Using Social Network Analysis to Enhance Nonprofit Organizational Research Capacity: A Case Study

As donor agencies become more specific in funding requirements, research that can demonstrate the collaborative efforts of a nonprofit agency with its organizational neighbors and how those efforts pay off in terms of capacity and provision of services is highly useful. Recognizing these benefits, a local funding agency in Virginia commissioned a study to look at the ways in which social network analysis (SNA) can enhance the data resources available to nonprofits for funding and grant requests. In this article, we present a case study of a network of 52 nonprofit organizations to illustrate the viability of SNA in terms of funding and research needs specific to nonprofit organizations. We discuss the outcomes of the case study in terms of how the visual and metric outputs of SNA can be used by nonprofits to enhance the accomplishment of their organizational missions and strengthen their grant requests.     

How Changes in Sending Countries Influenced Patterns of Interracial Families Through Intercountry Adoption

The racial characteristics of children adopted from abroad by American parents have fluctuated sharply over time in response to changing legislation and attitudes toward intercountry adoptions in the United States and the sending countries. This study investigates how the likelihood of parents adopting a White versus non-White child varies by the characteristics of the adopted child, the parents, and the household. Our analyses, using the 2008–2012 American Community Survey, show that parents consider the child's age, sex, and health; the presence of adopted and biologically related children in the household; and shifts in the availability of children across major sending countries.     

Involving Service Users in the Teaching and Learning of Social Work Students: Reflections on Experience

This article describes and reflects on a project to develop service user involvement in the teaching and learning of students on one social work degree course. It includes the perspectives of a service user who took a lead role in the project and a part‐time academic member of staff given a specific contract to develop the work. The aspects of the project discussed are: getting started; bringing people together; student learning from individual voices; and student learning from collective experiences. Achievements and obstacles are discussed and key points of learning noted.     

K-medoids inverse regression

Abstract

K-means inverse regression was developed as an easy-to-use dimension reduction procedure for multivariate regression. This approach is similar to the original sliced inverse regression method, with the exception that the slices are explicitly produced by a K-means clustering of the response vectors. In this article, we propose K-medoids clustering as an alternative clustering approach for slicing and compare its performance to K-means in a simulation study. Although the two methods often produce comparable results, K-medoids tends to yield better performance in the presence of outliers. In addition to isolation of outliers, K-medoids clustering also has the advantage of accommodating a broader range of dissimilarity measures, which could prove useful in other graphical regression applications where slicing is required.     

Homelessness,Public Space and Civil Disobedience

This paper argues that anti-social behaviour, in the context of homelessness, ought to be seen as acts of civil disobedience. Firstly, I identify public space as a hostile space for people experiencing homelessness. Secondly, I detail how this reveals a default interpretation of them as anti-social through their mere presence. Thirdly, I explore how this de-politicises. I go onto define and examine civil disobedience theory, as a counter narrative to anti-social behaviour. I then argue how acts of disruption by people experiencing homelessness in public space can qualify as civil disobedience. I acknowledge this as a wicked problem but claim that flipping the default framing of homelessness in this way has normative gain, undoing the de-politicising othering that anti-social behaviour narratives have caused.     

“Taking Back a Little Bit of Control”: Managing the Contaminated Body Through Consumption

In this article, we explore the lived experience of avoiding environmental chemicals through safer consumption, such as buying “eco‐friendly” products. Using focus groups and in‐depth interviews involving 50 subjects, we investigate how individuals become aware of environmental chemicals and how they adapt to this awareness. Our participants describe being surprised or alarmed to learn that chemicals are present in food and commodities that they believed were safe. They respond by developing a set of heuristics rendering the “dangerous” consumer landscape into a space that is amenable to personal control. They learn to read an ingredient label and look for organic certification seals on product packaging. We develop the idea of the “contingent boundary” to describe how participants perceive personal control as uneven: they believe they can activate a protective boundary in local and familiar contexts, but outside these contexts, they believe the boundary dissolves. They accept this contingency as normal and describe having to ignore some chemical exposures, for fear of becoming too “crazy.” We conclude that the individuals in our study accept that inverted quarantine (Szasz 2007) is out of reach, and instead try to impose order upon a ubiquitous risk.     

The Rheumatoid Arthritis Drug Development Model: a case study in Bayesian clinical trial simulation

The development of a new drug is a major undertaking and it is important to consider carefully the key decisions in the development process. Decisions are made in the presence of uncertainty and outcomes such as the probability of successful drug registration depend on the clinical development programmme. The Rheumatoid Arthritis Drug Development Model was developed to support key decisions for drugs in development for the treatment of rheumatoid arthritis. It is configured to simulate Phase 2b and 3 trials based on the efficacy of new drugs at the end of Phase 2a, evidence about the efficacy of existing treatments, and expert opinion regarding key safety criteria. The model evaluates the performance of different development programmes with respect to the duration of disease of the target population, Phase 2b and 3 sample sizes, the dose(s) of the experimental treatment, the choice of comparator, the duration of the Phase 2b clinical trial, the primary efficacy outcome and decision criteria for successfully passing Phases 2b and 3. It uses Bayesian clinical trial simulation to calculate the probability of successful drug registration based on the uncertainty about parameters of interest, thereby providing a more realistic assessment of the likely outcomes of individual trials and sequences of trials for the purpose of decision making. In this case study, the results show that, depending on the trial design, the new treatment has assurances of successful drug registration in the range 0.044–0.142 for an ACR20 outcome and 0.057–0.213 for an ACR50 outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluation of methods for interval estimation of model outputs, with application to survival models

When a published statistical model is also distributed as computer software, it will usually be desirable to present the outputs as interval, as well as point, estimates. The present paper compares three methods for approximate interval estimation about a model output, for use when the model form does not permit an exact interval estimate. The methods considered are first-order asymptotics, using second derivatives of the log-likelihood to estimate variance information; higher-order asymptotics based on the signed-root transformation; and the non-parametric bootstrap. The signed-root method is Bayesian, and uses an approximation for posterior moments that has not previously been tested in a real-world application. Use of the three methods is illustrated with reference to a software project arising in medical decision-making, the UKPDS Risk Engine. Intervals from the first-order and signed-root methods are near- identical, and typically 1% wider to 7% narrower than those from the non-parametric bootstrap. The asymptotic methods are markedly faster than the bootstrap method.