Jelte Wicherts has been awarded a prestigious 2 million euro Consolidator Grant from the European Research Council (ERC). With the money the meta-research group will be expanded with two postdocs and two PhD students.
A new manuscript by the Meta Research group is in press at Accountability in Research; primary author Coosje Veldkamp. The final paper will be available Open Access, but in the meantime find the abstract below and the postprint on PsyArxiv.
Do lay people and scientists themselves recognize that scientists are human and therefore prone to human fallibilities such as error, bias, and even dishonesty? In a series of three experimental studies and one correlational study (total N = 3,278) we found that the ‘storybook image of the scientist’ is pervasive: American lay people and scientists from over 60 countries attributed considerably more objectivity, rationality, open-mindedness, intelligence, integrity, and communality to scientists than other highly-educated people. Moreover, scientists perceived even larger differences than lay people did. Some groups of scientists also differentiated between different categories of scientists: established scientists attributed higher levels of the scientific traits to established scientists than to early-career scientists and PhD students, and higher levels to PhD students than to early-career scientists. Female scientists attributed considerably higher levels of the scientific traits to female scientists than to male scientists. A strong belief in the storybook image and the (human) tendency to attribute higher levels of desirable traits to people in one’s own group than to people in other groups may decrease scientists’ willingness to adopt recently proposed practices to reduce error, bias and dishonesty in science.
Michèle Nuijten and Sacha Epskamp are two of the nine winners of the 2016 Leamer-Rosenthal prize for Open Social Science for their work on statcheck. This prize is an initiative of the Berkeley Initiative for Transparency in the Social Sciences (BITSS), and comes with a prize of $10,000.
Lately there has been quite some media attention for statcheck. In a piece in Nature, Monya Baker has written a thorough and nuanced overview of statcheck and the PubPeer project of Chris Hartgerink, in which he scanned 50,000 papers and posted the statcheck results on the online forum PubPeer. In the Nature editorial this type of post-publication peer review is discussed.
Some other interesting coverage of statcheck can be found here:
Buranyi, S. (2016). Scientists are worried about `peer review by algorithm’. Motherboard (VICE). URL
Resnick, B. (2016). A bot crawled thousands of studies looking for simple math errors. The results are concerning. Vox. URL
Kershner, K. (2016). Statcheck: when bots `correct’ academics. How Stuff Works. URL
Keulemans, M. (2016). Worden sociale wetenschappen geterroriseerd door jonge onderzoekers?: Oorlog onder psychologen. De Volkskrant. URL
Our team member Marjan Bakker has just published a paper in Psychological Science, together with Chris Hartgerink, Jelte Wicherts and Han van der Maas. The abstract:
Many psychology studies are statistically underpowered. In part, this may be because many researchers rely on intuition, rules of thumb, and prior practice (along with practical considerations) to determine the number of subjects to test. In Study 1, we surveyed 291 published research psychologists and found large discrepancies between their reports of their preferred amount of power and the actual power of their studies (calculated from their reported typical cell size, typical effect size, and acceptable alpha). Furthermore, in Study 2, 89% of the 214 respondents overestimated the power of specific research designs with a small expected effect size, and 95% underestimated the sample size needed to obtain .80 power for detecting a small effect. Neither researchers’ experience nor their knowledge predicted the bias in their self-reported power intuitions. Because many respondents reported that they based their sample sizes on rules of thumb or common practice in the field, we recommend that researchers conduct and report formal power analyses for their studies.
The paper is available here (Open Access).
The Meta-Research group was well represented at the APS conference in Chicago. As a recap, we have shared all our slides. Feel free to view them and let us know if you have any questions or suggestions! Where applicable, Open Science Framework links are included, which makes the presentations citable as well as preserves them.
The Psychology of Statistics and the Statistics of Psychology
Honesty and Trust in Psychology Research
The Storybook Image of the Scientist
Why do so many researchers misreport p-values?
How to Deal with Publication Bias in Psychology? Illustrations and Recommendations
To be added
Publication Bias in IQ Research
Our team member Robbie van Aert recently got his paper accepted for publication in Perspectives on Psychological Science, together with Jelte Wicherts and Marcel van Assen. The abstract:
Because evidence of publication bias in psychology is overwhelming, it is important to develop techniques that correct meta-analytic estimates for publication bias. Van Assen, Van Aert, and Wicherts (2015) and Simonsohn, Nelson, and Simmons (2014a) developed p-uniform and p-curve, respectively. The methodology on which these methods are based has great promise for providing accurate meta-analytic estimates in the presence of publication bias. However, we show that in some situations p-curve behaves erratically while p-uniform may yield implausible negative effect size estimates. Moreover, we show that (and explain why) p-curve and p-uniform overestimate effect size under moderate to large heterogeneity, and may yield unpredictable bias when researchers employ p-hacking. We offer hands-on recommendations on applying and interpreting results of meta-analysis in general and p-uniform and p-curve in particular. Both methods as well as traditional methods are applied to a meta-analysis on the effect of weight on judgments of importance. We offer guidance for applying p-uniform or p-curve using R and a user-friendly web application for applying p-uniform (https://rvanaert.shinyapps.io/p-uniform).
An interesting read for anyone using these methods or interested in applying these methods! The paper will be published in a special issue on Methods and Practices.