Replicability, Robustness, and Reproducibility in Psychological Science
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Reproducibility and Replicability in Science (2019)
THE NATIONAL ACADEMIES PRESS This PDF is available at http://nap.edu/25303 SHARE Reproducibility and Replicability in Science (2019) DETAILS 256 pages | 6 x 9 | PAPERBACK ISBN 978-0-309-48616-3 | DOI 10.17226/25303 CONTRIBUTORS GET THIS BOOK Committee on Reproducibility and Replicability in Science; Board on Behavioral, Cognitive, and Sensory Sciences; Committee on National Statistics; Division of Behavioral and Social Sciences and Education; Nuclear and Radiation Studies FIND RELATED TITLES Board; Division on Earth and Life Studies; Board on Mathematical Sciences and Analytics; Committee on Applied and Theoretical Statistics; Division on SUGGESTEDEngineering an CITATIONd Physical Sciences; Board on Research Data and Information; NCaotmiomnaittl eAec oand eSmciiesn coef ,S Ecniegninceeesr, inEgn,g Mineedeircininge,, aanndd MPeudbilcicin Peo 2li0c1y;9 P. Roleicpyr oadnudcibility aGnlodb Rael Aplfifcaairbsi;li tNy ainti oSncaiel Ancea.d Wemaisehsi nogf tSonc,ie DnCce: sT,h Een Ngianteioenrainl gA, caandde Mmeiedsi cPinress. https://doi.org/10.17226/25303. Visit the National Academies Press at NAP.edu and login or register to get: – Access to free PDF downloads of thousands of scientific reports – 10% off the price of print titles – Email or social media notifications of new titles related to your interests – Special offers and discounts Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the National Academies Press. (Request Permission) Unless otherwise indicated, all materials in this -
Promoting an Open Research Culture
Promoting an open research culture Brian Nosek University of Virginia -- Center for Open Science http://briannosek.com/ -- http://cos.io/ The McGurk Effect Ba Ba? Da Da? Ga Ga? McGurk & MacDonald, 1976, Nature Adelson, 1995 Adelson, 1995 Norms Counternorms Communality Secrecy Open sharing Closed Norms Counternorms Communality Secrecy Open sharing Closed Universalism Particularlism Evaluate research on own merit Evaluate research by reputation Norms Counternorms Communality Secrecy Open sharing Closed Universalism Particularlism Evaluate research on own merit Evaluate research by reputation Disinterestedness Self-interestedness Motivated by knowledge and discovery Treat science as a competition Norms Counternorms Communality Secrecy Open sharing Closed Universalism Particularlism Evaluate research on own merit Evaluate research by reputation Disinterestedness Self-interestedness Motivated by knowledge and discovery Treat science as a competition Organized skepticism Organized dogmatism Consider all new evidence, even Invest career promoting one’s own against one’s prior work theories, findings Norms Counternorms Communality Secrecy Open sharing Closed Universalism Particularlism Evaluate research on own merit Evaluate research by reputation Disinterestedness Self-interestedness Motivated by knowledge and discovery Treat science as a competition Organized skepticism Organized dogmatism Consider all new evidence, even Invest career promoting one’s own against one’s prior work theories, findings Quality Quantity Anderson, Martinson, & DeVries, -
Sources of False Positives and False Negatives in the STATCHECK 1 Algorithm: Reply to Nuijten Et Al
Cite as: Schmidt, T. (2017). Sources of false positives and false negatives in the STATCHECK 1 algorithm: Reply to Nuijten et al. (2016). arXiv:1610.01010v8 [q-bio.NC] Sources of false positives and false negatives in the STATCHECK algorithm: Reply to Nuijten et al. (2016) arXiv:1610.01010v8 [q-bio.NC] Thomas Schmidt University of Kaiserslautern, Germany Experimental Psychology Unit www.sowi.uni-kl.de/psychologie [email protected] Abstract STATCHECK is an R algorithm designed to scan papers automatically for inconsistencies between test statistics and their associated p values (Nuijten et al., 2016). The goal of this comment is to point out an important and well-documented flaw in this busily applied algorithm: It cannot handle corrected p values. As a result, statistical tests applying appropriate corrections to the p value (e.g., for multiple tests, post-hoc tests, violations of assumptions, etc.) are likely to be flagged as reporting inconsistent statistics, whereas papers omitting necessary corrections