Day 2: Tuesday March 6

1. Angulo-Brunet, Ariadna: How to handle ordered responses with floor and ceiling effects in SEM using small samples?
2. Declercq, Lies: How can methodologists make multilevel modeling more accessible for applied researchers who use single-case experimental designs?
3. Gibertoni, Dino: How to correctly perform log-rank tests in a study population of 15 observations and less than 10 events?
4. Jamshidi, Laleh: The methodological quality of single-case experimental studies meta-analyses
5. Moeyaert, Mariola: How to Improve Bayesian Estimation When Synthesizing Single-Case Experimental Design Studies’ Random Effects’ Variance Components.
6. Ruiter, Naomi de: Studying temporal dependence within and between variables for (mixed-method) data with small samples?
7. Soukup, Petr: Inference for samples from small populations – Frequentist or Bayesian solution?
8. Vrolijk, Paula: How to deal with high attrition in a small complicated data set?

Day 3: Wednesday March 7

1. Beretvas, Tasha: How can we obtain unbiased ICC estimates for small sample size datasets?
2. Dijk, Rianne van: Longitudinal data from recently divorced parents and their children: What N is deemed sufficient?
3. Dong, Shuyang: Can latent growth modeling (LGM) and growth mixture modeling (GMM) be used with a sample size around 100?
4. Gmelin, Ole: How to deal with small sample sizes at the group-level for Dyadic Data Analysis in Speed-Dating Contexts?
5. Langeloo, Annegien: How to analyze a cross-lagged multilevel model to compare two small groups?
6. Mascareño, Mayra: Exploring patterns of sequential relations in classroom verbal interaction: How to balance within and between perspectives?
7. Radulescu, Silvia: How to analyze a sample with few data points per participant in a familiarization paradigm in artificial grammar learning?
8. Song, Yue:  How to analysis the development of sharing across three waves, also the relationship between parenting and sharing during these waves, using a sample size around 90?
9. Thauvoye, Evalyne: Is a Bayesian approach the solution for care transition research? Beyond descriptive analysis in a difficult to reach and drop-out prone population.