Fabian Dablander PhD Student Methods & Statistics

Submitted / under review

Dablander, F., Heesterbeek, H., Borsboom, D., & Drake, J.M. (under review). Overlapping Time Scales Obscure Early Warning Signals of the Second COVID-19 Wave. [Link]

Dablander, F., van den Bergh, D., Ly, A., & Wagenmakers, E.-J. (under review). Default Bayes factors for Testing the (In)equality of Several Population Variances. [Link][R package]

Dekker, M., Blanken, T., Dablander, F., Ou, J., Borsboom, D., & Debabrata, P. (under review). Quantifying agent impacts on contact sequences in social interactions. [Link]

Burger, J., Epskamp, S., Dablander, F., Schoevers, R. A., Fried, E. I., & Riese, H. (under review). A clinical PREMISE for personalized models: Towards a formal integration of case formulations and statistical networks. [Link]

Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & Van Mieghem, P. (under review). The lighting of the BECONs: A behavioral data science approach to tracking interventions in COVID-19 research. [Link]


Dablander, F., Huth, K., Gronau, Q. F., Etz, A., & Wagenmakers, E.-J. (accepted). A Puzzle of Proportions: Two Popular Bayesian Tests Can Yield Dramatically Different Conclusions. Statistics in Medicine. [Link]

Dablander, F., Pichler, A., Cika, A., & Bacilieri, A. (accepted). Anticipating Critical Transitions in Psychological Systems using Early Warning Signals: Theoretical and Practical Considerations. Psychological Methods. [Link]

Blanken, T., Tanis, C., Nauta, F., Dablander, F., Zijlstra, B., Bouten, R., … & Borsboom, D. (in press). Smart Distance Lab: A new methodology for assessing social distancing interventions. Scientific Report. [Link]

Haslbeck, J.M.B., Ryan, O., & Dablander, F. (in press). The Sum of All Fears: Comparing Networks Based on Symptom Sum-Scores. Psychological Methods. [Link]

Brown, J., Murray, D., Furlong, K., Coco, E., & Dablander, F. (2021). A Breeding Pool of Ideas: Analyzing Interdisciplinary Collaborations at the Complex Systems Summer School. PLoS One. [Link]

Tanis, C., Leach, N., Geiger, S. J., Nauta, F., Dablander, F., van Harreveld, F., … & Blanken, T. (2021). Smart Distance Lab Art Fair-An experimental data set on social distancing during the COVID-19 pandemic. Scientific Data [Link]


Dablander, F. (2020). An Introduction to Causal Inference. [Link]

Dablander, F., Ryan, O., & Haslbeck, J.M.B. (2020). Choosing between AR(1) and VAR(1) Models in Typical Psychological Applications. PLoS One. [Link]

Wagenmakers, E. J., Gronau, Q. F., Dablander, F., & Etz, A. (2020). The Support Interval. Erkenntnis. [Link]

van Doorn, J., van den Bergh, D., Bohm, U., Dablander, F., Derks, K., Draws, T., … & Hinne, M. (2020). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 1-14. [Link]

Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., … & Boehm, U. (2020). The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test. Computational Brain & Behavior, 3(2), 153-161. [Link]

van den Bergh, D., Van Doorn, J., Marsman, M., Draws, T., Van Kesteren, E. J., Derks, K., Dablander, F., … & Sarafoglou, A. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. LAnnee psychologique, 120(1), 73-96. [Link]


Dablander, F., & Hinne, M. (2019). Node centrality measures are a poor substitute for causal inference. Scientific Reports, 9, 6846. [Link]

Dablander, F., Epskamp, S., & Haslbeck, J.M.B. (2019). Studying Statistics Anxiety Requires Sound Statistics: A Comment on Siew, McCartney, and Vitevitch (2019). Scholarship of Teaching and Learning in Psychology. [Link]

Jakob, L., Garcia-Garzon, E., Jarke, H., & Dablander, F. (2019). The Science Behind the Magic? The Relation of the Harry Potter “Sorting Hat Quiz” to Personality and Human Values. Collabra: Psychology, 5(1), 31. [Link]

Edelsbrunner, P. A., & Dablander, F. (2019). The Psychometric Modeling of Scientific Reasoning: A Review and Recommendations for Future Avenues. Educational Psychology Review, 31(1), 1-34. [Link]

Marsman, M., Waldorp, L., Dablander, F., & Wagenmakers, E. J. (2019). Bayesian estimation of explained variance in ANOVA designs. Statistica Neerlandica, 73(3), 351-372. [Link]


Dablander, F., van den Bergh, D., & Wagenmakers, E. J. (2018). Another Paradox? A Comment on Lindley (1997). PsyArXiv [Link]

Dablander, F. (2018). In Review: Ten Great Ideas About Chance. Significance. [Link]

Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25(1), 219-234. [Link]

Orben, A., Mutak, A., Dablander, F., Hecht, M., Krawiec, J. M., Valkovičová, N., & Kosīte, D. (2018). From Face-to-Face to Facebook: Probing the effects of passive consumption on interpersonal attraction. Frontiers in Psychology, 9, 1163. [Link]


Dablander, F. (2017). Validating Driver Profiles in the Daimler Traffic Simulation. Unpublished Master’s Thesis. [Link] [Online Supplement]


Franke, M., Dablander, F., Schöller, A., Bennett, E., Degen, J., Tessler, M. H., … & Goodman, N. D. (2016). What does the crowd believe? A hierarchical approach to estimating subjective beliefs from empirical data. In: Proceedings of CogSci 38, Ed. by Anna Papafragou et al., pp. 2669-2674. [Link]

King, M., Dablander, F., Jakob, L., Agan, M. L., Huber, F., Haslbeck, J. M., & Brecht, K. F. (2016). Registered Reports for Student Research. Journal of European Psychology Students, 7(1), 20-23. [Link]


In my younger and more vulnerable years, I have written a few blog posts for the Journal of European Psychology Students. You can find them here. I occasionally co-author blog posts at bayesianspectacles.