Nielsen, K.S., Dablander, F., Debnath, R., Emegor, C.A., Ghai, S. Gwozdz, W., Hahnel, U.J.J., Hofmann, W., & Bauer, J.M. (submitted). Perceived plasticity of climate-relevant behaviors and policy support among high- and lower-income individuals. [Link]
Dablander, F.⭑, Wimmer, S.⭑, & Haslbeck, J.M.B.⭑ (under review). Media Coverage of Climate Activist Groups in Germany. [Link]
Ryan, O.⭑, & Dablander, F.⭑ (under review). Equilibrium Causal Models: Connecting Dynamical Systems Modeling and Cross-Sectional Data Analysis. [Link]
van den Bergh, D.⭑, & Dablander, F.⭑. (under review). Flexible Bayesian Multiple Comparison Adjustment Using Dirichlet Process and Beta-Binomial Model Priors. [Link]
Ryan, O.⭑, Dablander, F.⭑, & Haslbeck, J.M.B.⭑ (under review). Towards a Generative Model for Emotion Dynamics. [Link]
Dablander, F., Sachisthal, M., & Haslbeck, J.M.B. (2024). Climate Actions by Climate and Non-Climate Researchers. [Link]. npj Climate Action.
Dablander, F.⭑, Sachisthal, M.⭑, Cologna, V., Strahm, N., Bosshard, A., Grüning, N., Green, A., Brick, C., Aron, A., & Haslbeck, J.M.B.⭑ (2024). Climate Change Engagement of Scientists. Nature Climate Change. [Link] [Journal version]
Dablander, F.⭑, van den Bergh, D.⭑, Wagenmakers, E.-J., & Ly, A. (2024). Default Bayes factors for Testing the (In)equality of Several Population Variances. Bayesian Analysis, 19(3), 699-723. [Link] [R package]
Maier, M., Bartoš, F., Quintana, D., Dablander, F., van den Bergh, D., Marsman, M., Ly, A., & Wagenmakers, E. J. (2024). Model-averaged Bayesian t tests. Psychonomic Bulletin & Review [Link]
Dablander, F. (2023). Changing Systems: Statistical, Causal, and Dynamical Perspectives. Doctoral Thesis. [Link]
Dablander, F. (2023). Understanding and Preventing Climate Breakdown: A Guide for Social and Behavioral Scientists. [Link]
Dablander, F., Pichler, A., Cika, A., & Bacilieri, A. (2023). Anticipating Critical Transitions in Psychological Systems using Early Warning Signals: Theoretical and Practical Considerations. Psychological Methods, 28(4), 765-790. [Link] [Journal Version]
Haslbeck, J.M.B.⭑, Ryan, O.⭑, & Dablander, F.⭑ (2023). Multimodality and skewness in emotion time series. Emotion, 23(8), 2117–2141. [Link]
Eigenschink, M., Bellach, L., Leonard, S. R., Dablander, T. E., Maier, J., Dablander, F., & Sitte, H. H. (2023). Cross-sectional survey and Bayesian network model analysis of traditional Chinese medicine in Austria: Investigating public awareness, usage determinants and perception of scientific support. BMJ Open, 13(3), e060644. [Link]
Dablander, F., & Bury, T. M. (2022). Deep Learning for Tipping Points: Preprocessing Matters. Proceedings of the National Academy of Sciences, 119(37), e2207720119. [Link]
Dablander, F., Heesterbeek, H., Borsboom, D., & Drake, J.M. (2022). Overlapping Timescales Obscure Early Warning Signals of the Second COVID-19 Wave. Proceedings of the Royal Society B: Biological Sciences, 289(1968), 20211809. [Link]
Dablander, F., Huth, K., Gronau, Q. F., Etz, A., & Wagenmakers, E. J. (2022). A puzzle of proportions: Two popular Bayesian tests can yield dramatically different conclusions. Statistics in Medicine, 41(8), 1319-1333. [Link]
Dablander, F. & van Bork, R. (2022). Causal Inference. In Isvoranu, A. M., Epskamp, S., Waldorp, L.J., & Borsboom, D. (Eds.). Network Psychometrics with R: A Guide for Behavioral and Social Scientists. (pp. 93-110). Routledge. [Link]
Haslbeck, J.M.B.⭑, Ryan, O.⭑, & Dablander, F.⭑ (2022). The sum of all fears: Comparing networks based on symptom sum-scores. Psychological Methods, 27(6), 1061–1068. [Link] [Journal Version]
Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & Van Mieghem, P. (2022). The lighting of the BECONs: A behavioral data science approach to tracking interventions in COVID-19 research. Journal of Behavioral Data Science, 2(1), 1-34. [Link]
Burger, J., Epskamp, S., Dablander, F., Schoevers, R. A., Fried, E. I., & Riese, H. (2022). A clinical PREMISE for personalized models: Towards a formal integration of case formulations and statistical networks. Journal of Psychopathology and Clinical Science, 131(8), 906-916. [Link] [Journal Version]
Dekker, M., Blanken, T., Dablander, F., Ou, J., Borsboom, D., & Debabrata, P. (2022). Quantifying agent impacts on contact sequences in social interactions. Scientific Reports, 12, 3483. [Link]
Dablander, F.⭑, Blanken, T.⭑, Tanis, C.⭑, Breed, R., Coffeng, L., Crommelin, D., …, Heesterbeek, H., & Borsboom, D. (2021). A Multidisciplinary Perspective on COVID-19 Exit Strategies.
Blanken, T. F., Tanis, C. C., Nauta, F. H., Dablander, F., Zijlstra, B. J., Bouten, R. R., … & Borsboom, D. (2021). Promoting physical distancing during COVID-19: a systematic approach to compare behavioral interventions. Scientific Reports, 11(1), 19463. [Link]
Tanis, C. C., Leach, N. M., Geiger, S. J., Nauta, F. H., Dablander, F., van Harreveld, F., … & Blanken, T. F. (2021). Smart Distance Lab’s art fair, experimental data on social distancing during the COVID-19 pandemic. Scientific Data, 8(1), 179. [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, 16(2), e0246260. [Link]
van Doorn, J., van den Bergh, D., Bohm, U., Dablander, F., Derks, K., Draws, T., … & Wagenmakers, E.-J. (2021). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 28, 813–826. [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, 15(10), e0240730. [Link]
Wagenmakers, E. J., Gronau, Q. F., Dablander, F., & Etz, A. (2020). The Support Interval. Erkenntnis, 87, 589-601. [Link]
Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., … & Wagenmakers, E.-J. (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., … & Wagenmakers, E.-J. (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(1), 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.