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Silberzahn R, Uhlmann EL, Martin DP, Anselmi P, Aust F, Awtrey E, Bahník Š, Bai F, Bannard C, Bonnier E, Carlsson R, Cheung F, Christensen G, Clay R, Craig MA, Dalla Rosa A, Dam L, Evans MH, Flores Cervantes I, Fong N, Gamez-Djokic M, Glenz A, Gordon-McKeon S, Heaton TJ, Hederos K, Heene M, Hofelich Mohr AJ, Högden F, Hui K, Johannesson M, Kalodimos J, Kaszubowski E, Kennedy DM, Lei R, Lindsay TA, Liverani S, Madan CR, Molden D, Molleman E, Morey RD, Mulder LB, Nijstad BR, Pope NG, Pope B, Prenoveau JM, Rink F, Robusto E, Roderique H, Sandberg A, Schlüter E, Schönbrodt FD, Sherman MF, Sommer SA, Sotak K, Spain S, Spörlein C, Stafford T, Stefanutti L, Tauber S, Ullrich J, Vianello M, Wagenmakers EJ, Witkowiak M, Yoon S, Nosek BA. Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results. Advances in Methods and Practices in Psychological Science 2018. [DOI: 10.1177/2515245917747646] [Citation(s) in RCA: 267] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Twenty-nine teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skin-toned players. Analytic approaches varied widely across the teams, and the estimated effect sizes ranged from 0.89 to 2.93 ( Mdn = 1.31) in odds-ratio units. Twenty teams (69%) found a statistically significant positive effect, and 9 teams (31%) did not observe a significant relationship. Overall, the 29 different analyses used 21 unique combinations of covariates. Neither analysts’ prior beliefs about the effect of interest nor their level of expertise readily explained the variation in the outcomes of the analyses. Peer ratings of the quality of the analyses also did not account for the variability. These findings suggest that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy in which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective, analytic choices influence research results.
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Affiliation(s)
- R. Silberzahn
- Organisational Behaviour, University of Sussex Business School
| | | | - D. P. Martin
- Department of Psychology, University of Virginia
| | - P. Anselmi
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua
| | - F. Aust
- Department of Psychology, University of Cologne
| | - E. Awtrey
- Department of Management, University of Cincinnati
| | - Š. Bahník
- Department of Management, Faculty of Business Administration, University of Economics, Prague
| | - F. Bai
- Department of Management and Marketing, Hong Kong Polytechnic University
| | - C. Bannard
- Department of Psychology, University of Liverpool
| | - E. Bonnier
- Department of Economics, Stockholm School of Economics
| | - R. Carlsson
- Department of Psychology, Linnaeus University
| | - F. Cheung
- School of Public Health, University of Hong Kong
| | - G. Christensen
- Berkeley Institute for Data Science, University of California, Berkeley
| | - R. Clay
- Department of Psychology, College of Staten Island, City University of New York
| | - M. A. Craig
- Department of Psychology, New York University
| | - A. Dalla Rosa
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua
| | - L. Dam
- Faculty of Economics and Business, University of Groningen
| | - M. H. Evans
- Division of Neuroscience and Experimental Psychology, University of Manchester
| | | | - N. Fong
- Department of Marketing and Supply Chain Management, Temple University
| | - M. Gamez-Djokic
- Department of Management and Organizations, Kellogg School of Management, Northwestern University
| | - A. Glenz
- Department of Psychology, University of Zurich
| | | | - T. J. Heaton
- School of Mathematics and Statistics, University of Sheffield
| | - K. Hederos
- Swedish Institute for Social Research (SOFI), Stockholm University
| | - M. Heene
- Department of Psychology, Ludwig-Maximilians-Universität München
| | | | - F. Högden
- Department of Psychology, University of Cologne
| | - K. Hui
- School of Management, Xiamen University
| | | | | | - E. Kaszubowski
- Department of Psychology, Federal University of Santa Catarina
| | - D. M. Kennedy
- School of Business, University of Washington Bothell
| | - R. Lei
- Department of Psychology, New York University
| | | | - S. Liverani
- School of Mathematical Sciences, Queen Mary University of London
| | - C. R. Madan
- School of Psychology, University of Nottingham
| | - D. Molden
- Department of Psychology, Northwestern University
| | - E. Molleman
- Faculty of Economics and Business, University of Groningen
| | | | - L. B. Mulder
- Faculty of Economics and Business, University of Groningen
| | - B. R. Nijstad
- Faculty of Economics and Business, University of Groningen
| | - N. G. Pope
- Department of Economics, University of Maryland
| | - B. Pope
- Department of Economics, Brigham Young University
| | | | - F. Rink
- Faculty of Economics and Business, University of Groningen
| | - E. Robusto
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua
| | - H. Roderique
- Rotman School of Management, University of Toronto
| | - A. Sandberg
- Swedish Institute for Social Research (SOFI), Stockholm University
| | - E. Schlüter
- Department of Social Sciences and Cultural Studies, Institute of Sociology, Justus Liebig University, Giessen
| | - F. D. Schönbrodt
- Department of Psychology, Ludwig-Maximilians-Universität München
| | - M. F. Sherman
- Department of Psychology, Loyola University Maryland
| | | | - K. Sotak
- Department of Marketing and Management, SUNY Oswego
| | - S. Spain
- John Molson School of Business, Concordia University
| | - C. Spörlein
- Lehrstuhl für Soziologie, insb. Sozialstrukturanalyse, Otto-Friedrich-Universität Bamberg
| | - T. Stafford
- Department of Psychology, University of Sheffield
| | - L. Stefanutti
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua
| | - S. Tauber
- Faculty of Economics and Business, University of Groningen
| | - J. Ullrich
- Department of Psychology, University of Zurich
| | - M. Vianello
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua
| | | | | | - S. Yoon
- Department of Marketing and Supply Chain Management, Temple University
| | - B. A. Nosek
- Department of Psychology, University of Virginia
- Center for Open Science, Charlottesville, Virginia
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Wang Y, Glenz A, Muhler M, Wöll C. A new dual-purpose ultrahigh vacuum infrared spectroscopy apparatus optimized for grazing-incidence reflection as well as for transmission geometries. Rev Sci Instrum 2009; 80:113108. [PMID: 19947718 DOI: 10.1063/1.3257677] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
A newly designed ultrahigh vacuum (UHV) infrared spectroscopy apparatus dedicated to the spectroscopic characterization of oxides, singles crystals as well as powders, is described. It combines a state-of-the-art vacuum Fourier transform infrared (FTIR) spectrometer (Bruker, VERTEX 80v) with a novel UHV system (PREVAC) consisting of load-lock, distribution, measurement, and magazine chambers. The innovative design allows carrying out both reflection-absorption IR spectroscopy experiments at grazing incidence on well-defined oxide single crystal surfaces and FTIR transmission measurements for powder particles. A further unique feature of the apparatus is the entirely evacuated optical path to avoid background signals from gas phase H(2)O, CO(2), and other species, thus creating the possibility to record high-quality IR data with high sensitivity and stability, an essential prerequisite for monitoring molecular species adsorbed on oxide single-crystal surfaces. The unique performance of this new apparatus with regard to the spectroscopic characterization of adsorbates on oxide single crystals as well as on powder particles is demonstrated by case studies for two different materials, TiO(2) and ZnO.
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Affiliation(s)
- Y Wang
- Lehrstuhl für Physikalische Chemie I, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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