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.

Dyadic isolation is the tendency of some individuals to be involved in pairwise interactions rather than in larger group interactions. This article investigates the interpersonal processes associated with the dyadic isolation of individuals with depressive symptoms. We hypothesize that such individuals tend to initiate more and stay longer in dyadic interactions compared to group interactions (dyadic preference hypothesis) and that individuals—irrespective of their own level of depressive symptoms—tend to join and stay longer in interactions when interaction partners have lower levels of depressive symptoms (depression avoidance hypothesis). We analyze two data sets (N = 123) of face-to-face interaction events (N = 86,915) collected with proximity badges at a social event. Hypotheses are tested using a relational event model (DyNAM-i) specifically tailored for modeling group interactions. In line with the dyadic preference hypotheses, individuals with higher levels of depressive symptoms are found to be more likely to join and stay in dyadic interactions. Post hoc analyses reveal that this result only applies to female participants. We find limited support for the depression avoidance hypotheses.

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.

Important questions in the social sciences are concerned with the circumstances under which individuals, organizations, or states mutually agree to form social network ties. Examples of these coordination ties are found in such diverse domains as scientific collaboration, international treaties, and romantic relationships and marriage. This article introduces dynamic network actor models (DyNAM) for the statistical analysis of coordination networks through time. The strength of the models is that they explicitly address five aspects about coordination networks that empirical researchers will typically want to take into account: (1) that observations are dependent, (2) that ties reflect the opportunities and preferences of both actors involved, (3) that the creation of coordination ties is a two-sided process, (4) that data might be available in a time-stamped format, and (5) that processes typically differ between tie creation and dissolution (signed processes), shorter and longer time windows (windowed processes), and initial and repeated creation of ties (weighted processes). Two empirical case studies demonstrate the potential impact of DyNAM models: The first is concerned with the formation of romantic relationships in a high school over 18 months, and the second investigates the formation of international fisheries treaties from 1947 to 2010.

Individuals' favorite subjects in school can predetermine their educational and occupational careers. If girls develop weaker preferences for science, technology, engineering, and math (STEM), it can contribute to macrolevel gender inequalities in income and status. Relying on large-scale panel data on adolescents from Sweden (218 classrooms, 4,998 students), we observe a widening gender gap in preferring STEM subjects within a year (girls, 19 to 15 percent; boys, 21 to 20 percent). By applying newly developed random-coefficient multilevel stochastic actor-oriented models on social network data (27,428 friendships), we investigate how social context contributes to those changes. We find strong evidence that students adjust their preferences to those of their friends (friend influence). Moreover, girls tend to retain their STEM preferences when other girls in their classroom also like STEM (peer exposure). We conclude that these mechanisms amplify preexisting preferences and thereby contribute to the observed dramatic widening of the STEM gender gap.