Sequential Bayes Factor Analysis Balance Informativeness and Efficiency in Designing Experiments


The key of experimental design is to balance between informativeness and efficiency. However, power analysis only focuses on informativeness and is difficult to implement. Sequential Bayes factor analysis takes the advantage of Bayes factor’s ability to continuously update the evidence and reach a trade-off between informativeness and efficiency by setting Bayes factor criteria and the sequential analysis during data collection. The present primer demonstrates how to perform three steps of sequential Bayes factor analysis using open-source software JASP and R. This method considers practical issues in real research practices and is easy to implement, which can help researchers to design more efficient experiments.

In Chinese Journal of Applied Psychology
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