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How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data.
Fanelli, D. (2009). How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PLOS ONE, 4(5), e5738. https://doi.org/10.1371/journal.pone.0005738
Bibliometric Evidence for a Hierarchy of the Sciences.
Fanelli, D., & Glänzel, W. (2013). Bibliometric evidence for a hierarchy of the sciences. PLoS ONE, 8(6), e66938. https://doi.org/10.1371/journal.pone.0066938
Normal science: not uncritical or dogmatic.
Schindler, S. (2024). Normal science: Not uncritical or dogmatic. Synthese, 203(108). https://doi.org/10.1007/s11229-024-04527-w
Money, Time, and Grant Design.
Myers, K., & Tham, W. Y. (2023). Money, time, and grant design. arXiv:2312.06479v1 [econ.GN]. https://arxiv.org/abs/2312.06479
Publicly Shared Data: A Gap Analysis of Researcher Actions and Institutional Support throughout the Data Life Cycle.
Petters, J., Taylor, S., Hofelich Mohr, A., Carlson, J., Ge, L., Herndon, J., Kozlowski, W., Moore, J., & Hudson Vitale, C. (2024). Publicly shared data: A gap analysis of researcher actions and institutional support throughout the data life cycle. Association of Research Libraries. https://doi.org/10.29242/report.radsgapanalysis2024
Statistical Guidance to Authors at Top-Ranked Journals across Scientific Disciplines.
Hardwicke, T. E., Salholz-Hillel, M., Malički, M., Szűcs, D., Bendixen, T., & Ioannidis, J. P. A. (2022). Statistical Guidance to Authors at Top-Ranked Journals across Scientific Disciplines. The American Statistician, 77(3), 239–247. https://doi.org/10.1080/00031305.2022.2143897
Causal Inference Is Not Just a Statistics Problem.
D’Agostino McGowan, L., Gerke, T., & Barrett, M. (2024). Causal inference is not just a statistics problem. Journal of Statistics and Data Science Education, 32(2), 150–155. https://doi.org/10.1080/26939169.2023.2276446
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.
Greenland, S., Senn, S. J., Rothman, K. J., et al. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
On the challenges of drawing conclusions from p-values just below 0.05.
Lakens, D. (2015). On the challenges of drawing conclusions from p-values just below 0.05. PeerJ, 3, e1142. https://doi.org/10.7717/peerj.1142
Questionable research practices among researchers in the most research-productive management programs.
Kepes, S., Keener, S. K., McDaniel, M. A., & Hartman, N. S. (2022). Questionable research practices among researchers in the most research-productive management programs. Journal of Organizational Behavior, 43(7), 1190–1208. https://doi.org/10.1002/job.2623
A comparative study on characteristics of retracted publications across different open access levels
Zheng, E.T. & Fu, H.Z. A comparative study on characteristics of retracted publications across different open access levels. Journal of Data and Information Science, 2024, Sciendo, vol. 9 no. 2, pp. 22-40. https://doi.org/10.2478/jdis-2024-0010
The value of preregistration for psychological science: A conceptual analysis
Lakens, D. (2019, November 18). The Value of Preregistration for Psychological Science: A Conceptual Analysis. https://doi.org/10.31234/osf.io/jbh4w
Distinguishing between legitimate and illegitimate roles for values in transdisciplinary research.
Koskinen, I., & Rolin, K. (2022). Distinguishing between legitimate and illegitimate roles for values in transdisciplinary research. Studies in history and philosophy of science, 91, 191–198. https://doi.org/10.1016/j.shpsa.2021.12.001
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