An Exploratory Study of the Effects of Teacher Attractiveness on Undergraduates' Perceptions of Teacher–Student Sexual Involvement

This study explored whether the attractiveness of a teacher affected perceptions of teacher sexual misconduct. Respondents (120 female and 108 male undergraduates) read scenarios depicting teacher sexual misconduct varied by gender dyad (male teacher–female student and female teacher–male student) and two levels of attractiveness (very attractive or ordinary looking). The attractiveness of the teacher had little impact on respondents' perceptions. Significant interactions emerged on most variables between respondent gender and gender dyad. Specifically, male respondents tended to view the female teacher–male student dyad as less negative than the male teacher–female student dyad. Female respondents generally did not make a distinction based on the gender dyad.     

Sexual Decision Making for the “Better Than Average” College Student

Abstract

Objectives: To examine the impact of downward social comparison and the “known partner is a safe partner” heuristic on college students’ sexual decisions. Participants: One hundred-eighty heterosexual or bisexual undergraduate college students. Methods: Participants read dating vignettes that varied on perspective and familiarity and then rated the likelihood the couple would engage in sexual intercourse and use a condom. Results: There were no differences in rated likelihood based on familiarity, suggesting that the students did not view the 2 partner types as significantly different. Students rated the likelihood of sexual intercourse lower and condom use higher when the vignette was presented from the second person perspective. Conclusions: The students’ use of downward social comparison is consistent with the “better than average effect,” suggesting that the students perceive their own behavior as safe. The implication is that safer sex messages might be most effective if they focus on what students will gain by practicing safer sex behaviors, not just avoidance of a risk behavior.     

Deterring domestic violence: Do criminal sanctions reduce repeat offenses?

This study presents an empirical analysis of domestic violence case resolution in North Carolina for the years 2004 to 2010. The key hypothesis is that penalties at the level set for domestic violence crimes reduce recidivism (re-arrest on domestic violence charges or conviction in 2 years following an index arrest). We use state court data for all domestic violence-related arrests. Decisions to commit an act of domestic violence are based on a Bayesian process of updating subjective beliefs. Individuals have prior beliefs about penalties for domestic violence based on actual practice in their areas. An individual’s experience with an index arrest leads to belief updating. To address endogeneity of case outcomes, we use an instrumental variables strategy based on decisions of prosecutors and judges assigned to each index arrest in our sample. Contrary to our hypothesis, we find that penalities, at least as set at the current levels, do not deter future arrests and convictions.     

A Simple Distribution for the Sum of Correlated,Exchangeable Binary Data

This article describes a generalization of the binomial distribution. The closed form probability function for the probability of k successes out of n correlated, exchangeable Bernoulli trials depends on the number of trials and its two parameters: the common success probability and the common correlation. The distribution is derived under the assumption that the common correlation between all pairs of Bernoulli trials remains unchanged conditional on successes in all completed trials. The distribution was developed to model bond defaults but may be suited to biostatistical applications involving clusters of binary data encountered in repeated measurements or toxicity studies of families of organisms. Maximum likelihood estimates for the parameters of the distribution are found for a set of binary data from a developmental toxicity study on litters of mice.     

Choosing the best (wo)man for the job: The effects of mortality salience,sex, and gender stereotypes on leader evaluations

The present research investigated the impact of death-related thoughts on preferences for male and female leaders and tested alternative predictions derived from terror management theory: the stereotype bias effect was predicted to result in a global preference for male leaders (Study1) and a preference for agentic leaders (Study 2), whereas the ingroup bias effect was predicted to result in women favoring female leaders and men preferring male leaders. These hypotheses were examined in two experimental studies wherein participants were presented with a mortality salience or control manipulation and subsequently presented with campaign statements from male and female gubernatorial candidates. Results from Study 1 were in accord with the enhanced ingroup bias predictions: under mortality salience women preferred and voted for the female candidate more so than the male candidate, while men showed the reverse preference. Results from Study 2, which also manipulated gender stereotypic traits of the candidates, support the enhanced stereotype bias effect: under mortality salience female participants preferred and voted for the agentic candidate regardless of sex, and males preferred the agentic male candidate. Results support previous TMT research demonstrating both ingroup bias and stereotype bias processes.     

Insiders or Outsiders: normalisation and women with learning difficulties

This paper examines changing attitudes towards women with learning difficulties within the culture of normalisation. It builds on earlier critiques of the normalisation principle and, whilst welcoming these, argues the case for looking at women in particular. The authors review literature on sex education for people with learning difficulties to illustrate the ways in which gender has been ignored and women's sexuality has been treated. Writing from their perspective as lesbian feminists, they explore the oppressive nature of sex education in a culture that places a premium on normality. The paper ends with a discussion of alternative ways for women with learning difficulties to gain a sense of identity and belonging.     

On the use of corrections for overdispersion

In studying fluctuations in the size of a blackgrouse ( Tetrao tetrix ) population, an autoregressive model using climatic conditions appears to follow the change quite well. However, the deviance of the model is considerably larger than its number of degrees of freedom. A widely used statistical rule of thumb holds that overdispersion is present in such situations, but model selection based on a direct likelihood approach can produce opposing results. Two further examples, of binomial and of Poisson data, have models with deviances that are almost twice the degrees of freedom and yet various overdispersion models do not fit better than the standard model for independent data. This can arise because the rule of thumb only considers a point estimate of dispersion, without regard for any measure of its precision. A reasonable criterion for detecting overdispersion is that the deviance be at least twice the number of degrees of freedom, the familiar Akaike information criterion, but the actual presence of overdispersion should then be checked by some appropriate modelling procedure.     

Analysis of a longitudinal ordinal response clinical trial using dynamic models

Summary.  In many areas of pharmaceutical research, there has been increasing use of categorical data and more specifically ordinal responses. In many cases, complex models are required to account for different types of dependences among the responses. The clinical trial that is considered here involved patients who were required to remain in a particular state to enable the doctors to examine their heart. The aim of this trial was to study the relationship between the dose of the drug administered and the time that was spent by the patient in the state permitting examination. The patient's state was measured every second by a continuous Doppler signal which was categorized by the doctors into one of four ordered categories. Hence, the response consisted of repeated ordinal series. These series were of different lengths because the drug effect wore off faster (or slower) on certain patients depending on the drug dose administered and the infusion rate, and therefore the length of drug administration. A general method for generating new ordinal distributions is presented which is sufficiently flexible to handle unbalanced ordinal repeated measurements. It consists of obtaining a cumulative mixture distribution from a Laplace transform and introducing into it the integrated intensity of a binary logistic, continuation ratio or proportional odds model. Then, a multivariate distribution is constructed by a procedure that is similar to the updating process of the Kalman filter. Several types of history dependences are proposed.