1. Improving Openness and Reproducibility of Scientific Research
Brian Nosek
University of Virginia -- Center for Open Science
My general substantive interest in the gap between values and practices. The work that I am discussing today is a practical application of this interest to the gap between scientific values and practices. In particular, how can I best advance knowledge and my career at the same time?
Challenges I face when working to advance scientific knowledge and my career at the same time. And, how my scientific practices can be adapted to meet my scientific values.

2. Norms Counternorms Communality Secrecy Open sharing Closed
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume

3. Norms Counternorms Communality Universalism Secrecy Particularlism
Open sharing
Universalism
Evaluate research on own merit
Secrecy
Closed
Particularlism
Evaluate research by reputation
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume

4. Norms Counternorms Communality Universalism Disinterestedness Secrecy
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume

5. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume

6. Norms Counternorms Communality Universalism Disinterestedness
Open sharing
Universalism
Evaluate research on own merit
Disinterestedness
Motivated by knowledge and discovery
Organized skepticism
Consider all new evidence, even against one’s prior work
Quality
Secrecy
Closed
Particularlism
Evaluate research by reputation
Self-interestedness
Treat science as a competition
Organized dogmatism
Invest career promoting one’s own theories, findings
Quantity
Communality – open sharing with colleagues; Secrecy
Universalism – research evaluated only on its merit; Particularism – research evaluated by reputation/past productivity
Disinterestedness – scientists motivated by knowledge and discovery, not by personal gain; self-interestedness – treat science as a competition with other scientists
Organized skepticism – consider all new evidence, theory, data, even if it contradicts one’s prior work/point-of-view; organized dogmatism – invest career in promoting one’s own most important findings, theories, innovations
Quality – seek quality contributions; Quantity – seek high volume

7. Anderson, Martinson, & DeVries, 2007

8. Incentives for individual success are focused on getting it published, not getting it right
Nosek, Spies, & Motyl, 2012

9. Problems
Low power Flexibility in analysis Selective reporting Ignoring nulls Lack of replication
Examples from:
Button et al – Neuroscience
Ioannidis – why most results are false (Medicine)
GWAS
Biology
Two possibilities are that the percentage of positive results is inflated because negative results are much less likely to be published, and that we are pursuing our analysis freedoms to produce positive results that are not really there. These would lead to an inflation of false-positive results in the published literature.
Some evidence from bio-medical research suggests that this is occurring. Two different industrial laboratories attempted to replicate 40 or 50 basic science studies that showed positive evidence for markers for new cancer treatments or other issues in medicine. They did not select at random. Instead, they picked studies considered landmark findings. The success rates for replication were about 25% in one study and about 10% in the other. Further, some of the findings they could not replicate had spurred large literatures of hundreds of articles following up on the finding and its implications, but never having tested whether the evidence for the original finding was solid. This is a massive waste of resources.
Across the sciences, evidence like this has spurred lots of discussion and proposed actions to improve research efficiency and avoid the massive waste of resources linked to erroneous results getting in and staying in the literature, and about the culture of scientific practices that is rewarding publishing, perhaps at the expense of knowledge building. There have been a variety of suggestions for what to do. For example, the Nature article on the right suggests that publishing standards should be increased for basic science research.
[It is not in my interest to replicate – myself or others – to evaluate validity and improve precision in effect estimates (redundant). Replication is worth next to zero (Makel data on published replications; motivated to not call it replication; novelty is supreme – zero “error checking”; not in my interest to check my work, and not in your interest to check my work (let’s just each do our own thing and get rewarded for that)
Irreproducible results will get in and stay in the literature (examples from bio-med). Prinz and Begley articles (make sure to summarize accurately)
The Nature article by folks in bio-medicine is great. The solution they offer is a popular one in commentators from the other sciences -- raise publishing standards.
Sterling, 1959; Cohen, 1962; Lykken, 1968; Tukey, 1969; Greenwald, 1975; Meehl, 1978; Rosenthal, 1979

10. Figure credit: fivethirtyeight.com
Silberzahn et al., 2015

12. Median effect size (d) = .29 % p < .05 = 63%
Reported Tests (122)
Median p-value = .02
Median effect size (d) = .29
% p < .05 = 63%
Unreported Tests (147)
Median p-value = .35
Median effect size (d) = .13
% p < .05 = 23%
We find that about 40% of studies fail to fully report all experimental conditions and about 70% of studies do not report all outcome variables included in the questionnaire. Reported effect sizes are about twice as large as unreported effect sizes and are about 3 times more likely to be statistically
significant.
N = 32 studies in psychology
Unreported tests (147)
Median p-value = .35
Median d = .13
% significant = 23%
Reported tests (N = 122)
Median p = .02
Median d = .29
% sig p<.05 = 63%
Franco, Malhotra, & Simonovits, 2015, SPPS

