysfunction. This cross-sectional study aimed to test the mediating role of cognitive distraction on the relationship between dysfunctional sexual beliefs about sexual functioning shared by men and women and sexual function. The authors used a sample of 421 cisgender heterosexual participants involved in a monogamous relationship. The hypothesized mediation model was tested using a bootstrapped cross product of coefficients approach. Results showed a significant negative, indirect effect between dysfunctional sexual beliefs and women's sexual function through cognitive distraction. The discussion of this study highlights the importance of cognitive factors in sexual functioning.     

Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

Hypothesis Tests for Neyman's Bias in Case–Control Studies

Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.     

COWORDS: a probabilistic model for multiple word clouds

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.     

Some new constructions of circular balanced repeated measurements designs

Repeated measurements designs are widely used in medicine, pharmacology, animal sciences, and psychology. In this article, some infinite series are developed to generate the balanced repeated measurements designs for p (periods) even. For p odd, construction procedures are also described. Catalogues of the proposed designs are also presented for p = 5, 7, 9, when v ≤ 100.     

Sample size re‐estimation incorporating prior information on a nuisance parameter

Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter–based sample size re‐estimation in a design with an internal pilot study. We focus on trials with continuous endpoints in which the outcome variance is the nuisance parameter. For planning and analyzing the trial, frequentist methods are considered. Moreover, the external information on the variance is summarized by the Bayesian meta‐analytic‐predictive approach. To incorporate external information into the sample size re‐estimation, we propose to update the meta‐analytic‐predictive prior based on the results of the internal pilot study and to re‐estimate the sample size using an estimator from the posterior. By means of a simulation study, we compare the operating characteristics such as power and sample size distribution of the proposed procedure with the traditional sample size re‐estimation approach that uses the pooled variance estimator. The simulation study shows that, if no prior‐data conflict is present, incorporating external information into the sample size re‐estimation improves the operating characteristics compared to the traditional approach. In the case of a prior‐data conflict, that is, when the variance of the ongoing clinical trial is unequal to the prior location, the performance of the traditional sample size re‐estimation procedure is in general superior, even when the prior information is robustified. When considering to include prior information in sample size re‐estimation, the potential gains should be balanced against the risks.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.