Nullius in Verba

In advance of our episode Verify but Trust, a reading of John Hardwig's paper The Role of Trust in Science.

• Hardwig, J. (1991). The role of trust in knowledge. The Journal of Philosophy, 88(12), 693–708.

In this episode, we discuss the replication crisis in psychology which has been an important topic of discussion for the last decade. We revisit some key events from the start of the replication crisis, such as the publication of Daryl Bem's studies on precognition, the paper False Positive Psychology, and the Reproducibility Project and share personal anecdotes about how it was to live through the replication crisis.

Shownotes:

• Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100(3), 407–425. https://doi.org/10.1037/a0021524
• Ritchie, S. J., Wiseman, R., & French, C. C. (2012). Failing the Future: Three Unsuccessful Attempts to Replicate Bem’s ‘Retroactive Facilitation of Recall’ Effect. PLOS ONE, 7(3), Article e33423. https://doi.org/10.1371/journal.pone.0033423
• Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
• John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524–532. https://doi.org/10.1177/0956797611430953
• Fiedler, K., & Schwarz, N. (2016). Questionable Research Practices Revisited. Social Psychological and Personality Science, 7(1), 45–52. https://doi.org/10.1177/1948550615612150
  • NOTE: Daniel says in the podcast the paper below is by Fiedler and Strack - but it is by Fiedler and Schwarz.
• Ebersole, C. R., Mathur, M. B., Baranski, E., Bart-Plange, D.-J., Buttrick, N. R., Chartier, C. R., Corker, K. S., Corley, M., Hartshorne, J. K., IJzerman, H., Lazarević, L. B., Rabagliati, H., Ropovik, I., Aczel, B., Aeschbach, L. F., Andrighetto, L., Arnal, J. D., Arrow, H., Babincak, P., ...
• Nosek, B. A. (2020). Many Labs 5: Testing Pre-Data-Collection Peer Review as an Intervention to Increase Replicability. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920958687
• Luttrell, A., Petty, R. E., & Xu, M. (2017). Replicating and fixing failed replications: The case of need for cognition and argument quality. Journal of Experimental Social Psychology, 69, 178–183. https://doi.org/10.1016/j.jesp.2016.09.006
• Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on Generality (COG): A Proposed Addition to All Empirical Papers. Perspectives on Psychological Science, 12(6), 1123–1128. https://doi.org/10.1177/1745691617708630
• Simonsohn, U. (2015). Small Telescopes Detectability and the Evaluation of Replication Results. Psychological Science, 26(5), 559–569. https://doi.org/10.1177/0956797614567341

In today’s episode, we discuss the role of mentorship in academia. What are the characteristics of a good mentor-mentee relationship? What are the qualities of good mentors and good mentees? Does mentorship play a role in the development of scientific knowledge? And could mentors and mentees benefit from couples therapy?

Note: D.I.H.C is pronounced 'dick' but this is meant to be a family-friendly podcast :)

Shownotes

• https://www.psychologytoday.com/us/blog/emotional-fitness/201303/10-things-your-relationship-needs-thrive
• Roberts, L. R., Tinari, C. M., & Bandlow, R. (2019). An effective doctoral student mentor wears many hats and asks many questions. International Journal of Doctoral Studies, 14, 133.
• Sarabipour, S., Niemi, N. M., Burgess, S. J., Smith, C. T., Filho, A. W. B., Ibrahim, A., & Clark, K. (2023). Insights from a survey of mentorship experiences by graduate and postdoctoral researchers (p. 2023.05.05.539640). bioRxiv. https://doi.org/10.1101/2023.05.05.539640

In this episode, we discuss Quantifauxcation, described by statistician Philip Stark as “situations in which a number is, in effect, made up, and then is given credence merely because it is quantitative.” We give examples of quantifauxcation in psychology, including errors of the third kind. We spend the second half of the podcast discussing how to develop quantitative measures that are meaningful and bridge the divide between qualitative and quantitative observations.

Shownotes

• Statistics textbook by Philip Stark.
• Stark, P. B. (2022). Pay No attention to the model behind the curtain. Pure and Applied Geophysics, 179(11), 4121–4145. https://doi.org/10.1007/s00024-022-03137-2
• Burgess, E. W. (1927). Statistics and case studies as methods of sociological research, Vol 12(3), 103-120. (Thanks to Andy Grieve!)
• Nick Brown's role in pointing out flaws in the positivity ratio.
• Retraction notice of the positivity ratio paper.
• Blog by Tania Lombrozo on nonsensical formulas in abstracts.
• Kimball, A. W. (1957). Errors of the third kind in statistical consulting. Journal of the American Statistical Association, 52(278), 133–142. https://doi.org/10.1080/01621459.1957.10501374
• Type III errors: Philip Stark’s post of Deborah Mayo’s blog
• Brower, D. (1949). The problem of quantification in psychological science. Psychological Review, 56(6), 325–333. https://doi.org/10.1037/h0061802
• Guttman scales
• Wilson, M. (2023). Constructing measures: An item response modeling approach. Taylor & Francis.
• Wilson, M., Bathia, S., Morell, L., Gochyyev, P., Koo, B. W., & Smith, R. (2022). Seeking a better balance between efficiency and interpretability: Comparing the likert response format with the Guttman response format. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000462
• Bhatti, H.A., Mehta, S., McNeil, R., Wilson, M. (2023). A scientific approach to assessment: Rasch measurement and the four building blocks. In X. Liu & W. Boone (Eds.), Advances in Applications of Rash Measurement in Science Education. Springer Nature.

In our third episode, we discuss confirmation bias, which affects not only how scientists generate and test their own hypotheses, but also how they evaluate the scientific evidence presented by others. We discuss guardrails against confirmation bias that are already in place, and others that could potentially improve scientific practice if adopted.

Shownotes

• Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12(3), 129-140.
  
• Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
  
• Mellers, B., Hertwig, R., & Kahneman, D. (2001). Do frequency representations eliminate conjunction effects? An exercise in adversarial collaboration. Psychological Science, 12(4), 269-275.
  
• Coles, N. A., March, D. S., Marmolejo-Ramos, F., Larsen, J. T., Arinze, N. C., Ndukaihe, I. L., ... & Liuzza, M. T. (2022). A multi-lab test of the facial feedback hypothesis by the many smiles collaboration. Nature Human Behaviour, 1-12.
  
• Dutilh, G., Sarafoglou, A., & Wagenmakers, E. J. (2021). Flexible yet fair: Blinding analyses in experimental psychology. Synthese, 198(23), 5745-5772.
• Sarafoglou, A., Hoogeveen, S., & Wagenmakers, E. J. (2023). Comparing analysis blinding with preregistration in the many-analysts religion project. Advances in Methods and Practices in Psychological Science, 6(1), 25152459221128319.
• Faster-than-light neutrino anomaly