Scientific Utopia III: crowdsourcing science

Most scientific research is conducted by small teams of investigators who together formulate hypotheses, collect data, conduct analyses, and report novel findings. These teams operate independently as vertically integrated silos. Here we argue that scientific research that is horizontally distributed can provide substantial complementary value, aiming to maximize available resources, promote inclusiveness and transparency, and increase rigor and reliability. This alternative approach enables researchers to tackle ambitious projects that would not be possible under the standard model. Crowdsourced scientific initiatives vary in the degree of communication between project members from largely independent work curated by a coordination team to crowd collaboration on shared activities. The potential benefits and challenges of large-scale collaboration span the entire research process: ideation, study design, data collection, data analysis, reporting, and peer review. Complementing traditional small science with crowdsourced approaches can accelerate the progress of science and improve the quality of scientific research

Psychological Science Accelerator: A Promising Resource for Clinical Psychological Science

The Psychological Science Accelerator (PSA) is an international collaborative network of psychological scientists that facilitates rigorous and generalizable research. In this chapter, we describe how the PSA can help clinical psychologists and clinical psychological science more broadly. We first describe the PSA and outline how individual clinical psychologists can use the PSA as a helpful resource in numerous capacities: leading or contributing to clinical research or research with clinical relevance, building collaborative relationships, obtaining experience and expertise, and learning about systems and tools, particularly those related to open science practices, that they can adapt to their own research. We then describe how the PSA supports rigor and transparency at each stage of the research process. Finally, we discuss the challenges of the PSA’s large, collaborative approach to research

Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3–9; median total sample = 1,279.5, range = 276–3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (Δr = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00–.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19–.50).Additional co-authors: Ivan Ropovik, Balazs Aczel, Lena F. Aeschbach, Luca Andrighetto, Jack D. Arnal, Holly Arrow, Peter Babincak, Bence E. Bakos, Gabriel Baník, Ernest Baskin, Radomir Belopavlovic, Michael H. Bernstein, Michał Białek, Nicholas G. Bloxsom, Bojana Bodroža, Diane B. V. Bonfiglio, Leanne Boucher, Florian Brühlmann, Claudia C. Brumbaugh, Erica Casini, Yiling Chen, Carlo Chiorri, William J. Chopik, Oliver Christ, Antonia M. Ciunci, Heather M. Claypool, Sean Coary, Marija V. Cˇolic, W. Matthew Collins, Paul G. Curran, Chris R. Day, Anna Dreber, John E. Edlund, Filipe Falcão, Anna Fedor, Lily Feinberg, Ian R. Ferguson, Máire Ford, Michael C. Frank, Emily Fryberger, Alexander Garinther, Katarzyna Gawryluk, Kayla Ashbaugh, Mauro Giacomantonio, Steffen R. Giessner, Jon E. Grahe, Rosanna E. Guadagno, Ewa Hałasa, Rias A. Hilliard, Joachim Hüffmeier, Sean Hughes, Katarzyna Idzikowska, Michael Inzlicht, Alan Jern, William Jiménez-Leal, Magnus Johannesson, Jennifer A. Joy-Gaba, Mathias Kauff, Danielle J. Kellier, Grecia Kessinger, Mallory C. Kidwell, Amanda M. Kimbrough, Josiah P. J. King, Vanessa S. Kolb, Sabina Kołodziej, Marton Kovacs, Karolina Krasuska, Sue Kraus, Lacy E. Krueger, Katarzyna Kuchno, Caio Ambrosio Lage, Eleanor V. Langford, Carmel A. Levitan, Tiago Jessé Souza de Lima, Hause Lin, Samuel Lins, Jia E. Loy, Dylan Manfredi, Łukasz Markiewicz, Madhavi Menon, Brett Mercier, Mitchell Metzger, Venus Meyet, Jeremy K. Miller, Andres Montealegre, Don A. Moore, Rafał Muda, Gideon Nave, Austin Lee Nichols, Sarah A. Novak, Christian Nunnally, Ana Orlic, Anna Palinkas, Angelo Panno, Kimberly P. Parks, Ivana Pedovic, Emilian Pekala, Matthew R. Penner, Sebastiaan Pessers, Boban Petrovic, Thomas Pfeiffer, Damian Pienkosz, Emanuele Preti, Danka Puric, Tiago Ramos, Jonathan Ravid, Timothy S. Razza, Katrin Rentzsch, Juliette Richetin, Sean C. Rife, Anna Dalla Rosa, Kaylis Hase Rudy, Janos Salamon, Blair Saunders, Przemysław Sawicki, Kathleen Schmidt, Kurt Schuepfer, Thomas Schultze, Stefan Schulz-Hardt, Astrid Schütz, Ani N. Shabazian, Rachel L. Shubella, Adam Siegel, Rúben Silva, Barbara Sioma, Lauren Skorb, Luana Elayne Cunha de Souza, Sara Steegen, L. A. R. Stein, R. Weylin Sternglanz, Darko Stojilovic, Daniel Storage, Gavin Brent Sullivan, Barnabas Szaszi, Peter Szecsi, Orsolya Szöke, Attila Szuts, Manuela Thomae, Natasha D. Tidwell, Carly Tocco, Ann-Kathrin Torka, Francis Tuerlinckx, Wolf Vanpaemel, Leigh Ann Vaughn, Michelangelo Vianello, Domenico Viganola, Maria Vlachou, Ryan J. Walker, Sophia C. Weissgerber, Aaron L. Wichman, Bradford J. Wiggins, Daniel Wolf, Michael J. Wood, David Zealley, Iris Žeželj, Mark Zrubka, and Brian A. Nose

