Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 35
Number of items: 35
  • Haslbeck, J., Ryan, O., & Dablander, F. (2023). Multimodality and Skewness in Emotion Time Series. Emotion, 23(8), 2117-2141. https://doi.org/10.1037/emo0001218
  • 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. https://doi.org/10.1037/met0000450
  • Open Access
    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
  • Open Access
    Dablander, F. (2023). Changing systems: Statistical, causal, and dynamical perspectives. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Borsboom, D., Blanken, T. F., Dablander, F., van Harreveld, F., Tanis, C. C., & 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. https://doi.org/10.35566/jbds/v2n1/p1
  • Open Access
    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. https://doi.org/10.1002/sim.9278
  • Open Access
    Wagenmakers, E.-J., Gronau, Q. F., Dablander, F., & Etz, A. (2022). The Support Interval. Erkenntnis, 87(2), 589–601. https://doi.org/10.31234/osf.io/zwnxb, https://doi.org/10.1007/s10670-019-00209-z
  • Open Access
    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. https://doi.org/10.1037/met0000418
  • Open Access
    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
  • Open Access
    Dablander, F., & Bury, T. M. (2022). Deep learning for tipping points: Preprocessing matters. Proceedings of the National Academy of Sciences, 119(37), Article e2207720119. https://doi.org/10.1073/pnas.2207720119
Page 2 of 4