Discovering trends of social interaction behavior over time

Autor: Marlyne Meijerink-Bosman, Mitja Back, Katharina Geukes, Roger Leenders, Joris Mulder
Přispěvatelé: Department of Methodology and Statistics
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Behavior Research Methods, 55(3), 997-1023. Springer
ISSN: 1554-351X
DOI: 10.3758/s13428-022-01821-8
Popis: Real-life social interactions occur in continuous time and are driven by complex mechanisms. Each interaction is not only affected by the characteristics of individuals or the environmental context but also by the history of interactions. The relational event framework provides a flexible approach to studying the mechanisms that drive how a sequence of social interactions evolves over time. This paper presents an introduction of this new statistical framework and two of its extensions for psychological researchers. The relational event framework is illustrated with an exemplary study on social interactions between freshmen students at the start of their new studies. 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. Findings show that patterns of interaction developed early in the freshmen student network and remained relatively stable over time. Furthermore, clusters of interacting students formed quickly, and predominantly within a specific setting for interaction. Extraversion predicted rates of social interaction, and this effect was particularly pronounced on the weekends. 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.
Databáze: OpenAIRE
Externí odkaz: