Examining the correlates of engagement and disengagement coping among help-seeking battered women

This study examined several potential correlates of engagement and disengagement coping, including abuse-related factors, socioeconomic and social coping resources, and childhood trauma variables among a sample of battered women (N = 388). Relationship abuse frequency, particularly psychological aggression, and peritraumatic dissociation were the strongest positive predictors of the use of disengagement coping. Social coping resources, including tangible support and appraisals of social support and belonging, were associated with higher engagement coping and lower disengagement coping. A positive association was also found between interparental domestic violence and disengagement coping, and negative associations were found between both childhood physical and sexual abuse and engagement coping. Results suggest that coping strategies used by battered women are multidetermined and deserve further exploration.     

Pearls and pitfalls in evaluating a Student Assistance Program: a five-year case study

This article presents data from a five-year evaluation-research case study of a large urban schools district's internal Student Assistance Program (SAP). The district employed specially trained and licensed school-based counselors to implement an internal SAP expanded to include tertiary prevention, and modeled after an employee assistance program established for adults. From the population of more than 10,000 students who received some type of program assistance for at-risk behaviors, a stratified random sample of 2,238 cases, over a five year period, were reviewed. Results indicate a statistically significant decline (p < .05) in students' drug use and a significant improvement in internal locus of control occurred each year. While not statistically significant, the average number of student discipline referrals was less than the year prior to program admission; however, there was no significant change in academic performance. A significant correlation between students' drug use and the amount of time in program, the type of assistance received, and the student's custody status was also found.     

Environmental changes to increase physical activity: perceptions of older urban ethnic-minority women

Despite the numerous benefits of physical activity, older adults continue to be more sedentary than their younger counterparts, and sedentary behavior is more prevalent among older racial and ethnic minorities than among Whites. This study used the nominal group technique (NGT) to examine participants' perceptions of what neighborhood environmental changes would encourage greater physical activity for older African American and Hispanic women. Participants age 50-75 years were recruited from 2 urban community health clinics. Nine NGT sessions (45 participants) were conducted. The women were asked what changes in their neighborhood environment would encourage them to become more physically active. Responses to the research question were tabulated, and qualitative analysis was used to identify themes and categories. Major categories were physical environment changes, safety, and activities/social support. Although the physical environment received the greatest number of points, concerns for personal safety cut across categories. Participants indicated the need for more facilities in which to be active.     

A family systems-based model of organizational intervention

Employee assistance professionals are expected to be proficient at intervening in organizations and creating meaningful behavioral change in interpersonal functioning. Because of their training in family systems theories and concepts, marriage and family therapists (MFTs) are well suited to serve organizations as "systems consultants." Unfortunately, the authors were unable to identify any family systems-based models for organizational intervention that have been empirically tested and supported. In this article, the authors present a family systems-based model of intervention that they developed while working in an employee assistance program (EAP). They also present research that was used to refine the model and to provide initial support for its effectiveness.     

A DECISION THEORY EXAMPLE IN FOOTBALL

In the 1967 Harvard-Cornell football game, Harvard was ahead 14–0 late in the game when Cornell scored two touchdowns. On both occasions, Cornell tried and failed on the two-point conversion attempt and lost the game 14–12. Postgame arguments were divided on the merits of Cornell's strategy. For this frequently occurring scenario in college football, we derive a maximum expected utility decision rule for the decision of kicking versus running/passing based on the relative utility of a win, a tie, or a loss and the probabilities of success with a kick as opposed to a run/pass.     

Regression coefficient analysis for correlated binomial outcomes

At present, the generalized estimating equation (GEE) and weighted least-squares (WLS) regression methods are the most widely used methods for analyzing correlated binomial data; both are easily implemented using existing software packages. We propose an alternative technique, i.e. regression coefficient analysis (RCA), for this type of data. In RCA, a regression equation is computed for each of n individuals; regression coefficients are averaged across the n equations to produce a regression equation, which predicts marginal probabilities and which can be tested to address hypotheses of different slopes between groups, slopes different from zero, different intercepts, etc. The method is computationally simple and can be performed using standard software. Simulations and examples are used to compare the power and robustness of RCA with those of the standard GEE and WLS methods. We find that RCA is comparable with the GEE method under the conditions tested, and suggest that RCA, within specified limitations, is a viable alternative to the GEE and WLS methods in the analysis of correlated binomial data.     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.