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Building Evidence in Education: Workshop for EEF evaluators 2 nd June: York 6 th June: London The EEF by numbers. 34 topics in the Toolkit. 2,300 schools participating in projects. 5 00,000 pupils involved in EEF projects. 14 members of EEF team.

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Building Evidence in Education:Workshop for EEF evaluators2nd June: York6th June:

The eef by numbers
The EEF by numbers

34topics in the Toolkit

2,300 schools participating in projects

500,000pupils involved in EEF projects

14 members of EEF team

6,000heads presented to since launch

£210mestimated spend over lifetime of the EEF

16independent evaluation teams

83 evaluations

funded to date


reports published

Session 1: DesignAdapting DesignCarole Torgerson (Durham) David Torgerson (York Trials Unit)Calculating effect sizesAdetayoKasim (Durham)

Main points
Main Points

  • Over estimation of effect size when CLUSTER LEVEL ANALYSIS is used.

  • Disconnection between hypothesis testing and effect size from MULTILEVEL MODELS

Calculating effect size1
Calculating Effect Size

  • Cluster level analysis (CLA) - Two stage approach

    • Summarise data to cluster level

    • Calculate effect size using summarised data

  • Multilevel models (MLM)

    • Analyse pupils level data, but accounts for intra cluster correlation

    • Calculate effect size using WITHIN cluster variability

Calculating effect size2
Calculating Effect Size

  • Illustration 1: Simulation study

Calculating effect size3
Calculating Effect Size

  • Simulation 1: MEAN, SE and SD

  • MEAN(SD) from 10,000 simulated data

Calculating effect size4
Calculating Effect Size

  • Simulation 2: Calculating effect size assuming within cluster variance

  • MEAN(SD) of Hedges Effect Size from 10,000 simulated data

Calculating effect size5
Calculating Effect Size

  • Cluster level analysis may overestimates effect size when between variability is negligible and there is substantial variability within clusters

  • Effect sizes based on within cluster variance and total variance from multilevel model are comparable when between cluster variance is negligible

  • Using only within cluster variance could result in different conclusions based on effect sizes and hypothesis testing when there is a substantial variability between clusters


Cluster level analysis

  • OR

Within cluster Variance from multilevel model

  • OR

Total variance from multilevel model



  • Tymms P., Merrell C. and Henderson B. (1997) The first year at school: a quantitative investigation of the attainment and progress of pupils. Educational research and Evaluation, 3(2), pp. 101 - 118

  • Larry V. Hedges (2007) Effect sizes in cluster-randomized designs. Journal of Educational and Behavioural Statistics, 32(4), pp. 341-370

  • A. Brand, M.T. Bradley, L.A. Best, G. Stoica (2008) Accuracy of effect size estimates from published psychological research. Perceptual and Motor Skills, 106 (2) (2008), pp. 645–649