Personalized Z-Curve Project

Last year, I started posting z-curves for social psychologists, including myself (Schimmack, 2021). This year, I want to expand this project to a broader range of psychologists, in areas I am less familiar with. To do so, I would like to ask you for your help. Please provide me with a list of all articles published by a researcher you find interesting, including yourself. There should be at least 5, but ideally more articles in the journals that I am currently covering (Schimmack, 2022). To make it easy for me to find the z-scores for this researcher, I need the list of articles from WebOfScience (WOS). I created a tutorial that shows how to get it (Tutorial). The tutorial also shows how I use that information to get the z-scores and the z-curve plot. It also explains how the z-curve plot can be interpreted. The researcher featured in the tutorial is Alexander Todorov.

The final result is this z-curve with alpha = .01 (z ~ 2.6). Using alpha = .01, produces a very low point estimate of the false discovery risk, 1%, and even the upper level of the 95% confidence interval is just 5%. So, we can conclude that most published results with p-values below .01 are true findings (the effect size is not zero). Moreover, the expected replication rate with alpha = .01 is 77%. Thus, most of these published results are expected to produce a significant result again in a replication study, if the original conditions can be replicated exactly. If the replication study is not exact, the replication rate is expected to be between the discovery rate of 47% and the replication rate of 77%. The average of these two values predicts that 62% of the results would replicate at p < .01, even more at p < .05. Overall, this z-curve suggests that Alexander Todorov has published many credible findings.

Do you find this evaluation of Alexander Todorov’s results useful? Are you curious about some researchers in your field? If so, you can send me their WOS article list and I will send you the z-values and a z-curve free of charge. Now this is what I call collaborative open science.

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