![]() In this article, we demonstrate how time-stamped sensor data of social interactions can be modeled with multistate survival models. So far, however, smartphone sensor data have often been aggregated over time, thus, not doing justice to the fine-grained temporality of these data. Fine-grained temporal data, such as smartphone sensor data, allow gaining new insights into the dynamics of social interactions in day-to-day life and how these are associated with psychosocial phenomena – such as loneliness. More and more data are being collected using combined active (e.g., surveys) and passive (e.g., smartphone sensors) ambulatory assessment methods. These results illustrate how the relational event framework and its extensions can lead to new insights on social interactions and how they are affected both by the interacting individuals and the dynamic social environment. Extraversion predicted rates of social interaction, and this effect was particularly pronounced on the weekends. Furthermore, clusters of interacting students formed quickly, and predominantly within a specific setting for interaction. Findings show that patterns of interaction developed early in the freshmen student network and remained relatively stable over time. We show how the framework can be used to study: (a) which predictors are important drivers of social interactions between freshmen students who start interacting at zero acquaintance (b) how the effects of predictors change over time as acquaintance increases and (c) the dynamics between the different settings in which students interact. ![]() ![]() The relational event framework is illustrated with an exemplary study on social interactions between freshmen students at the start of their new studies. ![]() This paper presents an introduction of this new statistical framework and two of its extensions for psychological researchers. The relational event framework provides a flexible approach to studying the mechanisms that drive how a sequence of social interactions evolves over time. Each interaction is not only affected by the characteristics of individuals or the environmental context but also by the history of interactions. Real-life social interactions occur in continuous time and are driven by complex mechanisms. 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. 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. ![]() 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. 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. 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. Face-to-face interactions in social groups are a central aspect of human social lives. ![]()
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