About me

A (Psych Methods) PhD Candidate at the University of Amsterdam. I’m interested in meta-analyses, publication bias, replicability, and Bayesian inference.

Publications (also see Google Scholar; * Shared first authorship):

  • Maier*, M., Bartoš*, F.,, Stanley, T. D., Shanks, D., Harris, A. J., & Wagenmakers, E. J. (2022). No evidence for nudging after adjusting for publication bias. Proceedings of the National Academy of Sciences, 119(31). (open-access)
  • Bartoš, F., Maier, M., Wagenmakers, E. J., Doucouliagos, H., & Stanley, T. D. (2022). Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods. Research Synthesis Methods. (open-access)
  • Maier*, M., Bartoš*, F., & Wagenmakers, E. J. (2022). Robust Bayesian meta-analysis: Addressing publication bias with model-averaging. Psychological Methods. (article, preprint at PsyArXiv)
  • Bartoš, F., Aust, F., & Haaf, J. M. (2022). Informed Bayesian survival analysis.  BMC Medical Research Methodology, 22(238). (open-access)
  • Bartoš, F., & Schimmack, U. (2022). Z-curve 2.0: Estimating replication rates and discovery rates. Meta-Psychology, 6. (open-access)
  • Bartoš*, F., & Maier*, M. (accepted). Power or alpha? The better way of decreasing the false discovery rate. Meta-Psychology. (preprint at PsyArXiv)
  • Bartoš*, F., Maier*, M., Quintana, D. S, & Wagenmakers, E. J. (2022). Adjusting for publication bias in JASP and R—Selection models, PET-PEESE, and robust Bayesian meta-analysis. Advances in Methods and Practices in Psychological Science, 5(3). (open-access)
  • Bartoš, F., Gronau, Q. F., Timmers, B., Otte, W. M., Ly, A., & Wagenmakers, E. J. (2021). Bayesian model‐averaged meta‐analysis in medicine. Statistics in Medicine. (open-access)
  • Ly, A., van den Bergh, D., Bartoš, F., & Wagenmakers, E. J. (2021). Bayesian inference with JASP. The ISBA Bulletin28, 7-15. (open-access)
  • Bartoš, F., Martinková, P., & Brabec, M. (2019). Testing heterogeneity in inter-rater reliability. In The Annual Meeting of the Psychometric Society (pp. 347-364). Springer, Cham. (preprint at PsyArXiv)

Preprints:

  • Bartoš*, F., Maier*, M., Wagenmakers, E.-J., Nippold, F., Doucouliagos, H., Ioannidis, J. P. A., Otte, W. M., Sladekova, M., Fanelli, D., & Stanley, T. D. (2022). Footprint of publication selection bias on meta-analyses
    in medicine, economics, and psychology. (preprint at arXiv)
  • Bartoš, F., & Martinková, P. (2022). Selecting applicants based on multiple ratings: Using binary classification framework as an alternative to inter-rater reliability. (preprint at arXiv)
  • Martinková, P., Bartoš, F., & Brabec, M. (2022). Assessing inter-rater reliability with heterogeneous variance components models: Flexible approach accounting for contextual variables. (preprint at arXiv)
  • Bartoš*, F., Maier*, M., Shanks, D., Stanley, T. D., Sladekova, M., & Wagenmakers, E. J. (2022). Meta-analyses in psychology often overestimate evidence for and size of effects. (preprint at PsyArXiv)
  • Bartoš, F., Pawel, S., & Wagenmakers, E. J. (2022). When evidence and significance collide. (preprint at arXiv)
  • Bartoš, F., & Wagenmakers, E. J. (2022). Fast and accurate approximation to informed Bayes factors for focal parameters. at arXiv
  • Bartoš*, F., Maier*, M., Stanley, T. D., & Wagenmakers, E. (2022). Adjusting for publication bias reveals mixed evidence for the impact of cash transfers on subjective well-being and mental health. at PsyArXiv
  • Maier, M., Bartoš, F., Oh, M., Wagenmakers, E., Shanks, D., & Harris, A. J. L. (2022). Publication bias in research on construal level theory. at PsyArXiv
  • Efendic*, E., Bartoš*, F., Vranka, M., & Bahník, Š. (2019). Unpacking the justifiability of dishonesty: Behavioral and process-tracing investigation. at PsyArXiv

Software (also see GitHub):

  • RoBMA R package at CRAN
  • RoBSA R package at CRAN
  • zcurve R package at CRAN
  • BayesTools R package at CRAN
  • contributions to various JASP modules (Mixed Models, Learn Bayes, and Meta-Analysis)

Talks:

  • Bartoš, F. Adjusting for publication bias with Bayesian model-averaging and the RoBMA R package, ESMARConf, Online, 2022 (recording at YouTube)
  • Bartoš, F.,  Maier, M., Wagenmakers, E-J., Doucouliagos, H., & Stanley, T. D. No need to choose: Robust  Bayesian meta-analysis, MAER-NET, Piraeus, 22-23.10.2021
  • Bartoš, F.,  Maier, M., Wagenmakers, E-J., Doucouliagos, H., & Stanley, T. D. Extending RoBMA with PET-PEESE: Mitigating publication bias with Bayesian model averaging, International Meeting of Psychometric Society, Online, 2021
  • Bartoš, F.,  Maier, M., Wagenmakers, E-J., Doucouliagos, H., & Stanley, T. D. No need to choose: Extending robust Bayesian meta-analysis by model-averaging across different publication selection models, Quantitative Analysis of Publication Bias, Online, 2021
  • Bartoš, F. A minority deliberately cheats for one’s own and other’s benefit: An investigation of dicehonesty using mouse tracking, Prague Conference on Behavioral Sciences, Prague, 5-6.4.2019
  • Bartoš, F. Jak měřit podobnost fotografií pomocí neuronových sítí? (How to measure similarity between photographs using neural networks?), Kognitivní škola, Malá Skála, 24-27.1.2019
  • Bartoš, F., Houdek, P., Vranka, M., Smrčka, L., Machek, O. Recognition of Abilities in Random Noise: People Are Willing To Pay For The Illusion Of Success, Prague Conference on Behavioral Sciences, Prague, 4-5.5.2018

Presented posters:

  • Bartoš, F., Aust, F., & Haaf, J. M. Informed Bayesian survival analysis. International Society for Bayesian Analysis, Montreal, 2022
  • Bartoš, F., Martinková, P., Brabec, M. Testing heterogeneity in inter-rater reliability estimation. International Meeting of Psychometric Society, Santiago de Chile, 2019
  • Bartoš, F., Martinková, P., Brabec, M. Testing heterogeneity in inter-rater reliability estimation. International Meeting of Psychometric Society, Santiago de Chile, 2019
  • Efendic, E., Bartoš, F., Vranka, M. A., & Bahník, Š. Dicehonesty: Cheating Equally for One’s Own and Other’s Benefit. Society for Judgment and Decision Making, New Orleans, 2018 (http://sjdm.org/presentations/2018-Poster-Bartos-Frantisek-dicehonesty-cheating-equally~.pdf).
  • Bartoš, F., Lukavský, J., Děchtěrenko, F. Can predicting data improve model interpretation and inferences? Using Stan to fight the assumption of independence. StanCon, Helsinky, 2018 (https://doi.org/10.6084/m9.figshare.7074902.v1)

(Updated on 2nd of September 2022)