the eef by numbers
DESCRIPTION
Building Evidence in Education: Workshop for EEF evaluators 2 nd June: York 6 th June: London www.educationendowmentfoundation.org.uk. 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. - PowerPoint PPT PresentationTRANSCRIPT
Building Evidence in Education:Workshop for EEF evaluators
2nd June: York6th June: London
www.educationendowmentfoundation.org.uk
The EEF by numbers
83 evaluationsfunded to
date
2,300 schools
participating in projects
34 topics in
the Toolkit
16 independent evaluation
teams
500,000 pupils involved in EEF projects
14 members of EEF team
£210mestimated spend over lifetime of
the EEF
6,000 heads
presented to since launch
10 reports
published
Session 1: Design
Adapting DesignCarole Torgerson (Durham) David Torgerson (York Trials Unit)
Calculating effect sizesAdetayo Kasim (Durham)
Calculating Effect Sizes for
Cluster Randomized Trials
Adetayo Kasim
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 Size
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 Size
Illustration 1: Simulation study
Value Method
Mean
MLM 0.41(0.4) 0.39(0.59) 0.41(0.75) 0.39(0.91) 0.4(1.11) 0.38(1.33)
CLA 0.41(0.4) 0.39(0.59) 0.41(0.75) 0.39(0.91) 0.4(1.11) 0.38(1.33)
SE
MLM 0.43(0.06) 0.57(0.13) 0.73(0.18) 0.89(0.22) 1.07(0.27) 1.28(0.33)
CLA 0.39(0.10) 0.56(0.14) 0.73(0.18) 0.89(0.22) 1.07(0.27) 1.28(0.33)
SD
MLM - W 1.98(0.15) 1.99(0.15) 1.99(0.15) 2.00(0.15) 1.99(0.15) 2.00(0.15)
MLM - T 2.00(0.14) 2.10(0.16) 2.23(0.20) 2.38(0.24) 2.56(0.31) 2.80(0.39)
CLA 0.62(0.15) 0.89(0.23) 1.15(0.29) 1.40(0.35) 1.69(0.43) 2.03(0.52)
Calculating Effect Size
Simulation 1: MEAN, SE and SD
• MEAN(SD) from 10,000 simulated data
Calculating Effect Size
Simulation 2: Calculating effect size assuming within cluster variance
Effect size Method 0 0.1 0.2 0.3 0.4 0.5
0.2
MLM - W 0.20(0.20) 0.20(0.26) 0.21(0.30) 0.20(0.36) 0.2(0.42) 0.19(0.49)
MLM - T 0.20(0.20) 0.18(0.23) 0.17(0.26) 0.15(0.28) 0.13(0.29) 0.11(0.28)
CLA 0.64(0.69) 0.50(0.67) 0.43(0.68) 0.36(0.66) 0.31(0.67) 0.25(0.66)
0.3
MLM - W 0.30(0.20) 0.3(0.25) 0.3(0.30) 0.3(0.36) 0.3(0.42) 0.31(0.49)
MLM - T 0.29(0.20) 0.28(0.23) 0.25(0.26) 0.22(0.28) 0.20(0.29) 0.17(0.29)
CLA 0.95(0.72) 0.76(0.68) 0.63(0.69) 0.52(0.67) 0.46(0.67) 0.39(0.67)
0.4
MLM - W 0.41(0.21) 0.40(0.40) 0.40(0.31) 0.40(0.36) 0.40(0.42) 0.40(0.50)
MLM - T 0.39(0.20) 0.37(0.24) 0.34(0.26) 0.30(0.28) 0.26(0.29) 0.22(0.30)
CLA 1.26(0.74) 1.00(0.72) 0.84(0.70) 0.71(0.69) 0.61(0.69) 0.52(0.67)• MEAN(SD) of Hedges Effect Size from 10,000 simulated data
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
Discussion
Total variance from multilevel model
Cluster level analysis
Within cluster Variance from multilevel model
OR
OR
?
References
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
Larry V. Hedges (2007) Effect sizes in cluster-randomized designs. Journal of Educational and Behavioural Statistics, 32(4), pp. 341-370
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