Network results for Study 2 (Dynamics across days)

Network results for Study 1 (Dynamics within days)

We discuss theoretical implications for the dynamic regulation of social needs, especially regarding temporal processes and the role of positive affect in predicting social contact. Finally, we delineate future directions of multi-method research into daily-life social dynamics.

In contrast, at a daily timescale, social desire did not predict future contact. Bidirectional linkages of affect and social contact were also much denser hourly (vs. daily). Compared to in-person contact, calls and communication app usage generally showed distinct associations with affect.

Results were highly specific to the examined time scale. When measured at an hourly timescale, people desired more social contact than usual when they engaged in more in-person contact, and higher social desire predicted more future social contact in both experience sampling and smartphone sensing.

We used network models to analyze data from two separate studies based on experience sampling and unobtrusive smartphone sampling with roughly hourly assessments across 2 days (N = 303) and daily assessments across 14 days (N = 377).

Together with @maikeluhmann.bsky.social and bluesky-less Ricarda Steinmayr, Marcus Roth, Miriam Schmitz, and Alicia Neumann.

You can check out the final preprint for more details: osf.io/preprints/ps... (7/7)

Our findings underscore the importance of considering domain satisfaction as a key factor in understanding SWB. By applying psychometric network models, we hope to advance how we think about the structure and dynamics of well-being (6/7)

Interestingly, the dynamic relationships of SWB were very similar across different age groups in middle adulthood (5/7)

We found that the relationship between life satisfaction and domain satisfaction wasn’t one-directional (top-down or bottom-up). Instead, it was characterized by reciprocal connections (4/7)

From the domains considered, family, work, and income satisfaction tended to be the most central, highlighting their importance in overall well-being (3/7)

We used network modeling to analyze SWB dynamics across the lifespan. This is one of the first attempts to apply this method to a large, representative dataset (SOEP), capturing over 32,000 adults (2/7)