are certified as correct. The STATCHECK algorithm is thus valid for only a subset of scientific papers, and conclusions about the quality or integrity of statistical reports should never be based solely on this program. STATCHECK is an R algorithm designed to scan papers automatically for inconsistencies between test statistics and their reported p values (Nuijten, Hartgerink, van Assen, Epskamp, and Wicherts, 2016). The algorithm is currently part of these authors’ project to automatically scan thousands of scientific psychology papers and post the results on a website when the program detects some discrepancy, essentially flagging papers for reporting inconsistent test statistics. -
The Reproducibility Crisis in Research and Open Science Solutions Andrée Rathemacher University of Rhode Island, [email protected] Creative Commons License
University of Rhode Island DigitalCommons@URI Technical Services Faculty Presentations Technical Services 2017 "Fake Results": The Reproducibility Crisis in Research and Open Science Solutions Andrée Rathemacher University of Rhode Island, [email protected] Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License. Follow this and additional works at: http://digitalcommons.uri.edu/lib_ts_presentations Part of the Scholarly Communication Commons, and the Scholarly Publishing Commons Recommended Citation Rathemacher, Andrée, ""Fake Results": The Reproducibility Crisis in Research and Open Science Solutions" (2017). Technical Services Faculty Presentations. Paper 48. http://digitalcommons.uri.edu/lib_ts_presentations/48http://digitalcommons.uri.edu/lib_ts_presentations/48 This Speech is brought to you for free and open access by the Technical Services at DigitalCommons@URI. It has been accepted for inclusion in Technical Services Faculty Presentations by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. “Fake Results” The Reproducibility Crisis in Research and Open Science Solutions “It can be proven that most claimed research findings are false.” — John P. A. Ioannidis, 2005 Those are the words of John Ioannidis (yo-NEE-dees) in a highly-cited article from 2005. Now based at Stanford University, Ioannidis is a meta-scientist who conducts “research on research” with the goal of making improvements. Sources: Ionnidis, John P. A. “Why Most -
Changes on CRAN 2014-07-01 to 2014-12-31
NEWS AND NOTES 192 Changes on CRAN 2014-07-01 to 2014-12-31 by Kurt Hornik and Achim Zeileis New packages in CRAN task views Bayesian BayesTree. Cluster fclust, funFEM, funHDDC, pgmm, tclust. Distributions FatTailsR, RTDE, STAR, predfinitepop, statmod. Econometrics LinRegInteractive, MSBVAR, nonnest2, phtt. Environmetrics siplab. Finance GCPM, MSBVAR, OptionPricing, financial, fractal, riskSimul. HighPerformanceComputing GUIProfiler, PGICA, aprof. MachineLearning BayesTree, LogicForest, hdi, mlr, randomForestSRC, stabs, vcrpart. MetaAnalysis MAVIS, ecoreg, ipdmeta, metaplus. NumericalMathematics RootsExtremaInflections, Rserve, SimplicialCubature, fastGHQuad, optR. OfficialStatistics CoImp, RecordLinkage, rworldmap, tmap, vardpoor. Optimization RCEIM, blowtorch, globalOptTests, irace, isotone, lbfgs. Phylogenetics BAMMtools, BoSSA, DiscML, HyPhy, MPSEM, OutbreakTools, PBD, PCPS, PHYLOGR, RADami, RNeXML, Reol, Rphylip, adhoc, betapart, dendextend, ex- pands, expoTree, jaatha, kdetrees, mvMORPH, outbreaker, pastis, pegas, phyloTop, phyloland, rdryad, rphast, strap, surface, taxize. Psychometrics IRTShiny, PP, WrightMap, mirtCAT, pairwise. ReproducibleResearch NMOF. Robust TEEReg, WRS2, robeth, robustDA, robustgam, robustloggamma, robustreg, ror, rorutadis. Spatial PReMiuM. SpatioTemporal BayesianAnimalTracker, TrackReconstruction, fishmove, mkde, wildlifeDI. Survival DStree, ICsurv, IDPSurvival, MIICD, MST, MicSim, PHeval, PReMiuM, aft- gee, bshazard, bujar, coxinterval, gamboostMSM, imputeYn, invGauss, lsmeans, multipleNCC, paf, penMSM, spBayesSurv, -
2020 Impact Report
