Chapter published in:
Crossroads Semantics: Computation, experiment and grammar
Edited by Hilke Reckman, Lisa Lai-Shen Cheng, Maarten Hijzelendoorn and Rint Sybesma
[Not in series 210] 2017
► pp. 23–37

Experimental research

Problems and opportunities in the big-data era

| University of Amsterdam
Experimental research in psychology, psycholinguistics or medicine provides quantitative and therefore seemingly conclusive and trustworthy evidence. However, it has been convincingly shown that most research findings are actually false. This has hardly influenced the dominant scientific evaluation system which reflects a continued trust in the unbiasedness of data by a strong reliance on simple quantifications of scientific quality and productivity, such as number of publications and number of citations. This state of affairs is remarkable in the light of a long history of strong criticism of commonly used inference methods and scientific evaluation systems, which is now backed by large-scale research projects directly questioning the reproducibility of scientific findings. This way, the large amounts of data – “big-data” – have helped to uncover some of these problematic issues, but also provided a more open attitude towards data and code sharing. In addition, novel analytic frameworks may help to better integrate empirical data with computational models.
Keywords: Experimental research, big-data, statistics, scientific evaluation, theory testing
Published online: 12 April 2017
https://doi.org/10.1075/z.210.02cre
References

References

Bargh, John A., Mark Chen & Lara Burrows
1996 Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology 71(2). 230–244. Crossref.Google Scholar
Bornmann, Lutz, Rüdiger Mutz & Hans-Dieter Daniel
2010 A reliability-generalization study of journal peer reviews: a multilevel meta-analysis of inter-rater reliability and its determinants. PLoS ONE 5(12). e14331. CrossrefGoogle Scholar
Breiman, Leo
2001 Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science 16(3). 199–231. CrossrefGoogle Scholar
Button, Katherine S., John P. A. Ioannidis, Claire Mokrysz, Brian A. Nosek, Jonathan Flint, Emma S. J. Robinson & Marcus R. Munafò
2013 Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience 14(5). 365–376. CrossrefGoogle Scholar
Carp, Joshua
2012 The secret lives of experiments: Methods reporting in the fMRI literature. NeuroImage 63(1). 289–300. CrossrefGoogle Scholar
Cohen, Jacob
1994 The earth is round (p < .05). American Psychologist 49(12). 997–1003. CrossrefGoogle Scholar
Collobert, Ronan & Jason Weston
2008 A unified architecture for natural language processing: deep neural networks with multitask learning. the 25th international conference. New York, NY: ACM. CrossrefGoogle Scholar
Coyne, James C & Jacob N. de Voogd
2012 Are we witnessing the decline effect in the Type D personality literature? What can be learned? Journal of psychosomatic research 73(6). 401–407. CrossrefGoogle Scholar
Doyen, Stéphane, Olivier Klein, Cora-Lise Pichon & Axel Cleeremans
2012 Behavioral priming: It’s all in the mind, but whose mind? PLoS ONE 7(1). e29081. CrossrefGoogle Scholar
Dreber, Anna, Thomas Pfeiffer, Johan Almenberg, Siri Isaksson, Brad Wilson, Yiling Chen, Brian A. Nosek & Magnus Johannesson
2015 Using prediction markets to estimate the reproducibility of scientific research. Proceedings of the National Academy of Sciences 112(50). 15343–15347. CrossrefGoogle Scholar
Falk, Ruma & Charles W. Greenbaum
1995 Significance tests die hard: The amazing persistence of a probabilistic misconception. Theory & Psychology 5(1). 75–98. CrossrefGoogle Scholar
Fanelli, Daniele
2010 “Positive” results increase down the hierarchy of the sciences. PLoS ONE 5(4). e10068. CrossrefGoogle Scholar
Fugelsang, Jonathan A., Courtney B. Stein, Adam E. Green & Kevin N. Dunbar
2004 Theory and data interactions of the scientific mind: Evidence from the molecular and the cognitive laboratory. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 58(2). 86–95. CrossrefGoogle Scholar
Gigerenzer, Gerd
2004 Mindless statistics. The Journal of Socio-Economics 33(5). 587–606. CrossrefGoogle Scholar
Gilbert, Daniel T., Gary King, Stephen Pettigrew & Timothy D. Wilson
2016 Comment on “Estimating the reproducibility of psychological science”. Science 351(6277). 1037–1037. CrossrefGoogle Scholar
Greenberg, Steven A.
2009 How citation distortions create unfounded authority: analysis of a citation network. BMJ (Clinical research ed.) 339. b2680. CrossrefGoogle Scholar
Greenwald, Anthony G.
2012 There is nothing so theoretical as a good method. Perspectives on Psychological Science. CrossrefGoogle Scholar
Hill, Ray
2004 Multiple sudden infant deaths – coincidence or beyond coincidence? Paediatric and perinatal epidemiology 18(5). 320–326. Blackwell Science Ltd. CrossrefGoogle Scholar
Ioannidis, John P. A.
2005 Why most published research findings are false. PLoS Medicine 2(8). e124. CrossrefGoogle Scholar
Ioannidis, John P A.
2008 Why most discovered true associations are inflated. Epidemiology 19(5). 640–648. CrossrefGoogle Scholar
Ioannidis, John P.A.
2012 Why science is not necessarily self-correcting. Perspectives on Psychological Science: A Journal of the Association for Psychological Science 7(6). 645–654. CrossrefGoogle Scholar
Ioannidis, John P. A., Kevin W. Boyack, Henry Small, Aaron A. Sorensen & Richard Klavans
2014 Bibliometrics: Is your most cited work your best? Nature 514(7524). 561–562. CrossrefGoogle Scholar
Kahneman, Daniel
2013 Thinking, fast and slow. Farrar, Straus and GirouxGoogle Scholar
Klein, Richard A., Kate A. Ratliff, Michelangelo Vianello, Reginald B. Adams, Štěpán Bahník, Michael J. Bernstein, Konrad Bocian, et al.
2014 Investigating variation in replicability. Social Psychology 45(3). 142–152. CrossrefGoogle Scholar
Kramer, Adam D. I., Jamie E. Guillory & Jeffrey T. Hancock
2014 Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111(24). 8788–8790. CrossrefGoogle Scholar
Kriegeskorte, Nikolaus
2012 An emerging consensus for open evaluation: 18 visions for the future of scientific publishing. 1–5. CrossrefGoogle Scholar
Kriegeskorte, Nikolaus, Marieke Mur & Peter Bandettini
2008 Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience 2Google Scholar
Lazer, David, Ryan Kennedy, Gary King & Alessandro Vespignani
2014 Big data. The parable of Google Flu: Traps in big data analysis. Science 343(6176). 1203–1205. CrossrefGoogle Scholar
Lindley, Dennis V.
2000 The philosophy of statistics. Journal of the Royal Statistical Society: Series D (The Statistician) 49(3). 293–337Google Scholar
Loftus, Geoffrey R.
1993 A picture is worth a thousand p values: On the irrelevance of hypothesis testing in the microcomputer age. Behavior Research Methods, Instruments, & Computers 25(2). 250–256. CrossrefGoogle Scholar
Maxwell, Scott E.
2004 The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods 9(2). 147–163. CrossrefGoogle Scholar
Meehl, Paul E.
1978 Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology. CrossrefGoogle Scholar
1993 Philosophy of science: Help or hindrance? Psychological Reports 72(3). 707–733. CrossrefGoogle Scholar
Meehl, PE.
