Who wants to move? The role of neighbourhood change

This is the author accepted manuscript. The final version is available from SAGE via http://dx.doi.org/10.1177/0308518X15615367 There is growing interest in how, when and where neighbourhoods affect individual behaviours and outcomes. In Britain, falling levels of owner-occupation and the growth of ethnic minority populations have sparked a debate about how neighbourhood characteristics and neighbourhood change intersect with the decision to move. In this paper we investigate how mobility preferences vary with neighbourhood characteristics and neighbourhood change. We use multilevel logistic regression models to test whether this is configured by personal attributes or attachment to one's neighbourhood and perceived similarity to one's neighbours. The results show that neighbourhood deprivation, changes in neighbourhood ethnic composition and changes in tenure mix are associated with preferring to move. Importantly, we show that a feeling of belonging to the neighbourhood or feeling similar to others in the neighbourhood significantly reduces the desire to move. </jats:p

Using smart‐messaging to enhance mindfulness‐based cognitive therapy for cancer patients: A mixed methods proof of concept evaluation

Objective Depression and anxiety lead to reduced treatment adherence, poorer quality of life, and increased care costs amongst cancer patients. Mindfulness‐based cognitive therapy (MBCT) is an effective treatment, but dropout reduces potential benefits. Smart‐message reminders can prevent dropout and improve effectiveness. However, smart‐messaging is untested for MBCT in cancer. This study evaluates smart‐messaging to reduce dropout and improve effectiveness in MBCT for cancer patients with depression or anxiety.MethodsFifty‐one cancer patients attending MBCT in a psycho‐oncology service were offered a smart‐messaging intervention, which reminded them of prescribed between‐session activities. Thirty patients accepted smart‐messaging and 21 did not. Assessments of depression and anxiety were taken at baseline, session‐by‐session, and one‐month follow‐up. Logistic regression and multilevel modelling compared the groups on treatment completion and clinical effectiveness. Fifteen post‐treatment patient interviews explored smart‐messaging use.ResultsThe odds of programme completion were eight times greater for patients using smart‐messaging compared with non‐users, controlling for age, gender, baseline depression, and baseline anxiety (OR = 7.79, 95% CI 1.75 to 34.58, p = .007). Smart‐messaging users also reported greater improvement in depression over the programme (B = ‐2.33, SEB = .78, p = .004) when controlling for baseline severity, change over time, age, and number of sessions attended. There was no difference between groups in anxiety improvement (B = ‐1.46, SEB = .86, p = .097). In interviews, smart‐messaging was described as a motivating reminder and source of personal connection. ConclusionsSmart‐messaging may be an easily integrated telehealth intervention to improve MBCT for cancer patients

Area and individual differences in personal crime victimization incidence: The role of individual, lifestyle/routine activities and contextual predictors

This article examines how personal crime differences between areas and between individuals are predicted by area and population heterogeneity and their synergies. It draws on lifestyle/routine activities and social disorganization theories to model the number of personal victimization incidents over individuals including routine activities and area characteristics, respectively, as well as their (cross-cluster) interactions. The methodology employs multilevel or hierarchical negative binomial regression with extra binomial variation using data from the British Crime Survey and the UK Census. Personal crime rates differ substantially across areas, reflecting to a large degree the clustering of individuals with measured vulnerability factors in the same areas. Most factors suggested by theory and previous research are conducive to frequent personal victimization except the following new results. Pensioners living alone in densely populated areas face disproportionally high numbers of personal crimes. Frequent club and pub visits are associated with more personal crimes only for males and adults living with young children, respectively. Ethnic minority individuals experience fewer personal crimes than whites. The findings suggest integrating social disorganization and lifestyle theories and prioritizing resources to the most vulnerable, rather than all, residents of poor and densely populated areas to prevent personal crimes

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Entrepreneurs’ age, institutions, and social value creation goals: a multi-country study

This study explores the relationship between an entrepreneur's age and his/her social value creation goals. Building on the lifespan developmental psychology literature and institutional theory, we hypothesize a U-shaped relationship between entrepreneurs’ age and their choice to create social value through their ventures, such that younger and older entrepreneurs create more social value with their businesses while middle age entrepreneurs are relatively more economically and less socially oriented with their ventures. We further hypothesize that the quality of a country’s formal institutions in terms of economic, social, and political freedom steepen the U-shaped relationship between entrepreneurs’ age and their choice to pursue social value creation as supportive institutional environments allow entrepreneurs to follow their age-based preferences. We confirm our predictions using multilevel mixed-effects linear regressions on a sample of over 15,000 entrepreneurs (aged between 18 and 64 years) in 45 countries from Global Entrepreneurship Monitor data. The findings are robust to several alternative specifications. Based on our findings, we discuss implications for theory and practice, and we propose future research directions

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table

New material of Laophis crotaloides, an enigmatic giant snake from Greece, with an overview of the largest fossil European vipers

Laophis crotaloides was described by Richard Owen as a new and very large fossil viperid snake species from Greece. The type material is apparently lost and the taxon was mostly neglected for more than a century. We here describe a new partial viperid vertebra, collected from the same locality and of equivalent size to the type material. This vertebra indicates that at least one of the three morphological characters that could be used to diagnose L. crotaloides is probably an artifact of the lithographer who prepared the illustration supporting the original description. A revised diagnosis of L. crotaloides is provided on the basis of the new specimen. Despite the fragmentary nature of the new vertebra, it confirms the validity of L. crotaloides, although its exact relationships within Viperidae remain unknown. The new find supports the presence of a large viperid snake in the early Pliocene of northern Greece, adding further data to the diversity of giant vipers from Europe