Relational sociology,pragmatism, transactions and social fields

Starting from the idea that relational sociology has been founded on various and incompatible social ontologies, I argue that it is at risk of losing its raison d'être if we do not answer two fundamental practical and ontological questions: (1) Why do we need relational sociology? and (2) What do we study in relational sociology? In this respect, I propose a deep, transactional sociology partly and freely inspired by the work of J. Dewey which clearly detaches relational sociology from social determinism and co-determinism.     

Australia and the Global South: Knowledge and the Ambiguities of Place and Identity

Australia was settled as a colony of Britain from the 17th century, and its early history of violent occupation has gradually given way to a relatively peaceful, wealthy, multicultural society. As a post-colonial country, its people share characteristics with those of Britain, but, as a multicultural society, national identity is increasingly influenced by the cultures of many countries, from both the global North and South. In this paper, the question of Australia's placement as a country of the global North or South is explored. Considerations of geography, the economy, political regimes and national identity are the backdrop to an investigation of Australian scholarship and the attitudes of scholars to the inclusion of Australia as a country of the global South.     

Two Case Studies on Family Work with Eating Disorders and Body Image Issues

This article highlights the need for clinicians who work with eating disorders and body image problems to address the deeper family issues underlying these symptoms. The authors propose an eclectic approach including family systems, structural and experiential family therapy theory as a guide. The paper begins with an overview of family therapy theories currently used in the treatment of eating disorders, and goes into greater detail about the three theories highlighted in the eclectic model proposed. The authors present two cases with actual case dialogue, demonstrating how they used this composite model. This article has both theoretical and practical implications for clinicians working in the field.     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.     

Multivariate forests with missing mixed outcomes

In this article, we propose a multivariate random forest method for multiple responses of mixed types with missing responses. Imputation is performed for each bootstrap sample used to build the individual trees that form the forest. The individual trees are built using a weighted splitting rule allowing downweighting of imputed observations. A simulation study shows the benefits of this approach over complete case analysis when missing responses are missing completely at random and missing at random (MAR). In particular, the gain in prediction accuracy of the proposed method is larger in the MAR case and also increases as the proportion of missing increases.     

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis

We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal component method dedicated to categorical data: multiple correspondence analysis (MCA). The uncertainty concerning the parameters of the imputation model is reflected using a non-parametric bootstrap. Multiple imputation using MCA (MIMCA) requires estimating a small number of parameters due to the dimensionality reduction property of MCA. It allows the user to impute a large range of data sets. In particular, a high number of categories per variable, a high number of variables or a small number of individuals are not an issue for MIMCA. Through a simulation study based on real data sets, the method is assessed and compared to the reference methods (multiple imputation using the loglinear model, multiple imputation by logistic regressions) as well to the latest works on the topic (multiple imputation by random forests or by the Dirichlet process mixture of products of multinomial distributions model). The proposed method provides a good point estimate of the parameters of the analysis model considered, such as the coefficients of a main effects logistic regression model, and a reliable estimate of the variability of the estimators. In addition, MIMCA has the great advantage that it is substantially less time consuming on data sets of high dimensions than the other multiple imputation methods.     

An Empirical Process View of Inverse Regression

A common approach taken in high‐dimensional regression analysis is sliced inverse regression, which separates the range of the response variable into non‐overlapping regions, called ‘slices’. Asymptotic results are usually shown assuming that the slices are fixed, while in practice, estimators are computed with random slices containing the same number of observations. Based on empirical process theory, we present a unified theoretical framework to study these techniques, and revisit popular inverse regression estimators. Furthermore, we introduce a bootstrap methodology that reproduces the laws of Cramér–von Mises test statistics of interest to model dimension, effects of specified covariates and whether or not a sliced inverse regression estimator is appropriate. Finally, we investigate the accuracy of different bootstrap procedures by means of simulations.     

Improving OpenURL Menus: User Testing of Revisions to SFX® Menus

Twenty-one volunteers tested the usability of revisions to the Texas A&M University Libraries' SFX® OpenURL link resolver menus, including the addition of Ex Libris' new bX™ recommendation service and a plug-in which pulls additional information about the journal into the menu. The volunteers also evaluated the quality and desirability of the bX recommendations and discussed their preferences for help options and full-text format. Results of the usability testing are reported along with the resultant menu changes. This study will be of interest to librarians implementing or redesigning OpenURL menus as well as those interested in the user experience.