A pre-registered, multi-lab non-replication of the Action-sentence Compatibility Effect (ACE)

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.g., toward) matches the direction of the action in the to-be-judged sentence (e.g., Art gave you the pen describes action toward you). We report on a pre-registered, multi-lab replication of one version of the ACE. The results show that none of the 18 labs involved in the study observed a reliable ACE, and that the meta-analytic estimate of the size of the ACE was essentially zero.Fil: Morey, Richard. Cardiff University; Reino UnidoFil: Kaschak, Michael. Florida State University; Estados UnidosFil: Díez Álamo, Antonio. Universidad de Salamanca; España. Arizona State University; Estados UnidosFil: Glenberg, Arthur. Arizona State University; Estados Unidos. Universidad de Salamanca; EspañaFil: Zwaan, Rolf A.. Erasmus University Rotterdam; Países BajosFil: Lakens, Daniël. Eindhoven University of Technology; Países BajosFil: Ibáñez, Santiago Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of San Francisco; Estados Unidos. Universidad Adolfo Ibañez; Chile. Trinity College Dublin; IrlandaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of San Francisco; Estados Unidos. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina. Universidad de Santiago de Chile; ChileFil: Gianelli, Claudia. Universitat Potsdam; Alemania. Scuola Universitaria Superiore; ItaliaFil: Jones, John L.. Florida State University; Estados UnidosFil: Madden, Julie. University of Tennessee; Estados UnidosFil: Alifano Ferrero, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bergen, Benjamin. University of California at San Diego; Estados UnidosFil: Bloxsom, Nicholas G.. Ashland University; Estados UnidosFil: Bub, Daniel N.. University of Victoria; CanadáFil: Cai, Zhenguang G.. The Chinese University; Hong KongFil: Chartier, Christopher R.. Ashland University; Estados UnidosFil: Chatterjee, Anjan. University of Pennsylvania; Estados UnidosFil: Conwell, Erin. North Dakota State University; Estados UnidosFil: Wagner Cook, Susan. University of Iowa; Estados UnidosFil: Davis, Joshua D.. University of California at San Diego; Estados UnidosFil: Evers, Ellen R. K.. University of California at Berkeley; Estados UnidosFil: Girard, Sandrine. University of Carnegie Mellon; Estados UnidosFil: Harter, Derek. Texas A&m University Commerce; Estados UnidosFil: Hartung, Franziska. University of Pennsylvania; Estados UnidosFil: Herrera, Eduar. Universidad ICESI; ColombiaFil: Huettig, Falk. Max Planck Institute for Psycholinguistics; Países BajosFil: Humphries, Stacey. University of Pennsylvania; Estados UnidosFil: Juanchich, Marie. University of Essex; Reino UnidoFil: Kühne, Katharina. Universitat Potsdam; AlemaniaFil: Lu, Shulan. Texas A&m University Commerce; Estados UnidosFil: Lynes, Tom. University of East Anglia; Reino UnidoFil: Masson, Michael E. J.. University of Victoria; CanadáFil: Ostarek, Markus. Max Planck Institute for Psycholinguistics; Países BajosFil: Pessers, Sebastiaan. Katholikie Universiteit Leuven; BélgicaFil: Reglin, Rebecca. Universitat Potsdam; AlemaniaFil: Steegen, Sara. Katholikie Universiteit Leuven; BélgicaFil: Thiessen, Erik D.. University of Carnegie Mellon; Estados UnidosFil: Thomas, Laura E.. North Dakota State University; Estados UnidosFil: Trott, Sean. University of California at San Diego; Estados UnidosFil: Vandekerckhove, Joachim. University of California at Irvine; Estados UnidosFil: Vanpaeme, Wolf. Katholikie Universiteit Leuven; BélgicaFil: Vlachou, Maria. Katholikie Universiteit Leuven; BélgicaFil: Williams, Kristina. Texas A&m University Commerce; Estados UnidosFil: Ziv Crispel, Noam. BehavioralSight; Estados Unido

Type 2 diabetes and risk of hospital admission or death for chronic liver diseases

Background &amp; aims: the impact of type 2 diabetes (T2DM) on hospital admissions and deaths due to common chronic liver diseases (CLDs) is uncertain. Our aim was to investigate associations between T2DM and CLDs in a national retrospective cohort study and to investigate the role of sex and socio-economic status (SES).Methods: we used International Classification of Disease codes to identify incident alcoholic liver disease (ALD), autoimmune liver disease, haemochromatosis, hepatocellular carcinoma, non-alcoholic fatty liver disease (NAFLD) and viral liver disease from linked diabetes, hospital, cancer and death records for people of 40–89 years of age in Scotland 2004–2013. We used quasi Poisson regression to estimate rate ratios (RR).Results: there were 6667 and 33624 first mentions of CLD in hospital, cancer and death records over ?1.8 and 24 million person-years in people with and without T2DM, respectively. The most common liver disease was ALD among people without diabetes and was NAFLD among people with T2DM. Age-adjusted RR for T2DM compared to the non-diabetic population (95% confidence intervals) varied between 1.27 (1.04–1.55) for autoimmune liver disease and 5.36 (4.41–6.51) for NAFLD. RRs were lower for men than women and for more compared to less deprived populations for both ALD and NAFLD.Conclusions: T2DM is associated with increased risk of hospital admission or death for all common CLDs and the strength of the association varies by type of CLD, sex and SES. Increasing prevalence of T2DM is likely to result in increasing burden of all CLD

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

Many Labs 5:Testing pre-data collection peer review as an intervention to increase replicability

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3?9; median total sample = 1,279.5, range = 276?3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (?r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00?.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19?.50)

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