Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.
The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.
Significance
Communicating in ways that motivate engagement in social distancing remains a critical global public health priority during the COVID-19 pandemic. This study tested motivational qualities of messages about social distancing (those that promoted choice and agency vs. those that were forceful and shaming) in 25,718 people in 89 countries. The autonomy-supportive message decreased feelings of defying social distancing recommendations relative to the controlling message, and the controlling message increased controlled motivation, a less effective form of motivation, relative to no message. Message type did not impact intentions to socially distance, but people’s existing motivations were related to intentions. Findings were generalizable across a geographically diverse sample and may inform public health communication strategies in this and future global health emergencies.
The authors conducted a close replication of a study by Georgiou et al (2020), who found amongst 660 (reported in abstract) or 640 (reported in participant section) participants that 1) Covid-19 related conspiracy theory beliefs were strongly related to broader conspiracy theory beliefs, that 2) Covid-19 related conspiracy beliefs were higher in those with lower levels of education, and that 3) Covid-19 related conspiracy beliefs were positively (although weakly) correlated with more negative attitudes towards different individual items measuring the government’s response. Finally, they find that 4) Covid-19 beliefs were unrelated to self-reported stress. In a pre-registered replication and extension in a study sufficiently well-powered to detect f2 = 0.05, at an alpha level of .05, with an a priori power of .95, and with 5 Predictors in a multiple regression analysis, we do not find the same results. First, we find that education level is unrelated to Covid-19 related conspiracy beliefs, that stress is related to Covid-19 related conspiracy beliefs, but that the government’s response is indeed related to Covid-19 related conspiracy beliefs. We point out measurement problems in measuring conspiracy beliefs, extend the study through supervised machine learning by finding that attachment avoidance and anxiety are important predictors of conspiracy beliefs (Covid-19-related and beyond). Part of the differences between their and our study are likely due to differences in analysis approach; others may be due to the errors in Georgiou et al.’s (2020 reporting.
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