The Importance of Collaboration in Bayesian Analyses with Small Samples

Abstract

This chapter addresses Bayesian estimation with (weakly) informative priors as a solution for small sample size issues. Special attention is paid to the problems that may arise in the analysis process, showing that Bayesian estimation should not be considered a quick solution for small sample size problems in complex models. The analysis steps are described and illustrated with an empirical example for which the planned analysis goes awry. Several solutions are presented for the problems that arise, and the chapter shows that different solutions can result in different posterior summaries and substantive conclusions. Therefore, statistical solutions should always be evaluated in the context of the substantive research question. This emphasizes the need for a constant interaction and collaboration between applied researchers and statisticians.

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Small Sample Size Solutions.
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Duco Veen

I currently hold a post-doctoral position at the department of Methodology & Statistics of Utrecht University in the Netherlands. From December until July I’m consulting as a scientific project manager for Rick Grobbee at the University Medical Center Utrecht. My research interests lie primarily in the area of Bayesian statistics. My docteral thesis, under the supervision of Rens van de Schoot, concerned the use of Bayesian statistics, expert elicitation and information theory in the social sciences. I defended my thesis on March 13 at the acadamy building in Utrecht. In June I joined the Stan development team for my work on shinystan.