1967 Theory-testing in psychology and physics: A methodological paradox. Philosophy of science 34(2). 103–115. CrossrefGoogle Scholar
Miller, G.
2012 The smartphone psychology manifesto. Perspectives on Psychological Science: A Journal of the Association for Psychological Science 7(3). 221–237. CrossrefGoogle Scholar
Munafo, Marcus
2013 Deep impact: Unintended consequences of journal rank. 1–33.Google Scholar
Nickerson, R. S.
2000 Null hypothesis significance testing: A review of an old and continuing controversy. Psychological methods 5(2). 241–301. CrossrefGoogle Scholar
Nuijten, Michèle B., Chris H. J. Hartgerink, Marcel A. L. M. van Assen, Sacha Epskamp & Jelte M. Wicherts
2015 The prevalence of statistical reporting errors in psychology (1985–2013). Behavior Research Methods. 1–22. CrossrefGoogle Scholar
Open Science Collaboration
2015 Estimating the reproducibility of psychological science. Science 349(6251). aac4716–aac4716. CrossrefGoogle Scholar
Pfeiffer, Thomas, Lars Bertram & John P. A. Ioannidis
2011 Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias. PLoS ONE 6(3). e18362. CrossrefGoogle Scholar
Rodriguez-Esteban, Raul, Ivan Iossifov & Andrey Rzhetsky
2006 Imitating manual curation of text-mined facts in biomedicine. PLoS computational biology 2(9). e118. CrossrefGoogle Scholar
Rosenthal, Robert
1979 The file drawer problem and tolerance for null results. Psychological Bulletin 86(3). 638–641. CrossrefGoogle Scholar
Rosenthal, Robert & John Gaito
1963 The interpretation of levels of significance by psychological researchers. The Journal of Psychology 55(1). 33–38. CrossrefGoogle Scholar
Rutledge, Robb B., Nikolina Skandali, Peter Dayan & Raymond J. Dolan
2014 A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences 111(33). 12252–12257. CrossrefGoogle Scholar
Shmueli, Galit
2010 To explain or to predict? Statistical Science 25(3). 289–310. CrossrefGoogle Scholar
Simmons, Joseph P., Leif D. Nelson & Uri Simonsohn
2011 False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science 22(11). 1359–1366. CrossrefGoogle Scholar
Simonsohn, Uri, Leif D. Nelson & Joseph P. Simmons
2014 P-curve: A key to the file-drawer. Journal of Experimental Psychology: General 143(2). 534. CrossrefGoogle Scholar
Thompson, William C. & Edward L. Schumann
1987 Interpretation of statistical evidence in criminal trials: The prosecutor’s fallacy and the defense attorney’s fallacy. Law and Human Behavior 11(3). Springer. 167–187. CrossrefGoogle Scholar
Tversky, Amos & Daniel Kahneman
1971 Belief in the law of small numbers. Psychological Bulletin 76(2). 105. CrossrefGoogle Scholar
Tyler, Lorraine K, Teresa P. L. Cheung, Barry J. Devereux & Alex Clarke
2013 Syntactic computations in the language network: Characterizing dynamic network properties using representational similarity analysis. Frontiers in Psychology 4. 271. CrossrefGoogle Scholar
Wainer, Howard
2007 The most dangerous equation. American Scientist. CrossrefGoogle Scholar
Wicherts, Jelte M. & Marcel A. L. M. van Assen
2012 Research fraud: Speed up reviews of misconduct. Nature 488(7413). 591–591. CrossrefGoogle Scholar
Yarkoni, Tal, Russell A. Poldrack, Thomas E. Nichols, David C. Van Essen & Tor D. Wager
2011 Large-scale automated synthesis of human functional neuroimaging data. Nature methods 8(8). 665–670. CrossrefGoogle Scholar
Yong, Ed.
2012 Replication studies: Bad copy. Nature. CrossrefGoogle Scholar
Young, Neal S., John P. A. Ioannidis & Omar Al-Ubaydli
2008 Why current publication practices may distort science. PLoS Medicine 5(10). e201. CrossrefGoogle Scholar