Center for Open Science IMPACT REPORT 2020 Maximizing the impact of science together. COS Mission Our mission is to increase the openness, integrity, and reproducibility of research. But we don’t do this alone. COS partners with stakeholders across the research community to advance the infrastructure, methods, norms, incentives, and policies shaping the future of research to achieve the greatest impact on improving credibility and accelerating discovery. Letter from the Executive Director “Show me” not “trust me”: Science doesn’t ask for Science is trustworthy because it does not trust itself. Transparency is a replacement for trust. Transparency fosters self-correction when there are errors trust, it earns trust with transparency. and increases confidence when there are not. The credibility of science has center stage in 2020. A raging pandemic. Partisan Transparency is critical for maintaining science’s credibility and earning public interests. Economic and health consequences. Misinformation everywhere. An trust. The events of 2020 make clear the urgency and potential consequences of amplified desire for certainty on what will happen and how to address it. losing that credibility and trust. In this climate, all public health and economic research will be politicized. All The Center for Open Science is profoundly grateful for all of the collaborators, findings are understood through a political lens. When the findings are against partners, and supporters who have helped advance its mission to increase partisan interests, the scientists are accused of reporting the outcomes they want openness, integrity, and reproducibility of research. Despite the practical, and avoiding the ones they don’t. When the findings are aligned with partisan economic, and health challenges, 2020 was a remarkable year for open science. -
1 Maximizing the Reproducibility of Your Research Open
1 Maximizing the Reproducibility of Your Research Open Science Collaboration1 Open Science Collaboration (in press). Maximizing the reproducibility of your research. In S. O. Lilienfeld & I. D. Waldman (Eds.), Psychological Science Under Scrutiny: Recent Challenges and Proposed Solutions. New York, NY: Wiley. Authors’ Note: Preparation of this chapter was supported by the Center for Open Science and by a Veni Grant (016.145.049) awarded to Hans IJzerman. Correspondence can be addressed to Brian Nosek, [email protected]. 1 Alexander A. Aarts, Nuenen, The Netherlands; Frank A. Bosco, Virginia Commonwealth University; Katherine S. Button, University of Bristol; Joshua Carp, Center for Open Science; Susann Fiedler, Max Planck Institut for Research on Collective Goods; James G. Field, Virginia Commonwealth University; Roger Giner-Sorolla, University of Kent; Hans IJzerman, Tilburg University; Melissa Lewis, Center for Open Science; Marcus Munafò, University of Bristol; Brian A. Nosek, University of Virginia; Jason M. Prenoveau, Loyola University Maryland; Jeffrey R. Spies, Center for Open Science 2 Commentators in this book and elsewhere describe evidence that modal scientific practices in design, analysis, and reporting are interfering with the credibility and veracity of the published literature (Begley & Ellis, 2012; Ioannidis, 2005; Miguel et al., 2014; Simmons, Nelson, & Simonsohn, 2011). The reproducibility of published findings is unknown (Open Science Collaboration, 2012a), but concern that is lower than desirable is widespread - -
The Reproducibility Crisis in Scientific Research
James Madison University JMU Scholarly Commons Senior Honors Projects, 2010-current Honors College Spring 2019 The reproducibility crisis in scientific esearr ch Sarah Eline Follow this and additional works at: https://commons.lib.jmu.edu/honors201019 Part of the Medicine and Health Sciences Commons, and the Statistics and Probability Commons Recommended Citation Eline, Sarah, "The reproducibility crisis in scientific esearr ch" (2019). Senior Honors Projects, 2010-current. 667. https://commons.lib.jmu.edu/honors201019/667 This Thesis is brought to you for free and open access by the Honors College at JMU Scholarly Commons. It has been accepted for inclusion in Senior Honors Projects, 2010-current by an authorized administrator of JMU Scholarly Commons. For more information, please contact [email protected]. Running Head: REPRODUCIBILITY CRISIS 1 The Reproducibility Crisis in Scientific Research Sarah Eline Senior Honors Thesis Running Head: REPRODUCIBILITY CRISIS 2 While evidence-based medicine has its origins well before the 19th century, it was not until the beginning of the 1990s that it began dominating the field of science. Evidence-based practice is defined as “the conscientious and judicious use of current best evidence in conjunction with clinical expertise and patient values to guide health care decisions” (Titler, 2008, para. 3). In 1992, only two journal articles mentioned the phrase evidence-based medicine; however just five years later, that number rose to over 1000. In a very short period of time, evidence-based medicine had evolved to become synonymous with the practices that encompassed the medical field (Sackett, 1996). With evidence-based medicine came a decline in qualitative research and a shift towards quantitative research. -
