Elmer Timon, Lodder Gerine
Department of Psychometrics and Statistics, Faculty of Social and Behavioural Sciences, University of Groningen, Groningen, The Netherlands.
Department of Humanities, Social and Political Sciences, ETH Zürich, Zürich, Switzerland.
J Soc Pers Relat. 2023 Feb;40(2):654-669. doi: 10.1177/02654075221122069. Epub 2022 Aug 20.
More and more data are being collected using combined active (e.g., surveys) and passive (e.g., smartphone sensors) ambulatory assessment methods. 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. So far, however, smartphone sensor data have often been aggregated over time, thus, not doing justice to the fine-grained temporality of these data. In this article, we demonstrate how time-stamped sensor data of social interactions can be modeled with multistate survival models. We examine how loneliness is associated with (a) the time between social interaction (i.e., interaction rate) and (b) the duration of social interactions in a student population (N = 45, N = 74,645). Before a 10-week ambulatory assessment phase, participants completed the UCLA loneliness scale, covering subscales on intimate, relational, and collective loneliness. Results from the multistate survival models indicated that loneliness subscales were not significantly associated with differences in social interaction rate and duration - only relational loneliness predicted shorter social interaction encounters. These findings illustrate how the combination of new measurement and modeling methods can advance knowledge on social interaction dynamics in daily life settings and how they relate to psychosocial phenomena such as loneliness.
越来越多的数据是通过主动（如调查）和被动（如智能手机传感器）相结合的动态评估方法收集的。细粒度的时间数据，如智能手机传感器数据，有助于深入了解日常生活中社交互动的动态以及这些互动如何与诸如孤独等社会心理现象相关联。然而，到目前为止，智能手机传感器数据常常是随时间汇总的，因此没有充分体现这些数据的细粒度时间性。在本文中，我们展示了如何用多状态生存模型对社交互动的时间戳传感器数据进行建模。我们研究了在一个学生群体（N = 45，N = 74,645）中，孤独感如何与（a）社交互动之间的时间间隔（即互动率）以及（b）社交互动的持续时间相关联。在为期10周的动态评估阶段之前，参与者完成了加州大学洛杉矶分校孤独量表，该量表涵盖了亲密、关系和集体孤独等子量表。多状态生存模型的结果表明，孤独感子量表与社交互动率和持续时间的差异没有显著关联——只有关系孤独感预示着社交互动遭遇时间较短。这些发现说明了新的测量和建模方法的结合如何能够推进对日常生活环境中社交互动动态以及它们如何与诸如孤独感等社会心理现象相关联的认识。
