Selecting social work students: lessons from research in Scotland

The issue of selection of students to social work programmes is one that remains highly contested. While it is clear that there is no single way of choosing the next generation of social work students, nevertheless, there are a number of strongly held beliefs about what ‘best practice’ means in this fraught field. These can be difficult to challenge, and even harder to shift, in spite of contrary evidence. This paper presents research conducted in Scotland in 2016 as part of the Scottish Government-sponsored Review of Social Work Education. The research set out to consider what selection processes were being used in Scotland and why; more fundamentally, it sought to explore the views of those involved in social work education alongside evidence about the outcomes of the selection processes (that is, data on student retention and success). The article concludes that while there is little evidence that one method of selection to social work programmes is intrinsically better than another, issues of fairness and transparency in selection, as well as diversity, remain pressing.     

Variable selection and prediction in biased samples with censored outcomes

With the increasing availability of large prospective disease registries, scientists studying the course of chronic conditions often have access to multiple data sources, with each source generated based on its own entry conditions. The different entry conditions of the various registries may be explicitly based on the response process of interest, in which case the statistical analysis must recognize the unique truncation schemes. Moreover, intermittent assessment of individuals in the registries can lead to interval-censored times of interest. We consider the problem of selecting important prognostic biomarkers from a large set of candidates when the event times of interest are truncated and right- or interval-censored. Methods for penalized regression are adapted to handle truncation via a Turnbull-type complete data likelihood. An expectation–maximization algorithm is described which is empirically shown to perform well. Inverse probability weights are used to adjust for the selection bias when assessing predictive accuracy based on individuals whose event status is known at a time of interest. Application to the motivating study of the development of psoriatic arthritis in patients with psoriasis in both the psoriasis cohort and the psoriatic arthritis cohort illustrates the procedure.     

Examples of Decompositions of Chi-Squared Variables

Two examples of absolutely continuous bivariate distributions are given. The first example illustrates the fact that the sum of two random variables can be χ², one of the variables χ², the other variable positive but not necessarily χ². The second example illustrates the fact that the sum of the variables can be χ², each variable can be χ², the degrees of freedom add up properly but the two variables need not be independent.     

Men and Erotic Oases

This article presents a sociological examination of men who have sex with men in public places, commercial sex locations, and via the internet. Through a review of types of erotic oases, characteristics of men who frequent such places, communication modes, dangers, and the processes of seeking, finding, and conquering other men as sexual partners, this overview highlights the ways that sexuality and gender intersect. Focus is on the fluid and malleable nature of gender and sexuality, and the multitude of ways that such concepts are experienced.     

Discriminating among probability weighting functions using adaptive design optimization

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear-in-Log-Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models.     

On the Optimal Use of Suboptimal Forecasts of Explanatory Variables

Ashley (1983) gave a simple condition for determining when a forecast of an explanatory variable (X_t ) is sufficiently inaccurate that direct replacement of X_t by the forecast yields worse forecasts of the dependent variable than does respecification of the equation to omit X_t . Many available macroeconomic forecasts were shown to be of limited usefulness in direct replacement. Direct replacement, however, is not optimal if the forecast's distribution is known. Here optimal linear forms in commercial forecasts of several macroeconomic variables are obtained by using estimates of their distributions. Although they are an improvement on the raw forecasts (direct replacement), these optimal forms are still too inaccurate to be useful in replacing the actual explanatory variables in forecasting models. The results strongly indicate that optimal forms involving several commercial forecasts will not be very useful either. Thus Ashley's (1983) sufficient condition retains its value in gauging the usefulness of a forecast of an explanatory variable in a forecasting model, even though it focuses on direct replacement.     

Measuring Measurement Error in Economic Time Series

A spectral decomposition method is described for obtaining an upper bound on the amount of measurement error in a time series. The method is applied to generated data and to M1b, real GNP, and the CPI. The bounds provide insight into both the amount of measurement error in these series and the stochastic specification of the errors.     

Fifty Years of Refugee Studies: From Theory to Policy

This article reviews the growth of the field of refugee studies, focusing on its links with, and impact on, refugee policy. The last fifty years, and especially the last two decades, have witnessed both a dramatic increase in academic work on refugees and significant institutional development in the field. It is argued that these institutions have developed strong links with policymakers, although this has often failed to translate into significant policy impacts. Areas in which future policy‐orientated work might be developed are considered.