Using auxiliary data to model nonresponse bias The challenge of knowing too much about nonrespondents rather than too little?

The ADDResponse project (www.addresponse.org) explores the potential for using auxiliary data from multiple sources to understand and correct for nonresponse bias in general social surveys in the UK. Data from the census and other administrative sources together with consumer profiling data and geographic information about local neighbourhoods have been matched to data from Round 6 of the European Social Survey in the UK.1 Preliminary bivariate analysis suggests that a large number of these variables may be associated with response propensity and worthy of further investigation. Here we discuss some of the preliminary steps we have taken to try and identify the most likely candidates for nonresponse adjustment and compare the results from propensity models employing theory-driven vs. automated variable selection

Interacting non-Abelian anyons as Majorana fermions in the honeycomb lattice model

We study the collective states of interacting non-Abelian anyons that emerge in Kitaev's honeycomb lattice model. Vortex-vortex interactions are shown to lead to the lifting of the topological degeneracy and the energy is discovered to exhibit oscillations that are consistent with Majorana fermions being localized at vortex cores. We show how to construct states corresponding to the fusion channel degrees of freedom and obtain the energy gaps characterizing the stability of the topological low energy spectrum. To study the collective behavior of many vortices, we introduce an effective lattice model of Majorana fermions. We find necessary conditions for it to approximate the spectrum of the honeycomb lattice model and show that bi-partite interactions are responsible for the degeneracy lifting also in many vortex systems.Comment: 22 pages, 12 figures, published versio

Non-Abelian statistics as a Berry phase in exactly solvable models

We demonstrate how to directly study non-Abelian statistics for a wide class of exactly solvable many-body quantum systems. By employing exact eigenstates to simulate the adiabatic transport of a model's quasiparticles, the resulting Berry phase provides a direct demonstration of their non-Abelian statistics. We apply this technique to Kitaev's honeycomb lattice model and explicitly demonstrate the existence of non-Abelian Ising anyons confirming the previous conjectures. Finally, we present the manipulations needed to transport and detect the statistics of these quasiparticles in the laboratory. Various physically realistic system sizes are considered and exact predictions for such experiments are provided.Comment: 10 pages, 3 figures. To appear in New Journal of Physic

Using geographically weighted regression to explore spatial variation in survey data

Nonresponse can undermine the quality of social survey data. Understanding who does/does not respond to surveys is important for those involved in the collection and analysis of these data. Levels of nonresponse are known to vary geographically. However, there has been little consideration of how the predictors of survey nonresponse might vary geographically within countries. This study examines the possibility of spatial variation in response behavior using regional interactions and geographically weighted regression. Our results suggest that there is geographical variation in response behavior. Relying on “one size fits all” global models in nonresponse modelling might, therefore, be insufficient

Lactobacillus species causing obesity in humans: where is the evidence?

By definition, probiotics are to provide health benefits, and are expected not to cause any adverse effects in the general population. Recently, it has been suggested that probiotics, and in particular lactobacilli are contributing to human obesity. Here, we critically review the data available on this topic. The main misconception in this hypothesis is that growth in livestock and children equals with obesity in adults. The former two are expected to grow and probiotics may, by reducing disease risk, contribute to an improved growth. It is not correct to extrapolate this growth (of all tissues) to body weight gain (growth of adipose tissue) in adults. Furthermore, when looking at animal models of obesity, it even appears the lactobacilli may potentially contribute to a reduction in body weight. Epidemiological studies lend strength to this. We therefore conclude that there is no evidence that consumption of lactobacilli or probiotics in general would contribute to obesity in humans

Rapid and Direct Preparation of Lignin Nanoparticles from Alkaline Pulping Liquor by Mild Ultrasonication

The production of lignin nanoparticles (LNPs) has opened new routes to the utilization of lignin in advanced applications. The existing challenge, however, is to develop a production method that can easily be adapted on an industrial scale. In this study, we demonstrated a green and rapid method of preparing LNPs directly from a sulfur-free alkaline pulping liquor by combining acid precipitation and ultrasonication. The combined method produced spherical LNPs, with a hierarchical nanostructure and a highly negative surface charge, within only 5 min of sonication. The mild, rapid sonication was achieved by sonicating directly without prior drying of the acid-precipitated and dialyzed lignin. Optimization of the method revealed the potential for minimizing acid consumption, shortening the dialysis time, and processing directly the alkaline liquor with as much as 20 wt % lignin. The isolated LNPs were stable during storage for 180 days, at a pH range of 4–7, and in a dispersing medium below 0.1 M NaCl. The LNPs also displayed excellent emulsifying properties, stabilizing oil-in-water emulsions. Thus, this simple and energy-efficient method opens a sustainable, straightforward, and scalable route to the production of organic solvent-free LNPs, with high potential as interface stabilizers of multiphase systems in the food and medical industries.Peer reviewe

Informing Non-Response Bias Model Creation in Social Surveys with Visualisation

Through an ongoing process of co-design and co-discovery we are developing and using visualization to explore large amounts of auxiliary data from unfamiliar sources to understand non-response bias in social surveys. We present auxiliary data in their geographical contexts and show how this can complement traditional data analysis and provide a more comprehensive understanding of the data. This is helping select variables for non-response modelling. These processes are not just limited to non-response analysis, but have potential to be used in wider quantitative analysis in social science

Supporting Theoretically-grounded Model Building in the Social Sciences through Interactive Visualisation

The primary purpose for which statistical models are employed in the social sciences is to understand and explain phenomena occurring in the world around us. In order to be scientifically valid and actionable, the construction of such models need to be strongly informed by theory. To accomplish this, there is a need for methodologies that can enable scientists to utilise their domain knowledge effectively even in the absence of strong a priori hypotheses or whilst dealing with complex datasets containing hundreds of variables and leading to large numbers of potential models. In this paper, we describe enhanced model building processes in which we use interactive visualisations as the underlying mechanism to facilitate the construction and documentation of theory-driven models. We report our observations from a collaborative project involving social and computer scientists, and identify key roles for visualisation to support model building within the context of social science. We describe a suite of techniques to facilitate the exploration of statistical summaries of input variables, to compare the quality of alternative models, and to keep track of the model-building process. We demonstrate how these techniques operate in coordination to allow social scientists to efficiently generate models that are tightly underpinned by domain specific theory