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  • Daniel Lakens
Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology
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Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to re-use or check published research. These benefits will only emerge if researchers can reproduce the analysis reported in published articles, and if data is annotated well enough so that it is clear what all variables mean. Because most researchers have not been trained in computational reproducibility, it is important to evaluate current practices to identify practices that can be improved. We examined data and code sharing, as well as computational reproducibility of the main results, without contacting the original authors, for Registered Reports published in the psychological literature between 2014 and 2018. Of the 62 articles that met our inclusion criteria, data was available for 40 articles, and analysis scripts for 37 articles. For the 35 articles that shared both data and code and performed analyses in SPSS, R, Python, MATLAB, or JASP, we could run the scripts for 31 articles, and reproduce the main results for 20 articles. Although the articles that shared both data and code (35 out of 62, or 56%) and articles that could be computationally reproduced (20 out of 35, or 57%) was relatively high compared to other studies, there is clear room for improvement. We provide practical recommendations based on our observations, and link to examples of good research practices in the papers we reproduced.

Subject:
Psychology
Material Type:
Reading
Author:
Daniel Lakens
Jaroslav Gottfried
Nicholas Alvaro Coles
Pepijn Obels
Seth Ariel Green
Date Added:
08/07/2020
An excess of positive results: Comparing the standard Psychology literature with Registered Reports
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When studies with positive results that support the tested hypotheses have a higher probability of being published than studies with negative results, the literature will give a distorted view of the evidence for scientific claims. Psychological scientists have been concerned about the degree of distortion in their literature due to publication bias and inflated Type-1 error rates. Registered Reports were developed with the goal to minimise such biases: In this new publication format, peer review and the decision to publish take place before the study results are known. We compared the results in the full population of published Registered Reports in Psychology (N = 71 as of November 2018) with a random sample of hypothesis-testing studies from the standard literature (N = 152) by searching 633 journals for the phrase ‘test* the hypothes*’ (replicating a method by Fanelli, 2010). Analysing the first hypothesis reported in each paper, we found 96% positive results in standard reports, but only 44% positive results in Registered Reports. The difference remained nearly as large when direct replications were excluded from the analysis (96% vs 50% positive results). This large gap suggests that psychologists underreport negative results to an extent that threatens cumulative science. Although our study did not directly test the effectiveness of Registered Reports at reducing bias, these results show that the introduction of Registered Reports has led to a much larger proportion of negative results appearing in the published literature compared to standard reports.

Subject:
Psychology
Material Type:
Reading
Author:
Anne M. Scheel
Daniel Lakens
Mitchell Schijen
Date Added:
08/07/2020