48 research outputs found
The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network
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\u27s 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 Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network
Source at https://doi.org/10.1177/2515245918797607.Concerns about the veracity of psychological research have been growing. 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 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 that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability
Situational factors shape moral judgements in the trolley dilemma in Eastern, Southern and Western countries in a culturally diverse sample
Examining the generalizability of research findings from archival data
This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples
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
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 to 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 to 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 intentions 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
To which world regions does the valence–dominance model of social perception apply?
Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of
how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social
judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether
these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across
11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy,
the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated
dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance
model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed
when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007);
L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from
CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social
Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing
Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were
supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E.
Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council
of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad
de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant
from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick
and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak
Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported
by a French National Research Agency ‘Investissements d’Avenir’ programme grant
(ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research
Training Program Scholarship. The Raipur Group is thankful to: (1) the University
Grants Commission, New Delhi, India for the research grants received through its
SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science;
and (2) the Center for Translational Chronobiology at the School of Studies in Life
Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by
a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was
supported by grants from the Beijing Natural Science Foundation (5184035) and CAS
Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported
by the National Science Foundation Graduate Research Fellowship (R010138018). We
acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States
International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E.
Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova);
S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of
Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R.
C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New
Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T.
Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera);
J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B.
Todorova (University of Vienna, Austria). The funders had no role in study design, data
collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog