A global experience-sampling method study of well-being during times of crisis : The CoCo project

We present a global experience-sampling method (ESM) study aimed at describing, predicting, and understanding individual differences in well-being during times of crisis such as the COVID-19 pandemic. This international ESM study is a collaborative effort of over 60 interdisciplinary researchers from around the world in the “Coping with Corona” (CoCo) project. The study comprises trait-, state-, and daily-level data of 7490 participants from over 20 countries (total ESM measurements = 207,263; total daily measurements = 73,295) collected between October 2021 and August 2022. We provide a brief overview of the theoretical background and aims of the study, present the applied methods (including a description of the study design, data collection procedures, data cleaning, and final sample), and discuss exemplary research questions to which these data can be applied. We end by inviting collaborations on the CoCo dataset

In-person, online, and up close: The cross-contextual consistency of expressive accuracy

People vary widely in their expressive accuracy, the tendency to be viewed in line with one’s unique traits. It is unclear, however, whether expressive accuracy is a stable individual difference that transcends social contexts or a more piecemeal, context-specific characteristic. The current research therefore examined the consistency of expressive accuracy across three social contexts: face-to-face initial interactions, close relationships, and on social media. There was clear evidence for cross-contextual consistency such that expressive accuracy in face-to-face first impressions, based on brief round-robin interactions, was associated with expressive accuracy with close others (Sample 1; Ntargets = 514; Ndyads = 1656) and based on viewing only Facebook profiles (Samples 2 and 3: Ntargets = 126–132; Ndyads = 1170–1476). This was found on average across traits and for high and low observability traits. Further, unique predictors emerged for different types of expressive accuracy, such that psychological adjustment and conscientiousness most consistently predicted overall expressive accuracy, extraversion most consistently predicted high observability expressive accuracy, and neuroticism most consistently predicted low observability expressive accuracy. In sum, expressive accuracy appears to emerge robustly and consistently across contexts, though its predictors may differ depending on the type of trait

Social Anxiety and Comparisons on Instagram

Data file, metadata, and R script for data analysi