The Reproducibility of Statistical Results in Psychological Research: an Investigation Using Unpublished Raw Data
Psychological Methods © 2020 American Psychological Association 2020, Vol. 2, No. 999, 000 ISSN: 1082-989X https://doi.org/10.1037/met0000365 The Reproducibility of Statistical Results in Psychological Research: An Investigation Using Unpublished Raw Data Richard Artner, Thomas Verliefde, Sara Steegen, Sara Gomes, Frits Traets, Francis Tuerlinckx, and Wolf Vanpaemel Faculty of Psychology and Educational Sciences, KU Leuven Abstract We investigated the reproducibility of the major statistical conclusions drawn in 46 articles published in 2012 in three APA journals. After having identified 232 key statistical claims, we tried to reproduce, for each claim, the test statistic, its degrees of freedom, and the corresponding p value, starting from the raw data that were provided by the authors and closely following the Method section in the article. Out of the 232 claims, we were able to successfully reproduce 163 (70%), 18 of which only by deviating from the article’s analytical description. Thirteen (7%) of the 185 claims deemed significant by the authors are no longer so. The reproduction successes were often the result of cumbersome and time-consuming trial-and-error work, suggesting that APA style reporting in conjunction with raw data makes numerical verification at least hard, if not impossible. This article discusses the types of mistakes we could identify and the tediousness of our reproduction efforts in the light of a newly developed taxonomy for reproducibility. We then link our findings with other findings of empirical research on this topic, give practical recommendations on how to achieve reproducibility, and discuss the challenges of large-scale reproducibility checks as well as promising ideas that could considerably increase the reproducibility of psychological research. -
Using the OSF (Open Science Framework)
Using the OSF Center for Open Science at BITSS 2014 Johanna Cohoon & Caner Uguz Pull out your laptop and visit www.osf.io Anderson, Martinson, & DeVries, 2007 NORMS COUNTERNORMS Communality Secrecy Open sharing Closed Universalism Particularism Evaluate research on own merit Evaluate research by reputation Disinterestedness Self interestedness Motivated by knowledge and discovery Treat science as a competition Organized skepticism Organized dogmatism Consider all new evidence, even Invest career promoting one’s own against one’s prior work theories, findings Quality Quantity COUNTERNORMS NORMS Incentives Incentives for individual success are focused on getting it published, not getting it right. Change the incentives. Center for Open Science COMMUNITY INFRASTRUCTURE METASCIENCE Community ■ Foster discussion of transparency and reproducibility issues ■ Support community efforts to increase openness in the sciences ■ Promote the establishment of reporting standards Center for Open Science COMMUNITY INFRASTRUCTURE METASCIENCE Infrastructure ■ Tools for scientists that increase transparency and access to resources ■ Connect services and allow users to easily organize and share their work Center for Open Science COMMUNITY INFRASTRUCTURE METASCIENCE Metascience ■ How are scientists currently behaving? ■ Do transparency and replication have a positive impact on research? ■ Reproducibility Project: Psychology and Cancer Biology; Many Labs 1, 2, and 3; Archival Project; CREP Open your laptop! www.osf.io OSF Nudge the incentive system ■ Support pre-registration ■ Create alternative means of measuring impact ■ Enable sharing of materials Facilitate good research ■ Organize files ■ Allow collaboration meanwhile Logs Commenting Tags Statistics Download counts Addons OSF Nudge the incentive system ■ Support pre-registration ■ Create alternative means of measuring impact ■ Enable sharing of materials Facilitate good research ■ Organize files ■ Allow collaboration In the future ■ Check lists ■ Notifications ■ Messaging ■ Dataverse, Evernote, Trello, Plot.ly. -
