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In education, multisite trials involve randomization of pupils into intervention and comparison groups within schools. Most analytical models in multisite educational trials ignore that the impact of an intervention may be school dependent. This study investigates the impact of statistical models on the uncertainty associated with an effect size using comparable outcomes and covariates from ten multisite educational trials funded by the UK's Education Endowment Foundation. Ordinary least squares (OLS) models often assume that the pupil's outcomes within schools are independent, which is not always true. Multilevel models address this limitation by incorporating heterogeneity between schools to account for intra-school dependency. This inflates the confidence interval of an effect size obtained from the multilevel models than from an OLS model. For a multisite trial, the heterogeneity between schools also includes the differences in the expected impact of intervention between schools. Ignoring this additional school-by-intervention variation in a multisite trial could affect both its interpretation and conclusions. A robust approach to estimate the confidence intervals for effect size from multisite trials is by treating effect size as a parameter with its distribution. This paper is important for evaluating evidence from multisite trials by accounting for all sources of variability.

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.

In education, multisite trials involve randomization of pupils into intervention and comparison groups within schools. Most analytical models in multisite educational trials ignore that the impact of an intervention may be school dependent. This study investigates the impact of statistical models on the uncertainty associated with an effect size using comparable outcomes and covariates from ten multisite educational trials funded by the UK's Education Endowment Foundation. Ordinary least squares (OLS) models often assume that the pupil's outcomes within schools are independent, which is not always true. Multilevel models address this limitation by incorporating heterogeneity between schools to account for intra-school dependency. This inflates the confidence interval of an effect size obtained from the multilevel models than from an OLS model. For a multisite trial, the heterogeneity between schools also includes the differences in the expected impact of intervention between schools. Ignoring this additional school-by-intervention variation in a multisite trial could affect both its interpretation and conclusions. A robust approach to estimate the confidence intervals for effect size from multisite trials is by treating effect size as a parameter with its distribution. This paper is important for evaluating evidence from multisite trials by accounting for all sources of variability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]