Application of Bayesian posterior probabilistic inference in educational trials. Issue 5 (20th October 2021) - Uwimpuhwe, Germaine; Singh, Akansha; Higgins, Steve; Kasim, Adetayo
ABSTRACT: Educational researchers advocate the use of an effect size and its confidence interval to assess the effectiveness of interventions instead of relying on a p -value, which has been blamed for lack of reproducibility of research findings and the misuse of statistics. The aim of this study is to provide a framework, which can provide direct evidence of whether an intervention works for the study participants in an educational trial as the first step before generalizing evidence to the wider population. A hierarchical Bayesian model was applied to ten cluster and multisite educational trials funded by the Education Endowment Foundation in England, to estimate the effect size and associated credible intervals. The use of posterior probability is proposed as an alternative to p -values as a simple and easily interpretable metric of whether an intervention worked or not. The probability of at least one month's progression or any other appropriate threshold is proposed to use in education outcomes instead of using a threshold of zero to determine a positive impact. The results show that the probability of at least one month's progress ranges from 0.09 for one trial, GraphoGame Rime, to 0.94 for another, the Improving Numeracy and Literacy trial.