Suchergebnisse

Eine Möglichkeit, soziale Kontexte zu bestimmen, ist, sie als Stichprobe von Alteri zu definieren. Um individuelles Handeln zu verstehen, ist es wichtig zu wissen, woher diese Alteri kommen und wie sie miteinander verbunden sind. Einem Ansatz zufolge führen Beziehungen zwischen Alteri zu lokalen Abhängigkeiten. Es entstehen Regeln der sozialen Interaktion, die endogen die beobachtete Netzwerkstruktur von sozialen Situationen (settings) ausmachen. Hier geht es um die sozialen Wahlen. Einem anderen Ansatz nach sind soziale Situationen Sammlungen von sozialen Foci, also physischen oder symbolischen Räumen, in denen sich Personen treffen. Weil die Alteri eher aus den Foci stammen, werden soziale Foci häufig als die wichtigsten Ursachen für Netzwerk-Bindungen, und damit der Struktur der Situation, angesehen. Die Bindung an einen sozialen Focus ist das zentrale Interesse in diesem zweiten Ansatz. In unserem Beitrag zeigen wir, wie sich stochastische Akteurs-orientierte Modelle (SAOMs), die ursprünglich für die Analyse dynamischer multipler Netzwerke gedacht waren, auf miteinander verbundene Systeme von Entscheidungen (Wahl und Zugehörigkeit) in einem einheitlichen analytischen Bezugsrahmen anwenden lassen. Wir zeigen den empirischen Wert unseres Modells an einer Längsschnitt-Studie von Jugendlichen in der Glasgow Teenage Friends and Lifestyle Study. Die sozialen Wahlen werden im Kontext von Netzwerken von Freundschaften untersucht; dabei werden musikalische Genres als der wichtigste soziale Focus herausgearbeitet.

AbstractDynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of the event, modeled by a conditional multinomial logit model. The DyNAM assumes that the parameters are constant over the actors and the context. This homogeneity assumption, albeit statistically and computationally convenient, is difficult to justify, e.g., in the presence of unobserved differences between actors or contexts. In this paper, we extend DyNAMs by including random-effects parameters that vary across actors or contexts and allow controlling for unknown sources of heterogeneity. We illustrate the model by analyzing relational events among the users of an online community of aspiring and professional digital and graphic designers.

AbstractSocial ties are strongly related to well-being. But what characterizes this relationship? This study investigates social mechanisms explaining how social ties affect well-being through social integration and social influence, and how well-being affects social ties through social selection. We hypothesize that highly integrated individuals–those with more extensive and dense friendship networks–report higher emotional well-being than others. Moreover, emotional well-being should be influenced by the well-being of close friends. Finally, well-being should affect friendship selection when individuals prefer others with higher levels of well-being, and others whose well-being is similar to theirs. We test our hypotheses using longitudinal social network and well-being data of 117 individuals living in a graduate housing community. The application of a novel extension of Stochastic Actor-Oriented Models for ordered networks (ordered SAOMs) allows us to detail and test our hypotheses for weak- and strong-tied friendship networks simultaneously. Results do not support our social integration and social influence hypotheses but provide evidence for selection: individuals with higher emotional well-being tend to have more strong-tied friends, and there are homophily processes regarding emotional well-being in strong-tied networks. Our study highlights the two-directional relationship between social ties and well-being, and demonstrates the importance of considering different tie strengths for various social processes.

AbstractFace-to-face interactions in social groups are a central aspect of human social lives. Although the composition of such groups has received ample attention in various fields—e.g., sociology, social psychology, management, and educational science—their micro-level dynamics are rarely analyzed empirically. In this article, we present a new statistical network model (DyNAM-i) that can represent the dynamics of conversation groups and interpersonal interaction in different social contexts. Taking an actor-oriented perspective, this model can be applied to test how individuals' interaction patterns differ and how they choose and change their interaction groups. It moves beyond dyadic interaction mechanisms and translates central social network mechanisms—such as homophily, transitivity, and popularity—to the context of interactions in group settings. The utility and practical applicability of the new model are illustrated in two social network studies that investigate face-to-face interactions in a small party and an office setting.

AbstractIn this paper, we seek to advance an updated concept of social space that integrates the multilayer and dynamic statistical network methods currently at the disposal of social network researchers. We demonstrate the analytic value of the new concept of social space that we propose with the help of an illustrative analysis of an organizational field involving organizations' external and internal decisions that congeal into a multilevel system of action that shapes the space of possibilities for other participants in the field. Through these internal and external decisions, organizations seek certain positions in their social space while simultaneously modifying that social space over time. We conclude by arguing that network researchers' choices of goodness-of-fit statistics should reflect a consideration about the dimensions of social space of most interest to the nodes involved.

The emergence of disliking relations depends on how adolescents perceive the relative informal status of their peers. This phenomenon is examined on a longitudinal sample using dynamic network analysis (585 students across 16 classes in five schools). As hypothesized, individuals dislike those who they look down on (disdain), and conform to others by disliking those who they perceive as being looked down on by their peers (conformity). The inconsistency between status perceptions also leads to disliking, when individuals do not look up to those who they perceive to be admired by peers (frustration). Adolescents are not more likely to dislike those who they look up to (admiration). The results demonstrate the role of status perceptions on disliking tie formation.

AbstractRelational event models (REMs) for the analysis of social interaction were first introduced 15 years ago. Since then, a number of important substantive and methodological contributions have produced their progressive refinement and hence facilitated their increased adoption in studies of social and other networks. Today REMs represent a well-established class of statistical models for relational processes. This special issue of Network Science demonstrates the standing and recognition that REMs have achieved within the network analysis and networks science communities. We wrote this brief introductory editorial essay with four main objectives in mind: (i) positioning relational event data and models in the larger context of contemporary network science and social network research; (ii) reviewing some of the most important recent developments; (iii) presenting the innovative studies collected in this special issue as evidence of the empirical value of REMs, and (iv) identifying open questions and future research directions.