How Do We Assess Civic Attitudes Toward Equal Rights? Data and Methodology

This open access thematic report identifies factors and conditions that can help schools and education systems promote tolerance in a globalized world. The IEA’s International Civic and Citizenship Study (ICCS) is a comparative research program designed to investigate the ways in which young people are prepared to undertake their roles as citizens, and provides a wealth of data permitting not only comparison between countries but also comparisons between schools within countries, and students within countries. Advanced analytical methods provide insights into relationships between students’ attitudes towards cultural diversity and the characteristics of the students themselves, their families, their teachers and school principals. The rich diversity of educational and cultural contexts in the 38 countries who participated in ICCS 2009 are also acknowledged and addressed. Readers interested in civic education and adolescents’ attitudes towards cultural diversity will find the theoretical perspectives explored engaging. For readers interested in methodology, the advanced analytical methods employed present textbook examples of how to address cross-cultural comparability of measurement instruments and multilevel data structures in international large-scale assessments (ILSA). Meanwhile, those interested in educational policy should find the identification and comparison of malleable factors across education systems that contribute to positive student attitudes towards cultural diversity a useful and thought-provoking resource

Rewarding work : cross-national differences in benefits, volunteering during unemployment, well-being and mental health

Due to increasing labour market flexibilisation a growing number of people are likely to experience unemployment and, as a consequence, lower mental health and well-being. This article examines cross-national differences in well-being and mental health between unemployed people who engage in voluntary work and those who do not, using multilevel data from the European Quality of Life Survey on unemployed individuals in 29 European countries and other external sources. This article finds that, regardless of their voluntary activity, unemployed people have higher levels of well-being and mental health in countries with more generous unemployment benefits. Unexpectedly, the results also suggest that regular volunteering can actually be detrimental for mental health in countries with less generous unemployment benefits. This article concludes that individual agency exercised through voluntary work can partially improve well-being but the generosity of unemployment benefits is vital for alleviating the negative mental health effects of unemployment

Gene expression profiling of hypertrophic cardiomyocytes identifies new players in pathological remodelling

Aims: Pathological cardiac remodelling is characterized by cardiomyocyte (CM) hypertrophy and fibroblast activation, which can ultimately lead to maladaptive hypertrophy and heart failure (HF). Genome-wide expression analysis on heart tissue has been instrumental for the identification of molecular mechanisms at play. However, these data were based on signals derived from all cardiac cell types. Here, we aimed for a more detailed view on molecular changes driving maladaptive CM hypertrophy to aid in the development of therapies to reverse pathological remodelling. Methods and results: Utilizing CM-specific reporter mice exposed to pressure overload by transverse aortic banding and CM isolation by flow cytometry, we obtained gene expression profiles of hypertrophic CMs in the more immediate phase after stress, and CMs showing pathological hypertrophy. We identified subsets of genes differentially regulated and specific for either stage. Among the genes specifically up-regulated in the CMs during the maladaptive phase we found known stress markers, such as Nppb and Myh7, but additionally identified a set of genes with unknown roles in pathological hypertrophy, including the platelet isoform of phosphofructokinase (PFKP). Norepinephrine-angiotensin II treatment of cultured human CMs induced the secretion of N-terminal-pro-B-type natriuretic peptide (NT-pro-BNP) and recapitulated the up-regulation of these genes, indicating conservation of the up-regulation in failing CMs. Moreover, several genes induced during pathological hypertrophy were also found to be increased in human HF, with their expression positively correlating to the known stress markers NPPB and MYH7. Mechanistically, suppression of Pfkp in primary CMs attenuated stress-induced gene expression and hypertrophy, indicating that Pfkp is an important novel player in pathological remodelling of CMs. Conclusion: Using CM-specific transcriptomic analysis, we identified novel genes induced during pathological hypertrophy that are relevant for human HF, and we show that PFKP is a conserved failure-induced gene that can modulate the CM stress response

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

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

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

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

Measuring affective well-being at work using short-form scales : implications for affective structures and participant instructions

Measuring affective well-being in organizational studies has become increasingly widespread, given its association with key work-performance and other markers of organizational functioning. As such, researchers and policy-makers need to be confident that well-being measures are valid, reliable and robust. To reduce the burden on participants in applied settings, short-form measures of affective well-being are proving popular. However, these scales are seldom validated as standalone, comprehensive measures in their own right. In this article, we used a short-form measure of affective well-being with 10 items: the Daniels five-factor measure of affective well-being (D-FAW). In Study 1, across six applied sample groups (N = 2624), we found that the factor structure of the short-form D-FAW is robust when issued as a standalone measure, and that it should be scored differently depending on the participant instruction used. When participant instructions focus on now or today, then affect is best represented by five discrete emotion factors. When participant instructions focus on the past week, then affect is best represented by two or three mood-based factors. In Study 2 (N = 39), we found good construct convergent validity of short-form D-FAW with another widely used scale (PANAS). Implications for the measurement and structure of affect are discussed