Multisite educational trials: estimating the effect size and its confidence intervals. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Singh, Akansha; Uwimpuhwe, Germaine; Li, Mengchu; Einbeck, Jochen; Higgins, Steve; Kasim, Adetayo
• Is Part Of: International journal of research & method in education. Volume 45:Issue 1(2022)
• Page Start: 18
• Page End: 38
• Publication Date: 2022-01-01
• Subjects: Multisite trial -- education -- multilevel model -- effect size; Education -- Research -- Periodicals; Education -- Periodicals; 370.705
• DOI: 10.1080/1743727X.2021.1882416
• Languages: English
• ISSNs: 1743-727X

Abstract

ABSTRACT: 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.