13. Positive Result Rate dropped from 57% to 8% after preregistration required.

14. Barriers Perceived norms (Anderson, Martinson, & DeVries, 2007)
Motivated reasoning (Kunda, 1990)
Minimal accountability (Lerner & Tetlock, 1999)
I am busy (Me & You, 2016)
We can understand the nature of the challenge with existing psychological theory. For example:
1. The goals and rewards of publishing are immediate and concrete; the rewards of getting it right are distal and abstract (Trope & Liberman)
2. I have beliefs, ideologies, and achievement motivations that influence how I interpret and report my research (motivated reasoning; Kunda, 1990). And, even if I am trying to resist this motivated reasoning. I may simply be unable to detect it in myself, even when I can see those biases in others.
3. And, what biases might influence me. Well, pick your favorite. My favorite in this context is the hindsight bias.
4. What’s more is we face these potential biases in a context of minimal accountability. What you know of my laboratory work is only what you get in the published report. …
5. Finally, even if I am prepared to accept that I have these biases and am motivated to address them so that I can get it right. I am busy. So are you. If I introduce a whole bunch of new things that I must now do to check and correct for my biases, I will kill my productivity and that of my collaborators. So, the incentives lead me to think that my best course of action is to just to the best I can and hope that I’m doing it okay.

15. MEANS REWARDS Novel, Positive, Clean Transparency, Reproducibility
Outcomes
Process
MEANS
Research Content
Data and Materials
Publication
REWARDS

16. Signals: Making Behaviors Visible Promotes Adoption
Badges
Open Data
Open Materials
Preregistration
Psychological Science (Jan 2014)
Kidwell et al., 2016

17. 40%
30%
% Articles reporting data available in repository
20%
10% 0%

18. PREREGISTRATION Context of Justification Confirmation Data independent
Hypothesis testing
Context of Discovery Exploration Data contingent Hypothesis generating
p-values interpretable
p-values NOT interpretable
PREREGISTRATION
Presenting exploratory as confirmatory increases publishability of results at the cost of credibility of results
Study 1
Study 1
Study 2

19. Positive Result Rate dropped from 57% to 8% after preregistration required.

20. Are you okay with receiving treatment based on clinical trials that were not preregistered?
“oh but clinical trials are important”
Why would I waste my time on something that isn’t important enough to be worth doing the best that I can do it?

21. Preregistration Challenge http://cos.io/prereg

22. Registered Reports PEER REVIEW Design Collect & Analyze Report Publish
Review of intro and methods prior to data collection; published regardless of outcome
Beauty vs. accuracy of reporting
Publishing negative results
Conducting replications
Peer review focuses on quality of methods
Committee Chair: Chris Chambers

23. Registered Reports 39 journals so far
AIMS Neuroscience
Attention, Percept., & Psychophys
Cognition and Emotion
Cognitive Research
Comp. Results in Social Psychology
Cortex
Drug and Alcohol Dependence
eLife
Euro Journal of Neuroscience
Experimental Psychology
Human Movement Science
Int’l Journal of Psychophysiology
Journal of Accounting Research
Journal of Business and Psychology
Journal of Euro. Psych. Students
Journal of Expt’l Political Science
Journal of Personnel Psychology
Journal of Media Psychology
Leadership Quarterly
Nature Human Behaviour
Nicotine and Tobacco Research
NFS Journal
Nutrition and Food Science Journal
Perspectives on Psych. Science
Royal Society Open Science
Social Psychology
Stress and Health
Work, Aging, and Retirement
Review of intro and methods prior to data collection; published regardless of outcome
Beauty vs. accuracy of reporting
Publishing negative results
Conducting replications
Peer review focuses on quality of methods
Committee Chair: Chris Chambers

24. Mundane and Big Challenges for Reproducibility
Forgetting
Losing materials and data

26. Tyranny of the publication
Solution = Separate publication and evaluation
Nosek & Bar-Anan, 2012

29. Technology to enable change Training to enact change Incentives to embrace change
Improving scientific ecosystem

30. UNIVERSITIES
ecosystem
PUBLISHING
FUNDERS
SOCIETIES

31. What can you do? Try OSF, http://osf.io/
Prereg Challenge,
Share a preprint,
Editors: Badges, Registered Reports, TOP
Departments: OSF-Reproducibility workshops, hiring and promotion criteria
Individuals: COS Ambassador or

33. So is there a crisis? Nature survey.
Who are these respondents?

34. And why are they saying that – pervasive challenge across disciplines in reproducing prior results. A fundamental feature of science is that scientific claims gain credibility compared to other kinds of claims based on the potential to independently reproduce the evidence.
But, of course, whether there is a reproducibility crisis or not isn’t really an a relevant question. The real question is whether there are cultural norms and practices that are undermining the efficiency and effectiveness of knowledge building, and are there ways that we can do better?

