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Professor Steven Higgins' Outputs (10)

Improving power calculations in educational trials (2023)
Report
Singh, A., Uwimpuhwe, G., Vallis, D., Akhter, N., Coolen-Maturi, T., Einbeck, J., Higgins, S., Culliney, M., & Demack, S. (2023). Improving power calculations in educational trials. Education Endowment Foundation

The aim of this study was to investigate and empirically derive parameters commonly used for statistical power and sample size calculations to better inform future trial design. Towards achieving this aim, the research project leveraged the richness... Read More about Improving power calculations in educational trials.

Individual participant data meta-analysis: pooled effect of EEF funded educational trials on low baseline attaining group (2023)
Presentation / Conference Contribution
Uwimpuhwe, G., Singh, A., Akhter, N., Ashraf, B., Coolen-Maturi, T., Robinson, T., Higgins, S., & Einbeck, J. (2023, July). Individual participant data meta-analysis: pooled effect of EEF funded educational trials on low baseline attaining group. Presented at International Workshop on Statistical Modelling, Dortmund

The Education Endowment Foundation (EEF), a charity aiming to break the link between socioeconomic disadvantage and pupil attainment, has commissioned over 200 randomised controlled trials. The collection of data from these trials, the `EEF Data Arch... Read More about Individual participant data meta-analysis: pooled effect of EEF funded educational trials on low baseline attaining group.

The Teaching and Learning Toolkit: Communicating research evidence to inform decision‐making for policy and practice in education (2022)
Journal Article
Higgins, S., Katsipataki, M., Villanueva Aguilera, A., Alaidde, B., Dobson, E., Gascoine, L., Rajab, T., Reardon, J., Stafford, J., & Uwimpuhwe, G. (2022). The Teaching and Learning Toolkit: Communicating research evidence to inform decision‐making for policy and practice in education. Review of Education, 10(1), Article e3327. https://doi.org/10.1002/rev3.3327

This article compares and contrasts two versions of the Education Endowment Foundation's (EEF) Teaching and Learning Toolkit (‘Toolkit’), a web-based summary of international evidence on teaching 3–18 year-olds. The Toolkit has localised versions in... Read More about The Teaching and Learning Toolkit: Communicating research evidence to inform decision‐making for policy and practice in education.

Individual Participant Data Meta-analysis of the Impact of Educational Interventions on Pupils Eligible for Free School Meals (2021)
Journal Article
Ashraf, B., Singh, A., Uwimpuhwe, G., Higgins, S., & Kasim, A. (2021). Individual Participant Data Meta-analysis of the Impact of Educational Interventions on Pupils Eligible for Free School Meals. British Educational Research Journal, 47(6), 1675-1699. https://doi.org/10.1002/berj.3749

Meta-analysis is the synthesis of findings from research projects, which enables an estimate of the average or pooled effect across various studies. This study presents findings from the intention to treat analysis for a series of educational evaluat... Read More about Individual Participant Data Meta-analysis of the Impact of Educational Interventions on Pupils Eligible for Free School Meals.

Multisite educational trials: estimating the effect size and its confidence intervals (2021)
Journal Article
Singh, A., Uwimpuhwe, G., Li, M., Einbeck, J., Higgins, S., & Kasim, A. (2022). Multisite educational trials: estimating the effect size and its confidence intervals. International Journal of Research & Method in Education, 45(1), 18-38. https://doi.org/10.1080/1743727x.2021.1882416

In education, multisite trials involve randomisation 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 stud... Read More about Multisite educational trials: estimating the effect size and its confidence intervals.

Application of Bayesian posterior probabilistic inference in educational trials (2020)
Journal Article
Uwimpuhwe, G., Singh, A., Higgins, S., & Kasim, A. (2021). Application of Bayesian posterior probabilistic inference in educational trials. International Journal of Research & Method in Education, 44(5), 533-554. https://doi.org/10.1080/1743727X.2020.1856067

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... Read More about Application of Bayesian posterior probabilistic inference in educational trials.

Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention? (2020)
Journal Article
Uwimpuhwe, G., Singh, A., Higgins, S., Coux, M., Xiao, Z., Shkedy, Z., & Kasim, A. (2022). Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention?. The Journal of Experimental Education, 90(2), 404-418. https://doi.org/10.1080/00220973.2020.1767021

Educational stakeholders are keen to know the magnitude and importance of different interventions. However, the way evidence is communicated to support understanding of the effectiveness of an intervention is controversial. Typically studies in educa... Read More about Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention?.

An Empirical Unravelling of Lord’s Paradox (2017)
Journal Article
Xiao, Z., Higgins, S., & Kasim, A. (2019). An Empirical Unravelling of Lord’s Paradox. The Journal of Experimental Education, 87(1), 17-32. https://doi.org/10.1080/00220973.2017.1380591

Lord's Paradox occurs when a continuous covariate is statistically controlled for and the relationship between a continuous outcome and group status indicator changes in both magnitude and direction. This phenomenon poses a challenge to the notion of... Read More about An Empirical Unravelling of Lord’s Paradox.

Same Difference? Understanding Variation in the Estimation of Effect Sizes from Educational Trials (2016)
Journal Article
Xiao, Z., Higgins, S., & Kasim, A. (2016). Same Difference? Understanding Variation in the Estimation of Effect Sizes from Educational Trials. International Journal of Educational Research, 77, 1-14. https://doi.org/10.1016/j.ijer.2016.02.001

By applying four analytic models with comparable outcomes and covariates to a dataset of 20 outcomes from 17 educational trials, we found results closely matching in well-powered studies without serious implementation problems. The interventions and... Read More about Same Difference? Understanding Variation in the Estimation of Effect Sizes from Educational Trials.