Researchers Overturn Landmark Study on the Replicability of Psychological Science
Researchers overturn landmark study on the replicability of psychological science By Peter Reuell Harvard Staff Writer Category: HarvardScience Subcategory: Culture & Society KEYWORDS: psychology, psychological science, replication, replicate, reproduce, reproducibility, Center for Open Science, Gilbert, Daniel Gilbert, King, Gary King, Science, Harvard, FAS, Faculty of Arts and Sciences, Reuell, Peter Reuell Summary: A 2015 study claiming that more than half of all psychology studies cannot be replicated turns out to be wrong. Harvard researchers have discovered that the study contains several statistical and methodological mistakes, and that when these are corrected, the study actually shows that the replication rate in psychology is quite high – indeed, it is statistically indistinguishable from 100%. RELATED LINKS: According to two Harvard professors and their collaborators, a 2015 landmark study showing that more than half of all psychology studies cannot be replicated is actually wrong. In an attempt to determine the replicability of psychological science, a consortium of 270 scientists known as The Open Science Collaboration (OSC) tried to replicate the results of 100 published studies. More than half of them failed, creating sensational headlines worldwide about the “replication crisis” in psychology. But an in-depth examination of the data by Daniel Gilbert (Edgar Pierce Professor of Psychology at Harvard University), Gary King (Albert J. Weatherhead III University Professor at Harvard University), Stephen Pettigrew (doctoral student in the Department of Government at Harvard University), and Timothy Wilson (Sherrell J. Aston Professor of Psychology at the University of Virginia) has revealed that the OSC made some serious mistakes that make this pessimistic conclusion completely unwarranted: The methods of many of the replication studies turn out to be remarkably different from the originals and, according to Gilbert, King, Pettigrew, and Wilson, these “infidelities” had two important consequences. -
The Life Outcomes of Personality Replication (LOOPR) Project Was Therefore Conducted to Estimate the Replicability of the Personality-Outcome Literature
PSSXXX10.1177/0956797619831612SotoReplicability of Trait–Outcome Associations 831612research-article2019 ASSOCIATION FOR Research Article PSYCHOLOGICAL SCIENCE Psychological Science 2019, Vol. 30(5) 711 –727 How Replicable Are Links Between © The Author(s) 2019 Article reuse guidelines: sagepub.com/journals-permissions Personality Traits and Consequential DOI:https://doi.org/10.1177/0956797619831612 10.1177/0956797619831612 Life Outcomes? The Life Outcomes of www.psychologicalscience.org/PS Personality Replication Project TC Christopher J. Soto Department of Psychology, Colby College Abstract The Big Five personality traits have been linked to dozens of life outcomes. However, metascientific research has raised questions about the replicability of behavioral science. The Life Outcomes of Personality Replication (LOOPR) Project was therefore conducted to estimate the replicability of the personality-outcome literature. Specifically, I conducted preregistered, high-powered (median N = 1,504) replications of 78 previously published trait–outcome associations. Overall, 87% of the replication attempts were statistically significant in the expected direction. The replication effects were typically 77% as strong as the corresponding original effects, which represents a significant decline in effect size. The replicability of individual effects was predicted by the effect size and design of the original study, as well as the sample size and statistical power of the replication. These results indicate that the personality-outcome literature provides a reasonably accurate map of trait–outcome associations but also that it stands to benefit from efforts to improve replicability. Keywords Big Five, life outcomes, metascience, personality traits, replication, open data, open materials, preregistered Received 7/6/18; Revision accepted 11/29/18 Do personality characteristics reliably predict conse- effort to estimate the replicability of the personality- quential life outcomes? A sizable research literature has outcome literature.