Yannick Roos

Social relationships are central to well-being because they fulfill social affiliation needs. To explain how social needs are regulated, theories describe daily-life processes among social desire, social contact, and affect. Still, these processes remain empirically underexplored because of their complexity. In this study, we estimated multivariate associations of social desire and affect with social contact across different modalities (in-person, digital), time scales (hourly, daily), and levels of analysis (between-person, contemporaneous, temporally lagged). Participants from two age-heterogeneous samples answered experience sampling questions and contributed data through unobtrusive smartphone sensing, with roughly hourly assessments across 2 days (N = 303) and daily assessments across 14 days (N = 377). Multilevel vector autoregressive network models revealed associations between social contact, social desire, and affect across levels of analysis. 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. 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 with in-person contact, calls and communication app usage generally showed distinct associations with affect. We discuss theoretical implications for the dynamic regulation of social needs, especially regarding homeostatic temporal processes and the role of positive affect in predicting social contact. Finally, we delineate future directions of multimethod research into daily-life social dynamics. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

In psychology, mobile sensing is increasingly used to record behavior in real-life situations. However, little is known about the selectivity of samples participating in these new data collection approaches and thus about potential risks to the validity of research findings. We therefore investigated two potential sources of selection bias in smartphone-based data collections. Specifically, we examined whether smartphone system ownership (Android vs. iOS, i.e., platform-related differences) and willingness to participate (nonparticipation vs. intention to participate vs. actual participation, i.e., nonresponse error) are associated with sociodemographic, socioeconomic, and personality characteristics. Using two large-scale panel studies, we found replicable patterns for platform-related differences (N = 1,218 and N = 5,123) and nonresponse error (N = 1,673 and N = 2,337): The ownership of Android devices (in comparison to iOS devices) was associated with lower levels of education, income, and extraversion. The willingness to participate in mobile sensing studies was found to be higher among younger age groups, males, those with higher levels of openness to experience, and those with lower levels of neuroticism. Furthermore, different person characteristics played different roles at different stages of the recruitment process. Taken together, the results show that some selection bias in mobile sensing studies exists and that the effects were small to moderate in magnitude as well as comparable to selection bias for other, more common data collection approaches, such as online surveys. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Individual differences in social traits such as the affiliation motive are closely linked to the formation and maintenance of social relationships. Most previous research focused on long-term characteristics or momentary assessments of social relationships (e.g., social network size, relationship quality), whereas theoretical accounts have emphasized the temporal dynamics, that is, how social interactions unfold over time. The present studies examined how social interactions unfold within days as well as between days, taking personality traits and situational affordances into account. In two multimethod studies (Study 1: N = 307, age 18-80 years, 51% female; Study 2: N = 385, age 19-84 years, 48% female), we assessed participants' social interactions in daily life using ecological momentary assessments and mobile sensing over 2 and 14 days, respectively. Furthermore, participants answered questionnaires on affiliation, additional social traits, and situational affordances, for example, the voluntariness of social situations. Multilevel lead-lag analyses showed that affiliation predicted momentary social desires but not future social interactions, except when social interactions were assessed with unobtrusive mobile sensing. Situational affordances, such as the valence and voluntariness of social interactions, additionally predicted social desires and future contact. The results were largely specific to affiliation and not observed for extraversion. Future research on social interactions would benefit from (a) examining and specifying meaningful timescales of social relationship processes, (b) following the renewed interactionist call for considering person and situation factors, and (c) integrating the myriad of social trait concepts in theories and measurements. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Current psychological theories on daily social interactions emphasize individual differences yet are underspecified regarding contextual factors. We aim to extend this research by examining how two context factors shape social interactions in daily life: how many relationships people maintain and how densely people live together. In Study 1, 307 German participants (M_age = 39.44 years, SD_age = 14.14) answered up to 20 experience sampling questionnaires regarding their social interactions over 2 days. In Study 2, 313 German participants (M_age = 48.96 years, SD_age = 15.54) summarized their daily interactions in daily diaries for 14 days. Participants reported on their social network size and the social density (i.e., household and neighborhood density) of their living situations. Mobile sensing provided additional measures of social interactions and network size. The results showed that participants living in densely populated households transitioned faster from solitude to social interactions but slower from social interactions to solitude. Participants living in dwellings with more homes also transitioned slower from solitude to social interactions. Contrary to the hypothesis, social network size was inconsistently linked with transitions from solitude to social interactions and vice versa. Furthermore, current social desires predicted subsequent social interactions within days, but not across days-irrespective of individuals' social network size or social density. Together the results point out that people live their daily life in social contexts, which contribute to how they engage in social interactions. The findings thus call for a greater integration of contextual factors in personality theories of social interactions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

