Are victims of bullying primarily social outcasts? Person-group dissimilarities in relational, socio-behavioral, and physical characteristics as predictors of victimization

Existing literature has mostly explained the occurrence of bullying victimization by individual socioemotional maladjustment. Instead, this study tested the person-group dissimilarity model (Wright et al., Journal of Personality and Social Psychology, 50: 523–536, 1986) by examining whether individuals’ deviation from developmentally important (relational, socio-behavioral, and physical) descriptive classroom norms predicted victimization. Adolescents (N = 1267, k = 56 classrooms; Mage = 13.2; 48.7% boys; 83.4% Dutch) provided self-reported and peer-nomination data throughout one school year (three timepoints). Results from group actor–partner interdependence models indicated that more person-group dissimilarity in relational characteristics (fewer friendships; incidence rate ratios [IRR]T2 = 0.28, IRRT3 = 0.16, fewer social media connections; IRRT3 = 0.13) and, particularly, lower disruptive behaviors (IRRT2 = 0.35, IRRT3 = 0.26) predicted victimization throughout the school year

Justify your alpha

Benjamin et al. proposed changing the conventional “statistical significance” threshold (i.e.,the alpha level) from p ≤ .05 to p ≤ .005 for all novel claims with relatively low prior odds. They provided two arguments for why lowering the significance threshold would “immediately improve the reproducibility of scientific research.” First, a p-value near .05provides weak evidence for the alternative hypothesis. Second, under certain assumptions, an alpha of .05 leads to high false positive report probabilities (FPRP2 ; the probability that a significant finding is a false positive

Justify your alpha

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level

Eara 2018 - Friendships experiences of lonely adolescents, or why we should make better graphs

Powerpoint slides for my presentation in the EARA 2018 session "Understanding social problems with different study designs". The intention was to present you the results of some social network analyses, but that escalated quickly into a demonstration on why including more information in graphs is crucial if we want to learn the truth about human behavior. Most of what I presented is derived from what I learned from Gert Stulp (https://www.gertstulp.com/)

Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness

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-sensors 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) duration of social interactions in a student population (N_participants = 45, N_observations = 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

Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness

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-sensors 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) duration of social interactions in a student population (N_participants = 45, N_observations = 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

Supplementary Materials: Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness

These Supplementary Materials provide additional analyses for the publication "Modeling social interaction dynamics measured with smartphone sensors: An ambulatory assessment study on social interactions and loneliness". In addition to robustness analyses and a post-hoc power analysis, this document also describes an analysis in which we examined how frequency and duration of social interactions predict changes in loneliness. Thirty-six students responded to the UCLA loneliness scale at T1 and T2 (ten weeks later), covering the subscales of intimate, relational, and collective loneliness. Between those assessments, social interactions were measured using social sensors on students' smartphones for a period of 10 weeks. In predicting changes in loneliness subscales (T1-T2), only the mean duration of social interactions was negatively associated with collective loneliness. Future studies should examine these relations in larger samples. Despite the small sample size of this exploratory analysis, it is one of the first to study how social interaction dynamics are associated with changes in loneliness

Changes in Loneliness and Coping Strategies During COVID-19

The Scientific paper is based on a Dutch Report for the Ministry of Health, Welfare and Sport. Dutch title: Eenzaamheid voorkomen in een periode van social distancing (COVID-19). This project was a collaborative effort, all authors equally contributed to the report