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S4 conference

Talks (in alphabetical order)

  1. Busija, Lucy: When less is not more: Trials and tribulations of achieving the required sample size in a ‘real-world’ cluster RCT.
  2. De Neve, Jan: Regression models for rank tests when samples are small.
  3. Egberts, Marthe & Veen, Duco: Increasing power of statistical analyses through collaboration.
  4. Erp, Sara van: Shrinkage priors for Bayesian penalized regression: An overview and tutorial using Stan.
  5. Hoijtink, Herbert: Small Data is Becoming a Bigger Challenge.
  6. Johnson, Alan R.: Ring out the old, Ring in the new: Field Research Designs for New Venture Teams using S4.
  7. Kavelaars, Xynthia: Going multivariate in clinical trial studies: Increasing efficiency using Bayesian adaptive methods for information sharing.
  8. Klaassen, Fayette: All for one or some for all? Bayesian evaluation of multiple N=1 hypotheses.
  9. Klerk, Maartje de & Veen, Duco: Improving the Assessment of Individual Phoneme Discrimination Performance.
  10. Lek, Kimberley: Extreme survival guide: what to do with a single person and few observations?
  11. Lissa, Caspar van: MetaForest: Exploring heterogeneity in meta-analysis using insights from machine learning.
  12. McMenamin, Martina: Improving the efficiency of rare disease trials using composite endpoints.
  13. Nane, Tina: In and out of sample validation for structured expert judgment – a small sample size analysis
  14. Rosseel, Yves: Small Sample Solutions for SEM.
  15. Slavec, Ana: Determining the number of replicates in experimental studies with wood samples: how low can we go?
  16. Smid, Sanne: Bayesian SEM with Informative Priors: Precautions and Guidelines.
  17. Vanbrabant, Leonard: Sample-size reduction by order constraints.
  18. Vincze, Laszlo: Problems with power in a mixed ANOVA.
  19. Zavala-Rojas, Diana: Bayesian Estimation of the True Score Multitrait–Multimethod Model with a Split-Ballot Design.
  20. Zondervan-Zwijnenburg, Mariëlle: Searching prior information to solve small sample size issues in SEM.