A Case Study Using Photovoice to Explore Racial and Social Identity Among Young Black Men: Implications for Social Work Research and Practice

This article describes an exploratory qualitative case study using a research method novel to social work known as Photovoice. This case study of the Photovoice process assesses its value as a community-based participatory research (CBPR) method with marginalized communities within social work research. Photovoice was used to engage young Black men as researchers into their own lived experience. Through a photographic and group participatory project, participants discussed how intersections of race, gender, and sexuality affect their lives and health. While this methodology has proliferated within public health literature, little research exists within social work. Photovoice represents a research well-aligned with core social work values of empowerment and social justice. This method offers our profession a participatory, action-oriented tool to inform practice and policy and does so in the voices of communities who often go unheard.     

A Taxometric Analysis of Problem Gambling Data from a South African National Urban Sample

We investigate the question whether problem gambling (PG) in a recent South African sample, as measured by the Problem Gambling Severity Index (PGSI), is dimensional or categorical. We use two taxometric procedures, Mean Above Minus Below A Cut (MAMBAC) and Maxim Covariance (MAXCOV), to investigate the taxonic structure of PG as constructed by the PGSI. Data are from the 2010 South African National Urban Prevalence Study of Gambling Behavior. A representative sample of the urban adult population in South Africa (N = 3,000). Responses are to the 9 item PGSI. MAMBAC provided positive but modest evidence that PG as measured by the PGSI was taxonic. MAXCOV pointed more strongly to the same conclusion. These analyses also provide evidence that a PGSI cutoff score of 10 rather than the standard 8 may be called for. PG as constructed by the PGSI may best be thought of as categorical, but further studies with more theory based measurements are needed to determine whether this holds in a wider range of samples and for other screens. A higher cutoff score may be called for on the PGSI when it is used for research purposes to avoid false positives.     

Strategic leadership and the chief executive

This article is based on research undertaken among South African chief executives occupying the most senior operational positions in public-quoted companies. In looking at distinctive characteristics in these individuals the research attempts to bring together the activities that shape the chief executive's job and explore possible trends in their background; career route and behaviour patterns, and from the compositie picture to provide guidelines for executive development.     

Fast and Accurate Inference for the Smoothing Parameter in Semiparametric Models

A fast and accurate method of confidence interval construction for the smoothing parameter in penalised spline and partially linear models is proposed. The method is akin to a parametric percentile bootstrap where Monte Carlo simulation is replaced by saddlepoint approximation, and can therefore be viewed as an approximate bootstrap. It is applicable in a quite general setting, requiring only that the underlying estimator be the root of an estimating equation that is a quadratic form in normal random variables. This is the case under a variety of optimality criteria such as those commonly denoted by maximum likelihood (ML), restricted ML (REML), generalized cross validation (GCV) and Akaike's information criteria (AIC). Simulation studies reveal that under the ML and REML criteria, the method delivers a near‐exact performance with computational speeds that are an order of magnitude faster than existing exact methods, and two orders of magnitude faster than a classical bootstrap. Perhaps most importantly, the proposed method also offers a computationally feasible alternative when no known exact or asymptotic methods exist, e.g. GCV and AIC. An application is illustrated by applying the methodology to well‐known fossil data. Giving a range of plausible smoothed values in this instance can help answer questions about the statistical significance of apparent features in the data.