Suchergebnisse

Are you struggling to design your social network research? Are you looking for a book that covers more than social network analysis? If so, this is the book for you! With straight-forward guidance on research design and data collection, as well as social network analysis, this book takes you start to finish through the whole process of doing network research. Open the book and you'll find practical, 'how to' advice and worked examples relevant to PhD students and researchers from across the social and behavioural sciences. The book covers: Fundamental network concepts and theories; Research questions and study design; Social systems and data structures; Network observation and measurement; Methods for data collection; Ethical issues for social network research; Network visualization; Methods for social network analysis; Drawing conclusions from social network results. This is a perfect guide for all students and researchers looking to do empirical social network research.

This article is a theoretical examination of Connell's social theory of gender, discussing how hegemonic, complicit, subordinate, and marginalized masculinities interact and relate to one another in the men's everyday lives in particular social contexts. Connell's theory is articulated in global terms that need to be localized to examine the actual interactions of men with one another. The theory implies a multilevel framework that the authors develop more explicitly. They investigate two interrelated theoretical concerns: (a) inadequately detailed interdependencies between structural, individual, and cultural factors with respect to masculinities, and (b) the lack of contextualization of masculinities in specific relational settings. The authors suggest that theoretical insights gained from social network theory and analysis allow such issues to be addressed and assist in local-level accounts of gendered power relations. The authors conclude by specifying Connell's theory into particular, testable hypotheses for use with statistical models for social networks.

2.3 Brief History of ERGMs2.4 Network Data Amenable to ERGMs; 3 Formation of Social Network Structure; 3.1 Tie Formation: Emergence of Structure; 3.1.1 Formation of Social Ties; 3.1.2 Network Configurations: Consequential Network Patterns and Related Processes; 3.1.3 Local Network Processes; 3.1.4 Dependency (and Theories of Network Dependence); 3.1.5 Complex Combination of Multiple and Nested Social Processes; 3.2 Framework for Explanations of Tie Formation; 3.2.1 Network Self-Organization; 3.2.2 Individual Attributes; 3.2.3 Exogenous Contextual Factors: Dyadic Covariates.

This article argues that it is not just trust-generating but also trust-inhibiting mechanisms that operate in teams, and that these cooperative and competitive structures of interpersonal relations of trust within teams may affect team performance. Specifically, we propose that the presence of trust-generating structures (e.g., reciprocity, trusting in the referrals of others we trust, trusting in high performers and more experienced people) and the absence of trust-inhibiting structures (e.g., not trusting in the referrals of others we trust) are more likely to be associated with successful teams. Using exponential random graph models, a particular class of statistical model for social networks, we examine three professional sporting teams from the Australian Football League for the presence and absence of these mechanisms of interpersonal relations of trust. Quantitative network results indicate a differential presence of these postulated structures of trust relations in line with our hypotheses. Qualitative comparisons of these quantitative findings with team performance measures suggest a link between trust-generating and trust-inhibiting mechanisms of trust and team performance. Further theorization on other trust-inhibiting structures of trust relations and related empirical work is likely to shed further light on these connections.

In this article we propose structural preconditions for effective network governance, including network structures that can facilitate effective coordination of action (such as relational and structural embeddedness), and agreement among network actors about goals and actions. We illustrate circumstances in which these preconditions do not seem to be met through a case study of environmental governance of a major water resource – the Swan River in Western Australia. This governance system, since changed by legislation, did not exhibit embeddedness among its constituent organizations, and crucial relations involved contestation, with organizations apparently pursuing different goals. This paper demonstrates how advanced social network methods can be used to analyse a networked governance system.

To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs), and stochastic actor‐oriented models. We focus most attention on ERGMs by providing an illustrative example of a model for a strategic information network within a local government. We draw inferences about the structural role played by individuals recognized as key innovators and conclude that such an approach has much to offer in analyzing the policy process.

AbstractThis is the beginning of Network Science. The journal has been created because network science is exploding. As is typical for a field in formation, the discussions about its scope, contents, and foundations are intense. On these first few pages of the first issue of our new journal, we would like to share our own vision of the emerging science of networks.