| Website | https://adamkuczynski.com |
| EMA App | https://pingr.co |
| R Training | https://adamkucz.github.io/psych548 |
| Website | https://adamkuczynski.com |
| EMA App | https://pingr.co |
| R Training | https://adamkucz.github.io/psych548 |
Very nice discussion of modeling #IntensiveLongitudinalData in #QuantitudePod - a must listen! They discuss the importance of theory driven RQs, the need to account for residual autocorrelation and seasonality, how to deal with time trends, and differences between SEM and mixed effects models 👂
Fantastic paper by @maikeluhmann, Susanne Buecker, and Marilena Rüsberg discussing macro- and micro-level predictors of loneliness. They highlight the likely complex interaction between macro- and micro-level factors and need for more robust measurement and research strategies. Such great nuance!
People feel lonely when their social needs are not met by the quantity and quality of their social relationships. Most research has focused on individual-level predictors of loneliness. However, macro-level factors related to historical time and geographic space might influence loneliness through their effects on individual-level predictors. In this Review, we summarize empirical findings on differences in the prevalence of loneliness across historical time and geographical space and discuss four groups of macro-level factors that might account for these differences: values and norms, family and social lives, technology and digitalization, and living conditions and availability of individual resources. Regarding historical time, media reports convey that loneliness is on the rise, but the empirical evidence is mixed, at least before the COVID-19 pandemic. Regarding geographical space, national differences in loneliness are linked to differences in cultural values (such as individualism) but might also be due to differences in the sociodemographic composition of the population. Research on within-country differences in loneliness is scarce but suggests an influence of neighbourhood characteristics. We conclude that a more nuanced understanding of the effects of macro-level factors on loneliness is necessary because of their relevance for public policy and propose specific directions for future research. People feel lonely when their social needs are not met, which can lead to long-term health issues. In this Review, Luhmann et al. summarize empirical findings on differences in the prevalence of loneliness across time and space and consider macro-level factors that might account for these differences.
Nothing beats seeing #MountRainier on your way into work at #UWmedicine
Psych residency isn't so bad!
Excited to announce that we'll be hiring a 3-year postdoc for our WARN-D project (www.warn-d.com). The postdoc's main tasks will include to help us build an early warning system for depression, based on the 2-year prospective dataset (including smartwatch & smartphone data) we're currently collecting in 2000 students.
Position should be online soon, I'll make sure to post it here! We have a massive & immensely challenging dataset, & are eager to further grow the team to avoid the below :)