People have a need to form and maintain fulfilling social contact, yet they differ with respect to with whom they satisfy the need and how quickly this need is deprived or overly satiated. These social dynamics across relationships and across time are theoretically delineated in the current article. Furthermore, we developed a questionnaire to measure individual differences in three aspects of such social dynamics: (a) Family-friends interdependence, (b) Social deprivation, and (c) Social oversatiation. In a longitudinal study spanning 9 weeks in spring 2020, in total 471 participants (18–75 years, 47% women) answered the newly developed items on social dynamics, questionnaires on social dispositions (e.g., affiliation motive, need to be alone, social anxiety), and questions on personal and indirect contact with family and friends during nationwide contact restrictions related to COVID-19. The results showed that individual differences in Family-friends interdependence, Social deprivation, and Social oversatiation can be measured reliably, validly, and with predictive value for changes in daily contact as contact restrictions were loosened. We discuss potential applications of the Social Dynamics Scale (SDS) for studying social relationships in healthy and clinical populations, and conclude that the brief self-report questionnaire of social dynamics can be useful for situations and samples where direct behavioral observations are not feasible.

Mobile sensing is a promising method that allows researchers to directly observe human social behavior in daily life using people’s mobile phones. To date, limited knowledge exists on how well mobile sensing can assess the quantity and quality of social interactions. We therefore examined the agreement among experience sampling, day reconstruction, and mobile sensing in the assessment of multiple aspects of daily social interactions (i.e., face-to-face interactions, calls, and text messages) and the possible unique access to social interactions that each method has. Over 2 days, 320 smartphone users (51% female, age range = 18–80, M = 39.53 years) answered up to 20 experience-sampling questionnaires about their social behavior and reconstructed their days in a daily diary. Meanwhile, face-to-face and smartphone-mediated social interactions were assessed with mobile sensing. The results showed some agreement between measurements of face-to-face interactions and high agreement between measurements of smartphone-mediated interactions. Still, a large number of social interactions were captured by only one of the methods, and the quality of social interactions is still difficult to capture with mobile sensing. We discuss limitations and the unique benefits of day reconstruction, experience sampling, and mobile sensing for assessing social behavior in daily life.

Smartphones are ubiquitous today, yet opinions differ as to whether the benefits or downsides of smartphone use predominate. Using the age-representative ‘Innovation Sample’ of the German Socio-Economic Panel study (N = 5,131, age M = 52.26, 17–96 years), this study examined associations between self-reported smartphone use and three life domains: individual well-being (psychological well-being, sleep); interpersonal relationships (social isolation, network size); and work (working hours, job satisfaction). For younger adults, increased smartphone use was associated with higher psychological well-being and longer sleep. In middle and late adulthood, moderate smartphone use was associated with the highest psychological well-being, and increased smartphone use was associated with less sleep. The more adults used the communicative functions of their smartphone, the more close friends they reported – irrespective of age. Employed adults who used work-related functions of their smartphone reported working more overtime and in the evening. Exploratory structural equation model trees examined non-linear combinations of smartphone use and demographic characteristics as moderators of these associations, but observed only few replicable patterns. The generally small associations might indicate that differences in ‘normal ranges’ of smartphone use are less consequential for the broad population than often assumed. We discuss challenges and future approaches for research on consequences of smartphone use.