Abstract
The popularity and use of Bayesian methods have increased across many research domains. The current article demonstrates how some less familiar Bayesian methods can be used. Specifically, we applied expert elicitation, testing for prior-data conflicts, the Bayesian Truth Serum, and testing for replication effects via Bayes Factors in a series of four studies investigating the use of questionable research practices (QRPs). Scientifically fraudulent or unethical research practices have caused quite a stir in academia and beyond. Improving science starts with educating Ph.D. candidates: the scholars of tomorrow. In four studies concerning 765 Ph.D. candidates, we investigate whether Ph.D. candidates can differentiate between ethical and unethical or even fraudulent research practices. We probed the Ph.D.s’ willingness to publish research from such practices and tested whether this is influenced by (un)ethical behavior pressure from supervisors or peers. Furthermore, 36 academic leaders (deans, vice-deans, and heads of research) were interviewed and asked to predict what Ph.D.s would answer for different vignettes. Our study shows, and replicates, that some Ph.D. candidates are willing to publish results deriving from even blatant fraudulent behavior–data fabrication. Additionally, some academic leaders underestimated this behavior, which is alarming. Academic leaders have to keep in mind that Ph.D. candidates can be under more pressure than they realize and might be susceptible to using QRPs. As an inspiring example and to encourage others to make their Bayesian work reproducible, we published data, annotated scripts, and detailed output on the Open Science Framework (OSF).
Highlights
Several systematic reviews have shown that applied researchers have become more familiar with the typical tools of the Bayesian toolbelt (Johnson et al, 2010a; König and van de Schoot, 2017; van de Schoot et al, 2017, 2021a; Fragoso et al, 2018; Smid et al, 2020; Hon et al, 2021)
In four studies concerning 765 Ph.D. candidates, we investigate whether Ph.D. candidates can differentiate between ethical and unethical or even fraudulent research practices
We illustrate how some less familiar tools can be applied to empirical data: A Bayesian expertelicitation method (O’Hagan et al, 2006; Anca et al, 2021) – described in van de Schoot et al (2021b), a test for prior-data conflict using the prior predictive p-value (Box, 1980) and the Data Agreement Criterion (DAC) (Veen et al, 2018), a Bayes truth serum to correct for socially desirable responses (Prelec, 2004), and testing for replication effects via the Bayes Factor (Bayarri and Mayoral, 2002; Verhagen and Wagenmakers, 2014)
Summary
Several systematic reviews have shown that applied researchers have become more familiar with the typical tools of the Bayesian toolbelt (Johnson et al, 2010a; König and van de Schoot, 2017; van de Schoot et al, 2017, 2021a; Fragoso et al, 2018; Smid et al, 2020; Hon et al, 2021). We illustrate how some less familiar tools can be applied to empirical data: A Bayesian expertelicitation method (O’Hagan et al, 2006; Anca et al, 2021) – described in van de Schoot et al (2021b), a test for prior-data conflict using the prior predictive p-value (Box, 1980) and the Data Agreement Criterion (DAC) (Veen et al, 2018), a Bayes truth serum to correct for socially desirable responses (Prelec, 2004), and testing for replication effects via the Bayes Factor (Bayarri and Mayoral, 2002; Verhagen and Wagenmakers, 2014) These methods are applied to the case of how Ph.D. students respond to academic publication pressure in terms of conducting questionable research practices (QRPs). Supplementary Appendix A–C contains additional details referred to throughout the text
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