The psychological science accelerator’s COVID-19 rapid-response dataset

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

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

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

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

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

Presentations

Part of the OSF project on Google Personalized Search and Political Polarization. In this component you can find the abstract and presentation during dataset challenge at Euro CSS 2018 on December 5th 2018. Data can be found on https://github.com/jgarciab/eurocss_challeng

The Semantic Scale Network:An online tool to detect semantic overlap of psychological scales and prevent scale redundancies

Psychological measurement and theory are afflicted with an ongoing proliferation of new constructs and scales. Given the often redundant nature of new scales, psychological science is struggling with arbitrary measurement, construct dilution, and disconnection between research groups. To address these issues, we introduce an easy-to-use online application: the Semantic Scale Network. The purpose of this application is to automatically detect semantic overlap between scales through latent semantic analysis. Authors and reviewers can enter the items of a new scale into the application, and receive quantifications of semantic overlap with related scales in the application's corpus. Contrary to traditional assessments of scale overlap, the application can support expert judgments on scale redundancy without access to empirical data or awareness of every potentially related scale. After a brief introduction to measures of semantic similarity in texts, we introduce the Semantic Scale Network and provide best practices for interpreting its outputs. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Does Passive Facebook Use Promote Feelings of Social Connectedness?

Supplementary material for Pit, I.L., Veling, H., & Karremans, J.C. (2022) Can Passive Facebook Use Promote Feelings of Social Connectedness? Media and Communication, 10(2), 119-129. https://doi.org/10.17645/mac.v10i2.500

The Semantic Scale Network: An online tool to detect semantic overlap of psychological scales and prevent scale redundancies

Psychological measurement and theory are afflicted with an ongoing proliferation of new constructs and scales. Given the often redundant nature of new scales, psychological science is struggling with arbitrary measurement, construct dilution, and disconnection between research groups. To address these issues, we introduce an easy-to-use online application: the Semantic Scale Network. The purpose of this application is to automatically detect semantic overlap between scales through latent semantic analysis. Authors and reviewers can enter the items of a new scale into the application, and receive quantifications of semantic overlap with related scales in the application's corpus. Contrary to traditional assessments of scale overlap, the application can support expert judgments on scale redundancy without access to empirical data or awareness of every potentially related scale. After a brief introduction to measures of semantic similarity in texts, we introduce the Semantic Scale Network and provide best practices for interpreting its outputs. (PsycINFO Database Record (c) 2019 APA, all rights reserved)