Sequential analyses in psychological research using Bayesian statistics
Abstract: It is important in psychological research to use well planned methods that are as time and resource efficient as possible, without jeopardizing the reliability and validity of psychological science. The present paper aims to test how sequential analyses could be implemented in psychological research using Bayesian statistics. With sequential analyses it is possible to stop an experiment or study in the data collection stage for success or futility. To avoid offset estimation and false alarms, a mixture of model testing with Bayes Factor and Bayesian parameter estimation were used as stopping rules. After several runs of Monte Carlo simulations, it appears as a Bayes’ Factor (BF) boundary of 6 together with 95% Highest density interval (HDI) width under a SD'0.60 served as suitable stopping rules under conditions of simulations. However, the generalizability is limited by the simulations settings and the stopping rules are recommended to be implemented on data from real conducted experiments.
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