Bridges and Barriers: Religion and Immigrant Occupational Attainment across Integration Contexts

This article advances knowledge about context‐dependent impacts of religion on immigrant structural integration. Drawing on theories of inter‐generational immigrant integration, it identifies and spells out two context‐dependent mechanisms through which religion impinges upon structural integration – as ethnic marker prompting exclusion and discrimination, or as social organization providing access to tangible resources. The propositions are empirically tested with nationally representative data on occupational attainment in three different integration contexts which vary in religious boundary configurations and religious field characteristics – the United States, Canada, and Western Europe. Using data from the US General Social Survey, the Canadian Ethnic Diversity Survey, and the European Social Survey, the article analyzes indirect and direct effects of religious affiliation and participation on occupational attainment among first and second generation immigrants. The analyses find only limited evidence for the assumption that in contexts with strong religious boundaries (such as Western Europe and, to a lesser extent, Canada), immigrants face religious penalties in structural integration. By contrast, the analyses support the assumption that in contexts with a thriving religious field (such as the United States and, to a lesser extent, Canada), religious attendance tends to be positively related to occupational attainment, especially for the second generation. For the first time, the article empirically tests arguments about transatlantic differences in the role of religion for immigrant structural integration, and it suggests ways of better integrating micro‐oriented survey research with macro‐oriented institutional analysis.     

An agent-based random-utility-maximization model to generate social networks with transitivity in geographic space

Stochastic actor-based approaches receive increasing interest in the generation of social networks for simulation in time and space. Existing models however cannot be readily integrated in agent-based models that assume random-utility-maximizing behavior of agents. We propose an agent-based model to generate social networks explicitly in geographic space which is formulated in the random-utility-maximizing (RUM) framework. The proposed model consists of a friendship formation mechanism and a component to simulate social encounters in a population. We show how transitivity can be incorporated in both components and how the model can be estimated based on data of personal networks using likelihood estimation. In an application to the Swiss context, we demonstrate the estimation and ability of the model to reproduce relevant characteristics of networks, such as geographic proximity, attribute similarity (homophily), size of personal networks (degree distribution) and clustering (transitivity). We conclude that the proposed social-network model fits seamlessly in existing large-scale micro-simulation systems which assume RUM behavior of agents.     

Auslandsentsendungen – ein Ansatz zum Einzel- und Paarcoaching

Using different case studies the author concentrates on the analysis of international assignments with regard to executive and couple coaching. It becomes clear that the success of assignments abroad largely depends on the modalities (with vs. without family) and duration of the stay, the expatriates´ cross-cultural competence and the cultural dimensions of the host country. Coaching helps to improve the work-life-balance against the background of an imminent culture shock.     

Risk Prediction for Prostate Cancer Recurrence Through Regularized Estimation with Simultaneous Adjustment for Nonlinear Clinical Effects

In biomedical studies, it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring. In the presence of well-established clinical risk factors, investigators often prefer a procedure that also adjusts for these clinical variables. While accelerated failure time (AFT) models are a useful tool for the analysis of censored outcome data, it assumes that covariate effects on the logarithm of time-to-event are linear, which is often unrealistic in practice. We propose to build risk prediction scores through regularized rank estimation in partly linear AFT models, where high-dimensional data such as gene expression data are modeled linearly and important clinical variables are modeled nonlinearly using penalized regression splines. We show through simulation studies that our model has better operating characteristics compared to several existing models. In particular, we show that there is a non-negligible effect on prediction as well as feature selection when nonlinear clinical effects are misspecified as linear. This work is motivated by a recent prostate cancer study, where investigators collected gene expression data along with established prognostic clinical variables and the primary endpoint is time to prostate cancer recurrence. We analyzed the prostate cancer data and evaluated prediction performance of several models based on the extended c statistic for censored data, showing that 1) the relationship between the clinical variable, prostate specific antigen, and the prostate cancer recurrence is likely nonlinear, i.e., the time to recurrence decreases as PSA increases and it starts to level off when PSA becomes greater than 11; 2) correct specification of this nonlinear effect improves performance in prediction and feature selection; and 3) addition of gene expression data does not seem to further improve the performance of the resultant risk prediction scores.