Bayesian sample size planning for developmental studies

Abstract

Running developmental experiments, particularly with infants, is often time-consuming and intensive, and the recruitment of participants is hard and expensive. Thus, an important goal for developmental researchers is to optimize sampling plans such that neither too many nor too few participants are tested given the hypothesis of interest. One approach that enables such optimization is the use of Bayesian sequential designs. The use of such sequential designs allows data collection to be terminated as soon as the evidence is deemed sufficiently strong, without compromising the interpretability of the test outcome. In this tutorial, we illustrate how to plan a Bayesian sequential testing design prior to data collection by the method of Bayes factor design analysis—the Bayesian equivalent of power analysis—and discuss the relevance of this for developmental psychologists. The tutorial provides a step-by-step guide to perform such analyses, and the methods are illustrated using commonly used statistics in a typical infant-looking time paradigm such that researchers can easily adapt these methods for their studies. Highlights: Bayesian Sequential Testing can be used to optimize sample sizes and save on data collection. Bayes Factor Design Analysis can be used to analyze a sequential testing study prior to data collection. Step-by-step guide for performing Bayes Sequential Testing and Bayes Factor Design Analysis.