Violence against women with physical disabilities

This study explored risk factors for violence among a sample of adult women with physical disabilities. Fifty-six percent (100) of the 177 women participating in the study indicated a positive history of abuse. Of the women who reported abuse, most reported multiple abuse situations and abusers who were typically their male partners. In addition, only a small proportion of women sought and received adequate help. Women who indicated that they did not seek help were asked why this was the case. Their responses included: feeling that they could handle it themselves, having other sources of support available, being unaware of where to go, feeling embarrassed, feeling guilty about being a burden or that it was their fault, fear that abuser would come after them, fear of not being believed, and, to a lesser extent, concern that the shelter would lack appropriate accommodations. These findings highlight the importance of intervention strategies including advocacy activities for women with disabilities, activities with schools, activities to deter and prevent partner and caregiver violence, community awareness activities, and dissemination activities.     

Examining the link between social responsibility and decision styles

The paper examines the factors that influence socially responsible decision making by individuals. The study found four social responsibility styles: Playing it Safe, Weather The Storm, Problem to Solve, and Hope it Goes Away. These styles describe individuals on the basis of decision style, propensity for risk, and coping style. The styles explain why people with different values might come to the same decision in the same circumstances.     

Theories of Learning in Games and Heterogeneity Bias

Comparisons of learning models in repeated games have been a central preoccupation of experimental and behavioral economics over the last decade. Much of this work begins with pooled estimation of the model(s) under scrutiny. I show that in the presence of parameter heterogeneity, pooled estimation can produce a severe bias that tends to unduly favor reinforcement learning relative to belief learning. This occurs when comparisons are based on goodness of fit and when comparisons are based on the relative importance of the two kinds of learning in hybrid structural models. Even misspecified random parameter estimators can greatly reduce the bias relative to pooled estimation.     

Survival analysis with long‐term survivors and partially observed covariates

The authors describe a method for fitting failure time mixture models that postulate the existence of both susceptibles and long‐term survivors when covariate data are only partially observed. Their method is based on a joint model that combines a Weibull regression model for the susceptibles, a logistic regression model for the probability of being a susceptible, and a general location model for the distribution of the covariates. A Bayesian approach is taken, and Gibbs sampling is used to fit the model to the incomplete data. An application to clinical data on tonsil cancer and a small Monte Carlo study indicate potential large gains in efficiency over standard complete‐case analysis as well as reasonable performance in a variety of situations.