Dablander, F., Hickey, C., Sandberg, M., Zell-Ziegler, C., & Grin, J. (2025). Embracing sufficiency to accelerate the energy transition. Energy Research and Social Science, 120, Article 103907. https://doi.org/10.1016/j.erss.2024.103907
Grin, J., Dablander, F., Hickey, C. J., Sandberg, M., & Zell-Ziegler, C. (2025). Embracing sufficiency to accelerate the energy transition. Energy Research & Social Science, 120, Article 103907.
2024
Dablander, F., Sachisthal, M. S. M., & Haslbeck, J. M. B. (2024). Climate Actions by Climate and Non-Climate Researchers. npj Climate Action, 3, Article 105. https://doi.org/10.1038/s44168-024-00187-1
Dablander, F., Sachisthal, M. S. M., Cologna, V., Strahm, N., Bosshard, A., Grüning, N.-M., Green, A. J. K., Brick, C., Aron, A. R., & Haslbeck, J. M. B. (2024). Climate change engagement of scientists. Nature Climate Change, 14, 1033-1039. https://doi.org/10.1038/s41558-024-02091-2[details]
Dablander, F., Wimmer, S., & Haslbeck, J. M. B. (2024). Media Coverage of Climate Activist Groups in Germany. Manuscript submitted for publication. https://doi.org/10.31234/osf.io/yhn54
Maier, M., Bartoš, F., Quintana, D., Dablander, F., van den Bergh, D., Marsman, M., Ly, A., & Wagenmakers, E. M. (2024). Model-Averaged Bayesian t-Tests. Psychonomic Bulletin & Review. Advance online publication. https://doi.org/10.3758/s13423-024-02590-5
Eigenschink, M., Bellach, L., Leonard, S., 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), Article e060644. https://doi.org/10.1136/bmjopen-2021-060644[details]
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), Article 20211809. https://doi.org/10.1098/rspb.2021.1809[details]
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. Advance online publication. https://doi.org/10.1002/sim.9278[details]
Dablander, F., Pichler, A., Cika, A., & Bacilieri, A. (2022). Anticipating Critical Transitions in Psychological Systems Using Early Warning Signals: Theoretical and Practical Considerations. Psychological Methods, 28(4), 765-790. https://doi.org/10.1037/met0000450
Dekker, M. M., Blanken, T. F., Dablander, F., Ou, J., Borsboom, D., & Panja, D. (2022). Quantifying agent impacts on contact sequences in social interactions. Scientific Reports, 12, Article 3483. https://doi.org/10.1038/s41598-022-07384-0[details]
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. Advance online publication. https://doi.org/10.1037/met0000418[details]
Blanken, T. F., Tanis, C. C., Nauta, F. H., Dablander, F., Zijlstra, B. J. H., Bouten, R. R. M., Oostvogel, Q. H., Boersma, M. J., van der Steenhoven, M. V., van Harreveld, F., de Wit, S., & Borsboom, D. (2021). Promoting physical distancing during COVID-19: a systematic approach to compare behavioral interventions. Scientific Reports, 11, Article 19463. https://doi.org/10.1038/s41598-021-98964-z[details]
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, Article e0246260. https://doi.org/10.1371/journal.pone.0246260
Tanis, C. C., Leach, N. M., Geiger, S. J., Nauta, F. H., Dablander, F., van Harreveld, F., de Wit, S., Kanters, G., Knoppers, J., Markus, D. A. W., Bouten, R. R. M., Oostvogel, Q. H., Boersma, M. J., van der Steenhoven, M. V., Borsboom, D., & Blanken, T. F. (2021). Smart Distance Lab’s art fair, experimental data on social distancing during the COVID-19 pandemic. Scientific Data, 8, Article 179. https://doi.org/10.1038/s41597-021-00971-2[details]
Tanis, C. C., Leach, N. M., Geiger, S., Nauta, F. H., Dablander, F., van Harreveld, F., de Wit, S., Kanters, G., Knoppers, J., Markus, D. A. W., Bouten, R. R. M., Oostvogel, Q. H., Boersma, M. J., van der Steenhoven, M. V., Borsboom, D. & Blanken, T. F. (2021). Metadata record for: Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Figshare. https://doi.org/10.6084/m9.figshare.14312180.v1
Tanis, C. C., Leach, N. M., Geiger, S., Nauta, F. H., Dablander, F., van Harreveld, F., de Wit, S., Kanters, G., Knoppers, J., Markus, D. A. W., Bouten, R. R. M., Oostvogel, Q. H., Boersma, M. J., van der Steenhoven, M. V., Borsboom, D. & Blanken, T. F. (2021). Metadata record for: Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Figshare. https://doi.org/10.6084/m9.figshare.14312180.v1
van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., Haaf, J. M., Hinne, M., Kucharský, Š., Ly, A., Marsman, M., Matzke, D., Komarlu Narendra Gupta, A. R., Sarafoglou, A., Stefan, A., Voelkel, J. G., & Wagenmakers, E-J. (2021). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 28(3), 813–826. Advance online publication. https://doi.org/10.3758/s13423-020-01798-5[details]
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), Article e0240730. https://doi.org/10.1371/journal.pone.0240730[details]
Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., Kucharský, S., Derks, K., Gronau, Q. F., Raj, A., Boehm, U., van Kesteren, E-J., Hinne, M., Matzke, D., Marsman, M., & 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. Advance online publication. https://doi.org/10.31234/osf.io/dhb7x, https://doi.org/10.1007/s42113-019-00070-x[details]
van den Bergh, D., van Doorn, J., Marsman, M., Draws, T., van Kesteren, E-J., Derks, K., Dablander, F., Gronau, Q. F., Kucharský, Š., Komarlu Narendra Gupta, A. R., Sarafoglou, A., Voelkel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagenmakers, E-J. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. Année Psychologique, 120(1), 73-96. https://doi.org/10.31234/osf.io/spreb, https://doi.org/10.3917/anpsy1.201.0073[details]
Edelsbrunner, P., & Dablander, F. (2019). The Psychometric Modeling of Scientific Reasoning: a Review and Recommendations for Future Avenues. Educational Psychology Review, 31(1), 1-34. https://doi.org/10.1007%2Fs10648-018-9455-5[details]
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, Article 31. https://doi.org/10.1525/collabra.240[details]
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. https://doi.org/10.3758/s13423-017-1317-5[details]
2022
Dablander, F., & Bury, T. M. (2022). Deep learning for tipping points: Preprocessing matters. Proceedings of the National Academy of Sciences of the United States of America, 119(37), Article e2207720119. https://doi.org/10.1073/pnas.2207720119
2023
Dablander, F. (2023). Changing systems: Statistical, causal, and dynamical perspectives. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
Tanis, C. C., Leach, N. M., Geiger, S., Nauta, F. H., Dablander, F., van Harreveld, F., de Wit, S., Kanters, G., Knoppers, J., Markus, D. A. W., Bouten, R. R. M., Oostvogel, Q. H., Boersma, M. J., van der Steenhoven, M. V., Borsboom, D. & Blanken, T. F. (2021). Metadata record for: Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Figshare. https://doi.org/10.6084/m9.figshare.14312180.v1
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