Perceived parental approval of drinking and its impact on problem drinking behaviors among first-year college students

There is a paucity of research investigating the impact that parents may have on college drinking. In this study, the authors investigated the relationship between students' perceptions of parent approval of drinking and problem drinking occurrence. They conducted a Web-based survey of 265 first-year students living on campus during their second semester. The authors used logistic regression to examine the relationship between students' perceptions of their mothers' and fathers' attitudes toward their drinking, their mothers' and fathers' drinking habits, and problem drinking since they had begun college. Sixty-nine percent of respondents reported experiencing at least 1 drinking problem. Over one third of students perceived that their parents would approve of them drinking occasionally. Students perceiving more parental approval for their drinking were more likely to report at least 1 drinking problem. Student perceptions of parental approval of drinking warrant further investigation as a potentially mutable correlate of problem drinking.     

Social work and aging in the emerging health care world

Social work practice with older adults and their families is increasingly recognized by the profession as a major field of practice in a wide range of health care and community-based settings. This article reviews emerging trends and issues in the fields of aging and health care, drawing on gerontological health care research which bridges these areas. Given the growing number and diversity of older adults in our society, and dramatic changes in the organization and delivery of health care, the authors suggest skills and competencies essential to enhancing the well-being of older adults and their families in the 21st Century.     

A Monte Carlo study of traditional and rank-based picked-points analyses

In analysis of covariance with heteroscedastic slopes a picked-points analysis is often performed. Least-squares based picked-points analyses often lose efficiency (at times substantial) for nonnormal error distributions. Robust rank-based picked-points analyses are developed which are optimizable for heavy-tailed and/or skewed error distributions. The results of a Monte Carlo investigation of these analyses are presented. The situations include the normal model and models which violate it in one or several ways. Empirically the rank-based analyses appear to be valid over all these situations and more powerful than the least squares analysis for all the nonnormal models, while losing little efficiency at the normal model.     

Hybrid Strategies: Allocating Involvement in the Digital Age

An important trait of mobile phones is their capacity to superimpose multiple social interactions in time and space. Little research examines how individuals choose between face‐to‐face and digitally mediated interactions in specific social contexts. Drawing upon focus group interviews with a diverse sample of university students in the United States, we argue that, contrary to a perspective that is popular in theory and journalistic commentary, mobile phone users do not experience the digital and the face‐to‐face as distinct realms. In deciding where to direct their attention, users enmesh the distant and the present, making moves that are expressive and strategic in their own right that reveal the interest, intimacy and urgency that users place in multiple, coinciding social involvements.     

The Effects of the Specialist Schools Education Policy on School and Post‐School Outcomes in England

We investigate the effects of the specialist schools policy, introduced in 1994 in England, on test scores and truancy for pupils during compulsory schooling. We also assess the effects on labour market status and post‐secondary test score performance. We use matching models and data from the Youth Cohort Surveys for pupils who left school in either 2002 or 2004. We find that the policy did raise test scores, as well as increase the probability of employment. The evidence on post‐secondary test scores is mixed insofar as the policy increased the number of A levels studied but reduced average points scores.     

Adoptive and Nonadoptive Mother–Child Behavioral Interaction: A Comparative Study at 4 Years of Age

Comparable samples of low-risk adopted and non-adopted children and mothers were observed during three tasks at age 4 years. Quality of mother–child interactions, child level of functioning in four domains, and maternal parenting satisfaction and social support were assessed. Adopted children were as competent as non-adopted children on measures of developmental functioning. Both groups of mothers expressed high satisfaction and support as parents. However, ratings of child, maternal, and dyadic behavior when interacting were all lower for adoptive dyads than for non-adoptive dyads, and adoptive dyads with boys accounted for the maternal and dyadic group differences.     

Benefits of Model-based Drug Development: A Rigorous,Planned Case Study

Model-based drug development (MBDD) is useful to make better quantitative decisions within drug development. However, rigorous evaluation of the benefits has been scarce in the literature. In this study, we take a completed development program, retrospectively repeat it in a virtual setting using MBDD methodologies, and compare it to the traditional drug development process. The conclusion is that the use of MBDD could have facilitated more efficient use of resources.     

A candidate-set-free algorithm for generating D-optimal split-plot designs

Summary.  We introduce a new method for generating optimal split-plot designs. These designs are optimal in the sense that they are efficient for estimating the fixed effects of the statistical model that is appropriate given the split-plot design structure. One advantage of the method is that it does not require the prior specification of a candidate set. This makes the production of split-plot designs computationally feasible in situations where the candidate set is too large to be tractable. The method allows for flexible choice of the sample size and supports inclusion of both continuous and categorical factors. The model can be any linear regression model and may include arbitrary polynomial terms in the continuous factors and interaction terms of any order. We demonstrate the usefulness of this flexibility with a 100-run polypropylene experiment involving 11 factors where we found a design that is substantially more efficient than designs that are produced by using other approaches.