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  PSACR: The Psychological Science Accelerator's COVID-19 Rapid-response Dataset

Buchanan, E. M., C. Lewis, S., Paris, B., Forscher, P. S., Pavlacic, J. M., Beshears, J. E., et al. (2023). PSACR: The Psychological Science Accelerator's COVID-19 Rapid-response Dataset. OSF Preprints. doi:10.31219/osf.io/byqha.

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Buchanan, Erin M., Author
C. Lewis, Savannah, Author
Paris, Bastien, Author
Forscher, Patrick S., Author
Pavlacic, Jeffrey M., Author
Beshears, Juli E., Author
Gourdon-Kanhukamwe, Amélie, Author
Rahal, Rima-Maria1, Author           
et al., Author
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1Max Planck Institute for Research on Collective Goods, Max Planck Society, ou_2173688              

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 Abstract: In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data.

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 Dates: 2023-05-27
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.31219/osf.io/byqha
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Title: OSF Preprints
Source Genre: Journal
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