35. Conclusion
Psychology is not under threat, it is leading the way to more open, reproducible science
Start with conclusion….
Why such focus on psych? We are getting the attention not because we have bigger problems, but because we are bothering to face them and we have productive ideas and are taking steps to deal with them.
Think about how much time you spend not caring about what is happening in physics, chemistry, earth sciences, and biology. Turns out, that is just about the same amount of time that physicists, chemists, earth scientists, and biologists spend not caring about us. That’s why the “reproducibility crisis” is actually an opportunity for us, not a threat.
Other areas of science will care about us more and more not when we solve our challenges, but when we solve theirs. And, that’s already underway…

36. What can you do? Try out OSF, http://osf.io/
Prereg Challenge,
Share a preprint,
Join SIPS,
These slides are shared at:
[take a picture of this slide] or

39. TOP Guidelines Data citation Design transparency
Research materials transparency
Data transparency
Analytic methods (code) transparency
Preregistration of studies
Preregistration of analysis plans
Replication

40. Data sharing 1 2 3
Article states whether data are available, and, if so, where to access them
Data must be posted to a trusted repository. Exceptions must be identified at article submission.
Data must be posted to a trusted repository, and reported analyses will be reproduced independently prior to publication.

41. 749 Journals, 62 Organizations
AAAS/Science
American Academy of Neurology
American Geophysical Union
American Heart Association
American Meterological Society
American Society for Cell Biology
Association for Psychological Science
Association for Research in Personality
Association of Research Libraries
Behavioral Science and Policy Association
BioMed Central
Committee on Publication Ethics
Electrochemical Society
Frontiers
MDPI
Nature Publishing Group
PeerJ
Pensoft Publishers
Public Library of Science
The Royal Society
Society for Personality and Social Psychology
Society for a Science of Clinical Psychology
Ubiquity Press
Wiley

42. Open Access Open Data Open Workflows
Outcomes
Content
Process
Improving scientific ecosystem
Make outcomes more accessible
Make research content more accessible
Make research process more accessible

44. OpenSesame

46. Dreber et al.,
in press, PNAS

47. Dreber et al.,
in press, PNAS

48. Dreber et al., in press, PNAS
Fig. 3. Probability of a hypothesis being true at three different stages of
testing: before the initial study (p0), after the initial study but before the
replication (p1), and after replication (p2). “Error bars” (or whiskers) represent
range, boxes are first to third quartiles, and thick lines are medians.
Initially, priors of the tested hypothesis are relatively low, with a median of
8.8% (range, 0.7–66%). A positive result in an initial publication then moves
the prior into a broad range of intermediate levels, with a median of 56%
(range, 10–97%). If replicated successfully, the probability moves further up,
with a median of 98% (range, 93.0–99.2%). If the replication fails, the
probability moves back to a range close to the initial prior, with a median of
6.3% (range, 0.01–80%).
Dreber et al.,
in press, PNAS

49. 98% 56% 8.8% 6.3% Dreber et al., in press, PNAS
Fig. 3. Probability of a hypothesis being true at three different stages of
testing: before the initial study (p0), after the initial study but before the
replication (p1), and after replication (p2). “Error bars” (or whiskers) represent
range, boxes are first to third quartiles, and thick lines are medians.
Initially, priors of the tested hypothesis are relatively low, with a median of
8.8% (range, 0.7–66%). A positive result in an initial publication then moves
the prior into a broad range of intermediate levels, with a median of 56%
(range, 10–97%). If replicated successfully, the probability moves further up,
with a median of 98% (range, 93.0–99.2%). If the replication fails, the
probability moves back to a range close to the initial prior, with a median of
6.3% (range, 0.01–80%).
8.8%
6.3%
Dreber et al.,
in press, PNAS

50. 97%
37% xx
Open Science Collaboration, 2015, Science

51. Mr = .396
Mr = .198
Open Science Collaboration, 2015, Science

52. New Reproducibility Projects
Economics
Cancer Biology
Many Babies
Michael Frank
Computer Science
Tropical Ecology
Health Sciences
RPCB, Economics – Camerer at CalTech
Where are we going?
-Support teams in many disciplines to adopt the Reproducibility Project model
-Christian Collberg and Todd Proebsting have proposing a replication project in computer science
-Emilio Bruna: Replication Project: Tropical Ecology
-Michael Frank: Many Babies
-Leslie McIntosh and Cynthia Hudson-Vitale: Electronic health records proposal: An internal replication at University of Washington, st. louis to
replicate a study that was performed with a data set that is not accessible to the general public.
How do we get there?
-Focus on evaluating our initiatives and interventions
KEVIN M. ESTERLING professor of political science at UC Riverside
Christian Collberg Todd Proebsting
Emilio Bruna
Leslie McIntosh
Cynthia Hudson-Vitale